CN116665489A - Method for identifying congestion area of airway network - Google Patents
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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
Technical Field
The invention belongs to the technical field of allocation of channel network resources, and particularly relates to a channel network congestion area identification method.
Background
The airway network is a main carrier for air transportation and is a guarantee foundation for efficient operation of air traffic. The plurality of navigation points and navigation stations form a complex navigation network by points and lines, the geographical positions of the nodes are different, and the borne traffic loads are different, so that the importance of the nodes in the navigation network is different. Network function and operation efficiency are often affected by a small part of nodes in the network, the network performance is reduced due to the functional failure of the part of nodes, if corresponding measures are not taken in time, the failure effect of the part of nodes can rapidly reach the whole network, the network is finally paralyzed, and serious consequences are brought to the network, and the part of nodes are called key nodes. Based on the method, the importance degree of the nodes of the airway network is judged, and the searching of the key nodes has important significance for understanding the characteristics, the structure and the functions of the airway network, relieving the congestion of the airway network and improving the overall efficiency of air transportation.
At present, network congestion is studied mostly by only considering network topology indexes, and evaluation of nodes is carried out only based on network local structure information, so that more accurate judgment can not be made on the influence of the nodes in a global network, and the accuracy of congestion identification is reduced.
Therefore, there is a need for a way network congestion area identification method.
Disclosure of Invention
The invention aims to provide a road network congestion area identification method.
In order to solve the technical problems, the invention provides a method for identifying a congestion area of a route network, which comprises the following steps:
step1: constructing a route network G= (V, E), and setting the capacity of each node as M i False, falseImportance C of fixed 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 =ft, the simulation ends.
Further, the method for constructing the airway network g= (V, E) includes: 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.
Further, the importance degree 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.
Further, the establishing the channel network node importance evaluation index system includes: network topology index, network vulnerability index and network operation state index.
Further, the network topology index includes: center of degree, center of betweenness, center of proximity, center of feature vector.
Further, the network vulnerability index includes: network efficiency loss, operating efficiency loss, maximum subgraph loss.
Further, the network operation state index includes: traffic concentration, peak hour traffic, peak time.
Further, 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 Calculated value of the j index representing the i-th waypoint, S' ij Is the index value after normalization;
and then normalization and solving are carried out to obtain:
further, the method for comprehensively evaluating the importance of each waypoint comprises the following steps:
(1) Index pretreatment:
assuming a constructed airway networkN waypoints are shared, and each waypoint is provided with 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:
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:
further, the benefit type index includes: network topology index, network vulnerability index and network operation state index.
The invention has the beneficial effects that the method for identifying the congestion area of the airway network firstly establishes an airway network node importance evaluation index system for three dimensions of network topology characteristics, network vulnerability and network running state of airway network nodes, then carries out combined weighting on each evaluation index through an entropy weighting method-CRITIC, and improves an approximate ideal value ranking (TOPSIS) method by introducing a relative entropy and grey correlation analysis method to comprehensively evaluate the importance of each airway 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.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious 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 objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a simulation flow chart of the congestion identification of the airway network in the preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a network topology and corresponding adjacency matrix in accordance with a preferred embodiment of the present invention;
FIG. 3 is a system of importance assessment indicators for nodes of the airway network in accordance with a preferred embodiment of the present invention;
FIG. 4 is a defect chart of a distance calculation method of the conventional TOPSIS method;
FIG. 5 is a waypoint importance integrated assessment flow according to a preferred embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present embodiment provides a method for identifying a congestion area of an airway network, including:
step1: constructing a route network G= (V, E) and setting each nodeCapacity of 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 ，F t The total inflowing flight quantity in the current network;
step2: according to the parameters set by Step1, the number of flights entering the network is sequentially increased by setting proper flight generation interval time, namely meeting the requirement of a safety interval;
step3: simulating the flight process, and satisfying the dynamic balance of the whole network while ensuring the flow balance (namely the flight quantity flowing into the node is always equal to the flight quantity flowing out of the node) inside each node and the connection between the nodes (namely the flight quantity flowing into each node is equal to the sum of the outflow quantities of all nodes connected in front of the node); recording the total inflowing flight quantity F in the network when the node is congested 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.
In this embodiment, optionally, the method for constructing the airway network g= (V, E) includes:
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; referring to FIG. 2, a network including 8 waypoints and corresponding adjacency matrix representation method is shown; based on this, the airway network is simplified into a network topology diagram composed of N nodes and M edges, and represented by an undirected graph g= (V, E), where V is a set of airway points and E is a set of airways connecting the airway points.
In this embodiment, optionally, the importance of the node C i The calculation method of (1) comprises the following steps: establishing a road network nodeA point importance evaluation index system; 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.
In this embodiment, the calculation of the evaluation index of each waypoint may be performed based on the constructed network topology, but since there are many waypoints in the waypoint network, if only a single index is used for evaluation, the situation may occur that the evaluation results of a plurality of waypoints are the same and cannot be distinguished. In order to avoid the problems, a route network node importance evaluation index system is established by integrating the centrality characteristic, the network topology index, the destructiveness characteristic, the network vulnerability index, the actual running condition of the route points, the network running state index and the like of each route point so as to evaluate the importance of the route network node more reasonably and accurately.
In this embodiment, the waypoint is also a complex network, and the basic topology of the complex network is generally described by indexes such as availability, betweenness, etc., which can roughly measure the influence of the nodes in the network structure, where several indexes such as centrality, betweenness centrality, proximity centrality and feature vector centrality of the selected waypoint evaluate the topology attribute of the waypoint.
(1) Degree of centrality
The degree of the waypoints refers to the number of neighbor waypoints around the waypoints, and is the most visual index for evaluating the importance of one waypoint. The greater the waypoint number, the more waypoints that are directly connected to it, and the greater the importance of the waypoint. And the degree centrality is to normalize the degree of the waypoints according to the total number of the waypoints in the network. Let the number of edges directly connected to waypoint i be D i Then:
wherein a is ij The adjacency relation of the waypoint i is represented by the value of the ith row and jth column of the adjacency matrix a representing the waynetwork.
The calculation formula of the centrality is as follows:
(2) Center of median
The betweenness of the waypoints is used for reflecting the importance degree of the position of the waypoints in the network, when the shortest path passing through a certain waypoint is more, the betweenness of the waypoints is larger, which means that the information quantity carried by the waypoints is larger, and the betweenness centrality is to normalize the waypoints by utilizing the total number of the waypoints in the network. Let the shortest path from waypoint j to waypoint k pass through node i by r jk (i) The number of all shortest paths from node j to node k is r jk The median centrality calculation formula of the node i is:
(3) Near centrality
The proximity centrality of a waypoint represents the proximity between the waypoint and other waypoints in the network, reflecting the distance required for the information related to the waypoint to be transferred in the network, and the smaller the sum of the distances from the waypoint to the other waypoints, the higher the proximity. The approach centrality calculation method of the waypoint is the average value of the reciprocals accumulated by the shortest path distances from the waypoint to all other waypoints, and the number of the routes or the sections contained in the shortest distance from the waypoint i to the waypoint j is assumed to be d ij The approximate centrality calculation formula of the waypoint i is:
(4) Feature vector centrality
The importance of a node is related to the number of the neighbor nodes, and the importance of the node is also closely related to the importance of the neighbor nodes, and the characteristic vector centrality of the node measures the characteristic of the node. The feature vector centrality of a waypoint is a value corresponding to the waypoint in the feature vector corresponding to the maximum feature value of the adjacent matrix of the network. Based on the assumption that the importance of the waypoints and the importance of the adjacent waypoints are in a linear relation, the following equation set is established:
Ax＝λx；
solving the equation set to obtain the eigenvalue lambda= [ lambda ] of the network adjacency matrix 1 ,λ 2 ,λ 3 L λ m ] T Wherein the maximum eigenvalue lambda max The corresponding principal eigenvector is x= [ x 1 ,x 2 ,x 3 …,x n ] T The characteristic vector centrality of the waypoint i is x i The calculation formula is as follows:
in this embodiment, the index is based on the location attribute of the waypoint in the waynetwork, and measures the importance of the waypoint in the current complete network state. In addition to such metrics, network changes with the waypoint removed may also evaluate the importance of the waypoint. The network efficiency loss degree, the node efficiency loss degree and the maximum subgraph loss degree are selected, and the degree of influence of the waypoints on the network vulnerability is measured.
(1) Network efficiency loss degree
The efficiency of the whole airway network is the average value of the efficiency of all paired airway points, the efficiency of the paired airway points is expressed by the reciprocal of the shortest distance between the airway points, the difficulty degree of communication between the airway points in the network is reflected, and the calculation formula is as follows:
wherein d ij Representing the shortest distance between the waypoint i and the waypoint j, and when the shortest distance is smaller, the network efficiency is larger, and the transmission efficiency of the corresponding network is higherThe higher the rate.
Based on the above concept of network efficiency, the network efficiency loss degree of the waypoint is defined as the change rate of the network efficiency before and after deleting the waypoint i, and the larger the change rate is, the more important the waypoint is. Assuming that the network efficiency before deleting the waypoint i is E, and the new network efficiency after deleting the waypoint i and the connected way or section is E i The calculation formula of the network efficiency loss degree of the waypoint i is as follows:
(2) Degree of loss of operating efficiency
To measure the operation efficiency loss degree of each waypoint, the operation efficiency concept of the waypoint needs to be defined first. When the actual running load flow of a waypoint is smaller than the capacity, the waypoint can normally run, the running efficiency is 1, when the actual load exceeds the critical capacity, congestion is generated at the waypoint, the running efficiency is reduced to 0, the running efficiency of the waypoint in the process is inversely proportional to the actual load, namely, the greater the actual load is, the lower the running efficiency is. Assume that the real-time operation load of the waypoint i at the time t isThe operating load at the initial time is +.>Its capacity is C i The acceptable overload coefficient is gamma, and the operation efficiency of the route point i can be obtained according to the gamma, wherein the operation efficiency is as follows:
wherein the method comprises the steps ofRepresenting the average load of the whole network at the initial moment.
The degree of loss of operating efficiency is defined asAnd removing the influence of a certain waypoint on the network operation efficiency from a waynetwork. Assuming that the maximum connected subgraph of the network is phi, the operation efficiency of each route point is eta when the network fails i The degree of loss of operating efficiency at waypoint i may be expressed as:
(3) Maximum subgraph loss degree
The maximum sub-graph loss degree of the route point is defined as the change degree of the route point contained in the maximum connected sub-graph of the network before and after deleting the route point from the network, and the route point contained in the maximum connected sub-graph after deleting the route point i is assumed to be N by assuming that the route point contained in the maximum connected sub-graph of the original route network is N i The maximum subgraph loss degree for waypoint i can be expressed as:
in this embodiment, the importance of the waypoint is not only related to the structure of the waynetwork in which it is located, but also has a close relationship with the traffic and capacity carried by the waypoint. In actual operation, attention is paid to the overall flow condition of the waypoints and the operation condition of the peak period. The traffic concentration, peak hour traffic and peak time index are selected to evaluate the operation state of the waypoint.
(1) Concentration of flow
When the state of the waypoint under the actual running condition is evaluated, the flow and the capacity of the waypoint need to be comprehensively considered. If a certain waypoint is lost, the network will also lose the traffic of the waypoint connected with the waypoint, so the load condition of the waypoint can reflect the importance degree of the waypoint, namely, the greater the load of the certain waypoint, and the more the routes connected with the waypoint, the higher the importance degree of the waypoint. Assuming that the average flow of the waypoint i isCapacity of C i The traffic concentration of the defined waypoint i is:
(2) Peak hour flow rate
The peak hour flow of the waypoint reflects the maximum flow degree of the waypoint in the network and can also be used as one of evaluation criteria of the importance degree of the waypoint. Let the flow of the waypoint i at t be f i t The peak hour traffic for that waypoint may be expressed as:
PF i ＝max(f i t )。
(3) Peak time
When the traffic of a part of waypoints in the waynetwork is always at a higher level, the waypoints are generally important waypoints in the network, namely, the peak time of the waypoints also reflects the importance of the waypoints. The hour traffic defining waypoint i is greater than its thresholdThe time length of (2) is the peak time length of the waypoint, namely
Wherein θ t And the variable is 0-1, and is used for judging whether the flow at each moment of the waypoint is in a peak flow state, and the variable is expressed as:
thus, an importance evaluation index system of the nodes of the airway network is established as shown in figure 3.
In this embodiment, after the importance evaluation index system of the airway network node is established, in order to evaluate the importance of the airway network node as objectively and accurately as possible, each index needs to be given a reasonable weight before subsequent evaluation. The entropy weighting method and the CRITIC method are combined to give weight to each evaluation index, and compared with the subjective weighting method, the objective weighting method avoids subjective randomness and can fully utilize data information to obtain more accurate weight. Meanwhile, because the index selected in the text is a multi-dimensional comprehensive index, direct subjective comparison is not easy to carry out, and the weight of each index is further obtained. The entropy weighting method is an objective weighting method applied to multiple fields, and relatively accurate weights can be obtained by measuring index value differences, but the method does not consider differences and relativity among indexes and possibly causes deviation to a final result, and the CRITIC method can optimize the weighting result by utilizing the contrast intensity and conflict among indexes, so that the defects of the entropy weighting method are overcome. Therefore, the combined weighting method based on the entropy weighting method and the CRITIC method can effectively reduce the result deviation caused by a single weighting method, so that the final weighting result is more reasonable and accurate, and the distinction between different waypoints can be reflected well.
Overview of entropy weight method
The entropy is a measure of the degree of confusion of a system, and the entropy weight method is to measure the entropy value of an index according to the degree of variation of the index so as to determine the weight of the index. When the variation range of the index value of the evaluation phenomenon is larger, the variation degree of the index is larger, namely the entropy value is larger, the amount of information contained is larger, the effect on the evaluation is larger, and the weight of the final index is larger. The weight calculation by using the entropy weight method comprises the following steps:
(1) Index normalization
Before the correlation calculation, in order to avoid larger errors caused by different magnitudes, the indexes are required to be normalized, and the calculation formula is as follows:
wherein s is ij Calculated value of the j index representing the i-th waypoint, s i ' j Is the normalized index value.
(2) Constructing a decision matrix
Calculating the specific gravity beta of the index value of the ith waypoint under the jth index according to the normalized numerical value ij The calculation formula is as follows:
thereby obtaining a decision matrix
(3) Calculating entropy of index
Based on the obtained decision matrix, calculating the entropy value of each index, wherein the calculation formula is as follows:
when beta is ij When 0, beta ij lnβ ij Taking 0.1-E j And the information entropy redundancy degree of the j index is represented.
(4) Calculating entropy weight of index
Obtaining the information entropy of each index as E according to the calculation formula of the information entropy j And then entropy weight of each index is calculated through information entropy, and a calculation formula is as follows:
CRITIC entitlement profile
CRITIC (Criteria Importance Though Intercrieria Correlation) is also an objective weighting method, which integrates the comparison strength of the indexes and the conflict between the indexes, and accurately calculates the weight of the indexes. The method not only judges the index value, but also considers the relativity among indexes, and utilizes the objective attribute of the data to carry out scientific evaluation. For the CRITIC method, the smaller the inter-index conflict is, the smaller the weight is at a constant standard deviation; conversely, the greater the weight. The index determination weight using CRITIC method is as follows:
(1) Constructing a normalized matrix
Similar to the initial processing of the entropy weight method, normalizing each index to obtain a normalized matrix
(2) Calculating correlations between metrics
Calculating the correlation coefficient between the indexes can measure the correlation between the indexes. The calculation formula is as follows:
wherein ρ is mn Is the correlation coefficient of the mth index and the nth index, s' im 、s' in The normalized values of the m and n index values of the ith waypoint obtained in (1),the average value of the normalized values of the corresponding indexes.
(3) Calculating information quantity of index
Based on the correlation coefficient matrix among the indexes in the last step, the informativeness of each index is calculated by combining the concept of the contrast intensity of the evaluation index and the conflict among the indexes, and the calculation formula is as follows:
wherein,,the mean square error of the normalized value of the mth index can reflect the difference between indexes, namely the contrast intensity, ρ mn The (m) th obtained in the (2) th stepCorrelation coefficient of each index and nth index, (1- ρ) mn ) Can reflect the conflict between the two indexes.
(4) Calculating the weight coefficient of the index
Based on the calculation results, the weights of the indexes can be obtained as follows:
in this embodiment, preferably, the calculation formula for performing combined weighting on each evaluation index by using 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；
Solving for alpha 1 And alpha 2 The process of (2) may be converted into a weighted optimization problem, which is modeled as follows:
solving according to the Lagrange extremum condition to obtain
Wherein S is ij Calculated value of the j index representing the i-th waypoint, S' ij Is the index value after normalization;
and then normalization and solving are carried out to obtain:
in this embodiment, since the index is large, the comprehensive evaluation method is a more comprehensive and accurate method when evaluating the object having the multidimensional attribute. The common comprehensive evaluation methods include comprehensive index method, analytic hierarchy process, rank sum ratio method, TOPSIS method, fuzzy comprehensive evaluation method, etc. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is a method capable of making full use of each index information to perform objective evaluation, is suitable for comprehensively evaluating a plurality of objects with various attributes, has no complex calculation, and is very suitable for evaluating importance of the nodes of the airway network.
Traditional TOPSIS method
The TOPSIS method is evaluated by calculating the integrated distance between each object and the positive and negative ideal solutions, but this method uses only euclidean distance in calculating the distance. As shown in FIG. 4, O 1 、O 2 For positive ideal solutions and negative ideal solutions obtained, for non-O 1 、O 2 Most of the objects on the midpoint of the line (e.g., point C, D) are based on their connection to O 1 、O 2 The evaluation result can be obtained by the integrated distance of (2), but for the position at O 1 、O 2 Objects on the middle vertical of the line (e.g., point A, B), which are connected with O 1 、O 2 The obtained evaluation results of these objects are the same, but in reality, the attributes of these objects are likely not the same, so that objective and correct evaluation results cannot be obtained.
Improved TOPSIS process
Aiming at the defects of the traditional TOPSIS method, the distance calculation method needs to be improved, and common methods for measuring the proximity degree or the association degree between objects or data include a relative entropy method, a gray association analysis method and the like. The relative entropy can be measured by calculating the KL distance between the objects, which is not a distance in a physical sense, but is not a symmetry, so that each evaluation object can be better distinguished. The gray correlation analysis method evaluates the correlation degree according to the trend similarity of each object, and can accurately measure the relationship degree without depending on a large amount of data. Therefore, the relative entropy and gray correlation coefficient of each evaluation object and positive and negative ideal solutions are calculated for comprehensive measurement, so that the method replaces the conventional method for judging by only utilizing Euclidean distance, and the problem that the importance degree of partial objects cannot be distinguished is effectively avoided.
Based on the above, after the importance evaluation index system of the route network node is established, the method firstly utilizes the entropy weight method-CRITIC method to carry out combined weighting, and on the basis, the TOPSIS method is improved by introducing a relative entropy and gray correlation analysis method, so that the importance comprehensive quantification result of each route point can be obtained.
In this embodiment, referring to fig. 5, the method for comprehensively evaluating importance of each waypoint includes:
(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:
meanwhile, in order to avoid calculation errors caused by different dimensions of index values of different dimensions of the waypoints, the initial matrix S needs to be standardized. And (3) respectively adopting different methods to perform standardization processing on each index category on the basis of distinguishing the index categories (being benefit type indexes or cost type indexes).
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;
because the distance calculation has defects in the traditional method, the improvement is carried out in the step, and the proximity degree is comprehensively evaluated by combining the relative entropy and gray correlation degree with the ideal solution;
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], the smaller the value of ρ ε [0,1] the larger the corresponding resolution, usually 0.5;
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:
wherein eta 1 And eta 2 Reflecting whether the distance is more emphasized or curved, where η is taken 1 ＝η 2 ＝0.5；
Finally, calculating the comprehensive importance proximity of each waypoint, namely the importance of the node:
in this embodiment, the network topology index, the network vulnerability index, and the network operation status index are optional benefit indexes.
The higher the overall importance of a node, the more frequent the communication between network flows, information flows, etc. at that node, the greater the attractiveness to flight flows, i.e., the stronger the node's impact on the network as a whole. In an airliner network, considering that the use of airspace is limited, the capacity of each node in the network is limited, when the number of flights flowing through the network is continuously increased, partial nodes can generate flight inflow larger than the capacity of the nodes to cause node congestion, if the congested nodes are not managed in time, the small-range congestion can be diffused and extended to the whole network to form large-range congestion, and then the normal operation of air traffic is affected.
In summary, the method for identifying the congestion area of the airway network of the invention firstly establishes an airway network node importance evaluation index system for three dimensions of network topology characteristics, network vulnerability and network operation state of airway network nodes, then carries out combined weighting on each evaluation index through an entropy weighting method-CRITIC, and improves an approach ideal value ranking (TOPSIS) method by introducing a relative entropy and gray correlation analysis method to comprehensively evaluate the importance of each airway 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.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of 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.
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| CN117314201A (en) * | 2023-11-28 | 2023-12-29 | 中国民用航空总局第二研究所 | Method and system for determining key links of flight guarantee service |
| CN117391543A (en) * | 2023-12-07 | 2024-01-12 | 武汉理工大学 | Method and system for evaluating quality of offshore route network generated by track data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117314201A (en) * | 2023-11-28 | 2023-12-29 | 中国民用航空总局第二研究所 | Method and system for determining key links of flight guarantee service |
| CN117314201B (en) * | 2023-11-28 | 2024-02-06 | 中国民用航空总局第二研究所 | Method and system for determining key links of flight guarantee service |
| CN117391543A (en) * | 2023-12-07 | 2024-01-12 | 武汉理工大学 | Method and system for evaluating quality of offshore route network generated by track data |
| CN117391543B (en) * | 2023-12-07 | 2024-03-15 | 武汉理工大学 | Method and system for evaluating quality of offshore route network generated by track data |
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