CN116665489A - Identification method of airway network congestion area - Google Patents

Abstract

The invention belongs to the technical field of allocation of channel network resources, and particularly relates to a channel network congestion area identification method. Firstly establishing an importance evaluation index system of the road network nodes aiming at three dimensions of network topology characteristics, network vulnerability and network operation state of the road network nodes, then carrying out combined weighting on each evaluation index through an entropy weighting method-CRITIC, introducing a relative entropy and gray correlation analysis method to improve a sequencing of approximation ideal values (TOPSIS), and comprehensively evaluating the importance of each road point; and finally, carrying out channel network congestion identification simulation according to the importance of each channel point, and identifying nodes which are easy to generate congestion so as to establish main research channels according to the nodes, find out flights expected to enter the congested channel network and provide a basis for researching channel network resource allocation.

Description

Identification method of airway network congestion area

technical field

The invention belongs to the technical field of airway network resource allocation, and in particular relates to a method for identifying an airway network congestion area.

Background technique

The air route network is the main carrier of air transportation and the guarantee basis for the efficient operation of air traffic. Numerous waypoints and navigation stations constitute a complex route network by points and lines, and the nodes are located in different geographical locations, and the traffic load they bear is different, which leads to different importance in the route network. Network function and operating efficiency are often affected by a small number of nodes in the network. The failure of this part of the node will lead to a decline in network performance, and if the corresponding measures are not taken in time, the impact of the failure of this part of the node will quickly spread to the entire network. And eventually paralyze the network, bringing serious consequences, these nodes are called key nodes. Based on this, it is of great significance to judge the importance of airway network nodes and find key nodes to understand the characteristics, structure and function of airway network, alleviate airway network congestion, and improve the overall efficiency of air transportation.

At present, most of the research on network congestion only considers the network topology index, and only evaluates nodes based on the local structure information of the network. It cannot make a more accurate judgment on the influence of nodes in the global network, thus reducing the accuracy of congestion identification. sex.

Therefore, there is a need for a method for identifying areas of airway network congestion.

Contents of the invention

The purpose of the present invention is to provide an airway network congestion area identification.

In order to solve the above-mentioned technical problems, the present invention provides a method for identifying an airway network congestion area, including:

Step1: Construct the air route network G=(V,E), set the capacity of each node as M _i , assume that the importance of the node C _i corresponds to the probability of the flight passing through the node, set the simulation step F _T =6000, and the flight flow of each node The quantity is f _i ^enter =F _t *C _i ;

Step2: According to the parameters set in Step1, increase the number of flights entering the network in turn by setting an appropriate flight generation interval;

Step3: Simulate the flight process, record the node where the congestion occurs and the total inflow flight volume F _ti in the network when the node is congested; wherein the node where the congestion occurs is a node whose f _i ^enter is greater than its capacity M _i ;

Step4: Repeat the process of Step2～Step3 until F _t =FT, and the simulation ends.

Further, the method for constructing the route network G=(V, E) includes: taking waypoints as nodes, simplifying the routes and route segments between nodes into edges, and using an adjacency matrix A(a _xy ) to represent the waypoints in the network The connection with the waypoint, x, y belong to the elements in the waypoint set, when a _xy = 1, it means that there is a route connection between the waypoint and the waypoint, otherwise it is a waypoint without connection; then the route network is simplified as A network topology graph composed of N nodes and M edges is represented by an undirected graph G=(V, E), where V is a set of waypoints, and E is a set of routes connecting each waypoint.

Further, the calculation method of the importance C _i of the nodes includes: establishing an evaluation index system for the importance of route network nodes; combining and weighting each evaluation index through the entropy weight method—CRITIC weight method; Overview.

Further, the establishment of the route network node importance evaluation index system includes: network topology index, network vulnerability index and network operation status index.

Further, the network topology indicators include: degree centrality, betweenness centrality, proximity centrality, and eigenvector centrality.

Further, the network vulnerability index includes: network efficiency loss degree, operating efficiency loss degree, and maximum subgraph loss degree.

Further, the network operation state indicators include: traffic concentration, peak hour traffic, and peak duration.

Further, the calculation formula for combining weighting of each evaluation index through the entropy weight method-CRITIC weight method is:

ω _j =α ₁ u _j +α ₂ v _j ;

Among them, ω _j represents the comprehensive weight of the jth evaluation index; α ₁ and α ₂ represent the weight ratios of the two weighting methods respectively, satisfying α ₁ , α ₂ ≥ 0 and α ₁ + α ₂ = 1; u _j is The entropy weight of the jth evaluation index; v _j is the weight of the CRITIC method of the jth evaluation index;

Methods for solving α ₁ and α ₂ include:

Solving according to the Lagrangian extreme value conditions, we can obtain

Among them, S _ij represents the calculated value of the j-th index of the i-th waypoint, and S' _ij is the normalized index value;

And then normalized to get:

Further, the method for comprehensively evaluating the importance of each waypoint includes:

(1) Index preprocessing:

Assuming that there are n waypoints in the constructed route network, there are m evaluation indicators for each waypoint, s _ij (i=1,2,3,...,n; j=1,2,3,..., m) represent the initial value of the nth waypoint under the mth evaluation index; construct the initial matrix S;

The standardized treatment for benefit-type indicators is:

The standardized treatment for cost-type indicators is:

After standardizing each index value, a standardized decision matrix Z=(z _ij ) _n×m is obtained;

(2) Calculate the weighting matrix:

According to the comprehensive weight ω _j , and ω _j satisfies The weighted matrix X is obtained by combining the obtained standardized decision matrix Z=(z _ij ) _n×m with each standardized index value:

X＝(x _ij ) _n×m ＝(z _ij ω _j ) _n×m ;

(3) Calculate the ideal solution:

According to the obtained weighting matrix, calculate its positive ideal solution X ⁺ and negative ideal solution X ^- :

(4) Calculate the comprehensive proximity;

First calculate the relative entropy with positive and negative ideal solutions and />

Then calculate the gray correlation degree between each waypoint and the positive and negative ideal solutions and />

Among them, ρ represents the resolution coefficient, ρ∈[0,1], the smaller the value, the greater the resolution;

Combining the relative entropy and gray relational degree between each waypoint and the positive and negative ideal solution, calculate the proximity of each waypoint to the positive ideal solution and the negative ideal solution:

Among them, what η ₁ and η ₂ reflect is more emphasis on distance or curve shape;

Finally, calculate the comprehensive important approach of each waypoint, that is, the importance of the node:

Further, the benefit-type indicators include: network topology indicators, network vulnerability indicators and network operation status indicators.

The beneficial effect of the present invention is that the airway network congestion area identification method of the present invention firstly establishes an airway network node importance evaluation index system based on the three dimensions of the airway network node's network topology characteristics, network vulnerability, and network operation status, and then uses the entropy weight to Method—CRITIC carries out combined weighting on each evaluation index, and introduces relative entropy and gray relational analysis method to improve the approach to ideal value sorting (TOPSIS) method, and comprehensively evaluates the importance of each waypoint; finally, according to the importance of each waypoint Congestion identification simulation of airway network can be carried out systematically, and the nodes that are more prone to congestion can be identified, so as to establish main research airways based on these nodes, and find out the flights that are expected to enter the congested airway network, and provide a basis for the research of airway network resource allocation.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

In order to make the above-mentioned objects, features and advantages of the present invention more comprehensible, preferred embodiments will be described in detail below together with the accompanying drawings.

Description of drawings

In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings that need to be used in the specific implementation or description of the prior art. Obviously, the accompanying drawings in the following description The drawings show some implementations of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

Fig. 1 is the emulation flow chart of route network congestion recognition simulation of preferred embodiment among the present invention;

Fig. 2 is a schematic diagram of a network topology and a corresponding adjacency matrix of a preferred embodiment of the present invention;

Fig. 3 is the route network node importance evaluation index system of the preferred embodiment of the present invention;

Figure 4 is a defect map of the distance calculation method of the traditional TOPSIS method;

Fig. 5 is a process for comprehensively evaluating the importance of waypoints in a preferred embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. the embodiment. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Referring to Fig. 1, this embodiment provides a method for identifying a route network congestion area, including:

Step1: Construct the air route network G=(V,E), set the capacity of each node as M _i , assume that the importance of the node C _i corresponds to the probability of the flight passing through the node, set the simulation step F _T =6000, and the flight flow of each node The quantity is f _i ^enter =F _t *C _i , where F _t is the total inbound flight volume in the current network;

Step2: According to the parameters set in Step1, by setting an appropriate flight generation interval, that is, to meet the safety interval requirements, the number of flights entering the network is sequentially increased;

Step3: Simulate the flight process, in order to ensure the flow balance inside each node (that is, the flight volume flowing into the node is always equal to the flight volume flowing out of the node), the connection between nodes (that is, the flight volume flowing into each node is equal to the The outflow sum of all nodes connected in front of the node) satisfies the dynamic balance of the entire network; record the node that is congested and the total inflow flight volume F _ti in the network when the node is congested; wherein the node that is congested is nodes whose f _i ^enter is greater than their capacity M _i ;

Step4: Repeat the process of Step2-Step3 until F _t =F _T , and the simulation ends.

In this embodiment, optionally, the method for constructing the route network G=(V, E) includes:

Taking waypoints as nodes, the routes and route segments between nodes are simplified into edges, and the adjacency matrix A(a _xy ) is used to represent the connection between waypoints and waypoints in the network, and x and y belong to the elements in the waypoint set. When When a _xy = 1, it means that there is a route connection between the waypoint and the waypoint, otherwise it is a waypoint without connection; see Figure 2, which is a network containing 8 waypoints and the corresponding adjacency matrix representation method; based on this, the waypoint The network is simplified to a network topology graph composed of N nodes and M edges, represented by an undirected graph G=(V, E), where V is the set of waypoints, and E is the set of routes connecting each waypoint.

In this embodiment, optionally, the calculation method of the importance C _i of the node includes: establishing an evaluation index system for the importance of route network nodes; performing combined weighting on each evaluation index through the entropy weight method—CRITIC weight method; Comprehensive evaluation of the importance of each waypoint.

In this embodiment, based on the constructed network topology, evaluation indicators for each waypoint can be calculated. However, due to the large number of waypoints in the route network, if only a single indicator is used for evaluation, multiple waypoints may be evaluated. Situations where the results are the same and cannot be distinguished. In order to avoid such problems, the "centrality" characteristics of each waypoint - network topology indicators, "destructive" characteristics - network vulnerability indicators, and the actual operation of waypoints - network operation status indicators are established here. The importance evaluation index system of route network nodes is established to achieve a more reasonable and accurate evaluation of the importance of route network nodes.

In this embodiment, the air route network is also a complex network, and the basic topology of the complex network can generally be described by indicators such as degree and betweenness. These indicators can roughly measure the influence of nodes in the network structure. Here Select degree centrality, betweenness centrality, proximity centrality and eigenvector centrality of waypoints to evaluate the topological properties of waypoints.

(1) Degree centrality

The degree of a waypoint refers to the number of neighboring waypoints around the waypoint, which is the most intuitive indicator for evaluating the importance of a waypoint. The greater the degree of a waypoint, the more waypoints directly connected to it, and the higher the importance of the waypoint. Degree centrality, on the other hand, normalizes the degree of waypoints according to the total number of waypoints in the network. Assuming that the number of edges directly connected to waypoint i is D _i , then:

Among them, a _ij is the value of the ith row and the jth column of the adjacency matrix A representing the route network, which represents the adjacent relationship of the waypoint i.

The formula for calculating degree centrality is:

(2) Betweenness centrality

The betweenness of a waypoint is used to reflect the importance of the position of the waypoint in the network. When there are more shortest paths passing through a waypoint, the greater the betweenness of the waypoint means that the amount of information carried by the waypoint The larger the , the betweenness centrality is normalized by the total number of waypoints in the network. Assuming that the number of the shortest path from waypoint j to waypoint k passing through node i is r _jk (i), and the number of all shortest paths from node j to node k is r _jk , then the betweenness centrality calculation formula of node i is :

(3) Proximity to centrality

The proximity centrality of a waypoint indicates the proximity between the waypoint and other waypoints in the network, reflecting the distance required for the information related to the waypoint to be transmitted in the network, and the sum of the distances from the waypoint to other waypoints The smaller the value, the higher the proximity. The approach centrality calculation method of a waypoint is the average value of the reciprocal of the sum of the shortest path distances from this waypoint to all other waypoints, assuming that the number of routes or segments contained in the shortest distance from waypoint i to waypoint j is d _ij , then the calculation formula of approach centrality of waypoint i is:

(4) Eigenvector Centrality

The importance of a node is not only related to the number of its neighbor nodes, but also closely related to the importance of its neighbor nodes. The eigenvector centrality of a node measures this characteristic of a node. The eigenvector centrality of a waypoint is the value corresponding to the waypoint in the eigenvector corresponding to the largest eigenvalue of the adjacency matrix of the network. Based on the assumption that the importance of the waypoint and the importance of its adjacent waypoints are linear, the following equations are established:

Ax=λx;

Solving this system of equations, the eigenvalue λ=[λ ₁ ,λ ₂ ,λ ₃ L λ _m ] ^T of the network adjacency matrix can be obtained, and the main eigenvector corresponding to the largest eigenvalue λ _max is x=[x ₁ ,x ₂ ,x ₃ …,x _n ] ^T , the eigenvector centrality of waypoint i is x _i , the calculation formula is:

In this embodiment, the above indicators are based on the position attributes of the waypoints in the route network, and measure the importance of the waypoints in the current complete network state. In addition to such metrics, an assessment of how critical the waypoint would be to changes in the network should the waypoint be removed can be assessed. Here, the network efficiency loss degree, node efficiency loss degree, and maximum subgraph loss degree are selected to measure the influence of waypoints on network vulnerability.

(1) Network efficiency loss degree

The efficiency of the entire route network refers to the mean value of the efficiency of all paired waypoints, and the efficiency of paired waypoints is represented by the reciprocal of the shortest distance between waypoints, which reflects the difficulty of connection between waypoints in the network. Its calculation formula is:

Among them, d _ij represents the shortest distance between waypoint i and waypoint j. When the shortest distance is smaller, the network efficiency is higher, and the transmission efficiency of the corresponding network is higher.

Based on the above concept of network efficiency, the network efficiency loss degree of waypoint is defined as the change rate of network efficiency before and after waypoint i is deleted, and the greater the change rate, the more important the waypoint is. Assuming that the network efficiency before deleting waypoint i is E, and the new network efficiency after deleting waypoint i and its connected route or segment is E _i , then the calculation formula of network efficiency loss degree of waypoint i is:

(2) Operating efficiency loss degree

To measure the operating efficiency loss of each waypoint, it is first necessary to clarify the concept of operating efficiency of the waypoint. When the actual operating load flow of a waypoint is less than the capacity, the waypoint can operate normally, and its operating efficiency is 1. When the actual load exceeds the critical capacity, congestion occurs at the waypoint, and the operating efficiency drops to 0. During the process, the waypoint The operating efficiency is inversely proportional to the actual load, that is, the greater the actual load, the lower the operating efficiency. Assume that the real-time operating load of waypoint i at time t is The operating load at the initial moment is /> Its capacity is C _i , and the acceptable overload factor is γ, so the operating efficiency of waypoint i can be obtained as:

in Indicates the average load of the entire network at the initial moment.

Operational efficiency loss is defined as the impact of removing a certain waypoint in a route network on the network's operational efficiency. Assuming that the maximum connected subgraph of the network is Φ, and the operating efficiency of each waypoint is η _i when the network fails, then the operating efficiency loss degree of waypoint i can be expressed as:

(3) Maximum subgraph loss degree

The maximum subgraph loss degree of a waypoint is defined as the degree of change in the number of waypoints contained in the largest connected subgraph of the network before and after the waypoint is deleted from the network. The number of waypoints contained in the maximum connected subgraph after i is N _i , and the maximum subgraph loss degree of waypoint i can be expressed as:

In this embodiment, the importance of a waypoint is not only related to the structure of the route network, but also closely related to the flow and capacity of the waypoint. In actual operation, it is necessary to pay attention to the overall traffic conditions of waypoints and the operation conditions during peak hours. Here, the indicators of flow concentration, peak hour flow and peak duration are selected to evaluate the operation status of waypoints.

(1) Traffic concentration

When evaluating the status of waypoints under actual operating conditions, it is necessary to comprehensively consider the flow and capacity of waypoints. If the network loses a certain waypoint, it will also lose the flow of the waypoints connected to the waypoint, so the load of the waypoint can also reflect the importance of the waypoint, that is, the greater the load of a certain waypoint, and The more routes connected to the waypoint, the higher the importance of the waypoint. Suppose the average flow at waypoint i is The capacity is C _i , and the flow concentration of waypoint i is defined as:

(2) Peak hour traffic

The peak hour flow rate of a waypoint reflects the maximum flow rate that the waypoint can bear in the network, and can also be used as one of the evaluation criteria for the importance of the waypoint. Assuming that the flow of waypoint i at time t is f _i ^t , the peak hourly flow of this waypoint can be expressed as:

PF _i =max( _fi ^t ).

(3) Peak hours

When the flow of some waypoints in the route network is at a high level all the time, this waypoint is generally an important waypoint in the network, that is, the peak duration of the waypoint also reflects its importance. Defines that the hourly flow of waypoint i is greater than its threshold The length of time is the peak duration of the waypoint, that is,

Among them, _θt is a 0-1 variable, which is used to judge whether the flow at each time point of the waypoint is in the peak flow state, expressed as:

So far, the establishment of the route network node importance evaluation index system is shown in Figure 3.

In this embodiment, after establishing the evaluation index system for the importance of airway network nodes, in order to evaluate the importance of airway network nodes as objectively and accurately as possible, it is necessary to assign reasonable weights to each index before the subsequent evaluation. In this paper, the combination of entropy weight method and CRITIC method is used to weight each evaluation index. Compared with the subjective weighting method, the objective weighting method avoids subjective arbitrariness and can make full use of data information to obtain more accurate weights. At the same time, because the indicators selected in this paper are multi-dimensional comprehensive indicators, it is not easy to conduct direct subjective comparisons to obtain the weight of each indicator. The entropy weight method is an objective weighting method applied in many fields. It can obtain more accurate weights by measuring the difference in index values, but this method does not take into account the differences and correlations between indicators. It may cause deviation to the final result, and the CRITIC method can optimize the weighting result by using the contrast strength and conflict between indicators, which makes up for the deficiency of the entropy weight method. Therefore, the combined weighting method based on the entropy weight method and the CRITIC method can effectively reduce the result deviation caused by the single weighting method, make the final weighting result more reasonable and accurate, and can better reflect the differences between different waypoints.

Overview of entropy weight method

Entropy is a measure of the degree of chaos in a system, and the entropy weight method is to measure the entropy value of the index by the variation degree of the index, and then determine the weight of the index. When the variation range of the index value of the evaluation phenomenon is larger, the degree of variation of the index is greater, that is, the greater the entropy value, the greater the amount of information contained, and the greater the effect on the evaluation, that is, the final value of the index is obtained. The greater the weight. The steps of weight calculation using the entropy weight method are as follows:

(1) Index normalization processing

In order to avoid large errors caused by different magnitudes before performing correlation calculations, it is necessary to normalize the indicators first, and the calculation formula is:

Among them, s _ij represents the calculated value of the j-th index of the i-th waypoint, and s _i ' _j is the normalized index value.

(2) Construct decision matrix

Calculate the proportion β _ij of the index value of the i-th waypoint under the j-th index according to the normalized value, and the calculation formula is:

From this we get the decision matrix

(3) Calculate the entropy value of the index

Based on the obtained decision matrix, the entropy value of each index is calculated, and the calculation formula is:

When β _ij is 0, β _ij lnβ _ij takes 0. 1-E _j represents the information entropy redundancy of the jth index.

(4) Calculate the entropy weight of the index

According to the calculation formula of information entropy, the information entropy of each index is obtained as E _j , and then the entropy weight of each index is calculated through information entropy. The calculation formula is:

Overview of the CRITIC Empowerment Act

The CRITIC (Criteria Importance Though Intercrieria Correlation) method is also an objective weighting method, which integrates the contrast strength of indicators and the conflict between indicators, and accurately calculates the weight of indicators. This method not only judges from the size of the index value, but also considers the correlation between the indicators, and uses the objective attributes of the data itself for scientific evaluation. For the CRITIC method, when the standard deviation is constant, the smaller the conflict between indicators, the smaller the weight; otherwise, the larger the weight. The steps to determine the weight of indicators using the CRITIC method are as follows:

(1) Construct a normalized matrix

Similar to the initial processing of the entropy weight method, the normalized matrix is obtained after normalizing each index

(2) Calculate the correlation between indicators

Calculating the correlation coefficient between indicators can measure the correlation between indicators. The calculation formula is:

Among them, ρ _mn is the correlation coefficient between the m-th index and the n-th index, s' _im and s' _in are the normalized values of the m-th and n-th index values of the i-th waypoint obtained in (1) , is the mean of the normalized values of the corresponding indicators.

(3) Calculate the amount of information of the index

Based on the correlation coefficient matrix between the indicators in the previous step, combined with the concept of the contrast strength of the evaluation indicators and the conflict between the indicators, the information degree of each indicator is calculated. The calculation formula is:

in, is the mean square error of the normalized value of the m-th index, which can reflect the difference between the indexes, that is, the contrast strength, ρ _mn is the correlation coefficient between the m-th index and the n-th index obtained in step (2), ( 1-ρ _mn ) can reflect the conflict between the two indicators.

(4) Calculate the weight coefficient of the index

Based on the above calculation results, the weight of each indicator can be obtained as follows:

In this embodiment, preferably, the calculation formula for combining and weighting each evaluation index through the entropy weight method—CRITIC weight method is:

ω _j = α ₁ u _j α + ₂ v _j ;

Among them, ω _j represents the comprehensive weight of the jth evaluation index; α ₁ and α ₂ represent the weight ratio of the two weighting methods respectively, satisfying α ₁ , α ₂ ≥0 and α ₁ +α ₂ =1;

The process of solving α ₁ and α ₂ can be transformed into a weighted optimization problem, and the problem model is as follows:

Solving according to the Lagrangian extreme value conditions, we can obtain

Among them, S _ij represents the calculated value of the j-th index of the i-th waypoint, and S' _ij is the normalized index value;

And then normalized to get:

In this embodiment, since there are many indicators involved, when evaluating such an object with multi-dimensional attributes, the comprehensive evaluation method is a more comprehensive and accurate method. Commonly used comprehensive evaluation methods include comprehensive index method, analytic hierarchy process, rank sum ratio method, TOPSIS method and fuzzy comprehensive evaluation method. Among them, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is a method that can make full use of the information of each index for objective evaluation, and is suitable for comprehensive evaluation of multiple objects with various attributes without complicated calculations. It is very suitable for the evaluation of the importance of route network nodes in this paper.

Traditional TOPSIS method

The TOPSIS method is evaluated by calculating the comprehensive distance between each object and the positive and negative ideal solutions, but this method only uses the Euclidean distance when calculating the distance. As shown in Figure 4, O ₁ and O ₂ are the obtained positive and negative ideal solutions. For most objects (such as points C and D) that are not on the mid-perpendicular line connecting O ₁ and O ₂ , according to The comprehensive distance between it _and O ₁ , O ₂ can _{get its} evaluation result _. The distances are the same, so the evaluation results of these objects are the same, but in reality, the attributes of these objects are likely to be different, so it is impossible to obtain objective and correct evaluation results.

Improved TOPSIS method

In view of the shortcomings of the traditional TOPSIS method, it is necessary to improve the distance calculation method. The commonly used methods for measuring the proximity or correlation between objects or data include relative entropy and gray relational analysis. Relative entropy can be measured by calculating the KL distance between objects. Although the KL distance is called a distance, it is not a distance in the physical sense and does not have symmetry, so each evaluation can be better Objects are distinguished. The gray correlation analysis method is to evaluate the degree of correlation of each object based on the similarity of the trend of each object. This method can make a more accurate measurement without relying on a large amount of data. Therefore, this paper calculates the relative entropy and gray correlation coefficient of each evaluation object and the positive and negative ideal solutions for comprehensive measurement, which replaces the traditional method of only using Euclidean distance for evaluation, and effectively avoids the problem of not being able to distinguish the importance of some objects .

Based on this, after establishing the evaluation index system of the importance of route network nodes, this embodiment first uses the entropy weight method-CRITIC method to carry out combined weighting. On this basis, the TOPSIS method is carried out by introducing relative entropy and gray correlation analysis method Improvement, in this way, the comprehensive quantitative results of the importance of each waypoint can be obtained.

In this embodiment, referring to FIG. 5, the method for comprehensively evaluating the importance of each waypoint includes:

(1) Index preprocessing:

Assuming that there are n waypoints in the constructed route network, there are m evaluation indicators for each waypoint, s _ij (i=1,2,3,...,n; j=1,2,3,..., m) represents the initial value of the nth waypoint under the mth evaluation index; construct the initial matrix S:

At the same time, in order to avoid the calculation error caused by the different dimensions of the index values of different dimensions of the waypoint, it is also necessary to standardize the initial matrix S. On the basis of distinguishing each index category (benefit-type index or cost-type index), different methods are used to standardize them.

The standardized treatment for benefit-type indicators is:

The standardized treatment for cost-type indicators is:

After standardizing each index value, a standardized decision matrix Z=(z _ij ) _n×m is obtained;

(2) Calculate the weighting matrix:

According to the comprehensive weight ω _j , and ω _j satisfies The weighted matrix X is obtained by combining the obtained standardized decision matrix Z=(z _ij ) _n×m with each standardized index value:

X＝(x _ij ) _n×m ＝(z _ij ω _j ) _n×m ;

(3) Calculate the ideal solution:

According to the obtained weighting matrix, calculate its positive ideal solution X ⁺ and negative ideal solution X ^- :

(4) Calculate the comprehensive proximity;

Because there are defects in the distance calculation in the traditional method, an improvement is made in this step, and the proximity is comprehensively evaluated by combining the relative entropy and gray relational degree with the ideal solution;

First calculate the relative entropy with positive and negative ideal solutions and />

Then calculate the gray correlation degree between each waypoint and the positive and negative ideal solutions and />

Among them, ρ represents the resolution coefficient, ρ∈[0,1], the smaller the value, the greater the resolution, usually 0.5;

Combining the relative entropy and gray relational degree between each waypoint and the positive and negative ideal solution, calculate the proximity of each waypoint to the positive ideal solution and the negative ideal solution:

Among them, η ₁ and η ₂ reflect whether the distance is more important or the shape of the curve, here η ₁ =η ₂ =0.5;

Finally, calculate the comprehensive important approach of each waypoint, that is, the importance of the node:

In this embodiment, optionally, the network topology index, the network vulnerability index, and the network operation status index are benefit-type indexes.

The higher the overall importance of a node, the more frequent the communication between the network flow and information flow at the node, and the greater the attraction to the flight flow, which means that the node has a stronger influence on the overall network. In the air route network, considering the limited use of airspace, the capacity of each node in the network is limited. When the number of flights passing through the network continues to increase, some nodes will experience flight inflows greater than their capacity, resulting in node congestion. If the congestion nodes are not controlled in time, small-scale congestion will spread to the entire network to form large-scale congestion, which will affect the normal operation of air traffic.

To sum up, the airway network congestion area identification method of the present invention first establishes an airway network node importance evaluation index system for the network topology characteristics of airway network nodes, network vulnerability and network operation status, and then uses the entropy weight method— CRITIC combines weights for each evaluation index, and introduces relative entropy and gray relational analysis method to improve the approach to ideal value sorting (TOPSIS) method, and comprehensively evaluates the importance of each waypoint; finally, according to the importance of each waypoint. The route network congestion identification simulation can identify the nodes that are more likely to cause congestion, so as to establish the main research routes based on these nodes, and find out the flights that are expected to enter the congested route network, providing a basis for the research of route network resource allocation.

Inspired by the above-mentioned ideal embodiment according to the present invention, through the above-mentioned description content, relevant workers can make various changes and modifications within the scope of not departing from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, but must be determined according to the scope of the claims.

Claims (10)

1. The method for identifying the congestion area of the airway network is characterized by comprising the following steps of:

step1: constructing a route network G= (V, E), and setting the capacity of each node as M _i Assume importance C of a node _i Setting a simulation step F corresponding to the probability of flight passing through the node _T =6000, flight inflow for each node f _i ^enter ＝F _t *C _i ；

Step2: sequentially increasing the number of flights entering the network by setting proper flight generation interval time according to the parameters set by Step 1;

step3: simulating a flight process, and recording total inflowing flight quantity F in a network when a node with congestion occurs _ti The method comprises the steps of carrying out a first treatment on the surface of the Wherein the node where congestion occurs is f _i ^enter Greater than its capacity M _i Is a node of (a);

step4: repeating the steps 2-3 until F _t ＝F _T And (5) finishing the simulation.

2. The method for identifying a congested area of an airway network of claim 1,

the method for constructing the airway network G= (V, E) comprises the following steps:

with the route points as nodes, the routes and route segments between the nodes are simplified to edges, and the adjacent matrix A (a _xy ) Representing the connection condition of the waypoints in the network and the waypoints, wherein x and y belong to elements in the waypoint set, and when a _xy When the number is=1, the way point is connected with the way, otherwise, the way point is a connectionless way point;

the airway network is simplified into a network topological graph formed by N nodes and M edges, and the network topological graph is represented by an undirected graph G= (V, E), wherein V is a set of airway points, and E is an airway set connecting the airway points.

3. The method for identifying a congested area of an airway network of claim 1,

importance C of the node _i The calculation method of (1) comprises the following steps:

establishing an importance evaluation index system of the nodes of the airway network;

the combination weighting is carried out on each evaluation index by an entropy weighting method-CRITIC weighting method;

and comprehensively evaluating the importance of each waypoint.

4. A method for identifying areas of congestion in an airway network as claimed in claim 3,

the establishment of the route network node importance evaluation index system comprises the following steps: network topology index, network vulnerability index and network operation state index.

5. The method for identifying a congested area of an airway network of claim 4,

the network topology index includes: center of degree, center of betweenness, center of proximity, center of feature vector.

6. The method for identifying a congested area of an airway network of claim 5,

the network vulnerability index includes: network efficiency loss, operating efficiency loss, maximum subgraph loss.

7. The method for identifying a congested area of an airway network of claim 6,

the network operation state index comprises: traffic concentration, peak hour traffic, peak time.

8. The method for identifying a congested area of an airway network of claim 7,

the calculation formula for carrying out combined weighting on each evaluation index by the entropy weighting method-CRITIC weighting method is as follows:

ω _j ＝α ₁ u _j +α ₂ v _j ；

wherein omega _j Comprehensive weight representing the j-th evaluation index; alpha ₁ And alpha ₂ Respectively represent the weight proportion of two weighting methods, satisfies alpha ₁ ,α ₂ Not less than 0 and alpha ₁ +α ₂ +1；u _j Entropy weight of the j-th evaluation index; v _j CRITIC method weight as j-th evaluation index;

solving for alpha ₁ And alpha ₂ The method of (1) comprises:

solving according to the Lagrange extremum condition to obtain

Wherein S is _ij A calculation value of the j index representing the i-th waypoint, S _i ' _j Is the index value after normalization;

and then normalization and solving are carried out to obtain:

9. the method for identifying a congested area of an airway network of claim 8,

the method for comprehensively evaluating the importance of each waypoint comprises the following steps:

(1) Index pretreatment:

assuming that the constructed route network has n route points in total, each route point has m evaluation indexes, s _ij (i=1, 2,3, …, n; j=1, 2,3, …, m) represents an initial value of the nth waypoint under the mth evaluation index; constructing an initial matrix S;

the standardized treatment for the benefit index is as follows:

the standardized processing for the cost index is as follows:

after normalizing each index value, a normalized decision matrix z= (Z) is obtained _ij ) _n×m ；

(2) Calculating a weighting matrix:

according to the integrated weight omega _j And omega _j Satisfy the following requirementsCombining the resulting normalized decision matrix z= (Z) _ij ) _n×m Obtaining a weighting matrix X by the standardized index values:

X＝(x _ij ) _n×m ＝(z _ij ·ω _j· ) _n×m ；

(3) Calculating an ideal solution:

based on the obtained weighting matrix, the positive ideal solution X is calculated ⁺ And negative ideal solution X ^- ：

(4) Calculating comprehensive proximity;

first, calculating the relative entropy of the ideal solution with positive and negativeAnd->

Then, gray correlation degree between each waypoint and positive and negative ideal solutions is calculatedAnd->

Wherein ρ represents a resolution coefficient, ρ ε [0,1] and the smaller the value, the larger the resolution;

the relative entropy and gray association degree of each waypoint and positive and negative ideal solutions are synthesized, and the proximity degree of each waypoint and positive and negative ideal solutions is calculated:

η ₁ +η ₂ ＝1；

η ₁ +η ₂ ＝1；

wherein eta ₁ And eta ₂ Whether the distance is more emphasized or the curve shape is reflected;

finally, calculating the comprehensive importance proximity of each waypoint, namely the importance of the node:

10. the method for identifying a congested area of an airway network of claim 9,

the benefit type index comprises: network topology index, network vulnerability index and network operation state index.