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

Abstract

The embodiment of the invention relates to a method and equipment for deploying an AI work assistant on a cloud desktop by intelligent equipment and a medium. The method comprises the following steps: a zero terminal receives user historical behavior data information sent by intelligent equipment; wherein the intelligent device comprises a head-mounted intelligent device and a wrist strap type intelligent device; analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan daily work content, and sending the daily work content to the cloud desktop; and analyzing the historical working time data through a machine learning algorithm to plan a daily working rhythm, and sending the daily working rhythm to the cloud desktop. According to the invention, through planning the daily work content and the daily work rhythm, an omnibearing intelligent service is provided for the user in work, the manual operation of the user is reduced through the intelligent service, and the work efficiency is improved.

Description

Background Art

[0002] Although cloud desktops have realized obtaining user information through smart wearable devices, they have not been able to analyze and utilize this user information for intelligent functions. For example, although smart wearable devices can capture simple actions of users from raising their hands to real-time voice commands and quickly transmit this information to the cloud desktop terminal. But this only transmits the actual operations of users to the terminal in a smart wearable way to replace the current keyboard and mouse operations, and has not analyzed and processed the user information collected by smart wearable devices and applied it to users' daily work to improve work efficiency and quality.

[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 Art section is only used to enhance the understanding of the background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. 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 work assistant on a cloud desktop by an intelligent device, thereby at least to some extent overcoming one or more problems caused by the limitations and defects of related technologies.

[0006] According to the first aspect of the embodiments of the present application, a method for deploying an AI work assistant on a cloud desktop by an intelligent device is provided. The method includes:

[0007] A zero terminal receives user historical behavior data information sent by an intelligent device; wherein, the intelligent device includes a head-mounted intelligent device and a wristband intelligent device, and the user historical behavior data information includes historical visual data, historical auditory data collected by the head-mounted intelligent device, and historical working time data collected by the wristband intelligent device; the historical visual data at least includes the work content, meeting content, software type, and software usage duration seen by the user, and the historical auditory data at least includes the work content, meeting content, and topic content heard by the user; the historical working time data at least includes past working hours and past rest times;

[0008] Analyze the historical visual data and the historical auditory data through a machine learning algorithm to plan daily work content, and send the daily work content to the cloud desktop;

[0009] Analyze the historical working time data through the machine learning algorithm to plan daily work rhythm, and send the daily work rhythm to the cloud desktop.

[0010] In an exemplary embodiment of the present application, in the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes:

[0011] If a keyword appears in the work content, the meeting content, or the topic content, predict that the work content, the meeting content, or the topic content is the core work content; wherein, the keyword includes: core or important;

[0012] If the usage duration of a certain software type used by the user is greater than or equal to the preset usage duration, predict that the content processed by the user using this software type is the core work content.

[0013] In an exemplary embodiment of the present application, in the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes:

[0014] If no keyword appears in the work content, the meeting content, or the topic content, predict that the work content, the meeting content, or the topic content is the secondary work content;

[0015] If the usage duration of a certain software type used by the user is less than the preset usage duration, predict that the content processed by the user using this software type is the secondary work content.

[0016] In an exemplary embodiment of the present application, in the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes:

[0017] According to the predicted core work content and secondary work content, perform priority planning on the user's daily work content; wherein, the priority planning is: set the core work content in the daily work content as the first to be completed, and set the secondary work content in the daily work content as the second to be completed.

[0018] In an exemplary embodiment of the present application, in the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes:

[0019] Generate a daily work report according to the completion situation of the core work content and the secondary work content in the daily work content; wherein, the daily work report includes the progress of the daily work content, and the progress of the daily work content includes the completed core work content, the completed secondary work content, the uncompleted core work content, and the uncompleted secondary work content.

[0020] In an exemplary embodiment of the present application, in the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes:

[0021] Predict office prompts according to the daily work content; wherein, the office prompts at least include: the progress of yesterday's work content, today's core work content, and today's secondary work content; wherein, the progress of yesterday's work content includes the completed yesterday's core work content, the completed yesterday's secondary work content, the uncompleted yesterday's core work content, and the uncompleted yesterday's secondary work content.

[0022] In an exemplary embodiment of the present application, in the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes:

[0023] Predict the working hours of the core work content and the working hours of the secondary work content according to the priority plan.

[0024] In an exemplary embodiment of the present application, in the step of analyzing the historical working time data through a machine learning algorithm to plan the daily work rhythm, it includes:

[0025] Plan the daily boot time and the opening time of commonly used software according to the user's past working time;

[0026] Plan the daily work rhythm according to the user's past working time and past rest time; wherein, the work rhythm includes that the user takes a preset rest period during a preset working period.

[0027] According to a second aspect of the embodiments of the present application, there is provided an electronic device, including:

[0028] A processor; and

[0029] A memory for storing executable instructions of the processor;

[0030] Wherein, the processor is configured to execute the steps of the method for deploying an AI work assistant on a cloud desktop by the intelligent device in any of the above embodiments via executing the executable instructions.

[0031] According to a third aspect of the embodiments of the present application, there is provided a computer program product, including a computer program or instruction, and when the computer program or instruction is executed by a processor, it implements the steps of the method for deploying an AI work assistant on a cloud desktop by the intelligent device in any of the above embodiments.

[0032] The technical solutions provided by the embodiments of the present application may include the following beneficial effects:

[0033] In the embodiments of the present application, through the above method, the historical visual data and historical auditory data collected by the head-mounted intelligent device are analyzed by a machine learning algorithm to plan the user's daily work content, making the work arrangement more in line with the user's work habits and actual needs, thereby improving the user's work efficiency. By analyzing the historical working time data collected by the wristband intelligent device through a machine learning algorithm, the user's daily work rhythm is planned, which is convenient for the user to better balance work and rest, avoid overwork, improve the sustainability of work and quality of life, and also improve the overall time utilization efficiency. By planning the daily work content and daily work rhythm, the present application provides the user with all-round intelligent services in work, reduces the user's manual operation through the intelligent services, and improves the work efficiency.

[0034] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0036] Figure 1 A flowchart showing the steps of a method for deploying an AI work assistant on a cloud desktop by an intelligent device in an exemplary embodiment of the present application;

[0037] Figure 2 An architecture diagram showing the deployment of an AI work assistant on a cloud desktop by an intelligent device in an exemplary embodiment of the present application;

[0038] Figure 3 A schematic diagram showing an electronic device in an exemplary embodiment of the present application;

[0039] Figure 4 A schematic diagram showing a program product in an exemplary embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments.

[0041] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0042] In this exemplary embodiment, a method for deploying an AI work assistant on a cloud desktop by an intelligent device is first provided. As shown in Figure 1 , this method includes steps S101 to S103.

[0043] Among them, step S101: The zero terminal receives the user historical behavior data information sent by the intelligent device; wherein, the intelligent device includes a head-mounted intelligent device and a wristband intelligent device, and the user historical behavior data information includes historical visual data, historical auditory data collected by the head-mounted intelligent device, and historical working time data collected by the wristband intelligent device; the historical visual data at least includes the work content, meeting content, software type, and software usage duration seen by the user, and the historical auditory data at least includes the work content, meeting content, and topic content heard by the user; the historical working time data at least includes the past working time and the past rest time.

[0044] Step S102: Analyze the historical visual data and historical auditory data through a machine learning algorithm to plan the daily work content, and send the daily work content to the cloud desktop.

[0045] Step S103: Analyze the historical working time data through a machine learning algorithm to plan the daily work rhythm, and send the daily work rhythm to the cloud desktop.

[0046] In the embodiment of the present application, through the above method, the historical visual data and historical auditory data collected by the head-mounted intelligent device are analyzed through a machine learning algorithm to plan the user's daily work content, making the work arrangement more in line with the user's work habits and actual needs, thereby improving the user's work efficiency. The historical working time data collected by the wristband intelligent device is analyzed through a machine learning algorithm to plan the user's daily work rhythm, which is convenient for the user to better balance work and rest, avoid overwork, improve the sustainability of work and the quality of life, and at the same time can also improve the overall time utilization efficiency. The present application provides users with all-round intelligent services in work by planning the daily work content and daily work rhythm, and reduces the user's manual operation through intelligent services, thereby improving work efficiency.

[0047] Next, with reference toFigures 1 to 2 A more detailed description of each step of the above method in this exemplary embodiment will be given.

[0048] In step S101, the user wears a smart device, and through the smart device, user historical behavior data information of the user can be collected. The smart device includes a head-mounted smart device and a wristband smart device. The head-mounted smart device can be an AR glasses of the Orion product, and there are 7 micro cameras installed on the AR glasses. The wristband smart device can be a neural wristband of the Orion product. The user historical behavior data includes historical visual data and auditory data. The user wears the AR glasses and the neural wristband. Through the AR glasses, the user's historical visual data and historical auditory data can be collected. Through the neural wristband, the user's historical working time data can be collected.

[0049] It should be noted that the historical visual data, historical auditory data, and historical working time data of the user in daily work in a certain period in the past are collected through the smart device. The certain period in the past is at least one month, and it can be specifically set according to the actual situation. This application will not elaborate on this.

[0050] The historical visual data includes at least the work content, meeting content, software type, and software usage duration seen by the user. The historical auditory data includes at least the work content, meeting content, and topic content heard by the user. By collecting the user's historical visual data and historical auditory data in daily work in a certain period in the past through the head-mounted smart device, it can provide data support for subsequent planning of daily work content.

[0051] The historical working time data includes at least the past working time and past rest time. By collecting the past working time and past rest time of the user in daily work in a certain period in the past through the wristband smart device, it can provide data support for planning the daily work rhythm.

[0052] In step S102, through machine learning algorithms, the work content, meeting content, software type, software usage duration seen by the user, the work content, meeting content, and topic content heard by the user are analyzed to accurately analyze the user's daily work content in a certain period in the past, precisely plan the user's daily work content in the future, make the work arrangement more in line with the user's work habits and actual needs, and thus improve the user's work efficiency.

[0053] After the daily work content is planned, it is sent to the cloud desktop so that the user can carry out daily work according to the daily work content.

[0054] In step S103, machine learning algorithms are used to analyze past working hours and past rest hours to accurately analyze the user's daily work rhythm over a certain period in the past, precisely plan the user's daily work content in the future, facilitate the user to better balance work and rest, avoid overexertion, improve work sustainability and quality of life, and also enhance the overall time utilization efficiency.

[0055] After planning the daily work rhythm, it is sent to the cloud desktop so that the user can reasonably arrange the daily working hours according to the daily work rhythm.

[0056] It should be noted that the machine learning algorithms include decision tree algorithms or neural network algorithms, etc. The decision tree algorithm and the neural network algorithm can be specifically understood with reference to the existing technology.

[0057] In one embodiment, in the step of planning the daily work content by analyzing historical visual data and historical auditory data through machine learning algorithms, it includes:

[0058] If keywords appear in the work content, meeting content, or topic content, then predict that the work content, meeting content, or topic content is the core work content; among them, the keywords include: core or important;

[0059] If the usage duration of a certain software type used by the user is greater than or equal to the preset usage duration, then predict that the content processed by the user using this software type is the core work content.

[0060] In one embodiment, in the step of planning the daily work content by analyzing historical visual data and historical auditory data through machine learning algorithms, it includes:

[0061] If keywords do not appear in the work content, meeting content, or topic content, then predict that the work content, meeting content, or topic content is the secondary work content;

[0062] If the usage duration of a certain software type used by the user is less than the preset usage duration, then predict that the content processed by the user using this software type is the secondary work content.

[0063] It can be understood that in the process of analyzing historical visual data and historical auditory data through machine learning algorithms, it is possible to predict whether the work content, meeting content, or topic content is the core work content based on whether keywords such as core or important appear in the work content, meeting content, or topic content. If keywords appear in the work content, meeting content, or topic content, then predict that the work content, meeting content, or topic content is the core work content. If keywords do not appear in the work content, meeting content, or topic content, then predict that the work content, meeting content, or topic content is the secondary work content.

[0064] In addition, it is also possible to predict whether the content processed by the user using a certain software type is the core work content based on the software usage duration of a certain software type used by the user and the size of the preset usage duration. If the software usage duration of a certain software type used by the user is greater than or equal to the preset usage duration, it is predicted that the content processed by the user using this software type is the core work content. If the software usage duration of a certain software type used by the user is less than the preset usage duration, it is predicted that the content processed by the user using this software type is the secondary work content.

[0065] It should be noted that according to whether keywords such as "core" or "important" appear in the work content, meeting content or topic content, the present application can accurately predict the core work content or the secondary work content. Similarly, based on the software usage duration of a certain software type used by the user and the size of the preset usage duration, the core work content or the secondary work content can also be accurately predicted. Among them, the preset usage duration can be 4h or 5h, etc. It can be specifically set according to the actual situation, and the present application will not elaborate on this.

[0066] In an exemplary case, CAD is generally used for drawing design. If CAD is mentioned as "core" or "important" in the work content, meeting content or topic content, at this time, the core work content of this user is drawing design.

[0067] In an exemplary case, the Python interpreter is generally used for programming. If the Python interpreter is mentioned as "core" or "important" in the work content, meeting content or topic content, at this time, the core work content of this user is programming.

[0068] In an exemplary case, if the software usage duration of the user using CAD is 7h and the preset usage duration is 5h, at this time, the core work content of this user is drawing design.

[0069] In an exemplary case, the Python interpreter is generally used for programming. If the software usage duration of the user using the Python interpreter is 6h and the preset usage duration is 4h, at this time, the core work content of this user is programming.

[0070] In an exemplary case, chat tools are generally used for chatting. If the software usage duration of the user using the chat tool is 1h and the preset usage duration is 4h, at this time, the secondary work content of this user is chatting.

[0071] In an exemplary case, Word is generally used for writing documents. If the software usage duration of the user using the chat tool is 2h and the preset usage duration is 4h, at this time, the secondary work content of this user is writing documents.

[0072] In one embodiment, in the step of analyzing historical visual data and historical auditory data through a machine learning algorithm to plan daily work content, it includes:

[0073] Perform priority planning on the user's daily work content according to the predicted core work content and secondary work content; wherein, the priority planning is: set the core work content in the daily work content as the priority to be completed, and set the secondary work content in the daily work content as the secondary to be completed.

[0074] It can be understood that after predicting the core work content and secondary work content, it is necessary to perform priority planning on them so that the user can reasonably arrange the daily work content, ensure that the user gives priority to completing the core work content in the daily work content, and then complete the secondary work content.

[0075] In one embodiment, in the step of analyzing historical visual data and historical auditory data through a machine learning algorithm to plan daily work content, it includes:

[0076] Generate a daily work report according to the completion status of the core work content and the secondary work content in the daily work content; wherein, the daily work report includes the progress of the daily work content, and the progress of the daily work content includes the completed core work content, the completed secondary work content, the uncompleted core work content, and the uncompleted secondary work content.

[0077] It can be understood that when the user completes the work according to the daily work content, in actual work, there may be situations where the core work content in the daily work content is completed or not completed, and the secondary work content in the daily work content is completed or not completed. Therefore, it is necessary to generate a daily work report according to the completion status of the core work content and the secondary work content in the daily work content. Through the daily work report, the user can timely discover problems in the daily work content to improve the subsequent work efficiency.

[0078] It should be noted that this application can also adjust the daily work content at any time according to the work changes of the user over a period of time.

[0079] In one embodiment, in the step of analyzing historical visual data and historical auditory data through a machine learning algorithm to plan daily work content, it includes:

[0080] Predict office prompts according to the daily work content; wherein, the office prompts at least include: the progress of yesterday's work content, today's core work content, and today's secondary work content; wherein, the progress of yesterday's work content includes the completed core work content of yesterday, the completed secondary work content of yesterday, the uncompleted core work content of yesterday, and the uncompleted secondary work content of yesterday.

[0081] It is understandable that predicting office prompts based on daily work content facilitates users to arrange their daily work according to the office prompts.

[0082] For example: Through office prompts, the user knows the progress of yesterday's work content, today's core work content, and today's secondary work content. When the user arranges today's work according to the office prompts, they can consider first completing the unfinished core work content of yesterday, then completing today's core work content, and then reasonably arranging the unfinished secondary work content of yesterday and today's secondary work content according to the actual situation. Through the office prompts of this application, it is convenient for users to arrange work content and improve work efficiency.

[0083] In one embodiment, in the step of analyzing historical visual data and historical auditory data through a machine learning algorithm to plan daily work content, it includes:

[0084] Planning the working hours of core work content and the working hours of secondary work content according to priority planning.

[0085] It is understandable that the working hours of core work content are generally longer than those of secondary work content. Therefore, it is necessary to reasonably plan the working hours of users when doing core work content and the working hours of users when doing secondary work content to better help users with their work rhythm and work arrangement.

[0086] In one embodiment, in the step of analyzing historical working time data through a machine learning algorithm to plan daily work rhythm, it includes:

[0087] Planning the daily computer startup time and the opening time of commonly used software according to the user's past working time;

[0088] Planning the daily work rhythm according to the user's past working time and past rest time; wherein, the work rhythm includes the user taking a preset rest period during the preset working period.

[0089] It is understandable that when planning the daily work rhythm, the daily computer startup time and the opening time of commonly used software can be planned according to the user's past working time. After planning the daily computer startup time and the opening time of commonly used software, before the user's daily work, the computer will be automatically turned on according to the planned daily computer startup time, and the commonly used work software will be automatically opened, such as opening CAD software. If the core work of yesterday was not completed, when CAD software is automatically opened, it will be loaded to the progress of yesterday's core work. If the core work of yesterday was completed, then just open CAD software automatically. Among them, the daily computer startup time is the time when the computer is turned on.

[0090] When planning the daily work rhythm, it can be planned according to the user's past working hours and past rest hours, so that the user can reasonably arrange their work according to the daily work rhythm.

[0091] In an exemplary case, the past working hours include the past work start time, the past work end time, and a preset working period, and the past rest hours include all the rest hours of the user on a working day, at least including the meal time, the lunch break time, and a preset rest period.

[0092] Specifically, the past work start time is 9:00, the past work end time is 6:00, and the preset working period is 1 hour. The meal time is from 12:00 to 1:00, the lunch break time is from 1:00 to 2:00, and the preset rest period is 10 minutes. According to the above past working hours and past rest hours, the daily work rhythm of the user is planned. For example, the user goes to work at 9:00, has lunch at 12:00, starts the lunch break at 1:00 pm, and gets off work at 6:00 pm. Among them, when the user is working daily, they rest for 10 minutes after working for 1 hour.

[0093] It should be noted that this application can also be set according to the styles of the software types commonly used by the user, the computer style, and usage habits, etc., and set up in advance for the user.

[0094] It should be noted that although the steps of the method in this application are described in a specific order in the drawings, however, this does not require or imply that these steps must be executed in this specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution, etc. Also, it is easily understood that these steps can be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0095] In an exemplary embodiment of this application, an electronic device is also provided. The electronic device can include a processor and a memory for storing executable instructions of the processor. Among them, the processor is configured to execute the steps of the method for deploying an AI work assistant on a cloud desktop by the intelligent device in any one of the above embodiments via executing the executable instructions.

[0096] Those skilled in the art of the relevant technical field can understand that various aspects of the present invention can be implemented as a system, a method, or a program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0097] Next, refer toFigure 3 Describe the electronic device 600 according to this embodiment of the present invention. Figure 3 The shown electronic device 600 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0098] As Figure 3 shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the 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 different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0099] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of deploying the AI work assistant on the cloud desktop for the intelligent device in the above description of this specification. For example, the processing unit 610 can execute the steps as Figure 1 shown in.

[0100] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0101] The storage unit 620 may further include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. The implementation of the network environment may be included in each or some combination of these examples.

[0102] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0103] The electronic device 600 can also communicate with one or more external devices 700 (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable users to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as routers, modems, etc.). Such communication can be carried out through the input / output (I / O) interface 650. Moreover, the electronic device 600 can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through the network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630. It should be understood that although not shown in the figure, 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, etc.

[0104] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments 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, USB flash drive, mobile hard disk, etc.) or on the network, including several instructions to enable a computing device (which can be a personal computer, server, or network device, etc.) to execute the method of deploying an AI work assistant on a cloud desktop by the intelligent device according to the above embodiments of the present application.

[0105] In an exemplary embodiment of the present application, a computer storage medium is further provided, on which a computer program is stored, and when the program is executed by, for example, a processor, the steps of the method of deploying an AI work assistant on a cloud desktop by the intelligent device described in any one of the above embodiments can be implemented.

[0106] In some possible implementation manners, various aspects of the present invention can also be implemented in the form of a computer program product, which includes a computer program or instruction. When the computer program product runs on a terminal device, the computer program code or instruction is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the part of the method of deploying an AI work assistant on a cloud desktop by the intelligent device in the present specification.

[0107] Reference Figure 4As shown, a program product 300 for implementing the above method according to an embodiment of the present application is described. It can be a portable compact disc read-only memory (CD-ROM) and includes program code, and can 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 can be any tangible medium that contains or stores a program, and the program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0108] The above program product can be written in any combination of one or more programming languages to write program code for performing the operations of the present invention. The programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent 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 can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0109] The computer software product can be stored in a computer storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0110] Other embodiments of the present application will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not disclosed in the present application.

Claims

1. A method for deploying an AI work assistant on a cloud desktop by an intelligent device, characterized in that The method includes: A zero-terminal receives user historical behavior data information sent by an intelligent device; wherein, the intelligent device includes a head-mounted intelligent device and a wristband intelligent device, and the user historical behavior data information includes historical visual data, historical auditory data collected by the head-mounted intelligent device, and historical working time data collected by the wristband intelligent device; the historical visual data at least includes the work content, meeting content, software type, and software usage duration seen by the user, and the historical auditory data at least includes the work content, meeting content, and topic content heard by the user; the historical working time data at least includes past working hours and past rest times; Analyze the historical visual data and the historical auditory data through a machine learning algorithm to plan daily work content, and send the daily work content to a cloud desktop; Analyze the historical working time data through the machine learning algorithm to plan a daily work rhythm, and send the daily work rhythm to the cloud desktop.

2. The method for deploying an AI work assistant on a cloud desktop for the intelligent device according to claim 1, wherein, In the step of analyzing the historical visual data and the historical auditory data through the machine learning algorithm to plan daily work content, it includes: If a keyword appears in the work content, the meeting content, or the topic content, predict that the work content, the meeting content, or the topic content is core work content; wherein, the keyword includes: core or important; If the usage duration of a certain software type used by the user is greater than or equal to a preset usage duration, predict that the content processed by the user using this software type is the core work content.

3. The method for deploying an AI work assistant on a cloud desktop for the intelligent device according to claim 2, wherein In the step of analyzing the historical visual data and the historical auditory data through the machine learning algorithm to plan daily work content, it includes: If no keyword appears in the work content, the meeting content, or the topic content, predict that the work content, the meeting content, or the topic content is secondary work content; If the usage duration of a certain software type used by the user is less than the preset usage duration, predict that the content processed by the user using this software type is the secondary work content.

4. The method for deploying an AI work assistant on a cloud desktop for the intelligent device according to claim 3, characterized in that, In the step of analyzing the historical visual data and the historical auditory data through the machine learning algorithm to plan daily work content, it includes: Perform priority planning on the user's daily work content according to the predicted core work content and secondary work content; wherein, the priority planning is: set the core work content in the daily work content to be completed first, and set the secondary work content in the daily work content to be completed second.

5. The method for deploying an AI work assistant on a cloud desktop by the intelligent device according to claim 4, wherein, In the step of analyzing the historical visual data and the historical auditory data through the machine learning algorithm to plan daily work content, it includes: Generate a daily work report based on the completion status of the core work content and the secondary work content in the daily work content; wherein, the daily work report includes the progress of the daily work content, and the progress of the daily work content includes the completed core work content, the completed secondary work content, the uncompleted core work content, and the uncompleted secondary work content.

6. The method for deploying an AI work assistant on a cloud desktop by the intelligent device according to claim 5, wherein In the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes: Predict office prompts based on the daily work content; wherein, the office prompts at least include: the progress of yesterday's work content, today's core work content, and today's secondary work content; wherein, the progress of yesterday's work content includes the completed core work content of yesterday, the completed secondary work content of yesterday, the uncompleted core work content of yesterday, and the uncompleted secondary work content of yesterday.

7. The method for deploying an AI work assistant on a cloud desktop for the intelligent device according to claim 4, wherein In the step of analyzing the historical visual data and the historical auditory data through a machine learning algorithm to plan the daily work content, it includes: Predict the working hours of the core work content and the working hours of the secondary work content according to the priority plan.

8. The method for deploying an AI work assistant on a cloud desktop by the intelligent device according to claim 1, characterized in that, In the step of analyzing the historical working time data through a machine learning algorithm to plan the daily work rhythm, it includes: Plan the daily boot time and the opening time of commonly used software according to the user's past working time; Plan the daily work rhythm according to the user's past working time and past rest time; wherein, the work rhythm includes that the user takes a break for a preset rest period during a preset working period.

9. An electronic device, characterized in that, It includes: A processor; And A memory for storing the executable instructions of the processor; Wherein, the processor is configured to execute the steps of the method for deploying an AI work assistant on a cloud desktop of the intelligent device according to any one of claims 1 to 8 by executing the executable instructions.

10. A computer storage medium, on which a computer program is stored, characterized in that, When the program is executed by the processor, it implements the steps of the method for deploying an AI work assistant on a cloud desktop of the intelligent device according to any one of claims 1 to 8.

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