CN-121979606-A - Mobile office scheme determining method and device for vehicle-mounted scene

Abstract

The application provides a mobile office scheme determining method and device for a vehicle-mounted scene, and relates to the technical field of mobile office software. Determining a target adaptation mechanism and a target scheduling strategy corresponding to the target context event based on the corresponding relation between the configured different context events and different adaptation mechanisms and the corresponding relation between the different context events and different scheduling strategies; and adjusting the configured man-machine interaction mode, office task execution flow and system resource allocation scheme by adopting a target adaptation mechanism and a target scheduling strategy to obtain a mobile office scheme matched with the vehicle-mounted dynamic environment. The application realizes intelligent adaptation of mobile office in vehicle-mounted dynamic environment, improves interaction safety and office efficiency, and optimizes system resource utilization.

Inventors

• HUO LIJUN
• SUN LONGFEI

Assignees

• 奇瑞汽车股份有限公司

Dates

Publication Date

20260505

Application Date

20260127

Claims (10)

1. 1. A mobile office proposal determining method for a vehicle-mounted scene, the method comprising: Reasoning the multisource data of the current vehicle through a trained reasoning model, and determining a target context event representing the current office environment; Determining a target adaptation mechanism and a target scheduling strategy corresponding to the target context event based on the corresponding relation between the configured different context events and different adaptation mechanisms and the corresponding relation between the different context events and different scheduling strategies; And adjusting the configured man-machine interaction mode, office task execution flow and system resource allocation scheme by adopting the target adaptation mechanism and the target scheduling strategy to obtain a mobile office scheme matched with the vehicle-mounted dynamic environment.
2. 2. The method of claim 1, wherein the multi-source data comprises vehicle dynamics data, environment and navigation data, and user status data; The vehicle dynamic data comprise triaxial acceleration, triaxial angular velocity, vehicle speed, steering wheel rotation angle and gear information; the environment and navigation data comprise travel routes, real-time road conditions, road section information, expected arrival time, network signal intensity and a prediction map; the user state data includes head orientation, gaze drop point, eye opening and closing degree, and gesture information.
3. 3. The method of claim 2, wherein the target context events include a vehicle motion state event, a network state event, a trip stationary event, and a user concentration state event.
4. 4. A method according to claim 3, wherein inferring the multisource data of the current vehicle by means of a trained inference model, determining a target context event characterizing the current office environment, comprises: Determining the vehicle motion state event based on the vehicle dynamic data, wherein the vehicle motion state event comprises a bump event and a steering event; Determining the network state event and/or the journey stationary period event based on the environment and navigation data, wherein the network state event comprises a network interruption early warning event and a network signal weakening event; Based on the user state data, the user attention state event is determined, the user attention state event including a line-of-sight focus screen event.
5. 5. The method of claim 1, wherein the inference model comprises a decision tree model, a logistic regression model, or a naive bayes model.
6. 6. The method of claim 1, wherein the target adaptation mechanism comprises a UI motion compensation sub-mechanism, a voice interaction optimization sub-mechanism, an input method adaptation mechanism, and a UI mode switching sub-mechanism.
7. 7. The method of claim 1, wherein the target scheduling policy comprises a task fragmentation scheduling sub-policy and a predictive caching sub-policy.
8. 8. A mobile office proposal determining device for an in-vehicle scene, the device comprising: the reasoning unit is used for reasoning the multisource data of the current vehicle through the trained reasoning model and determining a target context event representing the current office environment; The determining unit is used for determining a target adaptation mechanism and a target scheduling strategy corresponding to the target context event based on the corresponding relation between the configured different context events and different adaptation mechanisms and the corresponding relation between the different context events and different scheduling strategies; And the adjusting unit is used for adjusting the configured man-machine interaction mode, office task execution flow and system resource allocation scheme by adopting the target adaptation mechanism and the target scheduling strategy to obtain a mobile office scheme matched with the vehicle-mounted dynamic environment.
9. 9. An electronic device, characterized in that the electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are in communication with each other through the communication bus; a memory for storing a computer program; a processor for implementing the method steps of any of claims 1-7 when executing a program stored on a memory.
10. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-7.

Description

Mobile office scheme determining method and device for vehicle-mounted scene Technical Field The application relates to the technical field of mobile office software, in particular to a method and a device for determining a mobile office scheme oriented to a vehicle-mounted scene. Background With the rapid development of communication technology, internet of things and intelligent cabins, automobiles have evolved from pure vehicles to third living spaces, and vehicle commute time becomes a potential working time for business people. The mobile office related technology in the current Internet of vehicles field mainly comprises three types, namely basic interconnection and function transplantation, wherein the traditional office application is simply transplanted to a vehicle-mounted system, connection is realized instead of fusion, no perception is provided for the dynamic environment of the vehicle, pain points such as bumpy operation and network fluctuation cannot be solved, driver state monitoring and safety isolation, focusing driving safety are realized, office functions are limited by monitoring the driver state, office experience is not optimized for passengers, and the simple scene mode switching is realized, static office environment is only built by adjusting seats, lights and the like, and utilization of deep contexts such as real-time motion gestures of the vehicle, navigation prediction information and the like is lacking, and dynamic adaptation and look-ahead scheduling cannot be realized. In the prior art, vehicles are generally regarded as static offices or cabins with functions of drivers to be limited, dynamic context data of the vehicles cannot be deeply mined, so that when traditional office software is applied to a vehicle-mounted environment, the problems of interaction mode and environment conflict, task management and trip disjointing, lack of resource use and the like exist, and the requirements of safe, efficient and immersive mobile offices of passengers are difficult to meet. Disclosure of Invention The embodiment of the application aims to provide a mobile office scheme determining method and device for a vehicle-mounted scene, which are used for solving the problems in the prior art, realizing the self-adaptive adaptation of vehicle-mounted mobile office and improving the interaction fluency and office efficiency. In a first aspect, a mobile office scheme determining method facing to a vehicle-mounted scene is provided, and the method may include: Reasoning the multisource data of the current vehicle through a trained reasoning model, and determining a target context event representing the current office environment; Determining a target adaptation mechanism and a target scheduling strategy corresponding to the target context event based on the corresponding relation between the configured different context events and different adaptation mechanisms and the corresponding relation between the different context events and different scheduling strategies; And adjusting the configured man-machine interaction mode, office task execution flow and system resource allocation scheme by adopting the target adaptation mechanism and the target scheduling strategy to obtain a mobile office scheme matched with the vehicle-mounted dynamic environment. In one possible implementation, the multi-source data includes vehicle dynamics data, environment and navigation data, and user status data; The vehicle dynamic data comprise triaxial acceleration, triaxial angular velocity, vehicle speed, steering wheel rotation angle and gear information; the environment and navigation data comprise travel routes, real-time road conditions, road section information, expected arrival time, network signal intensity and a prediction map; the user state data includes head orientation, gaze drop point, eye opening and closing degree, and gesture information. In one possible implementation, the target context events include a vehicle motion state event, a network state event, a trip stationary event, and a user concentration state event. In one possible implementation, inferring multi-source data of a current vehicle by a trained inference model, determining a target context event characterizing the current office environment, comprising: Determining the vehicle motion state event based on the vehicle dynamic data, wherein the vehicle motion state event comprises a bump event and a steering event; Determining the network state event and/or the journey stationary period event based on the environment and navigation data, wherein the network state event comprises a network interruption early warning event and a network signal weakening event; Based on the user state data, the user attention state event is determined, the user attention state event including a line-of-sight focus screen event. In one possible implementation, the inference model includes a decision tree model, a logistic regression model, or a naive bay