A routing selection method for vehicular self-organizing networks based on topology evolution

Abstract

The present invention relates to the field of wireless communications, and in particular to a vehicle-mounted self-organizing network routing selection method based on topology evolution, the method comprising: obtaining vehicle-mounted self-organizing network parameters; constructing a dynamic key penetration model according to the network parameters, and predicting link changes through the model; constructing a dynamic topology evolution model according to the link change results, and using a network topology state transfer matrix to describe the dynamic change process of the network topology; predicting a steady-state network topology structure according to the stable distribution properties of the dynamic topology evolution model; obtaining all possible transmission paths of source and destination nodes as a candidate path set according to the steady-state network topology structure, and screening out an optimal transmission path; the present invention takes into account the network characteristics in VANET and the mutual influence relationship between links, and can select a stable and reliable path for data transmission based on the predicted network topology during the routing selection process, can effectively improve the data packet transmission rate of the routing process, and reduce the end-to-end transmission delay.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a vehicle-mounted self-organizing network routing selection method based on topology evolution. Background Art

[0002] Intelligent transportation systems play an indispensable role in the development of urban transportation. Vehicular Ad-Hoc Network (VANET), as an important part of intelligent transportation systems, solves problems such as traffic information dissemination and road safety by establishing wireless communications between vehicles and between vehicles and roadside units. Routing algorithms are the core issue to ensure communication quality, so it is of great significance to design efficient routing algorithms suitable for VANET scenarios. However, the high-speed moving vehicle nodes and complex network environment in VANET scenarios will increase the interruption probability of wireless links between vehicles, resulting in frequent changes in network topology. The time-varying topology makes data transmission discontinuous, which will enable the rerouting mechanism, resulting in a sharp deterioration in network packet transmission rate and end-to-end delay performance. Therefore, it is necessary to develop reliable routing protocols for VANET scenarios to improve network communication performance.

[0003] The introduction of software defined network (SDN) into the Internet of Vehicles can determine the best route from a global perspective based on the collected network information. However, the controller selects the route based on the current network information, and then sends the decision instruction to the data plane. When the vehicle node on the data plane receives the instruction, the instruction is prone to failure due to the time-varying nature of the topology structure. Therefore, characterizing the evolution of network topology becomes the key to designing a reliable routing solution, which is of great significance for decision makers to make effective routing decisions in advance according to network changes. Existing topology time evolution analysis methods describe the changes in network topology, and add the time dimension to the network evolution characterization process by establishing time-varying graphs and spatiotemporal graph models. However, existing research on topology evolution mainly focuses on constructing time-varying graph models to characterize the time-varying nature of topology to support routing decisions, and does not analyze the dynamic changes of network topology from the perspective of microscopic changes that cause network topology. The topology change representation accuracy in the existing graph model is low, which affects the accuracy of routing calculation and cannot obtain the optimal reliable path. Therefore, it is necessary to analyze the evolution of network topology structure from the perspective of node and link changes to improve the accuracy of characterizing the time-varying topology structure.

[0004] In order to characterize the dynamic characteristics of the network, existing research has established a link prediction model by extracting features between nodes and nodes. A large number of existing studies use the location information and mobility of nodes to predict the change of link connection status. However, in actual VANET scenarios, in addition to the high-speed movement of nodes, complex channel environment, and time-varying node resources, the mutual influence between links will also cause the key factor of frequent changes in link connection status, resulting in inaccurate routing paths. Summary of the invention

[0005] In order to solve the above problems in the prior art, the present invention proposes a vehicle-mounted self-organizing network routing selection method based on topology evolution, the method comprising:

[0006] S1: constructing a vehicle-mounted self-organizing network, obtaining network parameters, and determining a network wireless link state change diagram according to the obtained network parameters;

[0007] S2: Construct a dynamic key penetration model, use this model to calculate the mutual influence relationship between each link in the network wireless link state change graph, and predict the link change result based on the mutual influence relationship between the links;

[0008] S3: A dynamic topology evolution model is constructed based on the link change results, and the network topology state transition matrix of the dynamic topology evolution model is used to describe the dynamic change process of the network topology;

[0009] S4: Using dynamic topology evolution model to predict steady-state network topology;

[0010] S5: Obtain all possible transmission paths of the source and destination nodes according to the predicted steady-state network topology structure, and use all possible transmission paths as a candidate path set;

[0011] S6: Screen the paths in the candidate path set, and select the path with the least number of hops as the optimal transmission path.

[0012] Preferably, the network parameters include signal to interference plus noise ratio, node buffer queue length and link availability.

[0013] Preferably, determining the network wireless link state change diagram according to the network parameters includes: setting a signal to interference plus noise ratio threshold, calculating the state of the current link signal to interference plus noise ratio and the state of the link signal to interference plus noise ratio at the next moment according to the set threshold, and constructing a signal to interference plus noise ratio state transfer matrix according to the state of the current link signal to interference plus noise ratio and the state of the link signal to interference plus noise ratio at the next moment; using a probability mass function to calculate the cache queue length state of the current vehicle node and the cache queue length state of the current vehicle at the next moment, and constructing a node cache queue length state transfer matrix according to the cache queue length state of the current vehicle node and the cache queue length state of the current vehicle at the next moment; setting a time interval, calculating the probability of sustainable communication between two vehicle nodes in the current time period, calculating the probability of sustainable communication between two vehicle nodes in the next time interval according to the set time interval, and constructing a link availability state transfer matrix according to the probability of sustainable communication in the current time period and the probability of sustainable communication in the next time interval; constructing a link state transfer matrix according to the signal to interference plus noise ratio state transfer matrix, the node cache queue length state transfer matrix and the link availability state transfer matrix.

[0014] Furthermore, the link state transfer matrix is:

[0015]

[0016] in, represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from “failure” to “connection”, represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from "connection" to "failure".

[0017] Preferably, calculating the mutual influence relationship between each link in the network wireless link state change diagram includes: calculating link l ij The probability of changing from a "connected" state to a "failed" state within time Δt; calculate the link l ij The probability of changing from the "fault" state to the "connection" state within time Δt; based on the probability of changing from the "connection" state to the "fault" state and the probability of changing from the "fault" state to the "connection", a link connection state change matrix that increases the mutual influence between links is constructed.

[0018] Furthermore, the expression of the link connection state change matrix with the mutual influence between links is:

[0019]

[0020] in, represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from “failure” to “connection”, represents the impact of a “faulty” link in the adjacent link set on the link status change, ε i and ε j are the “connected” link sets in the adjacent link sets of nodes i and j, respectively. and are the set of “faulty” links, respectively, and f represents the mathematical relationship between the number of adjacent links connected to the nodes at both ends of the link, which describes the subtle differences in the impact of adjacent links. Indicates the impact of the "connected" link of the adjacent link set on the link state change.

[0021] Preferably, the process of constructing a dynamic topology evolution model includes: obtaining a link connection state change matrix F that increases the mutual influence between links ij ; Based on the analysis of link state changes, the state changes of links in the network are traversed to calculate the network topology state transition probability q(A,A′), where A represents the current network topology state and A′ is the predicted topology state; the network topology state transition matrix Q is derived according to the network topology state transition probability, and the network topology state transition matrix is ​​used to represent the changes in network topology to complete the construction of the dynamic topology evolution model.

[0022] Furthermore, the element calculation expression in the network topology state transfer matrix is:

[0023]

[0024]

[0025]

[0026] Among them, the set Λ represents the set of all network topology states, and the network topology state is represented by the adjacency matrix, A x is the xth network topology state. i,j represents the element in the i-th row and j-th column of the network topology state transition matrix, E represents the set of links in the network, q(A1,A k ) indicates that the network topology changes from state A1 to state A k The probability of the state, q(A i ,A j ) indicates that the network topology consists of A i The state is transferred to A kThe probability of the state, q(A i ,A j ) represents the probability of the network topology transitioning from state A to state A', l ij Represents the link l in the network ij , Indicates link l ij Link connection state transition, a ij represents the element in row i and column j in the adjacency matrix A, a ij ′ represents the element in the i-th row and j-th column of the adjacency matrix A'.

[0027] Preferably, the process of predicting a steady-state network topology structure according to the stable distribution property of the dynamic topology evolution model includes: constructing a steady-state network topology prediction model, and predicting a network structure with a stable state through the prediction model; the expression of the prediction model is:

[0028]

[0029] stC1:πQ=π

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] Among the network state stability probabilities calculated by the Markov process stability properties, the network state with the highest probability is the predicted maximum stable network topology state:

[0036]

[0037] Among them, π m represents the maximum stationary distribution probability among all network topology states, Indicates the network topology status. Indicates the second |E| network topology state, E represents the set of links in the network, C1 represents the calculation formula of the stable distribution probability of the network topology state, π represents the stable distribution probability vector of the network topology state, Q represents the network topology state transfer matrix, C2 represents the network topology state transfer matrix, q i,j represents the element in the i-th row and j-th column of the matrix Q, C3 represents the vector storing the stationary distribution probability of all network topological states, C4 represents the calculation method of the corresponding transfer matrix element, q(A1,A k) indicates that the network topology changes from state A1 to state A k The probability of the state, A k , A i , A j They represent the kth, ith and jth network topology states respectively, C5 indicates that the sum of the stationary distribution probabilities of all network topology states is 1, π(A k ) indicates network status A k The stationary distribution probability, C6 represents the stationary distribution probability and the elements of the network topology state transfer matrix need to satisfy the conditions greater than or equal to 0 and less than or equal to 1, A * represents the stable network topology state, A is the network adjacency matrix representing the network topology state, and Λ represents the set of all network topology states.

[0038] Preferably, the process of screening the paths in the candidate path set includes: calculating the number of hops of each path in the candidate path set, and taking the path with the smallest number of hops as the optimal transmission path, which is expressed as: p_opt = arg min h (p i ),i=1,2,…p; where p_opt represents the optimal transmission path, h(p i ) represents the candidate path p i , and p represents the i-th candidate transmission path.

[0039] Beneficial effects of the present invention:

[0040] Based on the dynamic characteristics analysis of VANET scenarios, the present invention integrates the mobility of vehicles and the changes of complex network environment into the node and link state modeling process in different time dimensions, and establishes a dynamic key penetration model to quantify the mutual influence relationship between links, so as to achieve accurate prediction of link connection state changes; then, a network topology state transition model is constructed based on the link model to complete the characterization of the dynamic evolution process of the network topology; finally, based on the steady-state analysis of the network topology evolution process, the steady-state network topology is predicted to support reliable routing decisions and improve data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A software-defined vehicle-mounted self-organizing network scenario diagram of the present invention;

[0042] Figure 2 The present invention is a flow chart of a routing selection method for a vehicle-mounted self-organizing network based on topology evolution. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The establishment of wireless links in VANET scenarios must meet the following conditions: First, the harsh channel environment and shadow fading make the signal transmission quality poor, so the SINR of the receiving node needs to be greater than the receiving threshold to ensure that the receiving node can successfully receive the data; second, the vehicle node needs to have sufficient cache space to store and process the received data; then, to ensure the stability of the wireless link, the link between the vehicle nodes needs to remain continuously available within the specified time; finally, due to time-varying business flows and network resources, when the traffic on a node or link exceeds the threshold, the data transmitted through the node or link will choose the adjacent link for transmission, which will increase the cache pressure and link load of the adjacent nodes, resulting in link interruption due to node or link overload, thereby causing cascading failures, so the mutual influence between links also needs to be considered.

[0045] A software-defined vehicle self-organizing network structure Figure 1 As shown in the figure, this architecture includes a data plane and a control plane. The data plane is composed of the on-board unit (OBU), roadside unit (RSU) and base station (BS) equipped by the vehicle node, and executes the operation instructions issued by the control plane. The RSU obtains the status information of the vehicle nodes in its coverage area by listening to the basic safety messages (BSM) broadcast by the vehicle, including the vehicle's location, speed and driving direction. The vehicle outside the RSU coverage updates its status information to the BS. The RSU and BS will send BSM information to the SDN controller, and the controller will form a network topology map and perform network topology prediction by aggregating the underlying network status information, so as to make reliable routing decisions.

[0046] A routing selection method for vehicular self-organizing networks based on topology evolution, such as Figure 2 As shown, the method includes:

[0047] S1: constructing a vehicle-mounted self-organizing network, obtaining network parameters, and determining a network wireless link state change diagram according to the obtained network parameters;

[0048] S2: Construct a dynamic key penetration model, use this model to calculate the mutual influence relationship between each link in the network wireless link state change graph, and predict the link change result based on the mutual influence relationship between the links;

[0049] S3: A dynamic topology evolution model is constructed based on the link change results, and the network topology state transition matrix of the dynamic topology evolution model is used to describe the dynamic change process of the network topology;

[0050] S4: Using dynamic topology evolution model to predict steady-state network topology;

[0051] S5: Obtain all possible transmission paths of the source and destination nodes according to the predicted steady-state network topology structure, and use all possible transmission paths as a candidate path set;

[0052] S6: Screen the paths in the candidate path set, and select the path with the least number of hops as the optimal transmission path.

[0053] A specific implementation method of a vehicle-mounted self-organizing network routing selection method based on topology evolution, the establishment of a wireless link in a VANET scenario needs to meet the following conditions: first, the SINR of the receiving node needs to be greater than the receiving threshold to ensure that the receiving node can successfully receive the data; second, the receiving node needs to have sufficient cache queue length to store and process the received data; finally, in order to maintain the stability of the link, the link needs to remain continuously available within a certain period of time. In view of the above key conditions, the present invention selects network parameters SINR, BQL and link availability to jointly quantify the link status.

[0054] 101. According to the network dynamic characteristics of the VANET scenario, the parameters SINR, BQL and link availability are selected to jointly quantify the link status;

[0055] In the example of the present invention, the establishment of a wireless link in a VANET scenario needs to meet the following conditions: first, the SINR of the receiving node needs to be greater than the receiving threshold to ensure that the receiving node can successfully receive the data; second, the receiving node needs to have sufficient cache queue length to store and process the received data; finally, to maintain the stability of the link, the link needs to remain continuously available within a certain period of time. In view of the above key conditions, the present invention selects network parameters SINR, BQL and link availability to jointly quantify the link status.

[0056] 102. Network parameter modeling is performed based on the meaning of SINR, BQL and link availability, and the dynamic characteristics of VANET, such as node mobility, channel environment, shadow fading, etc., are integrated into the network parameter derivation process. Based on the modeling of network parameters, a link connection state change model is established to describe the link state change process.

[0057] In the example of the present invention, the parameters are first modeled according to the state information of the node. For the parameter SINR, the high-speed movement of the node will cause the distance and interference between the nodes to change frequently. Therefore, when node i sends data to node j at time t1, the SINR calculation formula of node j after Δt is:

[0058]

[0059] Where P t Indicates the vehicle's transmission power, is the propagation loss, To receive the interference received by vehicle j.

[0060] The SINR state of a link is independent of the previous SINR state of the link, and is independent of the link availability state L and the buffer queue length B. The link SINR state transition probability is:

[0061]

[0062] Among them, P S represents the state transition probability of the network parameter SINR, S represents the SINR state at the current moment, S' represents the predicted SINR state, represents, P represents probability, P t Indicates the network node transmission power, It means predicting the link l after Δt at time t1 ij The propagation loss of It indicates the noise and interference received by the receiving node j after Δt predicted at time t1, and γ0 indicates the SINR threshold.

[0063] The state transition matrix of the network parameter SINR is represented by S, which is specifically expressed as follows:

[0064]

[0065] With respect to the parameter BQL, the nodes in the network have the same maximum buffer queue length F. At time t1, the buffer queue length used by the receiving node is m, and the BQL of the node is b=Fm.

[0066] The arrival process of vehicle data packets in VANET approximately follows Poisson distribution, with an average arrival probability of λ packets / second. The data processing speed of the node is h packets / second, the probability of successful data packet transmission follows binomial distribution, and the error probability of vehicle data packet transmission is p e The probability mass functions of the number of received packets x and the number of sent packets y are:

[0067]

[0068]

[0069] Among them, f X (.) represents the probability mass function of the number of received data packets x, λ represents the average arrival probability of data packets, Δt represents the set time interval, and f Y (.) represents the probability mass function of the number of packets y sent, m represents the length of the used buffer queue, represents the possibility of randomly selecting y packets for transmission from the m+x packets in the buffer, p e Indicates the error probability of sending a data packet.

[0070] Based on the analysis of the state transition probability of the cache queue length of the vehicle node, the BQL state transition matrix can be obtained as follows:

[0071]

[0072] Where B represents the BQL state transition matrix, P B represents the transition probability of the node cache queue length, B represents the current BQL state, and B' represents the predicted BQL state.

[0073] The elements of matrix B are calculated as follows:

[0074]

[0075]

[0076] Among them, h represents the processing speed of the node for the data packet, Δt represents the time difference between the current state and the predicted state of the parameter SINR, and b represents the remaining cache space length of the node.

[0077] To ensure the stability of the wireless link, it is necessary to select a link with link availability, where link availability is defined as the probability that the link between two vehicles remains continuously available within a specified time.

[0078] The current wireless link is available, indicating that the nodes at both ends of the link are still within the communication range of each other after Δt. The probability of the link remaining available in the next state, that is, the probability of the link being available, is calculated as follows:

[0079]

[0080] Where L represents the current link availability status, L′ represents the predicted link availability status, and f(T) is the probability density function of duration T. The expression of the probability density function of duration T is:

[0081]

[0082] Where R represents the communication range between vehicles, represents the standard deviation of the speed difference between vehicle i and vehicle j, represents the mean speed difference between vehicle i and vehicle j.

[0083] When the current wireless link is not available, the reliability of the next state wireless link is predicted. First, it is calculated whether the nodes at both ends of the next state link are within the communication range of each other. The expression is:

[0084]

[0085] The probability that the next state link is available is calculated based on whether the nodes at both ends of the next state link are within the communication range of each other. The expression is:

[0086]

[0087] The link availability state transition probability matrix is ​​represented by L, which is specifically expressed as:

[0088]

[0089] Based on the modeling analysis of network parameters SINR, BQL and link availability, the link connection status can be quantified and the link status change transfer matrix can be derived; link l ij The connection state transfer matrix is:

[0090]

[0091]

[0092]

[0093] in, P X The short form of (x,x'), for example It represents the transition probability of the link SINR state changing from state 0 to state 1.

[0094] 103. Based on the analysis of the mutual influence relationship between links, a dynamic key permeation model is constructed on the basis of step 102 to quantify the influence of the connectivity of adjacent links of the link on the change of the link state.

[0095] The failure probability of the i-th link in this model, that is, the probability that the link connection state changes from "connected" to "faulty", is related to the number of faulty links in the adjacent links in the network; the recovery probability of the link, that is, the probability that the link connection state changes from "faulty" to "connected", is related to the number of connected links in the adjacent links.

[0096] Link 1 ijThe probability of changing from the "connected" state to the "faulty" state within time Δt is exponentially distributed, and its calculation formula is:

[0097]

[0098] Among them, F ij The link connection status after considering the influence of network topology on link connection: F ij =1 indicates link l ij In the "connected" state, F ij =0 indicates that the link is in a "fault" state. Parameter γ>0 is the influence of network topology on link failure. When γ=1, link failure has no topology dependency. When γ≠1, the network topology will affect the link connection failure through a cascading function.

[0099] Link 1 ij The probability of changing from the "fault" state to the "connection" state within time Δt is exponentially distributed, and the calculation formula is:

[0100]

[0101] Among them, the parameter μ>0 is the influence strength of network topology on link connection recovery. When μ=1, link connection recovery has no topology dependence. When μ≠1, the network topology structure will affect the link connection recovery through the cascade function.

[0102] The link connection state transfer matrix after adding the mutual influence relationship between links is expressed as F ij , the formula is:

[0103]

[0104] in, represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from “failure” to “connection”, represents the impact of a “faulty” link in the adjacent link set on the link status change, ε i and ε j are the “connected” link sets in the adjacent link sets of nodes i and j, respectively. and are the set of “faulty” links, respectively, and f represents the mathematical relationship between the number of adjacent links connected to the nodes at both ends of the link, which describes the subtle differences in the impact of adjacent links. Indicates the impact of the "connected" link of the adjacent link set on the link state change.

[0105] 104. According to the changes in the links of the entire network, the Markov theory is used to construct a dynamic topology evolution model (Dynamic Topology Evolution, DTE), and the network topology state transfer matrix Q is used to describe the dynamic change process of the network topology. The specific calculation of the matrix elements is as follows:

[0106]

[0107]

[0108] Among them, A and A′ are adjacency matrices of two different network topology states, a ij with a ij ′ are the corresponding matrix elements respectively.

[0109] 105. The DTE model uses Markov chains and state transition matrices to represent topological evolution. The stationary probability of each network topological state is calculated based on the stationary distribution characteristics of the model, so that the topological state with the largest stationary probability becomes a stable network topology. Therefore, the topological state prediction problem is expressed as a stationary distribution probability solution problem, and a stable topological prediction model is designed as follows:

[0110]

[0111] stC1:πQ=π

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] Among them, π m represents the maximum stationary distribution probability among all network topology states, Indicates the network topology status. Indicates the second |E| network topology state, E represents the set of links in the network, C1 represents the calculation formula of the stable distribution probability of the network topology state, π represents the stable distribution probability vector of the network topology state, Q represents the network topology state transfer matrix, C2 represents the network topology state transfer matrix, q i,j represents the element in the i-th row and j-th column of the matrix Q, C3 represents the vector storing the stationary distribution probability of all network topological states, C4 represents the calculation method of the corresponding transfer matrix element, q(A1,A k) indicates that the network topology changes from state A1 to state A k The probability of the state, A k , A i , A j They represent the kth, ith and jth network topology states respectively, C5 indicates that the sum of the stationary distribution probabilities of all network topology states is 1, π(A k ) indicates network status A k The stationary distribution probability, C6 represents the stationary distribution probability and the elements of the network topology state transfer matrix need to satisfy the conditions greater than or equal to 0 and less than or equal to 1, A * represents the stable network topology state, A is the network adjacency matrix representing the network topology state, and Λ represents the set of all network topology states.

[0118] 106. Based on the steady-state network topology predicted by the DTE model, the selected path is more stable and less likely to be interrupted during data transmission. Therefore, the predicted steady-state network topology A * Select all possible transmission paths from the source node to the destination node and represent them as M(s,d)={p1,p2,...,p p}, and calculate the number of hops of all paths, and select the path with the least hops for data transmission. Therefore, the optimal transmission path is: p_opt = argminh (p i ),i=1,2,…p.

[0119] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle-mounted self-organizing network routing selection method based on topology evolution, characterized in that: The method includes: S1: constructing a vehicle-mounted self-organizing network, obtaining network parameters, and determining a network wireless link state change diagram according to the obtained network parameters; S2: construct a dynamic key penetration model, calculate the mutual influence relationship between each link in the network wireless link state change diagram through the model, and predict the link change result according to the mutual influence relationship between the links; calculating the mutual influence relationship between each link in the network wireless link state change diagram includes: calculating the link l ij The probability of changing from "connected" state to "failed" state within time Δt; calculate the link l ij The probability of changing from "fault" state to "connection" within time Δt; based on the probability of changing from "connection" state to "fault" and the probability of changing from "fault" state to "connection", a link connection state change matrix with increased mutual influence between links is constructed; its expression is: in, represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from "failure" to "connection", It represents the probability that link lij changes from "connected" to "faulty". Indicates the impact of the "faulty" link in the adjacent link set on the link state change, ε i and ε j are the "connected" link sets in the adjacent link sets of nodes i and j respectively, and are the set of "faulty" links, respectively, and f represents the mathematical relationship between the number of adjacent links connected to the nodes at both ends of the link, which describes the subtle differences in the impact of adjacent links. Indicates the impact of the "connected" link of the adjacent link set on the link state change; S3: A dynamic topology evolution model is constructed based on the link change results, and the network topology state transition matrix of the dynamic topology evolution model is used to describe the dynamic change process of the network topology; the process of constructing the dynamic topology evolution model includes: obtaining the link connection state change matrix F with the mutual influence between links ij ; Based on the analysis of link state changes, the state changes of links in the network are traversed to calculate the network topology state transition probability q(A,A′), where A represents the current network topology state and A′ represents the predicted topology state; the network topology state transition matrix Q is derived according to the network topology state transition probability, and the network topology state transition matrix is ​​used to represent the changes in network topology, completing the construction of the dynamic topology evolution model; The element calculation expression in the network topology state transfer matrix is: Among them, Λ represents the set of all network topology states, and the network topology state is represented by the adjacency matrix, A x is the xth network topology state, q i,j represents the element in the i-th row and j-th column of the network topology state transition matrix, E represents the set of links in the network, q(A1,A k ) indicates that the network topology changes from state A1 to state A k The probability of the state, q(A i ,A j ) represents the probability of the network topology transitioning from state Ai to state Ak, q(A,A′) represents the probability of the network topology transitioning from state A to state A′, l ij Represents the link l in the network ij , Indicates link l ij Link connection state transition, a ij represents the element in row i and column j in the adjacency matrix A, a ij ′ represents the element in the i-th row and j-th column of the adjacency matrix A'; S4: Use the dynamic topology evolution model to predict the steady-state network topology structure; the specific process includes: constructing a steady-state network topology prediction model, and predicting the network structure with a stable state through the prediction model; the expression of the prediction model is: stC1:πQ=π C2: C3: C4: C5: C6: Among the network state stability probabilities calculated by the Markov process stability properties, the network state with the highest probability is the predicted stable network topology state: Among them, π m represents the maximum stationary distribution probability among all network topology states, Indicates the network topology status. Indicates the second E network topology state, E represents the set of links in the network, C1 represents the calculation formula of the stable distribution probability of the network topology state, π represents the stable distribution probability vector of the network topology state, Q represents the network topology state transfer matrix, C2 represents the network topology state transfer matrix, q i,j represents the element in the i-th row and j-th column of the matrix Q, C3 represents the vector storing the stationary distribution probability of all network topological states, C4 represents the calculation method of the corresponding transfer matrix element, q(A1,A k ) indicates that the network topology changes from state A1 to state A k The probability of the state, A k , A i , A j They represent the kth, ith and jth network topology states respectively, C5 indicates that the sum of the stationary distribution probabilities of all network topology states is 1, π(A k ) indicates network status A k The stationary distribution probability, C6 represents the stationary distribution probability and the elements of the network topology state transfer matrix need to satisfy the conditions greater than or equal to 0 and less than or equal to 1, A * represents the stable network topology state, A is the network adjacency matrix representing the network topology state, and Λ represents the set of all network topology states; S5: Obtain all possible transmission paths of the source and destination nodes according to the predicted steady-state network topology structure, and use all possible transmission paths as a candidate path set; S6: Screen the paths in the candidate path set, and select the path with the least number of hops as the optimal transmission path.

2. According to the topology evolution-based on-vehicle self-organizing network routing selection method of claim 1, it is characterized in that: Network parameters include signal-to-interference-plus-noise ratio, node buffer queue length, and link availability.

3. The vehicle-mounted self-organizing network routing selection method based on topology evolution according to claim 1 is characterized in that: Determining the network wireless link state change diagram according to the network parameters includes: setting a signal to interference plus noise ratio threshold, calculating the state of the current link signal to interference plus noise ratio and the state of the link signal to interference plus noise ratio at the next moment according to the set threshold, and constructing a signal to interference plus noise ratio state transfer matrix according to the state of the current link signal to interference plus noise ratio and the state of the link signal to interference plus noise ratio at the next moment; using a probability mass function to calculate the cache queue length state of the current vehicle node and the cache queue length state of the current vehicle at the next moment, and constructing a node cache queue length state transfer matrix according to the cache queue length state of the current vehicle node and the cache queue length state of the current vehicle at the next moment; setting a time interval, calculating the probability of sustainable communication between two vehicle nodes in the current time period, calculating the probability of sustainable communication between two vehicle nodes in the next time interval according to the set time interval, and constructing a link availability state transfer matrix according to the probability of sustainable communication in the current time period and the probability of sustainable communication in the next time interval; constructing a link state transfer matrix according to the signal to interference plus noise ratio state transfer matrix, the node cache queue length state transfer matrix and the link availability state transfer matrix.

4. The vehicle-mounted self-organizing network routing selection method based on topology evolution according to claim 3 is characterized in that: The link state transition matrix is: in, represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from "failure" to "connection", represents the link l calculated based on the signal to interference plus noise ratio state transition matrix, the node cache queue length state transition matrix and the link availability state transition matrix ij The probability of transitioning from "connection" to "failure".

5. The method for selecting a route in a vehicle-mounted self-organizing network based on topology evolution according to claim 1, characterized in that: The process of screening the paths in the candidate path set includes: calculating the number of hops of each path in the candidate path set, and taking the path with the smallest number of hops as the optimal transmission path, which is expressed as: p_opt = argminh(p i ),i=1,2,…p; where p_opt represents the optimal transmission path, h(p i ) represents the candidate path p i The number of hops, p i represents the i-th candidate transmission path.

Images