In-vehicle application system, and implementation method
By containerizing in-vehicle functional modules and utilizing neural networks to monitor and dynamically adjust communication strategies, a low-cost and high-security in-vehicle system is achieved, solving the problems of high hardware redundancy or low security in existing technologies and improving the stability and security of the system.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- CHENGDU DESAY SV KAWA TECHNOLOGY CO LTD
- Filing Date
- 2025-06-30
- Publication Date
- 2026-07-09
Smart Images
Figure CN2025105285_09072026_PF_FP_ABST
Abstract
Description
A vehicle-mounted application system and its implementation method Technical Field
[0001] This application relates to the field of vehicle safety technology, and in particular to a vehicle application system and its implementation method. Background Technology
[0002] Safety is the primary consideration in the design and development of autonomous driving systems. While achieving a high level of safety, it is also necessary to control hardware costs to ensure the widespread availability and sustainability of the in-vehicle system.
[0003] However, existing solutions, while achieving high safety performance, also require high hardware redundancy, leading to higher overall system costs. Conversely, systems without hardware redundancy and with lower costs may not achieve high levels of safety. Clearly, current automotive safety technologies fail to strike a good balance between safety and hardware cost. Summary of the Invention
[0004] This application provides an in-vehicle application system and its implementation method to solve the above-mentioned technical problems.
[0005] Specifically, this application provides an in-vehicle application system, which includes multiple functional modules, each of which operates independently in a container; wherein, the in-vehicle application system includes at least: a memory for storing computer instructions for multiple functional layers, each functional layer including one or more neural networks, each of the one or more neural networks being associated with the container in which the respective functional module of the in-vehicle application runs; and a processor, communicating with the memory, for executing the computer instructions for each of the multiple functional layers stored in the memory, so that the processor executes each neural network.
[0006] The container monitoring and management function layer includes one or more neural networks used to analyze the connection relationships between various functional modules and the deployment relationships of each container in the current vehicle equipment, so as to obtain the communication topology information between each container based on the connection relationships and the deployment relationships; and the container communication function layer includes one or more neural networks used to receive container communication data and query the corresponding communication topology information based on the container communication data, so as to transmit the container communication data to the corresponding container according to the queried communication topology information and the current load balancing strategy.
[0007] In the above technical solution, by containerizing the workload (i.e. the functional module), the functions can be deployed on different systems, thereby decoupling the software and hardware and reducing the overall system cost to a certain extent. Containerization technology reduces potential security risks by isolating the operating environments of different functional modules. Security vulnerabilities in one functional module are less likely to affect other modules or the entire system. Furthermore, through real-time analysis of the container monitoring and management functional layer and querying of communication topology information, the system can dynamically adjust communication strategies, further improving system security.
[0008] Furthermore, the plurality of functional layers also includes a container runtime functional layer; one or more neural networks in the container runtime functional layer are used to provide a runtime environment for running each container, including at least creating the container process corresponding to the container and allocating system resources.
[0009] In the above technical solution, the container runtime functional layer can dynamically allocate system resources (such as CPU, memory, storage, etc.) according to the actual needs of each container, avoiding resource waste. Moreover, the resource allocation of each container is independent, and the high resource consumption of one container will not affect the operation of other containers, thereby improving the stability and reliability of the system. The container runtime functional layer is responsible for creating and managing container processes, simplifying the management of the container lifecycle.
[0010] Furthermore, one or more neural networks in the container monitoring and management function layer are also used to deploy the obtained communication topology information to the container communication function layer.
[0011] In the above technical solution, the container monitoring and management function layer obtains communication topology information by analyzing the connection relationship between functional modules and the deployment relationship of containers, and deploys it to the container communication function layer. This enables the container communication function layer to dynamically adjust the communication path according to the latest topology information, ensuring the efficiency and reliability of data transmission.
[0012] Furthermore, each container is equipped with a Watchdog API; one or more neural networks in the container monitoring and management function layer are also used to receive heartbeat signals sent by the Watchdog API, so as to monitor the operating status of the container based on the heartbeat signals.
[0013] In the above technical solution, the heartbeat signal is a health indicator of the container's operating status. By periodically receiving these heartbeat signals, the container monitoring and management function layer can understand the operating status of each container in real time and ensure that the system is in the best state. If a container fails to send a heartbeat signal on time, the container monitoring and management function layer can immediately detect the anomaly and take quick countermeasures.
[0014] Furthermore, one or more neural networks in the container monitoring and management function layer are also used to monitor the execution status of the container process and system resources, so as to monitor the running status of the container based on the execution status.
[0015] In the above technical solutions, in addition to heartbeat signals, the container monitoring and management function layer can also directly monitor container processes and system resources to gain a deeper understanding of the container's operating environment and resource usage, thereby capturing signs of failure.
[0016] Furthermore, one or more neural networks in the container monitoring and management function layer are also used to adjust the corresponding communication topology information based on the abnormal state when the operating state of any container is detected as abnormal.
[0017] In the above technical solution, when the container monitoring and management function layer detects that a container is in an abnormal state, it can immediately isolate the container to prevent the abnormality from spreading to other containers and reduce the impact on the overall stability of the system. By adjusting the communication topology information, the container monitoring and management function layer can reconfigure the communication path to ensure that even if a critical container fails, the system can still continue to operate normally through the remaining communication paths, thereby improving the fault tolerance of the system.
[0018] Furthermore, one or more neural networks in the container monitoring and management function layer are also used to operate the container; the operation includes at least creating the corresponding container based on the function module, and starting or stopping the corresponding container based on the running instructions issued by the vehicle application.
[0019] In the above technical solution, the container monitoring and management function layer can dynamically create corresponding containers according to the needs of the functional modules, ensuring that each functional module has its own exclusive and independent operating environment, avoiding resource conflicts and interference; according to the running instructions of the vehicle application, the container monitoring and management function layer can dynamically start or stop the containers, ensuring the efficient use of system resources and avoiding unnecessary resource waste.
[0020] Based on the same concept, this application also provides a method for implementing an in-vehicle application system. The method is applied to the in-vehicle application system and includes the following steps: analyzing the connection relationships between various functional modules and the deployment relationships of each container in the current in-vehicle device through a container monitoring and management functional layer, so as to obtain communication topology information between each container according to the connection relationships and the deployment relationships; receiving container communication data through a container communication functional layer and querying the corresponding communication topology information based on the container communication data; and transmitting the container communication data to the corresponding container through the container communication functional layer according to the queried communication topology information and the current load balancing strategy.
[0021] In the above technical solution, by analyzing the connection relationships between functional modules and container deployment relationships, the system can dynamically generate and update communication topology information, ensuring the real-time nature and flexibility of communication paths. Even if container deployment changes occur during the operation of the vehicle equipment (such as adding or removing containers), the communication topology can adapt quickly and maintain the system's communication efficiency. The container communication function layer can query the corresponding communication topology information based on the received container communication data, ensuring that the data can be accurately transmitted to the target container. This precise data distribution mechanism avoids data loss or incorrect transmission and enhances the stability of the system.
[0022] In addition, containerizing functional modules can reduce the overall cost of the system and reduce potential security risks by isolating the operating environments of different functional modules.
[0023] Furthermore, before analyzing the connection relationships between various functional modules through the container monitoring and management functional layer, the process also includes: creating corresponding containers based on the functional modules through the container monitoring and management functional layer.
[0024] Furthermore, the Watchdog API is pre-configured in each container; obtaining the communication topology information between containers also includes: receiving the heartbeat signal sent by the Watchdog API through the container monitoring and management function layer, so as to monitor the running status of the container based on the heartbeat signal.
[0025] Furthermore, obtaining the communication topology information between containers also includes: monitoring the execution status of container processes and system resources through the container monitoring and management function layer, so as to monitor the running status of the container based on the execution status.
[0026] Furthermore, obtaining the communication topology information between containers also includes: adjusting the corresponding communication topology information in real time based on the abnormal state when the container monitoring and management function layer detects that the operating state of any container is abnormal.
[0027] Furthermore, before receiving container communication data through the container communication function layer, the method further includes: deploying the communication topology information to the container communication function layer through the container monitoring and management function layer.
[0028] Compared with the prior art, the beneficial effects of this application are as follows:
[0029] This application decouples software and hardware by containerizing functional modules, reducing the overall system cost. Containerization technology reduces potential security risks by isolating the operating environments of different functional modules, and the system can dynamically adjust communication strategies to further improve system security. Attached Figure Description
[0030] Figure 1 is a framework diagram of the vehicle application system described in this application.
[0031] Figure 2 is a schematic diagram of the communication topology information under the normal operation scenario described in this application.
[0032] Figure 3 is a load balancing diagram at time t1 under the normal operation scenario described in this application.
[0033] Figure 4 is a load balancing diagram at time t2 under the normal operation scenario described in this application.
[0034] Figure 5 is a schematic diagram of the communication topology information after adjustment under abnormal conditions as described in this application.
[0035] Figure 6 is a load balancing diagram at time t1 under the abnormal state described in this application.
[0036] Figure 7 is a load balancing diagram at time t2 under the abnormal state described in this application.
[0037] Figure 8 is a flowchart of the implementation method of the vehicle application system described in this application. Detailed Implementation
[0038] The following describes in further detail an in-vehicle application system and its implementation method according to specific embodiments and accompanying drawings.
[0039] Please refer to Figure 1. This application provides an in-vehicle application system. The in-vehicle application 10 includes multiple functional modules, each of which operates independently within a container 101. The in-vehicle application system includes at least:
[0040] Memory 20 is used to store computer instructions for multiple functional layers, each functional layer including one or more neural networks, each of the one or more neural networks being associated with a container 101 on which various functional modules of the in-vehicle application 10 run; processor 30, communicating with memory 20, is used to execute the computer instructions for each of the multiple functional layers stored in memory 20, so that processor 30 executes each neural network.
[0041] The container monitoring and management function layer 201 includes one or more neural networks used to analyze the connection relationships between various functional modules and the deployment relationships of each container 101 in the current vehicle equipment, so as to obtain the communication topology information between each container 101 based on the connection relationships and the deployment relationships.
[0042] One or more neural networks in the container communication function layer 202 are used to receive container communication data and query the corresponding communication topology information based on the container communication data, so as to transmit the container communication data to the corresponding container 101 according to the queried communication topology information and the current load balancing strategy.
[0043] In some embodiments, the in-vehicle application system is deployed in multiple in-vehicle devices, such as intelligent driving hardware and intelligent cockpit hardware. Taking intelligent driving hardware as an example, it is assumed that the application on it has a first functional module (such as an environmental perception module, whose corresponding container communication data is such as environmental data around the vehicle) and a second functional module (such as a path planning and decision-making module). The redundancy configuration is determined based on the input-output relationship (i.e., the connection relationship) of the functional modules, for example, the second functional module can be deployed simultaneously in the intelligent cockpit hardware. Each functional module in the hardware corresponds to a container 101. For example, the first functional module (i.e., function 1 in Figure 3) and the second functional module (i.e., function 2 in Figure 3) in the intelligent driving hardware correspond to the first container and the second container, respectively, and the second functional module of the intelligent cockpit hardware corresponds to the third container.
[0044] Furthermore, it should be noted that whether redundant configuration of functional modules is required is determined based on the functional safety requirements of the modules. The number of functional modules can be set and adjusted by those skilled in the art according to actual application needs, and is not limited to this; the first container and the second container are merely to distinguish the containers 101 corresponding to different functional modules.
[0045] Furthermore, based on the input-output relationship of the above functional modules and the deployment relationship of each container 101, the communication topology information shown in Figure 2 is obtained.
[0046] Furthermore, the load balancing strategy, such as average allocation, allocation according to a certain ratio, or dynamic ratio allocation, can be selected and set by those skilled in the art according to actual application requirements. Assuming that under normal operating conditions, the second functional module operates normally on both hardware devices; as shown in Figure 3, at time t1, one or more neural networks of the container communication functional layer 202 send the container communication data output by the first functional module to the second functional module running in the second container based on the current communication topology information; as shown in Figure 4, at time t2, one or more neural networks of the container communication functional layer 202 send the container communication data output by the first functional module to the second functional module running in the third container.
[0047] Furthermore, the plurality of functional layers also include a container runtime functional layer 203; one or more neural networks of the container runtime functional layer 203 are used to provide a runtime environment for running each container 101, including at least creating the container process corresponding to the container 101 and allocating system resources.
[0048] In some embodiments, a container process is created for the first functional module, with an independent file system and process space allocated; container processes are created for the second functional module in the second and third containers respectively. Furthermore, CPU, memory, storage, etc., are allocated according to the needs of the functional modules to ensure that each container 101 receives sufficient resource support and to avoid resource contention.
[0049] In the above technical solution, the container runtime functional layer 203 can dynamically allocate system resources according to the actual needs of each container 101, avoiding resource waste. Moreover, the resource allocation of each container 101 is independent, and the high resource consumption of one container 101 will not affect the operation of other containers 101, thereby improving the stability and reliability of the system. The container runtime functional layer 203 is responsible for creating and managing container processes, simplifying the management of the container lifecycle.
[0050] Furthermore, one or more neural networks in the container monitoring and management function layer 201 are also used to deploy the obtained communication topology information to the container communication function layer 202.
[0051] In some embodiments, one or more neural networks of the container monitoring and management function layer 201 deploy communication topology information to the container communication function layer 202 through an API interface, so that one or more neural networks of the container communication function layer 202 transmit data based on the currently deployed communication topology information.
[0052] In the above technical solution, the container monitoring and management function layer 201 obtains communication topology information by analyzing the connection relationship between functional modules and the deployment relationship of container 101, and deploys it to the container communication function layer 202. This enables the container communication function layer 202 to dynamically adjust the communication path according to the latest topology information, ensuring the efficiency and reliability of data transmission.
[0053] Furthermore, each container 101 is configured with a Watchdog API; one or more neural networks of the container monitoring and management function layer 201 are also used to receive heartbeat signals sent by the Watchdog API, so as to monitor the operating status of the container 101 based on the heartbeat signals.
[0054] In some embodiments, the Watchdog API is integrated into the startup script or application of container 101 to set the frequency of heartbeat signal transmission (e.g., once every 10 seconds); wherein, the heartbeat signal may include the basic operating status of container 101 (e.g., CPU, memory, network, etc.) so that the monitoring and management module can perform more detailed analysis.
[0055] Furthermore, the container monitoring and management function layer 201 starts a background service specifically for receiving and processing Watchdog API heartbeat signals from each container 101. Heartbeat signals can be received through polling or event-driven methods and stored in memory or persistent storage. The container monitoring and management function layer 201 is equipped with a timeout mechanism. If a heartbeat signal from a container 101 is not received within a predetermined time (e.g., 30 seconds), the container 101 is considered to have malfunctioned.
[0056] In the above technical solution, the heartbeat signal is a health indicator of the operating status of container 101. By receiving these heartbeat signals periodically, the container monitoring and management function layer 201 can understand the operating status of each container 101 in real time and ensure that the system is in the best state. If a container 101 fails to send a heartbeat signal on time, the container monitoring and management function layer 201 can immediately detect the anomaly and take countermeasures quickly.
[0057] Furthermore, one or more neural networks in the container monitoring and management function layer 201 are also used to monitor the execution status of the container process and system resources, so as to monitor the running status of the container 101 based on the execution status.
[0058] In other embodiments, in addition to monitoring heartbeat signals, it is also possible to periodically detect whether there are any abnormalities in the container processes within the first, second, and third containers, and collect information such as CPU and memory usage; and comprehensively analyze the process and / or resource monitoring data to confirm whether all containers 101 are in a healthy operating state.
[0059] For example, if the container monitoring and management function layer 201 detects that the container process of the second container suddenly stops, or the system CPU utilization rate continues to exceed 90%, it is determined that the second container has failed.
[0060] In the above technical solution, in addition to heartbeat signals, the container monitoring and management function layer 201 can also directly monitor container processes and system resources to gain a deeper understanding of the container 101's operating environment and resource usage, thereby capturing signs of failure.
[0061] Furthermore, one or more neural networks in the container monitoring and management function layer 201 are also used to adjust the corresponding communication topology information based on the abnormal state when the operating state of any container 101 is detected as abnormal.
[0062] In some embodiments, when an anomaly is detected in container 101 based on either of the two monitoring methods described above, the communication topology information needs to be adjusted accordingly for the container 101 experiencing the anomaly. For example, if the container monitoring and management function layer 201 detects an anomaly in the second container running on the intelligent driving hardware, it needs to notify the container communication function layer 202 of the anomaly, causing the container communication function layer 202 to adjust the communication topology information. The adjusted communication topology information is shown in Figure 5.
[0063] At this point, as shown in Figures 6 and 7, at any time t1 or t2, the container communication functional layer 202 sends the container communication data of the first functional module to the second functional module running in the third container. In this case, the intelligent driving hardware utilizes the intelligent cockpit hardware to achieve redundancy and safety.
[0064] In addition, it should be noted that the container communication function layer 202 sends container communication data to the corresponding container 101 by calling the interface of the communication layer. For example, for the DDS communication layer, the DDS communication layer Filter interface is called to filter out containers 101 that do not need to be sent.
[0065] In other embodiments, an MQTT communication layer can also be used. For example, the first container acts as a publisher, sending data to a specified topic via MQTT, while the second and third containers act as subscribers, subscribing to the corresponding topics via MQTT. During subscription, wildcard topic filtering is used to receive only data of interest. The container communication function layer 202 manages the subscription and publication relationships of each container 101, dynamically adjusting the subscribed and published topics based on the communication topology information.
[0066] In the above technical solution, when the container monitoring and management function layer 201 detects that a container 101 is in an abnormal state, it can immediately isolate the container 101 to prevent the abnormality from spreading to other containers 101 and reduce the impact on the overall stability of the system. By adjusting the communication topology information, the container monitoring and management function layer 201 can reconfigure the communication path to ensure that even if a critical container 101 fails, the system can still continue to operate normally through the remaining communication paths, thereby improving the fault tolerance of the system.
[0067] Furthermore, one or more neural networks in the container monitoring and management function layer 201 are also used to operate the container 101; the operation includes at least creating the corresponding container 101 based on the function module, and starting or stopping the corresponding container 101 based on the running command issued by the vehicle application 10.
[0068] In some embodiments, such as the container monitoring and management function layer 201, the requirements for creating container 101 are obtained from the function module, including the image of container 101, environment variables, network configuration, etc., and the interface for creating container 101 is called through such as Docker API or Kubernetes API. After container 101 is created, the configuration of container 101 is verified to meet the requirements, and the creation log is recorded.
[0069] The Docker API and Kubernetes API provide powerful container management capabilities suitable for different application scenarios. The Docker API is suitable for container management in single-machine or small cluster environments, while the Kubernetes API is suitable for container orchestration and management in large-scale distributed clusters. Those skilled in the art can choose between them according to actual application needs to flexibly manage the container lifecycle and meet the high reliability and real-time requirements of in-vehicle application systems.
[0070] The container monitoring and management function layer 201 receives the startup command issued by the vehicle application 10, obtains the container ID or name to be started, and calls the interface to start container 101 through Docker API or Kubernetes API; during the startup process of container 101, it monitors the startup status of container 101 to ensure that container 101 starts successfully and records the startup log.
[0071] The container monitoring and management function layer 201 receives the stop command issued by the vehicle application 10, obtains the ID or name of the container to be stopped, and calls the interface to stop container 101 through the Docker API or Kubernetes API; during the stopping process of container 101, it monitors the stopping status of container 101 to ensure that container 101 stops successfully and records the stop log.
[0072] In the above technical solution, the container monitoring and management function layer 201 can dynamically create corresponding containers 101 according to the needs of the functional modules, ensuring that each functional module has its own exclusive and independent operating environment, avoiding resource conflicts and interference; according to the running instructions of the vehicle application 10, the container monitoring and management function layer 201 can dynamically start or stop the container 101, ensuring the efficient use of system resources and avoiding unnecessary resource waste.
[0073] Based on the same concept, please refer to Figure 8. This application also provides a method for implementing an in-vehicle application system. The method is applied to the in-vehicle application system and includes the following steps:
[0074] S1: The container monitoring and management function layer 201 analyzes the connection relationship between each functional module and the deployment relationship of each container 101 in the current vehicle equipment, so as to obtain the communication topology information between each container 101 based on the connection relationship and the deployment relationship.
[0075] In some embodiments, the container monitoring and management function layer 201 needs to first create corresponding containers 101 based on the functional modules, and pre-configure the Watchdog API in each container 101. Taking an in-vehicle device including intelligent driving hardware and intelligent cockpit hardware as an example, the intelligent driving hardware has a first functional module and a second functional module, and the intelligent cockpit hardware has a redundant second functional module based on functional safety requirements. Each functional module in the hardware corresponds to a container 101. For example, the first functional module and the second functional module in the intelligent driving hardware correspond to the first container and the second container, respectively, and the second functional module in the intelligent cockpit hardware corresponds to the third container. The corresponding communication topology information can be obtained based on the connection relationship of the functional modules and the deployment relationship of each container 101.
[0076] S2: Receive container communication data through the container communication function layer 202, and query the corresponding communication topology information based on the container communication data.
[0077] In some embodiments, the container monitoring and management function layer 201 deploys communication topology information to the container communication function layer 202, and the communication topology information can be adjusted in real time according to abnormal states. Therefore, the topology information in the container communication function layer 202 is dynamic.
[0078] The container communication function layer 202 receives container communication data sent by container 101 from the communication layer, and then queries the communication topology information stored in the current container communication function layer 202. Assuming that the functional module corresponding to the current container 101 is the environmental perception module, the container communication data is, for example, environmental data around the vehicle collected from vehicle sensors (such as cameras, radar, lidar, etc.).
[0079] The abnormal state of a container is obtained based on the container monitoring and management function layer 201, and the communication topology information is adjusted based on the abnormal state. Specifically, the container monitoring and management function layer 201 receives heartbeat signals sent by the Watchdog API to monitor the running status of container 101 based on the heartbeat signals; simultaneously, the container monitoring and management function layer 201 monitors the execution status of container processes and system resources to monitor the running status of container 101 based on the execution status. When the container monitoring and management function layer 201 detects that the running status of any container 101 is abnormal, it adjusts the corresponding communication topology information in real time based on the abnormal state.
[0080] S3: The container communication function layer 202 transmits the container communication data to the corresponding container 101 according to the communication topology information obtained from the query and the current load balancing strategy.
[0081] In some embodiments, the container communication function layer 202 obtains communication topology information based on a query and, in conjunction with the current load balancing strategy, determines which container 101 to send the container communication data to; wherein, the communication layer interface is called to send the data to the corresponding container 101.
[0082] In the above technical solution, by analyzing the connection relationship between functional modules and the deployment relationship of container 101, the system can dynamically generate and update communication topology information, ensuring the real-time performance and flexibility of the communication path. Even if the deployment of container 101 changes during the operation of the vehicle equipment (such as adding or removing container 101), the communication topology can adapt quickly and maintain the communication efficiency of the system. The container communication function layer 202 can query the corresponding communication topology information based on the received container communication data to ensure that the data can be accurately transmitted to the target container 101. This precise data distribution mechanism avoids data loss or incorrect transmission and enhances the stability of the system.
[0083] In addition, containerizing functional modules can reduce the overall cost of the system and reduce potential security risks by isolating the operating environments of different functional modules.
[0084] In summary, this application provides an in-vehicle application system and its implementation method. The in-vehicle application system includes at least a memory 20 storing multiple functional layer computer instructions and a processor 30 executing the computer instructions. The multiple functional layers include at least a container monitoring and management functional layer 201 and a container communication functional layer 202. One or more neural networks in the container monitoring and management functional layer 201 are used to analyze the connection relationships between various functional modules and the deployment relationships of each container 101 in the current in-vehicle device, so as to obtain communication topology information between each container 101 based on the connection relationships and the deployment relationships. One or more neural networks in the container communication functional layer 202 are used to receive container communication data and query the corresponding communication topology information based on the container communication data, so as to transmit the container communication data to the corresponding container 101 according to the queried communication topology information and the current load balancing strategy. This application achieves decoupling between software and hardware by containerizing functional modules, reducing the overall system cost. Containerization technology reduces potential security risks by isolating the operating environments of different functional modules, and the system can dynamically adjust communication strategies, further improving system security.
[0085] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.
[0086] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0087] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.
[0088] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.
[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0090] Although the description of this application has been made in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.
Claims
1. An in-vehicle application system, wherein the in-vehicle application (10) includes multiple functional modules, each functional module operating independently in a container (101); the in-vehicle application system includes at least: The memory (20) is used to store computer instructions for multiple functional layers, each functional layer including one or more neural networks, each of the one or more neural networks being associated with a container (101) on which the various functional modules of the vehicle application (10) are run; The processor (30) communicates with the memory (20) to execute computer instructions for each of the multiple functional layers stored in the memory (20) so that the processor (30) executes each neural network; Among them, the plurality of functional layers include at least a container monitoring and management functional layer (201) and a container communication functional layer (202). One or more neural networks of the container monitoring and management function layer (201) are used to analyze the connection relationship between each function module and the deployment relationship of each container (101) in the current vehicle equipment, so as to obtain the communication topology information between each container (101) according to the connection relationship and the deployment relationship. One or more neural networks of the container communication function layer (202) are used to receive container communication data and query the corresponding communication topology information based on the container communication data, so as to transmit the container communication data to the corresponding container (101) according to the query obtained communication topology information and the current load balancing strategy.
2. The vehicle application system according to claim 1, wherein the plurality of functional layers further includes a container runtime functional layer (203). One or more neural networks of the container runtime functional layer (203) are used to provide a runtime environment for running each container (101), including at least creating the container process corresponding to the container (101) and allocating system resources.
3. In the vehicle application system according to claim 1, one or more neural networks of the container monitoring and management function layer (201) are further used to deploy the obtained communication topology information to the container communication function layer (202).
4. In the vehicle application system according to claim 1, each container (101) is equipped with a Watchdog API.
5. In the vehicle application system according to claim 4, one or more neural networks of the container monitoring and management function layer (201) are further used to receive heartbeat signals sent by the Watchdog API, so as to monitor the operating status of the container (101) based on the heartbeat signals.
6. In the vehicle application system according to claim 5, one or more neural networks of the container monitoring and management function layer (201) are further used to monitor the execution status of the container process and system resources, so as to monitor the running status of the container (101) based on the execution status.
7. In the vehicle application system according to claim 6, one or more neural networks of the container monitoring and management function layer (201) are further used to adjust the corresponding communication topology information in real time based on the abnormal state when the operating state of any container (101) is monitored to be abnormal.
8. In the vehicle application system according to claim 1, one or more neural networks of the container monitoring and management function layer (201) are further used to operate the container (101).
9. The vehicle application system according to claim 8, wherein the operation includes at least creating a corresponding container (101) based on the functional module, and starting or stopping the corresponding container (101) based on the running command issued by the vehicle application (10).
10. A method for implementing an in-vehicle application system, the method being applied to the in-vehicle application system as described in claim 1, comprising the following steps: The container monitoring and management function layer (201) analyzes the connection relationship between each functional module and the deployment relationship of each container (101) in the current vehicle equipment to obtain the communication topology information between each container (101) based on the connection relationship and the deployment relationship (S1). Container communication data is received through the container communication function layer (202), and the corresponding communication topology information is queried based on the container communication data (S2). And, the container communication function layer (202) transmits the container communication data to the corresponding container (101) according to the communication topology information obtained by query and the current load balancing strategy (S3).
11. The method for implementing the vehicle application system according to claim 10, before analyzing the connection relationship between various functional modules through the container monitoring and management functional layer (201), further includes: The container monitoring and management function layer (201) creates the corresponding container (101) based on the function module.
12. The method for implementing the vehicle application system according to claim 10, wherein the Watchdog API is pre-configured in each container (101); the step of obtaining the communication topology information between each container (101) further includes: The container monitoring and management function layer (201) receives the heartbeat signal sent by the Watchdog API to monitor the running status of the container (101) based on the heartbeat signal.
13. The method for implementing the vehicle application system according to claim 11, wherein obtaining the communication topology information between each container (101) further includes: The execution status of container processes and system resources is monitored through the container monitoring and management function layer (201) to monitor the running status of the container (101) based on the execution status.
14. The method for implementing the vehicle application system according to claim 13, wherein obtaining the communication topology information between each container (101) further includes: When the container monitoring and management function layer (201) detects that the operating status of any container (101) is abnormal, it adjusts the corresponding communication topology information in real time based on the abnormal status.
15. The method for implementing an in-vehicle application system according to claim 10, further comprising, before receiving container communication data through the container communication function layer (202): The communication topology information is deployed to the container communication function layer (202) through the container monitoring and management function layer (201).