CN113450558A - Method, system and storage medium for identifying network key node - Google Patents
Method, system and storage medium for identifying network key node Download PDFInfo
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- CN113450558A CN113450558A CN202010229270.5A CN202010229270A CN113450558A CN 113450558 A CN113450558 A CN 113450558A CN 202010229270 A CN202010229270 A CN 202010229270A CN 113450558 A CN113450558 A CN 113450558A
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- 239000011159 matrix material Substances 0.000 claims description 40
- 238000004891 communication Methods 0.000 claims description 12
- 238000012546 transfer Methods 0.000 claims description 10
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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
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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
- G08G1/0125—Traffic data processing
Abstract
The invention provides a method, a system and a storage medium for identifying network key nodes, wherein the method for identifying the network key nodes comprises the following steps: acquiring road topology data, wherein the road topology data comprises the connection relation among all roads and the out degree of each road; iteratively calculating the weight value of each road according to the road topology data; and sequencing the weighted values of all roads, and finding out the key roads in the traffic network according to the weighted values. The method carries out Markov-based processing on the key nodes of the roads by globally considering the connection relation among the roads, and identifies the key nodes of the network by iteratively calculating the weight value of each road.
Description
Technical Field
The present invention relates to the field of complex network technologies, and in particular, to a method, a system, and a storage medium for identifying a key node in a network.
Background
With the great increase of the number of vehicles, urban traffic systems become large and complex, and the urban traffic problem becomes more severe.
The problem of determining traffic junctions or key nodes in urban traffic networks is one of the research topics of the complexity problem of urban traffic networks. The nodes in the network are located at different positions, and the importance degree of the nodes is different. The key nodes in the urban traffic network have a great influence on the safety, reliability and overall performance of the traffic network structure. The determination of the key nodes can provide accurate and effective traffic control, guidance and evacuation measures and provide a reasonable solution for planning, designing, rebuilding and expanding a traffic network.
Therefore, there is a need to provide a method for identifying network key nodes to solve the above problems.
Disclosure of Invention
In view of the foregoing, the present invention provides a method, system and storage medium for identifying key nodes in a network to identify key nodes in a traffic network.
The invention is realized by the following steps:
the invention firstly provides a method for identifying network key nodes, which comprises the following steps: acquiring road topology data, wherein the road topology data comprises the connection relation among all roads and the out degree of each road; iteratively calculating the weight value of each road according to the road topology data; and sequencing the weighted values of all roads, and finding out the key roads in the traffic network according to the weighted values.
Further, the step of iteratively calculating the weight value of each road according to the road topology data includes: establishing a road connection relation matrix X according to whether a connection relation exists between roadsnAnd giving an initial value to the initial value; generating a road transfer matrix P according to the road connection relation matrix; and iteratively calculating Xn+1＝P XnAnd is in Xn+1And XnWhen the distance of (2) is less than a predetermined value alpha, stopping the iterative computation, wherein n is a natural number.
Further, the step of establishing a road connection relationship matrix Xn according to whether there is a connection relationship between roads and assigning an initial value thereto includes: assuming that the weights of all roads are the same; each road in the road connection relation matrix Xn is given the same weight value.
Further, the step of generating the road transfer matrix P according to the road connection relationship matrix includes: obtaining the out-degree T of each road; and generating a transfer matrix P according to the output T.
Further, in the road transition matrix P, the element at the road intersection is 1/T, and the elements at other positions are 0.
Further, the method comprises the steps of: and taking the path with the largest weight value as the most critical node.
The invention also provides a system for identifying network key nodes, which comprises: a memory, a processor, a communication bus, and a program stored on the memory that identifies network critical nodes; the communication bus is used for realizing communication connection between the processor and the memory; the processor is configured to execute the program stored in the memory for identifying the network critical node to implement any one of the above methods for identifying the network critical node.
The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program for identifying a network key node, and when the program for identifying a network key node is executed by a processor, the computer-readable storage medium implements any one of the above methods for identifying a network key node.
The method carries out Markov-based processing on the key nodes of the roads by globally considering the connection relation among the roads, and identifies the key nodes of the network by iteratively calculating the weight value of each road.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a flowchart illustrating a method for identifying a key node in a network according to a preferred embodiment of the invention.
Fig. 2 is another flow chart illustrating a method for identifying a key node in a network according to a preferred embodiment of the invention.
FIG. 3 is a logic diagram of a method for identifying network key nodes in accordance with the preferred embodiment of the present invention.
FIG. 4 is a logic diagram of a system for identifying network critical nodes in accordance with a preferred embodiment of the present invention.
FIG. 5 is a block diagram of a system for identifying key nodes in a network in accordance with a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in a preferred embodiment of the present invention, a method for identifying a key node in a network, applied to a terminal, includes:
step S10, acquiring road topology data, wherein the road topology data comprises the connection relation among all roads and the output degree of each road;
step S30, according to the road topology data, iteratively calculating the weight value of each road;
and step S50, sorting the weight values of the roads, and finding out the key roads in the traffic network according to the weight values.
Specifically, the network may be a traffic network or a virtual network. The road may be an actual traffic road, a propagation path, or a virtual network.
In detail, the step S30, namely the step of iteratively calculating the weight value of each road according to the road topology data, further includes:
step S31, establishing a road connection relation matrix X according to whether there is a connection relation between roadsnAnd giving an initial value to the initial value;
step S33, generating a road transfer matrix P according to the road connection relation matrix; and
step S35, iterative computation Xn+1＝P XnAnd is in Xn+1And XnWhen the distance of (2) is less than a predetermined value alpha, stopping the iterative computation, wherein n is a natural number.
In detailIn other words, the step S31 is to establish a road connection relationship matrix X according to whether there is a connection relationship between roadsnAnd giving an initial value thereto, including:
step S311, assuming that the weights of all roads are the same;
step S313, connecting the relation matrix X to the roadnEach road in (1) is given the same weight value.
More specifically, the step S33 of generating the road transition matrix P according to the road connection relationship matrix includes:
step S331, obtaining the out-degree T of each road;
and S333, generating a transfer matrix P according to the output T.
Specifically, in the road transition matrix P, the element at the road intersection is 1/T, and the elements at other positions are 0.
In detail, the method may further include step S51: and taking the path with the largest weight value as the most critical node.
Specifically, for the traffic network, in step S31, a connection relationship matrix is established according to the following rule: assuming that the number of roads in the cell is N, a matrix X of N X N is establishednIf a connection relation exists between the two roads, the value of the matrix corresponding to the two roads is 1, otherwise, the value is 0, and an initial value is given to the road connection relation matrix X. Accordingly, in step S33, a road transition matrix P in the area may be generated according to the obtained road connection relation matrix in the small area, according to the following rule: and if the out-degree number of a certain road in the area is T, the coefficient of the modified road transfer matrix is 1/T, and the rest coefficients are 0. In step S35, the predetermined value α may approach zero infinitely, for example, may be 0.000001, and the distance between the two weight vectors is calculated as follows: each term in the vector X is added with the absolute value of the difference of the corresponding term in the vector Y, after the iterative computation is stopped, the weight of each road in the obtained area is different, and the value of X is the weight, so that the key of the traffic network in the area can be determinedThe node is the road with the highest calculated weight.
For example, if there are 4 roads A, B, C, D in a region, vehicles from road A may enter B, C or D and vehicles from road B may enter A or D, thus initializing a weight vector with a weight of 1 for all roads in the region, XnInitial value X of1Namely:
the transition matrix P is:
multiplying X by a transfer matrix P1To obtain X2Until X after the nth iterationn+1And XnIs smaller than a predetermined value alpha, e.g. may converge to [1.5,1,0.5,1 [ ]]I.e., A, B, C, D the effect of the four page final states. At this time, the a road having the largest weight value may be selected as the most critical node.
In summary, referring to fig. 3, the method for identifying a network key node of the present invention may sequentially include: establishing a road connection relation matrix; generating a road transfer matrix; initializing road weight; setting an iteration stop condition; and outputting the road weight. The method adopted by the invention is a global method, and the heavier the weight is, the heavier the weight of the road connected with the road is. For the problem that the weight of the road cannot be completely determined at first, the application initially assumes that the weights of all roads are the same, and then performs iterative calculation to select the road with the most important weight.
By using the method, a system for identifying the network key nodes can be constructed, and as shown in fig. 4, the system can comprise a data acquisition module, a data analysis module and a result presentation module. The data acquisition module is used for acquiring road data and road connection points in a certain area, such as data of intersections, and the connection matrix of the roads in the area can be obtained by carrying out algorithm arrangement on the road data, the data of restricted roads, one-way roads and the like in the area and the data of the road connection points. And the data analysis module is used for carrying out data analysis on the connection relation of the roads and the weights of the roads by the method for identifying the network key nodes according to the connection matrix of the roads in the area obtained by the data acquisition module, and calculating the weights of all the roads in the area through iteration. The result presentation module is used for presenting key nodes of the traffic network in the area, namely a certain road with the maximum weight as the most key node after simple arrangement according to the weight values of the roads in the area obtained by the data analysis module.
The system for identifying the network key node can be a PC (personal computer), and can also be a terminal device such as a smart phone, a tablet computer and a portable computer.
Specifically, as shown in FIG. 5, the system 200 for identifying network critical nodes may include a processor 202, a memory 204, and a communication bus 203. Wherein the communication bus 203 is used for realizing connection communication between the processor 202 and the memory 204. The memory 204 may be a high-speed RAM memory, an NVM (non-volatile memory), such as a disk memory, or a storage device independent of the processor 202.
Optionally, the system 200 for identifying network critical nodes may further include a user interface 206, a network interface 208, a camera, radio frequency circuitry, audio circuitry, a WiFi module, and the like. The user interface 206 may comprise a display screen, an input unit such as a keyboard, and the optional user interface 206 may also comprise a standard wired, wireless interface. The network interface 208 may optionally include a standard wired interface, a wireless interface, such as a WI-FI interface.
Those skilled in the art will appreciate that the system for identifying network critical nodes illustrated in fig. 5 does not constitute a limitation on the system for identifying network critical nodes and may include more or fewer components than illustrated, or some components in combination, or a different arrangement of components.
As shown in fig. 5, the memory 204, which is a type of computer storage medium, may include an operating system, a network communication module, and a program that identifies network critical nodes. The operating system is a program that manages and controls the system hardware and software resources that identify network critical nodes, supporting the execution of the program that identifies network critical nodes, as well as other software and/or programs. The network communication module is used to enable communication between the various components within memory 204, as well as with other hardware and software in the system that identify network critical nodes.
In the system for identifying network critical nodes shown in fig. 5, the processor 202 is configured to execute a program stored in the memory 204 for identifying network critical nodes, the program for identifying network critical nodes being configured to implement any of the above-mentioned methods for identifying network critical nodes.
The present invention also provides a computer-readable storage medium storing at least one program executable by at least one processor to implement the above-described method of identifying network critical nodes.
In an embodiment, the computer-readable storage medium provided by this embodiment may include any entity or device capable of carrying computer program code, a recording medium, such as ROM, RAM, magnetic disk, optical disk, flash memory, and the like.
In summary, the present invention performs markov-based processing on key nodes of roads by considering the connection relationship between roads globally. And finally, combining the road data of the Shanghai city to obtain that the weight of the loop in the Shanghai city is highest to be 41.82, and the weight of the loop in the Shanghai city is arranged in the front row, which basically accords with the condition of the road in the Shanghai city.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
As used herein, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, including not only those elements listed, but also other elements not expressly listed.
The present invention is not limited to the above preferred embodiments, and any modification, equivalent replacement or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method of identifying a network critical node, comprising:
acquiring road topology data, wherein the road topology data comprises the connection relation among all roads and the out degree of each road;
iteratively calculating the weight value of each road according to the road topology data; and
and sequencing the weighted values of all roads, and finding out the key roads in the traffic network according to the weighted values.
2. A method of identifying network critical nodes according to claim 1, characterized by: the step of iteratively calculating the weight value of each road according to the road topology data comprises the following steps:
establishing a road connection relation matrix X according to whether a connection relation exists between roadsnAnd giving an initial value to the initial value;
generating a road transfer matrix P according to the road connection relation matrix; and
iterative computation of Xn+1＝P XnAnd is in Xn+1And XnWhen the distance of (2) is less than a predetermined value alpha, stopping the iterative computation, wherein n is a natural number.
3. The method for identifying key nodes in network according to claim 2, wherein the road connection relation matrix X is established according to whether there is connection relation between each roadnAnd giving an initial value thereto, including:
assuming that the weights of all roads are the same;
to road connection relation matrix XnIn (1)Each road is given the same weight value.
4. The method for identifying network key nodes according to claim 2, wherein the step of generating a road transition matrix P according to the road connection relation matrix comprises:
obtaining the out-degree T of each road;
and generating a transfer matrix P according to the output T.
5. The method for identifying network key nodes according to claim 1, wherein in the road transition matrix P, the element at the road intersection is 1/T, and the elements at other places are 0.
6. Method for identifying network critical nodes according to claim 1, characterized in that it further comprises the steps of: and taking the path with the largest weight value as the most critical node.
7. A system for identifying key nodes of a network, comprising: the system for identifying the network key node comprises:
a memory, a processor, a communication bus, and a program stored on the memory that identifies network critical nodes;
the communication bus is used for realizing communication connection between the processor and the memory;
the processor is configured to execute a program stored on the memory for identifying network critical nodes to implement the method of identifying network critical nodes of any of claims 1 to 6.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for identifying network critical nodes, which program, when executed by a processor, implements a method for identifying network critical nodes according to any one of claims 1 to 6.
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