Control method and device of intelligent drinking water equipment and electronic equipment

Abstract

Embodiments of the present application provide a control method and device of an intelligent water drinking device and an electronic device. The method is applied to the intelligent water drinking device, and the method comprises: acquiring a water outlet influencing parameter of the intelligent water drinking device; determining a water outlet parameter of the intelligent water drinking device through an environmental parameter and a pre-stored water drinking model, the pre-stored water drinking model being determined by a server based on historical water drinking data of a user, the water outlet parameter comprising at least one of the following: water outlet temperature, water outlet volume, and water outlet type; and performing water outlet according to the water outlet parameter. The technical solution provided by the embodiments of the present application can meet the personalized water drinking demand of the user on the premise of saving the operation of the user in controlling the water outlet parameter.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and more specifically, to a control method, device, and electronic device for a smart drinking water device. Background Technology

[0002] Smart water dispensers are intelligent devices that provide drinking water services to users. Users can control parameters such as the temperature, volume, and type of water dispensed (e.g., boiled water, herbal tea, oolong tea, etc.) according to their own needs.

[0003] Taking temperature control as an example, the control panel of the smart water dispenser includes a temperature increment control, a temperature decrement control, and a screen that displays the set water temperature of the smart water dispenser. Users can select to trigger the temperature increment control or the temperature decrement control to make the set water temperature of the smart water dispenser meet their own needs.

[0004] The control methods for the aforementioned smart drinking water devices require manual control by the user, which is quite cumbersome. Summary of the Invention

[0005] This application provides a control method, apparatus, and electronic device for a smart drinking water device.

[0006] In a first aspect, embodiments of this application provide a control method for an intelligent drinking water device, applied to the intelligent drinking water device. The method includes: acquiring water output influence parameters of the intelligent drinking water device, wherein the water output influence parameters characterize parameters that affect the water output parameters of the intelligent drinking water device; determining the water output parameters of the intelligent drinking water device through the water output influence parameters and a pre-stored drinking water model, wherein the pre-stored drinking water model is determined based on the user's historical drinking water data, and the water output parameters include at least one of the following: water output temperature, water output volume, and water output type; and dispensing water according to the water output parameters.

[0007] Secondly, embodiments of this application provide a control method for an intelligent drinking water device, applied to a server. The method includes: receiving historical drinking water data of a user sent by at least one intelligent drinking water device, wherein the user's historical drinking water model includes at least one of the following from historical drinking water behavior: environmental information, water output parameter information, status information, operation information, and human vital sign information; generating a drinking water model based on the user's historical drinking water data; and sending the drinking water model to at least one intelligent drinking water device to instruct the intelligent drinking water device to determine the water output parameters according to the drinking water model and the water output influence parameters of the intelligent drinking water device.

[0008] Thirdly, embodiments of this application provide a control device for an intelligent drinking water device. The device includes: a parameter acquisition module for acquiring water output influence parameters of the intelligent drinking water device, wherein the water output influence parameters characterize parameters affecting the water output parameters of the intelligent drinking water device; a parameter determination module for determining the water output parameters of the intelligent drinking water device through the water output influence parameters and a pre-stored drinking water model, wherein the pre-stored drinking water model is determined based on the user's historical drinking water data, and the water output parameters include at least one of the following: water output temperature, water output volume, and water output type; and a water output module for dispensing water according to the water output parameters.

[0009] Fourthly, embodiments of this application provide a control device for a smart drinking water device. The device includes: a data receiving module for receiving historical drinking water data of a user sent by at least one smart drinking water device; the user's historical drinking water model includes at least one of the following from historical drinking water behavior: environmental information, water output parameter information, status information, operation information, and human vital sign information; a model generation module for generating a drinking water model based on the user's historical drinking water data; and a model sending module for sending the drinking water model to at least one smart drinking water device to instruct the smart drinking water device to determine the water output parameters according to the drinking water model and the water output influence parameters of the smart drinking water device.

[0010] Fifthly, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores computer program instructions, which are invoked by the processor to execute the methods described in the first or second aspect.

[0011] When the electronic device is a smart drinking water device, the computer program instructions are invoked by the processor to execute the method described in the first aspect; when the electronic device is a server, the computer program instructions are invoked by the processor to execute the method described in the second aspect.

[0012] Sixthly, this application also provides a computer-readable storage medium storing program code that, when executed by a processor, performs the methods described in the first or second aspect.

[0013] In a seventh aspect, this application also provides a computer program product that, when executed, implements the method described in the first or second aspect.

[0014] This application provides a control method for an intelligent drinking water device. A drinking water model is generated based on the user's historical drinking water data. This model is then sent to the intelligent drinking water device. Before dispensing water, the device can determine the dispensing parameters based on the current dispensing parameters and the drinking water model, and dispense water according to these parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters. Attached Figure Description

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

[0016] Figure 1 This is a schematic diagram of the implementation environment provided in the embodiments of this application.

[0017] Figure 2 This is a flowchart of a control method for an intelligent drinking water device provided in an embodiment of this application.

[0018] Figure 3 This is a flowchart of another control method for an intelligent drinking water device provided in an embodiment of this application.

[0019] Figure 4 This is a flowchart of another control method for an intelligent drinking water device provided in an embodiment of this application.

[0020] Figure 5 This is a flowchart of another control method for an intelligent drinking water device provided in an embodiment of this application.

[0021] Figure 6 This is a block diagram of a control device for an intelligent drinking water equipment provided in an embodiment of this application.

[0022] Figure 7 This is a block diagram of a control device for another intelligent drinking water device provided in an embodiment of this application.

[0023] Figure 8 This is a structural block diagram of the electronic device provided in the embodiments of this application.

[0024] Figure 9 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0026] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] This application provides a control method for an intelligent drinking water device. A drinking water model is generated based on the user's historical drinking water data. This model is then sent to the intelligent drinking water device. Before dispensing water, the device can determine the dispensing parameters based on the current dispensing parameters and the drinking water model, and dispense water according to these parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters.

[0028] The drinking water model will be explained below.

[0029] A drinking water model is generated based on a user's historical drinking water data and is used to predict the user's drinking habits. The drinking water model can be a statistical model or a neural network model; this application embodiment does not limit this. In this application embodiment, only a statistical model is used as an example for illustration. Statistical models can include multiple regression models, cluster analysis models, fitting models, etc.

[0030] A user's historical drinking water data includes at least one of the following from their historical drinking water behavior: environmental information, water output parameter information, status information, and operation information.

[0031] Environmental information regarding historical drinking behavior includes ambient temperature and parameters. Water output parameter information refers to the water output parameters during historical drinking behavior, including output volume, output temperature, and output type. Operational information refers to the operations performed by the user on the smart water dispenser when the historical drinking behavior occurred, including but not limited to: setting the output volume, selecting the output temperature, and selecting the output type. Status information includes the status of the smart water dispenser when the historical drinking behavior occurred, including but not limited to: water temperature, remaining water volume, and remaining water volume for different output types. In some embodiments, the user's historical drinking data also includes the time of the historical drinking behavior and the user identifier corresponding to the historical drinking behavior.

[0032] In some embodiments, the drinking water model can be generated by the server based on the historical drinking water data of a specified user, and is used to predict the drinking habits of the specified user. Optionally, the smart drinking water device carries a user identifier when reporting the user's historical drinking water data. The server obtains the historical drinking water data carrying the same user identifier, performs statistical analysis on it, and obtains the drinking water model. The user identifier can be obtained in the following way: the smart drinking water device can pre-store a first mapping relationship between different user identifiers and different facial features. After receiving a water dispensing command, it collects a facial image and determines the user identifier based on the facial features extracted from the facial image and the aforementioned first mapping relationship.

[0033] In other embodiments, the drinking water model can be generated by the server based on the historical drinking water data of users with a specified user profile, and is used to predict the drinking habits of users with that user profile. Optionally, the smart drinking water device carries a user identifier when reporting historical drinking water data. The server determines the user profile corresponding to the carried user identifier based on a second mapping relationship between pre-stored user identifiers and user profiles, and then performs statistical analysis on the historical drinking water data of each user profile to obtain the drinking water model. The user profile corresponding to each user identifier can be set by the user or obtained through statistical analysis of behavioral data; this embodiment does not limit this. The aforementioned user profiles include, but are not limited to, those related to health, trends, tradition, sweets, tea, etc.

[0034] In some embodiments, the drinking water model includes mapping relationships between different effluent parameters and different environmental parameters. Optionally, the drinking water model includes mapping relationships between ambient temperature and effluent temperature, and between ambient temperature and effluent volume.

[0035] Since users' drinking habits may differ at different times of day—for example, they might prefer warm water in the morning and ice water at noon—the server can generate a drinking model based on historical drinking data from different time periods. This model includes the mapping relationship between water output parameters and environmental parameters at different times. Optionally, the drinking model includes the mapping relationship between ambient temperature and water output temperature at different times, the mapping relationship between ambient temperature and water output volume at different times, and a mapping table between different time periods and different water output types.

[0036] like Figure 1 The diagram illustrates an implementation environment provided in this embodiment of the application. This implementation environment includes a server 110 and one or more smart drinking water devices 120.

[0037] Server 110 can be the backend server corresponding to the application used to manage the smart drinking water device 120. Server 110 can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center. In this embodiment, server 110 is used to receive historical drinking water data reported by the smart drinking water device 120, train a preset drinking water model based on the historical drinking water data, and send the preset drinking water model to the smart drinking water device 120 so that the smart drinking water device 120 can provide personalized drinking water services to users.

[0038] The intelligent water dispenser 120 includes a microprocessor unit, a water storage space, a temperature control device, a temperature detection device, and a water outlet. The water storage space, which can be multiple, stores different types of liquids (such as boiled water or herbal tea). The water storage space is connected to the water outlet of the intelligent water dispenser 120. The temperature control device heats or cools the liquid in the water storage space to achieve the set temperature. The temperature detection device detects the temperature of the liquid in the water storage space, enabling precise temperature control. The water outlet allows the liquid to flow out of the water storage space.

[0039] In this embodiment, the microprocessor unit in the smart water dispenser 120 is used to provide personalized drinking water services to users. Specifically, the smart water dispenser 120 receives a preset drinking water model from the server 110. After receiving a water dispensing command, the smart water dispenser 120 predicts the user's desired water parameters based on the preset drinking water model, including water temperature, water volume, water type, etc., and then dispenses water according to the predicted parameters. The smart water dispenser 120 can also report historical drinking water data to the server 110 after the current water dispensing or at a predetermined period, so that the server 110 can update the preset drinking water model.

[0040] In some embodiments, the implementation environment further includes a terminal device. The terminal device has the aforementioned application for managing the smart water dispenser installed. In some embodiments, the user interface of the application includes a switch for a personalized water function. Upon receiving an instruction to turn on the personalized water function, a notification message is sent to the smart water dispenser 120 to instruct the smart water dispenser 120 to set a flag representing the personalized water function to a preset value.

[0041] like Figure 2 As shown, it illustrates a control method for an intelligent drinking water device provided in one embodiment of this application, which is applied to... Figure 1 The method for intelligent drinking water devices includes the following steps.

[0042] Step S201: Obtain the water output parameters of the smart drinking water device.

[0043] The parameters affecting the water output of a smart water dispenser characterize the parameters that influence the water output of the smart water dispenser. These parameters include environmental parameters and human vital sign parameters. Environmental parameters describe the current environment in which the smart water dispenser is located. In this embodiment, the environmental parameters of the smart water dispenser include, but are not limited to, ambient temperature, ambient humidity, etc. Human vital sign parameters describe the user's physical characteristics, including, but not limited to, body temperature, heart rate, blood oxygen saturation, etc.

[0044] In some embodiments, the smart drinking water device is equipped with an environmental parameter acquisition module to collect environmental parameters. In one example, the environmental parameter acquisition module includes a temperature acquisition device to collect the ambient temperature. The temperature acquisition device can be a temperature sensor, thermometer, etc., and this application embodiment is not limited to this. In another example, the environmental parameter acquisition module includes a humidity acquisition device to collect the ambient humidity. In other embodiments, the smart drinking water device is equipped with a communication module to establish a communication connection with external devices and receive environmental parameters sent by external devices through this communication connection.

[0045] In some embodiments, the smart drinking water device is equipped with a communication module, which establishes a communication connection with a wearable device and receives human vital signs parameters sent by the wearable device through this communication connection. The wearable device can be a smartwatch, smart bracelet, etc.

[0046] In some embodiments, the smart water dispenser acquires water dispensing parameters after receiving a water dispensing command. Optionally, the smart water dispenser includes a water dispensing button; upon receiving a press signal on the button, a water dispensing command is received. Optionally, the smart water dispenser includes a sensor; when the sensor detects a cup below the water outlet, a water dispensing command is received. Optionally, the smart water dispenser receives voice information; if the voice information includes a specified keyword, a water dispensing command is received. The specified keyword is preset and can be "dispensing water," "drinking water," etc., and this embodiment does not limit its scope.

[0047] In other embodiments, the smart water dispenser acquires water output parameters at a preset time. This preset time is determined based on the user's historical drinking data. Optionally, the server predicts the user's habitual drinking time based on their historical drinking data, determines a target time before that habitual drinking time as the preset time, and sends this preset time to the smart water dispenser. The time interval between the target time and the preset time satisfies the parameter adjustment requirements of the smart water dispenser, ensuring that the water output parameters can be adjusted before the user's habitual drinking time. In a specific example, if the user typically drinks water at 7:15 AM, the server determines the habitual drinking time to be 7:15 AM, and the smart water dispenser requires 1 minute to complete parameter adjustment, then 7:14 AM is determined as the preset time.

[0048] Step S202: Determine the water output parameters of the smart drinking water device using the water output influence parameters and the pre-stored drinking water model.

[0049] The water output parameters of a smart water dispenser include at least one of the following: water temperature, water volume, and water type. Water types include boiled water, herbal tea, lemon tea, etc.

[0050] The pre-stored drinking water model is trained based on the user's historical drinking water data and sent to the local smart drinking water device. Before each drinking session, the smart drinking water device sets the relevant parameters for this drinking session based on the drinking water model determined by the user's historical drinking water data and the current water output influencing parameters, so that the smart drinking water device can provide users with personalized drinking water services without manual operation even when not connected to the network.

[0051] In some embodiments, the smart water dispenser includes a drinking model corresponding to a specified user. Upon receiving a water dispensing command, the smart water dispenser collects the user's facial information, extracts facial features from the collected facial information, and if the extracted facial features match pre-stored facial features, reads the drinking model corresponding to the specified user locally, and determines the water dispensing parameters of the smart water dispenser using the aforementioned water dispensing influence parameters and the drinking model corresponding to the specified user. The drinking model corresponding to the specified user can be obtained by the server analyzing the specified user's historical behavior data. The pre-stored facial features are facial features extracted from the specified user's facial image.

[0052] In other embodiments, the smart water dispenser includes a drinking model corresponding to a specified user profile. Upon receiving a water dispensing command, the smart water dispenser collects the user's facial information, extracts facial features from the collected facial information, and if the extracted facial features match pre-stored facial features, obtains the drinking model corresponding to the specified user profile. The water dispensing parameters of the smart water dispenser are then determined using the aforementioned water dispensing influence parameters and the drinking model corresponding to the specified user profile. The specified user profile can be input by the specified user or obtained by the server based on the specified user's historical drinking data.

[0053] It should be noted that in the two embodiments described above, if the facial features in the face image captured by the smart water dispenser are not pre-stored facial features, and this is the first time the face image has been captured, the current user sets the water dispensing parameters, and the smart water dispenser stores the set water dispensing parameters corresponding to the first captured facial features. If the facial features in the face image captured by the smart water dispenser are not pre-stored facial features, and this is not the first time the face image has been captured, the corresponding water dispensing parameters are obtained based on the stored information, and water is dispensed according to the obtained water dispensing parameters. For example, if user A visits user B's home, user A can set the water dispensing parameters when getting water for the first time. The smart water dispenser records the water dispensing parameters set by user A, and subsequent times user A gets water, the smart water dispenser dispenses water according to the recorded water dispensing parameters.

[0054] In other possible embodiments, the smart water dispenser establishes a communication connection with the server. Before determining the water output parameters, the smart water dispenser sends an update request to the server, carrying the locally stored water model. Based on the comparison between the latest water model and the locally stored water model, the server determines whether the local water model of the smart water dispenser needs to be updated. If an update is needed, the server sends the latest water model to the smart water dispenser. The smart water dispenser determines the water output parameters based on the latest water model and environmental parameters, so as to provide users with more accurate and personalized drinking water services.

[0055] Step S203: Discharge water according to the effluent parameters.

[0056] If the water output parameters include the water output temperature, and the current water temperature of the smart water dispenser is higher than the aforementioned water output temperature, the smart water dispenser controls the cooling device to operate, so that the current water temperature of the smart water dispenser drops to the water output temperature; if the current water temperature of the smart device is lower than the aforementioned water output temperature, the smart water dispenser controls the heating device to operate, so that the current water temperature of the smart water dispenser rises to the specified temperature.

[0057] When the water output parameters include the water output type, the smart drinking water device includes multiple water storage spaces. Different water storage spaces store different types of liquids. The smart drinking water device controls the water storage space corresponding to the water output type to be connected to the water outlet so that the water outlet can discharge the liquid of the aforementioned water output type.

[0058] When the water output parameters include the water output volume, the smart water dispenser monitors the water flow rate at the outlet in real time and stops dispensing water when the water flow rate reaches the aforementioned water output volume. Alternatively, the smart water dispenser calculates the dispensing time based on the water output volume and dispensing rate, and stops dispensing water once the actual dispensing time reaches the calculated dispensing time.

[0059] In some embodiments, if the smart water dispenser executes the step of acquiring the environment of the smart water dispenser at a preset time, the smart water dispenser can then execute the step of dispensing water according to the water dispensing parameters after receiving a water dispensing command. The explanation of the smart water dispenser receiving the water dispensing command can be found in step S201, and will not be repeated here. In other embodiments, if the smart water dispenser executes step S of acquiring the environmental parameters of the smart water dispenser after receiving a water dispensing command, the smart water dispenser can directly dispensing water according to the water dispensing parameters.

[0060] In some embodiments, before step S203, the smart water dispenser may issue an inquiry message to ask whether water should be dispensed according to the determined water dispensing parameters. Upon receiving a confirmation indication of the inquiry message, step S203 is executed. Upon receiving a rejection indication of the inquiry message, the user can then manually set the water dispensing parameters. The inquiry message can be in voice or text format, and this embodiment does not limit this. In some embodiments, after step S203, the smart water dispenser may also report the water dispensing parameters to the server, enabling the server to update the water dispensing model in a timely manner.

[0061] This application provides a control method for an intelligent drinking water device. A drinking water model is generated based on the user's historical drinking water data. This model is then sent to the intelligent drinking water device. Before dispensing water, the device can determine the dispensing parameters based on the current dispensing parameters and the drinking water model, and dispense water according to these parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters.

[0062] like Figure 3 As shown in the figure, this application provides a control method for an intelligent drinking water device. In this embodiment, the water output influencing parameter is an environmental parameter, and the method is applied to... Figure 1 The method for intelligent drinking water devices includes the following steps.

[0063] Step S301: Upon receiving a water dispensing command, acquire the environmental parameters of the smart drinking water device.

[0064] Environmental parameters are used to describe the environment in which a smart device is located.

[0065] Step S302: Obtain the target time period.

[0066] The target time period is the time period to which the water dispensing command's received timestamp belongs. The water dispensing command's received timestamp refers to the moment the water dispensing command is received. The smart water dispenser pre-divides multiple time periods. Upon receiving a water dispensing command, it records the received timestamp and then compares it sequentially with the pre-divided time periods to determine the target time period. For example, if the smart water dispenser divides the time period into [7:00-8:00], [10:00-11:00], [16:00-17:00], etc., and the user triggers a water dispensing command at 10:27, then the target time period is [10:00-11:00].

[0067] Step S303: Determine the water dispensing strategy of the smart drinking water device during the target time period based on the pre-stored drinking water model and target time period.

[0068] The water dispensing strategy of intelligent drinking water devices during a target time period includes at least one mapping relationship between water dispensing parameters and environmental parameters, including but not limited to: functional relationships, mapping tables, etc. Since users' drinking habits may differ at different times, to address these potential issues, the server pre-divides historical drinking data for different time periods during statistical analysis to obtain water dispensing strategies for those different time periods when developing the drinking water model. These different time-period water dispensing strategies constitute the drinking water model.

[0069] In some embodiments, environmental parameters include ambient temperature, water output parameters include water output temperature, and the water output strategy of the smart drinking water device includes a mapping relationship between ambient temperature and water output temperature. Step S303 can be implemented as follows: obtaining the standard ambient temperature corresponding to the target time period, determining the offset of the ambient temperature relative to the standard ambient temperature, and determining the mapping relationship between the water output temperature and the ambient temperature based on the offset range of the offset.

[0070] The standard ambient temperature corresponding to the target time period can be directly read from the drinking water model, and its determination process is completed by the server. The intelligent drinking water device determines the difference between the ambient temperature and the standard ambient temperature as the offset of the ambient temperature relative to the standard ambient temperature. For example, if the standard ambient temperature is 28.2℃ and the ambient temperature is 27.5℃, then the offset is -0.7℃.

[0071] Optionally, the drinking water model includes a third mapping relationship between the offset range within the target time period and the mapping relationship between the outlet water temperature and the ambient temperature. The intelligent drinking water device first determines the offset range in which the offset is located, and then searches for the above-mentioned third mapping relationship in the drinking water model to determine the mapping relationship between the outlet water temperature and the ambient temperature within the target time period.

[0072] In some embodiments, the effluent parameters include effluent type, and the drinking water model includes a fourth mapping relationship between different time periods and different effluent types. Table-1 below illustrates the fourth mapping relationship.

[0073] Table 1

[0074] [7:00-8:00] warm water [10:00-11:00] scented tea [16:00-17:00] ice water

[0075] In some embodiments, environmental parameters include ambient temperature, water output parameters include water output volume, and the water output strategy of the smart drinking water device includes a mapping relationship between ambient temperature and water output volume. Step S303 can be implemented by obtaining the standard ambient temperature corresponding to the target time period, determining the offset of the ambient temperature relative to the standard ambient temperature, and determining the mapping relationship between water output volume and ambient temperature within the target time period based on the offset range of the offset.

[0076] Optionally, the drinking water model includes a fifth mapping relationship between the offset range within the target time period and the mapping relationship between ambient temperature and water volume. The intelligent drinking water device first determines the offset range in which the offset is located, and then searches for the above-mentioned fifth mapping relationship in the drinking water model to determine the mapping relationship between water volume and ambient temperature within the target time period.

[0077] The third, fourth, and fifth mapping relationships mentioned above are all determined by the server based on the user's historical drinking water data. The determination process will be described in the following examples.

[0078] Step S304: Determine the effluent parameters based on environmental parameters and effluent strategy.

[0079] When the water dispensing strategy includes a first functional relationship between the water temperature and the ambient temperature within the target time period, the current environmental parameters of the smart water dispenser are substituted into the first functional relationship to calculate the water temperature. When the water dispensing strategy includes a mapping table of different time periods and different water types, the smart water dispenser obtains the water type corresponding to the target time period from the mapping table. When the water dispensing strategy includes a second functional relationship between the water volume and the ambient temperature within the target time period, the current environmental parameters of the smart water dispenser are substituted into the second functional relationship to calculate the water volume.

[0080] Step S305: Discharge water according to the effluent parameters.

[0081] In other embodiments, if the parameters affecting the effluent include human vital signs parameters, based on... Figure 2 In the optional embodiment provided by the illustrated example, step S202 can also be implemented as: obtaining the water dispensing strategy corresponding to the human vital signs parameters from the drinking water model; and determining the water dispensing parameters of the intelligent drinking water device according to the water dispensing strategy corresponding to the human vital signs parameters. In this embodiment, different water dispensing strategies are designed for different user vital signs data, such as providing hotter water when the body temperature is low, so that the water dispensing from the intelligent drinking water device meets the needs of different human vital signs. The process of generating the water dispensing strategy corresponding to the human vital signs parameters will be described in the following embodiments.

[0082] This application provides a control method for an intelligent drinking water device. A server generates a drinking water model based on the user's historical drinking water data and sends the model to the intelligent drinking water device. Before dispensing water, the device determines the dispensing parameters based on the current environmental parameters and the drinking water model, and dispenses water according to these parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters.

[0083] like Figure 4 As shown in the figure, this application provides a control method for an intelligent drinking water device, which includes the following steps S. The method includes the following steps.

[0084] Step S401: Obtain the on / off status of the personalized drinking water function.

[0085] The personalized drinking function determines the water output parameters based on a pre-stored drinking model. The personalized drinking function has two states: on and off.

[0086] In some embodiments, the internal memory of the smart water dispenser is equipped with a preset flag bit.

[0087] The preset flag value is used to indicate the on / off state of the personalized water drinking function. When the preset flag value is a preset value, the personalized water drinking function is in the on state. When the preset flag value is not a preset value, the personalized water drinking function is in the off state. In some embodiments, the preset value includes a first preset value. When the preset flag value is the first preset value, the personalized water drinking function is in the on state. When the preset flag value is a second preset value, the personalized water drinking function is in the off state. The first and second preset values ​​are set according to actual needs. For example, the first preset value is 1 and the second preset value is 0.

[0088] In some embodiments, the on / off state of the personalized drinking function may be the same or different for different users. After receiving a water dispensing command, the smart drinking device captures a facial image, extracts facial features from the captured facial image to determine the user's identity, and then determines the on / off state of the personalized drinking function matching that user's identity. Optionally, the smart drinking device locally stores multiple preset flag bits, with different preset flag bits corresponding to different users. The value of each user's preset flag bit is used to indicate the on / off state of the personalized drinking function for that user. For example, if the smart drinking device stores "Personalized service_userA:1", it means that the personalized drinking function for user A is in the on / off state.

[0089] In some embodiments, the smart water dispenser receives a notification message from a target terminal and sets the value of a preset flag to a preset value based on the notification message. The notification message indicates that the personalized drinking function's on / off state is switched from off to on. The target terminal is the terminal used to control the smart water dispenser. The target terminal can be a remote control device that controls the smart water dispenser via near-field communication technology, such as a remote control, universal remote control, etc. The target terminal can also be a terminal device with a specified application installed, such as a smartphone, tablet, personal computer, etc. The specified application is the application used to control the smart water dispenser.

[0090] In other embodiments, the smart drinking device receives a notification message from a target terminal and sets the value of a preset flag for a specified user to a preset value based on the notification message. The notification message is used to indicate that the on / off state of the personalized drinking function for the specified user is switched from the off state to the on state.

[0091] Step S402: With the personalized drinking function turned on, acquire the water output parameters of the smart drinking device.

[0092] When the personalized drinking function is enabled, the smart water dispenser will proceed with the following steps. Users can enable or disable the personalized drinking function on the target terminal according to their own needs.

[0093] When a smart water dispenser includes preset flags for multiple users, it captures a facial image upon receiving a water dispensing command. Facial features are extracted from the image to identify the user. The dispenser then checks if the user's preset flag value matches a preset value. If the preset flag value matches the preset value, the dispenser's environmental parameters are retrieved. In this way, when a smart water dispenser serves multiple users in a household, each user can enable or disable personalized functions according to their needs, thus meeting individual user preferences.

[0094] Step S403: Determine the water output parameters of the smart drinking water device using the water output influence parameters and the pre-stored drinking water model.

[0095] The pre-stored drinking water model is determined by the server based on the user's historical drinking water data, and the water output parameters include at least one of the following: water temperature, water volume, and water type.

[0096] Step S404: Discharge water according to the effluent parameters.

[0097] In summary, the technical solution provided in this application allows users to turn personalized drinking functions on or off in the application managing the smart water dispenser, enabling the smart water dispenser to operate according to user needs. Furthermore, when the smart water dispenser serves multiple users in a household, different users can choose to turn personalized functions on or off according to their own needs, thus meeting their individual requirements.

[0098] like Figure 5 As shown in the figure, this application provides a control method for an intelligent drinking water device. The method is applied to a server and includes the following steps.

[0099] Step S501: Receive historical drinking water data of the user sent by at least one smart drinking water device.

[0100] The server receives historical drinking water data from at least one smart water dispenser at predetermined intervals. Alternatively, the server receives historical drinking water data reported by the smart water dispenser after each dispensing of water. The specific content of the historical drinking water data can be found in the above embodiment and will not be repeated here.

[0101] In some embodiments, after receiving historical drinking water data, the server needs to filter out invalid data and preprocess the filtered historical drinking water data.

[0102] Optionally, the server filters out invalid data according to preset filtering rules. These preset filtering rules can be set by the server based on the water dispensing habits of the smart water dispenser, or they can be set by technical personnel; this embodiment does not limit this. In some embodiments, the preset filtering rules include that the water temperature must be between 0 and 100°C, the time interval between the end and start of water dispensing should be less than a preset value, the water volume cannot exceed the remaining water volume in the smart water dispenser, and the water type should be a specified drinking water type supported by the smart water dispenser, etc. The process by which the server filters out invalid data can be called an ETL (Extract-Transform-Load) process.

[0103] Optionally, after the filtering process is completed, the server preprocesses the filtered historical drinking water data. The preprocessing process includes extracting feature parameters from the filtered historical drinking water data, including the number of historical drinking behaviors in each time period, the average ambient temperature in each time period, the maximum ambient temperature in each time period, the average historical water temperature in each time period, the most frequently occurring historical water temperature in each time period, the average historical water volume in each time period, the most frequently occurring historical water volume in each time period, the number of occurrences of different water types in each time period, the frequency of occurrence, etc.

[0104] Step S502: Generate a drinking water model based on the user's historical drinking water data.

[0105] The server can learn users' drinking habits based on their historical drinking data and generate a drinking model based on the learned drinking habits. The drinking model is then sent to the smart drinking device, which determines the water output parameters based on the drinking model and the current environmental parameters. This allows users to obtain water that matches their drinking habits without having to manually adjust the water output parameters.

[0106] In some embodiments, step S502 can be implemented as follows: dividing the user's historical drinking data into historical drinking data for multiple time periods; generating a water dispensing strategy for the i-th time period based on the historical drinking data within the i-th time period, where i is a positive integer. The server divides the data into multiple time periods based on the occurrence time of each historical drinking behavior. Since users' drinking habits differ in different time periods, different water dispensing strategies need to be designed for different time periods to meet the user's drinking habits in different time periods.

[0107] In some embodiments, the water dispensing strategy for the i-th time period includes a first functional relationship between ambient temperature and water dispensing temperature within multiple offset ranges. The water dispensing strategy for the i-th time period is generated based on historical drinking water data within the i-th time period, including: determining the standard ambient temperature within the i-th time period based on the historical drinking water data within the i-th time period; dividing the historical drinking water data within the i-th time period into multiple offset ranges; and fitting the first functional relationship between ambient temperature and water dispensing temperature within the k-th offset range based on the historical ambient temperature and historical water dispensing temperature within the k-th offset range, where k is an integer.

[0108] The standard ambient temperature in the i-th time period can be the average of the historical ambient temperatures in the historical drinking water data in the i-th time period, or the historical ambient temperature that appears most frequently in the historical drinking water data in the i-th time period. This application does not limit this.

[0109] The offset range refers to the range of historical ambient temperatures in the historical drinking water data within the i-th time period relative to the standard ambient temperature within the i-th time period. The server can divide the historical ambient temperatures in the historical drinking water data within the i-th time period into multiple offset ranges, with each offset range containing approximately the same number of historical ambient temperatures.

[0110] For each offset range of historical ambient temperature, the server obtains the corresponding historical outlet water temperature and then fits a first functional relationship between the two. This first functional relationship includes the function type, coefficients, constant terms, etc. Function types include linear, quadratic, exponential, and power functions, etc.

[0111] In some embodiments, the water dispensing strategy for the i-th time period includes the water dispensing type corresponding to the i-th time period. Optionally, the server performs statistical analysis on the historical water dispensing types in the historical drinking water data within the i-th time period, and determines the historical water dispensing type that appears most frequently as the water dispensing type corresponding to the i-th time period.

[0112] In some embodiments, the water dispensing strategy for the i-th time period includes a second functional relationship between ambient temperature and water dispensing volume within multiple offset ranges. The water dispensing strategy for the i-th time period is generated based on historical drinking water data within the i-th time period, including: determining the standard ambient temperature within the i-th time period based on the historical drinking water data within the i-th time period; dividing the historical drinking water data within the i-th time period into multiple offset ranges; and fitting the second functional relationship between ambient temperature and water dispensing volume within the k-th offset range based on the historical ambient temperature and historical water dispensing volume within the k-th offset range, where k is an integer.

[0113] In other embodiments, step S502 can be implemented as follows: dividing the user's historical drinking water data into historical drinking water data with different human vital signs parameters, and then performing statistical analysis on the historical drinking water data under each human vital signs parameter to obtain the water dispensing strategy corresponding to the historical drinking water data with different human vital signs parameters.

[0114] Step S503: Send a drinking water model to at least one smart drinking water device to instruct the smart drinking water device to determine the water output parameters according to the drinking water model and the water output influence parameters of the smart drinking water device.

[0115] This application provides a control method for an intelligent drinking water device. A server generates a drinking water model based on the user's historical drinking water data and sends the model to the intelligent drinking water device. Before dispensing water, the device determines the dispensing parameters based on the current environmental parameters and the drinking water model, and dispenses water according to these parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters.

[0116] In a specific example, at least one smart drinking water device reports historical drinking water data as shown in Table-2 below to the server.

[0117] Table 2

[0118]

[0119]

[0120] In Table 2 above, the occurrence time of historical drinking behavior 1 is time1, the water temperature is 34.5℃, the water volume is 350ml, and the water type is flower tea.

[0121] The server performs an ETL process on the historical drinking water data in Table-2, and then preprocesses it to obtain the feature data shown in Table-3 below.

[0122] Table 3

[0123]

[0124] In Table 3 above, when the average ambient temperature for time period 1 was 29.5℃, the average water temperature was 33.6℃, ​​the average water volume was 320ml, the frequency of flower tea was 0.05, the frequency of plain water was 0.3, and the frequency of grapefruit tea was 0.08.

[0125] The server uses the feature data in Table 3 to fit the effluent strategy for each time period. Table 4 below shows the parameters involved in the first functional relationship between ambient temperature and effluent temperature for each time period.

[0126] Table 4

[0127]

[0128] In Table 4 above, within time period 1, if the deviation between the ambient temperature and the standard ambient temperature falls within the range of [-0.8, 2], then the functional relationship between the outlet water temperature and the ambient temperature is linear, with a coefficient of 0.6 and a constant of -1.9. That is, within time period 1, if the deviation between the ambient temperature and the standard ambient temperature falls within the range of [-0.8, 2], then the functional relationship between the outlet water temperature and the ambient temperature can be expressed by the following formula:

[0129] Outlet water temperature = 0.6 * ambient temperature - 1.9.

[0130] The server represents the mapping relationship between environmental parameters and water output parameters under different offset ranges in different time periods in the form of code to obtain a drinking water model, and then sends the drinking water model in code form to the smart drinking water device.

[0131] Based on the above analysis, the following conclusions can be drawn:

[0132] If it is during time period 1 and the ambient temperature is between 28.7-31.5℃, then the outlet water temperature is calculated using the formula (outlet water temperature = 0.6 * ambient temperature - 1.9); if it is during time period 1 and the ambient temperature is between 27.5-32.5℃, then the outlet water temperature is calculated using the formula (outlet water temperature = -0.12 * ambient temperature). 2 -9) This formula calculates the water temperature.

[0133] Figure 6This is a block diagram of the control device for an intelligent drinking water device provided in an embodiment of this application. The device is applied to an intelligent drinking water device. The control device for the intelligent drinking water device includes: a parameter acquisition module 610, a parameter determination module 620, and a water dispensing module 630.

[0134] The parameter acquisition module 610 is used to acquire the water output parameters of the smart water dispenser. These parameters represent the parameters that affect the water output parameters of the smart water dispenser. The parameter determination module 620 is used to determine the water output parameters of the smart water dispenser using the water output parameters and a pre-stored drinking water model. The pre-stored drinking water model is determined by the server based on the user's historical drinking water data. The water output parameters include at least one of the following: water temperature, water volume, and water type.

[0135] The water outlet module 630 is used to dispense water according to the water outlet parameters.

[0136] In summary, this application provides a control device for an intelligent drinking water device. A server generates a drinking water model based on the user's historical drinking water data and sends the model to the intelligent drinking water device. Before dispensing water, the intelligent drinking water device can determine the dispensing parameters based on the current environmental parameters and the drinking water model, and dispense water according to the determined parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters.

[0137] In some embodiments, the water dispensing influencing parameters include environmental parameters, which describe the environment in which the smart water dispenser is located. The parameter acquisition module 610 is used to acquire the environmental parameters of the smart water dispenser upon receiving a water dispensing command. The parameter determination module 620 is used to determine the water dispensing strategy of the smart water dispenser during the target time period based on a pre-stored water dispensing model and a target time period. The water dispensing strategy includes at least one mapping relationship between water dispensing parameters and environmental parameters. The water dispensing parameters are determined based on the environmental parameters and the water dispensing strategy.

[0138] In some embodiments, the environmental parameters include ambient temperature, the water output parameters include water output temperature, and the water output strategy of the smart drinking water device during the target time period includes a first functional relationship between the ambient temperature and the water output temperature during the target time period; the parameter determination module 620 is used to obtain the standard ambient temperature corresponding to the target time period; determine the offset of the ambient temperature relative to the standard ambient temperature; and determine the first functional relationship based on the offset range in which the offset is located.

[0139] In some embodiments, the parameters affecting water output include human vital signs parameters, which include at least one of the following: body temperature parameters and heart rate parameters; the parameter determination module 620 is used to obtain the water output strategy corresponding to the human vital signs parameters from the drinking water model; and to determine the water output parameters of the intelligent drinking water device according to the water output strategy corresponding to the human vital signs parameters.

[0140] Figure 7 This is a block diagram of the control device for an intelligent drinking water device provided in an embodiment of this application. The device is applied to a server. The control device for the intelligent drinking water device includes: a data receiving module 710, a model generation module 720, and a model sending module 730.

[0141] The data receiving module 710 is used to receive historical drinking water data of a user sent by at least one smart drinking water device. The user's historical drinking water model includes at least one of the following in historical drinking water behavior: environmental information, water output parameter information, status information, operation information, and human vital signs information.

[0142] The model generation module 720 is used to generate drinking water models based on users' historical drinking water data.

[0143] The model sending module 730 is used to send a drinking water model to at least one smart drinking water device to instruct the smart drinking water device to determine the water output parameters according to the drinking water model and the water output influence parameters of the smart drinking water device.

[0144] In summary, this application provides a control device for an intelligent drinking water device. A server generates a drinking water model based on the user's historical drinking water data and sends the model to the intelligent drinking water device. Before dispensing water, the intelligent drinking water device can determine the dispensing parameters based on the current environmental parameters and the drinking water model, and dispense water according to the determined parameters. Since the drinking water model is generated based on the user's historical drinking water data, the generated dispensing parameters are highly likely to match the user's drinking habits. Furthermore, the process of determining the dispensing parameters does not require manual control by the user. Therefore, the technical solution provided by this application can meet the user's personalized drinking water needs while saving the user the effort of controlling the dispensing parameters.

[0145] In some embodiments, the user's historical drinking water data includes historical drinking water data in different time periods, and the drinking water model includes water dispensing strategies in different time periods; the model generation module 720 is used to divide the user's historical drinking water data into historical drinking water data in multiple time periods; and to generate a water dispensing strategy for the i-th time period based on the historical drinking water data in the i-th time period, where i is a positive integer.

[0146] In some embodiments, the water dispensing strategy for the i-th time period includes a first functional relationship between ambient temperature and water dispensing temperature within multiple offset ranges. The model generation module 720 is used to determine the standard ambient temperature within the i-th time period based on historical drinking water data; divide multiple offset ranges based on historical drinking water data within the i-th time period, where the offset range refers to the range to which the historical ambient temperature in the historical drinking water data within the i-th time period is offset relative to the standard ambient temperature within the i-th time period; and fit the first functional relationship between ambient temperature and water dispensing temperature within the k-th offset range based on the historical ambient temperature and historical water dispensing temperature within the k-th offset range, where k is an integer.

[0147] like Figure 8 As shown, this application also provides an electronic device 800, which includes a processor 810 and a memory 820. The electronic device can be a smart water dispenser or a server. The memory 820 stores computer program instructions.

[0148] The processor 810 may include one or more processing cores. The processor 810 connects to various parts of the entire battery management system using various interfaces and lines, and performs various functions and processes data of the battery management system by running or executing instructions, programs, code sets, or instruction sets stored in the memory 820, and by calling data stored in the memory 820. Optionally, the processor 810 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 810 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 810 and may be implemented separately using a communication chip.

[0149] The memory 820 may include random access memory (RAM) or read-only memory (ROM). The memory 820 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 820 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing various methods described below. The data storage area may also store data created during the use of the electronic device (such as phonebook data, audio and video data, chat log data, etc.).

[0150] Please see Figure 9 The present application also provides a computer-readable storage medium 900, which stores computer program instructions 910 that can be invoked by a processor to perform the methods described in the above embodiments.

[0151] The computer-readable storage medium 900 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 900 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 900 has storage space for computer program instructions 910 that perform any of the method steps S described above. These computer program instructions 910 can be read from or written to one or more computer program products. The computer program instructions 910 may be compressed in an appropriate form.

[0152] The above are merely preferred examples of this application and are not intended to limit this application in any way. Although this application has disclosed the preferred examples above, they are not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent examples without departing from the scope of the technical solution of this application. Any simple modifications, equivalent changes and alterations made to the above examples based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A control method for an intelligent drinking water device, characterized in that, The method, applied to intelligent drinking water devices, includes: The water output parameters of the intelligent drinking water device are obtained, and the water output parameters represent the parameters that affect the water output parameters of the intelligent drinking water device; the water output parameters include environmental parameters; The water output parameters of the smart drinking water device are determined using the water output influence parameters and a pre-stored drinking water model. The pre-stored drinking water model is based on the user's historical drinking water data and includes water output strategies for multiple time periods. The water output strategy for the i-th time period includes a first functional relationship between ambient temperature and water output temperature within multiple offset ranges. The water output strategy for the i-th time period is obtained by fitting historical ambient temperature and historical water output temperature within different offset ranges within the i-th time period. The offset range refers to the range of historical ambient temperature offset from the standard ambient temperature within the i-th time period. The standard ambient temperature within the i-th time period is the average of historical ambient temperatures in the historical drinking water data within the i-th time period, or the standard ambient temperature within the i-th time period is the historical ambient temperature that appears most frequently in the historical drinking water data within the i-th time period. The water output parameters include at least one of the following: water output temperature, water output volume, and water output type. Water is discharged according to the stated discharge parameters.

2. The method according to claim 1, characterized in that, The environmental parameters are used to describe the environment in which the smart drinking water device is located. The step of obtaining the water output parameters of the smart drinking water device includes: Upon receiving a water dispensing command, the environmental parameters of the intelligent drinking water device are acquired. The process of determining the water output parameters of the intelligent drinking water device using the environmental parameters and a pre-stored drinking water model includes: Obtain the target time period, which represents the time period to which the timestamp of the water discharge command is received belongs; Based on the pre-stored drinking water model and the target time period, the water dispensing strategy of the smart drinking water device during the target time period is determined. The water dispensing strategy includes at least one mapping relationship between the water dispensing parameters and the environmental parameters. The effluent parameters are determined based on the environmental parameters and the effluent strategy.

3. The method according to claim 2, characterized in that, The environmental parameters include ambient temperature, the water output parameters include water output temperature, and the water output strategy of the intelligent drinking water device during the target time period includes the mapping relationship between ambient temperature and water output temperature during the target time period. The step of determining the water dispensing strategy of the smart drinking water device based on the pre-stored drinking water model and the target time period includes: Obtain the standard ambient temperature corresponding to the target time period; Determine the offset of the ambient temperature relative to the standard ambient temperature; The mapping relationship is determined based on the offset range in which the offset is located.

4. The method according to claim 1, characterized in that, The parameters affecting the water output also include human vital signs parameters, which include at least one of the following: body temperature parameter and heart rate parameter; The process of determining the water output parameters of the smart drinking water device using the water output influence parameters and a pre-stored drinking water model includes: Obtain the water dispensing strategy corresponding to the human vital signs parameters from the drinking water model; The water output parameters of the intelligent drinking water device are determined according to the water output strategy corresponding to the human vital signs parameters.

5. A control method for an intelligent drinking water device, characterized in that, Applied to a server, the method includes: The system receives historical drinking data of a user sent by at least one smart drinking water device. The user's historical drinking model includes at least one of the following from historical drinking behavior: environmental information, water output parameter information, status information, operation information, and human vital signs information. A drinking water model is generated based on the user's historical drinking water data. The drinking water model includes water dispensing strategies for multiple time periods. The water dispensing strategy for the i-th time period includes a first functional relationship between ambient temperature and water dispensing temperature within multiple offset ranges. The water dispensing strategy for the i-th time period is obtained by fitting historical ambient temperature and historical water dispensing temperature within different offset ranges within the i-th time period. The offset range refers to the range of the offset of historical ambient temperature in the historical drinking water data within the i-th time period relative to the standard ambient temperature within the i-th time period. The standard ambient temperature within the i-th time period is the average of historical ambient temperatures in the historical drinking water data within the i-th time period, or the standard ambient temperature within the i-th time period is the historical ambient temperature that appears most frequently in the historical drinking water data within the i-th time period. The drinking water model is sent to at least one of the smart drinking water devices to instruct the smart drinking water devices to determine the water output parameters according to the drinking water model and the water output influence parameters of the smart drinking water devices, the water output influence parameters including environmental parameters.

6. The method according to claim 5, characterized in that, The user's historical drinking water data includes historical drinking water data within different time periods, and the drinking water model includes water dispensing strategies for different time periods; The process of generating a drinking water model based on the user's historical drinking water data includes: The user's historical drinking water data is divided into historical drinking water data for multiple time periods; Based on the historical drinking water data within the i-th time period, a water dispensing strategy for the i-th time period is generated, where i is a positive integer.

7. The method according to claim 6, characterized in that, The water dispensing strategy for the i-th time period includes a mapping relationship between ambient temperature and water dispensing temperature within multiple offset ranges. Generating the water dispensing strategy for the i-th time period based on historical drinking water data within the i-th time period includes: The standard ambient temperature for the i-th time period is determined based on the historical drinking water data for the i-th time period. The historical drinking water data within the i-th time period is divided into multiple offset ranges, where each offset range refers to the range to which the historical ambient temperature in the historical drinking water data within the i-th time period is offset relative to the standard ambient temperature within the i-th time period. Based on the historical ambient temperature and historical effluent temperature within the k-th offset range, a first functional relationship between the ambient temperature and effluent temperature within the k-th offset range is fitted, where k is an integer.

8. A control device for an intelligent drinking water equipment, characterized in that, The device includes: The parameter acquisition module is used to acquire the water output impact parameters of the smart drinking water device. The water output impact parameters characterize the parameters that affect the water output parameters of the smart drinking water device; the water output impact parameters include environmental parameters. The parameter determination module is used to determine the water output parameters of the smart drinking water device through the water output influence parameters and a pre-stored drinking water model. The pre-stored drinking water model is determined based on the user's historical drinking water data. The pre-stored drinking water model includes water output strategies for multiple time periods. The water output strategy for the i-th time period includes a first functional relationship between ambient temperature and water output temperature within multiple offset ranges. The water output strategy for the i-th time period is obtained by fitting historical ambient temperature and historical water output temperature within different offset ranges within the i-th time period. The offset range refers to the range of the offset of historical ambient temperature in the historical drinking water data within the i-th time period relative to the standard ambient temperature within the i-th time period. The standard ambient temperature within the i-th time period is the average of historical ambient temperatures in the historical drinking water data within the i-th time period, or the standard ambient temperature within the i-th time period is the historical ambient temperature that appears most frequently in the historical drinking water data within the i-th time period. The water output parameters include at least one of the following: water output temperature, water output volume, and water output type. The water outlet module is used to dispense water according to the stated water outlet parameters.

9. A control device for an intelligent drinking water equipment, characterized in that, The device includes: The data receiving module is used to receive historical drinking data of a user sent by at least one smart drinking device. The user's historical drinking model includes at least one of the following in historical drinking behavior: environmental information, water output parameter information, status information, operation information, and human vital signs information. A model generation module is used to generate a drinking water model based on the user's historical drinking water data. The drinking water model includes water dispensing strategies for multiple time periods. The water dispensing strategy for the i-th time period includes a first functional relationship between ambient temperature and water dispensing temperature within multiple offset ranges. The water dispensing strategy for the i-th time period is obtained by fitting historical ambient temperature and historical water dispensing temperature within different offset ranges within the i-th time period. The offset range refers to the range of the offset of historical ambient temperature in the historical drinking water data within the i-th time period relative to the standard ambient temperature within the i-th time period. The standard ambient temperature within the i-th time period is the average of historical ambient temperatures in the historical drinking water data within the i-th time period, or the standard ambient temperature within the i-th time period is the historical ambient temperature that appears most frequently in the historical drinking water data within the i-th time period. The model sending module is used to send the drinking water model to at least one of the smart drinking water devices to instruct the smart drinking water devices to determine the water output parameters according to the drinking water model and the water output influence parameters of the smart drinking water devices, wherein the water output influence parameters include environmental parameters.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing computer program instructions, which are invoked by the processor to execute the method as described in any one of claims 1-7.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that is invoked by a processor to execute the method as described in any one of claims 1-7.

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