CN109686082B - Urban traffic monitoring system based on edge computing nodes and deployment method - Google Patents

Urban traffic monitoring system based on edge computing nodes and deployment method Download PDF

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CN109686082B
CN109686082B CN201811497213.4A CN201811497213A CN109686082B CN 109686082 B CN109686082 B CN 109686082B CN 201811497213 A CN201811497213 A CN 201811497213A CN 109686082 B CN109686082 B CN 109686082B
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CN109686082A (en
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陈晨
惠强
吕宁
裴庆祺
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Shaanxi Bilian Wuji Technology Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools

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Abstract

The invention relates to an urban traffic monitoring system based on edge computing nodes and a deployment method, wherein the deployment method comprises the following steps: dividing urban traffic to be deployed into a plurality of grids according to preset conditions, and counting the average traffic flow of traffic road sections in each grid; determining a deployment position of a first edge node according to each grid; constructing a distance matrix and a traffic flow matrix according to the deployment position of the first edge node and the average traffic flow of the traffic road sections in each grid; setting a target function and a constraint condition according to the distance matrix and the traffic flow matrix; and programming and solving the objective function and the constraint condition to obtain the deployment position of the second edge node. According to the urban traffic deployment method, the optimal model is solved, the minimum service quality is guaranteed on the basis of minimizing the deployment quantity of the first edge nodes, and the deployment cost of the edge nodes in urban traffic is reduced.

Description

Urban traffic monitoring system based on edge computing nodes and deployment method
Technical Field
The invention belongs to the technical field of communication, and particularly relates to an urban traffic monitoring system based on edge computing nodes and a deployment method.
Background
With the continuous increase of the economic level of China and the acceleration of the urbanization process, the role of the automobile in daily life is more and more important. With the rapid increase of the number of automobiles, a series of urban traffic problems are caused, such as urban road traffic congestion, increased traffic accidents, public traffic fading and the like directly caused by the flooding of private vehicles. The problem of traffic congestion has become a main bottleneck restricting economic development of various big cities in China and improving the quality of life of people. The real-time monitoring of the urban traffic condition can provide data support for reasonable shunting measures of urban traffic management departments, and can also provide early warning information for the path selection of drivers, thereby reducing the degree of urban traffic congestion.
At present, vehicles generally acquire traffic information from a cloud server, and only can acquire traffic information of partial road sections, and the local traffic condition information can not provide effective help for relieving urban traffic and reducing travel time; only under the global traffic condition information, the urban traffic department can make reasonable traffic control measures, and the driver can reasonably plan the path. And as the number of vehicles increases, the pressure of the cloud server is higher and higher, and the edge computing is carried out at the same time. By deploying edge nodes in a city, each edge node monitors the traffic condition of a certain area, and the traffic condition of the whole city can be monitored by data synchronization among the edge nodes. The vehicle can obtain the traffic information of the whole city in the coverage range of any edge node, the pressure of a cloud server is reduced, and the service quality is improved; hmotlagh et al proposed to use an unmanned aerial vehicle as an edge computing node in 2016, this method optimizing the energy consumption and runtime of an unmanned aerial vehicle by way of linear programming; bauza R et al utilize inter-vehicle crowd sensing and fuzzy logic to estimate traffic congestion.
The disadvantages brought by the way that the unmanned aerial vehicle is used as an edge computing node are as follows: the unmanned aerial vehicle itself needs to consume a large amount of energy, and if the unmanned aerial vehicle is added with a calculation function, the power consumption of the unmanned aerial vehicle becomes enormous. The cost of the unmanned aerial vehicle is high, and the unmanned aerial vehicle is seriously influenced by factors such as weather; the method for estimating the traffic congestion condition by using the inter-vehicle crowd sensing and fuzzy logic can only acquire the traffic information condition of a certain road section, cannot acquire the whole traffic condition, cannot plan a reasonable route to reduce the congestion time, and improves the driving experience.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an urban traffic monitoring system based on edge computing nodes and a deployment method thereof, and the technical problems to be solved by the invention are realized by the following technical scheme:
the embodiment of the invention provides an urban traffic deployment method based on edge computing nodes, which comprises the following steps:
dividing urban traffic to be deployed into a plurality of grids according to preset conditions, and counting the average traffic flow of traffic road sections in each grid;
determining a deployment position of a first edge node according to each grid;
constructing a distance matrix and a traffic flow matrix according to the deployment position of the first edge node and the average traffic flow of the traffic road sections in each grid;
setting a target function and a constraint condition according to the distance matrix and the traffic flow matrix;
and programming and solving the objective function and the constraint condition to obtain a second edge node.
In an embodiment of the present invention, determining the deployment location of the edge node of each grid according to the grid includes:
acquiring all traffic road sections in each grid;
determining center coordinates of the traffic road section;
and finding the position with the shortest distance to the center coordinate in each grid as the deployment position of the first edge node.
In one embodiment of the present invention, the objective function is:
Figure BDA0001897181220000031
wherein r isjDetermine a matrix for the region, rjWhen 0, it means that the second edge node is not deployed, rjA value of 1 indicates that the second edge node is deployed, and j indicates a node.
In one embodiment of the invention, the constraints include:
the area distance in the range of the second edge node is smaller than the coverage radius of the second edge node, the total number of the traffic managed by the second edge node is smaller than or equal to the maximum service number of the second edge node, the number of the second edge nodes corresponding to each grid is equal to 1, and the number of the grids covered by the second edge nodes is greater than or equal to 1.
In an embodiment of the present invention, the calculation formula of the area distance within the second edge node range is:
L＝pijxij
wherein p isijRepresenting said distance matrix, xijRepresents a grid decision matrix, xijA value of 0 indicates that the mesh is not covered by the second edge node, xijA value of 1 indicates that the mesh is covered by the second edge node, i indicates the mesh, and j indicates the node.
In an embodiment of the present invention, a calculation formula of a total number of traffic managed by the second edge node is:
Figure BDA0001897181220000032
wherein x isijRepresenting a grid decision matrix, CiAnd representing the traffic flow matrix, wherein n is the grid number.
In an embodiment of the present invention, the programming solution of the objective function and the constraint condition to obtain a second edge node includes:
and programming and solving the objective function and the constraint condition by using data processing software Matlab or L ingo to obtain the second edge node.
Another embodiment of the present invention provides an urban traffic monitoring system based on edge computing nodes, including:
the local traffic monitoring modules are used for acquiring traffic data of different areas;
a second edge node obtaining module, configured to obtain a second edge node according to the method in any of the embodiments;
the second edge node is used for receiving the traffic data and performing calculation analysis to obtain local traffic information;
and the cloud server is used for receiving, storing and integrating the local area traffic information and feeding back the local area traffic information to the second edge node.
Compared with the prior art, the invention has the beneficial effects that:
1. the urban traffic deployment method provided by the invention realizes the monitoring of the whole urban traffic condition, provides data support for traffic control of traffic management departments and reasonable planning of travel routes by drivers, and is beneficial to relieving the congestion condition of urban traffic;
2. according to the urban traffic deployment method, the optimal model is solved, the minimum service quality is guaranteed on the basis of minimizing the deployment quantity of the first edge nodes, and the deployment cost of the edge nodes in urban traffic is reduced;
3. according to the urban traffic detection system, the local traffic conditions of the city are monitored through the second edge node module, and the global traffic conditions are monitored through data synchronization among the nodes, so that the system not only reduces the pressure of cloud service, but also ensures the service quality.
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FIG. 1 is a schematic flow chart of a method for urban traffic deployment based on edge computing nodes;
FIG. 2 is a schematic structural diagram of an urban traffic monitoring system based on edge computing nodes;
fig. 3 is a schematic structural diagram of another urban traffic monitoring system based on edge computing nodes.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a city traffic deployment method based on edge computing nodes. An urban traffic deployment method based on edge computing nodes comprises the following steps:
step (a): dividing the urban traffic to be deployed into a plurality of grids according to preset conditions, and counting the average traffic flow of traffic road sections in each grid.
The urban traffic to be deployed is divided into a plurality of grids according to a set area, for example, the urban traffic to be deployed is divided into the grids of 500 × 500 in units of 500 meters, the number of vehicles passing through the traffic road section in each grid on average is counted, and if no traffic road section exists in the grid, the grid is removed.
Further, the city is divided into n grids, each grid is taken as a point, and the n points can deploy the first edge node in general.
Step (b): a deployment location of the first edge node is determined from each grid.
The first edge node is deployed in such a way that the distance from the road segment in the corresponding grid to the deployed edge node in the grid is minimum to ensure the service quality.
It should be noted that, in edge computing, an edge computing server is generally deployed, and this server is referred to as an edge node.
Further, the step (b) may include the steps of:
step (b 1): all traffic segments in each grid are acquired.
Step (b 2): the center coordinates of the traffic segment are determined.
Step (b 3): and finding the position with the shortest distance coordinate from the center in each grid as the deployment position of the first edge node.
It should be noted that the first edge node refers to an edge node with an optimal position in a grid.
Step (c): and constructing a distance matrix and a traffic flow matrix according to the deployment position of the first edge node and the average traffic flow of the traffic road sections in each grid.
Establishing a distance matrix P between deployable first edge node locations between gridsijAnd a traffic flow matrix C counted in each gridiWhere i represents a mesh and j represents a node.
Step (d): and setting an objective function and constraint conditions according to the distance matrix and the traffic flow matrix.
According to the embodiment of the invention, an optimization model is constructed by using the objective function and the constraint condition, and the number and the positions of the first edge nodes are optimized by using the optimization model to obtain the second edge nodes with the optimal number and positions.
Turning the minimization of the deployment cost of the first edge node to the minimization of the deployment number of the first edge node, an objective function may be written that minimizes the deployment number of the first edge node.
Further, the objective function is:
Figure BDA0001897181220000061
wherein r isjIndicating whether a second edge node is deployed, rjWhen 0, it means that the second edge node is not deployed, rjA value of 1 indicates that a second edge node is deployed and j indicates a node.
It should be noted that the second edge node refers to an edge node with an optimal global position, that is, an edge node with an optimal position in the entire urban grid, and an object of the present invention is to obtain a deployment scheme of the second edge node with a goal of minimizing the number of first edge nodes and a condition of ensuring the minimum quality of service and maximizing the coverage area, in order to implement an edge border node deployment scheme based on global optimization.
Further, the constraints include:
(d1) the method comprises the following steps The distance of the area within the range of the second edge node is smaller than the coverage radius of the second edge node.
Determining the coverage of the second edge node, and in order to ensure that each grid can be served, the distance from the grid covered by the second edge node to the edge node should be smaller than the coverage radius thereof, so that a distance constraint condition in the deployment method can be written, that is:
L＝pijxij≤R
wherein p isijRepresenting a distance matrix, xijRepresents a grid decision matrix, xijA value of 0 indicates that the grid is not covered by the second edge node, xijA value of 1 indicates that the mesh is covered by the second edge node, i indicates the mesh,j denotes a node and R is the coverage radius of the second edge node.
It should be noted that if a first edge node is deployed in each grid, the deployment cost of the edge node is increased, and therefore, the embodiment of the present invention establishes a matrix x of n × nijI is a grid, j is a node, and whether a second edge node needs to be deployed or not at the j node is obtained through calculation processing, so xijA matrix is also determined for the grid.
It should be noted that the coverage area of the second edge node is determined by the coverage area of the wireless communication device carried by the second edge node.
(d2) The method comprises the following steps The total number of traffic managed by the second edge node is less than or equal to the maximum number of services of the second edge node.
Determining the maximum service quantity of the second edge node, and in order to ensure the service quality of the second edge node received in each grid within the range covered by the second edge node, the sum of the average number of passing vehicles in all grids covered by the second edge node should be less than or equal to the maximum service quantity of the second edge node, that is:
Figure BDA0001897181220000081
wherein x isijRepresenting a grid decision matrix, CiRepresenting a traffic flow matrix, N being the number of grids, N being the maximum number of services of the second edge node.
It should be noted that the maximum number of services of the second edge node depends on the performance of the second edge node, and in this application, the maximum number of services of the second edge node is known.
(d3) The method comprises the following steps The number of second edge nodes per mesh is equal to 1.
In order to minimize the deployment number of the second edge nodes and improve the effective coverage rate of the second edge nodes, each grid area is restricted to be within the coverage range of only one second edge node, that is, the number of the second edge nodes corresponding to each grid is equal to 1, that is, the number of the second edge nodes corresponding to each grid is 1
Figure BDA0001897181220000082
(d4) The method comprises the following steps The number of meshes covered by the second edge node must be greater than or equal to 1.
Determining matrix x in the gridijThe sum of each column represents the number of grid areas covered by the second edge node deployed in node j, when rjA value of 0 indicates that no second edge node is deployed at node j, and thus
Figure BDA0001897181220000083
If r isjA value of 1 indicates that a second edge node is deployed at node j, then
Figure BDA0001897181220000084
In summary, the constraint between the area judgment matrix and the grid judgment matrix can be written:
Figure BDA0001897181220000085
a step (e): and programming and solving the target function and the constraint condition to obtain a second edge node.
And programming and solving the objective function and the constraint condition by using data processing software Matlab or L ingo to obtain the second edge node, namely obtaining the position where the second edge node needs to be deployed, and simultaneously obtaining the number of the second edge nodes and which grids are covered by which second edge node.
The urban traffic deployment method provided by the embodiment of the invention realizes the monitoring of the whole urban traffic condition, provides data support for traffic control of traffic management departments and reasonable planning of travel routes by drivers, and is favorable for relieving the congestion condition of urban traffic.
According to the embodiment of the invention, by solving the optimization model, the minimum service quality is ensured on the basis of minimizing the deployment quantity of the first edge nodes, and the deployment cost of the edge nodes in urban traffic is reduced.
Example two
Referring to fig. 2 and fig. 3, fig. 2 is a schematic structural diagram of an urban traffic monitoring system based on edge computing nodes; fig. 3 is a schematic structural diagram of another urban traffic monitoring system based on edge computing nodes. On the basis of the embodiment, the embodiment of the invention provides an urban traffic monitoring system based on edge computing nodes, which comprises:
the local traffic monitoring modules 100 are used for acquiring traffic data of different areas;
a second edge node obtaining module 200, configured to obtain a second edge node according to the method described in the first embodiment.
The second edge node 300 is configured to receive traffic data and perform calculation analysis to obtain local traffic information;
and the cloud server 400 is configured to receive, store and integrate the local traffic information, and feed back the local traffic information to the second edge node 300.
Further, the local traffic monitoring modules 100 include radar, high-definition cameras, and roadside infrastructure, and the local traffic detection modules 100 are configured to collect traffic data in different areas and transmit the collected traffic data to the nearest second edge computing node.
Further, the second edge node obtaining module 200 obtains a plurality of second edge nodes 300 by using the urban traffic deployment method provided in the first embodiment, and please refer to the first embodiment for the specific obtaining method, which is not described herein again in the embodiments of the present invention.
Further, the second edge node 300 should have wireless communication capability, computing capability and service providing capability, and the second edge computing node 300 performs computing analysis on the data acquired by the local traffic monitoring module 100 to obtain regional traffic information; the automobile can communicate with the nearest second edge node and provide traffic information service for the city, all the second edge nodes can communicate with each other and provide the city traffic information service, all the second edge nodes can communicate with each other and maintain the traffic state database of the whole city together by adopting a distributed database technology.
Further, the cloud server 400 stores the latest urban global traffic condition information, integrates the regional traffic information obtained by the second edge nodes 300, and feeds back the regional traffic information to each second edge node, so that each second edge node has the traffic condition information of the whole city, and a vehicle can directly obtain data from the covered second edge node instead of directly accessing the second cloud server 400, thereby further reducing the pressure of the cloud server 400, and meanwhile, when the vehicle travels into a region covered by a certain second edge node, a driver or navigation software can wirelessly communicate with the corresponding second edge node to obtain the traffic information of the whole city, and the vehicle replans a traveling route through the service provided by the second edge node.
According to the embodiment of the invention, the local traffic condition of the city is monitored through the plurality of second edge nodes in the second edge node modules, and the global traffic condition is monitored through data synchronization among the nodes, so that the system not only reduces the pressure of cloud service, but also ensures the service quality.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (3)

1. A city traffic deployment method based on edge computing nodes is characterized by comprising the following steps:
dividing urban traffic to be deployed into a plurality of grids according to a set area, and counting the average traffic flow of traffic road sections in each grid;
determining a deployment position of a first edge node according to each grid;
the determining the deployment position of the edge node according to each grid comprises:
acquiring all traffic road sections in each grid;
determining center coordinates of the traffic road section;
finding a position with the shortest distance from the center coordinate in each grid as a deployment position of a first edge node;
constructing a distance matrix and a traffic flow matrix according to the deployment position of the first edge node and the average traffic flow of the traffic road sections in each grid;
setting a target function and a constraint condition according to the distance matrix and the traffic flow matrix;
the objective function is:
Figure FDA0002446323090000011
wherein r isjDetermine a matrix for the region, rjWhen 0, it means that the second edge node is not deployed, rjWhen the number is 1, the second edge node is deployed, and j represents a node;
the constraint conditions include: the area distance in the range of the second edge node is smaller than the coverage radius of the second edge node, the total number of the traffic managed by the second edge node is smaller than or equal to the maximum service number of the second edge node, the number of the second edge nodes corresponding to each grid is equal to 1, and the number of the grids covered by the second edge nodes is greater than or equal to 1;
the calculation formula of the area distance in the second edge node range is as follows:
L＝pijxij
wherein p isijRepresenting said distance matrix, xijRepresents a grid decision matrix, xijA value of 0 indicates that the mesh is not covered by the second edge node, xijA value of 1 indicates that the mesh is covered by the second edge node, i indicates the mesh, and j indicates the node;
the calculation formula of the total amount of traffic managed by the second edge node is as follows:
Figure FDA0002446323090000021
wherein x isijRepresenting a grid decision matrix, CiRepresenting the traffic flow matrix, wherein n is the grid number; and programming and solving the objective function and the constraint condition to obtain a second edge node.
2. The method for deploying urban traffic based on edge computing nodes according to claim 1, wherein the programming solution of the objective function and the constraint condition to obtain a second edge node comprises:
and programming and solving the objective function and the constraint condition by using data processing software Matlab or L ingo to obtain the second edge node.
3. An urban traffic monitoring system based on edge computing nodes, comprising:
the local traffic monitoring modules are used for acquiring traffic data of different areas;
a second edge node obtaining module, configured to obtain a second edge node according to the method of any one of claims 1 to 2;
the second edge node is used for receiving the traffic data and performing calculation analysis to obtain local traffic information;
and the cloud server is used for receiving, storing and integrating the local traffic information and feeding back the local traffic information to the second edge node.
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