A system design method based on intelligent automobile information physical system field knowledge base

Abstract

This invention discloses a system design method belonging to the field of intelligent vehicle technology, specifically relating to a system design method based on an intelligent vehicle cyber-physical system (IVCPS) domain knowledge base. The method includes: analyzing the composition of information and physical elements of the intelligent vehicle cyber-physical system; constructing an IVCPS domain knowledge base using ontology representation methods; and applying techniques to construct the IVCPS domain knowledge base, including: achieving knowledge reuse based on the IVCPS domain ontology knowledge base through knowledge representation language conversion, and realizing auxiliary system modeling design based on the IVCPS domain knowledge base. The knowledge representation language conversion includes: constructing a mapping relationship between a knowledge modeling language and Extensible Markup Language (Extensible Markup Language) and a system modeling language. Based on the IVCPS domain knowledge base application techniques, the method implements system design based on the IVCPS domain knowledge base, uses typical traffic scenarios within the IVCPS domain for example analysis, verifies the effectiveness of the system design method, effectively assists system design research, reduces data collection time, and avoids repetitive work.

Description

Technical Field

[0001] This invention belongs to the field of intelligent vehicle technology, and specifically relates to a system design method based on a knowledge base of intelligent vehicle cyber-physical systems. Background Technology

[0002] Intelligent Vehicle Cyber-Physical Systems (IVCPS) can be seen as a further extension and development of Intelligent Traffic Systems (ITS). ITS is a large-scale, nonlinear, complex system with strongly coupled physical infrastructure, aiming to achieve full coordination and deep integration of elements such as people, vehicles, and roads. Due to the lack of extensive interconnectivity between various traffic entities, traditional ITS still has shortcomings in coordination and optimization, requiring the use of cyber-physical systems to achieve deep collaboration between information and physical space. IVCPS embeds information systems into physical systems such as ITS, achieving the integration of information and physical systems. This can solve problems such as insufficient reliability of intelligent vehicle perception systems, limited computing resources, and heterogeneous data issues in vehicle-road cooperative technologies during the development of intelligent vehicles. Utilizing cloud control systems to provide decision-making and control capabilities helps improve the driving safety and vehicle-road coordination capabilities of intelligent vehicles, enhances the controllability, efficiency, and reliability of physical systems, and makes intelligent vehicle systems more intelligent, energy-efficient, and comfortable.

[0003] With the increasing demands for vehicle automation, intelligent roadside infrastructure, and interconnected transportation networks, more and more intelligent devices and information resources are being applied to transportation systems, resulting in complex transportation systems with numerous elements and intricate relationships. One of the challenges facing the current and future product development processes of Intelligent Vehicle Cyber-Physical Systems (IVCPS) is ensuring the reliability of increasingly complex transportation systems and maintaining consistency in system development and design, particularly the traceability of requirements in systems with strong interconnections with different stakeholders. To utilize domain knowledge in the design process of IVCPS systems, it is essential to first consider using computer-recognizable and processable models to represent IVCPS domain knowledge and construct an IVCPS domain knowledge framework.

[0004] The proper application of knowledge representation and knowledge engineering methods can automate repetitive, non-creative design tasks, saving significant time and costs. Currently, designers spend approximately 20% of their time searching for and absorbing information; 40% of all design information needs are met through individual collection, even when more suitable information may already exist. This means that design information and knowledge are not stored in easily accessible knowledge bases, nor is domain-specific knowledge sharing achieved, resulting in a large amount of repetitive work being performed by different personnel during the R&D process. System designers of intelligent vehicle cyber-physical systems face significant challenges in representing, managing, sharing, and reusing domain knowledge.

[0005] Therefore, there is an urgent need for a system design methodology based on a knowledge base in the field of Intelligent Vehicle Cyber-Physical Systems (IVCPS). This methodology would utilize a single, centralized, and formalized knowledge representation method to achieve knowledge management in the IVCPS domain, as well as information exchange and knowledge sharing between subsystems. It would address the challenges of constructing and maintaining complex large-scale systems like IVCPS by leveraging a well-structured and knowledge-rich IVCPS knowledge base. This would help system developers quickly and easily find, understand, and use IVCPS domain knowledge, thereby better supporting system design work such as functional, logical, and physical design, and ensuring the efficiency and accuracy of system design. Summary of the Invention

[0006] The purpose of this invention is to provide a system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems, characterized by the following steps:

[0007] Step S1: Perform a compositional analysis of the information and physical elements of the intelligent vehicle cyber-physical system, and construct an IVCPS domain knowledge base using ontology representation methods;

[0008] Step S2: The application technology method for constructing the IVCPS domain knowledge base includes: realizing knowledge reuse based on the IVCPS domain ontology knowledge base through knowledge representation language conversion, and realizing auxiliary system modeling design based on the IVCPS domain knowledge base; the knowledge representation language conversion includes: constructing the mapping relationship between the knowledge modeling language and the Extensible Markup Language and the System Modeling Language;

[0009] Step S3: Based on the application technology method of the IVCPS domain knowledge base, implement the system design based on the IVCPS domain knowledge base, and use typical traffic scenarios in the IVCPS domain for case analysis to verify the effectiveness of the system design method.

[0010] The step S1 of constructing the IVCPS domain knowledge base using ontology representation methods includes:

[0011] The IVCPS domain knowledge base is constructed based on the analysis of domain knowledge involved in IVCPS, considering the "vehicle, road, cloud, network, and map" elements of IVCPS, to achieve multi-element and multi-level functional division and determine the characteristics of interaction relationships. The basic process of ontology modeling construction includes: problem analysis, requirements analysis, domain knowledge framework, and knowledge model establishment. The IVCPS knowledge modeling process is based on the characteristics of the smallest functional unit of all elements of IVCPS "vehicle, road, network, cloud, and map" and their information interaction relationships. It analyzes each knowledge element unit and its interaction relationships, classifies the elements hierarchically, and forms the IVCPS domain knowledge base.

[0012] The steps for constructing the IVCPS domain knowledge base include:

[0013] Step 1: Determine the scope of ontology knowledge in the IVCPS domain;

[0014] Step 2: List the important terms and concepts in the field of IVCPS;

[0015] Step 3: Establish the IVCPS ontology framework;

[0016] Step 4: Define the relationships between concepts in the IVCPS domain;

[0017] Step 5: Construct the IVCPS ontology model;

[0018] Step 6: Instantiate the IVCPS domain ontology;

[0019] Step 7: Examine the IVCPS domain knowledge base;

[0020] Step 8: Visualize the IVCPS domain knowledge base.

[0021] The mapping relationship between the knowledge modeling language and the Extensible Markup Language and System Modeling Language in step S2 includes:

[0022] Step 1: Analyze the basic structure of the IVCPS knowledge base language;

[0023] The IVCPS domain ontology knowledge base uses the syntax of a knowledge modeling language to represent and store IVCPS domain knowledge. It uses "classes, instances, object attributes, and data attributes" to formally represent IVCPS domain concepts and the relationships between them. The knowledge modeling language is a Web ontology language. Classes and attributes have subclasses and sub-attributes. In the IVCPS knowledge base, individuals and objects are represented as instances, concepts and categories as classes, and relationships as attributes. The attributes include object attributes and data attributes.

[0024] Step 2: Analyze the composition of the IVCPS modeling language; The IVCPS modeling language includes: System Modeling Language and Extensible Markup Language; System Modeling Language models entities in class systems with attributes and behaviors, and uses graphs to represent modeling elements; Extensible Markup Language is used to represent and transmit structured data, and the basic building blocks of Extensible Markup Language include: elements, attributes, and complex types;

[0025] Step 3: Construct the mapping relationship;

[0026] Web Ontology Language, as a further extension of Extensible Markup Language, expresses the semantics of data through attributes; each knowledge class, object attribute, and data attribute in the IVCPS domain knowledge base can be mapped to XML format in the corresponding form;

[0027] Both Web ontology languages ​​and system modeling languages ​​use the concept of classes. In Web ontology languages, a class is a collection of individuals, and the relationships between classes are represented by object attributes and data attributes. In system modeling languages, a class represents the system structure and includes attributes that define the structure, operations that define the behavior, and relationships with other classes. Object attributes in Web ontology languages ​​correspond to association, dependency, aggregation, and composition relationships in system modeling languages, subclasses correspond to generalization / inheritance relationships, and disjoint classes and equivalence classes are considered as a type of association relationship.

[0028] The application technology method based on the IVCPS domain knowledge base in step S3, which realizes the system design based on the IVCPS domain knowledge base, includes:

[0029] Step 1: Map the knowledge in the IVCPS domain knowledge base to the system modeling software;

[0030] Based on the language specification of the system modeling language, knowledge such as classes, object attributes, data attributes and instances in the IVCPS domain knowledge base is imported into the system modeling software according to the pre-set mapping rules and transformed into model elements supported by the system modeling language.

[0031] Step 2: Conduct knowledge-based system modeling and design;

[0032] Based on imported domain knowledge, system modeling and design for typical IVCPS scenarios are performed. In the system modeling software, relationships between classes are established according to the design requirements of typical IVCPS scenarios. Using tools and standard symbols provided by the system modeling language, the system design is modeled and described in detail, constructing the system's logical model and module diagram. Based on the modeling model of the system modeling language, corresponding outputs are generated, serving as the basis and reference for subsequent IVCPS typical scenario system development, implementation, and simulation verification. These outputs include: design documents, model diagrams, and system specifications.

[0033] Step 3: Construct a simulation scenario based on the IVCPS knowledge base;

[0034] Based on the requirements of the simulation software, simulation knowledge is extracted from the IVCPS domain knowledge base and stored in relevant traffic simulation files according to a pre-set format through language conversion. The IVCPS domain knowledge base provides complete knowledge modeling of road network parameters, simulation traffic flow parameters, and other knowledge required for the simulation of typical scenarios. During the simulation scenario construction process, relevant simulation knowledge is extracted from the knowledge base and converted into an extensible markup language that describes the simulation configuration file, thereby realizing the construction of simulation scenarios in the simulation software.

[0035] The construction of the simulation scenario based on the IVCPS knowledge base also includes storing the simulation results in the IVCPS domain knowledge base to facilitate subsequent analysis and use of the results.

[0036] The beneficial effects of this invention are as follows:

[0037] To avoid repetitive design work in intelligent vehicle cyber-physical systems (IVCPS) and achieve efficient design of typical scenarios, functions, and subsystems within the domain, this invention proposes a system design method based on an IVCPS domain knowledge base. This method uses the IVCPS domain knowledge base as a foundation, and through the mapping relationship between knowledge modeling languages ​​and system modeling languages, imports knowledge elements from the knowledge base into system modeling software tools to achieve system design based on the IVCPS domain knowledge base. This invention offers a new approach to addressing the issue of domain knowledge sourcing in the system design process. It can effectively utilize the IVCPS domain knowledge base to assist in system design research, reduce data collection time during the system design process, and avoid repetitive design work. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating a system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems according to the present invention.

[0039] Figure 2 This is a visual diagram of the IVCPS domain knowledge base according to an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram illustrating the mapping relationship between the knowledge representation language and the Extensible Markup Language in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram illustrating the mapping relationship between the knowledge representation language and the system modeling language in an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram illustrating the definition of a knowledge-based green wave speed guidance module according to an embodiment of the present invention.

[0043] Figure 6 This is a schematic diagram illustrating the construction process of the IVCPS domain ontology knowledge base according to an embodiment of the present invention. Detailed Implementation

[0044] This invention provides a system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems. The invention will be further described in detail below with reference to the accompanying drawings.

[0045] like Figure 1 The embodiment of the present invention shown discloses a system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems, including:

[0046] Step S1: Perform a compositional analysis of the information and physical elements of the intelligent vehicle cyber-physical system, and construct an IVCPS domain knowledge base using ontology representation methods;

[0047] Step S2: The application technology method for constructing the IVCPS domain knowledge base includes: realizing knowledge reuse based on the IVCPS domain ontology knowledge base through knowledge representation language conversion, and realizing auxiliary system modeling design based on the IVCPS domain knowledge base; the knowledge representation language conversion includes: constructing the mapping relationship between the knowledge modeling language and the Extensible Markup Language and the System Modeling Language;

[0048] Step S3: Based on the application technology method of the IVCPS domain knowledge base, implement the system design based on the IVCPS domain knowledge base, and use typical traffic scenarios in the IVCPS domain for case analysis to verify the effectiveness of the system design method.

[0049] The following provides a detailed explanation of each step.

[0050] (1) Construction of IVCPS domain knowledge base

[0051] The construction of the IVCPS domain knowledge base is based on the analysis of knowledge in the domains involved in IVCPS. It takes into account the multi-element and multi-level functional division and interaction characteristics of IVCPS such as "vehicle, road, cloud, network, and map". The elements are classified hierarchically, and finally an IVCPS domain knowledge base with a clear hierarchical structure, clear interaction relationships and flexible application of knowledge is formed.

[0052] In this embodiment, the steps for constructing the IVCPS domain knowledge base are as follows:

[0053] Step 1: Determine the scope of ontology knowledge in the IVCPS domain;

[0054] First, it is necessary to clarify the scope of knowledge covered by the constructed IVCPS ontology knowledge base and determine the physical and informational concepts covered by IVCPS. At the same time, it is also necessary to clarify the purpose of constructing the IVCPS knowledge base, namely, to achieve unified management, knowledge sharing and knowledge application of all elements of knowledge in the IVCPS domain.

[0055] Step 2: List the important terms and concepts in the field of IVCPS;

[0056] Based on relevant literature in the IVCPS field, a glossary is created listing all physical and informational concepts that need to be represented and covered in the IVCPS knowledge base. This glossary is a collection of IVCPS concepts; the listed terms do not need to be organized or have overlapping meanings.

[0057] Step 3: Establish the IVCPS ontology framework;

[0058] The numerous physical and informational concepts in the vocabulary are categorized and grouped according to certain rules to form different IVCPS ontology classes, thus constructing the IVCPS ontology knowledge framework. This process requires analyzing each physical and informational concept, selecting the concepts needed for the IVCPS domain knowledge base, and concisely and clearly expressing the IVCPS system knowledge. Simultaneously, the knowledge concepts are further supplemented and improved based on the IVCPS ontology framework.

[0059] Step 4: Define the relationships between concepts in the IVCPS domain;

[0060] Define the relationships between concepts in IVCPS, and establish the relationships, attributes, and constraints of knowledge classes, such as the relationship between physical concepts and information concepts, and the data attributes of physical concepts themselves.

[0061] Step 5: Construct the IVCPS ontology model;

[0062] The IVCPS domain knowledge classes, relationships, and attributes are constructed in a unified manner to form an IVCPS ontology model containing a large amount of domain knowledge and relationships.

[0063] Step 6: Instantiate the IVCPS domain ontology;

[0064] By consulting and collecting knowledge, classes in the IVCPS domain ontology model are instantiated, such as sensor devices, component facilities, road networks, and software algorithms. Instantiation of the IVCPS domain ontology not only enriches the content of the IVCPS domain knowledge base, providing more knowledge resources for subsequent design needs, but also verifies the comprehensiveness and rationality of the class and attribute relationships within the IVCPS knowledge base.

[0065] Step 7: Examine the IVCPS domain knowledge base

[0066] The consistency test of the knowledge base in the IVCPS domain is achieved through knowledge reasoning, which determines whether there are conflicts in the knowledge in the knowledge base in terms of logic, semantics, syntax, etc., and then the knowledge base is further modified and improved based on the test results.

[0067] Step 8: Visualize the IVCPS domain knowledge base;

[0068] like Figure 2 As shown, ontology modeling tools are used to visualize the ontology knowledge model, making it easier to observe the composition of the IVCPS ontology knowledge base and the attribute relationships between knowledge.

[0069] like Figure 6 As shown, the IVCPS knowledge base construction process can be iterated to ensure the comprehensiveness of the knowledge concepts covered and the rationality of the IVCPS ontology framework, making the constructed IVCPS domain ontology knowledge base more complete and reliable.

[0070] (2) Application techniques and methods of knowledge bases in the IVCPS domain:

[0071] This paper proposes a knowledge base application technology method for the IVCPS domain, outlining a mapping relationship between knowledge modeling languages, Extensible Markup Language (Extensible Markup Language), and system modeling languages. Through language conversion, it enables the generation of configuration files for traffic simulation software, code, and other applications based on the IVCPS domain ontology knowledge base, and assists in the design of IVCPS systems. The specific steps are as follows:

[0072] Step 1: Analyze the basic structure of the IVCPS knowledge base language.

[0073] The IVCPS domain ontology knowledge base uses the OWL knowledge modeling language syntax to represent and store a large amount of knowledge in the IVCPS domain. It uses elements such as "classes, instances, object attributes, and data attributes" to formally represent IVCPS domain concepts and the relationships between them. Attributes are further divided into object attributes and data attributes, and classes and attributes have subclasses and sub-attributes. In the IVCPS knowledge base, individuals and objects are represented as instances, concepts and categories as classes, and relationships as attributes.

[0074] Step 2: Perform IVCPS modeling language composition analysis;

[0075] IVCPS modeling languages ​​include: System Modeling Language and Extensible Markup Language;

[0076] SysML is a unified modeling language that uses graphical symbols to handle various aspects of software development, from database design to code module interaction. It models entities in class systems with attributes and behaviors and uses graphs to represent modeling elements.

[0077] Extensible Markup Language (XML) is a meta-language widely used for data exchange between applications to represent and transmit structured data. The basic building blocks of XML include elements, attributes, and complex types.

[0078] Step 3: Construct the mapping relationship;

[0079] In this embodiment, Web Ontology Language (OWL) is the knowledge modeling language.

[0080] OWL (Open Source Language) is also seen as a further extension of Extensible Markup Language (ESL), using rich attributes to further express the semantics of data and filling the gaps in XML's formalization and knowledge representation capabilities. In the IVCPS domain knowledge base, each knowledge class, object attribute, and data attribute can be mapped to XML format in an appropriate way, as shown in the specific mapping relationships. Figure 3 As shown.

[0081] The similarity between the OWL knowledge modeling language and system modeling languages ​​lies in their use of the concept of classes. In OWL, a class is a collection of individuals, and relationships between classes are represented using object attributes and data attributes. In system modeling languages, classes typically represent system structures and include attributes that define the structure, operations that define behavior, and relationships with other classes. Object attributes in OWL correspond to association, dependency, aggregation, and composition relationships in system modeling languages; subclasses correspond to generalization / inheritance relationships; and disjoint classes and equivalence classes can also be seen as a type of association. Specific mapping relationships are as follows: Figure 4 As shown.

[0082] (3) IVCPS system design method based on IVCPS domain knowledge base

[0083] The system design method based on the IVCPS domain knowledge base maps the relevant classes, instances, and attributes of physical and information elements in the IVCPS domain knowledge base to system modeling tools, forming a knowledge base-based system design method, and has been validated and analyzed through examples. It includes the following steps:

[0084] Step 1: Map the knowledge in the IVCPS domain knowledge base to the system modeling software

[0085] Based on the language specifications of the system modeling language, knowledge such as classes, object attributes, data attributes, and instances from the IVCPS domain knowledge base are imported into the system modeling software according to certain mapping rules, and transformed into model elements supported by the system modeling language. For example, classes in the knowledge base are transformed into appropriate model elements such as class diagrams, module diagrams, and activity diagrams in the system modeling language; attributes are transformed into corresponding attributes and relationships.

[0086] Step 2: Knowledge-based system modeling and design

[0087] System modeling and design for typical IVCPS scenarios are performed based on imported domain knowledge. In the system modeling software, relationships between classes are established according to the design requirements of typical IVCPS scenarios. Using tools and standard notation provided by the system modeling language, the system design is modeled and described in detail, resulting in the construction of the system's logical model, module diagram, etc.

[0088] Secondly, based on the modeling model of the system modeling language, corresponding outputs such as design documents, model diagrams, and system specifications are generated, serving as the basis and reference for subsequent design of typical IVCPS scenario system development, implementation, and simulation verification.

[0089] Step 3: Construct a simulation scenario based on the IVCPS knowledge base:

[0090] Simulation knowledge is extracted from the IVCPS domain knowledge base according to the simulation software requirements, and then stored in relevant traffic simulation files in a specific format through language conversion. The IVCPS domain knowledge base can provide complete knowledge modeling for road network parameters, simulated traffic flow parameters, and other knowledge required for typical scenario simulations. During simulation scenario construction, relevant simulation knowledge can be extracted from the knowledge base and converted into an extensible markup language (EXPLAIN) describing the simulation configuration file, thereby enabling the construction of simulation scenarios in the simulation software. Furthermore, the construction of simulation scenarios based on the IVCPS knowledge base also includes storing simulation results in the IVCPS domain knowledge base for subsequent analysis and use.

[0091] In this embodiment, the specific steps for constructing a simulation scenario based on the IVCPS knowledge base are as follows:

[0092] 1) Extracting simulation knowledge

[0093] An ontology knowledge base for typical IVCPS scenarios is constructed based on the IVCPS domain knowledge base, and simulation knowledge such as road network knowledge and simulated traffic flow is extracted from this knowledge base. Specifically, road network knowledge includes road network structure, intersection location, road attributes (such as number of lanes, speed limit, etc.), traffic light settings, etc., while simulated traffic flow information includes vehicle starting position, destination, departure time, and traffic flow.

[0094] 2) Generate simulation configuration file

[0095] The extracted simulation knowledge is converted into an Extensible Markup Language (XML) format supported by traffic simulation software tools. For example, road network information is converted into a simulated road network file (e.g., .net.xml) for traffic simulation software tools, including road segments, intersections, and connection relationships; traffic flow information is converted into a simulated traffic flow file (e.g., .rou.xml) for traffic simulation software tools, including vehicle generation rules and vehicle travel routes.

[0096] 3) Simulation Implementation and Simulation Result Storage

[0097] The generated simulation configuration file is imported into the traffic simulation software tool. Simulation parameters such as simulation time and simulation step size are set in the traffic simulation software tool according to the typical scenario ontology knowledge base. The simulation experiment is run and the simulation results are generated. The simulation vehicle operation results are stored in the IVCPS typical scenario ontology knowledge base for subsequent research and analysis.

[0098] Through the above process, road network parameter information and traffic flow information in the knowledge base can be transformed into simulation files required by traffic simulation software tools. Then, typical traffic scenarios of IVCPS can be simulated and verified in the traffic simulation software tools, thereby evaluating the performance and effectiveness of the traffic system and providing effective support for system design and optimization.

[0099] To verify the effectiveness of the system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems disclosed in this invention, the following experiment was conducted:

[0100] (1) Example Scenarios

[0101] This invention presents an example of a system design methodology based on a knowledge base in the field of intelligent vehicle cyber-physical systems, along with its software environment. The invention uses a green wave speed guidance scenario for intelligent vehicles as an example for analysis and verification, and implements the simulation in a traffic simulation software tool.

[0102] (2) Case Analysis

[0103] To verify the practicality of the IVCPS domain knowledge base and the effectiveness of the system design method assisted by the IVCPS domain knowledge base in this invention, a green wave speed guidance scenario for intelligent vehicles was used as an example. Based on the IVCPS domain knowledge base and scenario functional requirements analysis, the green wave speed guidance logic and functions were designed in detail, and simulation experiments were conducted. The specific implementation is as follows:

[0104] 1) Knowledge modeling for green wave speed guidance in intelligent vehicles based on the IVCPS domain knowledge base:

[0105] The knowledge modeling of green wave speed guidance for intelligent vehicles is a detailed representation of the physical equipment and software information required for the function implementation, thereby constructing a knowledge base for green wave speed guidance for intelligent vehicles. Based on the IVCPS domain knowledge base, a knowledge model for green wave speed guidance for intelligent vehicles was constructed, which includes sub-ontologies such as vehicle module, roadside module, cloud control module, communication module, and simulation module, covering all the information and physical elements required for function implementation.

[0106] After implementing the knowledge modeling of green wave speed guidance for intelligent vehicles, it is necessary to instantiate the relevant classes in the knowledge base of green wave speed guidance for intelligent vehicles in combination with the subsequent simulation requirements. Specifically, this includes the instantiation of input parameters, output parameters, and road network parameters. The output parameters are mainly used to store the simulation results after the simulation is completed.

[0107] 2) Design of a green wave speed guidance system for intelligent vehicles based on the IVCPS domain knowledge base:

[0108] IVCPS domain knowledge bases can assist in the construction of IVCPS green wave speed guidance ontology and the design of intelligent vehicle green wave speed guidance systems through knowledge reuse technology. Based on the modules, devices, data, and attribute information in the knowledge base, a green wave speed guidance function module diagram is constructed, such as... Figure 5 As shown. Based on the intelligent vehicle green wave speed guidance ontology knowledge base, the implementation of the intelligent vehicle green wave speed guidance function requires the joint support of the vehicle module, roadside module, cloud control module, and simulation verification module. The vehicle module mainly includes the on-board unit (OBU), the roadside module mainly includes the roadside unit (RSU) and the traffic light module, and the communication module mainly includes the V2X communication module and the road-cloud communication module.

[0109] 3) Simulation implementation of intelligent vehicle green wave speed guidance system:

[0110] The simulation parameters and road network module have been instantiated in the IVCPS green wave speed guidance knowledge base. Therefore, you only need to import the instantiated simulation parameters and road network parameters into the traffic simulation software tool, and you can build the road network file (e.g., .net.xml file) and traffic flow file (e.g., .rou.xml file) required for the simulation based on the road network knowledge and simulated traffic flow knowledge.

[0111] Based on relevant knowledge from the IVCPS knowledge base, a traffic simulation scenario was constructed, and the designed green wave speed guidance function in the IVCPS domain was simulated and verified, validating the applicability of the speed guidance design method. Simultaneously, the practicality of the IVCPS domain knowledge base in auxiliary system design was demonstrated.

Claims

1. A system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems, characterized in that, Includes the following steps: Step S1: Perform a compositional analysis of the information and physical elements of the intelligent vehicle cyber-physical system, and construct an IVCPS domain knowledge base using ontology representation methods; Step S2: The application technology method for constructing the IVCPS domain knowledge base includes: realizing knowledge reuse based on the IVCPS domain ontology knowledge base through knowledge representation language conversion, and realizing auxiliary system modeling design based on the IVCPS domain knowledge base; the knowledge representation language conversion includes: constructing the mapping relationship between the knowledge modeling language and the Extensible Markup Language and the System Modeling Language; Step S3: Based on the application technology method of the IVCPS domain knowledge base, implement the system design based on the IVCPS domain knowledge base, and use typical traffic scenarios in the IVCPS domain for case analysis to verify the effectiveness of the system design method; The mapping relationship between the knowledge modeling language and the Extensible Markup Language and System Modeling Language in step S2 includes: Step 1: Analyze the basic structure of the IVCPS knowledge base language; The IVCPS domain ontology knowledge base uses the syntax of a knowledge modeling language to represent and store IVCPS domain knowledge. It uses "classes, instances, object attributes, and data attributes" to formally represent IVCPS domain concepts and relationships between concepts. The knowledge modeling language is a Web ontology language. Classes and attributes have subclasses and sub-attributes. In the IVCPS knowledge base, individuals and objects are represented as instances, concepts and categories as classes, and relationships as attributes. The attributes include object attributes and data attributes. Step 2: Perform IVCPS modeling language composition analysis; The IVCPS modeling language includes: System Modeling Language and Extensible Markup Language; System Modeling Language models entities in class systems with attributes and behaviors, and uses graphs to represent modeling elements; Extensible Markup Language is used to represent and transmit structured data, and the basic building blocks of Extensible Markup Language include: elements, attributes, and complex types; Step 3: Construct the mapping relationship; Web Ontology Language, as a further extension of Extensible Markup Language, expresses the semantics of data through attributes; each knowledge class, object attribute, and data attribute in the IVCPS domain knowledge base can be mapped to XML format in the corresponding form; Both Web ontology languages ​​and system modeling languages ​​use the concept of classes. In Web ontology languages, a class is a collection of individuals, and the relationships between classes are represented by object attributes and data attributes. In system modeling languages, a class represents the system structure and includes attributes that define the structure, operations that define the behavior, and relationships with other classes. Object attributes in Web ontology languages ​​correspond to association, dependency, aggregation, and composition relationships in system modeling languages, subclasses correspond to generalization / inheritance relationships, and disjoint classes and equivalence classes are considered as a type of association relationship.

2. The system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems according to claim 1, characterized in that, The step S1 of constructing the IVCPS domain knowledge base using ontology representation methods includes: The IVCPS domain knowledge base is constructed based on the analysis of domain knowledge involved in IVCPS, considering the "vehicle, road, cloud, network, and map" elements of IVCPS, to achieve multi-element and multi-level functional division and determine the characteristics of interaction relationships. The basic process of ontology modeling construction includes: problem analysis - requirements analysis - domain knowledge framework - knowledge model establishment. The IVCPS knowledge modeling process is based on the characteristics of the smallest functional unit of all elements of IVCPS "vehicle, road, network, cloud, and map" and their information interaction relationships. It analyzes each knowledge element unit and interaction relationship, classifies the elements hierarchically, and forms the IVCPS domain knowledge base.

3. The system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems according to claim 1, characterized in that, The steps for constructing the IVCPS domain knowledge base include: Step 1: Determine the scope of ontology knowledge in the IVCPS domain; Step 2: List the important terms and concepts in the field of IVCPS; Step 3: Establish the IVCPS ontology framework; Step 4: Define the relationships between concepts in the IVCPS domain; Step 5: Construct the IVCPS ontology model; Step 6: Instantiate the IVCPS domain ontology; Step 7: Examine the IVCPS domain knowledge base; Step 8: Visualize the IVCPS domain knowledge base.

4. The system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems according to claim 1, characterized in that, The application technology method based on the IVCPS domain knowledge base in step S3, which realizes the system design based on the IVCPS domain knowledge base, includes: Step 1: Map the knowledge in the IVCPS domain knowledge base to the system modeling software; Based on the language specification of the system modeling language, knowledge such as classes, object attributes, data attributes and instances in the IVCPS domain knowledge base is imported into the system modeling software according to the pre-set mapping rules and transformed into model elements supported by the system modeling language. Step 2: Conduct knowledge-based system modeling and design; Based on imported domain knowledge, system modeling and design for typical IVCPS scenarios are performed. In the system modeling software, relationships between classes are established according to the design requirements of typical IVCPS scenarios. Using tools and standard symbols provided by the system modeling language, the system design is modeled and described in detail, constructing the system's logical model and module diagram. Based on the modeling model of the system modeling language, corresponding outputs are generated, serving as the basis and reference for subsequent IVCPS typical scenario system development, implementation, and simulation verification. These outputs include: design documents, model diagrams, and system specifications. Step 3: Construct a simulation scenario based on the IVCPS knowledge base; Based on the requirements of the simulation software, simulation knowledge is extracted from the IVCPS domain knowledge base and stored in relevant traffic simulation files according to a pre-set format through language conversion. The IVCPS domain knowledge base provides complete knowledge modeling of road network parameters, simulation traffic flow parameters, and other knowledge required for the simulation of typical scenarios. During the simulation scenario construction process, relevant simulation knowledge is extracted from the knowledge base and converted into an extensible markup language that describes the simulation configuration file, thereby realizing the construction of simulation scenarios in the simulation software.

5. The system design method based on a knowledge base in the field of intelligent vehicle cyber-physical systems according to claim 4, characterized in that, The construction of the simulation scenario based on the IVCPS knowledge base also includes storing the simulation results in the IVCPS domain knowledge base to facilitate subsequent analysis and use of the results.

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