CN115065964A - Vehicle accident information directional publishing method - Google Patents
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Abstract
The invention provides a directional releasing method of vehicle accident information, which mainly solves the problems of high oil consumption, fierce channel competition and incapability of meeting multi-service coexistence communication in the prior art, and the scheme is as follows: 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 for 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 rate loss of the large-bandwidth transmission service, and solving an optimal resource allocation matrix of the emergency service; and combining the optimal matrixes to obtain a directional distribution total matrix. The invention reduces the total consumption of a traffic system while maximizing the success rate of accident information transmission, and can be used for an urban road network.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a directional publishing method of vehicle accident information, which can be used for an urban road network.
Background
In recent years, the number of motor vehicles and travel demand suddenly increasing in urban road networks have led to frequent urban traffic accidents. In addition to direct economic losses, the losses caused by typical aperiodic traffic congestion caused by accidents in terms of travel time, fuel consumption, air pollution, etc. are also not insignificant. The negative impact of road congestion after an accident is gradually an obstacle 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 chance for solving the urgent need. Accident information is issued to all internet vehicles in the urban road network through a communication means, so that immeasurable economic loss caused by congestion expansion and propagation due to information lag is avoided. The non-directional distribution of accident information is crucial to reduce the number of accidents and alleviate the negative impact caused by congestion after an accident. With the increasing number of internet vehicles, the contradiction between limited wireless resources and unlimited resource requirements appears more and more dribbling, and the non-directional issuing performance of accident information is difficult to guarantee. This has prompted researchers to find new strategies to further alleviate the link pressure and performance impact of a large number of internet vehicle communications, and the idea of directional distribution of accident information has come into force.
At present, research on accident information directional release methods mainly focuses on semi-directional release strategies in an internet of vehicles scene, and can be divided into two types:
the first is a publishing method based on influential nodes. According to the method, the influence of the networked vehicles in the vehicle networking environment is sequenced through a vehicle terminal influence decision algorithm, and the networked vehicles with larger influence are selected as the relay nodes for information distribution when the information is distributed, namely, the accident information is firstly distributed to the relay nodes, and then the relay nodes distribute the accident information to all other networked vehicles, so that the channel competition pressure can be reduced at the same time, and the communication reliability is ensured.
The second is a distribution method based on transmission probability. In the method, the communication performance of different vehicle terminals when receiving information is determined by the relevance of the different vehicle terminals and the accident occurrence place, the internet connected vehicles related to the accident receive the accident information with high probability and low delay and high reliability, and the performance of other internet connected vehicles with lower relevance when receiving the information is possibly relatively poor. In the mode, semi-directional distribution is realized by controlling the distribution probability, so that the communication efficiency is improved, and the negative influence caused by congestion after an accident is relieved.
The related research of the existing accident information directional release method mainly focuses on the research aspect of communication performance of semi-directional release in an internet of vehicles scene, namely, information is released to all vehicle terminals by selecting influential nodes as relay forwarding nodes so as to reduce the competition pressure of links and improve the communication performance, and the method has the following three problems:
1) the influence of over-distribution of information on a traffic system is not considered, and the traffic system can be possibly congested and shifted to cause traffic delay, additional fuel consumption and other negative influences;
2) the complete directional distribution is not realized, so that the problem that a great amount of and continuously increased Internet vehicle information distribution still faces fierce channel competition is solved;
3) the method is not applied to a multi-service competition scene, and the reliability and the low time delay performance of accident information release in the scene are difficult to ensure.
Disclosure of Invention
The invention aims to provide a directional issuing method of vehicle accident information aiming at the defects of the prior art, so as to ensure low time delay and high reliability of the accident information under the condition of multi-service competition and relieve the negative influence of the excessive issuing of the accident information on the fuel consumption of a traffic system.
The technical scheme of the invention is as follows: modeling total consumption in a traffic system, obtaining an optimal accident information directional distribution 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, obtaining an arrival rule of accident information according to directional distribution matrix analysis, combining a resource distribution matrix of the large-bandwidth transmission service on the basis of the rule, maximizing the insertion transmission success rate of the emergency service and minimizing the rate loss of the large-bandwidth transmission service so as to obtain the optimal distribution matrix of the emergency service, and fusing the corresponding resource distribution matrix of each directional distribution matrix to realize final directional distribution. The concrete implementation steps comprise:
(1) selecting part of urban road networks, and establishing a traffic system model consisting of urban roads, vehicles and a mobile edge computing server;
(2) and 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 the accident road section and a mobile edge computing server of any other road section;
the second hop communication refers to a communication link between a non-accident road section mobile edge calculation server and a vehicle on the road section;
the communication service of each hop comprises two services, namely a large-bandwidth transmission service and an emergency service, wherein the accident information release belongs to the emergency service;
(3) setting directional distribution matrixes of first-hop communication and second-hop communication in the joint communication model as B, B respectively, and constructing a traffic system total consumption model c (t) related to B and B:
wherein ,b0,k B, distributing accident information of 0 th mobile edge computing server to k th mobile edge computing server in first hop communication directional distribution matrix B 0,k 0 means that the kth mobile edge computation server has not received the failure information, b 0,k 1 indicates that accident information is received; b k,v For second hop communicationB, issuing information of vehicles v in the jurisdiction range of the k mobile edge calculation server in the issuing matrix b k,v 0 means that the vehicle v does not receive the accident information issued by the kth mobile edge computing server, b k,v If 1, the accident information is received; c. C k,v (t) fuel consumption when the vehicle v does not receive accident information within the jurisdiction of the kth mobile edge computing server at the time tth; c' k,v (t) shows the oil consumption of the vehicle v after receiving the accident information and performing path replacement in the administrative range of the kth mobile edge calculation server at the moment tth;
k max representing the number of mobile edge compute servers;
v max representing the number of vehicles within 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 into two sub-optimization problems, and solving the two sub-optimization problems by adopting a one-dimensional search algorithm to obtain an optimal information directional distribution matrix B * and b* ；
(5) Setting a transmission power matrix of the large-bandwidth transmission service as P and a channel allocation matrix as omega, constructing an optimization problem P2 with optimization variables of P and omega and maximizing the average transmission rate of the large-bandwidth transmission service, and solving the problem P2 by using 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 the large bandwidth transmission service;
(6) issuing matrix b according to optimal orientation * Obtaining the number A (t) of the emergency services which arrive at the time slot t of each mobile edge computing server;
(7) based on optimal resource allocation matrix p * 、ω * And the arrival rule A (t) of the emergency service, and 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 is constructedSolving the optimal distribution matrix zeta of the emergency service * Realizing the optimal resource allocation of the two services;
(8) issuing the optimal directional distribution matrix B * and b* Optimal resource allocation matrix p of large bandwidth transmission service * and ω* And 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) and the negative influence of over-release of information on traffic oil consumption is relieved.
The invention models the traffic consumption of each road in the traffic system, constructs the optimization problem of minimizing the total consumption of the traffic system, and solves the optimization problem to obtain the optimal directional distribution matrix, thereby selectively controlling the vehicles to select the path again to avoid the accident road section. Compared with the traditional issuing strategy, the model realizes the directional issuing in the true sense, selectively issues the accident information to part of related vehicles, avoids the congestion transfer condition caused by the fact that the accident information is not directionally issued to all vehicles, and relieves the negative influence of the excessive issuing of the information on the aspect of traffic oil consumption.
(2) And the accident information communication performance is improved.
The invention integrates the directional release and the resource allocation into a whole, a part of vehicles related to the accident are screened out by the directional release matrix, and the resource allocation matrix optimizes the power and the channel occupation condition when the part of vehicles receive the information. Compared with the traditional communication performance optimization only by resource allocation, the method reduces the information sources needing to be transmitted through directional distribution, greatly reduces the communication energy consumption through the combination of the directional distribution and the resource allocation, and improves the communication performances of the transmission success rate and the transmission rate.
(3) And the effective release of the accident information is ensured when multiple service resources compete.
According to the invention, by constructing an optimization problem of maximizing the success rate of emergency service transmission and minimizing the average data rate of the large-bandwidth transmission service, the optimal allocation strategy of the large-bandwidth transmission service and the emergency service is obtained by solving on the premise of knowing a directional release matrix. Compared with the accident information release in the existing car networking environment, the accident information directional release method and the car networking system have the advantages that the accident information directional release strategy is considered to be fused with the multi-service scene, and the influence of multi-service competition in the current scene on the communication performance of the accident information directional release is solved.
Drawings
FIG. 1 is a block diagram of an implementation flow of the present invention;
FIG. 2 is a model diagram of a traffic system according to the present invention;
FIG. 3 is a sub-flowchart of the present invention for constructing a traffic system total consumption model;
FIG. 4 is a diagram of a portion of an urban traffic network used in the simulation of the present invention;
FIG. 5 is a comparison of total consumption simulation of the traffic network of FIG. 3 for the present invention and the prior art information distribution method;
fig. 6 is a comparison diagram of the communication fitness simulation of the information released by different users according to the present invention and the existing information release method.
Detailed Description
The embodiments and effects of the present invention are further described in detail below with reference to the accompanying drawings:
referring to fig. 1, the present example includes the steps of:
A part of road segments are selected from the road network, abstracted into a traffic system model consisting of urban roads, road infrastructure and autonomous vehicles, and it is assumed that an accident occurs at a certain road segment and causes road congestion, as shown in fig. 2.
Each road section is provided with a mobile edge computing server for collecting and publishing the traffic information of the vehicles on the road section, and the mobile edge computing server set is represented as
k max The number of servers is calculated for the moving edges in the road network.
The automatic driving vehicle integrates the functions of communication, perception and calculation and has self-driving functionA main decision function for representing the vehicle set on the road section managed by the mobile edge computing server k as
wherein vmax The number of vehicles on the road segment.
In the embodiment, a traffic system model is constructed by selecting partial road sections from a Beijing road network, 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.
A joint communication model consisting of two-hop communication links is constructed based on the location of each mobile edge computing server and vehicle in fig. 2. Wherein: the first hop communication link is a communication link between a mobile edge computing server of the accident road section and any other mobile edge computing server of the non-accident road section; and the second hop communication link refers to a communication link between the non-accident road section moving edge calculation server and the vehicle of the road section. Each hop of communication link is occupied by two 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 occurs, 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 calculation server to the non-accident road section moving edge calculation server in the transmission range of the accident road section moving edge calculation server to complete first-hop communication, and secondly issued from the non-accident road section moving edge calculation server to the automatic driving vehicle on the road section to complete second-hop communication. If the first hop communication does not occur, the corresponding second hop communication does not necessarily occur; if the first-hop communication is successful, the mobile edge computing server receiving the accident information selectively completes the second-hop communication.
The completion condition of two-hop communication during the directional distribution of the accident information is represented by a directional distribution matrix B of first-hop communication and a directional distribution matrix B of second-hop communication respectively. Wherein:
And 3, constructing a traffic system total consumption model c (t).
The total consumption of the traffic system refers to the sum of oil consumption of all vehicles in the traffic system, and the oil consumption of each vehicle can be influenced by the length of the currently selected path and the congestion degree of the vehicle. The principle of vehicle route selection is as follows: if the vehicle does not receive the accident information, the vehicle can drive according to the original planned path, and after receiving the accident information, the vehicle can plan the path and replace the optimal path again. Therefore, the total consumption of the traffic system can be divided into the sum of the fuel consumption of the vehicles receiving the accident information and the fuel consumption of the vehicles not receiving the accident information. And the oil consumption of the vehicle is calculated by the current oil price, the road section passing time delay and the road section oil consumption rate.
Referring to fig. 3, the specific implementation of this step is as follows:
3.1) setting the road state to have an accident state and a normal state, and calculating the average passing time delay C of the road section a in the two road states according to a queuing theory a ：
wherein ,λa Representing the vehicle arrival rate of the section a; mu denotes the service rate of the road under normal conditions, lambda 0 Indicating the incidence of accidents, gamma 0 Represents the service rate of the road in case of an accident;
3.2) calculating the oil consumption c when the vehicle v in the jurisdiction of the mobile edge calculation server k does not receive the accident information k,v (t)：
3.2.1) average transit time delay C according to road section a The average passing time of the road section i is delayed by C i Expressed as:
wherein ,λi For the vehicle arrival rate of the section i, i ∈ r k,v ，r k,v Representing the currently selected path of the vehicle v in the jurisdiction of the mobile edge calculation server k;
3.2.2) average transit time delay C according to road section i i Calculating the oil consumption c of the vehicles v in the jurisdiction of the mobile edge calculation server k when the vehicles v do not receive the accident information k,v (t)：
wherein ,fi Average fuel consumption of vehicles on section i, c fuel The oil price is shown as the price of oil,
3.3) calculating the fuel consumption c 'after the vehicle receives the accident information and carries out route replacement' k,v (t)：
3.3.1) average transit time delay C according to road section a The average passing time of the road section j is delayed by C j Expressed as:
wherein ,λj Is the vehicle arrival rate of link j, j ∈ r' k,v ，r’ k,v Representing a new path after a vehicle v in the jurisdiction of the mobile edge computing server k receives the accident information and carries out path replacement;
3.3.2) average transit time delay C according to road section j j Calculating the fuel consumption c 'after the vehicle v in the jurisdiction of the mobile edge calculation server k receives the accident information and carries out route replacement' k,v (t)：
wherein ,fj The average fuel consumption rate of the vehicle on the road section j;
3.4) oil consumption according to the accident information which is not received by the vehicle c k,v (t) and fuel consumption c 'after route replacement upon receipt of accident information' k,v (t) obtaining the total consumption c (t) of the traffic system:
From step 3, it can be known that the total system consumption c (t) is an expression about the two-hop directional distribution matrices B and B, and different distribution modes can cause different vehicle fuel consumptions. Because the vehicle can independently change the path after receiving the accident information, the congestion transfer phenomenon can be caused, and further the traffic system generates extra oil consumption, the total consumption of the traffic system can be reduced to the maximum extent by adjusting the information issuing condition.
4.1) expressing the optimization problem P1 as follows according to the total consumption c (t) of the traffic system:
4.2) decomposing the optimization problem P1 into two sub-optimization problems, P1-1 and P1-2:
4.3) solving the two sub-optimization problems P1-1 and P1-2 by adopting a one-dimensional search algorithm to obtain an optimal information directional distribution matrix B * and b* ：
wherein ,
represents the optimal accident information distribution condition of the 0 th mobile edge computing server to the k-th mobile edge computing server,
indicating that the k-th mobile edge computing server did not receive incident information,
indicating that the incident information was received, k ∈ {1,2 max }，k max Calculating the number of servers for the mobile edges in the road network;
represents the optimal accident information distribution condition of the k mobile edge computing server to the vehicles v in the jurisdiction range,
indicating that the vehicle v has not received the accident information,
indicating that vehicle v received accident information, v ∈ {1,2 max }，k max 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 traffic is allocated to a different resource block or transmission power to obtain different channel conditions and thus different transmission performance. Therefore, the transmission rate of the large-bandwidth transmission service can be improved by scheduling the resource block positions and the transmission power occupied by different services, and the existing fixed wireless resources are efficiently utilized.
Setting a transmission power matrix of large bandwidth transmission service as P and a channel allocation matrix as omega, and constructing an optimization problem P2 according to the following steps:
5.1) Transmission Rate of Large Bandwidth Transmission service u according to Shannon's theorem
Expressed as:
wherein ,fs Is the bandwidth of a radio resource block s; omega u,s Is the occupation condition of the large bandwidth transmission service u in the channel allocation matrix omega to the wireless resource block s, omega u,s 1 indicates that a radio resource block s is allocated to a service u, ω u,s 0 means that the radio resource block s is not allocated to the service u; β represents a constant related to the error rate; p is a radical of u 、g u Respectively representing the transmitting power and the channel gain of the large bandwidth transmission service u; sigma 2 Is the standard deviation of the system noise;
s max indicates that there is noThe number of the line resource blocks is,
u max representing the number of large bandwidth transmission services;
5.2) according to the transmission rate
Construction of transmission rate for large bandwidth transmission service
Related objective function
wherein ,
indicating the transmission rate status of the large bandwidth transmission service u in the time slot t,
the transmission rate of the service u is greater than or equal to the minimum rate value required by the large-bandwidth transmission service
The transmission requirements are met, and the device can be used,
indicating that the transmission rate of the service u is less than the required minimum rate value
Does not meet the transmission requirements;
5.3) according to the objective function
Constructing an optimization problem P2:
wherein ,pu,s Representing the corresponding sub-channel power value when a service u occupies a wireless resource block s in a power distribution matrix p of a large-bandwidth transmission service; the limiting conditions C1 and C2 jointly limit that one resource block can only be allocated to one large-bandwidth transmission service; the limit condition C3 represents a limit range of the total transmission power, where p max Represents the maximum value of the total transmission power of the base station;
5.4) solving the problem P2 by using a sparrow search algorithm to obtain the optimal resource allocation matrix P of the large-bandwidth transmission service * and ω* ：
wherein ,
represents the optimal transmission power value of the service u on the wireless resource block s in the large bandwidth transmission service, and at the time, u belongs to {1,2 max }，u max For the number of large bandwidth transmission services, s belongs to {1,2 max }，s max Is the number of radio resource blocks;
represents the optimal occupation situation of the wireless resource block s by the large bandwidth transmission service u,
indicating that the service u is occupied to a radio resource block s,
indicating unoccupied.
And 6, counting the arrival number A (t) of the emergency services.
Issuing matrix b according to optimal orientation * And obtaining the vehicle number of each road section receiving the accident information at the time slot t, and then counting all the vehicle numbers to obtain the number of the vehicles receiving the accident information at each road section, wherein the number is the arrival number A (t) of the emergency services at the time slot t:
In order to meet the self hard delay requirement, the emergency service punctures the resource occupied by the large bandwidth transmission service when arriving and ensures that the transmission of the emergency service is completed in a micro time slot, but the operation will affect the transmission rate of the original large bandwidth transmission service, and the operation can be based on the optimal resource allocation matrix p * 、ω * And an arrival rule A (t) of the emergency service, constructing a dual-target optimization problem which maximizes the transmission success rate of the emergency service and minimizes the loss rate of the large-bandwidth transmission service, and dynamically scheduling the position of the arrived emergency service puncturing resource block so as to meet the requirements of the two services as much as possible and maximize the resource utilization rate.
The specific implementation of this step 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 wireless resource block u (t)：
wherein ,yu (t) represents the occupation situation of the wireless resources by the large bandwidth transmission service u when there is no emergency service puncture; z is a radical of u (t) denotes a large bandThe resource condition of the punctured wide transmission service u;
representing the optimal power distribution value of the large bandwidth transmission service u; g u Representing the channel gain of a large bandwidth transmission service u; β represents a constant related to the error rate; sigma 2 Is the standard deviation of the system noise;
u max representing the number of large bandwidth transmission services;
7.2) calculating the Transmission Rate r of the Emergency service z z (t)：
wherein ,fs Is the bandwidth of a radio resource block s;
is the optimal channel distribution matrix omega of the large bandwidth transmission service * The occupation of the radio resource block s by the medium service u,
indicating that a radio resource block s is allocated to a service u,
indicating that the radio resource block s is not allocated to the service u; zeta u,s,z Is the puncture situation of the existing wireless resource block s of the emergency service z in the resource allocation matrix zeta to the large bandwidth transmission service u, zeta u,s,z 1 means that the emergency service z punctures into the allocated resource block s of the large bandwidth transmission service u, ζ u,s,z 0 means no puncture; β represents a constant related to the error rate; p is a radical of urllc 、g urllc Respectively representing the transmitting power and the channel gain of the emergency service; sigma 2 Is the standard deviation of the system noise;
z max the number of emergency services;
s max is the number of radio resource blocks;
7.3) calculating the average transmission success rate Delta of the emergency service suc ：
7.3.1) Transmission Rate r according to Emergency services z (t) calculating the successful number of transmission i of the emergency service z (t)：
Wherein D represents the bit value of the accident information, and tau represents the hard time delay requirement of the emergency service;
7.3.2) according to the number of transmission successes i z (t) calculating a transmission success rate Δ for emergency services suc ：
Wherein, A (t) represents the number of the emergency services arriving at the time slot t, and E (-) represents the operation of obtaining the expected value;
7.4) Transmission Rate r according to the Large Bandwidth Transmission traffic u (t) calculating the loss Delta caused by the puncture of the emergency service to the transmission rate of the large-bandwidth transmission service loss ：
wherein ,
indicating the transmission rate state of the large bandwidth transmission service u when the emergency service puncture does not exist;
the transmission rate of the large bandwidth transmission service u is shown when the emergency service puncture does not exist;
the minimum value of the data rate required by the large bandwidth transmission service is represented; i.e. i u (t) represents the transmission rate status of the large bandwidth transmission service u in the time slot t when the emergency service puncture exists, i u (t) ═ 1 indicates that the transmission rate of the service u is greater than or equal to the minimum rate value required by the large bandwidth transmission service
Satisfies the transmission requirement i u (t) ═ 0 indicates that the transmission rate of the service u is less than the required minimum rate value
Does not meet the transmission requirements;
7.5) according to the transmission success rate Delta suc And loss of delta loss Constructing an optimization problem P3:
wherein, the limiting conditions C1 and C2 jointly limit that each emergency service can only puncture the same resource block once; the limiting condition C3 limits the interruption probability of the emergency service, wherein P (E) represents the interruption probability of the emergency service and is related to the arrival number A (t) of the emergency service, and epsilon represents an interruption probability threshold; the restriction condition C4 indicates that the amount of resources that emergency traffic can puncture cannot exceed its available resources,
the overall resource allocated by the large bandwidth transmission service u is represented;
7.6) converting the nonlinear multi-objective optimization problem P3 into a single-objective optimization problem P3-1 according to a target product method:
7.7) solving the P3-1 according to the sparrow searching algorithm to obtain the optimal distribution matrix zeta of the emergency service * ：
wherein ,
the method represents the optimal puncturing condition of the wireless resources occupied by the large-bandwidth transmission service u by the emergency service, and u belongs to {1,2 max }，u max Number of traffic for large bandwidth transmission;
indicating the puncturing situation of the wireless resource block s occupied by the large bandwidth transmission service u by the emergency service z,
indicating that emergency traffic z is punctured into resource block s,
indicating that the emergency service z does not puncture into the resource block s, s ∈ {1,2 max }，s max Z ∈ {1, 2., z, which is the number of radio resource blocks max }，z max The number of emergency services.
And 8, combining to obtain an accident information directional distribution total matrix P.
Issuing the optimal directional distribution matrix B in first-hop communication * Optimal directional distribution matrix b in second hop communication * Optimal transmission power matrix p of large bandwidth transmission service * And an optimal channel allocation matrix omega * Optimal distribution matrix zeta of emergency services * Combining to obtain directional information issuing matrix B * ·b * ]And a resource allocation matrix [ omega ] * ·p * ·ζ * ]The accident information directional distribution total matrix P is composed of two parts:
P＝[B * ·b * ·ω * ·p * ·ζ * ]
where the symbol · represents the concatenation of the matrices.
And 9, issuing the accident information according to the information directional issuing total matrix P.
Directional distribution matrix [ B ] in directional distribution total matrix P * ·b * ]The element value is 1, which represents that the transmitting end issues accident information to the corresponding receiving end, and the element value is 0, which represents that the transmitting end does not issue the accident information to the corresponding receiving end.
In the information release process, the resource block position occupied by information transmission and the transmission power need to be allocated according to the resource allocation matrix [ omega ] * ·p * ·ζ * ]To allocate, the resource block position occupied by the large bandwidth transmission service and the transmission power obey [ omega ] * ·p * ]Rule, puncture location for emergency services [ ζ [ ] * ]And (4) rules.
The technical effects of the invention are further explained by combining simulation experiments as follows:
1. simulation conditions are as follows:
simulation software: adopting numerical simulation software MATLAB and traffic simulation software SUMO;
simulation scene: the urban part of the traffic network is shown in figure 4.
2. Simulation content and results:
as can be seen from fig. 5, the total system consumption of the two information distribution methods, namely the existing non-communication local control method NC and the non-selection semi-directional distribution method AC, is higher than the TDS of the present invention.
Compared with the existing NC method, the TDS has the advantages that the total system consumption of the average number of vehicles in any road section is larger in the NC method, because the vehicles avoid the accident road section due to the release of the accident information in the TDS, the traffic delay is reduced, the total system consumption is further reduced, and the effectiveness of the information release for reducing the system consumption is shown.
Compared with the existing AC method, the TDS is slightly in the downwind when the average vehicle number is smaller, because the TDS makes a decision not to release the information because fewer vehicles pass through the accident road section under a certain mobile edge computing server, the vehicles pass through the accident road section to cause more oil consumption, the AC strategy at the moment releases the information to all vehicles, the vehicles in the system are sparse, the possibility of congestion transfer is low, the vehicles are helped to avoid the accident road section to reduce the oil consumption, and meanwhile, the TDS gradually realizes the minimization of the total consumption of the system along with the increase of the average vehicle number of the road section, which shows that the directional release method has obvious effect on the aspect of reducing the total consumption of a traffic system compared with the semi-directional release method.
Simulation 2, under the simulation conditions, the communication fitness values when the information is respectively distributed to different numbers of users by using the invention and the existing non-selection semi-directional distribution method AC are simulated and compared, and the result is 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 at any number of users. This is because the TDS of the present invention is smaller than the number of emergency services arriving in the AC without selective semi-directed distribution method at any time, and the AC method has less resources allocated to each of the 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 transmission of both services. Because the communication adaptability 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 adaptability value of the AC method is greatly reduced, which shows that the TDS has superiority in guaranteeing the communication performance.
The foregoing description is only an example of the present invention and should not be construed as limiting the invention, as it will be apparent to those skilled in the art that various modifications and variations in form and detail can be made without departing from the principle and structure of the invention after understanding the present disclosure and the principles, but such modifications and variations are considered to be within the scope of the appended claims.
Claims (6)
1. A directional publishing method for vehicle accident information is characterized by comprising the following steps:
(1) selecting a part of urban road network, and establishing a traffic system model consisting of urban roads, vehicles and a mobile edge calculation server;
(2) and 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 link refers to a communication link between a mobile edge computing server of the accident road section and a mobile edge computing server of any other road section;
the second hop communication link refers to a communication link between the non-accident road section mobile edge calculation server and the vehicle on the road section;
the communication service of each hop of link comprises two services, namely a large-bandwidth transmission service and an emergency service, wherein the accident information release belongs to the emergency service;
(3) setting directional distribution matrixes of first-hop communication and second-hop communication in the joint communication model as B, B respectively, and constructing a traffic system total consumption model c (t) related to B and B:
wherein ,b0,k B, accident information distribution condition of 0 th mobile edge computing server to k th mobile edge computing server in first hop communication directional distribution matrix B 0,k 0 means that the kth mobile edge computation server has not received the failure information, b 0,k 1 indicates that accident information is received; b k,v The information of the k mobile edge calculation server in the second hop communication directional distribution matrix b to the vehicles v in the jurisdiction range is distributed, b k,v 0 means that the vehicle v does not receive the accident information issued by the kth mobile edge computing server, b k,v If 1, the accident information is received; c. C k,v (t) fuel consumption when the vehicle v does not receive accident information within the jurisdiction of the kth mobile edge computing server at the time tth; c' k,v (t) shows the oil consumption of the vehicle v after receiving the accident information and performing path replacement in the administrative range of the kth mobile edge calculation server at the moment tth;
k max representing the number of mobile edge compute servers;
v max representing the number of vehicles within 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 into two sub-optimization problems, and solving the two sub-optimization problems by adopting a one-dimensional search algorithm to obtain an optimal information directional distribution matrix B * and b* ；
(5) Setting a transmission power matrix of the large-bandwidth transmission service as P and a channel allocation matrix as omega, constructing an optimization problem P2 with optimization variables of P and omega and maximizing the average transmission rate of the large-bandwidth transmission service, and solving the problem P2 by using 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 the large bandwidth transmission service;
(6) issuing matrix b according to optimal orientation * Obtaining the number A (t) of the emergency services which arrive at the time slot t of each mobile edge computing server;
(7) based on optimal resource allocation matrix p * 、ω * And the arrival rule A (t) of the emergency service, and the construction maximizes the transmission success rate of the emergency service and minimizesThe optimization problem P3 of the loss rate of the large bandwidth transmission service is solved to obtain the optimal distribution matrix zeta of the emergency service * Realizing the optimal resource allocation of the two services;
(8) issuing the optimal directional distribution matrix B * and b* Optimal resource allocation matrix p of large bandwidth transmission service * and ω* And optimal allocation matrix ζ for emergency services * Combining to obtain an accident information directional release total matrix;
(9) and issuing the accident information according to the directional issuing total matrix.
2. The method of claim 1, wherein: the total consumption c (t) of the traffic system related to the directional distribution matrixes B and B is constructed in the step (3) and is realized as follows:
(3a) setting the road state to have an accident state and a normal state, and calculating the average passing time delay C of the road section a in the two road states according to the queuing theory a ：
wherein ,λa Representing the vehicle arrival rate of the section a; mu denotes the service rate of the road under normal conditions, lambda 0 Indicating the incidence of accidents, gamma 0 Represents the service rate of the road in case of an accident;
(3b) calculating the fuel consumption c when the vehicle does not receive the accident information k,v (t)：
(3b1) According to the average traffic time delay C of the road section a The average passing time of the road section i is delayed by C i Expressed as:
wherein ,λi For the vehicle arrival rate of the section i, i ∈ r k,v ，r k,v Representing the currently selected path of the vehicle v in the jurisdiction of the mobile edge calculation server k;
(3b2) average traffic time delay C according to road section i i Calculating the oil consumption c of the vehicles v in the jurisdiction of the mobile edge calculation server k when the vehicles v do not receive the accident information k,v (t)：
wherein ,fi Average fuel consumption of vehicles on section i, c fuel Represents the oil price;
(3c) calculating the fuel consumption c 'of the vehicle after the vehicle receives the accident information and changes the route' k,v (t)：
(3c1) According to the average traffic time delay C of the road section a The average passing time of the road section j is delayed by C j Expressed as:
wherein ,λj Is the vehicle arrival rate of link j, j ∈ r' k,v ，r′ k,v Representing a new path after a vehicle v in the jurisdiction of the mobile edge computing server k receives the accident information and carries out path replacement;
(3c2) average traffic time delay C according to road section j j Calculating the fuel consumption c 'after the vehicle v in the jurisdiction of the mobile edge calculation server k receives the accident information and carries out route replacement' k,v (t)：
wherein ,fj The average fuel consumption rate of the vehicle on the road section j;
(3d) according to the fuel consumption c of the vehicle not receiving the accident information k,v (t) and fuel consumption c 'after route replacement upon receipt of accident information' k,v (t) obtaining the total consumption c (t) of the traffic system:
3. the method of claim 1, wherein: and (4) constructing a minimum system total consumption optimization problem with the directional distribution matrixes B and B as optimization variables, wherein the minimum system total consumption optimization problem is realized as follows:
(4a) the optimization problem P1 for minimizing the total fuel consumption of the transportation system is expressed as:
(4b) the optimization problem P1 is decomposed into two sub-optimization problems P1-1 and P1-2:
4. the method of claim 1, wherein: the optimization problem P2 that the optimization variables are the transmission power matrix P and the channel allocation matrix ω and maximize the average transmission rate of the large-bandwidth transmission service is constructed in the step (5), and is realized as follows:
(5a) transmitting speed of large bandwidth transmission service u according to Shannon's theorem
Expressed as:
wherein ,fs Is the bandwidth of a radio resource block s; omega u,s Is the occupation condition of the large bandwidth transmission service u in the channel allocation matrix omega to the wireless resource block s, omega u,s 1 denotes that a radio resource block s is allocated to a service u, ω u,s 0 means that the radio resource block s is not allocated to the service u; β represents a constant related to the error rate; p is a radical of u 、g u Respectively representing the transmitting power and the channel gain of a large bandwidth transmission service u; sigma 2 Is the standard deviation of the system noise;
s max which indicates the number of radio resource blocks,
u max representing the number of large bandwidth transmission services;
(5b) according to the transmission rate
Construction of transmission rate for large bandwidth transmission service
Associated objective function
wherein ,
indicating the transmission rate status of the large bandwidth transmission service u in the time slot t,
the transmission rate of the service u is greater than or equal to the minimum rate value required by the large-bandwidth transmission service
The transmission requirements are met, and the device can be used,
indicating that the transmission rate of the service u is less than the required minimum rate value
Does not meet the transmission requirements;
(5c) according to the objective function
Constructing an optimization problem P2:
wherein ,pu,s Representing the corresponding sub-channel power value when a service u occupies a wireless resource block s in a power distribution matrix p of a large-bandwidth transmission service; the limiting conditions C1 and C2 jointly limit that one resource block can only be allocated to one large-bandwidth transmission service; the limit condition C3 represents a limit range of the total transmission power, where p max Representing the maximum value of the total transmission power of the base station.
5. The method of claim 1, wherein: issuing the matrix b according to the optimal orientation in the step (6) * Obtaining the number A (t) of the emergency services arriving at the time slot t of each mobile edge computing server, and firstly issuing a matrix b according to the optimal orientation * And obtaining the vehicle number of each road section receiving the accident information at the time slot t, and then counting all the vehicle numbers to obtain the number of the vehicles of each road section receiving the accident information, wherein the number is the emergency service arrival number A (t) at the time slot t.
6. The method of claim 1, wherein: in the step (7), an optimization problem P3 which maximizes the success rate of the emergency service transmission and minimizes the rate loss of the large-bandwidth transmission service is constructed, and the optimal distribution matrix ζ 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) represents the occupation situation of the service u in the large bandwidth transmission service to the wireless resource when there is no emergency service puncture; z is a radical of u (t) represents the resource situation that the service u is punctured in the large bandwidth transmission service;
representing the optimal power distribution value of the service u in the large bandwidth transmission service; g u Representing the channel gain of a large bandwidth transmission service u; β represents a constant related to the error rate; sigma 2 Is the standard deviation of the 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 a radio resource block s;
optimal channel allocation matrix omega for large bandwidth transmission service * The occupation of the radio resource block s by the medium service u,
indicating that a radio resource block s is allocated to a service u,
indicating that the radio resource block s is not allocated to the service u; zeta u,s,z Is the puncture situation of the existing wireless resource block s of the emergency service z in the resource allocation matrix zeta to the large bandwidth transmission service u, zeta u,s,z 1 means that the emergency service z punctures into the allocated resource block s of the large bandwidth transmission service u, ζ u,s,z 0 means no puncture; β represents a constant related to the error rate; p is a radical of urllc 、g urllc Respectively representing the transmitting power and the channel gain of the emergency service; sigma 2 Is the standard deviation of the system noise;
z max indicating the number of emergency services;
s max representing the number of radio resource blocks;
(7c) calculating average transmission success rate delta of emergency services suc ：
(7c1) According to the transmission rate r of the emergency service z (t) calculating the successful number of transmission i of the emergency service z (t)：
Wherein D represents the bit value of the accident information, and tau represents the hard time delay requirement of the emergency service;
(7c2) according to the transmission success number i z (t) calculating a transmission success rate Δ for emergency services suc ：
Wherein, A (t) represents the number of the emergency services arriving at the time slot t, and E (-) represents the operation of obtaining the expected value;
(7d) transmission rate r according to large bandwidth transmission service u (t) calculating the loss Delta caused by the puncture of the emergency service to the transmission rate of the large-bandwidth transmission service loss ：
wherein ,
indicating the transmission rate state of the large bandwidth transmission service u when the emergency service puncture does not exist;
the transmission rate of the large bandwidth transmission service u is shown when the emergency service puncture does not exist;
the minimum value of the data rate required by the large bandwidth transmission service is represented; i all right angle 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 indicates that the transmission rate of the service u is greater than or equal to the minimum rate value required by the large bandwidth transmission service
Satisfies the transmission requirement i u (t) ═ 0 indicates that the transmission rate of the service u is less than the required minimum rate value
Does not meet the transmission requirements;
(7e) according to transmission success rate delta suc And loss of delta loss Constructing an optimization problem P3:
minΔ loss (ζ u,s,z )
C3:P(E)≤ε
wherein, the limiting conditions C1 and C2 jointly limit that each emergency service can only puncture the same 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; the restriction C4 indicates that the amount of resources that emergency traffic can puncture cannot exceed its available resources,
indicating the total resource 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 P3-1 according to a sparrow search algorithm to obtain an optimal distribution matrix zeta of the emergency service * 。
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150055207A (en) * | 2013-11-12 | 2015-05-21 | 한국전자통신연구원 | Metohd and apparatus for centralized link based scheduling to supporrt directional transmission in wireless mesh network |
| CN107071854A (en) * | 2017-04-25 | 2017-08-18 | 西安电子科技大学 | The distributed multihop Radio Broadcasting Agreements of relay forwarding probability is maximized based on car networking |
| CN109302709A (en) * | 2018-09-14 | 2019-02-01 | 重庆邮电大学 | The unloading of car networking task and resource allocation policy towards mobile edge calculations |
| CN109388123A (en) * | 2017-08-10 | 2019-02-26 | 福特全球技术公司 | Vehicle communication |
| CN110379195A (en) * | 2019-08-22 | 2019-10-25 | 公安部交通管理科学研究所 | Traffic event information based on V2X orients delivery system |
| CN113163005A (en) * | 2021-04-15 | 2021-07-23 | 江苏安靖通电子信息科技有限公司 | Intelligent transportation operation and maintenance monitoring and data management and control system based on edge calculation |
| CN113904947A (en) * | 2021-11-15 | 2022-01-07 | 湖南大学无锡智能控制研究院 | Vehicle-road cooperative distributed edge computing task unloading and resource allocation method and system |
-
2022
- 2022-07-07 CN CN202210803453.2A patent/CN115065964B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150055207A (en) * | 2013-11-12 | 2015-05-21 | 한국전자통신연구원 | Metohd and apparatus for centralized link based scheduling to supporrt directional transmission in wireless mesh network |
| CN107071854A (en) * | 2017-04-25 | 2017-08-18 | 西安电子科技大学 | The distributed multihop Radio Broadcasting Agreements of relay forwarding probability is maximized based on car networking |
| CN109388123A (en) * | 2017-08-10 | 2019-02-26 | 福特全球技术公司 | Vehicle communication |
| CN109302709A (en) * | 2018-09-14 | 2019-02-01 | 重庆邮电大学 | The unloading of car networking task and resource allocation policy towards mobile edge calculations |
| CN110379195A (en) * | 2019-08-22 | 2019-10-25 | 公安部交通管理科学研究所 | Traffic event information based on V2X orients delivery system |
| CN113163005A (en) * | 2021-04-15 | 2021-07-23 | 江苏安靖通电子信息科技有限公司 | Intelligent transportation operation and maintenance monitoring and data management and control system based on edge calculation |
| CN113904947A (en) * | 2021-11-15 | 2022-01-07 | 湖南大学无锡智能控制研究院 | Vehicle-road cooperative distributed edge computing task unloading and resource allocation method and system |
Non-Patent Citations (2)
| Title |
|---|
| HEHE ZHANG: "Targeted Dissemination of Emergency Information: Joint Traffic and Communication Optimization", ICC 2021 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS * |
| 计星怡: "城市交通网络事故影响范围预测", 2020中国信息通信大会论文集 * |
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