SOA-based intelligent vehicle software architecture and software platform

Abstract

The invention relates to an intelligent vehicle software architecture and a software platform based on SOA (Service-Oriented Architecture). Wherein the software architecture comprises a component resource library, an SOA service architecture diagram, a business execution flow diagram, a model resource library and a vehicle resource library, and the component resource library comprises plug-and-play components, and information transmission between the components is seamlessly bridged through a configuration interface or a real-time data stream; the SOA service architecture diagram instantiates a component into a service node through a dragging operation, and supports service description in an XML (Extensible Markup Language) or JSON (JavaScript Object Notation) format; the service execution flow chart defines scheduling logic, an execution strategy and resource configuration during service operation; the model resource library constructs a service architecture and constructs a service execution process; the vehicle resource library is an integration and deployment environment of SOA services, is used for real-time data processing and vehicle algorithms, provides a hardware interface adaptation layer and supports OTA updating. According to the method, the software development life cycle can be effectively simplified, and a wide and deep knowledge support system is constructed for strategic planning and product innovation of enterprises.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle control, and in particular, to an SOA-based intelligent vehicle software architecture and software platform. Background Art

[0002] In recent years, intelligent vehicles have developed rapidly, and the entire industry is continuously shifting towards a software-defined car model. Domestic universities and major research institutes are also actively exploring the entire vehicle architecture system based on the SOA architecture.

[0003] At present, in the development of SOA-based autonomous driving systems, the construction of services that comply with automotive-grade standards relies heavily on dedicated and expensive AUTOSAR configuration tools. These tools are not only strongly bound to the hardware, resulting in low cross-platform reusability, but also the high cost limits the popularization and innovation of technology. There is an urgent need for a low-cost, high-efficiency, and platform-adaptable alternative to promote the flexible development and efficient deployment of service components.

[0004] Secondly, in the existing autonomous driving technology system, the tight coupling of equipment and algorithms and the lack of unified standard interfaces make system upgrades difficult and the integration of new equipment and algorithms complex and time-consuming, which limits the rapid iteration and flexible expansion of technology, increases development costs and resource consumption, and makes it difficult to efficiently respond to the growing diversity of needs and scenario adaptability challenges.

[0005] Thirdly, in the traditional autonomous driving algorithm development process, there is a lack of a highly integrated and customizable simulation environment, which requires algorithm engineers to spend a lot of resources to build it themselves or rely on limited real-world tests. This is not only inefficient, but also difficult to widely cover and reproduce the diversity and complexity of actual driving scenarios, which seriously hinders the rapid iteration and reliable verification of the algorithm.

[0006] Finally, the current deployment practice of autonomous driving algorithms generally faces challenges such as cumbersome manual configuration, low deployment efficiency, and lack of flexibility in post-service management. Especially in the scenario of large-scale deployment of multiple vehicles, it is difficult to achieve real-time and efficient version updates, status monitoring, and fault recovery. There is an urgent need for an automated and intelligent service lifecycle management system to optimize the deployment process and improve operation and maintenance efficiency and system stability.

[0007] At the same time, it is also noted that although the rapid development of autonomous driving technology has promoted the diversification and specialization of software tool chains, there is still a significant gap: there is a lack of a tool chain tailored for architects to systematically complete architecture design, task splitting, and full-process management that supports parallel development by multiple engineers. Such a tool should integrate high-level abstract capabilities of architectural design to make modularization of complex systems and interface standardization intuitive and easy; at the same time, it must also have task decomposition and coordination functions to ensure that the architecture blueprint can be smoothly converted into specific development tasks, assigned to different team members, and promote efficient collaborative work. This lack reflects the contradiction between the highly customized needs and the challenges of rapid iteration technology in the field of autonomous driving software development, and highlights the urgent need to develop a more integrated and intelligent tool chain.

[0008] Therefore, one or more methods are needed to solve the above problems.

[0009] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0010] The purpose of the present disclosure is to provide a SOA-based intelligent vehicle software architecture and software platform, thereby overcoming one or more problems caused by limitations and defects of related technologies at least to a certain extent.

[0011] According to one aspect of the present disclosure, a SOA-based intelligent vehicle software architecture is provided, the software architecture comprising a component resource library, a SOA service architecture diagram, a business execution flow chart, a model resource library, and a vehicle resource library, wherein:

[0012] The component resource library includes plug-and-play components for seamlessly bridging information transfer between components through configuration interfaces or real-time data streams;

[0013] The SOA service architecture diagram provides a graphical service modeling and orchestration tool for instantiating components as service nodes through drag and drop operations. The SOA service architecture diagram supports service descriptions in XML or JSON format;

[0014] The business execution flow chart is constructed based on the SOA service architecture diagram, which is used to define the service runtime scheduling logic, execution strategy and resource configuration, and implement execution and resource allocation using a directed acyclic graph;

[0015] The model resource library integrates a graphical editor and a configuration manager to build a service architecture and responsible business execution process, and implements version control and backtracking of services, scheduling strategies, deployment strategies, and lifecycle management strategies;

[0016] The vehicle resource library is an integration and deployment environment for SOA services, used for real-time data processing and vehicle algorithms, providing a hardware interface adaptation layer and supporting OTA updates.

[0017] In an exemplary embodiment of the present disclosure, the service componentization of the software architecture implements a component design with high cohesion and low coupling through a graphical configuration interface and a standardized service interface.

[0018] In an exemplary embodiment of the present disclosure, the service collaboration mechanism of the software architecture drives service nodes to collaborate closely through a data bus to form a responsive service network.

[0019] In an exemplary embodiment of the present disclosure, the business process orchestration of the software architecture adopts the BPMN 2.0 standard and provides a graphical interface to support complex logic configuration.

[0020] In an exemplary embodiment of the present disclosure, the one-click deployment of the software architecture adopts Ansible automated deployment technology, supports rapid integration and deployment of services, and has a built-in CI / CD process.

[0021] In one aspect of the present disclosure, a SOA-based intelligent vehicle software platform is provided, the software platform comprising a component resource library, an AI collaborative knowledge base, a task division management engine, a service development basic environment, and a protocol standardization management, wherein:

[0022] The component resource library provides plug-and-play functional units, including code generation, device abstraction, mature algorithms, operator optimization, simulation testing, service orchestration, and integrated deployment components;

[0023] The AI ​​collaborative knowledge base integrates industry big model resources and enterprise knowledge assets to form a collaborative knowledge ecosystem and provide data-driven decision support;

[0024] The task division management engine provides enterprise-level project management solutions, supporting the management of the entire SOA service development process from requirement definition to deployment;

[0025] The service development basic environment provides a communication mechanism and underlying framework to support the development and optimization of algorithm services;

[0026] The protocol standardization management is used to maintain all interface protocols and realize data iteration and seamless connection between interfaces.

[0027] In an exemplary embodiment of the present disclosure, the collaborative knowledge base forms a valuable knowledge system by analyzing and refining the historical experience data of the enterprise, thereby providing data-driven support for the vehicle SOA service architecture design, service simulation architecture design, and deployment strategy design. This support can help enterprises more effectively utilize the successful experiences and lessons learned from past projects, optimize the design process, improve the authenticity and accuracy of simulation tests, and develop more reasonable and effective deployment plans.

[0028] In an exemplary embodiment of the present disclosure, the software platform is a drag-and-drop software-defined vehicle computing model, providing an interface including standardized interface configuration, automated interface file generation, and graphical interface setting.

[0029] In an exemplary embodiment of the present disclosure, the software platform provides a configuration-driven SOA service rapid instantiation and building framework, supports intelligent service configuration and automatic code generation, service framework and automatic optimization compilation;

[0030] The software platform provides a support mechanism for algorithm / operator automatic optimization and multi-platform deployment capabilities, including intelligent strategy customization, dynamic adaptive optimization, one-click deployment and continuous optimization feedback loop.

[0031] The software platform provides a parameterized simulation test mechanism, integrates simulation components, supports refined configuration, service interconnection and test initiation, environment customization and real-time feedback;

[0032] The software platform provides a service scheduling and orchestration mechanism, including service node synchronization and classification identification, an interactive service grouping design interface, a dynamic policy configuration engine, and automatic service dependency analysis and policy generation.

[0033] In an exemplary embodiment of the present disclosure, the software platform provides an integrated deployment mechanism, supports visual component drag-and-drop deployment, property configuration assistant, policy-driven automated deployment engine, full lifecycle intelligent management, and dual-modal operation and maintenance control interface;

[0034] The software platform provides a task division management mechanism, including a unified development portal, precise task allocation and management, and real-time project progress monitoring.

[0035] An SOA-based intelligent vehicle software architecture and software platform in an exemplary embodiment of the present disclosure. The software architecture includes a component resource library, an SOA service architecture diagram, a business execution flow chart, a model resource library, and a vehicle resource library, wherein: the component resource library includes plug-and-play components, which are used to seamlessly bridge the information transmission between components through configuration interfaces or real-time data streams; the SOA service architecture diagram provides a graphical service modeling and orchestration tool, which is used to instantiate components as service nodes through drag and drop operations, and the SOA service architecture diagram supports service descriptions in XML or JSON format; the business execution flow chart is constructed based on the SOA service architecture diagram, which is used to define the service runtime scheduling logic, execution strategy and resource configuration, and implement execution and resource allocation using a directed acyclic graph; the model resource library integrates a graphical editor and a configuration manager, which is used to build a service architecture and a responsible business execution process, and implements service, scheduling strategy, deployment strategy, and lifecycle management strategy version control and backtracking; the vehicle resource library is an integration and deployment environment for SOA services, which is used for real-time data processing and vehicle algorithms, provides a hardware interface adaptation layer, and supports OTA updates. The disclosure can effectively streamline the software development life cycle and build a broad and in-depth knowledge support system for the company's strategic planning and product innovation.

[0036] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0038] Figure 1 A diagram of a SOA-based intelligent vehicle software architecture according to an exemplary embodiment of the present disclosure is shown;

[0039] Figure 2 A schematic diagram of an SOA-based intelligent vehicle software platform according to an exemplary embodiment of the present disclosure is shown;

[0040] Figure 3 A schematic diagram of a drag-and-drop software-defined vehicle computing model integrating design, development, simulation, orchestration, and deployment based on an efficient communication mechanism according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0041] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0042] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be adopted. In other cases, known structures, methods, devices, implementations, materials or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0043] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or these functional entities or parts of functional entities may be implemented in one or more software hardened modules, or these functional entities may be implemented in different networks and / or processor devices and / or microcontroller devices.

[0044] Embodiment 1:

[0045] In this example embodiment, a SOA-based intelligent vehicle software architecture is first provided; Figure 1 As shown in , the SOA-based intelligent vehicle software architecture includes:

[0046] Component resource library, SOA service architecture diagram, business execution flow chart, model resource library, vehicle resource library, including:

[0047] The component resource library includes plug-and-play components for seamlessly bridging information transfer between components through configuration interfaces or real-time data streams;

[0048] The SOA service architecture diagram provides a graphical service modeling and orchestration tool for instantiating components as service nodes through drag and drop operations. The SOA service architecture diagram supports service descriptions in XML or JSON format;

[0049] The business execution flow chart is constructed based on the SOA service architecture diagram, which is used to define the service runtime scheduling logic, execution strategy and resource configuration, and implement execution and resource allocation using a directed acyclic graph;

[0050] The model resource library integrates a graphical editor and a configuration manager to build a service architecture and responsible business execution process, and implements version control and backtracking of services, scheduling strategies, deployment strategies, and lifecycle management strategies;

[0051] The vehicle resource library is an integration and deployment environment for SOA services, used for real-time data processing and vehicle algorithms, providing a hardware interface adaptation layer and supporting OTA updates.

[0052] An SOA-based intelligent vehicle software architecture and software platform in an exemplary embodiment of the present disclosure. The software architecture includes a component resource library, an SOA service architecture diagram, a business execution flow chart, a model resource library, and a vehicle resource library, wherein: the component resource library includes plug-and-play components, which are used to seamlessly bridge the information transmission between components through configuration interfaces or real-time data streams; the SOA service architecture diagram provides a graphical service modeling and orchestration tool, which is used to instantiate components as service nodes through drag and drop operations, and the SOA service architecture diagram supports service descriptions in XML or JSON format; the business execution flow chart is constructed based on the SOA service architecture diagram, which is used to define the service runtime scheduling logic, execution strategy and resource configuration, and implement execution and resource allocation using a directed acyclic graph; the model resource library integrates a graphical editor and a configuration manager, which is used to build a service architecture and a responsible business execution process, and implements service, scheduling strategy, deployment strategy, and lifecycle management strategy version control and backtracking; the vehicle resource library is an integration and deployment environment for SOA services, which is used for real-time data processing and vehicle algorithms, provides a hardware interface adaptation layer, and supports OTA updates. The disclosure can effectively streamline the software development life cycle and build a broad and in-depth knowledge support system for the company's strategic planning and product innovation.

[0053] Next, a SOA-based intelligent vehicle software architecture in this example embodiment will be further described.

[0054] The SOA-based intelligent vehicle software architecture is characterized in that the software architecture includes a component resource library, an SOA service architecture diagram, a business execution flow chart, a model resource library, and a vehicle resource library, wherein:

[0055] The component resource library includes plug-and-play components for seamlessly bridging information transfer between components through configuration interfaces or real-time data streams;

[0056] The SOA service architecture diagram provides a graphical service modeling and orchestration tool for instantiating components as service nodes through drag and drop operations. The SOA service architecture diagram supports service descriptions in XML or JSON format;

[0057] The business execution flow chart is constructed based on the SOA service architecture diagram, which is used to define the service runtime scheduling logic, execution strategy and resource configuration, and implement execution and resource allocation using a directed acyclic graph;

[0058] The model resource library integrates a graphical editor and a configuration manager to build a service architecture and responsible business execution process, and implements version control and backtracking of services, scheduling strategies, deployment strategies, and lifecycle management strategies;

[0059] The vehicle resource library is an integration and deployment environment for SOA services, used for real-time data processing and vehicle algorithms, providing a hardware interface adaptation layer and supporting OTA updates.

[0060] In the embodiment of this example, the service componentization of the software architecture implements a component design with high cohesion and low coupling through a graphical configuration interface and a standardized service interface.

[0061] In the embodiment of this example, the service collaboration mechanism of the software architecture drives the service nodes to collaborate closely through the data bus to form a responsive service network.

[0062] In the embodiment of this example, the business process orchestration of the software architecture adopts the BPMN 2.0 standard and provides a graphical interface to support complex logic configuration.

[0063] In the embodiment of this example, the one-click deployment of the software architecture adopts Ansible automated deployment technology, supports rapid integration and deployment of services, and has a built-in CI / CD process.

[0064] It should be noted that although several modules or units of a SOA-based intelligent vehicle software architecture and software platform are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0065] In addition, in this example embodiment, a SOA-based intelligent vehicle software platform is also provided. The SOA-based intelligent vehicle software platform may include a component resource library, an AI collaborative knowledge base, a task division management engine, a service development basic environment, and a protocol standardization management, wherein:

[0066] The component resource library provides plug-and-play functional units, including code generation, device abstraction, mature algorithms, operator optimization, simulation testing, service orchestration, and integrated deployment components;

[0067] The AI ​​collaborative knowledge base integrates industry big model resources and enterprise knowledge assets to form a collaborative knowledge ecosystem and provide data-driven decision support;

[0068] The task division management engine provides enterprise-level project management solutions, supporting the management of the entire SOA service development process from requirement definition to deployment;

[0069] The service development basic environment provides a communication mechanism and underlying framework to support the development and optimization of algorithm services;

[0070] The protocol standardization management is used to maintain all interface protocols and realize data iteration and seamless connection between interfaces.

[0071] In the embodiment of this example, the collaborative knowledge base forms a valuable knowledge system by analyzing and refining the historical experience data of the enterprise, thereby providing data-driven support for the vehicle SOA service architecture design, service simulation architecture design, and deployment strategy design. This support can help enterprises more effectively utilize the successful experiences and lessons from past projects, optimize the design process, improve the authenticity and accuracy of simulation tests, and develop more reasonable and effective deployment plans.

[0072] In the embodiment of this example, the software platform is a drag-and-drop software-defined vehicle computing model, which provides interfaces including standardized interface configuration, automatic interface file generation, and graphical interface settings.

[0073] In the embodiment of this example, the software platform provides a configuration-driven SOA service rapid instantiation and building framework, supports intelligent service configuration and automatic code generation, service framework and automatic optimization compilation;

[0074] The software platform provides a support mechanism for algorithm / operator automatic optimization and multi-platform deployment capabilities, including intelligent strategy customization, dynamic adaptive optimization, one-click deployment and continuous optimization feedback loop.

[0075] The software platform provides a parameterized simulation test mechanism, integrates simulation components, supports refined configuration, service interconnection and test initiation, environment customization and real-time feedback;

[0076] The software platform provides a service scheduling and orchestration mechanism, including service node synchronization and classification identification, an interactive service grouping design interface, a dynamic policy configuration engine, and automatic service dependency analysis and policy generation.

[0077] In the embodiment of this example, the software platform provides an integrated deployment mechanism, supports visual component drag-and-drop deployment, property configuration assistant, policy-driven automated deployment engine, full lifecycle intelligent management, and dual-modal operation and maintenance control interface;

[0078] The software platform provides a task division management mechanism, including a unified development portal, precise task allocation and management, and real-time project progress monitoring.

[0079] Embodiment 2:

[0080] In the embodiment of this example, a SOA-based intelligent vehicle software architecture, such as Figure 1 As shown, including:

[0081] The component resource library not only integrates a highly flexible and reusable component system, but also realizes fine-grained modularization of key functions of vehicle systems, aiming to promote hardware-independent design and accelerate software iteration cycles. The internal design of components follows the principle of loose coupling, ensuring a streamlined and efficient structure.

[0082] As a graphical service modeling and orchestration tool, the SOA service architecture diagram allows users to instantiate components as service nodes through an intuitive drag-and-drop interface, clearly define the layout, interaction protocols and dependencies of services, and support service descriptions in XML or JSON format.

[0083] The business execution flowchart is a dynamic scheduling and resource management solution built on the SOA service architecture diagram. This solution defines the runtime scheduling logic, execution strategy and resource allocation of the service in detail, and uses a directed acyclic graph (DAG) to ensure efficient execution of service processes and reasonable allocation of resources.

[0084] The model resource library is the core design tool of the system. It integrates a graphical editor and a configuration manager, allowing developers to quickly build complex service architectures through intuitive drag-and-drop operations combined with rich configuration options, and implement service version control and backtracking functions.

[0085] The vehicle resource library, as an integration and deployment environment for SOA services, not only carries real-time data processing and decision-making algorithms, but also provides a hardware interface adaptation layer to ensure efficient integration of intelligent functions with the actual road environment and supports OTA update mechanisms.

[0086] The intelligent vehicle software architecture based on SOA services is as follows: Figure 1 As shown, it mainly includes:

[0087] Service componentization: Under the guidance of the SOA concept, this architecture has made a breakthrough in integrating sensor functions, mature algorithms, and the entire development-deployment chain into highly cohesive and low-coupling components. The internal logic of these components is closely connected, ensuring the simplicity and efficiency of the architecture. At the same time, by introducing a graphical configuration interface and standardized service interface, intuitive management and drag-and-drop assembly of component configuration are achieved, significantly enhancing the reusability and development flexibility of the software.

[0088] Service collaboration mechanism: The service nodes in the architecture are closely coordinated under the drive of high-performance data buses to form a highly responsive vehicle SOA service network. The service task scheduling group plays a key role, not only defining the service boundaries, but also serving as the core of intelligent scheduling to promote efficient information flow and accurate task execution. This design makes each service node a module that is both autonomous and reusable across scenarios, greatly improving the flexibility and deployment efficiency of the system.

[0089] Business process orchestration: Using the BPMN 2.0 standard, the business execution flowchart provides an intuitive and powerful process design platform that not only supports complex logic configurations such as conditional branches and loop control, but also enables users to directly operate service components for flexible grouping through a graphical orchestration interface to achieve rapid iteration and optimization of processes. This process fully supports dynamic policy configuration, further shortening the cycle from design to implementation.

[0090] One-click deployment: Relying on Ansible automated deployment technology, the architecture realizes rapid integration and deployment of services. Through the graphical interface, users can easily complete the packaging, deployment configuration and health monitoring settings of services, ensuring seamless connection from development to operation and maintenance, and built-in CI / CD process to accelerate the continuous integration and delivery of software, and improve the response speed and iteration efficiency of the overall project.

[0091] Based on the above software architecture, SOA-based intelligent vehicle software platform, such as Figure 2 As shown, including:

[0092] The component resource library includes code generation components, device abstraction components, mature algorithm components, operator / algorithm optimization, simulation test components, service scheduling and orchestration, and integrated deployment components. It provides plug-and-play functional units such as sensor data access and standardization, plug-and-play device abstraction management, mature algorithm encapsulation for rapid integration, automatic framework code generation and compilation, virtualized environment construction for simulation testing, dynamic service orchestration, and integrated deployment of vehicle applications.

[0093] The SOA service architecture diagram is a graphical service modeling and orchestration solution. The presentation layer uses a graphical interface to intuitively display the relationships and dependencies between services with nodes and lines, while the data layer uses JSON format files to structured store and describe detailed information on vehicle services.

[0094] The business execution flowchart is a dynamic scheduling and resource management solution built on the SOA service architecture diagram. It uses nodes and lines to intuitively display the relationship and dependencies between services, while the data layer uses JSON format files to structuredly store and describe the scheduling strategy and resource allocation information of vehicle services.

[0095] The model resource library is the core design tool of the system, including the interface management module, component management module and data management module. It is responsible for automatically instantiating SOA services and generating corresponding scheduling and deployment strategies, supporting real-time code editing and online simulation testing, and finally deploying to the target vehicle with one click, supporting the unattended operation and management of the deployed services in the vehicle.

[0096] The vehicle resource library is the final operating environment for business applications built based on SOA services. It includes hardware facilities, basic libraries required for the operation of SOA service applications, and basic libraries necessary for the full life cycle management of SOA services, providing infrastructure for the efficient operation of SOA services.

[0097] The AI ​​collaborative knowledge base is built on advanced artificial intelligence technology, integrating industry-leading large model resources and enterprise-specific knowledge assets to form a deeply collaborative knowledge ecosystem. The knowledge base aims to break down traditional knowledge barriers, promote cross-departmental and cross-domain knowledge sharing and innovation, provide data-driven decision support for the design of intelligent vehicle services and product strategy formulation, accelerate the transformation of technological achievements, and promote the forward-looking layout of corporate product strategies.

[0098] The task division management engine integrates a comprehensive enterprise-level project management solution, which not only covers the entire process from requirement definition, task allocation, progress tracking to deployment, but also ensures efficient collaboration at all stages of the project through intelligent task optimization and resource scheduling, thus improving team execution and project success rate. The introduction of this engine effectively balances R&D resources and enhances the transparency and flexibility of project management.

[0099] The service development basic environment is a high-performance service development base that provides an efficient communication mechanism for service operation and an underlying framework for operator / algorithm optimization of models. In addition, it includes a series of core mathematics and general basic libraries, preparing the necessary tools and resources for the smooth operation of algorithm services.

[0100] Protocol standardization management, including interface configuration management and interface parsing engine, automatically maintains all interface protocols used by the vehicle through user-selected interface standards and interface configuration information, ensuring rapid iteration and seamless docking capabilities, while reducing error rates and laying the foundation for a flexible infrastructure for future technology expansion.

[0101] The above-mentioned SOA-based intelligent vehicle software platform provides a drag-and-drop software-defined vehicle computing model that integrates design, development, simulation, orchestration, and deployment based on an efficient communication mechanism. Figure 3 As shown:

[0102] In the design phase, in response to the input functional requirements, the architect first needs to complete the automatic generation of all vehicle interface files through the interface standard management module; secondly, drag and drop the plug-and-play design components, add the corresponding business configuration information, and instantiate them into node services with specific functions; finally, define the dependencies between node services through intuitive connections on the interface. The R&D output of this phase is a vehicle SOA service architecture diagram that meets business requirements, and the architecture diagram already includes service interface files.

[0103] During the development phase, based on the vehicle SOA service architecture diagram and task division, development engineers first drag and drop development components to complete partial service architecture code generation, add business logic and complete service development; secondly, development engineers need to perform unit testing on plug-and-play node services and locally developed node services; finally, the R&D output of this phase is the SOA service architecture diagram and vehicle SOA services that meet functional requirements.

[0104] In the simulation test phase, based on the SOA service architecture diagram, vehicle SOA services, and task division, the simulation engineer first drags the simulation components and adds configuration information to complete the construction of the simulation test scenario; secondly, the services in the architecture are tested based on the simulation test scenario. The R&D outputs of this phase are the SOA service architecture diagram, vehicle SOA services, and simulation test reports. In addition, if the simulation test finds a problem, the results need to be fed back to the R&D engineer to re-execute the problem modification.

[0105] In the scheduling and orchestration stage, based on the SOA service architecture diagram, vehicle SOA services, and task division, the scheduling and orchestration engineer first needs to analyze the scheduling parallelism in the current architecture and complete the scheduling grouping; then drag and drop the scheduling and orchestration components, add scheduling configurations to the scheduling group, and complete the scheduling and orchestration. The R&D outputs of this stage are the SOA service architecture diagram, vehicle SOA services, and business execution flow charts.

[0106] During the integrated deployment phase, based on the SOA service architecture diagram, vehicle SOA services, business execution flowcharts, and task division, the operation and maintenance engineer first needs to drag and drop deployment components and add deployment configurations; secondly, the underlying support system will automatically generate deployment strategies and service lifecycle management policy files; finally, the underlying support system will automatically deploy the SOA service architecture diagram, vehicle SOA services, business execution flowcharts, and lifecycle management strategies to the vehicle based on the deployment strategy, and complete the service lifecycle management configuration at the same time.

[0107] The above-mentioned SOA-based intelligent vehicle software platform provides an interface generation mechanism of the SOA-based intelligent vehicle software platform. The specific implementation steps are as follows:

[0108] Standardized interface configuration: Architects first operate in a highly integrated interface parsing engine, which has built-in options for a variety of industry-recognized interface standards, such as AUTOSAR, SOME / IP, and enterprise-defined standards. Architects select the most appropriate interface standard based on project requirements and industry best practices. For easy configuration, the system allows architects to directly copy and paste interface description text from external specifications, or directly import standardized interface definition files (such as XML, JSON format). This step not only simplifies the tedious manual entry, but also ensures the consistency and accuracy of interface information.

[0109] Automatic interface file generation: Once the interface information is configured, the interface parsing engine immediately starts the intelligent analysis and code generation process. It automatically generates a series of interface files based on the configured interface standards and specific information. This automated process strictly follows the grammatical rules of the selected interface standard, ensuring the standardization and compatibility of the interface design, while significantly improving development efficiency and reducing errors caused by manual writing.

[0110] Interface setting: After dragging the abstract software component in the graphical interface, you can select the corresponding interface from the interface list to implement the interface setting for the node service; at the same time, when establishing a data flow connection between components, the user can select the bound interface as the data transmission channel. The system will automatically prompt the available data exchange mode (such as request-response, publish-subscribe, etc.), and configure the corresponding communication protocol and data format to ensure seamless integration between services and complete the design of the entire vehicle SOA service architecture.

[0111] The above-mentioned SOA-based intelligent vehicle software platform provides a configuration-driven SOA service rapid instantiation and construction framework. The specific implementation steps are as follows:

[0112] Intelligent service configuration and automatic code generation: The platform provides service development engineers with an unprecedented convenient service construction experience. First, engineers use a graphical interface to configure the required service attributes in detail, including but not limited to service interface definition, protocol selection, data format specification, etc. Then, the built-in advanced service coding engine automatically generates service framework code, service instantiation code and supporting unit test code based on these configuration information. This process not only includes the construction of the basic framework, but also intelligently reserves the business logic interface, so that developers only need to focus on filling in the business logic code at the preset interface, greatly shortening the development cycle. Furthermore, the engine automatically performs code static checking and supports a variety of inspection rule configurations, such as integrating industry-standard open source code inspection tools, safety specification inspection tools that comply with national standards (GB), and even strict review rules that follow military standards (GJB) to ensure code quality. After the inspection is completed, the engine immediately starts the automatic incremental compilation of the service code, and any changes can be fed back immediately to improve iteration efficiency.

[0113] High-performance service framework and automatic optimization compilation technology: The service code framework automatically generated by the platform deeply integrates a number of advanced technologies, such as zero-copy communication technology, which significantly reduces data replication operations and directly transmits data at the kernel level, thereby greatly improving the communication efficiency between services and the overall system performance. The framework also has built-in service governance mechanisms, such as intelligent load balancing, fault isolation and self-recovery strategies, to ensure that the service remains stable and reliable under high concurrency pressure. In addition, the service compilation system uses an innovative automatic tuning function for compilation parameters, which dynamically adjusts compilation options according to service characteristics and target hardware configuration, minimizes memory usage during service runtime, optimizes resource utilization efficiency, and provides a more lean and efficient operating environment for smart vehicle applications.

[0114] The above-mentioned SOA-based intelligent vehicle software platform provides a mechanism to support algorithm / operator automatic optimization and multi-platform deployment capabilities. The specific implementation steps are as follows:

[0115] Intelligent strategy customization and model insight: Reinforcement learning technology drives TVM strategy generation, that is, dynamically customizes the most suitable optimization path based on historical optimization data and real-time hardware indicator feedback. Not only limited to basic model analysis, we use deep feature map analysis and hardware feature mapping technology to deeply understand the collaborative space of model structure and hardware potential, and tailor an optimization blueprint for each operator.

[0116] Dynamic adaptive optimization and hardware-aware compilation: During the optimization phase, we introduced an adaptive fine-tuning algorithm that dynamically adjusts parameters during the optimization process, such as automatically balancing computation and memory usage based on the real-time load of the target device. At the same time, we adopted a hardware-aware intermediate representation (IR) that simulates the execution effects under different hardware environments before compilation, thereby guiding the generation of highly customized optimized code. In addition, we implemented a cross-layer optimization strategy, combining inter-layer dependency and data flow analysis to achieve performance improvements from a global perspective.

[0117] One-click deployment and continuous optimization feedback loop: Combined with the one-click deployment system, we can realize the automated deployment of models and related services. More importantly, we have established a closed-loop performance monitoring and feedback system. After the model is deployed, we will continue to collect operation data, analyze this data through machine learning models, and feed back to the optimization strategy library, forming a continuous evolutionary cycle of model optimization-deployment-feedback-re-optimization.

[0118] The above-mentioned SOA-based intelligent vehicle software platform provides a parameterized simulation test mechanism. The specific cases are as follows:

[0119] Integration of simulation components: In an autonomous driving algorithm verification scenario, simulation test engineers easily dragged the Carla simulation platform component into the software architecture through an intuitive graphical interface. This step demonstrates the high flexibility and ease of use of the SOA architecture.

[0120] Refined configuration: Subsequently, engineers conducted in-depth attribute configuration for the instantiated Carla simulation service, including but not limited to selecting an urban driving environment as the test scenario and configuring a sedan model with a complex dynamic traffic flow model. The specific parameters are set as follows: the vehicle mass is set to 1500kg, and the maximum acceleration is limited to 5m / s 2 , while enabling high-precision vehicle dynamics models and advanced pedestrian interaction simulation modes to ensure that the test environment is close to reality.

[0121] Service interconnection and test start: Through simple point-and-click operations, engineers connect the autonomous driving path planning service to be tested with the Carla simulation service and immediately start the simulation test. This plug-and-play approach greatly shortens the test preparation time.

[0122] Carla simulation environment customization: Carla simulation components show the convenience of a "order-a-la-carte system", allowing users to customize the test environment according to their needs. In this test, engineers selected a city road map and configured variable weather conditions (sunny to rainy), as well as different types of pedestrian and vehicle densities, to fully evaluate the performance of the algorithm in a complex environment.

[0123] Real-time feedback and performance evaluation: During the simulation process, Carla provides a highly simulated visual interface that shows the vehicle's driving status in the virtual environment, including route tracking accuracy, obstacle avoidance capabilities, and response speed to emergencies. Test data shows that the algorithm successfully avoided 96% of simulated obstacles without hardware intervention, and the average path tracking error was kept within 5cm, significantly improving the verification efficiency and confidence level before algorithm deployment. This mechanism effectively accelerates the iteration cycle from algorithm development to hardware integration, reducing the cost and risk of actual testing.

[0124] The above-mentioned SOA-based intelligent vehicle software platform provides a service scheduling and orchestration mechanism. The specific implementation steps are as follows:

[0125] Intelligent synchronization and classification identification of service nodes: The platform's service orchestration engine uses advanced algorithms to not only synchronize all functional service nodes in the service architecture, such as autonomous driving logic processing, sensor data analysis, etc., but also realizes intelligent classification identification of these nodes, automatically excluding auxiliary components such as deployment components, test components, and debugging components to ensure the efficiency and purity of service orchestration. This process is achieved through in-depth analysis of service interfaces, dependencies, and runtime behaviors, reducing manual intervention and improving the accuracy of service organization.

[0126] Interactive service group design interface: Engineers operate through a highly intuitive and interactive service group design interface, which integrates graphical drag-and-drop orchestration, import and export, debugging and other functions. Engineers can directly drag service icons into the workflow according to business needs, simplifying the organization of complex service architectures and improving design efficiency.

[0127] In-depth analysis of the dynamic policy configuration engine: The scheduling policy configuration module has built-in rich and multi-dimensional policy options, including but not limited to timing policies (triggered at a specific time point), periodic time policies (repeated execution at a preset frequency), and conditional trigger policies (triggered by environmental variables or events). Each policy supports fine-grained parameter configuration, such as time accuracy to the second level, customization of conditional logic expressions, etc. The configuration process uses a wizard to ensure that non-professional users can easily set complex policies.

[0128] At present, the system uses Python scripts as the core carrier of scheduling configuration, making full use of Python's readability and flexibility. Combined with the integrated deployment system, these Python scripts can be automatically detected, loaded, and seamlessly executed in the target environment. This transformation greatly accelerates the iteration cycle from policy design to actual deployment, and policy adjustments can be reflected in system operation in near real time without manual conversion or restarting services.

[0129] Automatic service dependency analysis and policy generation: The service orchestration engine uses graph theory algorithms and machine learning models to perform in-depth dependency analysis on configured services, automatically discover and resolve potential dependency conflicts, and ensure smooth service links. Based on the analysis results, the system automatically generates a comprehensive policy package including scheduling policy files, service lifecycle management policy files, and service deployment policy files. These policy files describe in detail the service startup sequence, resource allocation, fault recovery mechanism, etc., providing a solid script foundation for subsequent automated deployment and operation and maintenance, and achieving seamless connection and high automation from design to implementation.

[0130] The above SOA-based intelligent vehicle software platform provides an integrated deployment mechanism. The specific implementation steps are as follows:

[0131] Visual component drag-and-drop deployment: With a graphical interface design, service operation and maintenance engineers can intuitively use the mouse to drag and drop pre-configured deployment components to the specified location in the deployment topology diagram. This process not only simplifies deployment operations, but also helps engineers quickly build and adjust service layouts.

[0132] Property Configuration Assistant: After instantiating the component service, the system automatically pops up the Property Configuration Assistant. This assistant allows engineers to quickly complete the configuration of key properties such as device IP, port, user name, password, and deployment location. At the same time, the configuration assistant is embedded with a real-time verification function, which can immediately feedback configuration errors or conflicts, greatly reducing the deployment failure rate caused by improper configuration.

[0133] Policy-driven automated deployment engine: The service deployment engine deeply integrates the service deployment policy file, which is generated by the service orchestration engine in advance and contains service deployment details, service scheduling logic, and service life cycle management rules. The deployment engine automatically performs a series of deployment tasks such as basic library configuration and deployment file version verification by parsing these policies to ensure that the service is launched accurately according to the predetermined strategy. This process not only speeds up the deployment process, but also ensures the consistency and reliability of the deployment.

[0134] Full life cycle intelligent management: Once the service deployment is completed, the system automatically enables the service full life cycle management module, which provides monitoring and analysis functions and can automatically complete service health checks, dynamic resource adjustments, fault isolation and recovery operations according to preset strategies. The service automatically triggers corresponding management actions according to different stages of its life cycle (such as initialization, operation, upgrade, and retirement) to ensure that the service is always in the best operating state.

[0135] Dual-mode operation and maintenance control interface: In order to meet the needs of different operation and maintenance scenarios, the platform provides two management modes: command line interface (CLI) and Web graphical interface. The CLI mode is suitable for batch operations and script automation, and supports the rapid execution of batch start, stop, restart and other operations of services; while the Web interface is more intuitive and friendly, not only can it realize the fine control of single or group services, but also integrates the service status monitoring panel, log query system, performance indicator analysis and other functions, so that the operation and maintenance personnel can grasp the system operation status in real time and quickly locate and solve problems.

[0136] The above-mentioned SOA-based intelligent vehicle software platform provides a task division management mechanism. The specific implementation steps are as follows:

[0137] The platform provides a unified development portal that aims to fundamentally improve and accelerate the execution and management process of software architecture design and R&D tasks. The system innovatively uses an intuitive graphical drag-and-drop interface, enabling software architects to efficiently complete the layout and design of vehicle-level SOA services in a user-friendly environment, achieving real-time service component configuration and flexible layout, and significantly reducing design complexity.

[0138] After the design is finalized, the platform supports architects to accurately select service modules and seamlessly assign them to corresponding R&D engineers. At the same time, the task standards and requirements are defined in detail to ensure that the task instructions are clearly communicated. R&D team members can log in to the platform to easily obtain their personal task lists, claim and independently carry out development work, and provide instant feedback on progress information after the task is completed, providing project managers with a comprehensive progress view.

[0139] More importantly, the platform provides architects and project leaders with real-time project progress monitoring, allowing them to instantly grasp the latest status of R&D work and evaluate whether the established goals have been achieved based on detailed data analysis. Once the R&D output meets the expected standards, the platform will advance to the next stage of the task deployment process; if there are deviations in R&D, it supports adding revisions and guiding engineers to make necessary adjustments, thereby ensuring that the project can continue to evolve and optimize.

[0140] The above-mentioned SOA-based intelligent vehicle software platform provides an AI collaborative knowledge base, and the specific implementation steps are as follows:

[0141] Multi-source information intelligent extraction engine: The platform is equipped with a knowledge graph construction tool that uses natural language processing (NLP) and machine learning technology to automatically extract valuable information from diversified data sources within the enterprise. These data sources are not limited to private documents and technical standards, but also include code comments, email communications, discussion records in project management tools, etc., to ensure comprehensive coverage and in-depth mining of knowledge.

[0142] Adaptive knowledge graph modeling system: Engineers can use interactive tools to automatically build the knowledge graph framework with the help of intelligent algorithms, or make detailed manual adjustments as needed. This process combines semi-automatic annotation with intelligent association discovery functions to quickly establish mappings between entities, concepts, and relationships to form a highly structured private knowledge base. At the same time, the system supports a dynamic update mechanism to continuously absorb new knowledge and maintain the timeliness of the knowledge base.

[0143] High-performance lightweight model deployment framework: In response to the real challenge of resource constraints, the platform integrates and optimizes lightweight large models and FastGPT systems. Through algorithm compression and hardware acceleration technology, it greatly reduces the computing and storage resources required for operation while ensuring model accuracy. This enables efficient AI-assisted decision-making capabilities to run seamlessly in the R&D environment, and even scenarios with limited resources can experience smooth AI services.

[0144] The core of knowledge question-answering that deeply integrates FastGPT and private models: Through cleverly designed interfaces and algorithm fusion strategies, the powerful text generation and understanding capabilities of FastGPT are closely combined with the platform's private models to build a knowledge base question-answering system that has both extensive domain knowledge and in-depth understanding of enterprise-specific contexts. This system can not only respond to queries quickly, but also provide accurate, personalized solutions and innovative ideas, greatly improving problem-solving efficiency and the team's innovation capabilities.

[0145] Interactive knowledge graph exploration platform: The platform provides a highly visual knowledge graph interface that uses an intuitive graphical layout to display complex knowledge networks. Engineers can easily browse and analyze knowledge nodes and their associations through operations such as dragging, zooming, and filtering, and can directly add, delete, and modify knowledge points on the interface, promoting cross-departmental and cross-professional knowledge sharing and collaboration. In addition, the system also supports advanced functions such as knowledge heat analysis and knowledge gap identification, which further guide the continuous optimization of the knowledge base and the improvement of the team's knowledge structure.

[0146] The beneficial effects of the present invention are: a SOA-based intelligent vehicle software architecture and software platform in the exemplary embodiment of the present disclosure, wherein the platform includes: the platform greatly simplifies the construction process of the vehicle service architecture by virtue of its deep integration, modular design concept and plug-and-play characteristics, and realizes the high abstraction of key modules and the rapid construction through intuitive drag-and-drop operations. The platform's built-in standardized interface parsing engine is seamlessly integrated with a mature enterprise-level R&D process management system, which jointly accelerates the design and implementation of the SOA architecture that complies with strict automotive standards, ensures seamless collaboration in the R&D process, and effectively guarantees the efficient advancement of the project and the precise implementation of the goals.

[0147] The platform also uses an innovative graphical configuration tool that can automatically generate accurate architecture code and unit test scripts, while integrating zero-copy technology, load balancing strategies and advanced compilation optimization methods to comprehensively improve the efficiency and execution speed of vehicle service development. In addition, it provides a comprehensive graphical simulation test environment that supports pre-verification of algorithm functions without relying on specific hardware, greatly promoting the efficiency and accuracy of the early development stage.

[0148] Through its powerful service orchestration and deployment engine, the platform realizes integrated deployment and comprehensive lifecycle management of services, ensuring the flexibility and controllability of service deployment. Using TVM (Tensile Virtual Machine) technology to further optimize operational efficiency, the platform directly integrates the compiled and optimized models with the deployment components, greatly simplifying the end-to-end process from model optimization to GPU / NPU deployment.

[0149] It is particularly worth mentioning that the platform integrates a lightweight but powerful AI collaborative knowledge base, which not only contains a wide range of pre-trained models, but also seamlessly connects with the company's private knowledge to jointly support an efficient and customized question-and-answer system. With an intuitive and easy-to-use user interface, the platform actively promotes the sharing and circulation of knowledge and the accelerated collision of innovative thinking, opening up a new path for the intelligent upgrade of vehicle services.

[0150] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0151] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0152] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0153] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An intelligent vehicle software architecture based on SOA, characterized in that: The software architecture includes a component resource library, a SOA service architecture diagram, a business execution flow chart, a model resource library, and a vehicle resource library, wherein: The component resource library includes plug-and-play components for seamlessly bridging information transfer between components through configuration interfaces or real-time data streams; The SOA service architecture diagram provides a graphical service modeling and orchestration tool for instantiating components as service nodes through drag and drop operations. The SOA service architecture diagram supports service descriptions in XML or JSON format; The business execution flow chart is constructed based on the SOA service architecture diagram, which is used to define the service runtime scheduling logic, execution strategy and resource configuration, and implement execution and resource allocation using a directed acyclic graph; The model resource library integrates a graphical editor and a configuration manager to build a service architecture and responsible business execution process, and implements version control and backtracking of services, scheduling strategies, deployment strategies, and lifecycle management strategies; The vehicle resource library is an integration and deployment environment for SOA services, used for real-time data processing and vehicle algorithms, providing a hardware interface adaptation layer and supporting OTA updates.

2. The method according to claim 1, characterized in that The service componentization of the software architecture implements high-cohesion and low-coupling component design through a graphical configuration interface and a standardized service interface.

3. The method according to claim 1, characterized in that The service coordination mechanism of the software architecture drives service nodes to coordinate closely through a data bus to form a responsive service network.

4. The method according to claim 1, characterized in that The business process orchestration of the software architecture adopts the BPMN 2.0 standard and provides a graphical interface to support complex logic configuration.

5. The method according to claim 1, characterized in that The one-click deployment of the software architecture uses Ansible automated deployment technology, supports rapid integration and deployment of services, and has a built-in CI / CD process.

6. An intelligent vehicle software platform based on SOA, characterized in that: The software platform includes a component resource library, an AI collaborative knowledge base, a task division management engine, a service development basic environment, and protocol standardization management, among which: The component resource library provides plug-and-play functional units, including code generation, device abstraction, mature algorithms, operator optimization, simulation testing, service orchestration, and integrated deployment components; The AI ​​collaborative knowledge base integrates industry big model resources and enterprise knowledge assets to form a collaborative knowledge ecosystem and provide data-driven decision support; The task division management engine provides enterprise-level project management solutions, supporting the management of the entire SOA service development process from requirement definition to deployment; The service development basic environment provides a communication mechanism and underlying framework to support the development and optimization of algorithm services; The protocol standardization management is used to maintain all interface protocols and realize data iteration and seamless connection between interfaces.

7. The software platform according to claim 6, characterized in that: The collaborative knowledge base forms a valuable knowledge system by analyzing and refining the company's historical experience data, thereby providing data-driven support for the vehicle SOA service architecture design, service simulation architecture design, and deployment strategy design processes. This support can help companies more effectively utilize successful experiences and lessons from past projects, optimize design processes, improve the authenticity and accuracy of simulation tests, and develop more reasonable and effective deployment plans.

8. The software platform according to claim 6, characterized in that: The software platform is a drag-and-drop software-defined vehicle computing model that provides interfaces including standardized interface configuration, automated interface file generation, and graphical interface settings.

9. The software platform according to claim 6, characterized in that: The software platform provides a configuration-driven SOA service rapid instantiation and building framework, supports intelligent service configuration and automatic code generation, service framework and automatic optimization compilation; The software platform provides algorithm / operator automatic optimization and multi-platform deployment capability support mechanism, including intelligent strategy customization, dynamic adaptive optimization, one-click deployment and continuous optimization feedback loop; The software platform provides a parameterized simulation test mechanism, integrates simulation components, supports refined configuration, service interconnection and test initiation, environment customization and real-time feedback; The software platform provides a service scheduling and orchestration mechanism, including service node synchronization and classification identification, an interactive service grouping design interface, a dynamic policy configuration engine, and automatic service dependency analysis and policy generation.

10. The software platform according to claim 6, characterized in that: The software platform provides an integrated deployment mechanism, supports visual component drag-and-drop deployment, property configuration assistant, policy-driven automated deployment engine, full lifecycle intelligent management, and dual-modal operation and maintenance control interface; The software platform provides a task division management mechanism, including a unified development portal, precise task allocation and management, and real-time project progress monitoring.

Images