Silent mode control method and device, refrigeration equipment, medium and product

Abstract

The invention provides a mute mode control method and device, refrigeration equipment, a medium and a product. According to one embodiment of the invention, the method comprises the following steps: acquiring operation data of a user on household equipment in a historical preset time period; wherein the operation data is used for representing the work and rest time of the user; predicting a change trend of work and rest time based on the operation data by using a time period prediction model, and obtaining a predicted sleep time period of the user; and in the predicted sleep period, the refrigeration equipment is controlled to start a mute mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigeration equipment, and in particular to a control method and device for a silent mode, a refrigeration equipment, a medium and a product. BACKGROUND

[0002] With the improvement of consumers' requirements for the quality of life, in addition to meeting the basic use requirements, refrigeration equipment is increasingly focusing on comfort and environmental protection. Noise pollution, as an important indicator in the home environment, is gradually being concerned by consumers.

[0003] Therefore, major manufacturers have begun to develop and promote refrigeration equipment with a silent function. The silent mode can significantly reduce the noise level of the appliance during operation by adjusting the working mechanism inside the refrigeration equipment, such as reducing the fan speed and reducing the compressor working frequency, so as to meet the needs of consumers for a quiet home environment.

[0004] However, the start period of the silent mode currently needs to be manually set by the user in the client. The user's sleep period may also change, and the user also needs to modify the start period through the client, which is troublesome and often forgotten by the user. The working sound of the refrigeration equipment that fails to adjust the silent period in time will affect the user's rest. SUMMARY

[0005] To overcome the problems in the related art, the present application provides a control method and device for a silent mode, a refrigeration equipment, a medium and a product.

[0006] According to a first aspect of any of the embodiments of the present application, a control method for a silent mode is provided, the method comprising:

[0007] acquiring operation data of a user on a household device collected within a historical preset period; wherein the operation data is used to represent the user's work and rest time;

[0008] using a period prediction model to predict the trend of the work and rest time based on the operation data, and obtaining a predicted sleep period of the user;

[0009] controlling the refrigeration equipment to start the silent mode within the predicted sleep period.

[0010] According to a second aspect of any of the embodiments of the present application, a control device for a silent mode is provided, the device comprising:

[0011] an operation data acquisition module configured to acquire operation data of a user on a household device collected within a historical preset period; wherein the operation data is used to represent the user's work and rest time;

[0012] a sleep period prediction module configured to predict a change trend of the work-rest time of the user based on the operation data by using a period prediction model, and obtain a predicted sleep period of the user;

[0013] a mute mode starting module configured to control the refrigeration device to start a mute mode in the predicted sleep period.

[0014] According to a third aspect of any of the embodiments of the present application, a refrigeration device is provided, comprising:

[0015] a processor;

[0016] a memory for storing processor-executable instructions;

[0017] The processor implements the method described in any of the embodiments of the present application by running the executable instructions.

[0018] According to a fourth aspect of any of the embodiments of the present application, a computer-readable storage medium is provided, which stores computer instructions, and the instructions are executed by a processor to implement the method described in any of the embodiments of the present application.

[0019] According to a fifth aspect of any of the embodiments of the present application, a computer program product is provided, which stores computer program / instructions, and the computer program / instructions are executed by a processor to implement the method described in any of the embodiments of the present application.

[0020] The technical solutions provided in the present application can include the following beneficial effects:

[0021] According to the above embodiments, by acquiring the operation data of the user on the household device in the historical preset period, using a period prediction model, predicting the change trend of the work-rest time of the user based on the operation data, obtaining the predicted sleep period of the user, and controlling the refrigeration device to start a mute mode in the predicted sleep period, the actual work-rest time of the user is automatically predicted according to the use of the household device by the user, without the need for the user to manually modify the starting period of the mute mode, ensuring the consistency of the predicted sleep period and the starting period of the mute mode, thereby guaranteeing the best sleep quality of the user and meeting the needs of the user for undisturbed sleep.

[0022] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.

[0024] Figure 1is a flowchart of a control method of a mute mode according to an exemplary embodiment of the present application;

[0025] Figure 2 is an interaction diagram of a control method of a mute mode according to an exemplary embodiment of the present application;

[0026] Figure 3 is a schematic diagram of an interface for not starting a mute hosting mode according to an exemplary embodiment of the present application;

[0027] Figure 4 is a schematic diagram of an interface for starting a mute hosting mode according to an exemplary embodiment of the present application;

[0028] Figure 5 is a structural schematic diagram of a refrigeration device according to an exemplary embodiment of the present application;

[0029] Figure 6 is a block diagram of a control apparatus of a mute mode according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0030] The exemplary embodiments will now be described in detail with reference to the accompanying drawings. The following description, with reference to the drawings, is made for the purpose of illustrating the exemplary embodiments of the present application. The same reference numerals in different drawings denote the same or similar elements throughout the several exemplary embodiments. The following exemplary embodiments described in the following description do not represent all of the implementations consistent with the present application. Instead, they only represent different ways of implementing the present application, and do not limit the scope of the present application as defined by the appended claims.

[0031] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the description of the present application and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It also will be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0032] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used only as a shorthand notation to first, second, third, etc. terms in describing various embodiments. For example, a first information can be termed a second information, and similarly, a second information can be termed a first information without departing from the scope of the present application. Depending on the context, the word "if' as used herein can be interpreted to mean "when" or "in response to determining" or "in response to a determination".

[0033] At present, the opening and closing of the mute mode depend on the user manually modifying the start period of the mute mode in the client, which is more troublesome and often easily forgotten by the user, resulting in the working sound of the refrigeration equipment affecting the sleep quality of the user.

[0034] To solve the above problems, the present application provides a control method of a mute mode. To further illustrate the present application, the following embodiments are provided:

[0035] Please refer to Figure 1 , Figure 1 is a flowchart of a control method of a mute mode according to an exemplary embodiment. The control method of the mute mode can be applied to a refrigeration equipment, which is responsible for managing resources and data processing. The refrigeration equipment is an electronic equipment with a mute mode and networking function, such as a refrigerator, a freezer, an air conditioner, etc.

[0036] The control method of the mute mode can also be applied to a server, which communicates with the refrigeration equipment and controls the start state and the off state of the mute mode in the refrigeration equipment. The server is a computer device that manages resources and provides data processing services for the refrigeration equipment, such as a cloud server, a terminal server, etc.

[0037] Taking the control method of the mute mode applied to the server as an example, the control method of the mute mode can include the following steps:

[0038] Step 102: acquiring operation data of the user on the household equipment collected in a historical preset period; wherein the operation data is used to represent the user's work and rest time.

[0039] In this step, the server can collect the operation data of the user on the household equipment with networking function at regular intervals, and store the operation data in a storage module such as a database.

[0040] The server can acquire the collected operation data in the historical preset period, preprocess the operation data, remove noise and outliers in the operation data, and standardize the operation data for subsequent analysis.

[0041] Among them, the household equipment includes: refrigeration equipment, and other equipment. Other equipment is a device that has an association relationship with the refrigeration equipment, such as a television, a smart door, etc. The work and rest time is the activity time and sleep time of the user in daily life.

[0042] The association between the other device and the refrigeration device can be that the other device and the refrigeration device are bound to the same user account, that the other device and the refrigeration device are connected to the same wireless fidelity (WiFi) network, or that the other device and the refrigeration device are connected to the same Bluetooth, and the embodiments of the present application do not limit the association.

[0043] The operation data is use data of the user on the household device including the refrigeration device, for example, opening door time of the refrigerator, closing door time of the refrigerator, starting time of the television, opening / closing door time of the smart door, sleep data monitored by the smart bracelet, and the like.

[0044] The operation data can also include activity frequency of the user in each period, for example, the number of times of using the household device and the total length of time of using the household device. The period is a shorter period divided from a specific time period in the evening and the morning, such as an hour or half an hour, so as to more finely analyze the activity of the user.

[0045] The historical preset period is a period of time before the predicted sleep period, for example, the previous three weeks, the previous month, and the like.

[0046] In step 104, a period prediction model is used to predict a change trend of the work-rest time based on the operation data, and a predicted sleep period of the user is obtained.

[0047] In this step, the server uses a period prediction model to determine the fluctuation of the work-rest time of the user in the historical preset period based on the operation data, predicts the change trend of the work-rest time of the user in different quarters and different months, and obtains the predicted sleep period of the user next time or in the future.

[0048] The server can extract features related to the work-rest time from the preprocessed operation data, for example, the use length of the household device, the time period of use of the household device, the opening / closing sequence of the household device, and the like.

[0049] Particular attention is paid to the use behavior of the user on the household device before falling asleep and after waking up, such as turning off the light and closing the television before falling asleep, and turning on the light and opening the news application after waking up.

[0050] The operation data is input into the period prediction model, and the period prediction model is used to predict the wake-up time and the time of not using the household device of the user, and to predict the fluctuation of the sleep period of the user. According to the change trend of the work-rest time, the use behavior of the user on the household device in the historical preset period is combined, and the predicted sleep period is output.

[0051] The service end can also collect historical prediction sleep period, environment temperature where the refrigeration equipment is located, average sleep period of the region where the user is located, weather and other reference data. The service end inputs the operation data and the reference data into the period prediction model, and uses the period prediction model to obtain the prediction sleep period output by the period prediction model on the basis of predicting the change trend of the work and rest time of the user and in combination with the reference data.

[0052] The period prediction model is a prediction model constructed based on machine learning, deep learning and the like, and is used to predict the sleep period of the user. The prediction sleep period is the time period from the start of sleep to the end of sleep in the future of the user, for example, 22:00-6:00 the next day.

[0053] Before prediction by using the period prediction model, the service end can pre-collect historical data related to the work and rest time of the user. The historical data can include time series data, seasonal data, periodic data and the like.

[0054] The historical data is divided into a training set and a test set. The training set is used to train the initial period prediction model. In the training process, a method such as a loss function minimization method or a gradient descent method is used to fit the training set by continuously adjusting the model parameters. The test set is used to evaluate the performance of the period prediction model.

[0055] According to the evaluation result of the period prediction model, the parameters of the period prediction model are adjusted to obtain the trained period prediction model. With the change of the habits of the user and the generation of new operation data, the model is updated based on the latest operation data to maintain its prediction accuracy.

[0056] It can be understood that the training process of the period prediction model shown in the embodiments of the present application is only an example. The period prediction model can also be trained by using a cross-validation method or an ensemble learning method. The embodiments of the present application do not limit this.

[0057] Further, after obtaining the prediction sleep period, the service end can send the prediction sleep period to the client of the user, so that the client displays the prediction sleep period and prompts the user to start the quiet mode of the refrigeration equipment.

[0058] The client is an APP (Application) or a mini program associated with the refrigeration equipment through a user account, WiFi, Bluetooth or the like.

[0059] As described above, by sending the prediction sleep period to the client of the user, the client displays the prediction sleep period, informs the user of the start period of the prediction quiet mode, and improves the use experience of the user.

[0060] Step 106: controlling the refrigeration equipment to start the silent mode in the predicted sleep period.

[0061] In this step, in the case of reaching the start time point of the predicted sleep period, the server sends a start instruction to the refrigeration equipment to make the refrigeration equipment start the silent mode.

[0062] In the case of reaching the end time point of the predicted sleep period, the server sends a close instruction to the refrigeration equipment to make the refrigeration equipment close the silent mode, so as to control the refrigeration equipment to start the silent mode in the predicted sleep period.

[0063] The start instruction is used to instruct the refrigeration equipment to start the silent mode in the refrigeration equipment. The close instruction is used to instruct the refrigeration equipment to close the silent mode in the refrigeration equipment.

[0064] The control method of the silent mode in the embodiment, by acquiring the operation data of the user on the household equipment in the historical preset period, using the period prediction model, predicting the change trend of the user's work and rest time based on the operation data, obtaining the predicted sleep period of the refrigeration equipment, controlling the refrigeration equipment to start the silent mode in the predicted sleep period, automatically predicting the actual work and rest time of the user according to the use of the household equipment by the user, without manually modifying the start period of the silent mode by the user, ensuring the consistency of the actual sleep period of the user and the start period of the silent mode, so as to guarantee the best sleep quality of the user and meet the needs of the user to sleep undisturbed.

[0065] Moreover, based on the operation data of the user on each household equipment, the operation data is more comprehensive, the prediction is made by using the period prediction model, the operation data of complex data structure can be processed, the operation data in a longer period is analyzed, the long-term trend, seasonal change and periodic law in the operation data are more accurately captured and recognized, so that the predicted sleep period is more accurate.

[0066] In the foregoing embodiments, the silent mode is automatically started in the predicted sleep period by intelligently predicting the future sleep period of the user. In the following embodiments, how to further improve the accuracy of the predicted sleep period will be described in detail, and any of the above embodiments can be applied.

[0067] In an embodiment, the user can set the set silent period and the effective date of the set silent period through the client, the display screen of the refrigeration equipment, etc. The server can acquire the set silent period set by the user, for example: 21:00-6:00 the next day. The set silent period is analyzed, including the start time point, the end time point and the duration of the set silent period.

[0068] If the set silent period is still within the effective date, the server inputs the set silent period and the operation data into the period prediction model, the period prediction model predicts the change trend of the work-rest time based on the set silent period, analyzes the correlation between the operation data and the set silent period, such as the decrease of the activity frequency of the user before reaching the set silent period, which means that the user will enter the sleep state, and obtains the predicted sleep period output by the period prediction model.

[0069] The set silent period is a sleep period manually set by the user.

[0070] As described above, by obtaining the set silent period set by the user, the change trend of the work-rest time is predicted by using the period prediction model in combination with the operation data and the set silent period, and the predicted sleep period is obtained, so that the predicted sleep period output by the period prediction model is more accurate, and the consistency of the sleep period of the user and the start period of the silent mode is further ensured, and the sleep quality of the user is ensured.

[0071] In an embodiment, the refrigeration equipment can include a refrigerator, and the operation data can include the door opening time point and the door closing time point of the refrigerator.

[0072] The server can collect the door opening time point and the door closing time point of the refrigerator, and input the door opening time point and the door closing time point into the period prediction model to extract the time of each door opening, the duration of each door opening, the number of door openings per day, the last door opening time in the evening and other features from the door opening time point and the door closing time point.

[0073] The server inputs the features corresponding to the door opening time point and the door closing time point into the period prediction model, and uses the period prediction model to predict based on the operation data of the user on the refrigerator to obtain the predicted sleep period output by the period prediction model.

[0074] As described above, by inputting the door opening time point and the door closing time point of the refrigerator into the period prediction model, the predicted sleep period output by the period prediction model is obtained, and since the door opening time point and the door closing time point of the refrigerator are usually closely related to the daily life habits of the user, the use habits of the refrigerator are usually stable, and based on the door opening time point and the door closing time point of the refrigerator, the predicted sleep period is more accurate, and the sleep quality of the user is further ensured.

[0075] In an embodiment, the server inputs the operation data into the pre-trained period prediction model to obtain the predicted sleep period of the next time when the silent mode is started.

[0076] The time point of predicting the next sleep period, for example, can be after the refrigeration equipment is turned off each time, or can be a fixed time point each day, and the embodiments of the present application do not limit this.

[0077] As described above, by inputting the operation data into the pre-trained time period prediction model, obtaining the predicted sleep time period of the next start of the silent mode output by the time period prediction model, the sleep time period of the user each day is automatically predicted, and the start time period of the silent mode is ensured to be updated in time in the scene of business trip and the like.

[0078] In an embodiment, the server can construct and update the data portrait of the user by integrating and analyzing the identity data and the behavior data of the user. The data portrait of the user is obtained, and the age, gender, geographical location, industry and other characteristics of the user in the data portrait are analyzed.

[0079] The characteristics in the data portrait of the user are fused with the characteristics in the operation data, for example, the static characteristics such as the age and occupation of the user can be combined with the dynamic characteristics such as the use time and use frequency of the home device.

[0080] In the fused feature set, the characteristics highly related to the work and rest habits of the user are selected to reduce the complexity of the time period prediction model and improve the prediction accuracy.

[0081] By using the time period prediction model, based on the fused feature set of the operation data and the data portrait of the user, the work and rest habits of the corresponding population of the feature are analyzed, the work and rest habits and the operation data of the user are combined, the change trend of the work and rest time is predicted, and the predicted sleep time period is obtained.

[0082] Among them, the data portrait is used to reflect the living habits, preferences and behavior patterns of the user.

[0083] As described above, by obtaining the data portrait of the user, using the time period prediction model, combining the operation data and the work and rest habits corresponding to the data portrait, the predicted sleep time period is obtained, so that the predicted sleep time period is more accurate, and the sleep quality of the user is further guaranteed.

[0084] In order to further illustrate the control process of the server and the refrigeration device on the silent mode, Figure 2 An interaction diagram of a control method of a silent mode is shown. As Figure 2 shown, taking a refrigerator as an example, the control method of the silent mode can include the following steps:

[0085] Step 202: The server obtains the set silent time period set by the user.

[0086] In this step, after the user opens the silent hosting mode of the refrigerator, the user can set the set silent time period through the client corresponding to the refrigerator, and the server stores the set silent time period into the database. The server can obtain the set silent time period set by the user from the database.

[0087] Please refer to Figure 3 , Figure 3A schematic diagram of an interface for starting the silent hosting mode is shown. The user can start the silent hosting mode in the interface of the APP, and customize the start time point and the end time point of the silent period.

[0088] Figure 4 A schematic diagram of an interface for starting the silent hosting mode is shown. As shown in the figure, Figure 4 the user sets the start time point of the silent period to 22:00 and the end time point to 6:00. The user can also set the end date of starting the silent hosting mode and the reminder setting of reaching the end date in the interface.

[0089] Step 204: The server acquires the operation data collected in the historical preset period.

[0090] In this step, the server acquires the operation data of the refrigerator collected in the historical preset period, such as the opening time point and the closing time point.

[0091] Step 206: The server uses the period prediction model to predict the change trend of the work and rest time in combination with the operation data, the set silent period and the work and rest habit corresponding to the data portrait, and obtains the predicted sleep period of the user.

[0092] In this step, the server uses the period prediction model to predict the change trend of the work and rest time in combination with the operation data, the set silent period and the work and rest habit corresponding to the data portrait, and obtains the predicted sleep period of the user.

[0093] Step 208: The server sends the predicted sleep period to the client of the user, so that the client displays the predicted sleep period.

[0094] In this step, the server sends the predicted sleep period to the client of the user, and the client can display the predicted sleep period in the interface.

[0095] Step 210: The server sends a start instruction to the refrigeration equipment when the start time point of the predicted sleep period is reached.

[0096] In this step, the server sends a start instruction to the refrigeration equipment when the start time point of the predicted sleep period is reached, and also sends a start notification of the silent mode to the client. The client can display that the silent mode is in the start state in the interface in response to the received start notification.

[0097] Step 212: The refrigerator starts the silent mode in response to the received start instruction.

[0098] In this step, the refrigerator starts the silent mode in response to the received start instruction from the server, and adjusts the running frequency to the low-frequency and low-noise running mode.

[0099] Step 214: In the case where the service end reaches the end time point of the predicted sleep period, the service end sends a closing instruction to the refrigeration equipment.

[0100] In this step, in the case where the service end reaches the end time point of the predicted sleep period, the service end sends a closing instruction to the refrigeration equipment, and also sends a closing notification of the silent mode to the client. The client can display that the silent mode is in the closing state in the interface in response to the received closing notification.

[0101] Step 216: The refrigerator closes the silent mode in response to the received closing instruction.

[0102] In this step, the refrigerator closes the silent mode and adjusts the running frequency to the normal running mode in response to the received closing instruction sent by the service end.

[0103] Figure 5 is a structural schematic diagram of a refrigeration equipment according to an exemplary embodiment of the present application. The refrigeration equipment may, for example, be a refrigerator, a freezer, a refrigerator cabinet, a cabinet air conditioner, a central air conditioner, etc. Referring to Figure 5 At the hardware level, the refrigeration equipment includes a processor 502, an internal bus 504, a network interface 506, a memory 508, and a non-volatile memory 510, and of course, other hardware required by the business. The processor 502 reads the corresponding computer program from the non-volatile memory 510 into the memory 508 and then runs, and forms a silent mode control device at the logic level. Of course, in addition to the software implementation, the present application does not exclude other implementation manners, such as logic devices or software and hardware combined manner, etc., that is, the execution subject of the following processing flow is not limited to the logical units, but also can be hardware or logic devices.

[0104] Figure 6 is a block diagram of a silent mode control device according to an exemplary embodiment of the present application. Referring to Figure 6 The device can include an operation data acquisition module 602, a sleep period prediction module 604, and a silent mode starting module 608, wherein:

[0105] The operation data acquisition module 602 is configured to acquire operation data of a user on a household equipment collected in a historical preset period; wherein the operation data is used to represent the work and rest time of the user;

[0106] The sleep period prediction module 604 is configured to use a period prediction model to predict a change trend of the work and rest time based on the operation data, and obtain a predicted sleep period of the user;

[0107] The silent mode starting module 608 is configured to control the refrigeration equipment to start the silent mode in the predicted sleep period.

[0108] In an example, the sleep period prediction module 604, when used for obtaining the predicted sleep period of the user by using a period prediction model to predict the change trend of the work-rest time based on the operation data, comprises: obtaining a set quiet period set by the user; and using the period prediction model to predict the change trend of the work-rest time by combining the operation data and the set quiet period, to obtain the predicted sleep period.

[0109] In an example, the refrigeration device comprises a refrigerator; and the operation data comprises opening and closing time points of the refrigerator.

[0110] In an example, the sleep period prediction module 604, when used for obtaining the predicted sleep period of the user by using a period prediction model to predict the change trend of the work-rest time based on the operation data, comprises: inputting the operation data into the pre-trained period prediction model to obtain the predicted sleep period of the next time when the quiet mode is started, output by the period prediction model.

[0111] In an example, the sleep period prediction module 604, after being used for obtaining the predicted sleep period of the user by using a period prediction model to predict the change trend of the work-rest time based on the operation data, further comprises: sending the predicted sleep period to a client of the user, so that the client displays the predicted sleep period.

[0112] In an example, the sleep period prediction module 604, when used for obtaining the predicted sleep period of the user by using a period prediction model to predict the change trend of the work-rest time based on the operation data, comprises: obtaining a data portrait of the user; and using the period prediction model to obtain the predicted sleep period by combining the operation data and the work-rest habit corresponding to the data portrait.

[0113] The implementation processes of the functions and roles of the units in the above device are specifically described in the implementation processes of the corresponding steps in the above method, which will not be described here.

[0114] For the apparatus embodiments, since they basically correspond to the method embodiments, the relevant parts can be seen from the parts of the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected to achieve the purposes of the present application according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0115] In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as a memory comprising instructions, is also provided, which can be executed by a processor of the control device in the mute mode to implement the method according to any one of the above embodiments.

[0116] The non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc., and the present application is not limited thereto.

[0117] In an exemplary embodiment, a computer program product comprising computer programs / instructions is also provided, which can be executed by a processor of the control device in the mute mode to implement the method according to any one of the above embodiments.

[0118] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0119] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The specification and examples given are not intended to limit the scope of the application, which is set out in the claims. Various modifications and changes can be made thereto without departing from the spirit and scope of the application.

[0120] The above description is merely illustrative of the application, and is not to be taken in a limiting sense. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the application are included in the scope of the application.

Claims

1. A control method of a mute mode, characterized by, The method comprises: acquiring operation data of a user on a household device collected in a historical preset period; wherein the operation data is used to represent the user's sleep-wake time; using a period prediction model to predict the change trend of the sleep-wake time based on the operation data, and obtaining a predicted sleep period of the user; controlling the refrigeration device to start a silent mode in the predicted sleep period.

2. The method of claim 1, wherein, The method comprises: acquiring a set silent period set by the user; using the period prediction model to predict the change trend of the sleep-wake time in combination with the operation data and the set silent period, and obtaining the predicted sleep period.

3. The method of claim 1, wherein, The refrigeration device comprises a refrigerator; and the operation data comprises door opening and closing time points of the refrigerator.

4. The method of claim 1, wherein, The method comprises: inputting the operation data into the period prediction model to obtain a predicted sleep period for the next time the silent mode is started output by the period prediction model.

5. The method of claim 1, wherein, After the method of predicting the change trend of the sleep-wake time based on the operation data using the period prediction model and obtaining the predicted sleep period of the user, the method further comprises: sending the predicted sleep period to a client of the user to enable the client to display the predicted sleep period.

6. The method of claim 1, wherein, The method comprises: acquiring a data portrait of the user; using the period prediction model to obtain the predicted sleep period in combination with the sleep-wake habit corresponding to the data portrait.

7. A control device for a mute mode, characterized by The device comprises: an operation data acquisition module configured to acquire operation data of a user on a household device collected in a historical preset period; wherein the operation data is used to represent the user's sleep-wake time; a sleep period prediction module configured to use a period prediction model to predict the change trend of the sleep-wake time based on the operation data, and obtain a predicted sleep period of the user; a silent mode start module configured to control the refrigeration device to start a silent mode in the predicted sleep period.

8. A refrigeration appliance characterized in that, The device comprises: a processor; a memory for storing processor-executable instructions; wherein the processor executes the executable instructions to implement the method of any one of claims 1-6.

9. A computer readable storage medium having stored thereon computer instructions, wherein, The instructions are executed by the processor to implement the method of any one of claims 1-6.

10. A computer program product having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the method of any one of claims 1-6.

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