Schedule recommendation method and device, electronic equipment and storage medium

Abstract

The invention discloses a schedule recommendation method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-do event of a first user; and displaying schedule recommendation information of the to-be-handled event based on the to-be-handled event and the schedule information of the first user.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and specifically relates to a schedule recommendation method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of electronic device technology and mobile Internet, various humanized service functions on electronic devices are increasing. For example, the schedule provided by electronic devices can help users arrange their time, or remind users to handle or participate in certain activities in time.

[0003] In the related art, schedules are manually set by humans, and schedule recommendations are made to users according to the manually set schedules. However, if the user's schedule changes or an emergency occurs, the manually set fixed schedule cannot adjust the recommendation strategy in time to adapt to the user's new situation, and thus cannot meet the user's real-time needs or interest changes, making the schedule recommendation content mismatched with the user's current actual situation, reducing the effectiveness of the schedule recommendation. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a schedule recommendation method, device, electronic device and storage medium, which can improve the effectiveness of schedule recommendation.

[0005] In a first aspect, an embodiment of the present application provides a schedule recommendation method, comprising:

[0006] Get the to-do events of the first user;

[0007] Based on the to-do event and the schedule information of the first user, the schedule recommendation information of the to-do event is displayed.

[0008] In a second aspect, an embodiment of the present application provides a schedule recommendation device, including:

[0009] An acquisition module, used to acquire to-do events of the first user;

[0010] A display module is used to display the schedule recommendation information of the to-do event based on the to-do event and the schedule information of the first user.

[0011] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the schedule recommendation method shown in the first aspect.

[0012] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the schedule recommendation method shown in the first aspect are implemented.

[0013] In a fifth aspect, an embodiment of the present application provides a chip, the chip including a processor and a display interface, the display interface and the processor are coupled, and the processor is used to run a program or instruction to implement the steps of the schedule recommendation method shown in the first aspect.

[0014] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the schedule recommendation method shown in the first aspect.

[0015] In the embodiment of the present application, the to-do events of the first user can be obtained; based on the to-do events and the schedule information of the first user, the schedule recommendation information of the to-do events is displayed. In this way, schedule recommendations are made for the user based on the user's existing schedule information, so that the user can flexibly handle the to-do events in fragmented time, which can meet the user's real-time needs or interest changes and improve the effectiveness of schedule recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a schedule recommendation method provided for some embodiments of the present application;

[0017] Figure 2 A schematic diagram of an interface of a schedule recommendation method provided in some embodiments of the present application;

[0018] Figure 3 A schematic diagram of an interface of a schedule recommendation method provided in some embodiments of the present application;

[0019] Figure 4 A schematic diagram of an interface of a schedule recommendation method provided in some embodiments of the present application;

[0020] Figure 5 A schematic diagram of an interface of a schedule recommendation method provided in some embodiments of the present application;

[0021] Figure 6 A schematic diagram of the structure of a schedule recommendation device provided in some embodiments of the present application;

[0022] Figure 7 A schematic diagram of the structure of an electronic device provided for some embodiments of the present application;

[0023] Figure 8 A schematic diagram of the hardware structure of an electronic device provided for some embodiments of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0025] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0026] In order to solve the problems in the related art, the embodiments of the present application provide a schedule recommendation method, device and electronic device.

[0027] The definitions of abbreviations and key terms in the embodiments of the present application are as follows.

[0028] Personalized Artificial Intelligence (AI) assistant service in electronic devices is a highly personalized AI assistant that can deeply integrate user data in electronic devices, such as calendars, communication records, application usage, location information, etc., to fully understand the user's work and life plans for the day. The AI ​​assistant service can provide customized information services, task management, and schedule optimization suggestions by analyzing user behavior patterns, preferences, goals, and real-time status, helping users to arrange time more efficiently, complete tasks, and respond to emergencies. At the same time, the AI ​​assistant service can continuously learn and optimize its services based on user feedback and continuous usage habits to ensure that its suggestions and prompts are more in line with the user's personal needs and lifestyle.

[0029] Schedule management service refers to the AI ​​assistant service that dynamically optimizes and adjusts the user's schedule and tasks based on the user's user behavior information and real-time status to ensure that the user can complete daily tasks efficiently. The AI ​​assistant obtains the user's set schedule, analyzes the user's work habits, daily activities, communication records and other demands, the user's geographical location, user health data, attention level and other user behavior information, as well as information such as weather, traffic conditions and other emergency information, and dynamically adjusts and optimizes the user's schedule and task list. It can intelligently predict potential time conflicts or inefficiencies, reschedule task priorities, adjust activity time, and even provide personalized rest or work suggestions to ensure that users can achieve their daily work, study and life goals in an efficient manner. At the same time, the schedule management service also has a self-learning function. Through continuous user feedback and data accumulation, it continuously improves the schedule management strategy to make the schedule more intelligent, flexible and in line with user needs.

[0030] The personalized schedule recommendation service is the result of the AI ​​assistant service output based on the schedule management service. It combines in-depth analysis of the user's user behavior information, preferences, personalized goals and contextual information to provide customized reminders, suggestions and content recommendations. It can comprehensively consider the user's interests, learning and work habits, goal progress, emotional state and other factors, and actively recommend relevant content such as news, podcasts, courses, social activities, etc., and optimize the time and form of reminders to help users set more effective schedules at the right time and obtain valuable information and suggestions.

[0031] Based on this, the following Figures 1 to 5 , the schedule recommendation method provided by the embodiment of the present application is described in detail through specific embodiments and application scenarios.

[0032] First, combine Figure 1 A schedule recommendation method provided in an embodiment of the present application is described in detail.

[0033] Figure 1 A flowchart of a schedule recommendation method provided for some embodiments of the present application.

[0034] like Figure 1 As shown, the schedule recommendation method provided in the embodiment of the present application can be applied to an electronic device. Based on this, the schedule recommendation method can include steps 110 and 120 as shown below.

[0035] Step 110, obtaining to-do events of the first user; Step 120, displaying schedule recommendation information of the to-do events based on the to-do events and the schedule information of the first user.

[0036] For example, Figure 2As shown, to-do events related to the first user are obtained from the electronic device. The to-do events include invitation information proposed by the first user, Xiao Ming, to himself, such as watching 10 movies within 3 months, or a demand proposed by Xiao Ming to the second user, Xiao Mei, such as going shopping on the weekend, and the to-do events may also include invitation information proposed by Xiao Mei to Xiao Ming, such as holding a department meeting from 4 to 6 pm today. Based on this, the schedule information set by the first user can be combined, such as a dance class at 6 pm today, to determine the schedule recommendation information for to-do events. For example, based on watching 10 movies in 3 months, going shopping on the weekend, having a department meeting from 4 to 6 pm today, and having a dance class at 6 pm, the 10 movies can be divided into 3 months from 8 to 10 pm every Friday. If today is Friday, then the department meeting from 4 to 6 pm and the dance class at 6 pm may overlap in time. In this case, the 6 pm dance class will be postponed for half an hour, and the users related to the dance class will be notified. The notification content can be that Xiao Ming cannot arrive at the dance classroom as originally scheduled at 6 o'clock due to a department meeting, and requests a 30-minute extension. In this way, Figure 2 As shown, the dance class scheduled at 6 o'clock is adjusted to a dance class at 6:30, and the schedule recommendation information showing the to-do event may include "You have a department meeting from 4 to 6 o'clock, and there is a dance class at 6:30."

[0037] Therefore, schedule recommendations are made for users based on their existing schedule information, so that users can use fragmented time to flexibly handle pending events, meet users' real-time needs or interest changes, and improve the effectiveness of schedule recommendations.

[0038] It should be noted that in order to ensure that the schedule recommendation information of the to-do events has been accurately recommended to the user, the embodiment of the present application can determine the time for displaying the schedule recommendation information of the to-do events. Among them, based on the distance and vehicle condition between the meeting address and the dance class address, a recommended schedule such as self-driving, public transportation, cycling, etc. from the meeting address to the dance class address can be generated, so that the user can select and determine the time for displaying the schedule recommendation information of the to-do events according to the travel mode. For example, if the user selects the recommended schedule as cycling, the time for displaying the schedule recommendation information may be 6 o'clock, and if the user selects the recommended schedule as self-driving, the time for displaying the schedule recommendation information is 6:15. Here, the schedule recommendation information can be displayed in the artificial intelligence interactive interface of the electronic device from the time it is determined, or it can be displayed in any display interface of the electronic device according to the time for displaying the schedule recommendation information determined in the aforementioned manner.

[0039] The above steps are described in detail below. First, regarding step 110, in some embodiments of the present application, before step 110, a step of determining to-do events may be included, and the schedule recommendation method may also include steps 2101 to 2102.

[0040] Step 2101: receiving a first input from a first user while displaying an artificial intelligence interaction interface.

[0041] Step 2102, in response to the first input, determine the to-do events.

[0042] For example, Figure 3 As shown, the user can propose the user's schedule requirements to the AI ​​assistant service through the artificial intelligence interaction interface 30. For example, multiple options are displayed on the artificial intelligence interaction interface, namely, the option of watching 10 movies within 3 months, the option of reading a book within 1 month, and the option of traveling 3 times within 1 year, so that the user can choose. If the user chooses the option of watching 10 movies within 3 months and the option of reading a book within 1 month, the AI ​​assistant service can determine the to-do events based on the schedule requirements proposed by the user. For example, 10 movies can be divided into to-do events from 8 to 10 pm every Friday for 3 months, or 1 book can be divided into to-do events for half an hour before going to bed every day for 1 month.

[0043] As a result, the AI ​​assistant service can highly integrate the user's to-do items, gain an in-depth understanding of the user's work and life plans, and provide highly personalized information services and task management suggestions based on the user's behavior patterns, preferences, goals, and real-time status.

[0044] It should be noted that the condition for triggering the execution of step 110 may be that the user opens a service on the electronic device that displays a personalized recommended schedule, and the service automatically accesses the daily mode and analyzes the user's current schedule and task list to obtain the to-do items of the first user; it may be that the AI ​​assistant service obtains the user's specific schedule information and arrangements based on the schedule, to-do and other information manually entered by the user.

[0045] In other embodiments of the present application, the user's temporary schedule can be automatically identified when the pending events are resolved based on the instant messaging information, SMS information, and pending events formed during the meeting received by the user. Based on this, before step 110, another step of determining pending events can be included, and the schedule recommendation method can also include steps 2103 and 2104.

[0046] Step 2103, obtaining instant messaging information of the first user, where the instant messaging information includes invitation information sent by the first user and invitation information received by the first user.

[0047] Exemplarily, instant messaging information of at least one instant messaging application in an electronic device is obtained, and schedule-related instant messaging information is filtered from the instant messaging information, for example, invitation information sent by a first user, Xiao Ming, to a second user, Xiao Mei, is filtered from instant messaging application 1, such as "Let's go shopping on the weekend" and "I have booked a meeting room from 4 to 6 this afternoon, don't forget to come to the meeting"; and, invitation information received by the first user is filtered from instant messaging application 1, such as "Xiao Ming, don't forget to grab a high-speed rail ticket from Beijing to Shanghai on the weekend, I remind you."

[0048] Step 2104, determining to-do events based on the instant messaging information.

[0049] For example, if the instant messaging message is "I have booked a meeting room from 4 to 6 this afternoon, don't forget to come to the meeting", a to-do item for the meeting from 4 to 6 can be generated; if the instant messaging message is "Xiao Ming, don't forget to grab a high-speed rail ticket from Beijing to Shanghai this weekend, I remind you", a to-do item for purchasing high-speed rail tickets from Beijing to Shanghai on Saturday and Sunday can be generated.

[0050] It should be noted that the instant messaging information in the embodiments of the present application includes but is not limited to information of instant messaging applications, text message information, and to-do information generated during the meeting.

[0051] Therefore, in the embodiment of the present application, the user's temporary schedule can be automatically identified based on the instant messaging information received by the user. When the schedule changes, the AI ​​assistant service can automatically generate new to-do information based on the instant messaging information, such as notifying the temporary rescheduling of meetings, and quitting shopping time for some reason, etc. In this way, the user's real-time needs or interest changes can be met, and the effectiveness of schedule recommendations can be improved.

[0052] In some embodiments of the present application, when the schedule changes, the AI ​​assistant service can automatically generate new to-do information based on the instant messaging information, such as the AI ​​assistant service notifying that the meeting is temporarily rescheduled, and the date of the gathering with family members is changed. In some embodiments, the AI ​​assistant service can automatically determine the to-do events. In other embodiments, the AI ​​assistant service prompts the user to select and determines the to-do events based on the user's selection. Based on this, the above step 120 may specifically include:

[0053] Based on the to-do event and the schedule information of the first user, the schedule recommendation information and operation controls of the to-do event are displayed; the operation controls are used to trigger the processing of the to-do event.

[0054] For example, Figure 4As shown, the to-do event may include watching 10 movies within 3 months, and the first user's schedule information 41 includes working from 8 am to 5 pm and being free at other times. At this time, based on the to-do event and the schedule information of the first user, the schedule recommendation information 42 of the to-do event may be displayed in the artificial intelligence interaction interface 40, such as "There is not much work today, there is no schedule conflict, it is estimated that I can get off work on time, I haven't watched a movie this week, I can go to a movie with friends, and the following are the latest movies released". At this time, the operation control 43 for purchasing movie tickets may be displayed in the artificial intelligence interaction interface 40, such as the operation control for purchasing tickets for movie A, the operation control for purchasing tickets for movie B, and the operation control for purchasing tickets for movie C, so that the user can input the operation control to purchase movie tickets and then process the to-do event of watching a movie.

[0055] It should be noted that in an embodiment of the present application, the schedule information and to-do events may be manually set by the first user, or part of it may be manually set by the first user and the other part may be determined by the AI ​​assistant service, or both may be determined by the AI ​​assistant service. Based on this, examples are given below in combination with different scenarios.

[0056] In some embodiments, if the to-do events are determined by an AI assistant service, the AI ​​assistant service can recommend leisure activities based on the user's interests and preferences. For example, during the user's free time period, a to-do event can be generated for the user to watch a favorite TV series, read related books, or listen to a podcast of interest.

[0057] In some embodiments, if the to-do events are determined by the AI ​​assistant service, the AI ​​assistant service can recommend appropriate rest time, exercise plans or reading time based on the schedule information. For example, when the user is in the process of working, determine the to-do events that suggest the user to perform relaxing activities or a short break; for another example, when the user sits for a long time, determine the to-do events that remind the user to do light stretching exercises; for another example, when the user works late at night, determine the to-do events that suggest preparing soothing nighttime activities in advance, such as reading or meditation, to help the user maintain a calm mood and mental health.

[0058] In some embodiments, if the to-do events are manually set by the user and updated by the AI ​​assistant service, the AI ​​assistant service can detect the progress of at least two to-do events manually set by the user. If it is detected that the long-term to-do events set by the user are progressing slowly, such as reading a book or completing a project, the AI ​​assistant service can first redefine these slow-progressing to-do events based on the progress of the to-do events to help the user better achieve their goals. For example, the AI ​​assistant service can recommend slow-progressing to-do events based on the user's daily completion of some reading tasks and concentration in a certain period of time, and update the to-do items, such as breaking down these to-do events into smaller, actionable to-do event fragments.

[0059] In some embodiments, if the to-do events are determined by the AI ​​assistant service, the AI ​​assistant service can determine the to-do events that provide social activity suggestions based on the user's social behavior and frequency. For example, based on the user's social behavior and frequency, the user can be reminded to keep in touch with friends who have not been in touch for a long time, recommend weekend gatherings or participate in interest group activities; or when a friend's birthday or important anniversary is detected, the user can be reminded to send a blessing message or arrange a celebration.

[0060] In some embodiments, if the to-do event is determined by the AI ​​assistant service, the AI ​​assistant service can analyze the user's diet record, activity data, and health goals such as weight loss and muscle gain, and determine to-do events that recommend healthy diet and water supplementation to the user. For example, if it is detected that the user is losing weight, gaining muscle, etc., it can recommend an appropriate amount of water intake and nutritional combination every day; or, if it is detected that the user has not drunk water for a long time, remind the user to replenish water.

[0061] In some embodiments, if the to-do event is determined by the AI ​​assistant service, the AI ​​assistant service can determine the to-do event that prompts the user with travel or business trip plan information based on the first user's schedule information. For example, when the user decides to go out, the best departure time, transportation and restaurant options are recommended based on weather, traffic, itinerary and user preferences; relevant activities are recommended for the user's interests and hobbies, such as art exhibitions, concerts, book clubs, lectures and other activities; at the same time, according to the user's free time arrangement, combined with current real-time activities and price information, suitable activity participation opportunities and ticket booking options are recommended.

[0062] In addition, in some embodiments, if the schedule information in the embodiments of the present application is determined by an AI assistant service, it can be determined by analyzing the user's behavior data, positive feedback data and the user's real-time status. In this way, highly personalized and valuable suggestions and reminders can be provided to the user to help the user optimize daily decisions and life experience.

[0063] Therefore, the AI ​​assistant service can deeply understand the user's work and life plans by highly integrating schedule information and the user's to-do items, and provide highly personalized information services and task management suggestions based on the user's behavior patterns, preferences, goals and real-time status. The AI ​​assistant service can not only determine the to-do items based on the schedule information set by the user and when encountering emergencies such as health problems and accidents, but also gradually learn and optimize its adjustment process for determining to-do items through advanced machine learning models and contextual awareness technology, ensuring that the suggestions and reminders are more in line with the user's personal needs and lifestyle, thereby improving the quality of personalized services.

[0064] Next, step 120 is referred to. In some embodiments of the present application, step 120 may specifically include steps 1201 to 1203.

[0065] Step 1201, based on the to-do events and the set schedule, determine the initial schedule information of the to-do events.

[0066] Step 1202: adjust the initial schedule information of the to-do event according to the schedule impact information to obtain schedule recommendation information of the to-do event.

[0067] Step 1203, displaying the schedule recommendation information of the to-do event, wherein the schedule impact information includes at least one of the following: emergency information, user behavior information. The emergency information includes at least one of the following: weather information, traffic information, user health information, and the user behavior information includes at least one of the following: the travel mode of the first user in the first time period, the location of the first user in the second time period, and the first time period occurs earlier than the second time period.

[0068] Exemplarily, still taking the above as an example, considering that there may be traffic jams or road closures or traffic restrictions due to snow on the route from the meeting address and the dance class, at this time, adjustments can be made to the to-do events and the initial schedule information that has been set based on the schedule impact information.

[0069] Therefore, according to the schedule influencing information such as emergency information and user behavior information, the user's schedule and plan are automatically adjusted to improve the accuracy and rationality of the generated schedule recommendation information and ensure that the user's time is optimally utilized.

[0070] The following describes the process of obtaining schedule recommendation information based on different schedule impact information.

[0071] Based on this, the user's travel route and commuting time can be replanned according to the traffic information, and the initial schedule recommendation information can be adjusted. The above step 1202 can specifically include step 12021 and step 12022.

[0072] Step 12021: Based on the travel mode, first traffic information corresponding to the travel mode of the first user in the first time period is obtained from the traffic information.

[0073] Step 12022: According to the first traffic information, the initial schedule recommendation information is adjusted to obtain the schedule recommendation information.

[0074] Based on this, with respect to weather information, the above step 1202 may specifically include step 12023 and step 12024.

[0075] Step 12023, obtaining first weather information of the location of the first user in the first time period from the weather information.

[0076] Step 12024, according to the first weather information, adjust the initial schedule information and obtain the schedule recommendation information of the to-do event.

[0077] For example, when encountering bad weather or traffic congestion, the user is reminded to leave early or choose alternative means of transportation; if the weather suddenly turns cold or rains, the user is reminded to bring rain gear or wear warm clothes. Weather and traffic information can be intelligently obtained or analyzed through the user's schedule for the day, so as to adjust the initial schedule information and obtain schedule recommendation information. For example, if the user is going to the airport for a business trip to place A at 10:00 today, the local weather and traffic conditions from home to the airport in the nearby period can be automatically obtained through the schedule, and the take-off and landing conditions of flights and the weather at the destination can be intelligently analyzed.

[0078] Thus, the schedule information, user travel mode, traffic reminder and other functions set by the first user can be integrated to dynamically adjust the initial schedule information and obtain schedule recommendation information. In this way, potential time conflicts or inefficiencies can be predicted, the user's work and life arrangements can be deeply analyzed, schedule priorities can be rearranged, time periods can be reallocated, and customized schedule recommendation information can be provided to users to help users better manage various tasks and plans in daily life without excessive human involvement in schedule recommendation operations, thus simplifying user operations in the schedule recommendation process.

[0079] It should be noted that, in the embodiment of the present application, the initial schedule information may be adjusted according to at least one of the following factors: time dimension and processing priority.

[0080] In some embodiments, the emergency event information includes user health data. Based on this, the above step 1202 may specifically include step 12025 and step 12026.

[0081] Step 12025, determining the physical health evaluation value of the first user in the first time period through the user health data.

[0082] Step 12026, when the physical health evaluation value is less than or equal to the first preset threshold value, make at least one of the following adjustments to the initial schedule information: add a first category of to-do events to the initial schedule information, remove a second category of to-do events from the initial schedule information, and lower the arrangement density of to-do events in the initial schedule information to obtain schedule recommendation information; wherein, the first category of to-do events includes schedules related to the user's rest, and the second category of to-do events includes schedules related to the user's exercise.

[0083] Exemplarily, the user's health data such as number of steps, heart rate, sleep quality, etc. are obtained, and the exercise plan and rest time are adjusted in time. If the user does not get enough sleep, a first type of to-do event can be generated for the user to reduce certain non-essential activities and increase lunch break time; when the user's heart rate rises abnormally, a second type of to-do event can be generated for the user to take a temporary rest or perform relaxation exercises, such as meditation or deep breathing.

[0084] Here, it should be noted that the user health data in the embodiments of the present application can be obtained based on the user's mobile phone usage behavior, smart watches and other peripheral devices.

[0085] Therefore, by highly integrating to-do events and the first user's schedule information, we can have an in-depth understanding of the user's work and life plans. When encountering emergencies such as health problems and accidents, we can use the schedule impact information to readjust the initial schedule information to improve the accuracy and rationality of the generated schedule recommendation information, ensuring that the user's time is optimized.

[0086] In some embodiments, the user behavior information includes schedule change data. Based on this, the above step 1202 may specifically include steps 12027 to 120210.

[0087] Step 12027: Filter out the first to-do event associated with the changed data from the initial schedule recommendation information according to the changed data.

[0088] Step 12028, delete the first to-do event from the initial schedule recommendation information.

[0089] Step 12029, generate a second to-do event according to the changed data.

[0090] Step 120210: Based on the second to-do event, adjust the initial schedule recommendation information to obtain the schedule recommendation information.

[0091] Exemplarily, the user's work or study task changes, such as temporary meetings, task extensions, and other change data can be obtained, task priorities can be dynamically reallocated, and the initial schedule recommendation information can be adjusted. For example, if the user's meeting is suddenly cancelled, the original first to-do event can be screened out from the initial schedule recommendation information and deleted from the initial schedule recommendation information. Then, based on the time of the original first to-do event, a second to-do event, such as a second to-do event for a relaxation activity, is generated. At this time, the time of the second to-do event for the relaxation activity can be set to the time of the original first to-do event. Here, if the change data is to change the position of the original first to-do event, then a new meeting schedule is generated based on the change data, and the original meeting schedule in the initial schedule recommendation information is replaced based on the meeting schedule to obtain the schedule recommendation information.

[0092] In this way, when a user encounters a temporary situation and needs to adjust an already set schedule, the changed schedule can be adjusted without the user's participation, thereby achieving rapid adjustment of the schedule according to the user's new situation. While ensuring that the user can respond to various emergencies efficiently, the user operation of the schedule recommendation process is simplified, thereby improving the efficiency of schedule recommendation.

[0093] In some embodiments, the schedule impact information includes emergency event information and user behavior information. The emergency event information includes user physiological parameters, and the user behavior information includes status mode information of the user using the electronic device. Based on this, the above step 1202 can specifically include step 120211 and step 120212.

[0094] Step 120211, determining the attention level value of the first user based on the user's physiological parameters and state mode information.

[0095] For example, the attention level value of the first user may be determined based on information such as task density at the same time, usage time of state mode information, user feedback, blood oxygen saturation, heart rate variability, etc.

[0096] Step 120212, when the concentration level value is less than or equal to the second preset threshold, a third category of to-do events is added to the initial schedule recommendation information to obtain schedule recommendation information; wherein, the third category of to-do events includes at least one of the following: a schedule for a short break, a schedule for outdoor activities.

[0097] For example, it is possible to detect whether the first user's attention level value has a decreasing trend by detecting the user's daily usage habits, geographic location, daily commuting data, the user's physiological state detected by other devices such as a watch, whether the user has turned on the focus mode, the work mode, etc. If it is detected that the first user's attention level value has decreased, a short rest or outdoor activities can be added to the initial schedule information to restore energy.

[0098] In addition, when the attention level value is greater than the second preset threshold or when it is detected that the attention level value of the first user maintains a stable or rising trend, that is, when the user shows an efficient state, the schedule with a complexity or importance greater than the second preset threshold in the initial schedule information will be adjusted to this time period, so as to adjust the reminder time of the schedule in time according to the user status, thereby improving the user's work efficiency.

[0099] In some embodiments, the user behavior information includes user preference information. Based on this, the above step 1202 may specifically include step 12021 and step 12022.

[0100] Step 12021, obtain user preference information.

[0101] Step 12022: When the time period corresponding to the schedule recommendation information of the to-do event includes an idle time period, the schedule recommendation information corresponding to the user preference information is displayed based on the user preference information.

[0102] Exemplarily, during the user's free time period, a fourth to-do event can be generated for the user to watch a favorite TV series, read related books, or listen to a podcast of interest, and the fourth to-do event can be added to the user's free time period to recommend leisure activities based on the user's interests and preferences.

[0103] In this way, the user's social plan can be optimized and dynamically adjusted according to the current work and life status. For example, if the user has no social activities for most of the month, a schedule for the user to meet up with friends can be generated, and suitable restaurants can be recommended based on the user's food preferences and location, and reservations can be made on behalf of the user. Alternatively, the user's social plan can be intelligently obtained and analyzed through the user's communication records, notifications, and reminders using third-party software, and the user can be reminded to prepare gifts, book restaurants, etc. when the relevant schedule is approaching.

[0104] In addition, it is important to note that emergencies should be managed in a timely manner. For example, if it is detected that the battery of a user's device is too low and there is an important remote meeting next, a schedule can be generated to remind the user to charge as soon as possible, and the user can be reminded in a timely manner.

[0105] Therefore, the schedule recommendation information determined in the above step 120 of the present application can provide users with tailored suggestions and reminders through deep learning of the user's behavioral information and preferences, and realize deep personalized recommendations. Secondly, integrate various tasks such as work, study, life, and health, provide unified management and optimization, improve user efficiency, and realize comprehensive multi-task management. In addition, by combining the user's goal setting and physical and mental state, dynamically adjust the daily plan to help users better achieve their goals and realize health and goal management support. In addition, by comprehensively analyzing the user's historical behavior, interests and hobbies, learning and work habits, goal progress, emotional state and other factors, provide accurate content recommendations and personalized reminders, can actively recommend content related to the user's interests, and optimize the time and form of prompts, so that users can obtain the most valuable information and suggestions at the best time, and enhance personalized recommendation capabilities.

[0106] Then, in some embodiments of the present application, after step 120 , the schedule recommendation method may further include steps 1301 to 1303 .

[0107] Step 1301: When a time period corresponding to a to-do event conflicts with a time period corresponding to a first schedule, a first interface is displayed, wherein the first interface includes the to-do event and the first schedule.

[0108] Step 1302: receiving a second input from a first user selecting a first target object from to-do events and a first schedule.

[0109] Step 1303, in response to the second input, updating the schedule information based on the first target object, and taking the second target object as a to-do event, wherein the second target object is the to-do event and an object not selected by the user in the first schedule.

[0110] For example, Figure 5 As shown, to-do events related to the first user are obtained from the electronic device. Among them, the to-do event includes the invitation information proposed by the first user, Xiao Ming, to himself, such as watching 10 movies within 3 months, or the to-do event can also include the invitation information proposed by Xiao Mei to Xiao Ming, such as holding a department meeting at 4-6 pm today. Based on this, the schedule recommendation information of the to-do event can be determined in combination with the schedule information set by the first user, such as having a dance class at 6 pm today, for example, based on watching 10 movies within 3 months, shopping on the weekend, holding a department meeting at 4-6 pm today, and having a dance class at 6 pm, the 10 movies can be divided into 3 months every Friday from 8 to 10 pm. If today is Friday, then the department meeting at 4-6 pm and the dance class at 6 pm may overlap in time. At this time, the first interface can be displayed, and the first interface includes a department meeting at 4-6 pm and a dance class at 5 pm. If the user confirms whether to adjust the dance class at 6 pm to 6:30 pm, if a second input is received from the first user selecting the dance class from the department meeting and the dance class, the schedule information is updated based on the dance class and the dance class is added as a to-do item.

[0111] For example, automatically coordinate the schedules of users and others. When a user needs to collaborate with colleagues or friends to complete a task or activity, the AI ​​assistant service can automatically coordinate the free time of both parties, send schedule invitations, and ensure the smooth progress of the activity.

[0112] Therefore, the user's work and life arrangements can be deeply analyzed through the user's set schedule combined with the to-do events related to the first user, and customized to-do events can be provided to help users better manage various tasks and plans in daily life, without excessive human involvement in schedule recommendation operations, simplifying user operations in the schedule recommendation process. In addition, the schedule recommendation information of the to-do events is displayed to ensure that the user's time is fully optimized and utilized, especially to improve the utilization rate of fragmented time. Therefore, the schedule can be quickly adjusted according to the user's to-do items, and the user operation of the schedule recommendation process is simplified while ensuring that the user can efficiently respond to various emergencies, thereby improving the efficiency of schedule recommendation.

[0113] In some embodiments of the present application, after step 120 , the schedule recommendation method may further include steps 1401 and 1402 or steps 1403 and 1404 .

[0114] Step 1401, obtaining the execution status information of the to-do event executed by the first user.

[0115] Step 1402: Adjust and display the schedule recommendation information of the to-do events according to the execution status information.

[0116] or,

[0117] Step 1403: Obtain feedback information from the user on the schedule recommendation information for to-do events.

[0118] Step 1404: adjust the schedule recommendation information of the to-do events according to the feedback information.

[0119] Exemplarily, intelligent and adaptive real-time adjustments are made by continuously monitoring user feedback and behavior.

[0120] Specifically, the embodiment of the present application provides the following adaptive adjustment process, which is specifically shown below.

[0121] The electronic device not only records the user's dissatisfaction with the schedule adjustment or suggestion, but also analyzes the user's feedback pattern and behavior trend to intelligently predict the possible reasons for the user's dissatisfaction. For example, if the user frequently adjusts a specific type of suggestion such as social activities or work arrangements, similar suggestions can be proactively optimized in the future to reduce user dissatisfaction.

[0122] Furthermore, electronic devices can analyze user responses in real time, and AI assistant services will update their recommendation algorithms. For example, when users frequently adjust their work task schedules, AI assistant services will update their task priority strategies to make the recommended task schedules more in line with the user's actual needs, thus achieving dynamic learning and autonomous optimization.

[0123] Electronic devices not only provide adjustment suggestions, but also generate detailed response measures based on the context. For example, when abnormal health data of users is detected, the AI ​​assistant service will provide specific health management suggestions and synchronize data with the user's doctor or health application to ensure that health problems are fully addressed.

[0124] Electronic devices for real-time information interaction and automated notifications. Specifically, users are notified of new schedules or changes in real time through a variety of interactive interfaces such as electronic device notifications, voice assistants, smart watches, etc. The notification mechanism can automatically adjust the mode according to the user's current status and preferences, such as using non-disturb mode notifications during work hours and using more prominent reminders during leisure time, so as to achieve real-time information interaction and automated notifications with users and improve the efficiency of schedule recommendations.

[0125] Electronic devices can dynamically adjust the priority and frequency of notifications based on the user's response behavior and context, such as whether an important task is being performed or health status. For example, when the user is in a highly concentrated working state, the AI ​​assistant service will provide important notifications in a low-interference manner, and provide more detailed information when the user is idle or resting.

[0126] Electronic devices optimize schedules in schedule lists. Specifically, by analyzing users' long-term behavior and feedback, potential schedule conflicts or demand changes are predicted. For example, users' activity patterns are analyzed based on seasonal changes or annual cycles, and schedules are adjusted in advance to cope with upcoming special events or high-demand periods. Also, electronic devices automatically adjust task allocation based on users' daily habits and goals. For example, if a user often feels tired during a certain time period, the AI ​​assistant service will intelligently schedule high-priority tasks during the time period when the user is most energetic.

[0127] Therefore, by continuously monitoring user feedback and behavior and intelligently adjusting schedule recommendation strategies and suggested contents, it is possible to automatically optimize the recommendation algorithm based on user feedback patterns, reduce the types of suggestions that users are dissatisfied with, and apply forward-looking learning strategies to gradually improve the accuracy and personalization of schedule optimization.

[0128] Based on this, after at least one time period has ended, the day's schedule recommendations and task completion status can be intelligently summarized and continuously optimized, as shown below.

[0129] Electronic devices can generate comprehensive activity reports based on the schedule list, that is, automatically generate detailed comprehensive activity reports based on the user's activity data, task completion status, and schedule adjustment records. The report includes the user's completed task list, time usage, efficiency analysis of activity execution, and evaluation of the achievement of user goals. The report not only provides the completion status of tasks for the day, but also includes an evaluation of the effectiveness of schedule adjustments. For example, analyze the user's response to temporary adjustments to determine whether these adjustments have effectively improved the user's work efficiency and quality of life, thereby achieving dynamic feedback summary.

[0130] Electronic devices can intelligently optimize schedule recommendation information. Specifically, by analyzing user feedback and usage patterns, the AI ​​assistant service continuously optimizes schedule recommendation strategies. If it is found that a user has repeatedly ignored a certain type of suggestion, the system will adjust the content of these suggestions or reduce their frequency, while introducing new types of suggestions to improve user acceptance. In addition, machine learning algorithms are used to analyze long-term data to identify changing trends and potential needs of user behavior. For example, it can identify changes in user preferences in specific situations and adjust future recommendation strategies accordingly to ensure that the suggestions always meet the user's latest needs.

[0131] Electronic devices can apply the experience of the day to future schedules. By continuously accumulating user behavior information, the accuracy and personalization of schedule optimization can be gradually improved. For example, task priority allocation can be optimized, and future task allocation strategies can be adjusted based on the user's actual performance and feedback, thereby achieving experience adjustment and personalized recommendations.

[0132] In addition, the electronic device will apply the daily optimization results to long-term schedule planning based on the schedule list. For example, when it is found that the user performs well in a specific period of time, the important tasks are automatically adjusted to these periods; or when the user lacks a certain activity for a long time, relevant suggestions are added in a timely manner. In addition, by continuously monitoring the user's life and work status, real-time optimization suggestions are provided. For example, if the system detects that the user is facing an upcoming important deadline, the user is reminded in advance to make appropriate preparations. And according to the user's reading and feedback on the report, the report format and content presentation method can be continuously adjusted to better adapt to the user's reading habits and needs.

[0133] Therefore, through the above process, not only can the user's activities and tasks be summarized in real time, but also the schedule recommendation strategy can be intelligently optimized to ensure that the user obtains the best schedule and service recommendations on the electronic device, making the user's work, study and life more efficient and orderly. In addition, the daily summary experience can be applied to long-term schedules, and through self-learning and data accumulation, the task allocation strategy and schedule can be gradually optimized, and the long-term needs of users can be adapted to improve the intelligence level of schedule recommendations and user satisfaction.

[0134] The following uses four scenarios as examples to illustrate the use of schedule recommendation information in the schedule recommendation method provided in the embodiments of the present application.

[0135] Scenario 1: Weekday morning schedule optimization.

[0136] After the user wakes up, the electronic device can analyze and predict the user's preferred dressing style based on the schedule recommendation information and the user's browsing history or purchase history in shopping applications in the user behavior information. Then, according to the schedule recommendation information, the user is prompted with today's weather conditions and recommended to wear appropriate clothing. According to real-time traffic data, it is recommended to go out early to avoid traffic jams. At the same time, based on the time the user gets up today, the original morning reading time can be appropriately adjusted to the commute, and during the commute, the user can be recommended to read the corresponding chapter of the book or listen to a recent podcast to make full use of the commute time. Here, the user's reading plan can be manually entered by the user, or it can be intelligently recommended to the user based on the user's previous reading or listening habits of books and podcasts to improve the utilization of fragmented time. At the same time, the electronic device can also recommend additional information based on the meeting that the user needs to attend today or the topics that need to be discussed in the near future, social hot events, and key information related to the profession, so that the user can use fragmented time to prepare and input in advance.

[0137] Scenario 2: Dynamic adjustment of tasks during the workday.

[0138] After an afternoon meeting, the user received a reminder from the electronic device based on the schedule recommendation information, suggesting that he postpone the planned exercise time in the evening by half an hour in order to better recover. In addition, the electronic device can also prompt the user to adjust the dinner time based on the schedule recommendation information to better match the exercise schedule, and provide several healthy dinner options. The user's dinner choice can be combined with the user's recent fitness goals. At the same time, the AI ​​assistant service can provide dietary options suitable for the user's personal physical condition and preferences based on the user's physiological and psychological state, medical treatment, and drug purchase records.

[0139] Scenario 3: Life optimization suggestions for non-working days.

[0140] The user planned to go for a long run on the weekend, but the weather changed and affected the plan. The electronic device can adjust the schedule recommendation information based on the weather conditions and the user's health data, that is, change the outdoor long-distance run to indoor aerobic exercise, and provide suitable online courses and fitness videos. At the same time, the assistant also reminds the user to arrange a gathering with friends on the weekend and recommends several popular restaurants nearby.

[0141] Scenario 4: Daily goal and study plan management.

[0142] After a day's work, if the user's electronic device determines that the user is behind on the current reading plan based on the schedule recommendation information, the electronic device can prompt the user to allocate today's unfinished reading tasks to free time periods in the next few days based on the schedule recommendation information. In addition, relevant learning resources can be recommended based on the user's learning goals, and it is suggested to make reasonable use of fragmented time to achieve the goals.

[0143] In this way, users can get all-round schedule and task management support on one platform, improve the efficiency of life and work, and ensure the achievement of health and goals. Through intelligent and personalized services, this solution allows users to be more relaxed and efficient in their changing schedules.

[0144] In summary, the schedule recommendation method provided in the embodiment of the present application can help users better manage various tasks and plans in daily life through intelligent analysis and dynamic adjustment. The AI ​​assistant service can automatically optimize the user's daily schedule based on the short-term goals set by the user, combined with the user's behavioral information, real-time status and external factors, and can especially improve the utilization of fragmented time. The method can dynamically adjust plans such as exercise and reading according to the user's goals and habits, provide health management advice, and multi-task integration and optimization. By fully covering the user's daily activities, the AI ​​assistant service can provide the best decision support in various situations.

[0145] It should be noted that the schedule recommendation method provided in the embodiments of the present application can be executed by electronic devices such as mobile phones, tablet computers, laptops, PDAs, wearable devices, etc. In some embodiments of the present application, the schedule recommendation method provided in the embodiments of the present application is described by taking the electronic device as the execution subject to execute the schedule recommendation method.

[0146] The schedule recommendation method provided in the embodiment of the present application can be executed by a schedule recommendation device. In the embodiment of the present application, the schedule recommendation method is executed by a schedule recommendation device as an example to illustrate the device of the schedule recommendation method provided in the embodiment of the present application.

[0147] The present application also provides a schedule recommendation device. Figure 6 Provide detailed explanation.

[0148] Figure 6 A schematic diagram of the structure of a schedule recommendation device provided in some embodiments of the present application.

[0149] like Figure 6 As shown, the schedule recommendation device 60 can be applied to an electronic device, and the schedule recommendation device 60 can specifically include:

[0150] An acquisition module 601 is used to acquire to-do events of a first user;

[0151] The display module 602 displays schedule recommendation information of the to-do event based on the to-do event and the schedule information of the first user.

[0152] The schedule recommendation device 60 in the embodiment of the present application is described in detail below, as shown below.

[0153] In some embodiments of the present application,

[0154] The schedule recommendation device 60 in the embodiment of the present application may further include a receiving module for receiving a first input from a first user when the artificial intelligence interaction interface is displayed;

[0155] The schedule recommendation device 60 in the embodiment of the present application may further include a determination module, which is used to determine a to-do event in response to the first input.

[0156] In some embodiments of the present application, the display module 602 can be specifically used to display schedule recommendation information and operation controls of the to-do events based on the to-do events and the schedule information of the first user; the operation controls are used to trigger the processing of the to-do events.

[0157] In some embodiments of the present application, the schedule recommendation device 60 in the embodiment of the present application may further include a determination module for determining initial schedule information of the to-do event based on the to-do event and the schedule information of the first user;

[0158] The schedule recommendation device 60 in the embodiment of the present application may further include an adjustment module, which is used to adjust the initial schedule information according to the schedule impact information to obtain schedule recommendation information of the to-do event;

[0159] The display module 602 can be specifically used to display schedule recommendation information of to-do events.

[0160] In some embodiments of the present application, the acquisition module 601 may also be used to acquire user preference information;

[0161] The display module 602 may also be used to display the schedule recommendation information corresponding to the user preference information based on the user preference information when the time period corresponding to the schedule recommendation information of the to-do event includes an idle time period.

[0162] In some embodiments of the present application, the display module 602 may also be used to display a first interface including the to-do event and the first schedule when the time period corresponding to the to-do event conflicts with the time period corresponding to the first schedule;

[0163] The schedule recommendation device 60 in the embodiment of the present application may further include a receiving module for receiving a second input of the first user selecting a first target object from the to-do events and the first schedule;

[0164] The schedule recommendation device 60 in the embodiment of the present application may also include a processing module for updating the schedule information based on the first target object in response to the second input, and using the second target object as a to-do event, wherein the second target object is a to-do event and an object not selected by the user in the first schedule.

[0165] In some embodiments of the present application, the acquisition module 601 may also be used to acquire instant communication information of the first user, where the instant communication information includes invitation information sent by the first user and invitation information received by the first user;

[0166] The schedule recommendation device 60 in the embodiment of the present application may further include a determination module for determining to-do events based on instant messaging information.

[0167] The schedule recommendation device in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc. It can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0168] The schedule recommendation device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0169] The schedule recommendation device provided in the embodiment of the present application can achieve Figures 1 to 5 The various processes implemented in the illustrated schedule recommendation method embodiment achieve the same technical effect, and will not be described again here to avoid repetition.

[0170] Based on this, the schedule recommendation device provided in the embodiment of the present application can obtain the to-do events of the first user from the to-do events; based on the to-do events and the schedule information of the first user, the schedule recommendation information of the to-do events is displayed. In this way, schedule recommendations are made for users based on the user's existing schedule information, so that users can use fragmented time to flexibly handle to-do events, which can meet the user's real-time needs or interest changes, and improve the effectiveness of schedule recommendations.

[0171] Optional, such as Figure 7 As shown, an embodiment of the present application also provides an electronic device 70, including a processor 701 and a memory 702, wherein the memory 702 stores programs or instructions that can be executed on the processor 701, and when the program or instructions are executed by the processor 701, the various steps of the above-mentioned schedule recommendation method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not described here.

[0172] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0173] Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0174] The electronic device 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, a processor 810 and other components.

[0175] Those skilled in the art will appreciate that the electronic device 800 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 810 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system.

[0176] Figure 8 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0177] In the embodiment of the present application, the processor 810 is used to obtain the to-do events of the first user. The display unit 806 is used to display the schedule recommendation information of the to-do events based on the to-do events and the schedule information of the first user.

[0178] The electronic device 800 is described in detail below, as shown below.

[0179] In some embodiments of the present application, the user input unit 807 is used to receive a first input from a first user when the artificial intelligence interaction interface is displayed;

[0180] The processor 810 is configured to determine a to-do event in response to a first input.

[0181] In some embodiments of the present application, the display unit 806 can be specifically used to display schedule recommendation information and operation controls of the to-do events based on the to-do events and the schedule information of the first user; the operation controls are used to trigger the processing of the to-do events.

[0182] In some embodiments of the present application, the processor 810 is configured to determine initial schedule information of the to-do event based on the to-do event and schedule information of the first user;

[0183] Processor 810, configured to adjust initial schedule information according to schedule impact information to obtain schedule recommendation information of to-do events;

[0184] The display unit 806 can be specifically used to display schedule recommendation information of to-do events.

[0185] In some embodiments of the present application, the processor 810 may also be used to obtain user preference information;

[0186] The display unit 806 may also be configured to display the schedule recommendation information corresponding to the user preference information based on the user preference information when the time period corresponding to the schedule recommendation information of the to-do event includes an idle time period.

[0187] In some embodiments of the present application, the display unit 806 may also be configured to display a first interface including the to-do event and the first schedule when the time period corresponding to the to-do event conflicts with the time period corresponding to the first schedule;

[0188] The user input unit 807 is used to receive a second input from a first user selecting a first target object from to-do events and a first schedule;

[0189] Processor 810 is used to update the schedule information based on the first target object in response to the second input, and use the second target object as a to-do event, wherein the second target object is the to-do event and an object not selected by the user in the first schedule.

[0190] In some embodiments of the present application, the processor 810 may also be used to obtain instant communication information of the first user, where the instant communication information includes invitation information sent by the first user and invitation information received by the first user; and determine a to-do event based on the instant communication information.

[0191] It should be understood that the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of a static image or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 806 may include a display panel, and the display panel may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes at least one of a touch panel 8071 and other input devices 8072. The touch panel 8071 is also referred to as a touch screen. The touch panel 8071 may include two parts, a touch detection device and a touch display. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as a volume display button, a switch button, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0192] The memory 809 can be used to store software programs and various data. The memory 809 can mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area can store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 809 can include a volatile memory or a non-volatile memory, or the memory 809 can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0193] The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, and the modem processor mainly processes wireless display signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 810.

[0194] The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned schedule recommendation method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0195] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0196] In addition, an embodiment of the present application further provides a chip, which includes a processor and a display interface, the display interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned schedule recommendation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0197] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0198] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned schedule recommendation method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0199] 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.

[0200] In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0201] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0202] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A schedule recommendation method, characterized in that: include: Get the to-do events of the first user; Based on the to-do event and the schedule information of the first user, schedule recommendation information of the to-do event is displayed.

2. The method according to claim 1, characterized in that The method further comprises: In a case where the artificial intelligence interaction interface is displayed, receiving a first input from the first user; In response to the first input, the to-do event is determined.

3. The method according to claim 1, characterized in that The displaying schedule recommendation information of the to-do event based on the to-do event and the schedule information of the first user includes: Based on the to-do event and the schedule information of the first user, schedule recommendation information and operation controls of the to-do event are displayed; the operation controls are used to trigger the processing of the to-do event.

4. The method according to claim 1, characterized in that: The displaying schedule recommendation information of the to-do event based on the to-do event and the schedule information of the first user includes: Determining initial schedule information of the to-do event based on the to-do event and the schedule information of the first user; Adjust the initial schedule information according to the schedule impact information to obtain schedule recommendation information of the to-do event; The schedule recommendation information of the to-do event is displayed.

5. The method according to claim 1, characterized in that After displaying the schedule recommendation information of the to-do event based on the to-do event and the schedule information of the first user, the method further includes: Obtain user preference information; In a case where the time period corresponding to the schedule recommendation information of the to-do event includes an idle time period, the schedule recommendation information corresponding to the user preference information is displayed based on the user preference information.

6. The method according to claim 1, characterized in that The method further comprises: When the time period corresponding to the to-do event conflicts with the time period corresponding to the first schedule, displaying a first interface, wherein the first interface includes the to-do event and the first schedule; receiving a second input of the first user selecting a first target object from the to-do events and the first schedule; In response to the second input, the schedule information is updated based on the first target object, and a second target object is used as a to-do event, wherein the second target object is the to-do event and an object not selected by the user in the first schedule.

7. The method according to claim 1, characterized in that The method further comprises: Acquire instant communication information of the first user, where the instant communication information includes invitation information sent by the first user and invitation information received by the first user; The to-do event is determined based on the instant messaging information.

8. A schedule recommendation device, characterized in that: include: An acquisition module, used to acquire to-do events of the first user; The display module is used to display the schedule recommendation information of the to-do event based on the to-do event and the schedule information of the first user.

9. An electronic device, characterized in that: include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the schedule recommendation method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the steps of the schedule recommendation method according to any one of claims 1 to 7 are implemented.

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