Autonomous security assurance architecture construction method based on space-time whole process information interaction

Abstract

The invention provides an autonomous safety guarantee architecture construction method based on space-time whole-process traffic information interaction. Comprising the following steps: constructing a database, and generating security guarantee system components by utilizing multi-language large model fusion; performing spatial and functional attribute calibration on the components by using the large model; performing component relation calibration by utilizing large model reasoning; generating a component interaction time sequence information flow according to the component relationship; according to the function attribute and the information flow, constructing a logic architecture covering the function module and the information flow; and according to the spatial attribute and the information flow, constructing a physical architecture covering spatial distribution and the information flow. According to the method, the problems that due to rapid development of an autonomous road traffic system, the number of related safety guarantee components is large, the relation is complex, and information flow extraction and architecture construction are difficult are solved; a framework support is provided for the construction of an active intervention-oriented autonomous road safety guarantee system, and the road autonomous safety guarantee level is improved.

Description

Technical Field

[0001] The present invention relates to the field of road traffic safety assurance technology, and in particular to a method for constructing an autonomous safety assurance architecture based on full-process information interaction in time and space. Background Art

[0002] Autonomous road traffic system is the leading direction of the new round of global intelligent transportation technology development. It is committed to integrating technologies such as vehicle-road collaboration, autonomous driving, big data analysis, satellite positioning, and 5G communication, thereby reducing manual intervention in intelligent transportation systems and increasing their autonomy. As the complexity of the system continues to increase, how to ensure the safe and reliable operation of this intelligent system has become a key issue that needs to be solved urgently.

[0003] At present, although autonomous road traffic systems have achieved many research results at the technical level, their safety assurance system is still in its infancy, especially lacking a safety assurance architecture for autonomous decision-making intervention. There are the following difficulties in building an autonomous safety assurance architecture: the ITS framework and the autonomous traffic system architecture components are too complex and contain many elements and relationships that are not closely related to autonomous safety assurance; information technology is developing rapidly, and the functional attributes of the components in the existing architecture are difficult to adapt to technological development; the relationships between components are complex, and it is difficult to generate information flows for the safety assurance system for autonomous decision-making intervention.

[0004] Therefore, it is necessary to comprehensively utilize the current massive resources of the Internet and industry and research and development materials, use advanced artificial intelligence technologies such as language big models to fully grasp the current development of autonomous road traffic technology, extract the components of the road traffic autonomous safety assurance architecture for active decision-making intervention, and build an autonomous safety assurance architecture for the interaction of traffic information throughout the entire space and time process, provide autonomous perception-decision-making-intervention functions, provide architectural support for the construction of an autonomous road safety assurance system for active intervention, and improve the level of autonomous road safety assurance. Summary of the invention

[0005] The embodiments of the present invention provide a method for constructing an autonomous safety assurance architecture based on full-process information interaction in time and space, so as to effectively improve the level of autonomous road safety assurance.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme.

[0007] A method for constructing an autonomous safety assurance architecture based on the interaction of traffic information in the entire time and space, comprising:

[0008] Construct a combined database of local and Internet resources, and use a large multi-language model to perform natural language processing on the database to generate autonomous security system components;

[0009] Using the large model, the spatial and functional attributes of the safety assurance system components are calibrated, wherein the spatial attributes include the spatial location of the current last-level component, and the functional attributes include the function of the current last-level component in the road traffic system;

[0010] Using large model reasoning, calibrating component relationships of components of the safety assurance system;

[0011] generating a component interaction time sequence information flow according to the component relationship of the security assurance system;

[0012] Constructing a logical architecture covering functional modules and information flows according to the functional attributes of the components of the security assurance system and the information flows of the interaction sequence of the components;

[0013] According to the spatial properties of the components of the security assurance system and the temporal information flow of the components interacting with each other, a physical architecture covering spatial distribution and information flow is constructed.

[0014] Preferably, the construction of a local database and an Internet resource combination database, using a multi-language large model to perform natural language processing on the database to generate autonomous security system components, includes:

[0015] Step 1: Build a local database and calibrate the reference level of the data, generate the synonym database required to construct the problem, and define the first-level components including {perception system, decision-making system, intervention system, auxiliary system, and object of action};

[0016] Step 2: Select multiple language models for deployment, unify the data interface standards of the language models, and build a fusion framework for multiple language models according to the process of intent understanding, document parsing, document indexing and vector embedding, knowledge retrieval and re-ranking, and text answering;

[0017] Step 3: For each component at the current level, generate a question library based on the synonym library, use the large model fusion framework to generate reasoning results, extract keywords, and generate the next level candidate components corresponding to each current level component;

[0018] Step 4: Generate a synonym library for the keywords based on the reference degree of the keyword source for the next level of candidates, and select the keyword from the highest reference degree in the same group of synonyms as the component name;

[0019] Step 5: Use the expert scoring method to score the importance of the components, and retain the components with average scores exceeding the threshold to form the formal next-level components;

[0020] Step 6: If the fourth-level components have been generated or the next-level components are empty, stop; otherwise, go to step 3 and use all the generated level components to form an autonomous security assurance system.

[0021] Preferably, the components of the autonomous safety assurance system include multiple levels, and the high-level components include multiple low-level components; there are 5 first-level components, which are divided according to the basic logical order of perception-decision-intervention and the objects they act on and the auxiliary systems they rely on, namely perception system, decision-making system, intervention system, auxiliary system and objects of action; the second-level group is a system or module generated by the functional refinement of the first-level components; the third-level components and the fourth-level components are individual entity units generated by further refinement of each system or module in the second-level components.

[0022] Preferably, the method of using large model reasoning to calibrate the component relationships of the components of the safety assurance system includes:

[0023] (1) Extract the data used for the components to form a core database;

[0024] (2) Determine the specific relationship type according to the active / passive / interactive relationship, which includes: perception-being perceived, intervention-being intervened, sending information-receiving information-sending information / receiving information, and dependence-being dependent;

[0025] (3) Based on the relationship type, generate a question library for each component required to construct the relationship between components;

[0026] (4) Use the large model to generate inference results, perform relationship matching, and determine alternative relationships between components;

[0027] (5) Starting from the lowest level component, if the brother components at the same level all have a passive relationship with the same component A, the passive relationship between the brother components at the same level and component A is promoted to the parent component of the brother components, and the component relationship calibration is completed using the relationship between all components of the security system.

[0028] Preferably, generating a component interaction time sequence information flow according to the component relationship of the security assurance system includes:

[0029] (1) Obtain the component interaction sequence based on the basic sequence of perception-decision-intervention and the relationship mapping between components;

[0030] (2) Generate information flow between components using the component interaction sequence;

[0031] (3) Evaluate the rationality of information flows through expert scoring and remove information flows that do not meet the rationality threshold;

[0032] (4) Calibrate different intervention levels for reasonable information flows.

[0033] Preferably, the logic architecture covering functional modules and information flows is constructed according to the functional attributes of the components of the security assurance system and the information flows of the interaction sequence of the components, including: (1) constructing a basic functional logic framework according to the primary and secondary components of the security assurance system, including four basic frameworks of perception, decision-making, intervention and communication and an internal framework;

[0034] (2) mapping component functions to different positions of the functional logic framework according to the functional attributes of the third-level and fourth-level components of the security assurance system;

[0035] (3) Based on the component interaction time sequence information flow, the information flow within the logical functional architecture is constructed to generate a functional logical architecture that is suitable for different intervention levels.

[0036] Preferably, the physical architecture covering spatial distribution and information flow is constructed according to the spatial attributes of the components of the security system and the component interaction time sequence information flow, including:

[0037] (1) constructing a basic physical framework according to the spatial attribute categories of the components of the safety assurance system, the basic physical framework including clouds, roads, roadsides and other spaces;

[0038] (2) mapping the third and fourth level components to different positions of the basic physical framework according to the spatial relationship of the lowest components of the safety assurance system;

[0039] (3) Based on the component interaction time sequence information flow, the information flow within the physical architecture is constructed to generate a physical architecture covering different intervention levels.

[0040] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the present invention solves the problems of a large number of safety-related components, complex relationships, and difficulties in information flow extraction and architecture construction caused by the rapid development of autonomous road traffic systems; it provides architectural support for the construction of an autonomous road safety assurance system for active intervention, and improves the level of autonomous road safety assurance.

[0041] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1A flowchart of a method for constructing an autonomous safety assurance architecture based on full-time and space traffic information interaction provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a process for generating components of an autonomous security assurance system provided by an embodiment of the present invention;

[0045] Figure 3 An example diagram of components of an autonomous security assurance system provided by an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a component relationship calibration process provided by an embodiment of the present invention;

[0047] Figure 5 An example diagram of information flow of an autonomous security assurance system provided by an embodiment of the present invention;

[0048] Figure 6 An example diagram of an autonomous security assurance logic architecture provided by an embodiment of the present invention;

[0049] Figure 7 An example diagram of an autonomous security assurance physical architecture provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0051] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0052] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0053] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0054] The processing flow of a method for constructing an autonomous safety assurance architecture based on the full-time and space traffic information interaction provided by an embodiment of the present invention is as follows: Figure 1 As shown, the processing steps include the following:

[0055] Step S1: Build a local database and an Internet resource combination database, use a large multi-language model to perform natural language processing on the database, and generate autonomous security system components.

[0056] Step S2: Use the large model to calibrate the spatial and functional attributes of the safety assurance system components, where the spatial attributes include the spatial location of the current last-level components, and the functional attributes include the functions of the current last-level components in the road traffic system.

[0057] Step S3: Utilize large model reasoning to calibrate component relationships.

[0058] Step S4: Generate component interaction time sequence information flow based on component relationships.

[0059] Step S5: Construct a logical architecture covering functional modules and information flows based on functional attributes and information flows.

[0060] Step S6: Construct a physical architecture covering spatial distribution and information flow based on spatial attributes and information flow.

[0061] A schematic diagram of a process for generating components of an autonomous security assurance system provided by an embodiment of the present invention is as follows: Figure 2 As shown, the process includes the following steps:

[0062] Step 1: Use Chinese and English journals, dissertations, national standards, local standards, and group standards in the fields of autonomous transportation, vehicle-road collaboration, and intelligent driving to build a local database, calibrate the reference level of the data, and generate a synonym library required for constructing the problem. For example, {component, system, object, module, unit} is a group of synonyms used to enrich the description of the component generation process, and {intervention, control, manipulation} is a group of synonyms used to expand the scope of retrieval and the dimension of understanding, and frame the first-level components including {perception system, decision system, intervention system, auxiliary system, and object of action};

[0063] Step 2: Select ChatGLM3-6B, Qwen2-7B, Llama3, and Llama3-Chinese language models for layout and unify their data interface standards. Build a fusion framework of multiple language models according to the process of intent understanding, document parsing, document indexing and vector embedding, knowledge retrieval and re-ranking, and text answering. By combining the generation capability of the large model and the retrieval mechanism of the external knowledge base, the effect of natural language processing tasks is improved.

[0064] Step 3: For each component at the current level, generate a question library based on the synonym library, use the large model fusion framework to generate reasoning results, extract keywords, and generate the next level candidate components corresponding to each current level component;

[0065] Step 4: Generate a synonym library for the keywords based on the reference degree of the keyword source for the next level of candidates, and select the keyword from the highest reference degree in the same group of synonyms as the component name;

[0066] Step 5: Use the expert scoring method to score the importance of the components, and retain the components with average scores exceeding the threshold to form the formal next-level components;

[0067] Step 6: If the fourth-level components have been generated or the next-level components are empty, stop; otherwise, go to step 3 and use all the generated level components to form an autonomous security assurance system.

[0068] Figure 3 An example diagram of components of an autonomous security system provided by an embodiment of the present invention, referring to Figure 3 The components of the autonomous safety assurance system can be divided into multiple levels, and the high-level components contain multiple low-level components; there are 5 first-level components, which are divided according to the basic logical order of perception-decision-intervention and the objects they act on and the auxiliary systems they rely on, namely perception system, decision-making system, intervention system, auxiliary system and object of action; there are 15 second-level components, which are systems or modules generated by the functional refinement of the first-level components; the third-level and fourth-level components are the individual entity units generated by further refinement of the systems or modules in the second-level components.

[0069] For example, according to the function of the perception system (first-level component), the autonomous safety assurance system can be divided into three modules (second-level components), namely, the perception module, the perception positioning module and the perception communication module; the perception module can be further refined into the vehicle-side perception module and the road-side perception module (third-level component) according to its spatial attributes; in the vehicle-side perception module, there are three physical units (fourth-level component), namely, the vehicle-mounted camera, the vehicle-mounted laser radar and the vehicle-mounted millimeter-wave radar, which work together to enable the vehicle to perform various types of perception in an all-round and high-precision manner during road traffic.

[0070] Figure 4 A schematic diagram of a component relationship calibration process provided by an embodiment of the present invention, referring to Figure 4 , the component relationship calibration steps of the present invention are as follows:

[0071] Step 1: Extract the data used by the components to form a core database;

[0072] Step 2: Determine the specific relationship type according to the active / passive / interactive relationship, including: perception-being perceived, intervention-being intervened, sending information-receiving information-sending information / receiving information, dependence-being dependent;

[0073] Step 3: Based on the relationship type, for each component, generate the question library required to construct the relationship between components;

[0074] Step 4: Use the large model to generate inference results, perform relationship matching, and determine the candidate relationships between components;

[0075] Step 5: If there is a passive relationship between the brother components at the same level and the same component A, the passive relationship between the brother components at the same level and component A is promoted to the parent component of the brother components, and the calibration of the component relationship is completed using the relationship between all components of the security system.

[0076] Figure 5 An example diagram of an autonomous security system information flow provided by an embodiment of the present invention; refer to Figure 5Taking the traffic incident of non-motor vehicles running red lights at intersections as an example, the information flow process of the autonomous safety assurance system can be described as follows: at the beginning, the first-level intervention vehicle A and the second-level intervention vehicle B perceive the non-motor vehicle running red lights through the motion detection function of their respective vehicle-side perception modules, and the roadside auxiliary infrastructure D perceives the non-motor vehicle running red lights and the location of the third-level intervention vehicle C through the motion detection and position detection functions of the road-side perception modules; vehicles A, B and facility D upload the perceived information to the cloud-based decision-making communication module of the cloud platform through their respective perception communication units; the cloud-based decision-making module of the cloud platform makes decisions on the current traffic situation based on the perception information from the cloud-based communication module, and generates warning information and intervention parameters; then the cloud-based intervention communication module receives and sends the information to the cloud-based intervention generation module; the cloud-based intervention generation module generates an intervention instruction, and sends the intervention instruction to the vehicle-side intervention communication modules of vehicles A, B, and C through the cloud-based intervention communication module, and each vehicle-side intervention execution module receives and executes the intervention instruction. Among them, the intervention command of risk warning is executed on vehicles A and B, and the driver is reminded to brake through the driver interaction unit. The intervention command of braking is sent to the vehicle motion control unit of vehicle C; the driver of vehicle A operates the vehicle motion control unit of this vehicle to decelerate and brake to avoid, maintain a safe distance, and vehicle C brakes. After the driver interaction unit of vehicle B reminds the driver, it detects that there is no intention to brake. The cloud platform sends the intervention command again, executes the intervention command of driving suggestion to vehicle B and keeps monitoring. The driver interaction unit of vehicle B reminds the driver again. When the TTC (collision time) is monitored to be less than 6s, the intervention command of vehicle control takeover is executed on vehicle B, and vehicle B brakes urgently. After the non-motor vehicle passes, the system cancels the warning of vehicle A and the vehicle control takeover of vehicles B and C, and ends the information flow.

[0077] Figure 6 This is an example diagram of an autonomous security assurance logic architecture provided by an embodiment of the present invention. Figure 6 , the autonomous safety assurance logic architecture includes four functional layers: perception, decision-making, intervention and communication. According to the functional attribute coding, information flow and autonomy level of the components, the functions of the components are mapped to different positions of the functional logic framework. The perception function layer classifies the component perception functions into four categories: motion perception, distance perception, location perception and meteorological monitoring, so as to achieve all-round perception of various traffic elements and environments in the road traffic system; the decision function layer classifies the component decision functions into decision partitioning, hierarchical decision and layered decision according to the order of decision generation, so as to describe the detailed process of decision generation; the intervention function layer classifies the component intervention functions into two categories: intervention generation and intervention execution according to the process of intervention generation and execution, and also reflects the different intervention methods corresponding to different autonomy levels; in addition, in the process of realizing the perception, decision and intervention functions, the communication function layer provides important functional support for data transmission and information interaction throughout the process.

[0078] Figure 7 An example diagram of an autonomous security physical architecture provided by an embodiment of the present invention, referring to Figure 7 , the physical architecture includes the physical components and spatial attributes of the perception system, decision system, intervention system, auxiliary system and the object of action. According to the spatial attribute coding, information flow and autonomy level of the components, each component is mapped to different positions of the physical framework. Spatial attributes include spatial position and sub-space position, among which the spatial position can be divided into vehicle-side, road-side and cloud-side. Specifically, the modules and units used for vehicle-side perception, decision-making and intervention are located at the vehicle-side of each level of intervention vehicles; the spatial positions of the modules and units used for road-side perception, decision-making and intervention of the road-side auxiliary infrastructure and the intervention vehicles and other objects are all at the road-side, and can be further divided into two sub-space positions of road and roadside; the modules and units used for decision-making and intervention of the cloud platform are located in the cloud; other spaces are mainly the locations of the positioning system, communication system and geographic information system in the auxiliary system, including the earth orbit where the satellite positioning system is located, the vehicle carrier where the geographic information system and inertial navigation system are located, the geographic information system and cellular mobile communication positioning system and the cloud where the communication system is located, the cellular mobile communication positioning system and the base station of the communication system, etc. Different spatial components exchange information through the communication system and the communication units of each module in each system.

[0079] To sum up, the autonomous road safety assurance system for time-space full-process traffic information interaction of the present invention is designed with autonomous perception decision-making intervention as the main line and is highly targeted; it comprehensively utilizes the current massive Internet resources and industry and R&D materials, and uses advanced artificial intelligence technologies such as language big models to fully grasp the current development of autonomous road system technology, extract road traffic autonomous safety assurance architecture components for active decision-making intervention, and explore the functions and relationships of the components, to ensure the advancement and coverage of the component functions, as well as the consistency of the architecture, components and relationships, and to improve the operability of the architecture.

[0080] The present invention constructs an autonomous safety assurance architecture for the interaction of traffic information throughout the entire time and space, provides autonomous perception-decision-making-intervention components, functions and information flows, solves the problems of numerous safety assurance-related components, complex relationships, and difficulties in information flow extraction and architecture construction caused by the rapid development of autonomous road traffic systems; provides architectural support for the construction of an autonomous road safety assurance system for active intervention, and improves the level of autonomous road safety assurance.

[0081] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0082] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0083] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0084] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for constructing an autonomous safety assurance architecture based on the interaction of traffic information in the entire time and space, characterized in that: include: Construct a combined database of local and Internet resources, and use a large multi-language model to perform natural language processing on the database to generate autonomous security system components; Using the large model, the spatial and functional attributes of the safety assurance system components are calibrated, wherein the spatial attributes include the spatial location of the current last-level component, and the functional attributes include the function of the current last-level component in the road traffic system; Using large model reasoning, calibrating component relationships of components of the safety assurance system; generating a component interaction time sequence information flow according to the component relationship of the security assurance system; Constructing a logical architecture covering functional modules and information flows according to the functional attributes of the components of the security assurance system and the information flows of the interaction sequence of the components; According to the spatial properties of the components of the security assurance system and the temporal information flow of the components interacting with each other, a physical architecture covering spatial distribution and information flow is constructed.

2. The method according to claim 1, characterized in that The construction of a local database and an Internet resource combination database, using a multi-language large model to perform natural language processing on the database, and generating autonomous security system components, includes: Step 1: Build a local database and calibrate the reference level of the data, generate the synonym database required to construct the problem, and define the first-level components including {perception system, decision-making system, intervention system, auxiliary system, and object of action}; Step 2: Select multiple language models for deployment, unify the data interface standards of the language models, and build a fusion framework for multiple language models according to the process of intent understanding, document parsing, document indexing and vector embedding, knowledge retrieval and re-ranking, and text answering; Step 3: For each component at the current level, generate a question library based on the synonym library, use the large model fusion framework to generate reasoning results, extract keywords, and generate the next level candidate components corresponding to each current level component; Step 4: Generate a synonym library for the keywords based on the reference degree of the keyword source for the next level of candidates, and select the keyword from the highest reference degree in the same group of synonyms as the component name; Step 5: Use the expert scoring method to score the importance of the components, and retain the components with average scores exceeding the threshold to form the formal next-level components; Step 6: If the fourth-level components have been generated or the next-level components are empty, stop; otherwise, go to step 3 and use all the generated level components to form an autonomous security assurance system.

3. The method according to claim 2, characterized in that The autonomous safety assurance system components include multiple levels, and the high-level components include multiple low-level components; there are five first-level components, which are divided according to the basic logical order of perception-decision-intervention and the objects they act on and the auxiliary systems they rely on, namely, the perception system, the decision system, the intervention system, the auxiliary system and the object of action; The second-level group is a system or module generated by the functional refinement of the first-level component; the third-level and fourth-level components are individual entity units generated by further refinement of each system or module in the second-level component.

4. The method according to claim 1, characterized in that The method of using large model reasoning to calibrate the component relationships of the components of the safety assurance system includes: (1) Extract the data used for the components to form a core database; (2) Determine the specific relationship type according to the active / passive / interactive relationship, which includes: perception-being perceived, intervention-being intervened, sending information-receiving information-sending information / receiving information, and dependence-being dependent; (3) Based on the relationship type, generate a question library for each component required to construct the relationship between components; (4) Use the large model to generate inference results, perform relationship matching, and determine alternative relationships between components; (5) Starting from the lowest level component, if the brother components at the same level all have a passive relationship with the same component A, the passive relationship between the brother components at the same level and component A is promoted to the parent component of the brother components, and the component relationship calibration is completed using the relationship between all components of the security system.

5. The method according to claim 1, characterized in that The generating of component interaction time sequence information flow according to the component relationship of the security assurance system includes: (1) Obtain the component interaction sequence based on the basic sequence of perception-decision-intervention and the relationship mapping between components; (2) Generate information flow between components using the component interaction sequence; (3) Evaluate the rationality of information flows through expert scoring and remove information flows that do not meet the rationality threshold; (4) Calibrate different intervention levels for reasonable information flows.

6. The method according to claim 1, characterized in that The said constructing a logical architecture covering functional modules and information flows according to the functional attributes of the components of the security assurance system and the information flows of the interaction sequence of the components includes: (1) constructing a basic functional logical framework according to the primary and secondary components of the security assurance system, including four basic frameworks of perception, decision-making, intervention and communication and an internal framework; (2) mapping component functions to different positions of the functional logic framework according to the functional attributes of the third-level and fourth-level components of the security assurance system; (3) Based on the component interaction time sequence information flow, the information flow within the logical functional architecture is constructed to generate a functional logical architecture that is suitable for different intervention levels.

7. The method according to claim 1, characterized in that The physical architecture covering spatial distribution and information flow is constructed according to the spatial attributes of the components of the security system and the component interaction time sequence information flow, including: (1) constructing a basic physical framework according to the spatial attribute categories of the components of the safety assurance system, the basic physical framework including clouds, roads, roadsides and other spaces; (2) mapping the third and fourth level components to different positions of the basic physical framework according to the spatial relationship of the lowest components of the safety assurance system; (3) Based on the component interaction time sequence information flow, the information flow within the physical architecture is constructed to generate a physical architecture covering different intervention levels.

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