Intelligent connected vehicle group leader vehicle election system and method based on zero trust architecture

Abstract

The present invention discloses a system method for electing a leader vehicle in an intelligent connected vehicle group based on a zero-trust architecture. The system includes: a node establishment module, which is used to establish a corresponding vehicle node for each vehicle in an intelligent connected vehicle group under a zero-trust environment; a trust evaluation module, which is used to analyze the importance of vehicle nodes in the intelligent connected vehicle group by using a cross-entropy trust evaluation method under a zero-trust architecture, combined with the degree centrality and heterogeneity of vehicle nodes and neighboring vehicle nodes, and then calculate the trust of each vehicle node; a leader vehicle election module, which is used to determine the leader vehicle of the intelligent connected vehicle group according to the trust of each vehicle node by using a PBFT distributed consensus algorithm. The present invention uses a cross-entropy trust evaluation method to calculate the trust of each vehicle node in the intelligent connected vehicle group, and uses a PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent connected vehicle group according to the trust of each vehicle node, thereby improving the accuracy and reliability of the leader vehicle election.

Description

Technical Field

[0001] The present invention relates to the field of intelligent networked vehicle safety management, and in particular to an intelligent networked vehicle group leader vehicle election system and method based on a zero-trust architecture. Background Art

[0002] Intelligent and Connected Vehicle (ICV) refers to a new type of vehicle equipped with advanced on-board sensors, controllers, actuators and other equipment, and integrated with modern communication and network technologies, which realizes the interconnection between vehicles (V2V), vehicles and infrastructure (V2I), vehicles and pedestrians (V2P) and vehicles and the cloud (V2C). Among them, the election of the leader vehicle of the intelligent connected vehicle group is a key issue, which involves how to select a reliable and efficient vehicle as the leader to coordinate and manage the system functions and resource allocation of the entire vehicle group.

[0003] In a group of intelligent connected vehicles, the main responsibilities of the leader vehicle include but are not limited to internal communication coordination, operation status supervision, and ensuring the optimal allocation of fleet resources and functional system management. The selection of the leader vehicle is dynamic, and in existing technologies it is usually based on factors such as vehicle performance, trust, geographic location, and communication capabilities. By electing a leader vehicle, the resource allocation of the group can be better centralized and optimized, improving overall efficiency and safety.

[0004] However, in a zero-trust environment, the existing leader vehicle election methods cannot adapt to the dynamic changes of the zero-trust network environment and the actual vehicle group status. On the one hand, in the intelligent connected vehicle group under the zero-trust architecture, accurately evaluating the trust of vehicle nodes is a challenge. Since the behavior and performance of vehicle nodes may be affected by many factors, how to design an effective trust evaluation mechanism is a key issue. On the other hand, the threats and attacks faced by the intelligent connected vehicle group under the zero-trust architecture are diverse, and how to ensure the safety and reliability of the leader vehicle is also a key issue. Therefore, how to improve the accuracy and reliability of the election of the leader vehicle in the intelligent connected vehicle group under the zero-trust architecture is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide a method for electing a leader vehicle of an intelligent connected vehicle group based on a zero-trust architecture, using the cross-entropy trust evaluation method under the zero-trust architecture to calculate the trust of each vehicle node in the intelligent connected vehicle group, which can adapt to the dynamic changes of the actual vehicle group state in a zero-trust network environment, and at the same time use the PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent connected vehicle group according to the trust of each vehicle node, which can effectively solve the impact of factors such as malicious operations of individual vehicles or network attacks on the election of the leader vehicle, thereby improving the accuracy and reliability of the election of the leader vehicle of the intelligent connected vehicle group under the zero-trust architecture.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The intelligent connected vehicle group leader vehicle election system based on zero-trust architecture includes:

[0008] A node establishment module is used to establish a corresponding vehicle node for each vehicle in a group of intelligent connected vehicles in a zero-trust environment;

[0009] The trust assessment module is used to use the cross-entropy trust assessment method under the zero-trust architecture, combine the degree centrality and heterogeneity of the vehicle node and the neighboring vehicle nodes, analyze the importance of the vehicle node in the intelligent connected vehicle group, and then calculate the trust of each vehicle node in the zero-trust environment;

[0010] The leader vehicle election module is used to use the PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent connected vehicle group based on the trust level of each vehicle node.

[0011] The present invention also discloses a method for electing a leader vehicle of a smart connected vehicle group based on a zero-trust architecture, which is implemented based on the system for electing a leader vehicle of a smart connected vehicle group of the present invention, and includes:

[0012] S1: Establish a corresponding vehicle node for each vehicle in the intelligent connected vehicle group in a zero-trust environment;

[0013] S2: Using the cross-entropy trust evaluation method under the zero-trust architecture, combined with the degree centrality and heterogeneity of the vehicle node and its neighboring vehicle nodes, the importance of the vehicle node in the intelligent connected vehicle group is analyzed, and then the trust of each vehicle node in the zero-trust environment is calculated;

[0014] S3: Using the PBFT distributed consensus algorithm, the leading vehicle of the intelligent connected vehicle group is determined based on the trust level of each vehicle node.

[0015] Preferably, the vehicle node is established through the following steps:

[0016] S101: Identify a target vehicle that enters the communication range of the intelligent connected vehicle group and assign a node ID to the target vehicle;

[0017] S102: Initialize the node of the target vehicle through the following steps:

[0018] 1) Create a corresponding vehicle node according to the node ID of the target vehicle and allocate independent storage space;

[0019] 2) Establish and update the vehicle node adjacency matrix:

[0020] 3) Record the basic information of the target vehicle and store it in the corresponding storage space;

[0021] S103: Obtain the geographic location and operating status of the target vehicle, and store them in the corresponding storage space.

[0022] Preferably, the trustworthiness of the vehicle node is calculated by the following steps:

[0023] S201: Calculate the degree centrality of the vehicle node;

[0024] The formula is:

[0025]

[0026]

[0027] Where: DC i represents the degree centrality of vehicle node i; n represents the number of vehicle nodes in the intelligent connected vehicle group, that is, the sum of the row vectors of the adjacency matrix; a ij represents the connection status between vehicle node i and vehicle node j; k i is the degree of vehicle node i, that is, the number of other vehicle nodes directly connected to vehicle node i;

[0028] S202: Calculate the corresponding heterogeneity according to the degree centrality of the vehicle node;

[0029] The formula is:

[0030]

[0031] Where: I i represents the heterogeneity of vehicle node i;

[0032] S203: Calculate the corresponding trust level using a cross entropy-based algorithm according to the heterogeneity of the vehicle nodes;

[0033] The trust calculation formula of the algorithm based on cross entropy is expressed as:

[0034] TR i+ =-∑j∈Γ(i) I i i j ;

[0035] Where: TR i+ represents the trust degree of vehicle node i; j∈Γ(i) means that vehicle node j is a neighbor node of vehicle node i.

[0036] Preferably, if the trustworthiness of a certain vehicle node is lower than a set threshold, the vehicle node is considered suspicious or untrustworthy, and the access rights of the vehicle node are restricted or revoked.

[0037] Preferably, as the status of vehicle nodes changes and new vehicle nodes are added, the intelligent connected vehicle group periodically recalculates the trust of all vehicle nodes.

[0038] Preferably, the leading vehicle is selected by the following steps:

[0039] S301: Initialize the state of the intelligent connected vehicle group and set the number of Byzantine nodes f;

[0040] S302: Obtain the trustworthiness of all vehicle nodes, and select the vehicle node with the highest trustworthiness as the master node;

[0041] S303: The master node broadcasts pre-preparation information to each vehicle node;

[0042] S304: After receiving the pre-preparation information, each vehicle node verifies the pre-preparation message; after the verification passes, each vehicle node broadcasts the pre-preparation information to other vehicle nodes;

[0043] S305: After receiving the preparation information, each vehicle node verifies the preparation information; after the verification passes, each vehicle node broadcasts the submission information to other vehicle nodes;

[0044] S306: After receiving the submission information, each vehicle node verifies the preparation message;

[0045] S307: Repeat steps S301 to S306 until a certain vehicle node receives more than 2f+1 identical submission messages, and determines that the vehicle corresponding to the current master node is the leading vehicle.

[0046] Preferably, during the cycle, the status of the master node is monitored in real time, and once it is found that the master node behaves abnormally or the trustworthiness decreases, a re-election process is triggered to select a new master node.

[0047] Preferably, it also includes:

[0048] Step S4: After the leading vehicle is determined, the PBFT distributed consensus algorithm is used to determine the backup leading vehicle of the intelligent connected vehicle group based on the trust level of each vehicle node except the leading vehicle; when the leading vehicle is in an abnormal state, the backup leading vehicle takes over all responsibilities of the leading vehicle, that is, the leading vehicle and leadership are transferred to the backup leading vehicle.

[0049] Preferably, in step S4, the processing steps for backup and transfer of the leadership vehicle and leadership are as follows:

[0050] S401: The vehicle node selected as the backup leader vehicle always keeps synchronization with the leader vehicle;

[0051] S402: When the leading vehicle is in an abnormal state, the leadership of the leading vehicle is smoothly transferred to the backup leading vehicle, and a notification is sent to all vehicle nodes;

[0052] S403: After the leadership is successfully transferred to the backup leader vehicle, a backup election is performed again to designate a new backup leader vehicle;

[0053] S404: The new leader vehicle updates the log and status information of all vehicle nodes in the intelligent connected vehicle group;

[0054] S405: Monitor the status of the new leading vehicle and continuously evaluate its trustworthiness. When the new leading vehicle is in an abnormal state, perform the next round of leading vehicle backup and transfer process.

[0055] Compared with the prior art, the method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture in the present invention has the following advantages:

[0056] Beneficial effects:

[0057] In the task of electing a leader vehicle in a smart connected vehicle group under a zero-trust architecture, the present invention uses a cross-entropy trust evaluation method in combination with the degree centrality and heterogeneity of vehicle nodes and neighboring vehicle nodes to analyze the importance of vehicle nodes in the smart connected vehicle group and calculate the trust of each vehicle node. The cross-entropy method can measure the difference between the predicted probability and the actual observation, and combined with the node degree centrality and heterogeneity, it can more accurately evaluate the importance of vehicle nodes in the smart connected vehicle group, and then accurately evaluate the trust of vehicle nodes, and can adapt to the dynamic changes of the actual vehicle group state in a zero-trust network environment, which helps to ensure the accuracy and reliability of the leader vehicle election results, and make the leader vehicle election mechanism meet the zero-trust environment, thereby improving the accuracy and reliability of the leader vehicle election in the smart connected vehicle group under the zero-trust architecture.

[0058] The present invention further uses the PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent networked vehicle group according to the trust of each vehicle node based on the cross entropy trust evaluation method under the zero trust architecture to calculate the trust of the vehicle node. The PBFT algorithm can ensure that the correct decision can still be made when facing malicious or faulty nodes. Combined with the trust of the vehicle node, the accuracy of the decision can be further enhanced, because nodes with high trust are more likely to provide accurate and reliable information, and thus can effectively solve the impact of factors such as malicious operations or network attacks on the election of the leader vehicle caused by individual vehicles; and because the PBFT algorithm has the ability to handle Byzantine faults, the intelligent networked vehicle group can operate more stably and reliably when facing malicious attacks or node failures. By electing a high-trust vehicle node as the leader, the robustness of the election of the leader vehicle of the intelligent networked vehicle group can be further enhanced; in addition, combined with the PBFT algorithm and trust evaluation, security threats can be more effectively detected and prevented, and high-trust vehicle nodes are more likely to provide accurate and secure information, thereby helping the entire intelligent networked vehicle group system to improve security.

[0059] The node establishment module, trust assessment module, leader vehicle election module and subsequent backup and transfer modules in the present invention are all independent of each other, making the entire system modular, portable and changeable. The system has high flexibility and scalability and can adapt to different scenarios and needs.

[0060] The present invention introduces a mechanism for automatically backing up the leading vehicle and smoothly transferring leadership. When the leading vehicle fails or its trustworthiness decreases, the intelligent networked vehicle group can automatically start the backup leading vehicle and complete the smooth transfer of leadership. The introduction of this mechanism ensures the normal operation of the vehicle group and minimizes the impact of the failure on the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0062] Figure 1 A flowchart of a method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture;

[0063] Figure 2 This is a schematic diagram of the structure of the zero trust architecture;

[0064] Figure 3 Flowchart for trust evaluation of vehicle nodes;

[0065] Figure 4 This is the operation flow chart of the PBFT distributed consensus algorithm;

[0066] Figure 5Broadcast voting process for PBFT distributed consensus algorithm nodes. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0068] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0069] The following is a further detailed description through specific implementation methods:

[0070] Embodiment 1:

[0071] This embodiment discloses a system for electing a leader vehicle for a group of intelligent connected vehicles based on a zero-trust architecture.

[0072] like Figure 1 As shown, a leader vehicle election system for a group of intelligent connected vehicles based on a zero-trust architecture includes:

[0073] A node establishment module is used to establish a corresponding vehicle node for each vehicle in a group of intelligent connected vehicles in a zero-trust environment;

[0074] The trust evaluation module is used to use the cross entropy trust evaluation method under the zero-trust architecture, combine the degree centrality and heterogeneity of the vehicle node and the neighboring vehicle nodes, analyze the importance of the vehicle node in the intelligent connected vehicle group, and then calculate the trust of each vehicle node in the zero-trust environment;

[0075] Among them, cross entropy is often used in information theory to measure the difference between two probability distributions. The cross entropy trust evaluation method is a method that calculates the degree centrality and heterogeneity of vehicle nodes and neighboring vehicle nodes to analyze the importance of vehicle nodes in the intelligent connected vehicle group, and further calculates the trust of each vehicle. Among them, degree centrality is an evaluation indicator of the relative importance of a node in the network, and heterogeneity is a quantitative indicator to measure the difference in distribution between a node and its neighboring nodes.

[0076] The leader vehicle election module is used to use the PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent connected vehicle group based on the trust level of each vehicle node.

[0077] Among them, Byzantine Fault Tolerance (BFT) means that in a distributed system, some nodes of the system may have erroneous behaviors, and the system can tolerate the existence of a certain number of erroneous nodes and still work normally. Based on this, the distributed consensus algorithm (Practical Byzantine Fault Tolerance, PBFT) is an algorithm that can still reach a consensus when a certain number of nodes have erroneous behaviors. The PBFT distributed consensus algorithm can effectively reach a consensus in a distributed system without trust, and realize that in the trust voting of vehicles, even if there are malicious vehicle nodes or some vehicles have erroneous behaviors (such as unfair voting, providing wrong information, etc.), the system can still work normally and select the appropriate leader vehicle.

[0078] In the task of electing a leader vehicle in a smart connected vehicle group under a zero-trust architecture, the present invention uses a cross-entropy trust evaluation method in combination with the degree centrality and heterogeneity of vehicle nodes and neighboring vehicle nodes to analyze the importance of vehicle nodes in the smart connected vehicle group and calculate the trust of each vehicle node. The cross-entropy method can measure the difference between the predicted probability and the actual observation, and combined with the node degree centrality and heterogeneity, it can more accurately evaluate the importance of vehicle nodes in the smart connected vehicle group, and then accurately evaluate the trust of vehicle nodes, and can adapt to the dynamic changes of the actual vehicle group state in a zero-trust network environment, which helps to ensure the accuracy and reliability of the leader vehicle election results, and make the leader vehicle election mechanism meet the zero-trust environment, thereby improving the accuracy and reliability of the leader vehicle election in the smart connected vehicle group under the zero-trust architecture.

[0079] The present invention further uses the PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent networked vehicle group according to the trust of each vehicle node based on the cross entropy trust evaluation method under the zero trust architecture to calculate the trust of the vehicle node. The PBFT algorithm can ensure that the correct decision can still be made when facing malicious or faulty nodes. Combined with the trust of the vehicle node, the accuracy of the decision can be further enhanced, because nodes with high trust are more likely to provide accurate and reliable information, and thus can effectively solve the impact of factors such as malicious operations or network attacks on the election of the leader vehicle caused by individual vehicles; and because the PBFT algorithm has the ability to handle Byzantine faults, the intelligent networked vehicle group can operate more stably and reliably when facing malicious attacks or node failures. By electing a high-trust vehicle node as the leader, the robustness of the election of the leader vehicle of the intelligent networked vehicle group can be further enhanced; in addition, combined with the PBFT algorithm and trust evaluation, security threats can be more effectively detected and prevented, and high-trust vehicle nodes are more likely to provide accurate and secure information, thereby helping the entire intelligent networked vehicle group system to improve security.

[0080] The node establishment module, trust assessment module, leader vehicle election module and subsequent backup and transfer modules in the present invention are all independent of each other, making the entire system modular, portable and changeable. The system has high flexibility and scalability and can adapt to different scenarios and needs.

[0081] Embodiment 2:

[0082] This embodiment discloses a method for electing a leader vehicle in a smart connected vehicle group based on a zero-trust architecture, which is implemented based on the smart connected vehicle group leader vehicle election system described in Embodiment 1.

[0083] like Figure 1 As shown, a method for electing a leader vehicle of a group of intelligent connected vehicles based on a zero-trust architecture includes:

[0084] S1: Establish a corresponding vehicle node for each vehicle in the intelligent connected vehicle group in a zero-trust environment;

[0085] S2: Using the cross-entropy trust evaluation method under the zero-trust architecture, combined with the degree centrality and heterogeneity of the vehicle node and its neighboring vehicle nodes, the importance of the vehicle node in the intelligent connected vehicle group is analyzed, and then the trust of each vehicle node in the zero-trust environment is calculated;

[0086] S3: Using the PBFT distributed consensus algorithm, the leading vehicle of the intelligent connected vehicle group is determined based on the trust level of each vehicle node.

[0087] In the intelligent connected vehicle group leader vehicle election task under the zero-trust architecture, the present invention uses the cross-entropy trust evaluation method in combination with the degree centrality and heterogeneity of the vehicle node and the neighboring vehicle node to analyze the importance of the vehicle node in the intelligent connected vehicle group and calculate the trust of each vehicle node. The cross-entropy method can measure the difference between the predicted probability and the actual observation. Combined with the node degree centrality and heterogeneity, it can more accurately evaluate the importance of the vehicle node in the intelligent connected vehicle group, and then accurately evaluate the trust of the vehicle node. It can adapt to the dynamic changes of the actual vehicle group state in the zero-trust network environment, help ensure the accuracy and reliability of the leader vehicle election results, and make the leader vehicle election mechanism meet the zero-trust environment, thereby improving the accuracy and reliability of the leader vehicle election of the intelligent connected vehicle group under the zero-trust architecture.

[0088] The present invention further uses the PBFT distributed consensus algorithm to determine the leader vehicle of the intelligent networked vehicle group according to the trust of each vehicle node based on the cross entropy trust evaluation method under the zero trust architecture to calculate the trust of the vehicle node. The PBFT algorithm can ensure that the correct decision can still be made when facing malicious or faulty nodes. Combined with the trust of the vehicle node, the accuracy of the decision can be further enhanced, because nodes with high trust are more likely to provide accurate and reliable information, and thus can effectively solve the impact of factors such as malicious operations or network attacks on the election of the leader vehicle caused by individual vehicles; and because the PBFT algorithm has the ability to handle Byzantine faults, the intelligent networked vehicle group can operate more stably and reliably when facing malicious attacks or node failures. By electing a high-trust vehicle node as the leader, the robustness of the election of the leader vehicle of the intelligent networked vehicle group can be further enhanced; in addition, combined with the PBFT algorithm and trust evaluation, security threats can be more effectively detected and prevented, and high-trust vehicle nodes are more likely to provide accurate and secure information, thereby helping the entire intelligent networked vehicle group system to improve security.

[0089] During the specific implementation process, the vehicle node is established through the following steps:

[0090] S101: Identify a target vehicle that enters the communication range of the intelligent connected vehicle group and assign a node ID to the target vehicle;

[0091] In this embodiment, wireless communication technology, such as vehicle-mounted Wi-Fi or vehicle-mounted cellular network, is used to capture the unique identifier of the newly added vehicle. After completing vehicle identification, the system assigns a node ID to each vehicle.

[0092] S102: Initialize the node of the target vehicle through the following steps:

[0093] 1) Create a corresponding vehicle node according to the node ID of the target vehicle and allocate independent storage space;

[0094] 2) Establish and update the vehicle node adjacency matrix to describe the relationship and link status between vehicles; the adjacency matrix will be dynamically adjusted to reflect the real-time vehicle connection status.

[0095] In a group of intelligent connected vehicles, the node adjacency matrix is ​​usually constructed to represent the connection relationship between nodes (i.e., vehicles), so as to facilitate communication topology analysis and trust management. The specific steps are as follows:

[0096] a. Determine the matrix dimension: First, determine the total number of vehicle nodes in the intelligent connected vehicle group. Assuming there are N vehicles, the node adjacency matrix will be an N x N matrix, and each row and column of the matrix represents a node in the vehicle group.

[0097] b. Initialize the matrix: Initialize all elements in the N x N matrix to 0. At this time, 0 in the matrix means that there is no direct communication link between the corresponding vehicle nodes.

[0098] c. Determine adjacent vehicles: For each vehicle node, detect other vehicle nodes that are in direct communication with it based on factors such as communication range, vehicle status, and geographic location. Technologies such as radio frequency, wireless local area network (Wi-Fi), and dedicated short-range communication (DSRC) can be used to detect and determine which nodes are within the communication range.

[0099] d. Update the matrix: For node i, if it is within the communication range and can communicate directly with node j, then in the adjacency matrix, update the elements at positions (i, j) and (j, i) to 1, indicating that there is a direct communication link between the two nodes. The matrix is ​​symmetric about the main diagonal because the connectivity of the communication is bidirectional.

[0100] e. Considering the characteristics of the communication link: When it is necessary to consider the characteristics of the communication link, such as link quality, bandwidth, delay, etc. In this case, the adjacency matrix is ​​established as a weighted one, and no longer simply uses 0 or 1 to represent the connection between nodes, but uses a positive real number to represent the connection weight, which reflects the quality or trust of the link.

[0101] f. Dynamic update: Since the location of vehicle nodes and the communication environment may change at any time, the node adjacency matrix needs to be updated regularly or dynamically according to changes in the communication environment. Every time the location of the vehicle changes, resulting in changes in communication connectivity, the corresponding adjacency matrix must also be rebuilt.

[0102] 3) Record the basic information of the target vehicle and store it in the corresponding storage space;

[0103] In this embodiment, the basic information includes license plate number, vehicle model, etc.

[0104] S103: Obtain the geographic location and operating status of the target vehicle, and store them in the corresponding storage space.

[0105] In this embodiment, the global positioning system (GPS) is used to capture the geographical location information of the vehicle, and the running status information of the vehicle is obtained through various sensors installed on the vehicle, such as speed sensors, acceleration sensors, cameras, etc.

[0106] The captured basic information, geographic location and operating status are stored in a database (storage space), which is updated periodically or according to changes in vehicle status. An information processing mechanism is designed to ensure that the information stored in the database is up to date and can be accessed in real time.

[0107] In the specific implementation process, the intelligent connected vehicle group under the zero-trust architecture is as follows: Figure 2 As shown in Figure 1, in the design of modern intelligent connected vehicle systems, implementing a zero-trust model is an important strategy to improve system security. Different from traditional security frameworks, the zero-trust architecture does not presuppose the trust level of any network node, even internal nodes in the traditional sense. All nodes, whether from inside or outside the network, are considered potential risk carriers. Therefore, each node must pass a strict authentication and authorization procedure before it can access the resources managed by the system.

[0108] The zero-trust architecture system includes a clear distinction between the control plane and the data plane. The control plane is responsible for user authentication and access rights granting. Nodes that have been verified by the system can temporarily establish a secure communication channel on the data plane to access protected resources. The key entity in this process is the access proxy, which intercepts unauthorized information flows and ensures that only verified and authorized traffic can enter.

[0109] The dynamic trust evaluation engine is the core function of the zero-trust architecture, which provides a continuous flow of trust ratings. This mechanism is linked with access control to provide composite evaluation data, such as the authentication information, historical behavior and trust level of intelligent connected vehicles. This data is of fundamental significance to the formation and implementation of access control strategies. The system's continuous monitoring and evaluation of the trust level of vehicle nodes helps to detect and respond to potential security threats in a timely manner and adjust access rights accordingly.

[0110] In the application environment of intelligent connected vehicles, the access subjects may include but are not limited to vehicle terminals, mobile communication devices and various sensor units. These entities are connected to the vehicle communication system through a network interface. The access objects are key resources inside the vehicle, including status indications, operation commands and sensor data. The access subjects must be authorized through the authentication process to ensure the resource and functional safety of the vehicle.

[0111] In the intelligent connected vehicle system under the zero-trust architecture, it is very important to evaluate the trust of vehicle nodes. This implementation scheme introduces a vehicle node trust evaluation method based on cross entropy. Figure 3 As shown. This method uses the statistical characteristics of complex network theory to quantitatively analyze the trust between vehicle nodes based on the information of vehicle nodes and their adjacent nodes. Among them, cross entropy is a measure of the difference between two probability distributions, which is often used in statistical models such as logistic regression. The quantitative formula of this degree is given by the expected logarithmic difference between random distributions p and q, as shown in formula (1). Extended to network node trust evaluation, the concept of cross entropy can be used to distinguish the difference in topological information between nodes.

[0112] H(p,q)=E p [-lnq(x)]=-∑ x p(x)lnq(x)(1)

[0113] Specifically, the trust of the vehicle node is calculated through the following steps:

[0114] S201: Calculate the degree centrality of the vehicle node;

[0115] Degree centrality is a basic indicator to measure the relative importance of a node in its network, which depends on the number of neighbors connected to the node. The formula is expressed as:

[0116]

[0117]

[0118] Where: DC irepresents the degree centrality of vehicle node i; n represents the number of vehicle nodes in the intelligent connected vehicle group, that is, the sum of the row vectors of the adjacency matrix; in the established adjacency matrix, a ij represents the connection status between vehicle node i and vehicle node j. If there is a direct communication link between vehicle node i and vehicle node j, then a ij =1, otherwise, a ij =0; k i is the degree of vehicle node i, that is, the number of other vehicle nodes directly connected to vehicle node i;

[0119] S202: Calculate the corresponding heterogeneity according to the degree centrality of the vehicle node;

[0120] Considering that degree centrality can better reflect the topological structure of the sub-network composed of vehicle nodes and their neighboring vehicle nodes with low time complexity, the formula is expressed as:

[0121]

[0122] Where: I i represents the heterogeneity of vehicle node i;

[0123] Therefore, the heterogeneity between vehicle nodes can be well characterized. In the vehicle node trust evaluation method based on cross entropy under the zero trust architecture, by fusing the information structure of the central node and its local nodes and cross-considering each other, the difference in node influence can be more accurately characterized.

[0124] S203: Calculate the corresponding trust level using a cross entropy-based algorithm according to the heterogeneity of the vehicle nodes;

[0125] Cross entropy can measure the difference in the amount of information contained in a random variable. Similarly, there are differences in the topological information between different nodes in a complex network. Therefore, we consider introducing cross entropy to measure the statistical characteristics of nodes in a complex network. The principle of the cross-entropy-based intelligent connected vehicle node trust evaluation method under the zero-trust architecture is to comprehensively examine the overall importance of the vehicle node itself and the information of the surrounding vehicle nodes. Combining these two types of topological information, the cross entropy value can be used to quantify the differences between the distribution of vehicle nodes. Nodes with greater differences are given higher trust in the network.

[0126] Cross entropy is an indicator widely used in logistic regression model analysis. Its main concept is to describe the difference between two probability distributions, which can be regarded as a way to evaluate the similarity between variables. In general, as the cross entropy value increases, the difference between the two variables also increases.

[0127] The trust calculation formula of the algorithm based on cross entropy is expressed as:

[0128] TR i+ =-∑ j∈Γ(i) I i i j (5)

[0129] Where: TR i+ represents the trust of vehicle node i; j∈Γ(i) indicates that vehicle node j is a neighbor node of vehicle node i. This calculation quantifies the difference between the statistical characteristics of different nodes and provides an indicator to measure the trust of vehicle nodes in the system.

[0130] Specifically, if the trust level of a vehicle node is lower than the set threshold, the vehicle node is considered suspicious or untrustworthy. At this time, the intelligent connected vehicle group will take appropriate measures to limit or revoke the access rights of the vehicle node.

[0131] Specifically, as the status of vehicle nodes changes and new vehicle nodes are added, the intelligent connected vehicle group periodically recalculates the trust of all vehicle nodes and updates the security policy of the entire vehicle group.

[0132] In the specific implementation process, efficient and effective leader vehicle election is crucial to the stable operation of the communication protocol. This implementation describes a leader vehicle election method based on trust evaluation, using the practical PBFT distributed consensus algorithm as the consensus mechanism. The PBFT algorithm operation flow chart is as follows: Figure 4 As shown, in the PBFT algorithm, "f" represents the number of Byzantine nodes (nodes that may fail), and the algorithm ensures that consensus can be reached correctly when less than or equal to f nodes fail.

[0133] Specifically, the leading vehicle is selected through the following steps:

[0134] S301: Initialize the state of the intelligent connected vehicle group and set the number of Byzantine nodes f;

[0135] S302: Obtain the trustworthiness of all vehicle nodes, and select the vehicle node with the highest trustworthiness as the master node;

[0136] S303: The master node broadcasts pre-preparation information to each vehicle node;

[0137] S304: After receiving the pre-preparation information, each vehicle node verifies the pre-preparation message; after the verification passes, each vehicle node broadcasts the pre-preparation information to other vehicle nodes;

[0138] S305: After receiving the preparation information, each vehicle node verifies the preparation information; after the verification passes, each vehicle node broadcasts the submission information to other vehicle nodes;

[0139] S306: After receiving the submission information, each vehicle node verifies the preparation message;

[0140] S307: Repeat steps S301 to S306 until a certain vehicle node receives more than 2f+1 identical submission messages, and determines that the vehicle corresponding to the current master node is the leading vehicle.

[0141] During the cycle, the status of the master node is monitored in real time. Once the master node is found to be behaving abnormally or its trustworthiness is reduced, the re-election process is triggered to select a new master node, which ensures the continuous operation of the vehicle group.

[0142] In order to better illustrate the working logic of the PBFT algorithm, this embodiment discloses Figure 5 The node broadcast voting example shown. Assume that "0" is the vehicle master node, "1" and "2" are vehicle consensus nodes, and "3" is the faulty node in the vehicle consensus node. System c initiates a request to the network, which contains a specific operation description, timestamp, client identifier, and a request carrier (Request) including message content and summary. This carrier is digitally signed by the client to ensure its authenticity and non-repudiation.

[0143] In the Pre-prepare phase, after verifying the request, the master node assigns a sequence number to it and broadcasts a pre-prepare message to all consensus nodes. The pre-prepare message carries the view number, message content and its summary. After receiving the pre-prepare message, the consensus node verifies the signature, view number and message sequence number of the message. After successful verification, the node will enter the next step, the Prepare phase.

[0144] At this point, each consensus node broadcasts the prepare message to its peer nodes in the network and records the pre-prepare and prepare messages in its log. This phase requires nodes to verify the received prepare messages to confirm the compliance of the signature, view number, message sequence number, and number of prepare messages received. Once the requirements are met, the network will enter the commit phase.

[0145] In the commit phase, the consensus node broadcasts the commit message, and the receiving node verifies the legitimacy of these commit messages. When more than 2f+1 commit messages are observed, a node will return a reply message to the master node, where f represents the number of Byzantine nodes in the system. When the client receives 2f+1 consistent reply messages, it can be sure that its request has been agreed upon within the network.

[0146] The specific implementation process also includes:

[0147] Step S4: After the leading vehicle is determined, the PBFT distributed consensus algorithm is used to determine the backup leading vehicle of the intelligent connected vehicle group based on the trust level of each vehicle node except the leading vehicle; when the leading vehicle is in an abnormal state, the backup leading vehicle takes over all responsibilities of the leading vehicle, that is, the leading vehicle and leadership are transferred to the backup leading vehicle.

[0148] In this embodiment, the leadership of the leading vehicle includes any one or more of the route planning of the fleet, coordinated driving of the vehicle group, and data information management.

[0149] Specifically, the processing steps for leadership vehicle and leadership backup and transfer are as follows:

[0150] S401: The vehicle node selected as the backup leader vehicle always keeps synchronization with the leader vehicle;

[0151] In this embodiment, the content synchronized by the backup leader vehicle includes any one or more of the fleet route planning, strategy adjustment and status information implemented by the leader vehicle. At the same time, the vehicle node of the backup leader vehicle needs to keep updating its local data records so that it can seamlessly take over the leadership responsibility when needed.

[0152] S402: When the leader vehicle is in an abnormal state, the leadership of the leader vehicle is smoothly transferred to the backup leader vehicle, and a notification is sent to all vehicle nodes to confirm the leadership transfer and establish the identity of the new leader vehicle;

[0153] In this embodiment, the status of the leading vehicle is monitored and its trustworthiness is continuously evaluated. When the leading vehicle fails, responds with delay, and / or its trustworthiness is reduced, it is determined that its status is abnormal.

[0154] S403: After the leadership is successfully transferred to the backup leader vehicle, a backup election is conducted again to designate a new backup leader vehicle; the purpose is to ensure that the fleet can continue to operate normally even after multiple changes of the leader vehicle.

[0155] S404: The new leader vehicle updates the logs and status information of all vehicle nodes in the intelligent connected vehicle group to ensure data consistency and up-to-dateness of all vehicle nodes;

[0156] S405: Monitor the status of the new leading vehicle and continuously evaluate its trustworthiness. When the new leading vehicle is in an abnormal state, perform the next round of leading vehicle backup and transfer process.

[0157] The present invention introduces a mechanism for automatic backup of the leading vehicle and smooth transfer of leadership. When the leading vehicle fails or its trustworthiness decreases, the intelligent connected vehicle group can automatically start the backup leading vehicle and complete the smooth transfer of leadership. The introduction of this mechanism ensures the normal operation of the vehicle group and minimizes the impact of failures on the system.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture, characterized in that: Implementation of the leader vehicle election system based on intelligent connected vehicle groups; The system comprises: A node establishment module is used to establish a corresponding vehicle node for each vehicle in a group of intelligent connected vehicles in a zero-trust environment; The trust evaluation module is used to use the cross entropy trust evaluation method under the zero-trust architecture, combine the degree centrality and heterogeneity of the vehicle node and the neighboring vehicle nodes, analyze the importance of the vehicle node in the intelligent connected vehicle group, and then calculate the trust of each vehicle node in the zero-trust environment; The leader vehicle election module is used to determine the leader vehicle of the intelligent connected vehicle group based on the trust level of each vehicle node using the PBFT distributed consensus algorithm; The method comprises: S1: Establish a corresponding vehicle node for each vehicle in the intelligent connected vehicle group in a zero-trust environment; S2: Using the cross-entropy trust evaluation method under the zero-trust architecture, combined with the degree centrality and heterogeneity of the vehicle node and its neighboring vehicle nodes, the importance of the vehicle node in the intelligent connected vehicle group is analyzed, and then the trust of each vehicle node in the zero-trust environment is calculated; The principle of the cross-entropy trust evaluation method under the zero-trust architecture is to comprehensively examine the overall importance of the vehicle node itself and the information of the surrounding vehicle nodes, combine the topological information, and use the cross-entropy value to quantify the differences between the distribution of vehicle nodes. Nodes with greater differences are given higher trust in the network. In step S2, the trust of the vehicle node is calculated by the following steps: S201: Calculate the degree centrality of the vehicle node; The formula is: Where: DC i represents the degree centrality of vehicle node i; n represents the number of vehicle nodes in the intelligent connected vehicle group, that is, the sum of the row vectors of the adjacency matrix; a ij represents the connection status between vehicle node i and vehicle node j; k i is the degree of vehicle node i, that is, the number of other vehicle nodes directly connected to vehicle node i; S202: Calculate the corresponding heterogeneity according to the degree centrality of the vehicle node; The formula is: Where: I i represents the heterogeneity of vehicle node i; S203: Calculate the corresponding trust level using a cross entropy-based algorithm according to the heterogeneity of the vehicle nodes; The trust calculation formula of the algorithm based on cross entropy is expressed as: TR i+ =-∑ j∈Γ(i) I i lnI j ; Where: TR i+ represents the trust of vehicle node i; j∈Γ(i) means that vehicle node j is a neighbor node of vehicle node i; S3: Using the PBFT distributed consensus algorithm, the leading vehicle of the intelligent connected vehicle group is determined based on the trust level of each vehicle node.

2. The method for electing a leader vehicle in a smart connected vehicle group based on a zero-trust architecture as claimed in claim 1, characterized in that: In step S1, a vehicle node is established by the following steps: S101: Identify a target vehicle that enters the communication range of the intelligent connected vehicle group and assign a node ID to the target vehicle; S102: Initialize the node of the target vehicle through the following steps: 1) Create a corresponding vehicle node according to the node ID of the target vehicle and allocate independent storage space; 2) Establish and update the vehicle node adjacency matrix: 3) Record the basic information of the target vehicle and store it in the corresponding storage space; S103: Obtain the geographic location and operating status of the target vehicle, and store them in the corresponding storage space.

3. The method for electing a leader vehicle in a smart connected vehicle group based on a zero-trust architecture as claimed in claim 1, characterized in that: In step S2, if the trust level of a vehicle node is lower than a set threshold, the vehicle node is considered suspicious or untrustworthy, and the access rights of the vehicle node are restricted or revoked.

4. The method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture as claimed in claim 1, characterized in that: In step S2, as the status of vehicle nodes changes and new vehicle nodes are added, the intelligent connected vehicle group periodically recalculates the trust of all vehicle nodes.

5. The method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture as claimed in claim 1, characterized in that: In step S3, the leading vehicle is selected through the following steps: S301: Initialize the state of the intelligent connected vehicle group and set the number of Byzantine nodes f; S302: Obtain the trustworthiness of all vehicle nodes, and select the vehicle node with the highest trustworthiness as the master node; S303: The master node broadcasts pre-preparation information to each vehicle node; S304: After receiving the pre-preparation information, each vehicle node verifies the pre-preparation message; After verification, each vehicle node broadcasts the preparation information to other vehicle nodes; S305: After receiving the preparation information, each vehicle node verifies the preparation message; After verification, each vehicle node broadcasts submission information to other vehicle nodes; S306: After receiving the submission information, each vehicle node verifies the preparation message; S307: Repeat steps S301 to S306 until a certain vehicle node receives more than 2f+1 identical submission messages, and determines that the vehicle corresponding to the current master node is the leading vehicle.

6. The method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture as claimed in claim 1, characterized in that: In step S307, during the loop process, the status of the master node is monitored in real time. Once it is found that the master node behaves abnormally or the trustworthiness decreases, a re-election process is triggered to select a new master node.

7. The method for electing a leader vehicle in a smart connected vehicle group based on a zero-trust architecture as claimed in claim 1, characterized in that: Also includes: Step S4: After the leader vehicle is determined, the backup leader vehicle of the intelligent connected vehicle group is determined according to the trust of each vehicle node except the leader vehicle using the PBFT distributed consensus algorithm; When the leading vehicle is in an abnormal state, the backup leading vehicle takes over all responsibilities of the leading vehicle, that is, the leading vehicle and leadership are transferred to the backup leading vehicle.

8. The method for electing a leader vehicle in a group of intelligent connected vehicles based on a zero-trust architecture as claimed in claim 7, characterized in that: In step S4, the processing steps for backup and transfer of the leading vehicle and leadership are as follows: S401: The vehicle node selected as the backup leader vehicle always keeps synchronization with the leader vehicle; S402: When the leading vehicle is in an abnormal state, the leadership of the leading vehicle is smoothly transferred to the backup leading vehicle, and a notification is sent to all vehicle nodes; S403: After the leadership is successfully transferred to the backup leader vehicle, a backup election is performed again to designate a new backup leader vehicle; S404: The new leader vehicle updates the log and status information of all vehicle nodes in the intelligent connected vehicle group; S405: Monitor the status of the new leading vehicle and continuously evaluate its trustworthiness. When the new leading vehicle is in an abnormal state, perform the next round of leading vehicle backup and transfer process.

Images