CN117768351B

CN117768351B - Interference evaluation method and related equipment of Internet of vehicles system

Info

Publication number: CN117768351B
Application number: CN202410195726.9A
Authority: CN
Inventors: 张添; 贾焰; 韩伟红; 贾世准; 李小霞; 陈睿; 周密; 林文辉
Original assignee: Peng Cheng Laboratory
Current assignee: Peng Cheng Laboratory
Priority date: 2024-02-22
Filing date: 2024-02-22
Publication date: 2024-05-07
Anticipated expiration: 2044-02-22
Also published as: CN117768351A

Abstract

The method comprises the steps of firstly obtaining a normal network path diagram and a first shortest network path diagram of the normal network system in a normal network state, and obtaining an interfered network path diagram and a second shortest network path diagram of the normal network state after being interfered; next, calculating to obtain a node positive core based on the first network node and the second network node, and calculating to obtain an edge positive core based on the node positive core, the first sub-path and the second sub-path; and finally, calculating to obtain similar scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtaining an interference degree evaluation result of a normal network based on the similar scores, thereby effectively improving the real-time performance of evaluating interference in the Internet of vehicles system.

Description

Interference evaluation method and related equipment of Internet of vehicles system

Technical Field

The application relates to the technical field of Internet of vehicles, in particular to an interference assessment method and related equipment of an Internet of vehicles system.

Background

With the application and popularization of the internet of vehicles, network attacks to the internet of vehicles are more and more common, and the security problem of the V2X technology is also of great concern. The V2X communication node is easily subject to signal interference, communication delay and other problems in the information interaction process, so that the V2X communication node is easily subject to network attack. Therefore, the degree of the interference attack of the Internet of vehicles network can be effectively evaluated to better help to formulate an anti-interference strategy.

In the related art, the interference evaluation for the internet of vehicles is generally performed by performing analysis evaluation on an obtained communication environment, then analyzing and determining interference parameters from the communication environment, and then performing analysis and calculation on the interference parameters to obtain an interference score. However, since the vehicle-mounted unit in the internet of vehicles system is usually in a high-speed running state, the environment of the internet of vehicles is complex, and if the interference evaluation method is adopted in the internet of vehicles system, the calculation complexity and calculation time required for analyzing and determining the interference parameters are large, so that the real-time performance of interference scoring is low.

Disclosure of Invention

The embodiment of the application provides an interference evaluation method and related equipment for a vehicle networking system, which can improve the real-time performance of interference evaluation in the vehicle networking system.

To achieve the above object, a first aspect of an embodiment of the present application provides an interference evaluation method for an internet of vehicles system, the method including:

Acquiring a normal network path diagram of the Internet of vehicles system in a normal network state, and using an interfered network path diagram of the normal network state after being interfered;

Obtaining a first shortest network path diagram based on the normal network path diagram, and obtaining a second shortest network path diagram based on the interference network path diagram, wherein the first shortest network path diagram comprises first sub-paths between at least two first network nodes in a plurality of first network nodes, the second shortest network path diagram comprises second sub-paths between at least two second network nodes in a plurality of second network nodes, and the first network nodes are identical to the second network nodes corresponding to the first network nodes;

calculating to obtain a node positive core based on the first network node and the second network node, and calculating to obtain an edge positive core based on the node positive core, the first sub-path and the second sub-path;

And calculating to obtain similar scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive centering and the edge positive centering, and obtaining an interference degree evaluation result of a normal network based on the similar scores.

In some embodiments, the computing the edge positive core based on the node positive core, the first sub-path, and the second sub-path includes:

Determining that the first sub-path is identical to a second sub-path corresponding to the first sub-path;

determining a first sub-network node and a second sub-network node constituting the first sub-path from a plurality of the first network nodes, and determining a third sub-network node and a fourth sub-network node constituting the second sub-path from a plurality of the second network nodes; wherein the first sub-network node is the same as the third sub-network node, and the second sub-network node is the same as the fourth sub-network node;

obtaining edge positive core determination weight based on the first sub-path weight of the first sub-path and the second sub-path weight of the target second sub-path;

And accumulating the first node positive kernels of the first sub-network node and the third sub-network node, the edge positive kernels of the first sub-network node and the third sub-network node, and the second node positive kernels of the second sub-network node and the fourth sub-network node to obtain the edge positive kernels of the first sub-path and the target second sub-path.

In some embodiments, the calculating, based on the first network node and the second network node, a node positive core includes:

determining a first mapping relation of the first network node from the normal network path diagram to the first shortest network path diagram;

Determining a second mapping relationship of the second network node from the interfering network path graph to the second shortest network path graph;

and generating the node positive core of the first network node and the second network node based on the matching relation of the first mapping relation and the second mapping relation.

In some embodiments, the computing the similarity score for the first shortest network path graph and the second shortest network path graph based on the node positive core and the edge positive core comprises:

Acquiring a first adjacent matrix and a first diagonal matrix of the first shortest network path graph, and accumulating the first adjacent matrix and the first diagonal matrix to obtain a first normalized matrix;

acquiring a second adjacent matrix and a second diagonal matrix of the second shortest network path graph, and accumulating the second adjacent matrix and the second diagonal matrix to obtain a second normalized matrix;

Obtaining a normalization factor based on the product of the first matrix norm of the first normalization matrix and the second matrix norm of the second normalization matrix;

And accumulating all the node positive kernels and all the edge positive kernels, and dividing by the normalization factor to obtain the similarity scores.

In some embodiments, the internet of vehicles system includes a plurality of communication nodes, and the acquiring a normal network path diagram of the internet of vehicles system in a normal network state includes:

acquiring communication frames between two communication nodes, determining the communication frames with the number of the preset communication frames in a preset time threshold, and generating a communication link between the two communication nodes if the communication frames meet a preset intercommunication rule;

the normal network path graph is generated based on all of the communication nodes and all of the communication links.

In some embodiments, the obtaining the first shortest network path graph based on the normal network path graph includes:

Generating the first network node based on the communication node and determining the first sub-path based on the communication link;

Traversing matrix elements in an adjacent matrix of the normal network path diagram, and generating initial sub-path weights of the first sub-paths according to matrix element values of the matrix elements and element positions of the matrix elements;

Traversing all the initial sub-path weights, and updating the initial sub-path weights according to the comparison relation among every three initial sub-path weights to obtain a first sub-path weight of the first sub-path;

the first shortest network path graph is generated based on the first network node, the first sub-path, and the first sub-path weight.

In some embodiments, the method further comprises:

when the similarity score is lower than a preset safety threshold, obtaining the interference level of the interference according to the similarity score;

A security measure is determined based on the interference level and is performed such that the similarity score exceeds the preset security threshold.

To achieve the above object, a second aspect of an embodiment of the present application provides an interference estimation device for an internet of vehicles system, the device including:

The network path diagram acquisition module is used for acquiring a normal network path diagram of the Internet of vehicles system in a normal network state and an interference network path diagram of the normal network state after being interfered;

A shortest network path diagram obtaining module, configured to obtain a first shortest network path diagram based on the normal network path diagram, and obtain a second shortest network path diagram based on the interference network path diagram, where the first shortest network path diagram includes a first sub-path between at least two of the plurality of first network nodes, and the second shortest network path diagram includes a second sub-path between at least two of the plurality of second network nodes, where the first network node is the same as the second network node corresponding to the first network node;

the positive core determination calculation module is used for calculating to obtain a node positive core determination based on the first network node and the second network node, and calculating to obtain an edge positive core determination based on the node positive core determination, the first sub-path and the second sub-path;

and the interference evaluation module is used for calculating to obtain the similarity scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtaining the interference degree evaluation result of the normal network based on the similarity scores.

To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the method for evaluating interference of the internet of vehicles system according to the first aspect when executing the computer program.

To achieve the above object, a fourth aspect of the embodiments of the present application proposes a storage medium, which is a computer-readable storage medium storing a computer program that when executed by a processor implements the interference assessment method of the internet of vehicles system according to the first aspect.

The method comprises the steps of firstly obtaining a normal network path diagram of the Internet of vehicles system in a normal network state and an interference network path diagram of the normal network state after being interfered; then, a first shortest network path diagram is obtained based on the normal network path diagram, a second shortest network path diagram is obtained based on the interference network path diagram, the first shortest network path diagram comprises first sub-paths between at least two first network nodes in a plurality of first network nodes, the second shortest network path diagram comprises second sub-paths between at least two second network nodes in a plurality of second network nodes, and the first network nodes are identical to the second network nodes corresponding to the first network nodes; next, calculating to obtain a node positive core based on the first network node and the second network node, and calculating to obtain an edge positive core based on the node positive core, the first sub-path and the second sub-path; and finally, calculating to obtain similar scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtaining an interference degree evaluation result of the normal network based on the similar scores. According to the method and the system, the network path conditions of the normal network of the Internet of vehicles system before and after being interfered are represented by the first shortest network path diagram and the second shortest network path diagram, then the similarity scores of the first shortest network path diagram and the second shortest network path diagram are calculated by the node positive determination core and the node positive determination core, so that interference received in the Internet of vehicles system is estimated directly by the similarity scores, complex analysis on the communication environment of the Internet of vehicles system is not needed, the real-time performance of interference estimation in the Internet of vehicles system is effectively improved, and real-time interference estimation is facilitated to be utilized to timely formulate and take corresponding interference resisting strategies.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

Fig. 1 is a flowchart of an interference evaluation method of an internet of vehicles system according to an embodiment of the present application.

Fig. 2 is a flow chart of step 101 in fig. 1.

Fig. 3 is a schematic diagram of a communication link acknowledgement provided by another embodiment of the present application.

Fig. 4 is a schematic diagram of a normal network path diagram according to another embodiment of the present application.

Fig. 5 is a schematic diagram of an interfering network path diagram according to another embodiment of the present application.

Fig. 6 is a flow chart of step 102 in fig. 1.

Fig. 7 is a flow chart of step 103 in fig. 1.

Fig. 8 is a further flowchart of step 103 in fig. 1.

Fig. 9 is a flow chart of step 104 in fig. 1.

Fig. 10 is a flowchart of a security measure in an interference assessment method according to an embodiment of the present application.

Fig. 11 is a flowchart of a test interference estimation method according to an embodiment of the present application.

Fig. 12 is a schematic structural diagram of an interference estimation device of an internet of vehicles system according to an embodiment of the present application.

Fig. 13 is a schematic hardware structure of an electronic device according to an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.

First, several nouns involved in the present application are parsed:

the internet of vehicles (Connected Car) refers to connecting automobiles with internet technology, and realizing interconnection and interworking between vehicles, between vehicles and infrastructure, between vehicles and mobile phones, and the like. Through the internet of vehicles technology, the automobile can realize functions such as real-time data transmission, remote control, intelligent driving assistance, and the like, and driving safety, driving efficiency and vehicle owner experience are improved.

The network topology refers to a graph that graphically presents nodes in a network and connection relationships between them. In a network topology, a node typically represents a device (e.g., a router, a switch, a server, etc.) in a network, and a connection line represents a connection manner (e.g., wired connection, wireless connection, etc.) between the devices. The network topology may help one better understand the network structure, including the relationships between devices, the manner of connection, and the logical layout in the network.

The shortest path graph refers to a graph in which the shortest path between two nodes is calculated in the network and represented graphically. Shortest path refers to the shortest distance between two nodes, and is typically measured in terms of hops, bandwidth, or delay. The shortest path graph may help a network administrator find an optimal path from one node to another node in the network. This is very useful in router path selection, network planning and optimization, and network performance analysis.

Based on the method, the embodiment of the application utilizes the first shortest network path diagram and the second shortest network path diagram to represent the network path condition of the normal network of the Internet of vehicles system before and after being interfered, and then utilizes the node positive definite core and the edge positive definite core to calculate the similarity scores of the first shortest network path diagram and the second shortest network path diagram, thereby directly evaluating the interference suffered by the Internet of vehicles system by utilizing the similarity scores without complex analysis on the communication environment of the Internet of vehicles system, effectively improving the real-time performance of the interference evaluation in the Internet of vehicles system, and further facilitating the timely formulation and the corresponding interference resisting strategy by utilizing the real-time interference evaluation.

The embodiment of the application provides an interference evaluation method and related equipment of a vehicle networking system, and specifically, the following embodiment is used for explaining the interference evaluation method of the vehicle networking system in the embodiment of the application. The interference evaluation method of the internet of vehicles system provided by the embodiment can be applied to a server or a controller and the like in the internet of vehicles system.

Referring to fig. 1, an optional flowchart of a method for evaluating interference of an internet of vehicles system according to an embodiment of the present application is provided, where the method in fig. 1 may include, but is not limited to, steps 101 to 104. It should be understood that the order of steps 101 to 104 in fig. 1 is not particularly limited, and the order of steps may be adjusted, or some steps may be reduced or added according to actual requirements.

Step 101: and acquiring a normal network path diagram of the Internet of vehicles system in a normal network state and an interference network path diagram of the normal network state after being interfered.

Step 101 is described in detail below.

In some embodiments, after responding to an interference evaluation calculation request of the internet of vehicles system, a normal network path diagram of the internet of vehicles system in a normal network state is acquired in real time so as to be convenient for subsequent comparison with an interfered interference network path diagram, thereby evaluating the interference degree of interference, and further, complex analysis on a communication environment is not needed, so that the real-time performance of interference evaluation is improved.

Referring to fig. 2, a normal network path diagram of the internet of vehicles system in a normal network state is obtained, including the following steps 201 to 203.

Step 201: two communication nodes where a communication frame exists are determined as a first communication node and a second communication node.

Step 202: and acquiring a data transmission frame sent by the first communication node and a data confirmation frame sent by the second communication node within a preset time threshold, and generating a communication link between the first communication node and the second communication node.

Steps 201 to 202 are described in detail below.

In some embodiments, communication nodes in the internet of vehicles system are detected in real time, and when it is determined that a communication frame exists between any two communication nodes, the two communication nodes are determined to be a first communication node and a second communication node. And then acquiring continuous communication frames between the first communication node and the second communication node within a preset time threshold, analyzing the continuous communication frames, determining that a communication link exists between the first communication node and the second communication node if a data transmission frame Packet sent by the first communication node and a data acknowledgement frame ACK sent by the second communication node are acquired, and generating the communication link between the first communication node and the second communication node in a normal network path diagram. The data transmission frame Packet comprises data transmitted by the first communication node to the second communication node. The preset time threshold is used to confirm continuity of communication frames between the first communication node and the second communication node, which may be set according to an empirical value or a demand.

In some embodiments, to generate a more accurate normal network path graph, it is desirable to more accurately determine whether there is a true communication link between the first communication node and the second communication node. Referring to fig. 3, a schematic diagram of communication link confirmation provided by an embodiment of the present application is shown. When DSRC protocol is used in the Internet of vehicles system, four continuous communication frames exist between a first communication node and a second communication node according to RTS/CTS mechanism, wherein the first communication frame is a request-to-send frame RTS sent by the first communication node to the second communication node, and the duration time is thatWherein/>To send out the request frame time,/>A transmission time for transmitting the request frame; the second communication frame is an acknowledgement transmission frame CRS transmitted by the second communication node to the first communication node, and the duration is thatWherein/>To confirm the time of issuance of the request frame,/>A transmission time for transmitting the request frame; the third communication frame is a data transmission frame Packet sent by the first communication node, and the duration is thatWherein/>Time of issuance of data transmission frame,/>Transmission time of the data transmission frame; the fourth communication frame is the ACK frame of the data acknowledgement frame sent by the second communication node, and the duration is thatWherein/>Time of issuance of data acknowledgement frame,/>Transmission time of the data acknowledgement frame; after confirming that the first communication node and the second communication node have the four continuous communication frames, the communication link between the first communication node and the second communication node can be accurately determined, so that an accurate normal network path diagram is generated according to the communication links between the plurality of communication nodes.

It is understood that the DSRC protocol is a dedicated short-range communication protocol, and is widely used in the fields of intelligent transportation systems, inter-vehicle communication, and the like. The RTS/CTS mechanism is a communication mechanism in the DSRC protocol for solving channel contention and collision problems. RTS and CTS are control frames of two reserved channels used in the DSRC protocol to coordinate access and data transmission by communication nodes.

In some embodiments, when the internet of vehicles system uses a cellular network, the connectivity relationship between the communication nodes depends on their selection of parameters characterizing the quality of the communication channel, for example, setting parameters as: signal-to-noise ratio, CQI, signal strength indication (RSSI), etc. And when the two nodes are detected to be greater than the parameter selection threshold value, the two nodes are determined to be in a communicated state. Where the signal-to-noise ratio is an indicator of the ratio of the useful signal to the interference noise power, which is used to measure the signal quality. CQI is an index for evaluating channel quality in a wireless communication system. The CQI value is calculated based on the quality of the received signal and the interference condition to provide an estimate of the current channel condition, with higher CQI values indicating better channel quality and lower CQI values indicating worse channel quality. A signal strength Indicator (RECEIVED SIGNAL STRENGTH Indicator, RSSI) is an Indicator of the strength of a wireless signal, representing the strength of a received wireless signal, and is affected by the distance between the device and the signal source, the signal propagation path, and other environmental factors.

Step 203: a normal network path graph is generated based on all communication nodes and all communication links.

Step 203 is described in detail below.

In some embodiments, after the communication links between all communication nodes in the Internet of vehicles system are acquired, and for a period of timeNo new communication links are found, a normal network path graph may be generated based on all discovered communication nodes and the communication links therebetween. Referring to fig. 4, a schematic diagram of a normal network path diagram according to an embodiment of the present application is shown. In which five communication nodes are taken as examples, respectively/>Wherein the communication link comprisesThe node D is a detection node arranged on the server and used for detecting the link condition between the communication nodes. Based on the normal network path graph, a corresponding adjacency matrix can be generated as follows: /(I)Wherein each rank is a communication node/>。

It is understood that an adjacency matrix is a matrix representing the structure of a graph and is widely used in graph theory and network analysis. It is used to represent the connection relationships between nodes in the graph and may represent the weight or presence of edges by elements in the matrix. The adjacency matrix is typically a two-dimensional square matrix in which rows and columns represent nodes in the graph, respectively.

Through the steps 201 to 203, by using the communication frame condition of the continuous communication frame between every two communication nodes in the plurality of communication nodes in the normal network state of the internet of vehicles system, whether a communication link exists between the communication nodes can be more accurately determined, and an accurate normal network path diagram is generated according to the communication link, so that analysis and comparison with the condition of the internet of vehicles system after being interfered are facilitated, and the accuracy and reliability of interference assessment are improved.

In some embodiments, the normal network path diagram may also be constructed by pre-simulation, in particular initializing the communication network diagramNull diagram for N nodes,/>For the set of points,/>For the edge set, each node is labeled with a communication network node ID. And then judging the communication state of the two nodes by acquiring the communication parameters between the two nodes, thereby generating a corresponding normal network path diagram.

In some embodiments, after determining that the normal network state of the internet of vehicles system is interfered, the interference network path diagram of the normal network state of the internet of vehicles system after being interfered is obtained by a method similar to the method for generating the normal network path diagram. Specifically, a server generates communication nodes after the detection node detects the normal network state in the Internet of vehicles system and the communication links among the communication nodes, so that an interference network path diagram is generated. Referring to fig. 5, a schematic diagram of an interference network path diagram according to an embodiment of the present application is shown. Wherein FIG. 5 is an interfering network path diagram after a certain interference from the normal network path diagram shown in FIG. 4, wherein the communication nodes are five, respectively. But due to interference, the communication links between the communication nodes are reduced, leaving only. Based on the interference network path diagram, the communication nodes are respectively used as row and column coordinate axes, so that a corresponding adjacent matrix is generated. After the normal network path diagram and the interference network path diagram are obtained, the interference degree result of interference can be directly evaluated by analyzing the similarity between the normal network path diagram and the interference network path diagram. The interference can be generated by the fact that the Internet of vehicles system is suddenly attacked by the outside, or can be generated by testing requirements and self-defining.

Step 102: a first shortest network path graph is obtained based on the normal network path graph and a second shortest network path graph is obtained based on the interfering network path graph.

Step 102 is described in detail below.

In some embodiments, after obtaining the normal network path diagram and the interfering network path diagram, in order to better calculate the similarity between the normal network path diagram and the interfering network path diagram, the normal network path diagram and the interfering network path diagram need to be converted into corresponding first shortest network path diagram and second shortest network path diagram. How to convert from the normal network path diagram to the first shortest network path diagram will be described in detail below.

Referring to fig. 6, a first shortest network path diagram is obtained based on a normal network path diagram, including the following steps 601 to 604.

Step 601: a first network node is generated based on the communication node and a first sub-path is determined based on the communication link.

Step 602: traversing matrix elements in an adjacent matrix of the normal network path diagram, and generating initial sub-path weights of the first sub-paths according to matrix element values of the matrix elements and element positions of the matrix elements.

Step 603: traversing all the initial sub-path weights, and updating the initial sub-path weights according to the comparison relation among every three initial sub-path weights to obtain a first sub-path weight of the first sub-path.

Step 604: a first shortest network path graph is generated based on the first network node, the first sub-path, and the first sub-path weight.

Steps 601 to 604 are described in detail below.

In some embodiments, a normal network path graph is obtainedThereafter, the first shortest network Path Chart/>Point set and normal network path graph/>The same set of points, i.e. first network node/>With communication node/>The same applies. The conversion method of the edge set comprises the following steps: if the normal network path diagram/>Any two communication nodes/>A communication link exists between them, a first sub-path. Then by mapping/>For the first shortest network Path Chart/>The calculation method of the mapping relation of the first sub-path weighting values comprises the following steps: traversing matrix elements in an adjacent matrix of the normal network path diagram, and generating initial sub-path weights of the first sub-paths according to matrix element values of the matrix elements and element positions of the matrix elements; then traversing all initial sub-path weights, and updating the initial sub-path weights according to the comparison relation among every three initial sub-path weights to obtain a first sub-path weight cost [ i, j ] of a first sub-path, wherein i, j refer to two first network nodes forming the first sub-path; and generating a normal network path graph/>, based on the first network node, the first sub-path, and the first sub-path weightCorresponding first shortest network Path Chart/>. The first shortest network path diagram comprises a first sub-path between at least two first network nodes in the plurality of first network nodes.

The step of weighting the specific first sub-path is as follows:

(1) Traversing a normal network path graph When the element value of any one matrix element is equal to 1 and the matrix element is not in the diagonal of the adjacent matrix (i.e., (/ >)(I, j) =1) and (i+.j), i, j being any two communication nodes), determining that the matrix element corresponds to two communication nodes of the adjacency matrix based on the element position of the matrix element in the adjacency matrix; generating an initial sub-path weight (i.e., cost [ i, j ] = distance (i, j)) of the matrix element corresponding to the first sub-path based on the shortest path length between the two communication nodes;

(2) Traversing a normal network path graph When the element value of any one matrix element is not equal to 1 and the matrix element is in the diagonal of the adjacent matrix (i.e., (/ >)(I, j) =0) and (i=j), i, j being any two communication nodes), determining that the matrix element corresponds to two communication nodes of the adjacency matrix based on the element position of the matrix element in the adjacency matrix; taking 0 as an initial sub-path weight of the first sub-path corresponding to the matrix element (i.e. cost [ i, j ] =0);

(3) Traversing a normal network path graph When the element value of any one matrix element is not equal to 1 and the matrix element is in the diagonal of the adjacent matrix (i.e., (/ >)(I, j) = 0) and (i+.j), i, j being any two communication nodes), determining that the matrix element corresponds to two communication nodes of the adjacency matrix based on the element position of the matrix element in the adjacency matrix; taking positive infinity as an initial sub-path weight (namely cost [ i, j ] = infinity) of the first sub-path corresponding to the matrix element;

(4) Based on the initial sub-path weights of all the first sub-paths, an initial sub-path weight matrix is obtained;

(5) Traversing matrix elements in the initial sub-path weight matrix, and when the element value of any matrix element is larger than the sum of the element values of any two other matrix elements (i.e., (cost [ i, k ] +cost [ k, j ] < cost [ i, j ]), i, j, k is any three communication nodes), updating the element values of the larger matrix element (i.e., cost [ i, j ]: = cost [ i, k ] +cost [ k, j ]) based on the sum of the element values of the two smaller matrix elements to obtain the first sub-path weight of the first sub-path.

Wherein,For normal network path diagram/>Distance (i, j) is the normal network path graph/>In (i, j), the shortest path length of two communication nodes, cost (i, j) is the first shortest network path diagram/>Middle edge/>Is a weight of (a).

Through the steps 601 to 604, the initial sub-path weights are generated by using the element conditions of the matrix elements in the adjacency matrix of the normal network path diagram and the shortest path length of the communication link between every two communication nodes, and the first sub-path weights for accurately describing the path cost of the first sub-path in the normal network path diagram are updated based on the weight comparison condition of every three initial sub-path weights, so as to convert the normal network path diagram into the first shortest network path diagram, thereby facilitating the subsequent similar comparison between the first shortest network path diagram and the second shortest network path diagram after interference, so as to better evaluate the interference and further improve the accuracy and reliability of the interference evaluation.

In some embodiments, an interfering network path graph is obtainedThereafter, the second shortest network Path Chart/>Point set and interfering network path graph/>The same set of points, i.e. second network node/>With communication node/>The same applies. The conversion method of the edge set comprises the following steps: if the network path diagram is disturbed/>Any two communication nodes/>A communication link exists between them, then a second sub-path. Then by mapping/>For the second shortest network Path figure/>The calculation method of the mapping relation of the second sub-path empowerment of the sub-paths comprises the following steps: traversing matrix elements in a second adjacent matrix of the interference network path diagram, and generating initial sub-path weights of a second sub-path according to matrix element values of the matrix elements and element positions of the matrix elements; then traversing all initial sub-path weights, updating the initial sub-path weights according to the comparison relation among every three initial sub-path weights to obtain a second sub-path weight of a second sub-path, and generating an interference network path diagram/>, based on the second network node, the second sub-path and the second sub-path weightCorresponding second shortest network Path Chart/>. The second shortest network path diagram comprises a second sub-path between at least two second network nodes in the plurality of second network nodes, and the first network node is the same as the second network node corresponding to the first network node.

Step 103: and calculating to obtain a node positive core based on the first network node and the second network node, and calculating to obtain an edge positive core based on the node positive core, the first sub-path and the second sub-path.

Step 103 is described in detail below.

In some embodiments, a normal network path graph is obtainedCorresponding first shortest network Path Chart/>And interfering network path graph/>Corresponding second shortest network Path Chart/>Thereafter, by calculating the first shortest network Path graph/>And a second shortest network Path graph/>The similarity between the two can better evaluate the interference degree of the interference suffered by the Internet of vehicles system. It can be understood that the positive-definite kernel function maps the samples into a high-dimensional feature space and calculates the similarity between the samples in the feature space, so that the similarity calculation mode of the first shortest network path diagram and the second shortest network path diagram can be realized based on the node positive definite kernel and the edge positive definite kernel, and the direct calculation of the high-dimensional feature space is avoided, so that the real-time performance of interference evaluation is improved. How to calculate the first shortest network path graph/>, will be described in detail belowAnd a second shortest network Path graph/>Similarity between them.

In some embodiments, to calculate the first shortest network path graphAnd a second shortest network Path graph/>The similarity between the two network path diagrams needs to be calculated first to obtain a first shortest network path diagram/>And a second shortest network Path graph/>The nodes and edges between are core-defined. Thus, a first shortest network path graph/>, is obtainedAnd a second shortest network Path graph/>Thereafter, the first shortest network path graph/>, basedFirst and second shortest network Path diagrams/>The second network node of the network node is calculated to obtain a node positive definite core, and the first shortest network path diagram/>, based on the node positive definite coreFirst and second shortest network path graph/>And (3) calculating to obtain the edge positive definite core. The calculation steps of the node positive determination core and the edge positive determination core will be described in detail below.

Referring to fig. 7, the node positive determination core is calculated based on the first network node and the second network node, which includes the following steps 701 to 703.

Step 701: a first mapping relationship of the first network node from the normal network path graph to the first shortest network path graph is determined.

Step 702: a second mapping of the second network node from the interfering network path graph to a second shortest network path graph is determined.

Step 703: and generating node positive verification cores of the first network node and the second network node based on the matching relation of the first mapping relation and the second mapping relation.

Steps 701 to 703 are described in detail below.

In some embodiments, the interference estimation network is obtaining a first shortest network path graphAnd a second shortest network Path graph/>Thereafter, a first mapping relation/>, of the first network node from the normal network path diagram to the first shortest network path diagram, will be determinedAnd determining a second mapping relationship/>, of the second network node from the interfering network path graph to the second shortest network path graphGenerating a node positive centering/>, based on the matching relationship of the first mapping relationship and the second mapping relationship, of the first network node and the second network nodeThe following is indicated:

（1）

Wherein the method comprises the steps of For the first network node,/>Is a second network node corresponding to the first network node. Based on the method, all first shortest network path diagrams/>, can be obtainedIn a second shortest network path graph/>The node of the second network node corresponding to the first network node is being nucleated.

Through the steps 701 to 703, the mapping relationship between the first network node and the corresponding second network node is utilized to generate the node positive determination core of the first shortest network path diagram and the second shortest network path diagram, so as to facilitate the accurate calculation of the first shortest network path diagram by utilizing the node positive determination coreAnd a second shortest network Path graph/>Is a similarity of (3).

Referring to fig. 8, the edge positive core is calculated based on the node positive core, the first sub-path, and the second sub-path, and includes the following steps 801 to 804.

Step 801: a target second sub-path corresponding to the first sub-path among the plurality of second sub-paths is determined.

Step 802: the method comprises determining a first sub-network node and a second sub-network node from a plurality of first network nodes, which constitute a first sub-path, and determining a third sub-network node and a fourth sub-network node from a plurality of second network nodes, which constitute a target second sub-path.

Steps 801 to 802 are described in detail below.

In some embodiments, the first shortest network path graph is obtainedAnd a second shortest network Path graph/>After that, first the shortest network path graph/>, is determinedIn a second shortest network path graph/>Is determined to be the first sub-path/>, among the plurality of second sub-pathsCorresponding target second sub-path/>. A first sub-network node/>, which constitutes a first sub-path, is then determined from the plurality of first network nodesAnd a second subnetwork node/>And determining a constituent target second sub-path/>, from a plurality of second network nodesThird subnetwork node/>And fourth subnetwork node/>. Wherein the first shortest network Path graph/>First subnetwork node/>And at the second shortest network Path graph/>Third subnetwork node/>Same, first shortest network Path Chart/>Second subnetwork node/>And a second shortest network Path graph/>Fourth subnetwork node/>The same applies.

Step 803: and obtaining the edge positive core weight based on the first sub-path weight of the first sub-path and the second sub-path weight of the target second sub-path.

Step 803 is described in detail below.

In some embodiments, in determining the first sub-pathAnd a target second sub-path, according to the first shortest network path graph/>Determining the first sub-path/>First sub-path weight/>And according to the second shortest network path graph/>Determining target second sub-path/>Second sub-path weight/>. Then according to the first sub-path weight/>And a second sub-path weight/>Obtain the first sub-path/>The edge positive core weights are expressed as follows:

（2）

Wherein, Refers to the first shortest network path graph/>Set of multiple first sub-paths in/>Refers to the second shortest network path graph/>Is included in the set of the plurality of second sub-paths.

Step 804: and obtaining the edge positive core of the first sub-path and the target second sub-path according to the first node positive core, the edge positive core weight and the second node positive core of the second sub-network node and the fourth sub-network node of the first sub-network node and the third sub-network node.

Step 804 is described in detail below.

In some embodiments, the first sub-network node is determined in the above mannerAnd third subnetwork node/>Is nuclear/>First sub-path/>And target second sub-path/>Is of a positive edge-defined kernel weightSecond subnetwork node/>And fourth subnetwork node/>Is being nucleated by the second node of (2)Thereafter, the core/>, will be positively defined based on the first nodeSide-normal core weight/>Second node is determining core/>Calculating to obtain a first sub-path/>And target second sub-path/>Is characterized by the correct alignment of the edgesThe following is indicated:

（3）

Through the steps 801 to 804, the sub-path weights of the first sub-path and the second sub-path corresponding thereto are used to generate the edge positive determination weights, and the edge positive determination cores of the first shortest network path graph and the second shortest network path graph are accurately generated based on the node positive determination cores of the two corresponding network nodes of the first sub-path and the second sub-path and the edge positive determination weights, thereby facilitating the accurate calculation of the first shortest network path graph by using the edge positive determination cores And a second shortest network Path graph/>Is a similarity of (3).

Step 104: and calculating to obtain similar scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtaining an interference degree evaluation result after the normal network is interfered based on the similar scores.

Step 104 is described in detail below.

In some embodiments, the first shortest network path graph may be obtained based on the node positive verification calculation method of the above steps 701 to 703 and the edge positive verification calculation method of the above steps 801 to 804And a second shortest network Path graph/>The first shortest network path diagram/>, is calculated by utilizing the plurality of edge positive cores and the plurality of node positive coresAnd a second shortest network Path graph/>The similarity of the interference is convenient to evaluate the interference rapidly, and the real-time performance of the interference evaluation is improved.

In some embodiments, to calculate the first shortest network path graph more accuratelyAnd a second shortest network Path graph/>The similarity score between the two data is required to be normalized, and the normalization is a common data preprocessing technology used for converting the data with different scales and ranges into a unified standard range so as to better compare and analyze. Normalization can eliminate the dimensional difference of data and prevent the excessive influence of certain indexes on a model or an algorithm. The relevant steps of the normalization process will be further described below.

Referring to fig. 9, a similarity score for a first shortest network path graph and a second shortest network path graph is calculated based on node positive kernels and edge positive kernels, comprising the following steps 901 to 904.

Step 901: and acquiring a first adjacent matrix and a first diagonal matrix of the first shortest network path graph, and acquiring a first normalization matrix based on the first adjacent matrix and the first diagonal matrix.

Step 902: and acquiring a second adjacent matrix and a second diagonal matrix of the second shortest network path graph, and acquiring a second normalized matrix based on the second adjacent matrix and the second diagonal matrix.

Step 903: the normalization factor is obtained based on a product of a first matrix norm of the first normalization matrix and a second matrix norm of the second normalization matrix.

Step 904: and accumulating all node positive kernels and all edge positive kernels, and dividing by a normalization factor to obtain similar scores.

Steps 901 to 904 are described in detail below.

In some embodiments, the interference assessment device further obtains a first shortest network path graph while computing node positive core and edge positive coreFirst adjacency matrix/>And a second shortest network Path graph/>Second adjacency matrix/>Then according to the first adjacency matrix/>Calculating to obtain a corresponding first diagonal matrix/>And according to the second adjacency matrix/>Calculating to obtain a corresponding second diagonal matrix/>. Then, a first normalized matrix is calculated according to the first adjacent matrix and the first diagonal matrix, and the first normalized matrix is expressed as follows:

（4）

Similarly, a second normalized matrix calculated from the second adjacency matrix and the second diagonal matrix is represented as follows:

（5）

next, based on the first normalized matrix First matrix norm/>And a second normalized matrix/>Second matrix norm/>The product of (2) to obtain a normalization factor is expressed as follows:

（6）

Wherein, The Frobenius norm, also called as a matrix two-norm, is a form of matrix norm calculation, and is used for measuring the size or the characteristics of a matrix, and the process of the Frobenius norm calculation includes: the absolute squares of all elements of matrix a are added and then square root is opened. Based on this, the interference assessment means will accumulate all the resulting nodes positive core/>And all sides are core/>And divided by normalization factor/>The resulting similarity scores are expressed as follows:

/norm（7）

Through the steps 901 to 904, a first shortest network path graph can be obtained And a second shortest network Path graph/>And the similarity score between the two network stations can be used for obtaining an interference degree evaluation result of the normal network in the Internet of vehicles system after being interfered based on the similarity score. It will be appreciated that the higher the similarity score, the description of the first shortest network path graph/>And a second shortest network Path graph/>The more similar the interference is, the lower the interference degree evaluation result of the interference is further obtained; similarly, the lower the similarity score, the higher the interference degree evaluation result of the interference can be obtained. And the scores of the similar scores can be corresponding to each level of the interference degree evaluation result through equidistant division, equal difference division, experience value and other methods according to the score range of the similar scores, so that in the actual application of the Internet of vehicles system, the corresponding interference degree evaluation result can be directly obtained by acquiring the similar scores before and after the real-time normal network is interfered, and the real-time performance of interference evaluation is improved.

In some embodiments, after obtaining the similarity score, if the similarity score is found to be lower, it is indicated that the interference degree is higher at this time, and a corresponding safety measure needs to be adopted to improve the reliability of the interference evaluation method of the internet of vehicles system provided by the application. How to take corresponding measures based on the similarity scores will be described in detail below.

Referring to fig. 10, after obtaining the interference degree evaluation result after the normal network is interfered based on the similarity score, the method further includes:

step 1001: and when the similarity score is lower than a preset safety threshold, obtaining the interference level of the interference according to the similarity score.

Step 1002: and determining a safety measure based on the interference level, and executing the safety measure on the interfered car networking system.

Steps 1001 to 1002 are described in detail below.

In some embodiments, when the interference assessment device determines that the obtained similarity score is lower than the preset safety threshold, the interference assessment device indicates that the similarity score is in a lower condition, that is, the interference in the internet of vehicles system has affected the information interaction of the plurality of communication nodes in the normal network to a greater extent. At this time, the interference evaluation device determines the interference level of the interference according to the score of the similarity score, then determines the corresponding safety measure based on the interference level, and then takes the safety measure in the normal network of the internet of vehicles system to effectively reduce the influence of the interference in the internet of vehicles system, thereby improving the communication quality of a plurality of communication nodes in the internet of vehicles system, and further improving the reliability of the interference evaluation method of the internet of vehicles system.

It is to be appreciated that the interference levels may be generated based on the interference evaluation results, and that the determination of the different security measures corresponding to the different interference levels may be achieved through empirical values. Wherein the security measures include: authentication, encryption, intrusion detection and prevention systems, malware protection, security updates, and bug fixes, among others.

The embodiment of the application also provides a method for evaluating the test interference. Referring to fig. 11, a flowchart of a test interference estimation method according to an embodiment of the present application is shown. The test flow includes the following steps.

Step 1101: the normal network path diagram under any normal network is acquired in the pre-constructed simulated internet of vehicles system, and the acquisition method thereof can be acquired by the methods from step 201 to step 203.

Step 1102: any interference is applied in the normal network in step 1101.

Step 1103: and acquiring an interference network path diagram of a normal network of the analog internet of vehicles system after the interference is affected.

Step 1104: the normal network path diagram and the interference network path diagram are converted into a first shortest network path diagram corresponding to the normal network path diagram and a second shortest network path diagram corresponding to the interference network path diagram by a method similar to the steps 601 to 604.

Step 1105: through the methods from step 103 to step 104, the similarity scores of the first shortest network path diagram and the second shortest network path diagram are calculated, so that the interference degree evaluation result of the interference is obtained according to the similarity scores.

The embodiment of the application provides an interference evaluation method of an Internet of vehicles system, which comprises the steps of firstly obtaining a plurality of communication nodes of the Internet of vehicles system in a normal network state, determining the communication frame condition of continuous communication frames between every two communication nodes to generate communication links between the communication nodes, and generating a normal network path diagram of the Internet of vehicles system in the normal network state according to the plurality of communication nodes and the plurality of communication links; generating an interference network path diagram of the normal network state after being interfered according to the communication node of the normal network state of the Internet of vehicles system after being interfered and a communication link between the communication nodes; then, generating a first shortest network path diagram corresponding to the normal network path diagram based on the adjacency matrix of the normal network path diagram, and generating a second shortest network path diagram corresponding to the interference network path diagram based on the adjacency matrix of the interference network path diagram; next, calculating to obtain a node positive definite core based on the mapping relation between the first network node and the second network node, and calculating to obtain an edge positive definite core based on the node positive definite core, the first sub-path weight of the first sub-path and the second sub-path weight of the second sub-path; meanwhile, calculating a normalization factor by using a first adjacent matrix and a first diagonal matrix of the first shortest network path diagram and a second adjacent matrix and a second diagonal matrix of the second shortest network path diagram; and finally, calculating similar scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive centering, the edge positive centering and the normalization factor, obtaining an interference degree evaluation result of a normal network based on the similar scores, and taking corresponding safety measures in the normal network of the Internet of vehicles system based on the similar scores.

In the embodiment of the application, the communication links between the communication nodes are accurately generated by utilizing the communication frame condition of the continuous communication frames between the communication nodes before and after the interference of the Internet of vehicles system, and the normal network path diagram and the interference network path diagram of the Internet of vehicles system before and after the interference are accurately generated by utilizing the communication links. And then, calculating the similarity scores of the first shortest network path diagram and the second shortest network path diagram before and after the Internet of vehicles system is interfered in a normal network state by utilizing the node positive verification and the side positive verification, so that the interference suffered by the Internet of vehicles system is directly evaluated by utilizing the similarity scores, and the real-time performance of evaluating the interference in the Internet of vehicles system is effectively improved.

After the real-time interference degree evaluation result is obtained, corresponding safety measures are timely taken in the Internet of vehicles system by utilizing the real-time scoring result of the similarity scoring, so that the safety of the Internet of vehicles system is effectively improved.

The embodiment of the present application further provides an interference estimation device of an internet of vehicles system, which can implement the interference estimation method of the internet of vehicles system, and referring to fig. 12, the device 1200 includes:

the network path diagram acquiring module 1210 is configured to acquire a normal network path diagram of the internet of vehicles system in a normal network state and an interference network path diagram of the normal network state after being interfered;

The shortest network path diagram obtaining module 1220 is configured to obtain a first shortest network path diagram based on a normal network path diagram, and obtain a second shortest network path diagram based on an interference network path diagram, where the first shortest network path diagram includes a first sub-path between at least two first network nodes in a plurality of first network nodes, and the second shortest network path diagram includes a second sub-path between at least two second network nodes in a plurality of second network nodes, and the first network node is the same as a second network node corresponding to the first network node;

the positive core determining calculation module 1230 is configured to calculate a node positive core based on the first network node and the second network node, and calculate an edge positive core based on the node positive core, the first sub-path, and the second sub-path;

the interference evaluation module 1240 is configured to calculate a similarity score of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtain an interference degree evaluation result after the normal network is interfered based on the similarity score.

In some embodiments, the network path graph acquisition module 1210 is further configured to:

determining that two communication nodes with communication frames exist as a first communication node and a second communication node;

Acquiring a data transmission frame sent by a first communication node and a data confirmation frame sent by a second communication node within a preset time threshold, and generating a communication link between the first communication node and the second communication node;

a normal network path graph is generated based on all communication nodes and all communication links.

In some embodiments, the shortest network path graph acquisition module 1220 is further configured to:

generating a first network node based on the communication node and determining a first sub-path based on the communication link;

Traversing all initial sub-path weights, and updating the initial sub-path weights according to the comparison relation among every three initial sub-path weights to obtain a first sub-path weight of a first sub-path;

a first shortest network path graph is generated based on the first network node, the first sub-path, and the first sub-path weight.

In some embodiments, the positive nucleation computation module 1230 includes a node positive nucleation computation sub-module and an edge positive nucleation computation sub-module, wherein the node positive nucleation computation sub-module is to:

determining a first mapping relation of a first network node from a normal network path diagram to a first shortest network path diagram;

determining a second mapping relation of the second network node from the interference network path diagram to a second shortest network path diagram;

And generating node positive verification cores of the first network node and the second network node based on the matching relation of the first mapping relation and the second mapping relation.

In some embodiments, the edge positive verification computing module is to:

determining a corresponding target second sub-path of the first sub-path in the plurality of second sub-paths;

determining a first sub-network node and a second sub-network node constituting a first sub-path from a plurality of first network nodes, and determining a third sub-network node and a fourth sub-network node constituting a target second sub-path from a plurality of second network nodes;

And obtaining the edge positive core of the first sub-path and the target second sub-path according to the first node positive core, the edge positive core weight and the second node positive core of the second sub-network node and the fourth sub-network node of the first sub-network node and the third sub-network node.

In some embodiments, the interference assessment module 1240 is further to:

Acquiring a first adjacent matrix and a first diagonal matrix of a first shortest network path graph, and acquiring a first normalization matrix based on the first adjacent matrix and the first diagonal matrix;

acquiring a second adjacent matrix and a second diagonal matrix of the second shortest network path graph, and acquiring a second normalized matrix based on the second adjacent matrix and the second diagonal matrix;

And accumulating all node positive kernels and all edge positive kernels, and dividing by a normalization factor to obtain similar scores.

In some embodiments, the interference estimation device further comprises a safety measure device for:

When the similarity score is lower than a preset safety threshold, obtaining the interference level of interference according to the similarity score;

And determining a safety measure based on the interference level, and executing the safety measure on the interfered car networking system.

In the foregoing embodiments, the descriptions of the embodiments are focused on, and in a portion of an embodiment that is not described in detail, a specific implementation manner of the interference estimation device of the internet of vehicles system is substantially identical to a specific implementation manner of the interference estimation method of the internet of vehicles system, which is not described herein.

In the embodiment of the application, the interference estimation device accurately generates the communication links between the communication nodes by utilizing the communication frame condition of the continuous communication frames between the communication nodes before and after the interference of the Internet of vehicles system, and generates the accurate normal network path diagram and the accurate interference network path diagram of the Internet of vehicles system before and after the interference by utilizing the communication links. And then, calculating the similarity scores of the first shortest network path diagram and the second shortest network path diagram before and after the Internet of vehicles system is interfered in a normal network state by utilizing the node positive verification and the side positive verification, so that the interference suffered by the Internet of vehicles system is directly evaluated by utilizing the similarity scores, and the real-time performance of evaluating the interference in the Internet of vehicles system is effectively improved.

The embodiment of the application also provides electronic equipment, which comprises:

at least one memory;

At least one processor;

At least one program;

The program is stored in the memory, and the processor executes the at least one program to implement the method for evaluating the interference of the internet of vehicles system. The electronic device can be any intelligent terminal including a mobile phone, a tablet Personal computer, a Personal digital assistant (PDA for short), a vehicle-mounted computer and the like.

Referring to fig. 13, fig. 13 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:

The processor 1301 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs, so as to implement the technical solution provided by the embodiments of the present application;

The memory 1302 may be implemented in the form of a ROM (read only memory), a static storage device, a dynamic storage device, or a RAM (random access memory). The memory 1302 may store an operating system and other application programs, and when the technical solution provided in the embodiments of the present disclosure is implemented by software or firmware, relevant program codes are stored in the memory 1302, and the processor 1301 invokes an interference assessment method for executing the internet of vehicles system according to the embodiments of the present disclosure;

An input/output interface 1303 for implementing information input and output;

The communication interface 1304 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);

A bus 1305 to transfer information between the various components of the device (e.g., the processor 1301, memory 1302, input/output interfaces 1303, and communication interfaces 1304);

Wherein the processor 1301, the memory 1302, the input/output interface 1303 and the communication interface 1304 enable a communication connection between each other inside the device via a bus 1305.

The embodiment of the application also provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program which realizes the interference evaluation method of the Internet of vehicles system when being executed by a processor.

The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

It will be appreciated by persons skilled in the art that the embodiments of the application are not limited by the illustrations, and that more or fewer steps than those shown may be included, or certain steps may be combined, or different steps may be included.

The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.

The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. The coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.

The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.

Claims

1. An interference assessment method for an internet of vehicles system, the method comprising:

acquiring a normal network path diagram of the Internet of vehicles system in a normal network state and an interference network path diagram of the normal network state after being interfered;

calculating to obtain similar scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtaining an interference degree evaluation result after a normal network is interfered based on the similar scores;

The node positive determination core obtained based on the calculation of the first network node and the second network node comprises the following steps:

Generating the node positive cores of the first network node and the second network node based on the matching relation of the first mapping relation and the second mapping relation;

the computing the edge positive core based on the node positive core, the first sub-path and the second sub-path comprises the following steps:

determining a corresponding target second sub-path of the first sub-path in a plurality of second sub-paths;

Determining a first sub-network node and a second sub-network node which form the first sub-path from a plurality of the first network nodes, and determining a third sub-network node and a fourth sub-network node which form the target second sub-path from a plurality of the second network nodes, wherein the first sub-network node and the third sub-network node are identical, and the second sub-network node and the fourth sub-network node are identical;

2. The method for evaluating the interference of the internet of vehicles system according to claim 1, wherein the calculating the similarity score of the first shortest network path graph and the second shortest network path graph based on the node positive determination core and the edge positive determination core comprises:

acquiring a first adjacent matrix and a first diagonal matrix of the first shortest network path graph, and acquiring a first normalization matrix based on the first adjacent matrix and the first diagonal matrix;

3. The method for evaluating the interference of the internet of vehicles system according to claim 1, wherein the internet of vehicles system includes a plurality of communication nodes, and the acquiring the normal network path diagram of the internet of vehicles system in the normal network state includes:

acquiring a data transmission frame sent by the first communication node and a data confirmation frame sent by the second communication node within a preset time threshold, and generating a communication link between the first communication node and the second communication node;

4. The method for evaluating the interference of the internet of vehicles system according to claim 3, wherein the obtaining a first shortest network path diagram based on the normal network path diagram comprises:

5. The interference evaluation method of an internet of vehicles system according to claim 1, wherein after the interference degree evaluation result after the normal network is interfered is obtained based on the similarity score, the method further comprises:

And determining a safety measure based on the interference level, and executing the safety measure on the interfered internet of vehicles system.

6. An interference estimation device of an internet of vehicles system, wherein the device is configured to implement the interference estimation method of an internet of vehicles system according to any one of claims 1 to 5, the device comprising:

And the interference evaluation module is used for calculating to obtain the similarity scores of the first shortest network path diagram and the second shortest network path diagram based on the node positive determination core and the edge positive determination core, and obtaining an interference degree evaluation result after the normal network is interfered based on the similarity scores.

7. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the method of interference assessment of a car networking system according to any one of claims 1 to 5.

8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the interference assessment method of the internet of vehicles system according to any one of claims 1 to 5.