Intelligent networked automobile information physical system modularization quantitative evaluation method

Abstract

The invention belongs to the technical field of traffic information, and relates to an intelligent networked automobile information physical system modularization quantitative evaluation method comprising the following steps: S1, defining an IVCPS operation scene from three stages of a function scene, a logic scene and an information physical scene; s2, performing test verification on the IVCPS operation scene from three dimensions of test verification of a multi-scale scene, collaborative test verification of multiple subjects and collaborative test verification of multiple scales, multiple levels, multiple subjects and multiple functions; s3, quantitative evaluation of test verification of different scale combination scenes is completed through the multi-scale scene component, quantitative evaluation of test verification of different subjects and interaction cooperation relations among the subjects is completed through the information physical subject component, and quantitative evaluation of test verification of different functions is completed through the multi-scale multi-level multi-subject functional component; and S4, based on the quantitative evaluation result of the step S3, forming an IVCPS test verification quantitative evaluation result, and realizing the test verification effect of the vehicle-road cloud integrated system.

Description

Technical Field

[0001] The present invention belongs to the field of traffic information technology, and specifically relates to a component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle. Background Art

[0002] Multi-agent collaborative optimization of intelligent connected vehicle cyber-physical systems (IV CPS) is a technical system that achieves global optimal decision-making through dynamic coordination and resource integration of multiple heterogeneous subjects such as vehicles, roads, clouds, and people. In recent years, multi-agent collaborative optimization of intelligent connected vehicle cyber-physical systems has become a research hotspot. Multi-agent collaborative optimization can significantly improve the operating efficiency and safety of transportation systems. However, there is currently a lack of effective testing and evaluation methods for multi-agent collaborative optimization of intelligent connected vehicle cyber-physical systems. Traditional testing methods are difficult to fully quantify the effects of multi-agent collaborative optimization, especially in complex traffic scenarios. To address this problem, the present invention designs a component-based quantitative evaluation method for intelligent connected vehicle cyber-physical systems. By dividing the scenario components, subject components, and functional components, a comprehensive quantitative evaluation of the operating scenario is achieved, providing a scientific basis for system optimization. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle. The present invention aims to solve the problem that traditional testing methods are difficult to comprehensively quantify the collaborative optimization effect of multi-agent IV CPS in complex traffic scenarios.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle comprises the following steps:

[0006] S1. Define the operational scenarios of the cyber-physical system of intelligent connected vehicles from three stages: functional scenario, logical scenario, and cyber-physical scenario.

[0007] Functional scenarios include descriptions of the road network scale, road segment scale, and vehicle surrounding scale. The road network scale describes the road network-level traffic environment and driving tasks, the road segment scale describes the road segment-level traffic environment and driving trajectory, and the vehicle surrounding scale describes the vehicle surrounding traffic environment and driving behavior.

[0008] Logical scenarios include descriptions at the vehicle-road-cloud level, vehicle-road level, and vehicle-vehicle level. The vehicle-road-cloud level describes the implementation solutions and cloud-side functions for vehicle-road-cloud interaction and collaboration; the vehicle-road level describes the implementation solutions and road-side functions for vehicle-road interaction and collaboration; and the vehicle-vehicle level describes the implementation solutions and vehicle-side functions for vehicle-vehicle interaction and collaboration.

[0009] The cyber-physical scenario includes descriptions of the cloud, road, and vehicle sides. The cloud side describes the functions of each layer and the relationships between the functional modules at each layer. The road section describes the functions of each layer and the relationships between the functional modules at each layer. The vehicle side describes the functions of each layer and the relationships between the functional modules at each layer.

[0010] S2. Test and verify the operational scenarios of the cyber-physical system of intelligent connected vehicles from three dimensions: multi-scale scenario testing and verification, multi-agent collaborative testing and verification, and multi-scale, multi-level, multi-agent, and multi-functional collaborative testing and verification;

[0011] S3. Applying the concept of componentization, complete the quantitative evaluation of testing and verification of scenarios at different scales through multi-scale scenario components. Complete the quantitative evaluation of testing and verification of different subjects and their interactions through cyber-physical subject components. Complete the quantitative evaluation of testing and verification of different functions through multi-scale, multi-level, and multi-subject functional components.

[0012] S4. Combine the quantitative evaluation results of multi-scale scenario components, cyber-physical subject components, and multi-scale, multi-level, and multi-subject functional components to form the quantitative evaluation results of IVCPS test verification.

[0013] Furthermore, in step S3, completing the quantitative evaluation of the different scale combination scene test verification by the multi-scale scene component includes the following sub-steps:

[0014] S3.11 Quantitative evaluation of road components;

[0015] I. Conduct quantitative assessment based on road smoothness and road surface conditions;

[0016] The International Roughness Index (IRI) is used for road smoothness, and the road marking integrity rate is used for road facility conditions.

[0017] II. Scoring the road smoothness and pavement conditions against the assessment criteria, assigning weights and performing a weighted sum to obtain a quantitative score for the road component;

[0018] S1=A11×W11+A12×W12

[0019] Where S1 represents the quantitative score of road components; A11 represents the road smoothness score; A12 represents the road facility condition score; W11 represents the road smoothness weight coefficient; W12 represents the road facility condition weight coefficient;

[0020] S3.12 Quantitative assessment of environmental components;

[0021] I. Conduct quantitative assessment based on weather conditions and sunlight duration;

[0022] II. Score weather conditions and sunlight hours against the evaluation criteria, assign weights, and then sum the scores to obtain a quantitative score for the environmental component.

[0023] S2=A21×W21+A22×W22

[0024] Where S2 represents the quantitative score of the environmental component; A21 represents the weather condition score; A22 represents the light period score; W21 represents the weather condition weight coefficient; W22 represents the light period weight coefficient;

[0025] S3.13 Quantitative evaluation of traffic flow components;

[0026] I. Quantitative assessment based on traffic congestion index and average travel speed;

[0027] II. Scoring the traffic congestion index and average travel speed against the evaluation criteria, weighting them, and performing a weighted summation to obtain a quantitative score for the traffic flow component;

[0028] S3=A31×W31+A32×W22

[0029] Where S3 represents the traffic flow component quantitative score; A31 represents the traffic congestion index score; A32 represents the average travel speed score; W31 represents the traffic congestion index weight coefficient; W32 represents the average travel speed weight coefficient;

[0030] S3.14 Combine the scores of the road component, environment component, and traffic flow component after quantitative evaluation, divide the weights, and perform weighted summation to obtain the quantitative score of the multi-scale scene component;

[0031]

[0032] Where, Represents the quantitative score of the multi-scale scene component; W1 represents the weight coefficient of the road component; W2 represents the weight coefficient of the environment component; W3 represents the weight coefficient of the traffic flow component;

[0033] S3.15 Based on the association relationships among the multi-scale scene components, a combined scene component is formed, and a quantitative evaluation of the combined scene component is completed by dividing the weights;

[0034]

[0035] Where S c Indicates the quantitative score of the combined scene components; Represents the weight coefficient of the scene component; n represents the number of combined scenes.

[0036] Furthermore, in steps S3.11 to S3.15, the weight division is performed using the hierarchical analysis method.

[0037] Furthermore, in step S3, completing the test, verification, and quantitative evaluation of different subjects and the interaction relationships between subjects through the cyber-physical subject components includes the following sub-steps:

[0038] S3.21 Decouple the cyber-physical subject components into static attribute components, interactive relationship components, and subject participation form components;

[0039] The static properties of cyber-physical subject components include subject type, geometric dimensions, and intelligence;

[0040] The interactive relationship components of the cyber-physical main components include human-vehicle interaction and vehicle-vehicle interaction;

[0041] The subject participation form components of the cyber-physical subject components include the measured object and traffic participants;

[0042] S3.22 Use fuzzy comprehensive evaluation method to quantitatively evaluate the information-physical main components.

[0043] Furthermore, in step S3, completing the test verification and quantitative evaluation of different functions through multi-scale, multi-level, and multi-agent functional components includes the following sub-steps:

[0044] S3.31 decouples multi-scale, multi-level, and multi-agent functional components into eight dimensions: service object, function type, function subject, function scale, interaction relationship, data source, data cycle, and trigger mechanism;

[0045] S3.32 sets the functional scale, interaction relationship, data source, data cycle and trigger mechanism as the functional basic dimensions. Through the quantitative evaluation of the functional basic dimensions, it is judged whether the functional module composed of service objects, functional types and functional subjects can operate normally;

[0046] Among them, the multi-scale, multi-level and multi-agent functional components are also quantitatively evaluated using the fuzzy comprehensive evaluation method.

[0047] Furthermore, in step S4, the calculation expression of the quantitative evaluation result of the IVCPS test verification is:

[0048] S=S c +S z +S g

[0049] Where, S represents the quantitative evaluation score of IVCPS test verification; S z Represents the quantitative evaluation score of the cyber-physical main component; S g Represents the quantitative evaluation score of multi-scale, multi-level and multi-agent functional components.

[0050] Beneficial effects:

[0051] 1. The component-based quantitative evaluation method for the cyber-physical system of intelligent vehicles proposed in this invention realizes a comprehensive quantitative evaluation of the operating scenarios of the cyber-physical system of intelligent connected vehicles, avoiding the problem that traditional testing methods are difficult to fully quantify the effects of multi-agent collaborative optimization in complex traffic scenarios.

[0052] 2. This invention takes into account the correlation between multi-scale scenarios, the degree of correlation between various main components, and the interaction between multi-scale, multi-level, and multi-subject functions. It can adapt to complex and changing traffic scenarios and provide a strong guarantee for the stable operation of the cyber-physical system of intelligent connected vehicles in practical applications.

[0053] 3. The final test verification and quantitative evaluation results are presented in the form of scores, which are intuitive and easy to understand, allowing decision makers and relevant personnel to quickly understand the performance level of the system and make more reasonable decisions and improvement measures.

[0054] 4. The present invention adopts a component-based design concept, which allows for flexible selection and combination of corresponding components for evaluation when faced with different types of intelligent connected vehicle cyber-physical systems or different evaluation requirements. It has good scalability and adaptability, and can meet the evaluation needs of the evolving field of intelligent connected vehicle technology.

[0055] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A framework diagram for testing, verifying and quantitatively evaluating the cyber-physical system of intelligent vehicles;

[0057] Figure 2 Generate a flowchart for the running scenario;

[0058] Figure 3 It is the relationship diagram between scene components;

[0059] Figure 4 is the static attribute evaluation index diagram;

[0060] Figure 5 It is an evaluation index diagram of the human-vehicle interaction relationship;

[0061] Figure 6 This is the vehicle-to-vehicle interaction relationship evaluation index diagram;

[0062] Figure 7 This is the evaluation index diagram of the functional basic dimension. DETAILED DESCRIPTION

[0063] To make the technical solutions, advantages, and purposes of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0064] like Figure 1 As shown, the present invention provides a component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle, comprising the following steps:

[0065] S1. Using the MBSE concept, define the operation scenarios of the cyber-physical system of intelligent connected vehicles from three stages: functional scenario, logical scenario, and cyber-physical scenario. The operation scenario generation process is as follows: Figure 2 As shown;

[0066] Functional scenarios include descriptions of the road network scale, road segment scale, and vehicle surrounding scale. The road network scale describes the road network-level traffic environment and driving tasks, the road segment scale describes the road segment-level traffic environment and driving trajectory, and the vehicle surrounding scale describes the vehicle surrounding traffic environment and driving behavior.

[0067] Logical scenarios include descriptions at the vehicle-road-cloud level, vehicle-road level, and vehicle-vehicle level. The vehicle-road-cloud level describes the implementation of vehicle-road-cloud interaction and collaboration functions and cloud-side functions; the vehicle-road level describes the implementation of vehicle-road interaction and collaboration functions and road-side functions; and the vehicle-vehicle level describes the implementation of vehicle-vehicle interaction and collaboration functions and vehicle-side functions.

[0068] The cyber-physical scenario includes descriptions of the cloud, road, and vehicle. The cloud describes the specific functions of each layer and the relationships between functional modules at each layer; the road segment describes the specific functions of each layer at the road end and the relationships between functional modules at each layer; and the vehicle describes the specific functions of each layer at the vehicle end and the relationships between functional modules at each layer.

[0069] S2. Test and verify the operational scenarios of the cyber-physical system of intelligent connected vehicles from three dimensions: multi-scale scenario combination testing, multi-agent collaborative testing and verification, and multi-scale, multi-level, multi-agent, and multi-functional collaborative testing and verification;

[0070] The integrated vehicle-road-cloud functionality of intelligent connected vehicles can be broken down into vehicle-road-cloud-scale functions, vehicle-road-scale functions, and vehicle-vehicle-scale functions, which can then be integrated into cloud-side functions, road-side functions, vehicle-side functions, and the relationships between the cloud, road-side, and vehicle-side functions. Therefore, by testing and verifying the functionality of a specific entity through actual or simulated multi-agent operating states, sensory data, integrated sensory situations, and collaborative decision-making recommendations, the integrated vehicle-road-cloud system can be tested and verified.

[0071] S3. Applying the concept of componentization, complete the quantitative evaluation of testing and verification of scenarios at different scale combinations through multi-scale scenario components; complete the quantitative evaluation of testing and verification of different combinations of subjects through cyber-physical subject components; complete the quantitative evaluation of testing and verification of different combinations of functions through multi-scale, multi-level, and multi-subject functional components.

[0072] The quantitative evaluation of multi-scale scene components is decoupled into quantitative evaluation of road components, environment components, and traffic flow components, including the following steps:

[0073] Quantitative evaluation of road components:

[0074] Step 1: Conduct a quantitative assessment based on road smoothness and pavement infrastructure conditions. The International Roughness Index (IRI) was used for road smoothness, as shown in Table 1. The pavement infrastructure conditions were assessed using the pavement marking integrity rate, as shown in Table 2.

[0075] Step 2: Score the road smoothness and pavement facilities conditions according to the evaluation criteria, divide the weights, and perform weighted summation to obtain the quantitative score of the road component. The specific calculation formula is shown in (1.1):

[0076] S1=A11×W11+A12×W12(1.1)

[0077] S1: quantitative score of road components;

[0078] A11: road smoothness score;

[0079] A12: Road facilities condition score;

[0080] W11: road smoothness weight coefficient;

[0081] W12: weight coefficient of pavement facility conditions;

[0082] Table 1 International Flatness Index

[0083]

[0084] Table 2 Road sign and marking evaluation indicators

[0085]

[0086] Quantitative assessment of environmental components:

[0087] Step 1: Conduct a quantitative assessment based on weather conditions and sunlight duration. The standards used for weather conditions are shown in Table 3. The standards used for sunlight duration are shown in Table 4.

[0088] Step 2: Score the weather conditions and sunlight periods according to the evaluation criteria, divide the weights, and perform weighted summation to obtain the quantitative score of the environmental component. The specific calculation formula is shown in (1.2):

[0089] S2=A21×W21+A22×W22(1.2)

[0090] S2: Quantitative score of environmental components;

[0091] A21: Weather condition score;

[0092] A22: Light period score;

[0093] W21: Weather condition weight coefficient;

[0094] W22: Light period weight coefficient;

[0095] Table 3 Evaluation indicators of light period

[0096]

[0097] Table 4 Weather condition evaluation indicators

[0098]

[0099] Quantitative evaluation of traffic flow components:

[0100] Step 1: Conduct quantitative assessment based on traffic congestion index and average travel speed.

[0101] Step 2: Score the traffic congestion index and average travel speed against the evaluation criteria, divide the weights, and perform weighted summation to obtain the quantitative score of the traffic flow component. The specific calculation formula is shown in (1.3):

[0102] S3=A31×W31+A32×W22(1.3)

[0103] S3: Quantitative score of traffic flow components;

[0104] A31: Traffic congestion index score;

[0105] A32: Average travel speed score;

[0106] W31: Traffic congestion index weight coefficient;

[0107] W32: average travel speed weight coefficient;

[0108] Finally, the scores of the road component, environment component, and traffic flow component are combined and weighted, and then the weighted sum is calculated to obtain the quantitative score of the multi-scale scene component. The specific calculation formula is shown in (1.4):

[0109]

[0110] Table: Quantitative scores of multi-scale scene components;

[0111] W1: road component weight coefficient;

[0112] W2: weight coefficient of environmental component;

[0113] W3: Traffic flow component weight coefficient;

[0114] like Figure 3 ,According to the correlation relationship between multi-scale scene components, a combined scene component is formed, and the quantitative evaluation of the combined scene component is completed by dividing the weights.

[0115]

[0116] S c : Quantitative score of combined scene components;

[0117] Scene component weight coefficient;

[0118] n: number of combined scenes;

[0119] The weight division in the above scenario components is completed using the hierarchical analysis method.

[0120] The subjects in the cyber-physical subject component can be people, vehicles, roads, the cloud, networks, maps, etc. The cyber-physical subject component is decoupled into static attribute components, interactive relationship components, and subject participation form components. The fuzzy comprehensive evaluation method is used to quantitatively evaluate the cyber-physical subject component. Among them, the three major components are defined as follows:

[0121] The static properties of cyber-physical main components include main type, geometric size, and intelligence level;

[0122] The interactive relationship components of the cyber-physical main components include human-vehicle interaction, vehicle-vehicle interaction, etc.

[0123] The subject participation form components of the cyber-physical subject components include the measured objects and traffic participants.

[0124] The fuzzy comprehensive evaluation method includes the following steps:

[0125] Step 1: Establish a comprehensive evaluation factor set. The above decouples the cyber-physical main components into static attributes, interactive relationships, and participation forms. The evaluation of the cyber-physical main components, based on static attributes, interactive relationships, and participation forms, constitutes an evaluation index system geometrically denoted as: U = {static attribute u1, interactive relationship u2, participation form u3};

[0126] Step 2: Establish a comprehensive evaluation index set. For the cyber-physical main component, according to the actual needs, a comment set is formed by different judgments, which is recorded as: V = {simple v1, moderate v2, complex v3}.

[0127] Step 3: Determine the weight of each factor. i A weight a i , the fuzzy set of the weight set of each factor is represented by A: A = {a1, a2, a3}.

[0128] Fuzzy sets were confirmed based on the scaling and Delphi methods. First, the Delphi method was used to invite multiple experts in the professional field to score the importance of each factor, setting a scale of 1-9, as shown in Table 5, and comparing the importance differences between any two factors.

[0129] Table 5 Meaning of each scale in the scaling method

[0130]

[0131]

[0132] Considering the reversibility of the comparison relationship, if a ij For concept a i Compared with concept a j , then the concept a j and concept a i The scale of is the reciprocal:

[0133]

[0134] The scales corresponding to the results of the pairwise comparisons of all factors are combined to construct a scaling matrix:

[0135] A=[a ij ] nn (1.7)

[0136] Among them, n means there are n factors.

[0137] Apply the eigenvector method to analyze the judgment matrix of all concepts, calculate the eigenvector corresponding to the maximum eigenvalue of the scale matrix A, and normalize the eigenvector. To ensure the consistency and rationality of the relative importance calculation results, a consistency test is required for the scale matrix. The consistency test uses the consistency ratio (CR) to measure:

[0138]

[0139] Among them, CI is the consistency index, which is related to the matrix order n, and the calculation formula is as follows;

[0140]

[0141] RI is a random consistency index, and its value varies with the order of the scaling matrix, as shown in Table 6.

[0142] Table 6 Random consistency index values

[0143]

[0144] If the CR value is less than 0.1, the judgment matrix is ​​fairly consistent. If the CR value is greater than 0.1, the judgment matrix may need to be revised or reassessed. After the consistency test passes, the normalized eigenvector corresponding to the largest eigenvalue is the weight coefficient for that factor.

[0145] Step 4: Perform single factor fuzzy evaluation to obtain the evaluation matrix. Perform single factor evaluation on each evaluation factor to obtain the membership degree r of each evaluation level of the evaluation object under each evaluation factor pair. i For example, the fuzzy relationship vector of the first factor is R1 = [r1, r2, r3]. Single factor evaluation is performed on other factors to obtain the corresponding fuzzy relationships R2 and R3. These vectors are arranged in rows to form a fuzzy relationship matrix R = [R1; R2; R3].

[0146] In this step, the evaluation indicators of static attribute components are as follows Figure 4 As shown; the interaction relationship indicators of the subject interaction relationship components are as follows Figure 5 、 Figure 6 As shown in the figure, whether there is a collision risk between subjects is used as the final result judgment indicator; the subject participation form component is judged based on the degree of subject participation in the scene.

[0147] Step 5: Perform fuzzy evaluation synthesis. Perform fuzzy operation on the weight set A and the fuzzy relationship matrix R. Common operation methods include fuzzy synthesis operation. Using fuzzy synthesis operation, the operation formula is: in Represents the fuzzy synthesis operator. The comprehensive evaluation result vector B = [b1, b2, b3] is obtained through the operation, where b1, b2, and b3 represent the comprehensive membership of the evaluation object to each level in the evaluation set V.

[0148] Step 6: Analyze and sort the evaluation results. According to the comprehensive evaluation result vector, analyze the degree of membership of the evaluation object in each evaluation level. The maximum membership principle is usually used to determine the final evaluation level of the evaluation object, that is, the level with the largest membership in the comprehensive evaluation result vector is the final evaluation result. The final quantitative evaluation result is recorded as S z .

[0149] S3.3 Multi-scale, multi-level, and multi-agent functional components are decoupled into eight dimensions: service object, function type, function subject, function scale, interaction relationship, data source, data cycle, and trigger mechanism. Function scale, interaction relationship, data source, data cycle, and trigger mechanism are defined as the foundational dimensions of functionality. Through quantitative assessment of these foundational dimensions, it is possible to determine whether the functional module composed of service object, function type, and function subject is functioning properly.

[0150] Multi-scale, multi-level, and multi-agent functional components are also quantitatively evaluated using the fuzzy comprehensive evaluation method. The evaluation indicators of each factor in the functional basic dimension are as follows: Figure 7 As shown, the final quantitative evaluation result is recorded as S g .

[0151] S4. After completing the quantitative evaluation of the three major components, namely, the multi-scale scenario component, the cyber-physical main component, and the multi-scale, multi-level, multi-subject functional component, the quantitative evaluation results are divided by weight coefficient according to the importance level of each component to obtain the final test verification quantitative evaluation score, which is used to evaluate the effectiveness of the test verification. The calculation of the test verification quantitative evaluation score is shown in formula (1.10):

[0152] S=S c +S z +S g (1.10)

[0153] Where, S represents the quantitative evaluation score of IVCPS test verification; S z Represents the quantitative evaluation score of the cyber-physical main component; S g Represents the quantitative evaluation score of multi-scale, multi-level and multi-agent functional components.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of protection of the present invention.

Claims

1. A component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle, characterized by: The following steps are involved: S1. Define the operational scenarios of the cyber-physical system of intelligent connected vehicles from three stages: functional scenario, logical scenario, and cyber-physical scenario. Functional scenarios include descriptions of the road network scale, road segment scale, and vehicle surrounding scale. The road network scale describes the road network-level traffic environment and driving tasks, the road segment scale describes the road segment-level traffic environment and driving trajectory, and the vehicle surrounding scale describes the vehicle surrounding traffic environment and driving behavior. Logical scenarios include descriptions at the vehicle-road-cloud level, vehicle-road level, and vehicle-vehicle level. The vehicle-road-cloud level describes the implementation solutions and cloud-side functions for vehicle-road-cloud interaction and collaboration; the vehicle-road level describes the implementation solutions and road-side functions for vehicle-road interaction and collaboration; and the vehicle-vehicle level describes the implementation solutions and vehicle-side functions for vehicle-vehicle interaction and collaboration. The cyber-physical scenario includes descriptions of the cloud, road, and vehicle sides. The cloud side describes the functions of each layer and the relationships between the functional modules at each layer. The road section describes the functions of each layer and the relationships between the functional modules at each layer. The vehicle side describes the functions of each layer and the relationships between the functional modules at each layer. S2. Test and verify the cyber-physical system of intelligent connected vehicles from three dimensions: multi-scale scenario testing and verification, multi-agent collaborative testing and verification, and multi-scale, multi-level, multi-agent, and multi-functional collaborative testing and verification; S3. Applying the concept of componentization, complete the quantitative evaluation of testing and verification of scenarios at different scales through multi-scale scenario components. Complete the quantitative evaluation of testing and verification of different subjects and their interactions through cyber-physical subject components. Complete the quantitative evaluation of testing and verification of different functions through multi-scale, multi-level, and multi-subject functional components. S4. Combine the quantitative evaluation results of multi-scale scenario components, cyber-physical subject components, and multi-scale, multi-level, and multi-subject functional components to form the quantitative evaluation results of IVCPS test verification.

2. The component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle according to claim 1, characterized in that: In step S3, completing the quantitative evaluation of the different scale combination scene test verification by the multi-scale scene component includes the following sub-steps: S3.11 Quantitative evaluation of road components; I. Conduct quantitative assessment based on road smoothness and road surface conditions; The International Roughness Index (IRI) is used for road smoothness, and the road marking integrity rate is used for road facility conditions. II. Scoring the road smoothness and pavement conditions against the assessment criteria, assigning weights and performing a weighted sum to obtain a quantitative score for the road component; S1=A11×W11+A12×W12 Where S1 represents the quantitative score of road components; A11 represents the road smoothness score; A12 represents the road facility condition score; W11 represents the road smoothness weight coefficient; W12 represents the road facility condition weight coefficient; S3.12 Quantitative assessment of environmental components; I. Conduct quantitative assessment based on weather conditions and sunlight duration; II. Score weather conditions and sunlight hours against the evaluation criteria, assign weights, and then sum the scores to obtain a quantitative score for the environmental component. S2=A21×W21+A22×W22 Where S2 represents the quantitative score of the environmental component; A21 represents the weather condition score; A22 represents the light period score; W21 represents the weather condition weight coefficient; W22 represents the sunlight period weight coefficient; S3.13 Quantitative evaluation of traffic flow components; I. Quantitative assessment based on traffic congestion index and average travel speed; II. Scoring the traffic congestion index and average travel speed against the evaluation criteria, weighting them, and performing a weighted summation to obtain a quantitative score for the traffic flow component; S3=A31×W31+A32×W22 Where S3 represents the traffic flow component quantitative score; A31 represents the traffic congestion index score; A32 represents the average travel speed score; W31 represents the traffic congestion index weight coefficient; W32 represents the average travel speed weight coefficient; S3.14 Combine the scores of the road component, environment component, and traffic flow component after quantitative evaluation, divide the weights, and perform weighted summation to obtain the quantitative score of the multi-scale scene component; Where, Represents the quantitative score of the multi-scale scene component; W1 represents the weight coefficient of the road component; W2 represents the weight coefficient of the environment component; W3 represents the weight coefficient of the traffic flow component; S3.15 Based on the association relationships among the multi-scale scene components, a combined scene component is formed, and a quantitative evaluation of the combined scene component is completed by dividing the weights; Where S c Indicates the quantitative score of the combined scene components; Represents the weight coefficient of the scene component; n represents the number of combined scenes.

3. The component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle according to claim 2, characterized in that: In the steps S3.11 to S3.15, the weight division is performed using the hierarchical analysis method.

4. The component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle according to claim 3 is characterized in that: In step S3, completing the test, verification, and quantitative evaluation of different subjects and the interaction relationships between subjects through the cyber-physical subject components includes the following sub-steps: S3.21 Decouple the cyber-physical subject components into static attribute components, interactive relationship components, and subject participation form components; The static properties of cyber-physical subject components include subject type, geometric dimensions, and intelligence; The interactive relationship components of the cyber-physical main components include human-vehicle interaction and vehicle-vehicle interaction; The subject participation form components of the cyber-physical subject components include the measured object and traffic participants; S3.22 Use fuzzy comprehensive evaluation method to quantitatively evaluate the information-physical main components.

5. The component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle according to claim 4 is characterized in that: In step S3, the test verification and quantitative evaluation of different functions is completed by multi-scale, multi-level, and multi-agent functional components, including the following sub-steps: S3.31 decouples multi-scale, multi-level, and multi-agent functional components into eight dimensions: service object, function type, function subject, function scale, interaction relationship, data source, data cycle, and trigger mechanism; S3.32 sets the functional scale, interaction relationship, data source, data cycle and trigger mechanism as the functional basic dimensions. Through the quantitative evaluation of the functional basic dimensions, it is judged whether the functional module composed of service objects, functional types and functional subjects can operate normally; Among them, the multi-scale, multi-level and multi-agent functional components are also quantitatively evaluated using the fuzzy comprehensive evaluation method.

6. The component-based quantitative evaluation method for the cyber-physical system of an intelligent connected vehicle according to claim 5, characterized in that: In step S4, the calculation expression of the quantitative evaluation result of the IVCPS test verification is: S=S c +S z +S g Where, S represents the quantitative evaluation score of IVCPS test verification; S z Represents the quantitative evaluation score of the cyber-physical main component; S g Represents the quantitative evaluation score of multi-scale, multi-level and multi-agent functional components.

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