System design method based on intelligent automobile information physical system domain knowledge base

Abstract

The invention belongs to the technical field of intelligent automobiles, and particularly relates to a system design method based on an intelligent automobile cyber-physical system field knowledge base, which comprises the following steps of: performing composition analysis on information elements and physical elements of an intelligent automobile cyber-physical system, and constructing an IVCPS field knowledge base by using an ontology representation method; the application technology method for constructing the IVCPS domain knowledge base comprises the steps that knowledge reuse based on the IVCPS domain ontology knowledge base is achieved through knowledge representation language conversion, and auxiliary system modeling design based on the IVCPS domain knowledge base is achieved; the knowledge representation language conversion comprises the following steps: constructing a mapping relationship between a knowledge modeling language and an extensible markup language and a system modeling language; based on an IVCPS field knowledge base application technical method, system design based on the IVCPS field knowledge base is realized, instance analysis is performed by using typical traffic scenes in the IVCPS field, the effectiveness of the system design method is verified, system design research can be effectively assisted, data collection time is shortened, and repeated work is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent vehicles, and in particular relates to a system design method based on a knowledge base in the field of intelligent vehicle information-physical systems. Background Art

[0002] The cyber-physical system of intelligent vehicles can be regarded as a further extension and development of the intelligent traffic system (ITS). The intelligent traffic system is a large-scale nonlinear complex system with strong coupling of physical infrastructure, which is committed to achieving full coordination and deep integration of elements such as people, vehicles and roads. Due to the lack of extensive interconnection between various traffic entities, the traditional intelligent traffic system is still lacking in coordination and optimization, and it is necessary to use the cyber-physical fusion system to achieve deep collaboration between information and physical space. Based on the intelligent vehicle cyber-physical system (IVCPS), the information system is embedded into the physical system such as the intelligent traffic system to achieve the integration of the information system and the physical system, which can solve the problems of insufficient reliability of the intelligent vehicle perception system, limited computing resources, and heterogeneous data of vehicle-road cooperative technology in the development of intelligent vehicles. Using cloud control systems to provide decision-making and control capabilities will help improve the driving safety and vehicle-road coordination capabilities of intelligent vehicles, improve the controllability, efficiency and reliability of physical systems, and make intelligent vehicle systems more intelligent, energy-saving and comfortable.

[0003] With the demand for automobile automation, intelligent roadside facilities, and interconnected transportation networks, more and more intelligent devices and information resources are being applied to transportation systems, forming a complex transportation system with many elements and complex relationships. One of the challenges facing the current and future product development process of intelligent vehicle cyber-physical systems is to ensure the reliability of increasingly complex transportation systems and the consistency of system development and design, especially the traceability of requirements in strongly interconnected systems with different participants. In order to use domain knowledge in the design process of IVCPS systems, it is first necessary to consider using models that can be recognized and processed by computers to represent IVCPS domain knowledge and construct an IVCPS domain knowledge framework.

[0004] The rational use of knowledge representation and knowledge engineering methods can automate repetitive, non-creative design tasks, saving a lot of time and cost. Currently, designers spend about 20% of their time searching and absorbing information; 40% of all design information needs are collected by individuals, even though more appropriate information may already exist. This means that design information and knowledge are not stored in an easily accessible knowledge base, nor is knowledge sharing in the field achieved, resulting in a lot of repetitive work being done by different people during the R&D and design process. System designers of intelligent automotive cyber-physical systems face huge challenges in representing, managing, sharing, and reusing domain knowledge.

[0005] Therefore, a system design method based on the knowledge base of the intelligent vehicle cyber-physical system is urgently needed to achieve knowledge management in the IVCPS field and information exchange and knowledge sharing between subsystems through a single, centralized, and formal knowledge representation method. To solve the problem that IVCPS is difficult to build and maintain as a complex large system, a clearly structured and knowledge-rich IVCPS knowledge base is used to help system R&D designers 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 the present invention is to provide a system design method based on a knowledge base in the field of intelligent automobile cyber-physical system, characterized in that it comprises the following steps:

[0007] Step S1: Analyze the composition of information elements and physical elements of the intelligent vehicle cyber-physical system, and use the ontology representation method to build the IVCPS domain knowledge base;

[0008] Step S2: an application technology method for constructing an IVCPS domain knowledge base, including: 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 a mapping relationship between a knowledge modeling language and an extensible markup language and a system modeling language;

[0009] Step S3: Apply technical methods based on the IVCPS domain knowledge base to implement system design based on the IVCPS domain knowledge base, use typical traffic scenarios in the IVCPS field for example analysis, and verify the effectiveness of the system design method.

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

[0011] The use of IVCPS domain knowledge base is based on the analysis of the domain knowledge involved in IVCPS, taking into account 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-demand analysis-domain knowledge framework-knowledge model establishment; the IVCPS knowledge modeling process is based on the characteristics of the minimum functional unit of the IVCPS "vehicle, road, network, cloud, and map" full elements and their information interaction relationships, analyzes each knowledge element unit and interaction relationship, and classifies the elements hierarchically to form an IVCPS domain knowledge base.

[0012] The steps of 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 IVCPS ontology model;

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

[0019] Step 7: Examining 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 the 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 knowledge modeling language related syntax to represent and store IVCPS domain knowledge, and uses "classes, instances, object attributes, and data attributes" to formalize knowledge representation of IVCPS domain concepts and relationships between concepts; the knowledge modeling language is the Web ontology language; classes and attributes have subclasses and subattributes; in the IVCPS knowledge base, individuals and objects are represented as instances, concepts and categories are represented as classes, and relationships are represented as attributes; the attributes include: object attributes and data attributes;

[0024] Step 2: Analyze the structure of IVCPS modeling language; 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: Build mapping relationship;

[0026] As a further extension of Extensible Markup Language, Web Ontology Language further 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 the XML format in a corresponding form;

[0027] Both Web ontology language and system modeling language use the concept of class. The class in Web ontology language refers to a collection of individuals, and the relationship between classes is represented by object attributes and data attributes. The class in system modeling language represents the system structure, and includes attributes that specify the structure, operations that define the behavior, and the relationship with other classes. The object attributes in Web ontology language correspond to the association, dependency, aggregation and combination relationships in system modeling language, subclasses correspond to generalization / inheritance relationships, and disjoint classes and equivalent classes are regarded as an association relationship.

[0028] The technical method based on the IVCPS domain knowledge base application in step S3 to implement the system design based on the IVCPS domain knowledge base includes:

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

[0030] Based on the language specification of the system modeling language, the knowledge of classes, object attributes, data attributes and instances in the IVCPS domain knowledge base is imported into the system modeling software according to the preset mapping rules, and converted into model elements supported by the system modeling language;

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

[0032] Based on the imported domain knowledge, the system modeling and design of IVCPS typical scenarios are carried out. In the system modeling software, the relationship between classes is established according to the design requirements of IVCPS typical scenarios. The tools and standard symbols provided by the system modeling language are used to model and describe the system design in detail, and the logical model and module diagram of the system are constructed. According to the modeling model of the system modeling language, the corresponding output is generated as the basis and reference for the subsequent development, implementation and simulation verification of the IVCPS typical scenario system. The output includes: design documents, model diagrams, and system specifications.

[0033] Step 3: Construct simulation scenarios based on IVCPS knowledge base;

[0034] According to the requirements of the simulation software, the simulation knowledge in the IVCPS domain knowledge base is extracted, and the knowledge is stored in the relevant traffic simulation files in a preset format through language conversion; the road network parameters, simulated traffic flow parameters and other knowledge required for the typical scenario simulation are completely modeled in the IVCPS domain knowledge base; in the process of constructing the simulation scenario, the relevant simulation knowledge in the knowledge base is extracted and converted into an extensible markup language that describes the simulation configuration file, so as to realize the construction of the simulation scenario in the simulation software.

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

[0036] The beneficial effects of the present invention are:

[0037] In order to avoid repetitive design work of intelligent vehicle information-physical system and realize efficient design of typical scenarios, functions and subsystems in the field, the present invention proposes a system design method based on the domain knowledge base of intelligent vehicle information-physical system. This method is based on the IVCPS domain knowledge base, and through the mapping relationship between the knowledge modeling language and the system modeling language, the knowledge elements in the knowledge base are imported into the system modeling software tool to realize the system design based on the IVCPS domain knowledge base. The present invention proposes a new idea for the source of domain knowledge in the system design process. It can effectively use the IVCPS domain knowledge base to assist system design research, reduce the data collection time in the system design process, and avoid repetitive design work. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of a system design method based on a knowledge base in the field of intelligent automobile cyber-physical system according to the present invention;

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

[0040] Figure 3 A schematic diagram of the mapping relationship between the knowledge representation language and the extensible markup language in an embodiment of the present invention;

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

[0042] Figure 5 It is a schematic diagram of defining a green wave vehicle speed guidance module based on a knowledge base according to an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the process of constructing the IVCPS domain ontology knowledge base according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention provides a system design method based on a knowledge base in the field of intelligent automobile cyber-physical systems. The present invention is further described in detail below in conjunction with 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 automobile cyber-physical system, including:

[0046] Step S1: Analyze the composition of information elements and physical elements of the intelligent vehicle cyber-physical system, and use the ontology representation method to build the IVCPS domain knowledge base;

[0047] Step S2: an application technology method for constructing an IVCPS domain knowledge base, including: 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 a mapping relationship between a knowledge modeling language and an extensible markup language and a system modeling language;

[0048] Step S3: Apply technical methods based on the IVCPS domain knowledge base to implement system design based on the IVCPS domain knowledge base, use typical traffic scenarios in the IVCPS field for example analysis, and verify the effectiveness of the system design method.

[0049] The following is a detailed description 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 the domain knowledge involved in IVCPS. Taking into account the multi-element, multi-level functional division and interactive relationship characteristics of IVCPS such as "vehicle, road, cloud, network, and map", the elements are hierarchically classified, and finally an IVCPS domain knowledge base with a clear hierarchical structure, clear interactive relationships, and flexible knowledge application 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 concepts and information concepts covered by IVCPS; at the same time, it is also necessary to clarify the purpose of constructing the IVCPS knowledge base, that is, to achieve unified management, knowledge sharing and knowledge application of all elements of knowledge in the IVCPS field.

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

[0056] According to the relevant literature in the field of IVCPS, all physical and information concepts that need to be represented and covered in the IVCPS knowledge base are listed through the vocabulary. The vocabulary here is a stack of knowledge concepts in the field of IVCPS. There is no need for the listed words to have an organizational structure, nor is there any need to pay attention to whether the concepts overlap.

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

[0058] According to certain rules, a large number of physical and information concepts in the vocabulary are summarized and classified to form different IVCPS ontology classes, and the IVCPS ontology knowledge framework is constructed. In this process, it is necessary to analyze each physical and information concept, select the concepts required by the IVCPS domain knowledge base, and express the IVCPS system knowledge concisely and clearly; at the same time, the knowledge concepts are further supplemented and improved according to the IVCPS ontology framework.

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

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

[0061] Step 5: Construct IVCPS ontology model;

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

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

[0064] Through knowledge review and collection, the classes in the IVCPS domain ontology model are instantiated, such as sensor equipment, parts and facilities, road networks, and software algorithms. Through the instantiation of the IVCPS domain ontology, not only can the content of the IVCPS domain knowledge base be enriched, providing more knowledge resources for subsequent design needs, but also the comprehensiveness and rationality of the class and attribute relationship settings in the IVCPS knowledge base can be verified.

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

[0066] Through knowledge reasoning, the consistency test of the IVCPS domain knowledge base is realized to determine whether there are conflicts in logic, semantics, syntax, etc. in the knowledge base, and then the knowledge base is further modified and improved according to the test results.

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

[0068] like Figure 2 As shown in the figure, the ontology knowledge model is visualized with the help of ontology modeling tools, which facilitates the observation of the composition of the IVCPS ontology knowledge base and the attribute relationship 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) IVCPS domain knowledge base application technology methods:

[0071] The IVCPS domain knowledge base application technology method proposes the mapping relationship between the knowledge modeling language and the extensible markup language and the system modeling language. Through language conversion, the configuration file generation of traffic simulation software, code and other application programs based on the IVCPS domain ontology knowledge base is realized, and the IVCPS system design is assisted. 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 related syntax to represent and store a large amount of knowledge in the IVCPS domain, and uses elements such as "class, instance, object attribute, data attribute" to formalize the knowledge representation of IVCPS domain concepts and relationships between concepts. Among them, attributes are divided into object attributes and data attributes, and classes and attributes have subclasses and subattributes. In the IVCPS knowledge base, individuals and objects are represented as instances, concepts and categories are represented as classes, and relationships are represented as attributes.

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

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

[0076] The System Modeling Language (SysML) is a unified modeling language that uses graphical symbols to handle multiple aspects of the software development process, from database design to code module interaction. It models entities in a class system with attributes and behaviors, and uses diagrams to represent modeling elements.

[0077] Extensible Markup Language (XML) is a metalanguage that is widely used in 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: Build mapping relationship;

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

[0080] OWL knowledge modeling language is also seen as a further extension of extensible markup language. It further expresses the semantics of data through rich attributes, filling the gaps in XML in terms of formalization and knowledge representation capabilities. Each knowledge class, object attribute, and data attribute in the IVCPS domain knowledge base can be mapped to the XML format in an appropriate form. The specific mapping relationship is as follows: Figure 3 shown.

[0081] The similarity between the OWL knowledge modeling language and the system modeling language is that they both use the concept of class. The class in the OWL knowledge modeling language refers to a collection of individuals, and the relationships between classes are represented by object attributes and data attributes; while the class in the system modeling language usually represents the system structure, and includes attributes that specify the defined structure, operations that define the behavior, and relationships with other classes. The object attributes in the OWL knowledge modeling language correspond to the association, dependency, aggregation, and composition relationships in the system modeling language, the subclass corresponds to the generalization / inheritance relationship, and the disjoint class and the equivalence class can also be regarded as a kind of association relationship. The specific mapping relationship is as follows Figure 4 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 the physical elements and information elements in the IVCPS domain knowledge base to the system modeling tool, forming a system design method based on the knowledge base and conducting instance verification analysis. 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 specification of the system modeling language, the knowledge of classes, object attributes, data attributes, and instances in the IVCPS domain knowledge base is imported into the system modeling software according to certain mapping rules, and converted into model elements supported by the system modeling language. For example, the classes in the knowledge base are converted into appropriate model elements such as class diagrams, module diagrams, and activity diagrams of the system modeling language; attributes are converted into corresponding attributes, relationships, etc.

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

[0087] Based on the imported domain knowledge, the system modeling design of IVCPS typical scenarios is carried out. In the system modeling software, the relationship between classes is established according to the design requirements of IVCPS typical scenarios, and the tools and standard symbols provided by the system modeling language are used to model and describe the system design in detail, and build the system's logical model, module diagram, etc.

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

[0089] Step 3: Construct simulation scenarios based on IVCPS knowledge base:

[0090] According to the simulation software requirements, the simulation knowledge in the IVCPS domain knowledge base is extracted, and the knowledge is stored in a certain format in the relevant traffic simulation file through language conversion. The IVCPS domain knowledge base can perform complete knowledge modeling on the road network parameters, simulated traffic flow parameters and other knowledge required for typical scene simulation. In the simulation scene construction process, the relevant simulation knowledge in the knowledge base can be extracted and converted into an extensible markup language that describes the simulation configuration file, thereby realizing the construction of the simulation scene in the simulation software. In addition, the construction of the simulation scene based on the IVCPS knowledge base also includes: storing the simulation operation results in the IVCPS domain knowledge base to facilitate the subsequent analysis and use of the results.

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

[0092] 1) Extracting simulation knowledge

[0093] Based on the IVCPS domain knowledge base, the IVCPS typical scenario ontology knowledge base is constructed, and simulation knowledge such as road network knowledge and simulated traffic flow are extracted from the knowledge base. Among them, the road network knowledge specifically includes the road network structure, intersection location, road attributes (such as the number of lanes, speed limit, etc.), traffic signal light settings, etc. The simulated traffic flow information specifically includes the vehicle's starting position, destination, departure time, and traffic flow.

[0094] 2) Generate simulation configuration file

[0095] The extracted simulation knowledge is converted into the extensible markup language format supported by the traffic simulation software tool. For example, the road network information is converted into the simulated road network file (for example: .net.xml) of the traffic simulation software tool, including road sections, intersections, connection relationships, etc.; the traffic flow information is converted into the simulated traffic flow file (for example: .rou.xml) of the traffic simulation software tool, including vehicle generation rules and vehicle driving routes.

[0096] 3) Simulation implementation and simulation result storage

[0097] Import the converted simulation configuration file into the traffic simulation software tool, set the simulation parameters such as simulation time and simulation step in the traffic simulation software tool according to the typical scenario ontology knowledge base, run the simulation experiment and generate simulation results, and store the simulated vehicle operation results in the IVCPS typical scenario ontology knowledge base to facilitate subsequent research and analysis.

[0098] Through the above process, the road network parameter information and traffic flow information in the knowledge base can be converted into the simulation files required by the traffic simulation software tool, and the IVCPS typical traffic scenario simulation verification can be carried out in the traffic simulation software tool to evaluate the performance and effect of the traffic system and provide effective support for system design and optimization.

[0099] In order to verify the effectiveness of the system design method based on the domain knowledge base of intelligent automobile cyber-physical system disclosed in the present invention, the following experiments were conducted:

[0100] (1) Example scenario

[0101] An example and software environment for the implementation of a system design method based on a knowledge base in the field of cyber-physical systems for intelligent vehicles. The present invention takes the green wave speed guidance scenario of an intelligent vehicle as an example for example analysis and verification, and performs simulation implementation in a traffic simulation software tool.

[0102] (2) Example Analysis

[0103] In order 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 the present invention, the green wave speed guidance scenario of smart cars is taken as an example. Based on the IVCPS domain knowledge base and scenario function requirement analysis, the green wave speed guidance logic and functions are designed in detail, and simulation experiments are carried out. The specific implementation is as follows:

[0104] 1) Knowledge modeling of green wave speed guidance for intelligent vehicles based on 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 in the process of function realization, so as to build 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 is constructed, which includes sub-entities such as vehicle module, roadside module, cloud control module, communication module and simulation module, covering various information elements and physical elements required for function realization.

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

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

[0108] The IVCPS domain knowledge base can assist the construction of the IVCPS green wave speed guidance ontology and the design of the intelligent vehicle green wave speed guidance system through knowledge reuse technology. According to the modules, equipment, data and attribute information in the knowledge base, the speed guidance function is constructed to construct the green wave speed guidance function module diagram, such as Figure 5 As shown. Based on the smart car green wave speed guidance ontology knowledge base, the realization of the smart car 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 signal light module, and the communication module mainly includes the V2X communication module and the road-cloud communication module.

[0109] 3) Simulation and implementation of the green wave speed guidance system for intelligent vehicles:

[0110] The simulation parameters and road network modules 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 to build the road network files (e.g., .net.xml files) and traffic flow files (e.g., .rou.xml files) required for simulation based on the road network knowledge and simulated traffic flow knowledge.

[0111] According to the relevant knowledge in the IVCPS knowledge base, a traffic simulation scenario was constructed, and the designed IVCPS field green wave speed guidance function was simulated and verified to verify the applicability of the speed guidance design method. At the same time, the practicality of the IVCPS field knowledge base in the design of auxiliary systems was proved.

Claims

1. A system design method based on the domain knowledge base of intelligent automobile cyber-physical system, characterized in that: The steps include: Step S1: Analyze the composition of information elements and physical elements of the intelligent vehicle cyber-physical system, and use the ontology representation method to build the IVCPS domain knowledge base; Step S2: an application technology method for constructing an IVCPS domain knowledge base, including: 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 a mapping relationship between a knowledge modeling language and an extensible markup language and a system modeling language; Step S3: Apply technical methods based on the IVCPS domain knowledge base to implement system design based on the IVCPS domain knowledge base, use typical traffic scenarios in the IVCPS field for example analysis, and verify the effectiveness of the system design method.

2. The system design method based on the domain knowledge base of intelligent automobile cyber-physical system according to claim 1 is characterized in that: The step S1 of constructing the IVCPS domain knowledge base using the ontology representation method includes: The construction of the IVCPS domain knowledge base is based on the analysis of the domain knowledge involved in IVCPS, considering the "vehicle, road, cloud, network, and map" elements of IVCPS, realizing multi-element and multi-level functional division and determining the characteristics of the interaction relationship; the basic process of ontology modeling construction includes: problem analysis-demand analysis-domain knowledge framework-knowledge model establishment; the IVCPS knowledge modeling process is based on the characteristics of the minimum functional unit of the IVCPS "vehicle, road, network, cloud, and map" full elements and their information interaction relationship, analyzing each knowledge element unit and the interaction relationship, and classifying the elements hierarchically to form the IVCPS domain knowledge base.

3. The system design method based on the domain knowledge base of intelligent vehicle cyber-physical system according to claim 1 is characterized in that: The steps of 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 IVCPS ontology model; Step 6: Instantiate the IVCPS domain ontology; Step 7: Examining the IVCPS domain knowledge base; Step 8: Visualize the IVCPS domain knowledge base.

4. The system design method based on the domain knowledge base of intelligent vehicle cyber-physical system according to claim 1 is characterized in that: The mapping relationship between the knowledge modeling language and the extensible markup language and the 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 knowledge modeling language related syntax to represent and store IVCPS domain knowledge, and uses "classes, instances, object attributes, and data attributes" to formalize knowledge representation of IVCPS domain concepts and relationships between concepts; the knowledge modeling language is the Web ontology language; classes and attributes have subclasses and subattributes; in the IVCPS knowledge base, individuals and objects are represented as instances, concepts and categories are represented as classes, and relationships are represented as attributes; the attributes include: object attributes and data attributes; Step 2: Conduct IVCPS modeling language composition analysis; IVCPS modeling languages ​​include: system modeling language and extensible markup language; system modeling language models entities in a class system 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: Build mapping relationship; As a further extension of Extensible Markup Language, Web Ontology Language further 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 the XML format in a corresponding form; Both Web ontology language and system modeling language use the concept of class. The class in Web ontology language refers to a collection of individuals, and the relationship between classes is represented by object attributes and data attributes. The class in system modeling language represents the system structure, and includes attributes that specify the structure, operations that define the behavior, and the relationship with other classes. The object attributes in Web ontology language correspond to the association, dependency, aggregation and combination relationships in system modeling language, subclasses correspond to generalization / inheritance relationships, and disjoint classes and equivalent classes are regarded as an association relationship.

5. The system design method based on the domain knowledge base of intelligent vehicle cyber-physical system according to claim 1 is characterized in that: The technical method based on the IVCPS domain knowledge base application in step S3 to implement the system design based on the IVCPS domain knowledge base includes: Step 1: Map the knowledge in the IVCPS domain knowledge base into the system modeling software; Based on the language specification of the system modeling language, the knowledge of classes, object attributes, data attributes and instances in the IVCPS domain knowledge base is imported into the system modeling software according to the preset mapping rules, and converted into model elements supported by the system modeling language; Step 2: Conduct knowledge-based system modeling design; Based on the imported domain knowledge, the system modeling and design of IVCPS typical scenarios are carried out. In the system modeling software, the relationship between classes is established according to the design requirements of IVCPS typical scenarios. The tools and standard symbols provided by the system modeling language are used to model and describe the system design in detail, and the logical model and module diagram of the system are constructed. According to the modeling model of the system modeling language, the corresponding output is generated as the basis and reference for the subsequent development, implementation and simulation verification of the IVCPS typical scenario system. The output includes: design documents, model diagrams, and system specifications. Step 3: Construct simulation scenarios based on IVCPS knowledge base; According to the requirements of the simulation software, the simulation knowledge in the IVCPS domain knowledge base is extracted, and the knowledge is stored in the relevant traffic simulation files in a preset format through language conversion; the road network parameters, simulated traffic flow parameters and other knowledge required for the typical scenario simulation are completely modeled in the IVCPS domain knowledge base; in the process of constructing the simulation scenario, the relevant simulation knowledge in the knowledge base is extracted and converted into an extensible markup language that describes the simulation configuration file, so as to realize the construction of the simulation scenario in the simulation software.

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

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