CN115065964B - Directional release method for vehicle accident information - Google Patents
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Abstract
The invention provides a vehicle accident information directional release method, which mainly solves the problems of high oil consumption, strong channel competition and incapability of meeting multi-service coexistence communication in the prior art, and adopts the scheme that: establishing a traffic system model and a joint communication model containing two-hop communication; constructing a traffic system total consumption model on the basis of traffic and communication models; constructing an optimization problem of minimizing the total consumption of the system, and respectively solving an optimal directional release matrix of two-hop communication; constructing an optimization problem of minimizing the average transmission rate of the large-bandwidth transmission service, and solving an optimal resource allocation matrix of the optimization problem; constructing an optimization problem of maximizing the transmission success rate of the emergency service and minimizing the loss of the large bandwidth transmission service rate, and solving an optimal resource allocation matrix of the emergency service; combining these optimal matrices yields a directional release total matrix. The invention reduces the total consumption of the traffic system while maximizing the success rate of accident information transmission, and can be used for urban road networks.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation, and particularly relates to a vehicle accident information directional release method which can be used for an urban road network.
Background
In recent years, urban traffic accidents are frequent due to the increasing number of motor vehicles and travel demands in urban road networks. In addition to direct economic losses, typical aperiodic traffic congestion caused by accidents can also not be quite as small in terms of travel time, fuel consumption, air pollution, etc. The negative impact of road congestion after an accident is gradually a barrier to the construction of safe, efficient, green intelligent traffic systems.
As one of the most extensive fields of internet of things research, the internet of vehicles technology provides a opportunity for solving the urgent need. Accident information is issued to all network vehicles in the urban road network through a communication means, so that immeasurable economic losses caused by congestion expansion and propagation due to information lag are avoided. The non-directional release of accident information is critical to reduce the number of occurrences of the accident and to alleviate the negative impact of congestion after the accident. With the increasing number of network-connected vehicles, the contradiction between limited wireless resources and unlimited resource demands is more vivid, and the non-directional release performance of accident information is difficult to ensure. This has prompted researchers to find new strategies to further alleviate the link pressure and performance impact of large network-coupled communications, and the idea of accident information directed distribution has emerged.
At present, researches on an accident information directional release method are mainly focused on a semi-directional release strategy in a car networking scene, and can be divided into two types:
the first is a distribution method based on the influential nodes. According to the method, the influence of the network-connected vehicles in the vehicle networking environment is ordered through a vehicle terminal influence decision algorithm, and the network-connected vehicle with larger influence is selected as a relay node for information release during information release, namely, accident information is firstly released to the relay node, then the relay node releases the accident information to all other network-connected vehicles, so that channel competition pressure can be reduced at the same time, and communication reliability is ensured.
The second is a distribution method based on transmission probability. In this method, communication performance when different vehicle terminals receive information is determined by correlation between the vehicle terminals and accident places, and network-connected vehicles related to accidents receive accident information with high probability and low delay and high reliability, and performance when other network-connected vehicles with lower correlation degree receive information can be relatively poor. By controlling the release probability, the method realizes semi-directional release, improves the communication efficiency, and relieves the negative influence caused by post-accident congestion.
The related research of the conventional accident information directional release method is mainly focused on the aspect of communication performance research of semi-directional release in the scene of the internet of vehicles, namely, information is released to all vehicle terminals by selecting a node with influence as a relay forwarding node so as to reduce link competition pressure and improve communication performance, and the related research has the following three problems:
1) The influence of excessive information release on a traffic system is not considered, so that congestion transfer can be caused to cause negative influences such as traffic delay and extra fuel consumption;
2) The complete directional release is not realized, so that the release of a large amount of network-connected vehicle information which is continuously increased still faces the problem of strong channel competition;
3) The method is not applied to a multi-service competition scene, and the reliability and low-delay performance of accident information release under the scene are difficult to ensure.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a vehicle accident information directional release method which is used for guaranteeing low time delay and high reliability of accident information under the condition of multi-service competition and simultaneously relieving the negative influence on fuel consumption of a traffic system caused by excessive release of the accident information.
The technical scheme of the invention is as follows: modeling the total consumption in a traffic system, obtaining an optimal accident information directional release matrix by minimizing the total consumption of the system, distributing fixed wireless resources to a large-bandwidth transmission service to maximize the transmission rate of the large-bandwidth transmission service, analyzing the arrival rule of the accident information according to the directional release matrix, combining the resource distribution matrix of the large-bandwidth transmission service on the basis of the rule, maximizing the emergency service insertion transmission success rate and minimizing the rate loss of the large-bandwidth transmission service, thereby obtaining an optimal distribution matrix of the emergency service, and realizing final directional release by integrating the corresponding resource distribution matrix of each directional release matrix. The specific implementation steps comprise the following steps:
(1) Selecting part of urban road network, and establishing a traffic system model composed of urban roads, vehicles and a mobile edge calculation server;
(2) Constructing a joint communication model consisting of two-hop communication links according to the position of the mobile edge computing server:
the first-hop communication refers to a communication link between a mobile edge computing server of an accident road section and any other road section mobile edge computing server;
the second-hop communication refers to a communication link between the non-accident road section mobile edge calculation server and the road section vehicle;
the communication service of each hop comprises two services, namely a large bandwidth transmission service and an emergency service, wherein accident information release belongs to the emergency service;
(3) Setting the directional release matrixes of the first-hop communication and the second-hop communication in the joint communication model as B, B respectively, and constructing a traffic system total consumption model c (t) about B and B:
wherein ,b0,k B, distributing accident information of the 0 th mobile edge computing server to the k th mobile edge computing server in the first-hop communication directional distribution matrix B 0,k =0 indicates that the kth mobile edge computing server has not received the incident information, b 0,k =1 indicates that accident information is received; b k,v B, calculating information release conditions of a server to vehicles v in jurisdiction of the kth mobile edge in the second-hop communication directional release matrix b k,v =0 indicates that the vehicle v has not received the accident information issued by the kth moving edge computing server, b k,v =1 indicates that accident information was received; c k,v (t) represents the fuel consumption when no accident information is received by the vehicle v in the jurisdiction of the kth mobile edge computing server at the moment t; c' k,v (t) represents the fuel consumption of the vehicle v after receiving the accident information and performing path replacement in the jurisdiction of the kth mobile edge computing server at the moment t;k max representation shiftCalculating the number of servers by the dynamic edge; />v max Representing the number of vehicles in the jurisdiction of the kth mobile edge computing server;
(4) Constructing an optimization problem P1 for minimizing the total consumption of the system according to the total consumption c (t) of the traffic system, decomposing the optimization problem P1 into two sub-optimization problems, and solving the two sub-optimization problems by adopting a one-dimensional search algorithm to obtain an optimal information oriented distribution matrix B * and b* ；
(5) Setting the transmission power matrix of the large-bandwidth transmission service as P and the channel allocation matrix as omega, constructing an optimization problem P2 of the maximum average transmission rate of the large-bandwidth transmission service with optimization variables of P and omega, and solving the problem P2 by utilizing a sparrow search algorithm to obtain an optimal resource allocation matrix P of the large-bandwidth transmission service * and ω* Allocating fixed wireless resources to a large bandwidth transmission service;
(6) Based on the optimal directed distribution matrix b * Obtaining the emergency service number A (t) reached by each mobile edge computing server in a time slot t;
(7) Based on the optimal resource allocation matrix p * 、ω * And the arrival rule A (t) of the emergency service, constructing an optimization problem P3 for maximizing the transmission success rate of the emergency service and minimizing the loss rate of the large-bandwidth transmission service, and solving an optimal distribution matrix zeta of the emergency service * Optimal resource allocation of two services is realized;
(8) Will optimally orient the issue matrix B * and b* Optimal resource allocation matrix p for large bandwidth transmission traffic * and ω* Optimal allocation matrix ζ for emergency services * And combining to obtain the accident information directional release total matrix.
Compared with the prior art, the invention has the following advantages:
(1) The negative influence of excessive information release on traffic oil consumption is relieved.
The invention models the traffic consumption of each road in the traffic system, constructs an optimization problem for minimizing the total consumption of the traffic system, and solves the problem to obtain an optimal directional release matrix so as to selectively control the vehicles to carry out path selection again to avoid accident road sections. Compared with the traditional release strategy, the model truly realizes the directional release, selectively releases accident information to part of related vehicles, avoids congestion transfer caused by the fact that accident information is released to all vehicles in a non-directional way, and relieves the negative influence of excessive release of information on traffic oil consumption.
(2) And the accident information communication performance is improved.
The invention integrates the directional release and the resource allocation, the directional release matrix screens out a part of vehicles related to accidents, and the resource allocation matrix optimizes the power and the channel occupation condition when the part of vehicles receive information. Compared with the traditional communication performance optimization only for resource allocation, the method reduces the information sources to be transmitted through directional release, greatly reduces the communication energy consumption through the combination of the directional release and the resource allocation, and improves the communication performances of the transmission success rate and the transmission speed.
(3) And ensuring effective release of accident information when multi-service resources compete.
The invention solves the optimal allocation strategy of the large bandwidth transmission service and the emergency service on the premise of knowing the directional release matrix by constructing the optimization problem of maximizing the transmission success rate of the emergency service and minimizing the average data rate of the large bandwidth transmission service. Compared with the prior accident information release in the Internet of vehicles environment, the method and the system consider that the accident information directional release strategy is fused with the multi-service scene, and solve the influence of multi-service competition on the accident information directional release communication performance in the current scene.
Drawings
FIG. 1 is a block diagram of an implementation flow of the present invention;
FIG. 2 is a diagram of a traffic system model in accordance with the present invention;
FIG. 3 is a sub-flowchart of constructing a traffic system total consumption model in accordance with the present invention;
FIG. 4 is a diagram of a part of a traffic network in a city for use in the simulation of the present invention;
FIG. 5 is a graph showing the overall consumption simulation of the traffic network of FIG. 3 in comparison with the prior art information distribution method of the present invention;
fig. 6 is a simulated comparison chart of communication fitness of the present invention and the prior information distribution method for distributing information to different users.
Detailed Description
Embodiments and effects of the present invention are described in further detail below with reference to the attached drawings:
referring to fig. 1, the present example includes the steps of:
and step 1, establishing a traffic system model.
A part of road segments is selected from the road network, abstracted into a traffic system model composed of urban roads, road infrastructure and autonomous vehicles, and it is assumed that an accident occurs in a certain road segment and causes road congestion, as shown in fig. 2.
Each road segment is provided with a mobile edge computing server for collecting and distributing traffic information of vehicles in the road segment, and the mobile edge computing server is shown as a set of mobile edge computing serversk max The number of servers is calculated for the mobile edge in the road network.
The automatic driving vehicle integrates communication, perception and calculation functions and has an autonomous decision function, and the vehicle set on the road section managed by the mobile edge calculation server k is expressed as wherein vmax Is the number of vehicles on the road segment.
The example selects partial road sections from the Beijing road network to construct a traffic system model, wherein the number of mobile edge calculation servers in the partial road network is 16, and the maximum number of vehicles in each road section is 300. The present invention includes, but is not limited to, this example.
And 2, constructing a joint communication model.
From the location of each mobile edge computing server and vehicle in fig. 2, a joint communication model consisting of two-hop communication links is constructed. Wherein: a first-hop communication link refers to a communication link between a mobile edge calculation server of an accident road section and any other mobile edge calculation server of a non-accident road section; the second-hop communication link refers to a communication link between the non-accident road segment moving edge calculation server and the road segment vehicle. Each hop of communication link is occupied by two kinds of services, namely a large bandwidth transmission service and an emergency service, and accident information release belongs to the emergency service. The working principle of the joint communication model is as follows:
when an accident happens, the accident information completes a directional release process consisting of two-hop communication according to the two-hop communication link: the information is firstly issued from the accident road section moving edge computing server to the non-accident road section moving edge computing server in the transmission range to complete the first-hop communication, and secondly issued from the non-accident road section moving edge computing server to the automatic driving vehicle on the road section to complete the second-hop communication. If the first-hop communication does not occur, its corresponding second-hop communication must not occur; if the first-hop communication is successful, the mobile edge computing server that received the incident information will selectively complete the second-hop communication.
The completion condition of the two-hop communication during the directional release of the accident information is respectively represented by a directional release matrix B of the first-hop communication and a directional release matrix B of the second-hop communication. Wherein:
b 0,k b, distributing accident information of the 0 th mobile edge computing server to the k th mobile edge computing server in the first-hop communication directional distribution matrix B 0,k =0 indicates that the kth mobile edge computing server has not received the incident information, b 0,k =1 indicates that accident information is received;
b k,v calculation of the kth mobile edge in the directed distribution matrix b for second hop communicationsThe server issues information of vehicles v in the jurisdiction range of the server, b k,v =0 indicates that the vehicle v has not received the accident information issued by the kth moving edge computing server, b k,v And=1 indicates that accident information was received.
And 3, constructing a total consumption model c (t) of the traffic system.
The total consumption of the traffic system refers to the sum of the fuel consumption of all vehicles in the traffic system, and the fuel consumption of each vehicle is affected by the currently selected path length and congestion level of the vehicle. The principle of vehicle path selection is as follows: if the vehicle does not receive the accident information, the vehicle can run according to the original planned path, and after the vehicle receives the accident information, the vehicle can carry out path planning and replace the optimal path again. Thus, the total consumption of the traffic system can be divided into the sum of the fuel consumption of the vehicles that receive the accident information and the vehicles that do not receive the accident information. The fuel consumption of the vehicle is calculated by the current fuel price, the road section traffic delay and the road section fuel consumption rate.
Referring to fig. 3, the specific implementation of this step is as follows:
3.1 Setting the road state to have two accident states and normal states, and calculating the average traffic time delay C of the road section a under the two road states according to the queuing theory a ：
wherein ,λa Representing the vehicle arrival rate of the road section a; mu represents the service rate of the road under normal conditions, lambda 0 Indicating accident rate, gamma 0 Representing the service rate of the road under the accident condition;
3.2 Calculating fuel consumption c when vehicle v in jurisdiction of mobile edge calculation server k does not receive accident information k,v (t)：
3.2.1 According to the average transit time delay C of road sections a Average traffic delay C of road section i i Expressed as:
wherein ,λi For the vehicle arrival rate of road section i, i εr k,v ，r k,v Representing a path currently selected by the vehicle v in jurisdiction of the mobile edge computing server k;
3.2.2 According to the average transit time delay C of road section i i Calculating fuel consumption c of vehicle v in jurisdiction of mobile edge calculation server k when accident information is not received k,v (t)：
wherein ,fi For average fuel consumption of vehicles on road section i, c fuel The oil price is represented by the value of the oil,
3.3 Calculating the oil consumption c 'of the vehicle after the vehicle receives the accident information and performs path replacement' k,v (t)：
3.3.1 According to the average transit time delay C of road sections a Average traffic delay C of road section j j Expressed as:
wherein ,λj For road section j, vehicle arrival rate, j e r' k,v ，r’ k,v Representing a new path after the vehicle v in the jurisdiction of the mobile edge computing server k receives accident information and changes the path;
3.3.2 According to the average transit time delay C of the road section j j Calculating the oil consumption c 'of the vehicles v in the jurisdiction of the mobile edge calculation server k after receiving accident information and carrying out path replacement' k,v (t)：
wherein ,fj For average oil of vehicles on road section jConsumption rate;
3.4 Fuel consumption c according to accident information not received by vehicle k,v (t) and Fuel consumption c 'after route replacement by Accident information' k,v (t) obtaining the total consumption c (t) of the traffic system:
step 4, constructing an optimization problem P1 for minimizing the total consumption of the system, and solving an optimal oriented release matrix B * and b* 。
From step 3, it can be seen that the total system consumption c (t) is an expression of the two-hop directional distribution matrices B and B, and different distribution modes can cause different fuel consumption of the vehicle. The traffic system generates additional oil consumption due to the fact that the traffic system can automatically change the route after the vehicle receives the accident information, and therefore the total consumption of the traffic system can be reduced to the greatest extent by adjusting the release condition of the information.
4.1 Representing the optimization problem P1 as follows from the total traffic system consumption c (t):
4.2 Decomposing the optimization problem P1 into two sub-optimization problems P1-1 and P1-2:
4.3 Solving two sub-optimization problems P1-1 and P1-2 by adopting a one-dimensional search algorithm to obtain an optimal information oriented distribution matrix B * and b* ：
wherein ,indicating the optimal accident information distribution condition of the 0 th mobile edge computing server to the k th mobile edge computing server,/for the 0 th mobile edge computing server>Indicating that the kth mobile edge computing server did not receive the incident information,/>Indicating that accident information was received, k e {1, 2..k.) max }，k max Calculating the number of servers for the mobile edge in the road network; />Representing the optimal accident information distribution of the kth mobile edge computing server to the vehicles v in its jurisdiction,/for the vehicles v>Indicating that no accident information was received by vehicle v, +.>Indicating that the vehicle v received accident information, v e {1, 2., v max }，k max Is the maximum number of vehicles on the road section.
Step 5, constructing an optimization problem P2 for maximizing the average transmission rate of the large-bandwidth transmission service, and solving an optimal resource allocation matrix P * and ω* 。
Each service is allocated to a different resource block or transmission power and different channel conditions are available, thereby having different transmission performance. Therefore, the transmission rate of the large-bandwidth transmission service can be improved by scheduling the positions of the resource blocks occupied by different services and the transmission power, and the existing fixed wireless resources can be utilized efficiently.
Let the transmission power matrix of the large bandwidth transmission service be P, the channel allocation matrix be ω, and build the optimization problem P2 as follows:
5.1 Transmission rate of large bandwidth transmission service u according to shannon theoremExpressed as:
wherein ,fs Is the bandwidth of the radio resource block s; omega u,s Is the occupation condition of the large bandwidth transmission service u to the radio resource block s in the channel allocation matrix omega, omega u,s =1 indicates that the radio resource block s is allocated to the service u, ω u,s =0 indicates that the radio resource block s is not allocated to the service u; beta represents a constant related to the bit error rate; p is p u 、g u The transmitting power and the channel gain of the large bandwidth transmission service u are respectively represented; sigma (sigma) 2 Is the standard deviation of system noise;s max indicating the number of radio resource blocks,u max representing the number of large bandwidth transmission services;
5.2 According to the transmission rate)Constructing and transmitting the transmission rate of the business with large bandwidth>Related objective function
wherein ,representing the transmission rate state of the large bandwidth transmission service u in time slot t +.>Minimum speed value +.>Meet the transmission requirement->Representing that the transmission rate of service u is smaller than the required minimum rate value +.>Not meeting the transmission requirement;
5.3 According to the objective functionBuilding an optimization problem P2:
wherein ,pu,s Representing the corresponding sub-channel power value when the service u occupies the radio resource block s in the power distribution matrix p of the large bandwidth transmission service; the limiting conditions C1 and C2 limit that one resource block can only be allocated to one large bandwidth transmission service; constraint C3 represents a limit range of total transmission power, where p max Representing the maximum value of the total transmission power of the base station;
5.4 Solving the problem P2 by utilizing a sparrow search algorithm to obtain an optimal resource allocation matrix P of the large-bandwidth transmission service * and ω* ：
wherein ,represents the optimal transmission power value of service u on radio resource block s in a large bandwidth transmission service, where u e {1, 2., u max }，u max S e {1, 2..s for the number of large bandwidth transport services max }，s max The number of radio resource blocks; />Representing the optimal occupancy of the large bandwidth transmission service u to the radio resource block s,/for>Indicating that service u is occupied for radio resource block s, < >>Indicating that it is unoccupied.
And 6, counting the arrival number A (t) of the emergency service.
Based on the optimal directed distribution matrix b * Obtaining the number of vehicles receiving accident information on each road section in the time slot t, and counting all the vehicle numbers to obtain the number of vehicles receiving the accident information on each road section, wherein the number is the emergency service arrival number A (t) in the time slot t:
step 7, constructing an optimization problem P3 for maximizing the transmission success rate of the emergency service and minimizing the loss rate of the large-bandwidth transmission service, and solving an optimal distribution matrix zeta of the emergency service * 。
In order to meet the self hard delay requirement, the emergency service punctures the resources occupied by the large bandwidth transmission service and ensures that the transmission is completed in one micro time slot when the emergency service arrives, but the operation tends to influence the transmission rate of the original large bandwidth transmission service, and the emergency service can be based on the optimal resource allocation matrix p * 、ω * And the arrival rule A (t) of the emergency service, constructing a double-objective optimization problem of maximizing the transmission success rate of the emergency service and minimizing the loss rate of the large-bandwidth transmission service, and dynamically scheduling the positions of the arrived emergency service puncture resource blocks so as to meet the requirements of two services as much as possible and maximize the resource utilization rate.
The specific implementation of the steps is as follows:
7.1 Calculating the transmission rate r of the large bandwidth transmission service u when the emergency service arrives and punctures the radio resource block u (t)：
wherein ,yu (t) representing the occupation of the radio resource by the large bandwidth transmission service u without emergency service puncture; z u (t) represents the resource situation where the large bandwidth transmission service u is punctured;an optimal power allocation value representing a large bandwidth transmission service u; g u Channel gain representing large bandwidth transmission traffic u; beta represents a constant related to the bit error rate; sigma (sigma) 2 Is the standard deviation of system noise; />u max Representing the number of large bandwidth transmission services;
7.2 Computing emergency businessTransmission rate r of traffic z z (t)：
wherein ,fs Is the bandwidth of the radio resource block s;optimal channel allocation matrix omega for large bandwidth transmission service * Occupancy of radio resource block s by middle traffic u, < >>Indicating that radio resource block s is allocated to traffic u, for example>Indicating that the radio resource block s is not allocated to the service u; zeta type u,s,z Is the puncture condition of the emergency service z to the existing wireless resource block s of the large bandwidth transmission service u in the resource allocation matrix ζ u,s,z =1 indicates that the emergency traffic z punctures into the allocated resource block s of the large bandwidth transmission traffic u, ζ u,s,z =0 indicates unpunctured; beta represents a constant related to the bit error rate; p is p urllc 、g urllc Respectively representing the transmitting power and the channel gain of the emergency service; sigma (sigma) 2 Is the standard deviation of system noise; />z max Is the number of emergency services;s max is the number of radio resource blocks;
7.3 Calculating average transmission success rate delta of emergency service suc ：
7.3.1 According to the transmission rate r of emergency services z (t) calculating the transmission success number i of the emergency service z (t)：
Wherein D represents the bit value of accident information, and τ represents the hard delay requirement of emergency service;
7.3.2 According to the number i of successful transmissions z (t) calculating a transmission success rate delta of the emergency service suc ：
Wherein A (t) represents the number of emergency services reached by the time slot t, and E (·) represents the operation of solving the expected value;
7.4 Transmission rate r according to large bandwidth transmission traffic u (t) calculating loss delta of puncture of emergency service to transmission rate of large bandwidth transmission service loss ：
wherein ,the transmission rate state of the large bandwidth transmission service u without emergency service puncture is shown; />Indicating the transmission rate of the large bandwidth transmission service u without emergency service puncture; />Representing a minimum data rate required for a large bandwidth transmission service; i.e u (t) represents the transmission rate state of the large bandwidth transmission service u in the time slot t when the emergency service puncture exists, i u (t) =1 means that the transmission rate of the service u is equal to or higher than the minimum rate value required for the large bandwidth transmission service +.>Meet the transmission requirement, i u (t) =0 means that the transmission rate of the service u is smaller than the required minimum rate value +.>Not meeting the transmission requirement;
7.5 According to the transmission success rate delta suc And loss delta loss Building an optimization problem P3:
wherein, the limiting conditions C1 and C2 jointly limit that each emergency service can only puncture one resource block once; the limiting condition C3 limits the interruption probability of the emergency service, P (E) represents the interruption probability of the emergency service, and epsilon represents an interruption probability threshold value in relation to the arrival number A (t) of the emergency service; constraint C4 indicates that the amount of resources that emergency services can puncture cannot exceed their available resources,representing the total resources allocated by the large bandwidth transmission service u;
7.6 According to the target product method, converting the nonlinear multi-target optimization problem P3 into a single-target optimization problem P3-1:
7.7 Solving the P3-1 according to the sparrow search algorithm to obtain an optimal distribution matrix zeta of the emergency service * ：
wherein ,the optimal puncture condition of the emergency service on the wireless resource occupied by the large-bandwidth transmission service u is represented, u is {1, 2., u max }，u max The number of traffic for large bandwidth transmission; />Indicating the puncturing situation of the radio resource block s occupied by the emergency service z to the large bandwidth transmission service u,/>Indicating that emergency services z punctured into resource block s, are>Indicating that the emergency service z is not punctured into the resource block s, s e {1, 2., s max }，s max Z e {1, 2..z for the number of radio resource blocks max }，z max Is the number of emergency services.
And 8, combining to obtain the accident information directional release total matrix P.
Distributing matrix B optimally in first-hop communication * Optimal directed distribution matrix b in second-hop communication * Optimal transmission power matrix p for large bandwidth transmission traffic * And an optimal channel allocation matrix omega * Optimal allocation matrix ζ for emergency services * Combining to obtain information oriented release matrix [ B ] * ·b * ]And a resource allocation matrix [ omega ] * ·p * ·ζ * ]The accident information composed of two parts is directionally issued into a total matrix P:
P＝[B * ·b * ·ω * ·p * ·ζ * ]
wherein the symbol represents a concatenation of matrices.
And 9, publishing the accident information according to the information directional publishing total matrix P.
Directional issue total momentDirectional issue matrix [ B ] in matrix P * ·b * ]The element value is 1, which represents that the sending end issues accident information to the corresponding receiving end, and the element value is 0, which represents that the sending end does not issue accident information to the corresponding receiving end.
In the information release process, the position of the resource block occupied by the information transmission and the transmission power need to be according to the resource allocation matrix [ omega ] * ·p * ·ζ * ]To allocate, the resource block positions occupied by the large bandwidth transmission traffic and the transmission power obeys [ omega ] * ·p * ]Rules, puncture location compliance of emergency services [ ζ ] * ]Rules.
The technical effects of the invention are further described by combining simulation experiments:
1. simulation conditions:
simulation software: adopting numerical simulation software MATLAB and traffic simulation software SUMO;
simulation scene: the urban part traffic network is shown in fig. 4.
2. Simulation content and results:
simulation 1, under the simulation conditions, the method and the prior two information release methods are respectively applied to the traffic network of fig. 4 to release accident information, and the total traffic consumption generated when the average number of vehicles on road sections is changed by the three methods is simulated and compared, and the result is shown in fig. 5;
as can be seen from fig. 5, the total system consumption of the two information distribution methods, namely the existing communication-free local control method NC and the non-selective semi-directional distribution method AC, is higher than the TDS of the present invention.
Compared with the traditional NC method, the total system consumption of the NC method in any road section average vehicle number is larger, because the accident information is released in the TDS of the invention, so that vehicles avoid the accident road section, the passing time delay is reduced, and the total system consumption is further reduced, which indicates the effectiveness of information release for reducing the system consumption.
Compared with the existing AC method, the TDS is slightly leeward when the average number of vehicles is smaller, because the TDS can make a decision that the information is not released because the number of vehicles passing through an accident road section is smaller under a certain mobile edge calculation server, so that the vehicles can cause more oil consumption when passing through the accident road section, the AC strategy releases the information to all vehicles, the vehicles in the system are sparse, the possibility of congestion transfer is lower, the vehicles are helped to avoid the accident road section so as to reduce the oil consumption, and meanwhile, the TDS gradually achieves the minimization of the total consumption of the system along with the increase of the average number of vehicles on the road section, so that the directional release has an obvious effect on reducing the total consumption of a traffic system compared with the semi-directional release method.
Simulation 2, under the simulation conditions, performing simulation comparison on communication fitness values when information is released to different numbers of users by using the AC (alternating current) method and the AC method, and the results are shown in FIG. 6;
as can be seen from fig. 6, the AC communication fitness value of the prior art method is lower than the TDS of the present invention under any number of users. This is because the TDS of the present invention has fewer emergency services arriving at any time than the non-selective semi-directional distribution method AC, and the AC method has fewer resources allocated to each large bandwidth transmission service and the emergency service due to the excessive number of emergency services arriving, resulting in a greatly reduced success rate of the transmission of both services. Because the communication fitness value is positively correlated with the probability that the large-bandwidth transmission service meets the transmission requirement and the transmission success rate of the emergency service, the overall fitness value of the AC method is greatly reduced, which shows that the TDS has superiority in the aspect of guaranteeing the communication performance.
The above description is only one specific example of the invention and does not constitute any limitation of the invention, and it will be apparent to those skilled in the art that various modifications and changes in form and details may be made without departing from the principles, construction of the invention, but these modifications and changes based on the idea of the invention are still within the scope of the claims of the invention.
Claims (2)
1. The vehicle accident information directional release method is characterized by comprising the following steps of:
(1) Selecting part of urban road network, and establishing a traffic system model composed of urban roads, vehicles and a mobile edge calculation server;
(2) Constructing a joint communication model consisting of a first-hop communication link and a second-hop communication link according to the position of the mobile edge computing server:
the first-hop communication link refers to a communication link between a mobile edge computing server of an accident road section and any other road section mobile edge computing server;
the second-hop communication link refers to a communication link between the non-accident road segment mobile edge calculation server and the road segment vehicle;
the communication service of each hop link comprises two services, namely a large bandwidth transmission service and an emergency service, wherein accident information release belongs to the emergency service;
(3) Setting the directional release matrixes of the first-hop communication and the second-hop communication in the joint communication model as B, B respectively, and constructing a traffic system total consumption model c (t) about B and B:
wherein ,b0,k B, distributing accident information of the 0 th mobile edge computing server to the k th mobile edge computing server in the first-hop communication directional distribution matrix B 0,k =0 indicates that the kth mobile edge computing server has not received the incident information, b 0,k =1 indicates that accident information is received; b k,v B, calculating information release conditions of a server to vehicles v in jurisdiction of the kth mobile edge in the second-hop communication directional release matrix b k,v =0 indicates that the vehicle v has not received the accident information issued by the kth moving edge computing server, b k,v =1 indicates that accident information was received; c k,v (t) represents the fuel consumption when no accident information is received by the vehicle v in the jurisdiction of the kth mobile edge computing server at the moment t; c' k,v (t) represents the oil of the vehicle v after the path change of the accident information is received in the jurisdiction of the kth mobile edge computing server at the time tConsumption;k max representing the number of mobile edge computing servers; />v max Representing the number of vehicles in the jurisdiction of the kth mobile edge computing server;
the construction of the traffic system total consumption c (t) with respect to the directional distribution matrices B and B is realized as follows:
(3a) Setting the road state to have two accident states and normal states, and calculating the average passing time delay C of the road section a under the two road states according to the queuing theory a ：
wherein ,λa Representing the vehicle arrival rate of the road section a; mu represents the service rate of the road under normal conditions, lambda 0 Indicating accident rate, gamma 0 Representing the service rate of the road under the accident condition;
(3b) Calculating fuel consumption c when accident information is not received by vehicle k,v (t)：
(3b1) According to the average transit time delay C of road sections a Average traffic delay C of road section i i Expressed as:
wherein ,λi For the vehicle arrival rate of road section i, i εr k,v ，r k,v Representing a path currently selected by the vehicle v in jurisdiction of the mobile edge computing server k;
(3b2) According to the average passing time delay C of road section i i Calculating fuel consumption c of vehicle v in jurisdiction of mobile edge calculation server k when accident information is not received k,v (t)：
wherein ,fi For average fuel consumption of vehicles on road section i, c fuel Indicating the oil price;
(3c) Calculating oil consumption c 'of vehicle after receiving accident information and carrying out path replacement' k,v (t)：
(3c1) According to the average transit time delay C of road sections a Average traffic delay C of road section j j Expressed as:
wherein ,λj For road section j, vehicle arrival rate, j e r k ' ,v ，r k ' ,v Representing a new path after the vehicle v in the jurisdiction of the mobile edge computing server k receives accident information and changes the path;
(3c2) According to the average passing time delay C of the road section j j Calculating the oil consumption c 'of the vehicles v in the jurisdiction of the mobile edge calculation server k after receiving accident information and carrying out path replacement' k,v (t)：
wherein ,fj The average fuel consumption rate of the vehicles on the road section j;
(3d) Fuel consumption c according to accident information not received by vehicle k,v (t) and Fuel consumption c 'after route replacement by Accident information' k,v (t) obtaining the total consumption c (t) of the traffic system:
(4) Constructing an optimization problem P1 for minimizing the total consumption of the system according to the total consumption c (t) of the traffic system, decomposing the optimization problem P1 into two sub-optimization problems, and adopting a one-dimensional search algorithm to perform the optimization on the two sub-optimization problemsSolving sub-optimization problem to obtain optimal information oriented release matrix B * and b* ；
(5) Setting the transmission power matrix of the large-bandwidth transmission service as P and the channel allocation matrix as omega, constructing an optimization problem P2 of the maximum average transmission rate of the large-bandwidth transmission service with optimization variables of P and omega, and solving the problem P2 by utilizing a sparrow search algorithm to obtain an optimal resource allocation matrix P of the large-bandwidth transmission service * and ω* Allocating fixed wireless resources to a large bandwidth transmission service; the optimization problem P2 of the maximum average transmission rate of the large bandwidth transmission service is constructed by using the optimization variables of a transmission power matrix P and a channel allocation matrix omega, and is realized as follows:
(5a) The transmission rate of the large-bandwidth transmission service u is determined according to the shannon theoremExpressed as:
wherein ,fs Is the bandwidth of the radio resource block s; omega u,s Is the occupation condition of the large bandwidth transmission service u to the radio resource block s in the channel allocation matrix omega, omega u,s =1 indicates that the radio resource block s is allocated to the service u, ω u,s =0 indicates that the radio resource block s is not allocated to the service u; beta represents a constant related to the bit error rate; p is p u 、g u The transmitting power and the channel gain of the large bandwidth transmission service u are respectively represented; sigma (sigma) 2 Is the standard deviation of system noise;s max indicating the number of radio resource blocks,u max representing the number of large bandwidth transmission services;
(5b) According to the transmission rateConstructing and transmitting the transmission rate of the business with large bandwidth>Related objective function->
wherein ,representing the transmission rate state of the large bandwidth transmission service u in time slot t +.>Minimum speed value +.>Meet the transmission requirement->Representing that the transmission rate of service u is smaller than the required minimum rate value +.>Not meeting the transmission requirement;
(5c) According to an objective functionBuilding an optimization problem P2:
P2:
s.t.C1:
C2:
C3:
wherein ,pu,s Representing the corresponding sub-channel power value when the service u occupies the radio resource block s in the power distribution matrix p of the large bandwidth transmission service; the limiting conditions C1 and C2 limit that one resource block can only be allocated to one large bandwidth transmission service; constraint C3 represents a limit range of total transmission power, where p max Representing the maximum value of the total transmission power of the base station;
(6) Based on the optimal directed distribution matrix b * The emergency service number A (t) of each mobile edge computing server arriving at the time slot t is obtained by firstly distributing the matrix b according to the optimal orientation * Obtaining the number of vehicles receiving accident information in each road section when the time slot t is obtained, and counting all the vehicle numbers to obtain the number of vehicles receiving the accident information in each road section, wherein the number is the emergency service arrival number A (t) when the time slot t is obtained;
(7) Based on the optimal resource allocation matrix p * Omega and arrival rule A (t) of emergency service, constructing an optimization problem P3 for maximizing the transmission success rate of the emergency service and minimizing the loss rate of the large-bandwidth transmission service, and solving an optimal distribution matrix zeta of the emergency service * Optimal resource allocation of two services is realized; wherein an optimization problem P3 for maximizing the transmission success rate of the emergency service and minimizing the loss of the large bandwidth transmission service rate is constructed, and an optimal distribution matrix zeta of the emergency service is obtained by solving * The implementation is as follows:
(7a) Calculating the transmission rate r of the large bandwidth transmission service u when the emergency service arrives and punctures the wireless resource block u (t)：
wherein ,yu (t) representing the occupation of the service u to the radio resource in the large bandwidth transmission service without the emergency service puncture; z u (t) represents the resource situation where the traffic u is punctured in the large bandwidth transmission traffic;representing an optimal power allocation value for traffic u in the large bandwidth transmission traffic; g u Channel gain representing large bandwidth transmission traffic u; beta represents a constant related to the bit error rate; sigma (sigma) 2 Is the standard deviation of system noise; />u max Representing the number of large bandwidth transmission services;
(7b) Calculating the transmission rate r of the emergency service z z (t)：
wherein ,fs Is the bandwidth of the radio resource block s;optimal channel allocation matrix omega for large bandwidth transmission service * Occupancy of radio resource block s by middle traffic u, < >>Indicating that radio resource block s is allocated to traffic u, for example>Indicating that the radio resource block s is not allocated to the service u; zeta type u,s,z Is the puncture condition of the emergency service z to the existing wireless resource block s of the large bandwidth transmission service u in the resource allocation matrix ζ u,s,z =1 indicates that the emergency traffic z punctures into the allocated resource block s of the large bandwidth transmission traffic u, ζ u,s,z =0 indicates unpunctured; beta represents and errorCode rate dependent constants; p is p urllc 、g urllc Respectively representing the transmitting power and the channel gain of the emergency service; sigma (sigma) 2 Is the standard deviation of system noise; />z max Representing the number of emergency services;s max representing the number of radio resource blocks;
(7c) Calculating average transmission success rate delta of emergency service suc ：
(7c1) According to the transmission rate r of emergency services z (t) calculating the transmission success number i of the emergency service z (t)：
Wherein D represents the bit value of accident information, and τ represents the hard delay requirement of emergency service;
(7c2) According to the number i of successful transmission z (t) calculating a transmission success rate delta of the emergency service suc ：
Wherein A (t) represents the number of emergency services reached by the time slot t, and E (·) represents the operation of solving the expected value;
(7d) According to the transmission rate r of the large bandwidth transmission service u (t) calculating loss delta of puncture of emergency service to transmission rate of large bandwidth transmission service loss ：
wherein ,the transmission rate state of the large bandwidth transmission service u without emergency service puncture is shown; />Indicating the transmission rate of the large bandwidth transmission service u without emergency service puncture; />Representing a minimum data rate required for a large bandwidth transmission service; i.e u (t) represents the transmission rate state of the large bandwidth transmission service u in the time slot t when the emergency service puncture exists, i u (t) =1 means that the transmission rate of the service u is equal to or higher than the minimum rate value required for the large bandwidth transmission service +.>Meet the transmission requirement, i u (t) =0 means that the transmission rate of the service u is smaller than the required minimum rate value +.>Not meeting the transmission requirement;
(7e) According to the transmission success rate delta suc And loss delta loss Building an optimization problem P3:
P3:
minΔ loss (ζ u,s,z )
s.t.C1:
C2:
C3:P(E)≤ε
C4:
wherein, the limiting conditions C1 and C2 jointly limit that each emergency service can only puncture one resource block once; the limiting condition C3 limits the interruption probability of the emergency service, P (E) represents the interruption probability of the emergency service, and epsilon represents an interruption probability threshold; constraint C4 indicates that the amount of resources that emergency services can puncture cannot exceed their available resources,representing the total resources allocated by the large bandwidth transmission service u;
(7f) Converting the nonlinear multi-objective optimization problem P3 into a single-objective optimization problem P3-1 according to a target product method:
s.t.C1-C4
(7g) Solving the P3-1 according to a sparrow search algorithm to obtain an optimal distribution matrix zeta of emergency services * ；
(8) Will optimally orient the issue matrix B * and b* Optimal resource allocation matrix p for large bandwidth transmission traffic * and ω* Optimal allocation matrix ζ for emergency services * Combining to obtain an accident information directional release total matrix;
(9) And publishing the accident information according to the directional publishing total matrix.
2. The method according to claim 1, characterized in that: the minimization system total consumption optimization problem with the oriented release matrixes B and B as optimization variables is constructed in the step (4), and the implementation is as follows:
(4a) The optimization problem P1 of minimizing the total fuel consumption of the traffic system is expressed as:
P1:
s.t.C1:
C2:
(4b) Decomposing the optimization problem P1 into two sub-optimization problems P1-1 and P1-2:
P1-1:
s.t.C1:
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