Vehicle-mounted task management method, vehicle machine and program product

Abstract

The invention discloses a vehicle-mounted task management method, a vehicle machine and a program product, and relates to the technical field of intelligent cabins, and the method comprises the steps: analyzing the task type and priority in a voice instruction through a large model; dynamically adjusting the execution sequence of the tasks according to the vehicle state, the external environment and the currently executed tasks; potential conflicts among multiple tasks are identified, and a solution is provided when the potential conflicts are identified. According to the vehicle-mounted task management method, the vehicle machine and the program product provided by the invention, the problems of disordered task processing sequence, lack of dynamic adjustment capability and non-intelligent instruction conflict processing in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the field of intelligent cockpit technology, and in particular to in-vehicle task management methods, vehicle systems and software products. Background Technology

[0002] Currently, with the increasingly widespread application of intelligent voice assistants in in-vehicle environments, users are relying more and more on voice assistants for navigation, vehicle control, and information retrieval. However, existing voice assistants have certain limitations in task management, especially when receiving multiple commands simultaneously, failing to effectively manage priorities and optimize task execution. This leads to unreasonable execution order of multiple commands, poor user experience, and may even cause safety issues.

[0003] Problems with existing technology:

[0004] 1. Disordered task processing: When a user issues multiple voice commands in succession, the existing system cannot reasonably distinguish the priority of tasks, which may result in low-priority tasks being executed before urgent tasks, affecting driving safety.

[0005] 2. Lack of dynamic adjustment capability: The existing system cannot dynamically adjust the task execution order according to the external environment (such as traffic conditions) and the internal environment (such as vehicle status).

[0006] 3. Insufficiently intelligent handling of command conflicts: There may be conflicts between multiple voice commands (such as simultaneously requesting to play music and navigation prompts). Existing systems have difficulty intelligently handling these conflicts, causing users to need to repeat input or make manual adjustments. Summary of the Invention

[0007] The purpose of this invention is to provide an in-vehicle task management method, in-vehicle system and program product, which solves the problems of chaotic task processing order, lack of dynamic adjustment capability and unintelligent instruction conflict handling in the prior art.

[0008] This invention provides the following solution:

[0009] According to one aspect of the present invention, an in-vehicle task management method is provided, the in-vehicle task management method comprising:

[0010] Analyze the task types and priorities in voice commands using a large model;

[0011] The execution order of tasks is dynamically adjusted based on vehicle status, external environment, and the currently executing task.

[0012] Identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified.

[0013] Optionally, large-scale model analysis can be used to determine the task type and priority within voice commands, including:

[0014] Input voice commands into the large model;

[0015] Extract vehicle subsystem keywords from the feedback text of the large model;

[0016] Frequency statistics were performed on the extracted vehicle subsystem keywords;

[0017] Based on the word frequency statistics, determine the subsystem to which the voice command belongs;

[0018] Based on the identified subsystems, determine the task types.

[0019] Optionally, large-scale model analysis can be used to determine the task type and priority within voice commands, including:

[0020] Input voice commands into the large model;

[0021] Extract sentiment-related keywords from the feedback text of large models;

[0022] Frequency statistics were performed on the extracted sentiment-related keywords;

[0023] Based on word frequency statistics, determine the emotional tone of the voice commands;

[0024] Determine the task type based on the identified emotional tendency.

[0025] Optionally, large-scale model analysis can be used to determine the task type and priority within voice commands, including:

[0026] Extracting time markers from voice commands;

[0027] Determine task priority based on time markers.

[0028] Optionally, the execution order of tasks can be dynamically adjusted based on vehicle status, external environment, and the currently executing task, including:

[0029] For tasks that have not yet been executed, sort them by priority;

[0030] The tasks that have not yet been executed are executed sequentially according to their priority.

[0031] Optionally, identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified, including:

[0032] Get the list of system components involved in the task execution;

[0033] When the system component lists of multiple tasks overlap, they are identified as potentially conflicting tasks.

[0034] Optionally, identifying potential conflicts between multiple tasks and providing solutions when potential conflicts are identified also includes:

[0035] Acquire multiple tasks that have been identified as potentially conflicting tasks;

[0036] Obtain the necessary conditions for executing each task;

[0037] For tasks whose necessary conditions cannot be met at present, execution shall be suspended.

[0038] Tasks for which the necessary conditions for execution have already been met should be executed first.

[0039] According to a second aspect of the present invention, an in-vehicle task management device is provided, the in-vehicle task management device comprising:

[0040] The analysis module is used to analyze the task type and priority in voice commands using a large model;

[0041] The adjustment module is used to dynamically adjust the execution order of tasks based on vehicle status, external environment, and the currently executing task.

[0042] The identification module is used to identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified.

[0043] According to three aspects of the present invention, a vehicle infotainment system is provided, the vehicle infotainment system comprising: a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the vehicle task management method as described above.

[0044] According to four aspects of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the vehicle task management method as described above.

[0045] The above solution achieves the following beneficial technical effects:

[0046] 1. Improve task management efficiency: Through intelligent identification and optimized execution of task priorities using a large model, the system can effectively improve the efficiency of multi-task processing and avoid delays or chaos caused by unreasonable task order;

[0047] 2. Enhance system intelligence: The system can dynamically adjust the task execution order according to the real-time in-vehicle and out-of-vehicle environment to ensure the rationality and safety of tasks under various circumstances;

[0048] 3. Optimize user experience: Through intelligent conflict handling, users do not need to manually intervene or repeatedly enter commands, which improves the overall user experience and usability of the voice assistant. Attached Figure Description

[0049] Figure 1 This is a flowchart of an in-vehicle task management method provided in one or more embodiments of the present invention;

[0050] Figure 2 This is a flowchart of the analysis operation in the vehicle task management method provided by one or more embodiments of the present invention;

[0051] Figure 3 This is a flowchart of the analysis operation in the vehicle task management method provided by one or more embodiments of the present invention;

[0052] Figure 4 This is a flowchart of the analysis operation in the vehicle task management method provided by one or more embodiments of the present invention;

[0053] Figure 5 This is a flowchart of the adjustment operation in the vehicle task management method provided by one or more embodiments of the present invention;

[0054] Figure 6 This is a flowchart of the identification operation in the vehicle task management method provided by one or more embodiments of the present invention;

[0055] Figure 7 This is a flowchart of the identification operation in the vehicle task management method provided by one or more embodiments of the present invention;

[0056] Figure 8 This is a structural diagram of an in-vehicle task management device provided in one or more embodiments of the present invention;

[0057] Figure 9 This is a structural diagram of the vehicle system provided in one or more embodiments of the present invention. Detailed Implementation

[0058] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Figure 1 This is a flowchart of an in-vehicle task management method provided in one or more embodiments of the present invention. See also... Figure 1 The vehicle-mounted task management method includes the following steps:

[0060] S11 analyzes the task type and priority in voice commands using a large model.

[0061] S12 dynamically adjusts the execution order of tasks based on vehicle status, external environment, and the currently executing task.

[0062] S13 identifies potential conflicts between multiple tasks and provides solutions when potential conflicts are identified.

[0063] Voice commands are now widely used in the field of smart cockpits. Compared with other human-computer interaction methods, voice commands have the advantages of being easy to learn, easy to master, and having a low barrier to entry.

[0064] However, in actual cockpit applications, there are still many practical obstacles to the application of voice commands in human-computer interaction.

[0065] First, in the current process of executing computational tasks using voice commands, the order in which tasks should be executed is chaotic and disordered.

[0066] Secondly, during the execution of voice commands, there are often situations where two voice commands compete for resources, making it difficult to execute the computing tasks proposed by the voice commands normally.

[0067] To address the aforementioned situation, this embodiment provides a solution: managing computational tasks initiated via voice commands according to task type and priority. Furthermore, for computational tasks that may conflict during execution, potential conflicts are identified, and based on the identification results, computational resources are allocated to ensure the effective execution of each task.

[0068] To manage computing tasks by type and priority, the primary requirement is the ability to classify and prioritize different computing tasks.

[0069] Category recognition is performed based on the feedback text from a large language model. The feedback text from a large language model has the advantage of richer data, which can effectively avoid the drawback of sparse data in the original speech command.

[0070] There can be multiple different classification criteria for the computational tasks corresponding to voice commands. Moreover, multiple different classification criteria are allowed to coexist in the same system.

[0071] Within the same system, the existence of multiple different classification criteria for computing tasks can enrich the dimensions for classifying computing tasks and make it easier to find the target computing task.

[0072] This is similar to classifying books in a library. You can classify books by the Chinese Library Classification number, by the publisher, or by the year of publication.

[0073] Because there are many different classification methods, or classification standards, when categorizing books, it means that when searching for books, you can use different classification methods to find the books you need more easily.

[0074] For prioritizing computational tasks, instead of using the feedback text from a large model as a basis, the voice commands themselves can be graded directly to complete the priority allocation.

[0075] The main reason for this choice is to avoid noise in the feedback text, which could lead to incorrect assessment of the urgency of the task and cause errors in the allocation of priority data.

[0076] After completing the type recognition and priority allocation of voice commands, the execution order of computing tasks can be dynamically adjusted.

[0077] There are several prerequisites to consider when allocating the execution order. The first is the vehicle status.

[0078] Vehicle state is a broad concept encompassing the states of various subsystems within the vehicle. The vehicle's state significantly impacts the scheduling of tasks. For example, if the vehicle is currently parked, computational tasks that can only be effectively executed while the vehicle is in motion should be suspended. Similarly, if the vehicle's air conditioning system malfunctions, computational tasks related to the air conditioning system should be postponed.

[0079] Another important consideration is the external environment. Here, the external environment refers to things other than the vehicle. For example, weather is an external environmental factor. Road conditions are also an external environmental factor.

[0080] For example, if the current weather is cloudy, it means that the sunlight from outside is not strong. Therefore, the corresponding computing task for a voice command like "turn on the sunshade" does not need to be executed immediately, but should be prioritized for other computing tasks.

[0081] In addition, it should be noted that when there are multiple computing tasks from different sources and with different purposes in the system, these computing tasks may conflict with each other.

[0082] For example, two computational tasks both aim to modify a segment of data at the same address in the cache. Furthermore, the target values ​​for the two tasks are different. That is, if the requirement of one task is met, the requirement of the other task cannot be met. In this situation, we can say that the execution of the two computational tasks is conflicting.

[0083] For two or more computing tasks that conflict, solutions can be found by analyzing the specific points of conflict.

[0084] For example, two computational tasks both need to access the status parameters of the air conditioning equipment in the system in real time, but there are no specific requirements regarding the exact time when each task accesses these parameters. In this case, the timeline can be divided into different time slots, allowing different computational tasks to perform the access operations within those time slots.

[0085] Figure 2 This is a flowchart of the analysis operation in the vehicle-mounted task management method provided in one or more embodiments of the present invention. See also Figure 2 The large-scale model analyzes the task type and priority in voice commands, including the following operational steps:

[0086] S21, input the voice command into the large model.

[0087] S22, Extract vehicle subsystem keywords from the feedback text of the large model.

[0088] S23, perform word frequency statistics on the extracted vehicle subsystem keywords.

[0089] S24. Based on the word frequency statistics, determine the subsystem to which the voice command belongs.

[0090] S25, Determine the task type based on the identified subsystem.

[0091] In this embodiment, the process of determining the task type corresponding to the voice command is described in detail.

[0092] It should be understood that the vehicles we use daily are actually composed of multiple different subsystems. Each subsystem performs a specific function of the vehicle. These subsystems work together to form the organic whole of the vehicle.

[0093] For example, a typical vehicle will have: a powertrain, a braking system, a control system, and some auxiliary systems. These auxiliary systems may include: an auxiliary power supply system, an air conditioning and ventilation system, a vehicle lighting system, and so on.

[0094] Normally, each voice command should be directed to a specific object of action execution. This object is typically a specific component on the vehicle. Any component of the vehicle should correspond to a subsystem. Therefore, by analyzing the object to which a voice command is directed, we can determine which subsystem of the vehicle the voice command corresponds to, thus defining the task type of the voice command.

[0095] For example, if a user in the vehicle inputs the voice command "turn on the air conditioning," this command clearly refers to the vehicle's air conditioning system. The target of the voice command is the air conditioning, which belongs to the vehicle's air conditioning and ventilation system. Therefore, the category corresponding to this voice command should be the air conditioning and ventilation system.

[0096] For example, a user inputs the voice command "Turn on the fog lights." This voice command is executed on the fog lights, which are part of the vehicle's lighting system. Therefore, the task type corresponding to this voice command should be the vehicle lighting system.

[0097] It should be understood that the voice commands actually input by users usually suffer from data sparsity. To address the data sparsity problem encountered in the task category determination process, large language models, also known as generative language models, are introduced.

[0098] At the beginning of the task type recognition process provided in this embodiment, the voice command is first input into the large language model. The large language model will provide corresponding text feedback, and the task category of the voice command can be determined by analyzing the feedback text.

[0099] Specifically, keywords related to various subsystems of the vehicle are identified from the feedback text. These keywords are called vehicle subsystem keywords.

[0100] After extracting vehicle subsystem keywords from the feedback text, the frequency of each keyword is statistically analyzed. Then, the total frequency of each subsystem is summed. Furthermore, the subsystems ranking highest in the total frequency ranking are identified. From these top-ranking subsystems, the most likely subsystem is determined; this subsystem corresponds to the voice command, thus defining the category of the voice command.

[0101] Figure 3 This is a flowchart of the analysis operation in the vehicle-mounted task management method provided in one or more embodiments of the present invention. See also Figure 3 The large-scale model analyzes the task type and priority in voice commands, including the following operational steps:

[0102] S31 inputs voice commands into the large model.

[0103] S32, extract sentiment keywords from the feedback text of the large model.

[0104] S33, perform word frequency statistics on the extracted sentiment-related keywords.

[0105] S34. Based on word frequency statistics, determine the emotional tone of the voice command.

[0106] S35. Determine the task type according to the determined emotional tendency.

[0107] Different from the method of determining the category of voice commands according to the vehicle subsystem corresponding to keywords described in the foregoing embodiments of the present invention, in this embodiment, the task category of the voice command is determined by identifying the emotional keywords in the feedback text.

[0108] This embodiment is also applicable to the feedback text of the large model, aiming to solve the problem of data sparsity of the original voice command.

[0109] It should be understood that there are some words with emotional tendencies in the daily used voice. We can call these words emotional tendency keywords.

[0110] The emotional tendency keywords can include positive emotional keywords, negative emotional keywords, and neutral emotional keywords.

[0111] For example, "happy" is a typical positive emotional keyword. "Depressed" is a typical negative emotional keyword. "Open" is a typical neutral emotional keyword.

[0112] By counting the word frequencies of the emotional tendency keywords with various emotional tendencies in the feedback text, the emotional tendency with the highest word frequency is obtained. This emotional tendency is the emotional tendency corresponding to this voice command, that is, the task category recognition result of this voice command.

[0113] Figure 4 It is a flowchart of the analysis operation in the vehicle task management method provided by one or more embodiments of the present invention. Refer to Figure 4 , analyze the task type and priority in the voice command through the large model, including the following operation steps:

[0114] S41. Extract the temporal auxiliary words in the voice command.

[0115] S42. Determine the priority of the task according to the temporal auxiliary words.

[0116] In the voice commands we usually use, there are a large number of temporal auxiliary words. By analyzing and judging these temporal auxiliary words, the urgency of the task can be determined, and then the priority of the task can be determined.

[0117] For example, such temporal auxiliary words are: le, zhe, guo, etc.

[0118] Of course, in the actual used urgency judgment logic, not only the temporal auxiliary words need to be extracted and analyzed. In order to further clarify the urgency of the corresponding task, usually, the entity words such as the verbs adjacent to the temporal auxiliary words also need to be analyzed and recorded to obtain a more accurate urgency judgment result.

[0119] For example, for the voice command "The phone has crashed and needs to be restarted", the temporal particle "了" is extracted. After extracting the temporal particle "了", the verb "crashed" adjacent to its position is obtained. The combination of the verb and the particle expresses a state of the current phone.

[0120] "Crashed" is generally a very serious incident. Therefore, the priority assigned to this voice command should be relatively high.

[0121] In the process of determining the priority, the large language model is not used. The main consideration for not using the large language model in the process of determining the priority is to avoid introducing more data noise due to the use of the large language model.

[0122] Figure 5 It is a flowchart of the adjustment operation in the vehicle task management method provided by one or more embodiments of the present invention. Refer to Figure 5 , according to the vehicle state, external environment, and the tasks currently being executed, dynamically adjust the execution order of the tasks, including:

[0123] S51, sort the unexecuted tasks according to the priority.

[0124] S52, execute the unexecuted tasks in sequence according to the sorting result of the priority sorting.

[0125] This embodiment gives an example of the dynamic adjustment of the execution order.

[0126] It should be understood that from the input of the voice command into the in-vehicle system to the execution of the corresponding computing task, a certain amount of time is required. During this period, the computing task is completely in a waiting state, waiting to obtain the control right of the processor.

[0127] These computing tasks in the waiting state are the unexecuted computing tasks.

[0128] In this embodiment, for all unexecuted computing tasks, they are sorted according to the priority parameters of the computing tasks themselves. Then, according to the sorting result, the execution order of these computing tasks is determined.

[0129] After the above dynamic adjustment, the computing tasks with higher priority parameters will obtain the control right of the processor more preferentially and be executed more preferentially.

[0130] For computing tasks with the same priority parameters, they can be sorted again according to other attributes of the computing tasks. This re-sorting operation is called sub-sorting. Then, according to the sorting result of the sub-sorting, it is determined which of the computing tasks with the same priority parameters should be executed first and which should be executed later.

[0131] After the above dynamic adjustment, computing tasks with higher priority attributes will be executed first, ensuring the effective utilization of computing resources.

[0132] Figure 6 This is a flowchart of the identification operation in the vehicle task management method provided by one or more embodiments of the present invention. See also Figure 6 Identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified, including:

[0133] S61, obtain the list of system components involved in task execution.

[0134] S62: When the system component lists of multiple tasks overlap, they are identified as potentially conflicting tasks.

[0135] This embodiment focuses on illustrating the prerequisites for identifying potentially conflicting computational tasks.

[0136] It should be understood that the execution of each computing task typically involves multiple system components within the system. For example, the voice command "turn on the air conditioner" inevitably involves system components related to the air conditioner.

[0137] These system components can be obtained by calculating the list of functions called in the execution code of the task.

[0138] After obtaining the list of system components involved in the execution, if two computing tasks need to call the same system components during execution, it can be preliminarily determined that the two computing tasks are potential conflicting tasks.

[0139] It's important to note that potentially conflicting tasks don't necessarily clashed during actual execution. For example, if two computational tasks both call the same system component, but their calls to the system component are time-division multiplexed and their timing doesn't conflict, then these two potentially conflicting tasks wouldn't actually be considered conflicting tasks during execution.

[0140] Figure 7 This is a flowchart of the identification operation in the vehicle task management method provided by one or more embodiments of the present invention. See also Figure 7 Identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified, including:

[0141] S71, obtain the list of system components involved in task execution.

[0142] S72: When the system component lists of multiple tasks overlap, they are identified as potentially conflicting tasks.

[0143] S73: Obtain multiple tasks that have been identified as potentially conflicting tasks.

[0144] S74, obtain the necessary conditions for the execution of each task.

[0145] S75: For tasks whose necessary conditions cannot be met at present, execution shall be suspended.

[0146] S76. Tasks whose necessary conditions for execution have already been met shall be executed first.

[0147] This embodiment focuses on how to provide a solution for the execution order of each computing task after identifying potentially conflicting tasks.

[0148] After identifying potential conflicting tasks, each potential conflicting task needs to be treated differently to ensure that computing resources can be effectively utilized by each computing task and to prevent a large number of computing task deadlocks.

[0149] For each computational task among the identified potential conflicting tasks, obtain the necessary conditions for each computational task to be executed effectively. The meaning of necessary conditions is that the computational task will only be executed effectively if all these conditions are met.

[0150] If all the necessary conditions for the execution of a computational task have been met, then the computational task should be executed as soon as possible.

[0151] If some of the necessary conditions for the execution of a computational task have not been met, then the execution of that computational task will be suspended.

[0152] Figure 8 This is a structural diagram of an in-vehicle task management device provided in one or more embodiments of the present invention. See also... Figure 8 The vehicle-mounted task management device includes:

[0153] Analysis module 81 is used to analyze the task type and priority in voice commands using a large model.

[0154] The adjustment module 82 is used to dynamically adjust the execution order of tasks based on the vehicle status, external environment, and currently executing tasks.

[0155] The identification module 83 is used to identify potential conflicts between multiple tasks and to provide solutions when potential conflicts are identified.

[0156] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0157] like Figure 9 As shown, the present invention also provides a vehicle infotainment system, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the vehicle task management method.

[0158] Figure 9 This is a structural schematic diagram of a vehicle-mounted infotainment system provided in an embodiment of the present invention. Figure 9 The structure shown in this embodiment of the invention includes one or more processors 910 and a memory 920; the processors 910 in the vehicle infotainment system can be one or more. Figure 9 Taking a processor 910 as an example; the memory 920 is used to store one or more programs; the one or more programs are executed by the one or more processors 910, so that the one or more processors 910 implement the vehicle task management method as described in any one of the embodiments of the present invention.

[0159] The vehicle infotainment system may also include an input device 930 and an output device 940.

[0160] The processor 910, memory 920, input device 930, and output device 940 in the vehicle's infotainment system can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0161] The memory 920 in the vehicle infotainment system serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the vehicle task management method provided in this embodiment. The processor 910 executes various functional applications and data processing of the vehicle infotainment system by running the software programs, instructions, and modules stored in the memory 920, thereby implementing the vehicle task management method described in the above embodiment.

[0162] The memory 920 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the vehicle's infotainment system. Furthermore, the memory 920 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 920 may further include memory remotely located relative to the processor 910, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0163] Input device 930 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the vehicle system. Output device 940 may include display devices such as a display screen.

[0164] The present invention also provides a computer-readable storage medium storing a computer program executable by a vehicle-mounted system, which, when run on the vehicle-mounted system, causes the vehicle-mounted system to perform the steps of an in-vehicle task management method.

[0165] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0166] The present invention also provides a vehicle equipped with the on-board task management device described above.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle-mounted task management method, characterized in that, The vehicle-mounted task management method includes: Analyze the task types and priorities in voice commands using a large model; The execution order of tasks is dynamically adjusted based on vehicle status, external environment, and the currently executing task. Identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified.

2. The method according to claim 1, characterized in that, Large-scale model analysis is used to determine the task type and priority in voice commands, including: Input voice commands into the large model; Extract vehicle subsystem keywords from the feedback text of the large model; Frequency statistics were performed on the extracted vehicle subsystem keywords; Based on the word frequency statistics, determine the subsystem to which the voice command belongs; Based on the identified subsystems, determine the task types.

3. The method according to claim 1, characterized in that, Large-scale model analysis is used to determine the task type and priority in voice commands, including: Input voice commands into the large model; Extract sentiment-related keywords from the feedback text of large models; Frequency statistics were performed on the extracted sentiment-related keywords; Based on word frequency statistics, determine the emotional tone of the voice commands; Determine the task type based on the identified emotional tendency.

4. The method according to claim 1, characterized in that, Large-scale model analysis is used to determine the task type and priority in voice commands, including: Extracting time markers from voice commands; Determine task priority based on time markers.

5. The method according to claim 1, characterized in that, The execution order of tasks is dynamically adjusted based on vehicle status, external environment, and the currently executing task, including: For tasks that have not yet been executed, sort them by priority; The tasks that have not yet been executed are executed sequentially according to their priority.

6. The method according to claim 1, characterized in that, Identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified, including: Get the list of system components involved in the task execution; When the system component lists of multiple tasks overlap, they are identified as potentially conflicting tasks.

7. The method according to claim 6, characterized in that, Identifying potential conflicts between multiple tasks and providing solutions when potential conflicts are identified also includes: Acquire multiple tasks that have been identified as potentially conflicting tasks; Obtain the necessary conditions for executing each task; For tasks whose necessary conditions cannot be met at present, execution shall be suspended. Tasks for which the necessary conditions for execution have already been met should be executed first.

8. A vehicle-mounted task management device, characterized in that, The vehicle-mounted task management device includes: The analysis module is used to analyze the task type and priority in voice commands using a large model; The adjustment module is used to dynamically adjust the execution order of tasks based on vehicle status, external environment, and the currently executing task. The identification module is used to identify potential conflicts between multiple tasks and provide solutions when potential conflicts are identified.

9. A vehicle infotainment system, characterized in that, The vehicle infotainment system includes a processor and a memory, wherein the memory stores a computer program, which is loaded and executed by the processor to implement the vehicle task management method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle task management method as described in any one of claims 1 to 7.

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