Automatic cooking method and device, electronic equipment and storage medium

Abstract

The invention discloses an automatic cooking method and device, electronic equipment and a storage medium, and the automatic cooking method comprises the steps: obtaining sensor monitoring data and user historical sleep data in response to an appointment cooking operation for an intelligent cooking machine; based on the sensor monitoring data and the historical sleep data of the user, calculating a prediction time period of getting up of the user; obtaining a preset menu and a recommended menu corresponding to the prediction time period, and determining a target menu at the current time according to the weight values of the preset menu and the recommended menu; and based on the target menu, executing an automatic cooking operation for the intelligent cooking machine. According to the scheme, the target menu of the cooking task is determined by predicting the getting-up time of the user and combining the weight values of the preset menu and the recommended menu, so that the automatic cooking task is executed according to the target menu, a reasonable and personalized automatic cooking service is provided for the user, and the user experience is improved while cooking automation is realized. And the intelligent degree of automatic cooking and the user experience are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an automatic cooking method, device, electronic device and storage medium. Background Art

[0002] With the continuous development of science and technology, smart homes have begun to penetrate into all aspects of family life. For example, smart rice cookers, smart induction cookers, and smart cooking machines are gradually replacing traditional automated cooking equipment.

[0003] At present, automatic cooking equipment can obtain the cooking menu pre-set by the user when the preset cooking time arrives, and then automatically cook according to the cooking menu. However, in actual automatic cooking scenarios, when automatic cooking tasks need to be performed, the existing automatic cooking equipment is generally set to cook only according to the cooking menu pre-set by the user, and cannot automatically select the appropriate cooking menu for automatic cooking. It can be seen that the current automatic cooking solution cannot provide users with reasonable and personalized automatic cooking services. Summary of the invention

[0004] The present application provides an automatic cooking method, device, electronic device and storage medium, which can provide users with reasonable and personalized automatic cooking services, while realizing cooking automation, improving the intelligence level of automatic cooking and user experience.

[0005] In a first aspect, the present application provides an automatic cooking method, comprising:

[0006] In response to a scheduled cooking operation for the smart cooking machine, acquiring sensor monitoring data and user historical sleep data;

[0007] Calculate the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data;

[0008] Acquire the preset menu and the recommended menu corresponding to the predicted time period, and determine the target menu for the current time period according to the weight values ​​of the preset menu and the recommended menu;

[0009] Based on the target menu, an automatic cooking operation for the intelligent cooking machine is performed.

[0010] In a second aspect, the present application provides an automatic cooking device, comprising:

[0011] An acquisition module, configured to acquire sensor monitoring data and user historical sleep data in response to a scheduled cooking operation on the smart cooking machine;

[0012] A prediction module, used to calculate the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data;

[0013] A determination module, used to obtain the preset menu and the recommended menu corresponding to the predicted time period, and determine the target menu for the current time according to the weight values ​​of the preset menu and the recommended menu;

[0014] The execution module is used to execute the automatic cooking operation for the intelligent cooking machine based on the target menu.

[0015] Optionally, in some embodiments of the present application, the determining module includes:

[0016] A configuration unit, used to configure the weight function of the preset menu and the recommended menu;

[0017] A weight unit, used to calculate the weight values ​​of the preset menu and the recommended menu respectively according to the current time;

[0018] The first determining unit is used to determine a target menu at a current time based on the weight values ​​of the preset menu and the recommended menu.

[0019] Optionally, in some embodiments of the present application, the weight unit includes:

[0020] An extraction subunit, used for receiving sensor monitoring data and extracting user historical behavior data from the sensor monitoring data;

[0021] An estimation subunit, used to calculate the user's estimated time to go out based on the user's historical behavior data and the predicted time period;

[0022] The weight calculation subunit is used to calculate the time difference between the current time and the predicted going out time, and calculate the current weight values ​​of the preset menu and the recommended menu according to the time difference and the weight function.

[0023] Optionally, in some embodiments of the present application, the device further includes an updating module, and the updating module includes:

[0024] A second extraction unit, configured to receive the sensor monitoring data and extract the user health monitoring data and the user's historical dining data from the sensor monitoring data;

[0025] An updating unit, configured to update the recommended menu based on the user health monitoring data and the user's historical dining data;

[0026] The second determining unit is used to determine that the updated recommended menu is the target menu at the current time.

[0027] Optionally, in some embodiments of the present application, the second determining unit is specifically configured to:

[0028] Displaying a recommendation interface including the recommendation menu;

[0029] In response to a selection operation on the recommendation interface, a selected recommendation menu is determined as a target menu.

[0030] Optionally, in some embodiments of the present application, the prediction module includes:

[0031] A first extraction unit, configured to receive sensor monitoring data and extract device operating data from the sensor monitoring data;

[0032] A first prediction unit, configured to predict a user's sleeping time based on the device working data;

[0033] A second prediction unit, configured to calculate an average sleep duration of the user based on the historical sleep data of the user;

[0034] The third prediction unit is used to calculate the predicted time period for the user to wake up based on the sleeping time and the average sleeping time.

[0035] In a third aspect, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned automatic cooking method when executing the program.

[0036] In a fourth aspect, the present application further provides a storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above-mentioned automatic cooking method when executed by a processor.

[0037] As described above, the present application provides an automatic cooking method, device, electronic device, and storage medium. After obtaining sensor monitoring data and user historical sleep data in response to a scheduled cooking operation for an intelligent cooking machine, the predicted time period for the user to get up is calculated based on the sensor monitoring data and the user historical sleep data; then, the preset menu and recommended menu corresponding to the predicted time period are obtained, and the target menu for the current time is determined according to the weight values ​​of the preset menu and the recommended menu; finally, based on the target menu, the automatic cooking operation for the intelligent cooking machine is performed. In the automatic cooking scheme provided by the present application, the target menu for this automatic cooking task can be automatically determined according to the weight values ​​of the preset menu and the recommended menu at the current time, without the user manually adjusting the target menu, so that the intelligent cooking device can automatically cook according to the target menu, thereby providing users with reasonable and personalized automatic cooking services, and improving the intelligence level of automatic cooking and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] in:

[0040] Figure 1 It is a scene schematic diagram of the automatic cooking method provided in an embodiment of the present application.

[0041] Figure 2 It is a flow chart of the automatic cooking method provided in the embodiment of the present application.

[0042] Figure 3 It is a schematic diagram of a recommended interface in the automatic cooking method provided in an embodiment of the present application.

[0043] Figure 4 is a structural schematic diagram of an automatic cooking device provided in an embodiment of the present application;

[0044] Figure 5 is another structural schematic diagram of the automatic cooking device provided in an embodiment of the present application;

[0045] Figure 6 It is a structural schematic diagram of the intelligent cooking machine provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] The present application provides an automatic cooking method, device, electronic device and storage medium.

[0050] The automatic cooking device can be integrated into an electronic device, which can be a smart phone, a tablet computer, a laptop computer, a desktop computer or an intelligent cooking machine, but is not limited thereto. The electronic device can be directly or indirectly connected to the server through wired or wireless communication. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. This application does not limit this.

[0051] For example, see Figure 1 The automatic cooking device is integrated in the intelligent cooking machine 10. The cooking menu can be displayed on the display screen of the intelligent cooking machine 10, and the user can interact with the intelligent cooking machine 10 by touching the display screen of the intelligent cooking machine 10. In the process of automatic cooking, the intelligent cooking machine 10 responds to the scheduled cooking operation for the intelligent cooking machine and obtains the sensor monitoring data and the user's historical sleep data; the scheduled cooking operation can be triggered by the user 20 or automatically triggered within the set time, which can be determined according to the actual situation. Then, the intelligent cooking machine 10 calculates the predicted time period for the user 20 to get up based on the sensor monitoring data and the user's historical sleep data; then the intelligent cooking machine 10 obtains the preset menu and recommended menu corresponding to the predicted time period, and determines the target menu of the current time according to the weight values ​​of the preset menu and the recommended menu; finally, based on the target menu, the automatic cooking operation for the intelligent cooking machine 10 is executed.

[0052] The automatic cooking method provided in the present application can automatically determine the target menu of this automatic cooking task according to the weight values ​​of the preset menu and the recommended menu at the current time. It can be seen that the present application does not require the user to manually adjust the target menu, so that the intelligent cooking equipment can automatically cook according to the target menu, thereby providing users with reasonable and personalized automatic cooking services, thereby improving the intelligence level of automatic cooking and user experience.

[0053] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0054] An automatic cooking method comprises: in response to a scheduled cooking operation for an intelligent cooking machine, obtaining sensor monitoring data and user historical sleep data; calculating a predicted time period for the user to get up based on the sensor monitoring data and the user historical sleep data; obtaining a preset menu and a recommended menu corresponding to the predicted time period, and determining a target menu for the current time according to weight values ​​of the preset menu and the recommended menu; and executing an automatic cooking operation for the intelligent cooking machine based on the target menu.

[0055] See also Figure 2 , Figure 2 This is a flow chart of the automatic cooking method provided by this application. The specific flow of the automatic cooking method can be as follows:

[0056] 101. In response to a scheduled cooking operation on a smart cooking machine, obtain sensor monitoring data and user historical sleep data.

[0057] The scheduled cooking operation can be triggered by the user, such as displaying a scheduled cooking button on the display screen of the intelligent cooking machine, and triggering a scheduled cooking instruction in response to the user's click operation on the scheduled cooking button, and then triggering the scheduled cooking operation according to the scheduled cooking instruction. Of course, the user can also set the scheduled cooking time in the intelligent cooking machine in advance, that is, when the current time reaches the preset automatic cooking time, the scheduled cooking operation is triggered. The preset automatic cooking time can be set by the user or by the system. Optionally, in some embodiments, the scheduled cooking instruction issued by the user through the intelligent terminal can be received, for example, the user displays the scheduled cooking button on the display screen of the intelligent terminal, and in response to the user's click operation on the scheduled cooking button, the scheduled cooking instruction is issued, and the scheduled cooking operation is triggered by monitoring whether the scheduled cooking instruction issued by the user is received, that is, the step of responding to the scheduled cooking operation can not only be triggered by pre-setting the trigger time of the intelligent cooking machine, but also by receiving the scheduled cooking instruction sent by the user at the current moment.

[0058] In addition, after responding to the scheduled cooking operation, sensor monitoring data and user historical sleep data are obtained. The sensor monitoring data and user sleep data can be obtained from a cloud server through the network, or read from a local database. For example, they can be stored in a smart cooking machine, related smart home devices or a data storage module of a user terminal. After collecting monitoring data or user historical sleep data through relevant sensors, the sensor monitoring data and user historical sleep data can be uploaded to a cloud server for storage, or stored in a local database, which is not specifically limited here. Among them, the sensor monitoring data includes device working data, user health monitoring data and user historical dining data. The sensor monitoring data can be obtained through relevant sensors of different smart home devices or smart terminals. The device working data can be the working data of other smart home devices, such as smart bedroom lights, smart refrigerators, smart toilets and other devices. The user health monitoring data can be at least one of the target user's health status information and its corresponding dietary requirement information, exercise information and its corresponding dietary requirement information, emotional status information and its corresponding dietary requirement information, sleep status information and its corresponding dietary requirement information. The dietary requirement information includes the target user's taboo foods, recommended foods, recommended food amounts and recommended flavors, etc. The user's historical dining data includes meal duration, meal time period and number of meals, etc.

[0059] 102. Calculate the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data.

[0060] Specifically, based on the acquired sensor monitoring data and the user's historical sleep data, the predicted time period of the user's sleep from the next day is estimated, so that the corresponding preset menu and recommended menu can be obtained according to the predicted time period.

[0061] Optionally, in some embodiments, the step of “calculating the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data” may specifically include:

[0062] (61) receiving sensor monitoring data and extracting equipment working data from the sensor monitoring data;

[0063] (62) Predict the user’s sleep time based on device operation data;

[0064] (63) Calculate the user’s average sleep duration based on the user’s historical sleep data;

[0065] (64) Based on the time of falling asleep and the average sleep duration, the predicted time period for the user to wake up is calculated.

[0066] Specifically, the sensor monitoring data can be the time when the corresponding smart home devices are turned on or off by monitoring the relevant sensors in smart home devices such as bedroom lights, bedroom curtains, bathroom lights, and toilets. For example, the bedroom sensors monitor the opening or closing time of bedroom lights and bedroom curtains to preliminarily estimate the target user's sleep time, and the bathroom sensors monitor the working status of bathroom lights and toilets to determine whether the user goes to the bathroom during sleep, and estimate the user's toilet time; the user's historical sleep data includes data such as the user's sleep status, historical sleep time, and average sleep duration. The user's historical user data can be monitored by bedroom sensors and then stored in a local or cloud server. The bedroom sensors monitor the opening of curtains, or monitor the pressure condition of the bed to determine the user's sleep status, or monitor the user's sleep status through smart terminals such as smart watches or smart bracelets worn by the user. For example, the built-in sensors of the smart terminal are used to detect the user's heart rate, wrist movement frequency and amplitude, so as to calculate the user's average sleep duration. After obtaining the user's historical sleep data, it is uploaded to the cloud server or stored locally, and combined with the above sensor monitoring data and the user's historical sleep data, the user's wake-up time period the next day is estimated. For example, the bedroom sensor detects that the user turns off the bedroom lights and curtains at 23:00, and the bathroom sensor detects that the user last went to the bathroom before 23:00. The average sleep duration of the user's historical sleep data is 8 hours. Combined with the preset estimated time error range (such as the difference between the previous and the next is no more than 15 minutes), the estimated time for the user to get up is 06:45-07:15 the next day, thereby determining the user's estimated time to go out. This embodiment estimates the predicted time period for the target user to get up the next day based on the acquired sensor monitoring data and the user's historical sleep data, so as to subsequently obtain the preset menu and recommended menu for the predicted time period.

[0067] 103. Obtain a preset menu and a recommended menu corresponding to the predicted time period, and determine a target menu for the current time according to weight values ​​of the preset menu and the recommended menu.

[0068] Specifically, after estimating the predicted time period when the target user will wake up the next day, the preset menu and recommended menu corresponding to the predicted time are obtained. The preset menu and recommended menu can be obtained from a cloud server through the network. The preset menu and recommended menu can specifically include cooking dish information, cooking dish sorting, and preparation information and ingredient processing procedures corresponding to different cooking dishes; or, the preset menu and recommendation can also be stored locally. After the user sets the cooking menu in advance, it is stored in the local database of the automatic cooking machine. When the current time reaches the preset cooking time, the preset menu in the local database is automatically read. The recommended menu can also be obtained through crawler technology; of course, it can also be a preset menu confirmed by the user at the current moment. The specific selection can be made according to the actual situation. This application is not limited; the recommended menu is a cooking menu recommended based on the user's health monitoring data and the user's historical dining data; then, the target menu for the current time is determined based on the weight values ​​of the preset menu and the recommended menu at the current time. For example, the current time may correspond to the automatic cooking time preset by the user. The user can set the preset automatic cooking time in the smart cooking machine or the smart terminal in advance. When the current time reaches the preset automatic cooking time, the preset menu or the recommended menu is determined as the target menu for this automatic cooking task based on the weight values ​​of the preset menu and the recommended menu at the current time. By automatically adjusting the target menu of this automatic cooking task, there is no need for the user to manually adjust the target menu, and a target menu corresponding to the cooking scene that matches the current time is provided to the user.

[0069] 104. Based on the target menu, perform an automatic cooking operation on the intelligent cooking machine.

[0070] Specifically, after determining the target menu for this automatic cooking task, the intelligent cooking machine is controlled to perform the automatic cooking task, so that the intelligent cooking machine performs automatic cooking operations according to the target menu, completes this automatic cooking task, provides users with reasonable and personalized automatic cooking tasks, and improves the intelligence level of automatic cooking and user experience.

[0071] Optionally, in some embodiments, the step of “obtaining a preset menu and a recommended menu corresponding to the predicted time period, and determining a target menu for the current time according to the weight values ​​of the preset menu and the recommended menu” may specifically include:

[0072] (11) Configuring weight functions for preset menus and recommended menus;

[0073] (12) Calculate the weight values ​​of the preset menu and the recommended menu respectively according to the current time;

[0074] (13) Based on the weight values ​​of the preset menu and the recommended menu, determine the target menu for the current time.

[0075] Specifically, the specific steps for determining the target menu for the current time are as follows: first, respectively configure the weight functions of the preset menu and the recommended menu corresponding to the predicted time period, the weight function represents the weight value of the preset menu / recommended menu that changes with the size of the time difference, and set the initial weight value of the preset menu to be higher than the initial weight value of the recommended menu. As the time difference increases, the more time there is, the weight value of the preset menu decreases accordingly, and the weight value of the recommended menu increases accordingly. When the preset cooking time is reached, the weight values ​​corresponding to the preset menu and the recommended menu are calculated respectively according to the current time and the weight function, so as to determine the target menu for the current time (preset cooking time) by comparing the weight value of the preset menu with the weight value of the recommended menu, and determine a reasonable target menu for different cooking conditions to provide users with a reasonable and highly flexible automatic cooking service.

[0076] Optionally, in some embodiments, the step of “calculating the weight values ​​of the preset menu and the recommended menu respectively according to the current time” may specifically include:

[0077] (21) receiving sensor monitoring data and extracting user historical behavior data from the sensor monitoring data;

[0078] (22) Calculate the user’s estimated time to go out based on the user’s historical behavior data and the predicted time period;

[0079] (23) Calculate the time difference between the current time and the predicted departure time, and calculate the current weight values ​​of the preset menu and the recommended menu based on the time difference and the weight function.

[0080] Specifically, sensor monitoring data is obtained, and user historical behavior data in the sensor monitoring data is extracted. The user historical behavior data may include user sleep data, user washing data, user eating data, and user going out time. The acquisition of sensor monitoring data can be obtained from a cloud server through the network, or it can be stored in a local database, such as a smart home device or a data storage module of a user terminal. After collecting user historical behavior data through relevant sensors, the sensor monitoring data or user historical behavior data is uploaded to the cloud server for storage, or stored in a local database. For example, the user's sleeping action and sleep state are monitored through the bedroom sensor, or the user's sleep duration, sleep state and sleep time are collected through the user terminal, and the user's washing action and time are collected through the bathroom sensor, thereby obtaining the user's washing data, the user's action and time of taking and collecting tableware are monitored through the kitchen sensor, the user's eating time is monitored through the restaurant sensor, and the user's action and time of opening or closing the door are monitored through the door sensor, thereby obtaining the user's eating data and the user's going out time. Next, the user's washing and eating time after getting up is estimated based on the user's historical behavior data, and the user's estimated time to go out is calculated in combination with the predicted time period. For example, the user's washing and eating time is estimated to be 10 minutes, the eating time is 15 minutes, and the predicted time period for the user to get up is 06:45-07:15, then the user's estimated time to go out is preliminarily calculated to be 7:10-7:40. In addition, in order to further improve the accuracy of the estimated time to go out, the user's historical behavior data also includes the grooming time, which can obtain the time required for the user to groom after eating and before going out. For example, the user's changing time is monitored by the sensor in the cloakroom, the user's shoe-wearing time is monitored by the sensor in the shoe cabinet, and the time when the user opens and closes the door for the last time before going out is monitored by the sensor in the door, so as to determine the user's estimated time to go out based on the predicted time period and the user's historical behavior data; finally, the time difference between the current time and the predicted time to go out is calculated, and the time difference is substituted into the weight function of the preset menu and the preset function of the recommended menu, so as to calculate the current weight values ​​of the preset menu and the recommended menu, so that the target menu for the current time can be determined according to the weight values ​​of the two later.

[0081] Optionally, in some embodiments, the step of “determining the target menu at the current time based on the weight values ​​of the preset menu and the recommended menu” may specifically include:

[0082] (31) Obtaining the weight values ​​of the preset menu and the recommended menu at the current time;

[0083] (32) when it is determined that the weight value of the preset menu is greater than the weight value of the recommended menu, determining the preset menu as the target menu for the current time;

[0084] (33) When it is determined that the weight value of the preset menu is less than or equal to the weight value of the recommended menu, the recommended menu is determined to be the target menu for the current time.

[0085] Specifically, regarding determining the target menu for the current time, the present embodiment first substitutes the time difference between the current time and the predicted time to go out into the weight functions of the preset menu and the recommended menu, respectively, so as to calculate the weight values ​​of the preset menu and the recommended menu at the current time. By comparing the weight values ​​of the two, when it is determined that the weight value of the preset menu is greater than the weight value of the recommended menu, it is considered that the current time difference is small, the time before the user goes out is short, the cooking task is urgent, and it is not suitable to use the recommended menu, and the preset menu is preferred, so the preset menu is determined to be the target menu for the current time and then automatically cooked according to the target menu; when it is determined that the weight value of the preset menu is less than or equal to the weight of the recommended menu, it is considered that the current time difference is large, the time before the user goes out is long, there is sufficient cooking time, and there is more cooking time to cook the dishes of the recommended menu, so the recommended menu is determined to be the target menu for the current time and then automatically cooked according to the target menu. In addition, after determining that the recommended menu is the target menu, a reminder will be sent to the user, for example, a reminder of menu change confirmation is displayed on the display interface of the intelligent cooking machine or the display screen of the intelligent terminal, reminding the user that the menu has changed at this time, or reminding the user to confirm the menu change.

[0086] Optionally, in some embodiments, after the step of “determining the recommended menu as the target menu at the current time”, the following may be specifically included:

[0087] (41) receiving sensor monitoring data, and extracting user health monitoring data and user historical dining data from the sensor monitoring data;

[0088] (42) Updating the recommended menu based on the user’s health monitoring data and the user’s historical dining data;

[0089] (43) Determine the updated recommended menu as the target menu at the current time.

[0090] Specifically, after determining that the recommended menu is the target menu for the current time, the present embodiment also provides a method for updating the recommended menu, obtaining sensor monitoring data, wherein the acquisition method may be to obtain from a cloud server via a network, or to read from a stored local database; extracting user health monitoring data and user historical dining data from the sensor monitoring data, wherein the user health monitoring data may be at least one of the target user's health status information and its corresponding dietary requirement information, exercise status information and its corresponding dietary requirement information, emotional status information and its corresponding dietary requirement information, and sleep status information and its corresponding dietary requirement information, and the dietary requirement information includes the target user's taboo foods, recommended foods, recommended flavors, etc.; for example, the user's excrement may be collected through a smart toilet, the user's blood pressure data may be collected through a blood pressure detector, and the user's weight may be collected through a weight scale. The user's health status information is monitored through the data, so as to determine the dietary requirement information corresponding to the user's current health status; the user's exercise status information, emotional status information and sleep status information are collected through smart terminals such as smart bracelets, so as to determine the dietary requirement information corresponding to the exercise status information, emotional status information and sleep status information; at the same time, the user's historical dining data is collected through smart cooking machines and smart refrigerators, and the user's dining information in the recent period is counted, including information such as dishes, food intake and eating preferences, so as to further determine the user's current nutritional intake status and health status based on the historical dining data; finally, based on the user's health monitoring data and the user's historical dining data, the recommended menu is updated, and the updated recommended menu is determined as the target menu for the current time, so that the recommended menu can be more suitable for the user's current physical health status, and provide the user with a balanced diet and a menu that suits the user's personality.

[0091] In a specific embodiment, when the recommended menu is determined to be the target menu for the current time, it is equivalent to believing that the target user has more time at this time, so the health monitoring data of the target user is obtained, the remaining time can be calculated according to the meal duration and time period, and the cooking time is updated according to the remaining time. The updated cooking time and the health monitoring data are used as menu selection conditions, the cooking menu is reselected, and the original cooking menu is updated with the reselected menu.

[0092] In this embodiment, the recommended menu can be updated based on the user health monitoring data and the user's historical dining data extracted from the sensor monitoring data. If there is sufficient time, the user can be provided with a personalized recommended menu update service in combination with the user's health monitoring data, thereby improving the intelligence level of the smart cooking equipment and improving the user experience.

[0093] Further, when it is determined that the updated recommended menu is the target menu for the current time, a recommendation interface may be displayed, and then the user may select the updated recommended menu as the target menu for the current time in the recommendation interface. That is, optionally, in some embodiments, the step of "determining that the updated recommended menu is the target menu for the current time" may specifically include:

[0094] (51) displaying a recommendation interface including a recommendation menu;

[0095] (52) In response to a selection operation on the recommendation interface, the selected recommendation menu is determined as a target menu.

[0096] For example, see Figure 4 In this application, a recommendation interface is provided. After the recommendation menu is updated,

[0097] The recommendation interface displays a recommendation menu, which includes dishes m1, m2, and m3. In response to the user's selection operation on the recommendation menu, each dish in the recommendation menu is confirmed or adjusted. For example, if the user does not want to eat a certain dish today, the dish can be deleted or replaced with another dish. If the user has no objection to the recommended menu, the user can click the confirmation button to confirm and determine the selected recommended menu as the target menu. In addition, the smart cooking machine can also output a replenishment reminder to remind the user to replenish ingredients based on the dishes and preparation information.

[0098] After determining that the updated recommended menu is the target menu for the current time, this embodiment can also receive secondary adjustments to the target menu from the user, thereby determining the final target menu, improving the practicality and flexibility of the menu adjustment, and providing users with intelligent and personalized automatic cooking services.

[0099] The above is the automatic cooking process of this application.

[0100] As described above, the present application provides an automatic cooking method, in response to the scheduled cooking operation for the intelligent cooking machine, after obtaining the sensor monitoring data and the user's historical sleep data, the predicted time period for the user to get up is calculated based on the sensor monitoring data and the user's historical sleep data; then, the preset menu and the recommended menu corresponding to the predicted time period are obtained, and the target menu of the current time is determined according to the weight value of the preset menu and the recommended menu; finally, the automatic cooking operation for the intelligent cooking machine is performed based on the target menu. In the automatic cooking scheme provided by the present application, the predicted time period for the user to get up is calculated according to the obtained sensor monitoring data and the user's historical sleep data, and the preset menu and the recommended menu of the predicted time period are obtained. When the preset cooking time is reached, the target menu of the current time is determined according to the weight value of the preset menu and the recommended menu, so as to automatically cook according to the target menu. It can be seen that when the automatic cooking task needs to be executed, the target menu of this automatic cooking task can be determined according to the weight value of the preset menu and the recommended menu at the current time, without the user manually adjusting the target menu, so that the intelligent cooking device automatically cooks according to the target menu, and can provide users with reasonable and personalized automatic cooking services, thereby improving the intelligence level of automatic cooking and user experience.

[0101] In order to better implement the automatic cooking method of the embodiment of the present application, the embodiment of the present invention also provides a device based on the automatic cooking method (hereinafter referred to as the automatic cooking device). The meanings of the terms are the same as those in the automatic cooking method, and the specific implementation details can refer to the description in the method embodiment.

[0102] See also Figure 4 , Figure 4 The structure diagram of the automatic cooking device provided in the embodiment of the present application, wherein the automatic cooking device may include an acquisition module 201, a prediction module 202, a determination module 203 and an execution module 204, which may be specifically as follows:

[0103] The acquisition module 201 is used to acquire sensor monitoring data and user historical sleep data in response to a scheduled cooking operation on the intelligent cooking machine.

[0104] Specifically, the scheduled cooking operation can be triggered by the user, such as displaying a scheduled cooking button on the display screen of the smart cooking machine, and in response to the user clicking the scheduled cooking button, triggering a scheduled cooking instruction, and then triggering the scheduled cooking operation according to the scheduled cooking instruction. After responding to the scheduled cooking operation, the sensor monitoring data and the user's historical sleep data are obtained. The acquisition of the sensor monitoring data and the user's sleep data can be obtained from a cloud server through the network, or can be stored in a local database and read.

[0105] The prediction module 202 is used to calculate the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data.

[0106] Specifically, based on the acquired sensor monitoring data and the user's historical sleep data, the predicted time period of the user's sleep from the next day is estimated, so that the corresponding preset menu and recommended menu can be obtained according to the predicted time period.

[0107] The determination module 203 is used to obtain the preset menu and the recommended menu corresponding to the predicted time period, and determine the target menu for the current time according to the weight values ​​of the preset menu and the recommended menu.

[0108] Specifically, after estimating the predicted time period when the target user will wake up the next day, the preset menu and recommended menu corresponding to the predicted time are obtained. The preset menu and recommended menu can be obtained from a cloud server via the network or read from a local database. The target menu for the current time is determined based on the weight values ​​of the preset menu and the recommended menu at the current time.

[0109] The execution module 204 is used to execute the automatic cooking operation for the intelligent cooking machine based on the target menu.

[0110] Specifically, after determining the target menu for this automatic cooking task, the intelligent cooking machine is controlled to perform the automatic cooking task, so that the intelligent cooking machine performs automatic cooking operations according to the target menu, completes this automatic cooking task, provides users with reasonable and personalized automatic cooking tasks, and improves the intelligence level of automatic cooking and user experience.

[0111] Optionally, in some embodiments, the determination module 203 may specifically include:

[0112] A configuration unit, used to configure the weight function of the preset menu and the recommended menu;

[0113] A weight unit, used to calculate the weight values ​​of the preset menu and the recommended menu respectively according to the current time;

[0114] The first determining unit is used to determine a target menu at a current time based on weight values ​​of the preset menu and the recommended menu.

[0115] Optionally, in some embodiments, the weight unit may specifically include:

[0116] An extraction subunit, used for receiving sensor monitoring data and extracting user historical behavior data from the sensor monitoring data;

[0117] The estimation subunit is used to calculate the user's estimated time to go out based on the user's historical behavior data and the predicted time period;

[0118] The weight calculation subunit is used to calculate the time difference between the current time and the predicted going out time, and calculate the current weight values ​​of the preset menu and the recommended menu according to the time difference and the weight function.

[0119] Optionally, in some embodiments, see Figure 5 The automatic cooking device of the present application may further include an update module 205, which may include:

[0120] A second extraction unit, configured to receive the sensor monitoring data and extract the user health monitoring data and the user's historical dining data from the sensor monitoring data;

[0121] An updating unit, used for updating the recommended menu based on the user's health monitoring data and the user's historical dining data;

[0122] The second determining unit is used to determine that the updated recommended menu is the target menu at the current time.

[0123] Optionally, in some embodiments of the present application, the second determining unit is specifically configured to: display a recommendation interface including a recommendation menu; and in response to a selection operation on the recommendation interface, determine the selected recommendation menu as a target menu.

[0124] Optionally, in some embodiments, the prediction module 202 may specifically include:

[0125] A first extraction unit, used for receiving sensor monitoring data and extracting equipment working data from the sensor monitoring data;

[0126] A first prediction unit, configured to predict a user's sleeping time based on device working data;

[0127] A second prediction unit, configured to calculate an average sleep duration of a user based on the user's historical sleep data;

[0128] The third prediction unit is used to calculate the predicted time period for the user to wake up based on the sleeping time and the average sleeping time.

[0129] In summary, the embodiment of the present application provides an automatic cooking device, which obtains sensor monitoring data and user historical sleep data in response to the scheduled cooking operation for the intelligent cooking machine through the acquisition module 201; the prediction module 202 calculates the predicted time period for the user to get up based on the sensor monitoring data and the user's historical sleep data; the determination module 203 obtains the preset menu and recommended menu corresponding to the predicted time period, and determines the target menu for the current time according to the weight values ​​of the preset menu and the recommended menu; the execution module 204 executes the automatic cooking operation for the intelligent cooking machine based on the target menu. The automatic cooking device provided by the present application can automatically determine the target menu of this automatic cooking task according to the weight values ​​of the preset menu and the recommended menu at the current time. It can be seen that this solution does not require the user to manually adjust the target menu, so that the intelligent cooking device can automatically cook according to the target menu, thereby providing users with reasonable and personalized automatic cooking services, and improving the intelligence level of automatic cooking and user experience.

[0130] It should be noted that in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0131] Correspondingly, an embodiment of the present application also discloses a storage medium storing a computer program that can be loaded by a processor and execute the above-mentioned automatic cooking method.

[0132] The present application also discloses a smart cooking device, such as Figure 6 As shown, it includes a processor 100, at least one communication bus 200, a user interface 300, at least one external communication interface 400 and a memory 500. The processor 100 is used to provide computing and control capabilities, the communication bus 200 is configured to achieve connection and communication between these components, the user interface 300 may include a display screen, the external communication interface 400 may include a standard wired interface and a wireless interface, and the automatic cooking method is stored in the memory 500. The processor 100 is also used to execute the steps of the automatic cooking method stored in the memory 500.

[0133] Optionally, in some embodiments, the processor implements the following steps when executing the computer program:

[0134] In response to a scheduled cooking operation for the smart cooking machine, sensor monitoring data and the user's historical sleep data are obtained, and based on the sensor monitoring data and the user's historical sleep data, a predicted time period for the user to get up is calculated; a preset menu and a recommended menu corresponding to the predicted time period are obtained, and a target menu for the current time is determined based on the weight values ​​of the preset menu and the recommended menu; based on the target menu, an automatic cooking operation for the smart cooking machine is performed.

[0135] In an embodiment of the present application, the target menu of this automatic cooking task can be automatically determined based on the weight values ​​of the preset menu and the recommended menu at the current time. There is no need for the user to manually adjust the target menu, so that the intelligent cooking equipment can automatically cook according to the target menu, thereby providing users with reasonable and personalized automatic cooking services, thereby improving the intelligence level of automatic cooking and user experience.

[0136] It should be understood that "one embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0137] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0138] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0139] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0140] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately configured as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0141] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0142] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0143] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. An automatic cooking method, characterized in that: include: In response to a scheduled cooking operation for the smart cooking machine, acquiring sensor monitoring data and user historical sleep data; Calculate the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data; Acquire the preset menu and the recommended menu corresponding to the predicted time period, and determine the target menu for the current time period according to the weight values ​​of the preset menu and the recommended menu; Based on the target menu, an automatic cooking operation for the intelligent cooking machine is performed.

2. The automatic cooking method according to claim 1, characterized in that: The obtaining of the preset menu and the recommended menu corresponding to the predicted time period, and determining the target menu for the current time according to the weight values ​​of the preset menu and the recommended menu, includes: Configuring weight functions of the preset menu and the recommended menu; Calculate the weight values ​​of the preset menu and the recommended menu respectively according to the current time; Based on the weight values ​​of the preset menus and the recommended menus, a target menu for the current time is determined.

3. The automatic cooking method according to claim 2, characterized in that: The calculating the weight values ​​of the preset menu and the recommended menu respectively according to the current time includes: Receiving sensor monitoring data, and extracting user historical behavior data from the sensor monitoring data; Calculate the user's estimated time to go out based on the user's historical behavior data and the predicted time period; The time difference between the current time and the predicted going-out time is calculated, and the current weight values ​​of the preset menu and the recommended menu are calculated according to the time difference and the weight function.

4. The automatic cooking method according to claim 2, characterized in that: The determining the target menu at the current time based on the weight values ​​of the preset menu and the recommended menu includes: Obtaining weight values ​​of the preset menu and the recommended menu at the current time; When it is determined that the weight value of the preset menu is greater than the weight value of the recommended menu, determining the preset menu as the target menu for the current time; When it is determined that the weight value of the preset menu is less than or equal to the weight value of the recommended menu, the recommended menu is determined to be the target menu at the current time.

5. The automatic cooking method according to claim 4, characterized in that: After determining that the recommended menu is the target menu at the current time, the method further includes: Receiving sensor monitoring data, and extracting user health monitoring data and user historical dining data from the sensor monitoring data; Based on the user health monitoring data and the user's historical dining data, updating the recommended menu; The updated recommended menu is determined to be the target menu at the current time.

6. The automatic cooking method according to claim 5, characterized in that: The step of determining the updated recommended menu as the target menu at the current time includes: Displaying a recommendation interface including the recommendation menu; In response to a selection operation on the recommendation interface, a selected recommendation menu is determined as a target menu.

7. The automatic cooking method according to claim 1, characterized in that: The calculating the predicted time period for the user to get up based on the sensor monitoring data and the user's historical sleep data includes: Receiving sensor monitoring data, and extracting equipment working data from the sensor monitoring data; Predicting the user's sleeping time based on the device working data; Calculate the average sleep duration of the user based on the historical sleep data of the user; Based on the sleeping time and the average sleeping duration, a predicted time period for the user to wake up is calculated.

8. An automatic cooking device, characterized in that: include: An acquisition module, configured to acquire sensor monitoring data and user historical sleep data in response to a scheduled cooking operation on the smart cooking machine; A prediction module, used to calculate the predicted time period for the user to wake up based on the sensor monitoring data and the user's historical sleep data; A determination module, used to obtain the preset menu and the recommended menu corresponding to the predicted time period, and determine the target menu for the current time according to the weight values ​​of the preset menu and the recommended menu; The execution module is used to execute the automatic cooking operation for the intelligent cooking machine based on the target menu.

9. An electronic device, characterized in that: include: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the automatic cooking method according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the steps of the automatic cooking method according to any one of claims 1 to 7 are implemented.

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