Intelligent cabin dual-system resource dynamic regulation and control method, device and system

Abstract

The invention provides an intelligent cabin dual-system resource dynamic regulation and control method, device and system, and the dynamic regulation and control method comprises the steps: monitoring the resource use conditions of an Android system and a Linux system in an intelligent cabin in real time, and calculating the total resource utilization rate; identifying the current use scene of the user based on the HMI layer information of the vehicle screen; dynamically generating a resource adjustment strategy according to the total resource utilization rate and the scene information; and executing the resource adjustment strategy, and performing resource adjustment or process management on the target process. The technical problems that an existing intelligent cockpit system lacks dynamic perception and resource scheduling capability for an actual user scene, and accurate and differentiated resource allocation and guarantee are difficult to achieve when system resources are insufficient are solved. According to the method, the resource use condition is detected in real time, and the resource adjustment strategy is dynamically generated in combination with the current use scene, so that crash or no response caused by resource competition of some processes is avoided, and the stability and reliability of the whole system are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent cockpit technology, and more specifically, to a method, apparatus, and system for dynamic control of dual-system resources in an intelligent cockpit. Background Technology

[0002] With the rapid development of automotive intelligent technology, the functions of in-vehicle intelligent cockpits are becoming increasingly rich, gradually integrating navigation, audio-visual entertainment, communication, and various third-party applications to enhance the user's driving experience. To support these complex functions, most intelligent cockpits currently adopt a dual-system architecture, with one system responsible for infotainment and the other for system control. However, during actual vehicle operation, as various applications are used in parallel, the system's consumption of critical resources such as CPU and memory continues to rise. When the system load is too high and resources are close to saturation, problems such as entertainment system lag, application unresponsiveness, or even unexpected restarts can easily occur, seriously affecting the user experience and posing a potential risk to driving safety.

[0003] The problem is that existing intelligent cockpit systems lack the ability to dynamically perceive and schedule resources in actual user scenarios, making it difficult to achieve accurate and differentiated resource allocation and support when system resources are scarce. Summary of the Invention

[0004] This invention addresses the technical problem of existing intelligent cockpit systems lacking dynamic perception and resource scheduling capabilities for real-world user scenarios, making it difficult to achieve accurate and differentiated resource allocation and protection when system resources are strained. By real-time monitoring of resource usage and dynamically generating resource adjustment strategies based on the current usage scenario, this invention avoids crashes or unresponsiveness in certain processes due to resource contention, thereby improving the overall system stability and reliability.

[0005] To address the aforementioned issues, this invention provides a method for dynamic resource control of dual systems in an intelligent cockpit, comprising: real-time monitoring of resource usage of the Android and Linux systems in the intelligent cockpit and calculating the total resource utilization rate; identifying the user's current usage scenario based on HMI layer information from the vehicle screen; dynamically generating a resource adjustment strategy based on the total resource utilization rate and scenario information; and executing the resource adjustment strategy to adjust resources or manage processes of the target process.

[0006] Compared to existing technologies, this technical solution achieves the following effects: real-time monitoring of resource usage in both Android and Linux systems allows the intelligent cockpit system to promptly grasp the current resource operation status, providing data support for dynamic adjustments. Simultaneously, through HMI layer information recognition, the system can accurately identify user scenarios; this dynamic perception capability enables the intelligent cockpit to better understand user needs and behavioral patterns. Combining total resource utilization with scenario information, the dynamically generated resource adjustment strategy can implement precise and differentiated resource allocation for different usage needs, thereby optimizing resource utilization efficiency and ensuring that critical user needs are met even under resource constraints. Furthermore, the dynamic adjustment strategy can prevent crashes or unresponsiveness in certain processes due to resource contention, improving the overall system stability and reliability.

[0007] In one possible design, each process has a preset target threshold and a maximum threshold. The resource adjustment strategy includes: triggering an automatic optimization process when the total resource utilization reaches the first preset threshold; dynamically adjusting the target threshold of each process based on the current scenario; and adopting differentiated adjustment strategies according to the priority of the current scenario and the detection priority of the process.

[0008] Compared to existing technologies, this technical solution achieves the following technical benefits: By presetting target and maximum thresholds for each process, the system can more clearly define the resource requirements and limits of each process. This structured management helps improve the accuracy of resource allocation. When the total resource utilization reaches the first set threshold, an automatic optimization process is triggered, enabling the system to automatically respond to resource stress without relying on manual intervention. This real-time response mechanism greatly improves processing efficiency and the system's intelligence level. Simultaneously, dynamically adjusting the target threshold for each process based on the current scenario makes resource allocation more flexible and adaptable to current user needs. Furthermore, when user scenarios change, the system can instantly adjust resource allocation strategies to better meet the needs of different usage scenarios. Moreover, by adopting differentiated adjustment strategies based on the priority of the current scenario and the detection priority of processes, critical applications or important functions can still be prioritized during resource stress, thereby improving the overall reliability of the system and the user experience.

[0009] In one possible design, based on the priority of the current scenario and the detection priority of the process, a differentiated adjustment strategy is adopted, including: classifying the processes in the system into the following categories: critical processes: core processes directly related to security or the current scenario; related processes: non-core processes related to the current scenario, or foreground processes that are perceptible to the user; irrelevant processes: background processes that are irrelevant to the current scenario and are imperceptible to the user; the adjustment strategy prioritizes resource restriction or removal for irrelevant processes, followed by service quality degradation for related processes, while ensuring the resource requirements of critical processes.

[0010] Compared to existing technologies, this technical solution achieves the following technical benefits: By classifying processes into critical, relevant, and irrelevant processes, the system can manage different types of processes accordingly. This clear prioritization allows for more precise resource allocation, ensuring that important processes always receive the resources they need. Specifically, prioritizing resource restrictions or removal for irrelevant processes effectively frees up system resources and reduces system load. Implementing service quality degradation for relevant processes, rather than direct termination, maintains system responsiveness even under resource constraints, providing users with an acceptable experience. Therefore, through differentiated adjustment strategies for different processes, the system can allocate resources more efficiently, maximizing the use of existing resources and maintaining optimal system performance.

[0011] In one possible design, the differentiated adjustment strategy includes: for processes whose CPU usage is between their target threshold and maximum threshold, performing a minimization operation, which includes reducing the frame rate or reducing the resolution; for processes whose CPU usage exceeds their maximum threshold, performing a process kill operation and logging the process information.

[0012] Compared to existing technologies, this technical solution achieves the following advantages: by killing processes whose CPU usage exceeds the maximum threshold, it effectively avoids system overload-induced crashes or severe performance degradation, thereby maintaining overall system stability. Simultaneously, for processes whose CPU usage falls between the target and maximum thresholds, minimization effectively reduces resource consumption while maintaining basic functionality. This frees up some computing resources for other, more important tasks.

[0013] In one possible design, process information is recorded to automatically restart killed processes when the total system resource utilization falls below a second set threshold.

[0014] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by automatically restarting killed processes when the total system resource utilization rate is lower than the second set threshold, user needs can be better met, user inconvenience caused by abnormal process termination can be reduced, and user experience can be improved.

[0015] In one possible design, the user's current usage scenario is identified based on the HMI layer information of the vehicle screen, including: obtaining the layer stack information of the HMI system; parsing out the active application window at the top of the stack from the layer stack information; determining the foreground application currently used by the user based on the identifier of the application window, and identifying the type of the foreground application as the current usage scenario.

[0016] Compared to existing technologies, the technical benefits of this solution are as follows: By acquiring the layer stack information of the HMI system, the system can accurately parse the currently active application window. This layer-based approach can quickly understand the user's display environment, ensuring more accurate recognition of the user's current scene and reducing the possibility of misidentification or omission. Furthermore, HMI layer recognition can be performed in real time. Compared to other recognition methods, layer information can respond more quickly to changes in user operations, ensuring immediate feedback from the system to user activities and improving the smoothness of interaction.

[0017] In one possible design, the objects to be monitored in real time include the CPU utilization, memory usage, and disk activity of the Android and Linux systems.

[0018] Compared to existing technologies, the technical benefits of this solution include: comprehensive monitoring of system performance through real-time monitoring of various system resources, including CPU, memory, and disk usage. This multi-dimensional monitoring provides a better picture of the system's operational status. Furthermore, by continuously monitoring CPU, memory, and disk usage, the system obtains sufficient data to allocate resources more rationally and efficiently, reducing waste, improving overall utilization, and ensuring the smooth operation of critical applications.

[0019] This application also provides a dynamic resource control device for a dual-system intelligent cockpit. The dynamic resource control method for a dual-system intelligent cockpit is implemented through the dynamic control device, which includes: a resource monitoring module for real-time monitoring of resource usage in the Android and Linux systems; a scene perception module for identifying the user's current usage scene based on HMI layer information; a strategy decision module for dynamically generating resource control strategies based on resource usage data and scene information; and an execution module for executing the resource control strategies to adjust resources or manage processes of the target process.

[0020] Compared with the prior art, the technical effects achieved by adopting this technical solution are as follows: The intelligent cockpit dual-system resource dynamic control device of this application is used to realize the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application. Therefore, it has all the beneficial effects of the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application, which will not be repeated here.

[0021] In one possible design, the strategy decision module pre-stores a scenario priority mapping table and process detection priority information, which is used to dynamically generate differentiated control strategies based on the current scenario priority and process detection priority.

[0022] Compared with existing technologies, the technical effects achieved by this solution are as follows: the pre-stored scene priority mapping table and process detection priority information can accelerate the decision-making process, avoid complex calculations in real-time scenarios, and reduce the time required to generate resource adjustment strategies. Furthermore, the scene priority mapping table enables the system to clearly define the importance and priority of each task in different scenarios, achieving more precise allocation of computing resources and ensuring that high-priority scenes and processes receive priority response, thereby optimizing resource utilization efficiency and reducing resource contention and waste.

[0023] This application also provides an intelligent cockpit system, and an intelligent cockpit dual-system resource dynamic control method is applied to the intelligent cockpit system.

[0024] Compared with the prior art, the technical effects achieved by adopting this technical solution are as follows: The intelligent cockpit dual-system resource dynamic control method of any technical solution of this application is applied to the intelligent cockpit system of this application. Therefore, the intelligent cockpit system of this application has all the beneficial effects of the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application, which will not be repeated here. Attached Figure Description

[0025] Figure 1 A flowchart of a method for dynamic resource control of a dual-system intelligent cockpit provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of a smart cockpit dual-system resource dynamic control device provided in an embodiment of this application. Detailed Implementation

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] See Figure 1This invention provides a method for dynamic resource control of dual systems in an intelligent cockpit, comprising: real-time monitoring of resource usage of the Android and Linux systems in the intelligent cockpit and calculating the total resource utilization rate; identifying the user's current usage scenario based on the HMI layer information of the vehicle screen; dynamically generating a resource adjustment strategy based on the total resource utilization rate and scenario information; and executing the resource adjustment strategy to adjust resources or manage processes of the target process.

[0028] Specifically, traditional smart cockpits often employ static resource allocation strategies, pre-allocating fixed proportions of CPU, memory, and other resources to each process. Once allocated, these resources are difficult to dynamically adjust based on actual usage. Some existing solutions only monitor resources for a single operating system (such as Android or Linux), lacking consideration for the collaborative operation of dual systems. Static resource allocation cannot be dynamically adjusted according to actual needs, leading to resource waste or insufficiency; monitoring a single system can easily cause the entertainment system to lag or crash at critical moments due to insufficient resources, impacting user experience. Therefore, existing solutions do not fully consider the differences in user resource needs in different scenarios, such as navigation, video playback, and background applications, resulting in a lack of targeted control strategies.

[0029] In this embodiment, by monitoring and dynamically adjusting resources in real time, sufficient resources can be ensured for smooth operation under different usage scenarios, reducing lag and latency, thereby improving user response speed and overall experience. Furthermore, in situations of resource scarcity, resource adjustment and process management can effectively prevent system crashes or unresponsiveness due to resource exhaustion, improving system stability. Moreover, by identifying the user's current usage scenario based on HMI layer information, targeted resource guarantees can be provided for critical applications or services in specific scenarios, ensuring optimal performance.

[0030] In one embodiment of this application, each process is preset with a target threshold and a maximum threshold. The resource adjustment strategy includes: triggering an automatic optimization process when the total resource utilization reaches a first preset threshold; dynamically adjusting the target threshold of each process based on the current scenario; and adopting differentiated adjustment strategies according to the priority of the current scenario and the detection priority of the process.

[0031] Specifically, in this embodiment, by integrating dynamic scene recognition and differentiated strategies, the system can achieve more efficient resource allocation, reduce resource waste, and improve overall resource utilization. When the system can intelligently adjust process resource allocation according to the user's actual scenario, the user will experience smoother operation and faster response speed when using the smart cockpit, thereby improving user satisfaction.

[0032] In one embodiment of this application, a differentiated adjustment strategy is adopted based on the priority of the current scenario and the detection priority of the process, including: classifying the processes in the system into the following categories: critical processes: core processes directly related to security or the current scenario; related processes: non-core processes related to the current scenario, or foreground processes that are perceptible to the user; irrelevant processes: background processes that are irrelevant to the current scenario and are imperceptible to the user; the adjustment strategy prioritizes resource restriction or removal for irrelevant processes, then degrades the service quality of related processes, and ensures the resource requirements of critical processes.

[0033] Specifically, the intelligent cockpit is a complex system integrating safe driving and rich entertainment functions, and not all processes are equally important at all times. Critical processes have the highest priority and must not be sacrificed; related processes directly impact user experience, but their importance is secondary to safety; irrelevant processes have the lowest priority, as they have no direct impact on the current user experience. Therefore, by clearly defining critical processes and prioritizing their resource needs, it is possible to ensure the stable operation of vehicle safety functions, driver assistance systems, and the user's most essential operations (such as navigation), avoiding the risk of accidents or failure of critical functions due to insufficient resources. For user-perceptible foreground processes, their resource needs will be met as much as possible, provided that critical processes are guaranteed. Even when resources are scarce, service quality is only degraded (e.g., adjusting the frame rate of navigation projection, reducing the resolution of video applications, stopping background application animation rendering, etc.), rather than being stopped outright, thus maintaining an acceptable user experience. For irrelevant processes, the system can boldly restrict or even eliminate resources. This frees up CPU, memory, and I / O resources for critical and related processes, thereby improving the overall system efficiency and performance.

[0034] In one embodiment of this application, the differentiated adjustment strategy includes: for processes whose CPU utilization is between their target threshold and maximum threshold, performing a minimization operation, which includes reducing the frame rate or reducing the resolution; for processes whose CPU utilization exceeds their maximum threshold, performing a process kill operation and recording process information.

[0035] Specifically, in this embodiment, the first set threshold is 95%. When the total resource utilization of the cockpit system reaches 95%, an automatic optimization process is initiated: the cockpit system checks the CPU load of each process according to the detection priority of the current scenario and process; if the CPU utilization of the target process is between the target threshold and the maximum threshold, then a minimization process is adopted, such as adjusting the frame rate of navigation projection, reducing the resolution of video applications, and stopping the animation rendering of background applications; if the CPU utilization of the target process exceeds the maximum threshold, then the target process is killed directly and resources are released, while process information is recorded for subsequent automatic restart.

[0036] For example, a user is using the navigation function while a video application and a music application are running in the background. If the resource monitoring module detects that the total system resource utilization reaches 95%, and the scene perception module identifies the current scene as a navigation scene, the strategy decision module will prioritize the resource needs of the navigation process based on the priority of the navigation scene, reducing the resolution and frame rate of the video application and the music application. The execution module will then regulate the resources of the video application and the music application to ensure the stable operation of the navigation process.

[0037] For example, a user is watching a video while multiple applications are running in the background. If the resource monitoring module detects that the total system resource utilization reaches 95%, and the scene perception module identifies the current scene as a video playback scene, the strategy decision module will prioritize the resource needs of the video application based on the priority of the video playback scene, kill unnecessary background applications, and the execution module will manage the processes of unnecessary background applications to release resources to ensure smooth playback of the video application.

[0038] In one embodiment of this application, the process information is recorded to automatically restart the killed process when the total system resource utilization rate is lower than a second set threshold.

[0039] Specifically, killing processes is an emergency measure to prevent runaway processes from crippling the system. However, these killed processes often correspond to functions that users may need. If system resources are sufficient, there is no need to permanently interrupt these services. Automatically restarting them can maximize service availability, avoid manual user intervention, and thus improve the continuity of the user experience.

[0040] In one embodiment of this application, identifying the user's current usage scenario based on the HMI layer information of the vehicle screen includes: obtaining the layer stack information of the HMI system; parsing out the application window that is active at the top of the stack from the layer stack information; determining the foreground application currently used by the user based on the identifier of the application window, and identifying the type of the foreground application as the current usage scenario.

[0041] Specifically, the HMI system is the vehicle's screen display system, containing all the interface elements that the user can see and interact with. In the HMI system, the interfaces of various applications (such as navigation and music players) are stacked on top of each other in a layered manner. The topmost and most clearly visible layer is the one the user is currently using. The layer stack describes the order and state of these layers. Therefore, by parsing the active application window at the top of the stack from the layer stack information, the user can determine the foreground application currently in use.

[0042] In one embodiment of this application, the real-time monitoring objects include the CPU utilization, memory usage, and disk activity of the Android and Linux systems.

[0043] Specifically, CPU, memory, and disk are interdependent. The CPU needs memory to execute programs, while memory read / write operations depend on disk speed and availability. Good system performance often requires good coordination among these three resources. Furthermore, the utilization rate of a single resource is insufficient to judge overall resource utilization. For example, the CPU may be idle, but if memory is exhausted, the system will still be significantly affected. Therefore, combining these three indicators can more comprehensively reflect the system's operating status and resource usage.

[0044] See Figure 2 This application also provides a dynamic resource control device for a dual-system intelligent cockpit. The dynamic resource control method for a dual-system intelligent cockpit is implemented through the dynamic control device, which includes: a resource monitoring module for real-time monitoring of resource usage in the Android and Linux systems; a scene perception module for identifying the user's current usage scene based on HMI layer information; a strategy decision module for dynamically generating resource control strategies based on resource usage data and scene information; and an execution module for executing the resource control strategies to adjust resources or manage processes of the target process.

[0045] Specifically, the intelligent cockpit dual-system resource dynamic control device of this application is used to implement the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application. Therefore, it has all the beneficial effects of the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application, which will not be repeated here.

[0046] In one embodiment of this application, the strategy decision module pre-stores a scene priority mapping table and process detection priority information, which is used to dynamically generate differentiated control strategies based on the current scene priority and process detection priority.

[0047] Specifically, the pre-defined scenario priority mapping table and process detection priority information enable the system to make rapid decisions during real-time operation. When the system faces different work scenarios, it does not need to calculate or query priorities in real time, but can obtain them directly from the pre-stored information, significantly reducing decision-making time.

[0048] This application also provides an intelligent cockpit system, and an intelligent cockpit dual-system resource dynamic control method is applied to the intelligent cockpit system.

[0049] Specifically, the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application is applied to the intelligent cockpit system of this application. Therefore, the intelligent cockpit system of this application has all the beneficial effects of the intelligent cockpit dual-system resource dynamic control method of any technical solution of this application, which will not be repeated here.

[0050] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for dynamic resource control of a dual-system intelligent cockpit, characterized in that, include: Real-time monitoring of resource usage in the Android and Linux systems within the smart cockpit, and calculation of total resource utilization; Identify the user's current usage scenario based on HMI layer information from the vehicle screen; Based on the total resource utilization rate and scenario information, a resource adjustment strategy is dynamically generated; The resource adjustment strategy is executed to adjust resources or manage processes of the target process.

2. The method for dynamic resource control of a dual-system intelligent cockpit according to claim 1, characterized in that, Each process has a preset target threshold and a maximum threshold, and the resource adjustment strategy includes: When the total resource utilization rate reaches a first set threshold, an automatic optimization process is triggered; Dynamically adjust the target threshold for each process based on the current scenario; Differentiated adjustment strategies are adopted based on the priority of the current scenario and the detection priority of the process.

3. The method for dynamic resource control of a dual-system intelligent cockpit according to claim 2, characterized in that, The differentiated adjustment strategy based on the priority of the current scenario and the detection priority of the process includes: The processes in the system are classified into the following categories: Critical processes: Core processes directly related to security or the current scenario; Related processes: Non-core processes related to the current scenario, or foreground processes that are perceptible to the user; Irrelevant processes: Background processes that are unrelated to the current scenario and are not noticed by the user; The adjustment strategy prioritizes resource restrictions or removal for irrelevant processes, followed by service quality degradation for relevant processes, while ensuring the resource requirements of critical processes.

4. The method for dynamic resource control of a dual-system intelligent cockpit according to claim 2, characterized in that, The differentiated adjustment strategy includes: For processes whose CPU utilization is between their target threshold and maximum threshold, a minimization operation is performed, which includes reducing the frame rate or reducing the resolution. For processes whose CPU usage exceeds their maximum threshold, execute the process kill operation and record process information.

5. The method for dynamic resource control of a dual-system intelligent cockpit according to claim 4, characterized in that, The recorded process information is used to automatically restart killed processes when the total system resource utilization rate is lower than a second set threshold.

6. The method for dynamic resource control of a dual-system intelligent cockpit according to claim 1, characterized in that, The identification of the user's current usage scenario based on the HMI layer information of the vehicle screen includes: Obtain the layer stack information of the HMI system; The active application window located at the top of the stack is parsed from the layer stack information; Based on the identifier of the application window, determine the foreground application currently being used by the user, and identify the type of the foreground application as the current usage scenario.

7. The method for dynamic resource control of a dual-system intelligent cockpit according to claim 1, characterized in that, The real-time monitoring targets include CPU utilization, memory usage, and disk activity of the Android and Linux systems.

8. A dynamic resource control device for a dual-system intelligent cockpit, characterized in that, The intelligent cockpit dual-system resource dynamic control method as described in any one of claims 1-7 is implemented through a dynamic control device, which includes: The resource monitoring module is used to monitor the resource usage of the Android and Linux systems in real time. The scene awareness module is used to identify the user's current usage scenario based on HMI layer information; The strategy decision-making module is used to dynamically generate resource control strategies based on resource usage data and scenario information; The execution module is used to execute the resource control strategy and adjust resources or manage processes for the target process.

9. The intelligent cockpit dual-system resource dynamic control device according to claim 1, characterized in that, The strategy decision module pre-stores a scenario priority mapping table and process detection priority information, which is used to dynamically generate differentiated control strategies based on the current scenario priority and process detection priority.

10. An intelligent cockpit system, characterized in that, The intelligent cockpit dual-system resource dynamic control method as described in any one of claims 1-7 is applied to an intelligent cockpit system.

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