Method and device for intelligent device to deploy AI life assistant on cloud desktop, and medium

Abstract

The invention relates to a method and equipment for deploying an AI life assistant on a cloud desktop by intelligent equipment, and a medium. Comprising the steps that a zero terminal receives historical daily data information, sent by intelligent equipment, of a user in a certain period of time in the past; wherein the intelligent device comprises a head-mounted intelligent device and a wrist strap type intelligent device, and the historical daily data information comprises historical visual data collected by the head-mounted intelligent device and historical health data collected by the wrist strap type intelligent device; analyzing the historical visual data through a machine learning algorithm to plan daily browsing information of the user, and sending the daily browsing information to the cloud desktop; and analyzing the historical exercise data through a machine learning algorithm to plan a daily exercise plan of the user. By planning the daily browsing information, the daily activity plan, the daily rest plan and the daily recipe plan of the user, comprehensive intelligent service is provided for the user in life, and the life quality of the user is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a method, device, and medium for deploying an AI life assistant on a cloud desktop using a smart device. Background Art

[0002] While cloud desktops have achieved the ability to obtain user information through smart wearable devices, they haven't yet analyzed and utilized this information for intelligent functions. For example, while smart wearable devices can capture user actions, from simple hand gestures to real-time voice commands, and quickly transmit this information to the cloud desktop terminal, this merely transmits the user's actual actions to the terminal through the smart wearable device, replacing the current keyboard and mouse operations. The user information collected by the smart wearable device hasn't been analyzed and processed, and applied to the user's daily life to manage and optimize their lives, thereby improving their quality of life.

[0003] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method, device and medium for deploying an AI life assistant on a cloud desktop on a smart device, thereby overcoming one or more problems caused by the limitations and defects of related technologies to at least a certain extent.

[0006] According to a first aspect of an embodiment of the present application, a method for deploying an AI life assistant on a cloud desktop using a smart device is provided, comprising:

[0007] The zero terminal receives historical daily data information of a user over a certain period of time in the past sent by a smart device; wherein the smart device includes a head-mounted smart device and a wrist-mounted smart device, and the historical daily data information includes historical visual data collected by the head-mounted smart device and historical health data collected by the wrist-mounted smart device; the historical visual data at least includes browsing news, browsing pictures or videos, and the historical health data at least includes historical exercise data, historical activity data, historical sleep quality data and historical body fat percentage data; the historical exercise data at least includes past exercise duration and past exercise distance, the historical activity data at least includes past sedentary time and past activity time, the historical sleep quality data at least includes past sleep time and past lunch break time, and the historical body fat percentage data includes past body fat percentage;

[0008] Analyzing the historical visual data through a machine learning algorithm to plan the user's daily browsing information, and sending the daily browsing information to a cloud desktop;

[0009] Analyzing the historical exercise data by the machine learning algorithm to plan a daily exercise plan for the user, and sending the daily exercise plan to the cloud desktop;

[0010] Analyzing the historical activity data using the machine learning algorithm to plan a daily activity plan for the user, and sending the daily activity plan to the cloud desktop;

[0011] Analyzing the historical sleep quality data through the machine learning algorithm to plan a daily routine for the user, and sending the daily routine to the cloud desktop;

[0012] The historical body fat percentage data is analyzed by the machine learning algorithm to plan a daily diet plan for the user, and the daily diet plan is sent to the cloud desktop.

[0013] In an exemplary embodiment of the present application, the step of analyzing the historical visual data using a machine learning algorithm to plan the user's daily browsing information includes:

[0014] If the time the user spends browsing news under a certain topic exceeds a first preset browsing time, it is predicted that the browsing news corresponding to the topic is the content that the user focuses on, and the content that the user focuses on is used as the user's daily browsing information;

[0015] If the time the user spends browsing pictures or videos under a certain topic exceeds a second preset browsing time, it is predicted that the browsing pictures or videos corresponding to the topic are the content that the user focuses on, and the content that the user focuses on is used as the user's daily browsing information.

[0016] In an exemplary embodiment of the present application, the step of analyzing the historical exercise data by the machine learning algorithm to plan the user's daily exercise plan and sending the daily exercise plan to the cloud desktop includes:

[0017] deriving the average daily exercise duration of the user in the past period of time based on all the past exercise durations of the user in the past period of time;

[0018] Calculating the user's average daily exercise distance over the past period of time based on all the user's past exercise distances over the past period of time;

[0019] The daily exercise time, main exercise mode and daily exercise distance of the user in a future period are generated based on the daily average exercise duration and the daily average exercise distance of the user in the past period.

[0020] In an exemplary embodiment of the present application, the step of analyzing the historical activity data by the machine learning algorithm to plan the user's daily activity plan and sending the daily activity plan to the cloud desktop includes:

[0021] deriving the average daily sedentary time of the user in the past period of time based on all the past sedentary time of the user in the past period of time;

[0022] Calculating the average daily activity duration of the user in the past period of time based on all the past activity durations of the user in the past period of time;

[0023] The daily sedentary time and daily activity time of the user in a future period of time are generated based on all the daily average sedentary time and the daily average activity time of the user in a past period of time.

[0024] In an exemplary embodiment of the present application, the step of analyzing the historical sleep quality data by the machine learning algorithm to plan the user's daily schedule, and sending the daily schedule to the cloud desktop includes:

[0025] deriving the average daily sleep duration of the user in the past period of time based on all the past sleep durations of the user in the past period of time;

[0026] Calculating the average daily lunch break duration of the user in the past period of time based on all the past lunch break durations of the user in the past period of time;

[0027] The daily sleep time and daily lunch break time of the user in a future period of time are generated according to the daily average sleep time and the daily average lunch break time of the user in a past period of time.

[0028] In an exemplary embodiment of the present application, the step of analyzing the historical body fat percentage data by the machine learning algorithm to plan the user's daily diet plan, and sending the daily diet plan to the cloud desktop includes:

[0029] Calculating the user's daily average body fat percentage over the past period of time based on all of the user's past body fat percentages over the past period of time;

[0030] recommending three meals a day for the user in the future based on the daily average body fat percentage;

[0031] If the daily average body fat percentage is less than the preset lower limit of the body fat percentage, a daily recipe that can increase fat is pushed;

[0032] If the daily average body fat percentage is greater than or equal to the lower limit of the preset body fat percentage and less than or equal to the upper limit of the preset body fat percentage, a daily recipe that can maintain the current body fat percentage is pushed;

[0033] If the daily average body fat percentage is greater than the upper limit of the preset body fat percentage, a daily recipe that can reduce fat is pushed.

[0034] In an exemplary embodiment of the present application, the step of analyzing the historical body fat percentage data using the machine learning algorithm to plan the user's daily diet plan, and sending the daily diet plan to the cloud desktop further includes:

[0035] The user's daily diet that can increase body fat, maintain the current body fat percentage, or reduce body fat is collected, daily calories corresponding to the daily diet are estimated, and a first calorie curve is generated.

[0036] In an exemplary embodiment of the present application, the method further includes:

[0037] Collecting food pictures of the user during a certain period of time in the past, determining the category of the food, estimating the calories of the past food, obtaining the average calories of the past food, and generating a corresponding second calorie curve;

[0038] When the daily calorie intake is less than the average calorie intake of food in the past days, reminding the user;

[0039] When the daily calorie intake is greater than the average calorie intake of past food, an additional exercise plan is planned; wherein the additional exercise plan refers to an exercise plan that can consume excess calories in the daily calorie intake.

[0040] According to a second aspect of an embodiment of the present application, there is provided an electronic device, including:

[0041] processor; and

[0042] a memory for storing executable instructions of the processor;

[0043] The processor is configured to execute the steps of the method for deploying an AI life assistant on a cloud desktop by a smart device in any of the above embodiments by executing the executable instructions.

[0044] According to a third aspect of an embodiment of the present application, a computer storage medium is provided, on which a computer program is stored, which is used to implement the steps of the method of deploying an AI life assistant on a cloud desktop by an intelligent device as described in any of the above embodiments when executed by a processor.

[0045] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0046] In the embodiments of the present application, the above-described method uses a machine learning algorithm to analyze historical visual data collected by a head-mounted smart device to plan a user's daily browsing information. This not only makes the pushed daily browsing information more consistent with the user's browsing habits, but also saves browsing time and enhances the user's browsing experience. A machine learning algorithm also uses a method to analyze historical exercise data collected by a wristband smart device to plan a user's daily exercise plan, facilitating the user's daily exercise schedule and avoiding insufficient or excessive exercise. A machine learning algorithm also uses a method to analyze historical activity data to plan a user's daily activity schedule, facilitating the user's rational allocation of activity time, reducing sedentary time, increasing physical activity, and promoting health. A machine learning algorithm also uses a method to analyze historical sleep quality data to plan a user's daily routine, facilitating the user's development of healthy sleep habits, arranging rest time, and improving sleep quality. This allows the user to recover their strength and energy through adequate sleep, improving their daily life and work. A machine learning algorithm also uses a method to analyze historical body fat percentage data to plan a user's daily diet plan, facilitating the user's rational allocation of calorie needs. This application provides users with all-round intelligent services in life by planning their daily browsing information, daily activity plan, daily rest plan and daily recipe plan, thereby improving their quality of life.

[0047] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0049] Figure 1 A flowchart showing the steps of a method for deploying an AI life assistant on a cloud desktop using a smart device in an exemplary embodiment of the present application is shown;

[0050] Figure 2An architectural diagram illustrating deployment of an AI life assistant on a cloud desktop using a smart device in an exemplary embodiment of the present application is shown;

[0051] Figure 3 Schematically illustrates a schematic diagram of an electronic device in an exemplary embodiment of the present application;

[0052] Figure 4 A schematic diagram of a program product in an exemplary embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0053] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0054] In addition, the accompanying drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0055] This example implementation first provides a method for deploying an AI life assistant on a cloud desktop using a smart device. Figure 1 As shown in , the method includes steps S101 to S102.

[0056] Among them, step S101: the zero terminal receives the historical daily data information of the user in the past period of time sent by the smart device; wherein the smart device includes a head-mounted smart device and a wrist-mounted smart device, and the historical daily data information includes historical visual data collected by the head-mounted smart device and historical health data collected by the wrist-mounted smart device; the historical visual data at least includes browsing news, browsing pictures or videos, the historical health data at least includes historical exercise data, historical activity data, historical sleep quality data and historical body fat rate data, the historical exercise data at least includes past exercise duration and past exercise distance, the historical activity data at least includes past sedentary duration and past activity duration, the historical sleep quality data at least includes past sleep duration and past lunch break duration, and the historical body fat rate data includes past body fat rate.

[0057] Step S102: Analyze historical visual data through a machine learning algorithm to plan the user's daily browsing information, and send the daily browsing information to the cloud desktop.

[0058] Step S103: Analyze historical exercise data through a machine learning algorithm to plan the user's daily exercise plan, and send the daily exercise plan to the cloud desktop.

[0059] Step S104: Analyze historical activity data through a machine learning algorithm to plan the user's daily activity plan, and send the daily activity plan to the cloud desktop.

[0060] Step S105: Analyze the historical sleep quality data through a machine learning algorithm to plan the user's daily routine, and send the daily routine to the cloud desktop.

[0061] Step S106: Analyze historical body fat percentage data through a machine learning algorithm to plan the user's daily diet plan, and send the daily diet plan to the cloud desktop.

[0062] Through the above method, a machine learning algorithm analyzes historical visual data collected by a head-mounted smart device to plan a user's daily browsing information. This not only makes the pushed daily browsing information more consistent with the user's browsing habits, but also saves browsing time and enhances the user's browsing experience. A machine learning algorithm also analyzes historical exercise data collected by a wristband smart device to plan a user's daily exercise plan, helping the user to exercise rationally and avoid insufficient or excessive exercise. A machine learning algorithm also analyzes historical activity data to plan a user's daily activity plan, helping the user to rationally schedule their activity time, reduce sedentary time, increase physical activity, and promote health. A machine learning algorithm also analyzes historical sleep quality data to plan a user's daily routine, helping the user establish good sleep habits, schedule rest time appropriately, and improve sleep quality. This allows the user to recover their strength and energy through adequate sleep, improving their daily life and work. A machine learning algorithm also analyzes historical body fat percentage data to plan a user's daily diet plan, helping the user to rationally plan their calorie needs. This application provides users with all-round intelligent services in life by planning their daily browsing information, daily activity plan, daily rest plan and daily recipe plan, thereby improving their quality of life.

[0063] Below, we will refer to Figures 1 to 2 Each step of the above method in this exemplary embodiment is described in more detail.

[0064] In step S101, a user wears a smart device, which can collect the user's historical daily data information. The smart device includes a head-mounted smart device and a wristband smart device. The head-mounted smart device can be Orion's AR glasses, which are equipped with seven micro cameras, and the wristband smart device can be Orion's neural wristband. The user's historical behavior data includes historical visual data and auditory data. The user wears AR glasses and a neural wristband, and the AR glasses can collect the user's historical visual data, and the neural wristband can collect the user's historical health data.

[0065] When collecting a user's historical daily data, this application collects data from the user over a certain period of time in the past. The past period of time is at least one month, and the past period of time can be the past one month, three months, six months, or eight months, etc. The specific setting can be based on actual conditions and this application does not impose any restrictions on this.

[0066] In step S102, the historical visual data includes at least browsing news, browsing pictures, or browsing videos. The head-mounted smart device can collect the user's daily browsing news, browsing pictures, or browsing videos over a certain period of time in the past, which can provide data support for the subsequent planning of daily browsing plans. By planning a daily browsing plan for the user, not only is the pushed daily browsing information more in line with the user's browsing habits, but it also saves browsing time and improves the user's experience when browsing content. The specific planning of the daily browsing plan will be explained in the following embodiments and will not be elaborated on in this application.

[0067] Historical health data includes at least historical exercise data, historical activity data, historical sleep quality data, and historical body fat percentage data. Historical exercise data includes at least past exercise duration and distance, historical activity data includes at least past sedentary time and past activity time, historical sleep quality data includes at least past sleep time and past lunch break time, and historical body fat percentage data includes past body fat percentage.

[0068] In step S103, the wristband-type smart device can collect the user's past exercise duration and distance over a certain period of time, providing data support for the subsequent planning of the user's daily exercise plan. By planning a daily exercise plan for the user, it is possible to facilitate the user to exercise rationally every day and avoid insufficient or excessive exercise. The specific planning of the daily exercise plan will be described in the following embodiments and will not be detailed here.

[0069] In step S104, the wristband-type smart device can collect the user's past sedentary time and past active time over a certain period of time, providing data support for the subsequent planning of the user's daily activity plan. By planning a daily activity plan for the user, it is possible to help the user rationally arrange their activity time, reduce sedentary time, increase physical activity, and promote physical health. The specific planning of the daily activity plan will be described in the following embodiments and will not be detailed here.

[0070] In step S105, the wristband-type smart device can collect the user's past sleep duration and past lunch break duration over a certain period of time, which can provide data support for the subsequent planning of the user's daily work and rest plan. By planning a daily rest plan for the user, it is easy for the user to establish good work and rest habits, arrange rest time reasonably, improve sleep quality, and allow the user to restore physical strength and energy through adequate sleep, thereby improving daily life and work status. The specific planning of the daily work and rest plan will be explained in the following embodiments, and this application will not be repeated here.

[0071] In step S106, the wristband-type smart device can collect the user's past body fat percentage over a certain period of time, providing data support for the subsequent planning of the user's daily diet plan. By planning a daily diet plan for the user, it is convenient for the user to rationally plan the calorie needs of the body. The specific planning of the daily diet plan will be described in the following embodiments and will not be detailed in this application.

[0072] It should be noted that machine learning algorithms include decision tree algorithms or neural network algorithms, etc. The decision tree algorithms and neural network algorithms can be understood in detail by referring to existing technologies.

[0073] In one embodiment, the step of analyzing historical visual data using a machine learning algorithm to plan a user's daily browsing information includes:

[0074] If the time a user spends browsing news under a certain topic exceeds a first preset browsing time, the browsing news corresponding to the topic is predicted to be the content that the user focuses on, and the content that the user focuses on is used as the user's daily browsing information;

[0075] If the time a user spends browsing pictures or videos under a certain topic exceeds a second preset browsing time, it is predicted that the browsing pictures or videos corresponding to the topic are the content that the user focuses on, and the content that the user focuses on is used as the user's daily browsing information.

[0076] It is understandable that news generally covers topics such as politics, economy, society, technology, entertainment, etc., while pictures or videos generally cover topics such as natural scenery, people, animals, food, art, etc.

[0077] It generally takes a longer time for users to browse news, and a shorter time to browse pictures or videos. Therefore, in this application, the first preset browsing time is greater than the second preset browsing time. By comparing the time a user spends browsing news under a certain topic with the first preset browsing time, it is possible to more accurately predict whether the browsing news corresponding to the topic is the content that the user focuses on. By comparing the time a user spends browsing pictures or videos under a certain topic with the second preset browsing time, it is possible to more accurately predict whether the browsing pictures or videos corresponding to the topic are the content that the user focuses on.

[0078] In one example, suppose a user typically browses news with a first preset browsing time of 3 minutes, spends 5 minutes browsing economic news, and spends 1 minute browsing entertainment news. If the user spends 5 minutes browsing economic news, which exceeds the first preset browsing time of 3 minutes, then economic news will be considered as the user's daily browsing information. Therefore, the smart device pushes economic news to the user. However, if the user spends 1 minute browsing entertainment news, which is less than the first preset browsing time of 3 minutes, then entertainment news will not be considered as the user's daily browsing information.

[0079] It should be noted that the specific value of the first preset browsing time can be set according to actual conditions, and this application will not elaborate on this.

[0080] In one example, suppose a user typically browses images or videos with a second preset browsing time of 40 seconds, spends 2 minutes browsing animal images or videos, and spends 20 seconds browsing art images or videos. If the user spends 2 minutes browsing animal images or videos, which exceeds the second preset browsing time of 40 seconds, the animal images or videos will be included as the user's daily browsing information. Therefore, the smart device pushes animal images or videos to the user. However, if the user spends 20 seconds browsing art images or videos, which is less than the second preset browsing time of 40 seconds, the art images or videos will not be included as the user's daily browsing information.

[0081] It should be noted that the specific value of the second preset browsing time can be set according to actual conditions, and this application will not elaborate on this.

[0082] In one embodiment, the steps of analyzing historical exercise data using a machine learning algorithm to plan a user's daily exercise plan and sending the daily exercise plan to the cloud desktop include:

[0083] deriving the average daily exercise duration of the user in the past period of time based on all the past exercise durations of the user in the past period of time;

[0084] Calculating the user's average daily exercise distance over the past period of time based on all the user's past exercise distances over the past period of time;

[0085] The daily exercise time, main exercise mode and daily exercise distance of the user in a future period are generated based on the daily average exercise duration and the daily average exercise distance of the user in the past period.

[0086] It is understandable that by using machine learning algorithms to count all the user's past exercise durations over a certain period of time, the user's average daily exercise duration over a certain period of time can be accurately obtained.

[0087] By calculating the user's past exercise distances over a certain period of time, the user's average daily exercise distance over the past period of time can be accurately obtained.

[0088] By analyzing the user's average daily exercise duration, main exercise mode, and average daily exercise distance over the past period, you can reasonably plan the user's daily exercise time, main exercise mode, and exercise distance for the next period of time. Daily exercise time includes the start time and end time of daily exercise.

[0089] Plan the start time and end time of the user's daily exercise according to the user's average daily exercise duration. The user's average daily exercise speed can be calculated based on the user's average daily exercise distance and the average daily exercise duration. The user's main exercise mode can be given by the user's average daily speed. The user's main exercise mode generally includes running and walking. If the calculated average daily exercise speed is close to the user's (normal adult's) normal walking speed, the user's main exercise mode is determined to be walking. If the calculated average daily exercise speed is close to 3 times to 5 times the user's (normal adult's) normal walking speed, the user's main exercise mode is determined to be running. Plan the user's daily exercise distance in the future based on the user's average daily exercise distance. Among them, the future period can be set according to actual conditions, and this application will not go into details.

[0090] Based on the primary exercise mode type and daily exercise distance, a daily exercise plan can be created for the user. For example, if the primary exercise mode type is running and the exercise distance is 6 kilometers, a daily running plan can be created for the user at 6:30 AM. The user starts running at 6:30 AM every day to complete 6 kilometers.

[0091] In one embodiment, the steps of analyzing historical activity data using a machine learning algorithm to plan a user's daily activity plan and sending the daily activity plan to the cloud desktop include:

[0092] Based on the user's past sedentary time in the past period of time, the user's average daily sedentary time in the past period of time is obtained;

[0093] Based on the user's past daily activity duration over a certain period of time, the user's average daily activity duration over a certain period of time is obtained;

[0094] Based on the user's average daily sedentary time and average daily activity time in the past period of time, the user's daily sedentary time and daily activity time in the future period of time are generated.

[0095] It is understandable that by using a machine learning algorithm to count all the past sedentary time of a user over a certain period of time, it is possible to accurately determine the user's average daily sedentary time over the past period of time and remind the user not to sit for a long time. By using a machine learning algorithm to count all the past activity time of a user over a certain period of time, it is possible to accurately determine the user's daily activity time over the past period of time and remind the user to be active, so that the user can understand his or her activity status and living habits over the past period of time. For example, if you know that you have been sitting for a long time on average every day, you may realize that you need to increase your activity level to improve your health.

[0096] This application generates the daily sedentary time and daily activity time for a period of time in the future through the user's average daily sedentary time and average daily activity time in the past. It can help users have a preliminary plan and expectation for their future lifestyle. Users can arrange time for activities in advance to avoid excessive sitting, which helps to develop good living habits and have a positive impact on physical health. For example: the user's average daily sedentary time in the past period of time was 2.1 hours, and the average daily activity time was 9.5 minutes. The user is planned to have a daily sedentary time of 2 hours and a daily activity time of 10 minutes. When the user sits for 2 hours a day, the user is reminded to have a daily activity time of 10 minutes.

[0097] It should be noted that the user's daily activity mode can be walking, aerobics, etc., which will not be elaborated in this application.

[0098] In one embodiment, the steps of analyzing historical sleep quality data using a machine learning algorithm to plan a user's daily routine and sending the daily routine to a cloud desktop include:

[0099] Based on the user's previous sleep duration over a certain period of time, the user's average daily sleep duration over the past period of time is obtained;

[0100] Based on the user's lunch break duration in the past, the average daily lunch break duration of the user in the past period is obtained;

[0101] Generate the user's daily sleep time and daily lunch break time in the future based on the user's average daily sleep time and average daily lunch break time in the past period of time.

[0102] It is understandable that by using a machine learning algorithm to count all the user's past sleep durations over a certain period of time, the user's average daily sleep duration over the past period of time can be accurately calculated. By using a machine learning algorithm to count all the user's lunch break durations over a certain period of time, the user's average daily lunch break duration over the past period of time can be accurately calculated. Then, based on the user's average daily sleep duration over the past period of time, the user's daily sleep time over the future period of time can be reasonably planned. At the same time, based on the user's average daily lunch break duration over the past period of time, the user's daily lunch break duration over the future period of time can be reasonably planned.

[0103] For example: If the user's average daily sleep duration analyzed in the past 30 days is 7.6 hours, then based on this, the user's daily sleep time for a certain period of time in the future (such as the next 30 days) will be given, and the daily sleep time includes the sleep start time and the sleep end time. The sleep start time is 22:00, the sleep end time is 6:00, and the user is reminded to sleep at 22:00. If the user's average daily lunch break duration analyzed in the past 30 days is 30 minutes, then based on this, the user's daily lunch break time for a certain period of time in the future (such as the next 30 days) will be given, and the daily lunch break time includes the lunch break start time and the lunch break end time. The lunch break start time is 13:00, the lunch break end time is 13:30, and the user is reminded to take a lunch break at 13:00.

[0104] In one embodiment, the steps of analyzing historical body fat percentage data using a machine learning algorithm to plan a user's daily diet plan and sending the daily diet plan to the cloud desktop include:

[0105] Based on the user's body fat percentage in the past period of time, the user's daily average body fat percentage in the past period of time is obtained;

[0106] Recommend three meals a day for users in the future based on their average daily body fat percentage;

[0107] If the daily average body fat percentage is lower than the preset lower limit, a daily recipe that can increase body fat will be pushed;

[0108] If the daily average body fat percentage is greater than or equal to the preset lower limit and less than or equal to the preset upper limit, a daily recipe that can maintain the current body fat percentage will be pushed;

[0109] If the daily average body fat percentage is greater than the preset upper limit of the body fat percentage, a daily recipe that can help lose fat will be pushed.

[0110] It is understood that the user's body fat percentage can be determined by height, weight and gender. Therefore, the user's height, weight and gender can be input into the wristband smart device. The specific calculation process of body fat percentage is as follows:

[0111] (1) Body Mass Index (BMI) = weight (kg) ÷ (height × height (m)).

[0112] (2) Body fat percentage: 1.2 × BMI + 0.23 × age - 5.4 - 10.8 × sex (when the sex is male, the sex value is 1, when the sex is female, the sex value is 0).

[0113] The normal body fat percentage for adult males is approximately between 10% and 20%, and for adult females it is approximately between 17% and 30%.

[0114] It should be noted that the preset body fat percentage can be set according to the user's gender. This application uses the example of a male user:

[0115] The preset lower limit for body fat percentage is 10%, and the preset upper limit for body fat percentage is 20%. If the user's calculated daily average body fat percentage over a certain period of time is 8%, and 8% is less than the preset lower limit of 10%, the user will be prompted with a daily recipe that can help them gain weight. Breakfast: a bowl of oatmeal porridge, two boiled eggs, a cup of milk, and a handful of blueberries. Oatmeal is rich in dietary fiber and complex carbohydrates, providing long-lasting energy; eggs are a source of high-quality protein; milk supplements protein and calcium; and blueberries are rich in antioxidants. Lunch: a bowl of brown rice, a serving of seared steak, and stir-fried seasonal vegetables (such as broccoli, carrots, snow peas, etc.). Compared to white rice, brown rice retains more nutrients and provides a longer-lasting feeling of fullness; steak is a good source of high-quality protein and healthy fats, helping to build muscle mass; seasonal vegetables provide vitamins, minerals, and dietary fiber. Dinner: a sweet potato, a piece of grilled salmon, and a cold cucumber salad. Sweet potatoes are a high-quality source of carbohydrates and are rich in dietary fiber; salmon is rich in high-quality protein and unsaturated fatty acids, which are good for health; cucumbers are low in calories and rich in water and dietary fiber.

[0116] If the user's calculated daily average body fat percentage over a certain period is 15%, and 15% is greater than the preset lower limit of 10% and less than the preset upper limit of 20%, the user will be prompted with a daily menu that helps maintain their current body fat percentage. Breakfast: two slices of whole-wheat bread, a cup of low-fat yogurt, a banana, and a handful of nuts. Whole-wheat bread is rich in dietary fiber and complex carbohydrates; yogurt provides protein and probiotics, which promote gut health; bananas provide energy and potassium; and nuts are rich in healthy fats, protein, and vitamin E. Lunch: half a head of corn, a pan-fried chicken breast, and stir-fried vegetables (such as bok choy and romaine lettuce). Corn, a whole grain, provides a rich source of dietary fiber and carbohydrates; chicken breast is a source of high-quality protein and low in fat; and the vegetables are rich in vitamins and minerals. Dinner: a bowl of pumpkin porridge, a serving of steamed tofu, and stir-fried bean sprouts. Pumpkin is rich in carotene and dietary fiber; tofu is a good source of plant-based protein; and bean sprouts are low in calories and rich in vitamin C and dietary fiber.

[0117] If the user's calculated daily average body fat percentage over a certain period of time is 36%, and 26% is greater than the preset upper limit of 10%, the user will be prompted with a daily recipe that can help them lose weight. Breakfast: one boiled corn, one boiled egg, and one cup of black coffee. Corn provides satiety and dietary fiber; eggs provide high-quality protein; and black coffee helps boost metabolism. Lunch: half a bowl of brown rice, stir-fried winter melon with shrimp, and cold shredded kelp salad. Brown rice is rich in dietary fiber and has a low glycemic index; shrimp is high-quality protein and low in fat; winter melon and kelp are both low-calorie, high-fiber ingredients that help reduce calorie intake and promote intestinal motility. Dinner: Vegetable salad (such as lettuce, cucumber, tomato, purple cabbage, etc., can be seasoned with a little olive oil and vinegar), and a steamed purple sweet potato. Vegetable salad is rich in vitamins, minerals, and dietary fiber, and is extremely low in calories; purple sweet potato is an excellent source of carbohydrates and rich in dietary fiber, which can increase satiety.

[0118] In one embodiment, the step of analyzing historical body fat percentage data using a machine learning algorithm to plan a user's daily diet plan and sending the daily diet plan to the cloud desktop further includes:

[0119] The user's daily diet is collected based on a daily diet that can increase body fat, a daily diet that can maintain the current body fat percentage, or a daily diet that can reduce fat, and the daily calories corresponding to the daily diet are estimated to generate a first calorie curve.

[0120] It is understandable that a daily diet that increases body fat may have a higher daily calorie count, a daily diet that reduces body fat may have a lower daily calorie count, and a daily diet that maintains a current body fat percentage may have a normal daily calorie count. By generating a first calorie curve for daily calories under a daily diet, users can better understand their daily calorie intake.

[0121] It should be noted that the calories of each ingredient in the daily recipe = = ingredient weight (g) × (calories per 100 grams of ingredient ÷ 100. Among them, the calories per 100 grams of ingredient can be obtained by referring to the existing technology, and this application will not go into details. According to the calorie calculation formula for each ingredient in each recipe, the daily calories corresponding to the daily recipe can be estimated.

[0122] In one embodiment, the method further comprises:

[0123] Collect food pictures of the user during a certain period of time in the past, determine the food category, estimate the calories of all past food, obtain the average calories of past food, and generate the corresponding second calorie curve;

[0124] When the daily calorie intake is less than the average calorie intake of previous days' food, the user will be reminded;

[0125] When daily calories are greater than the average calories of previous food, plan an additional exercise plan; the additional exercise plan refers to an exercise plan that can consume the excess calories in the daily calories.

[0126] It is understood that based on the calorie calculation formula for each ingredient in each recipe, the calorie intake of previous meals can be estimated, thereby calculating the average calorie intake of previous meals. If the daily calorie intake is less than the average calorie intake of previous meals, the user is reminded to avoid calorie excess. If the daily calorie intake is greater than the average calorie intake of previous meals, an additional exercise plan is planned for the user and the user is reminded to perform the additional exercise plan. The additional exercise plan refers to an exercise plan that can burn off the excess daily calories. The additional exercise plan can include walking, running, skipping rope, etc., and this application does not limit this.

[0127] It should be noted that a corresponding second calorie curve can be generated based on the average calorie intake of food in the past, so that the user can understand the calorie intake in the past.

[0128] It should be noted that although the steps of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all steps must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be decomposed into multiple steps. In addition, it is also easy to understand that these steps may be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0129] In an exemplary embodiment of the present application, an electronic device is further provided, which may include a processor and a memory for storing executable instructions of the processor. The processor is configured to execute the executable instructions to perform the steps of the method for deploying an AI life assistant on a cloud desktop on a smart device described in any of the above embodiments.

[0130] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods, or program products. Therefore, various aspects of the present invention may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0131] Refer to the following Figure 3 An electronic device 600 according to this embodiment of the present invention will be described. Figure 3 The electronic device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0132] like Figure 3 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting various system components (including storage unit 620 and processing unit 610), a display unit 640, and the like.

[0133] The storage unit stores program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps of various exemplary embodiments of the present invention described in the method section of deploying an AI life assistant on a cloud desktop on a smart device. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0134] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0135] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0136] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0137] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0138] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the method of deploying an AI life assistant on a cloud desktop by the above-mentioned smart device according to the embodiment of the present application.

[0139] In an exemplary embodiment of the present application, a computer storage medium is also provided, on which a computer program is stored. When the program is executed by, for example, a processor, it can implement the steps of the method of deploying an AI life assistant on a cloud desktop on a smart device as described in any of the above embodiments.

[0140] In some possible implementations, various aspects of the present invention can also be implemented in the form of a computer program product, which includes a computer program or instructions. When the computer program product is run on a terminal device, the computer program code or instructions are used to enable the terminal device to execute the steps of various exemplary embodiments of the present invention described in the above-mentioned method section of this specification for deploying an AI life assistant on a cloud desktop by a smart device.

[0141] refer to Figure 4 As shown, a program product 300 for implementing the above method according to an embodiment of the present application is described. The program product 300 may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0142] The program product described above may be written in any combination of one or more programming languages to implement the program code of the present invention, including object-oriented programming languages such as Java, C++, and the like, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0143] The computer software product may be stored in a computer storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0144] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

Claims

1. A method for deploying an AI life assistant on a cloud desktop using a smart device, characterized in that: The method includes: The zero terminal receives historical daily data information of a user over a certain period of time in the past sent by a smart device; wherein the smart device includes a head-mounted smart device and a wrist-mounted smart device, and the historical daily data information includes historical visual data collected by the head-mounted smart device and historical health data collected by the wrist-mounted smart device; the historical visual data at least includes browsing news, browsing pictures or videos, and the historical health data at least includes historical exercise data, historical activity data, historical sleep quality data and historical body fat percentage data; the historical exercise data at least includes past exercise duration and past exercise distance, the historical activity data at least includes past sedentary time and past activity time, the historical sleep quality data at least includes past sleep time and past lunch break time, and the historical body fat percentage data includes past body fat percentage; Analyzing the historical visual data through a machine learning algorithm to plan the user's daily browsing information, and sending the daily browsing information to a cloud desktop; Analyzing the historical exercise data by the machine learning algorithm to plan a daily exercise plan for the user, and sending the daily exercise plan to the cloud desktop; Analyzing the historical activity data using the machine learning algorithm to plan a daily activity plan for the user, and sending the daily activity plan to the cloud desktop; Analyzing the historical sleep quality data through the machine learning algorithm to plan a daily routine for the user, and sending the daily routine to the cloud desktop; The historical body fat percentage data is analyzed by the machine learning algorithm to plan a daily diet plan for the user, and the daily diet plan is sent to the cloud desktop.

2. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 1, wherein: The step of analyzing the historical visual data by a machine learning algorithm to plan the user's daily browsing information includes: If the time the user spends browsing news under a certain topic exceeds a first preset browsing time, it is predicted that the browsing news corresponding to the topic is the content that the user focuses on, and the content that the user focuses on is used as the user's daily browsing information; If the time the user spends browsing pictures or videos under a certain topic exceeds a second preset browsing time, it is predicted that the browsing pictures or videos corresponding to the topic are the content that the user focuses on, and the content that the user focuses on is used as the user's daily browsing information.

3. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 1, wherein: The step of analyzing the historical exercise data by the machine learning algorithm to plan the user's daily exercise plan, and sending the daily exercise plan to the cloud desktop includes: deriving the average daily exercise duration of the user in the past period of time based on all the past exercise durations of the user in the past period of time; Calculating the user's average daily exercise distance over the past period of time based on all of the user's past exercise distances over the past period of time; The daily exercise time, main exercise mode and daily exercise distance of the user in a future period are generated based on the daily average exercise duration and the daily average exercise distance of the user in the past period.

4. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 1, wherein: The step of analyzing the historical activity data by the machine learning algorithm to plan the user's daily activity plan and sending the daily activity plan to the cloud desktop includes: deriving the average daily sedentary time of the user in the past period of time based on all the past sedentary time of the user in the past period of time; Calculating the average daily activity duration of the user in the past period of time based on all the past activity durations of the user in the past period of time; The daily sedentary time and daily activity time of the user in a future period of time are generated based on all the daily average sedentary time and the daily average activity time of the user in a past period of time.

5. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 1, wherein: The step of analyzing the historical sleep quality data by the machine learning algorithm to plan the user's daily schedule, and sending the daily schedule to the cloud desktop includes: deriving the average daily sleep duration of the user in the past period of time based on all the past sleep durations of the user in the past period of time; Calculating the average daily lunch break duration of the user in the past period of time based on all the past lunch break durations of the user in the past period of time; The daily sleep time and daily lunch break time of the user in a future period of time are generated according to the daily average sleep time and the daily average lunch break time of the user in a past period of time.

6. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 1, wherein: The step of analyzing the historical body fat percentage data by the machine learning algorithm to plan a daily diet plan for the user, and sending the daily diet plan to the cloud desktop includes: Calculating the user's daily average body fat percentage over the past period of time based on all of the user's past body fat percentages over the past period of time; recommending three meals a day for the user in the future based on the daily average body fat percentage; If the daily average body fat percentage is less than the preset lower limit of the body fat percentage, a daily recipe that can increase fat is pushed; If the daily average body fat percentage is greater than or equal to the lower limit of the preset body fat percentage and less than or equal to the upper limit of the preset body fat percentage, a daily recipe that can maintain the current body fat percentage is pushed; If the daily average body fat percentage is greater than the upper limit of the preset body fat percentage, a daily recipe that can reduce fat is pushed.

7. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 6, wherein: The step of analyzing the historical body fat percentage data by the machine learning algorithm to plan a daily diet plan for the user, and sending the daily diet plan to the cloud desktop further includes: The user's daily diet that can increase body fat, maintain the current body fat percentage, or reduce body fat is collected, daily calories corresponding to the daily diet are estimated, and a first calorie curve is generated.

8. The method for deploying an AI life assistant on a cloud desktop using a smart device according to claim 7, wherein: The method further comprises: Collecting food pictures of the user during a certain period of time in the past, determining the category of the food, estimating the calories of the past food, obtaining the average calories of the past food, and generating a corresponding second calorie curve; When the daily calorie intake is less than the average calorie intake of food in the past days, the user is reminded; When the daily calorie intake is greater than the average calorie intake of past food, an additional exercise plan is planned; wherein the additional exercise plan refers to an exercise plan that can consume excess calories in the daily calorie intake.

9. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the steps of the method for deploying an AI life assistant on a cloud desktop by a smart device according to any one of claims 1 to 8 by executing the executable instructions.

10. A computer storage medium having a computer program stored thereon, characterized in that: The program is used to implement the steps of the method for deploying an AI life assistant on a cloud desktop by a smart device as described in any one of claims 1 to 8 when executed by a processor.

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