CN116562692B - Urban low-altitude unmanned aerial vehicle airway network evaluation method - Google Patents
Urban low-altitude unmanned aerial vehicle airway network evaluation method Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle airway network design, and discloses an urban low-altitude unmanned aerial vehicle airway network evaluation method, which comprises the following steps: step one, acquiring multisource information data of an unmanned aerial vehicle route network system based on communication, navigation and monitoring equipment; step two, constructing a course network evaluation index set from two levels of unmanned aerial vehicle course network structure and network operation; step three, screening the channel network evaluation key indexes by calculating various index variation coefficients and correlation coefficients based on basic information data; and step four, evaluating the airway network by adopting an approximation ideal solution ordering method based on entropy weight according to the screened key indexes, and calculating to obtain a comprehensive evaluation index. The urban low-altitude unmanned aerial vehicle route network evaluation method is adopted, network evaluation key indexes are screened, comprehensive quantitative evaluation is carried out on the route network, and method support is provided for guaranteeing stable and orderly operation of large-scale unmanned aerial vehicles.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle airway network design, in particular to an urban low-altitude unmanned aerial vehicle airway network evaluation method.
Background
In recent years, the urban process is continuously accelerated, the land space tends to be saturated, the underground space is difficult to develop, and the unmanned aerial vehicle has the advantages of good maneuverability, high flexibility, strong timeliness and the like, and becomes a main carrier for urban air traffic in the future. Along with the continuous expansion of the operation scale of the urban unmanned aerial vehicle, the airway network is used as a main flight medium of various unmanned aerial vehicles, the performance of the airway network greatly influences the order and stability of the operation of the unmanned aerial vehicle, and the airway network construction test point work of the unmanned aerial vehicle is developed in many areas so as to design an airway network scheme suitable for local environment and requirements.
At present, the unmanned aerial vehicle airway network design is still in the primary stage, and comprehensive evaluation is required to be carried out on the unmanned aerial vehicle airway network by establishing proper indexes, so that a scientific and reasonable airway network is configured. The existing airway network evaluation method is mainly used for carrying out qualitative and quantitative analysis on a network structure, and neglecting network performance change of an actual operation level. Therefore, the unmanned aerial vehicle route network performance needs to be comprehensively analyzed from the structure and the running layer, and the unmanned aerial vehicle route network performance analysis method has important significance for guaranteeing safe and efficient flight of the unmanned aerial vehicle and improving the air traffic management level of the unmanned aerial vehicle.
Disclosure of Invention
The invention aims to provide an urban low-altitude unmanned aerial vehicle route network evaluation method, which solves the problems in the background technology.
In order to achieve the above purpose, the invention provides an urban low-altitude unmanned aerial vehicle airway network evaluation method, which comprises the following steps:
step one, acquiring multisource information data of an unmanned aerial vehicle route network system based on communication, navigation and monitoring equipment;
step two, constructing a course network evaluation index set from two levels of unmanned aerial vehicle course network structure and network operation;
step three, screening the channel network evaluation key indexes by calculating various index variation coefficients and correlation coefficients based on basic information data;
and step four, evaluating the airway network by adopting an approximation ideal solution ordering method based on entropy weight according to the screened key indexes, and calculating to obtain a comprehensive evaluation index.
Preferably, in the first step, based on communication, navigation and monitoring equipment between the airborne of the unmanned aerial vehicle and the ground, multi-source information data of an unmanned aerial vehicle airway network system is obtained, wherein the multi-source information data comprises service range data, airway network coordinate data, unmanned aerial vehicle flight plan data and unmanned aerial vehicle track data.
Preferably, in the second step, the airway network is expressed asFor the set of all target nodes in the airway network, < > for>A= (a) for the set of all edges in the airway network ij ) n×n For the adjacency matrix of the airway network, if a ij =1, representing node v i And v j There is a direct connection section between, otherwise a ij ＝0；
The urban low-altitude airspace adopts a layered airway strategy, all the airway segments in each airway network are positioned at the same altitude layer, the unmanned aerial vehicle executes vertical ascending or descending operation at a destination node taking-off and landing airport, and based on the operation strategy, an airway network evaluation index set is constructed from two layers of the airway network structure and network operation of the unmanned aerial vehicle.
Preferably, the channel network structure index includes a total network length, a network average degree, a network efficiency, an average cluster coefficient, a characteristic channel length, a degree distribution entropy, an average nonlinear coefficient, and a network connectivity, which are respectively expressed as:
the total network length D, defined as the sum of the lengths of all the direct-connected legs in the road network, is expressed as:
wherein d ij V is i And v j The actual shortest path length between nodes;
the network average degree K is defined as the ratio of the sum of all target node degrees to the number of target nodes in the route network, and is expressed as:
wherein a is ij The element value of the ith row and the jth column of the adjacent matrix of the airway network is given, and n is the number of target nodes;
network efficiency E, which is defined as the average value of the reciprocal of the actual route length among all target nodes of the route network, is positively correlated with network transport performance and is expressed as:
wherein a is ij For the element value of row j column of the route network adjacency matrix, a ik For the element value of row k column of the route network adjacency matrix, a jk The element value of the j row and k column of the adjacent matrix of the airway network;
the average clustering coefficient C is defined as the average value of all target node clustering coefficients in the airway network and expressed as:
the characteristic route length L is defined as the average value of the shortest route between any two nodes in the route network, and is expressed as:
the degree distribution entropy H, which represents the heterogeneity of the road network degree distribution, is expressed as:
in the formula g i For node v i The ratio of the degree of (c) to the degree of all target nodes is expressed as:
the average nonlinear coefficient I, defined as the average of nonlinear coefficients between all nodes in the airway network, is expressed as:
wherein s is ij V is i And v j The space linear length between the nodes;
the network connectivity J is defined as the intensity of mutual communication of all target nodes in the planning low-altitude space depending on the route, and is expressed as:
wherein D is the total length of the route network, lambda is the ratio of the total length of the route between nodes to the total length of the space straight line, and A is the horizontal area of the planned low-altitude airspace.
Preferably, the route network operation indexes include network saturation, network reachability, operation cost, traffic entropy, traffic density, delay time, conflict number and conflict occurrence rate, which are respectively expressed as:
the network saturation S is defined as the ratio of the traffic volume to the capacity of the airway network at a certain moment, and the value range is 0 to 1, and is expressed as:
wherein V is net C, setting up times for unmanned aerial vehicle running in airway network net Is the capacity of the airway network;
network reachability G, defined as the average shortest path length per unit time for an unmanned aerial vehicle to fly in a path network, expressed as:
wherein f ij Node v is unit time i And v j The flying flow between the unmanned frames is the unmanned frame number;
and the running cost M, wherein if the running cost of the unit mileage of the unmanned aerial vehicle is approximately equal, the running cost of the air route network in unit time is measured through the total flying mileage of the unmanned aerial vehicle, and the running cost is expressed as:
the flow entropy F represents the equilibrium state of the air route network flight flow distribution in unit time, and is expressed as:
wherein k is i Node v is unit time i The ratio of the flying flow to the entire course network flow is expressed as:
wherein t is i Node v is unit time i Unmanned aerial vehicle flight flow rate;
traffic density T, defined as the number of unmanned aerial vehicle frames in the course network per unit time, is expressed as:
delay time P, the average delay time of unmanned aerial vehicle in unit time is expressed as:
wherein m is the flying flow of the unmanned aerial vehicle of the airway network in unit time,for the actual moment when the ith unmanned aerial vehicle arrives at the target node,/-or->The planned moment when the ith unmanned aerial vehicle reaches the target node;
the number of conflict R is defined as the total number of times that the unmanned aerial vehicle of the air route network generates flight conflict in unit time, and is expressed as:
wherein r is i The number of times of collision for the ith unmanned aerial vehicle;
the conflict occurrence rate Q is defined as the ratio of the number of times of conflict of the unmanned aerial vehicle in the unit time route network to the flight flow, and is expressed as:
preferably, in the third step, based on basic information data, designing N groups of low-altitude unmanned aerial vehicle route network experiments, calculating route network structures and network operation indexes, and carrying out normalization processing on original positive index and negative index data by a range method;
and quantizing the index information quantity by using the variation coefficient, wherein the variation coefficient is in direct proportion to the information quantity, sorting the variation coefficients of all indexes in descending order, and screening the indexes by accumulating the variation coefficient ratio, wherein the screened t indexes are key indexes.
Preferably, in the fourth step, based on the key indexes of the screened t unmanned aerial vehicle airway networks, comprehensive evaluation is performed on a plurality of airway networks in different running states by adopting an approximate ideal solution ordering method based on entropy weight, and the comprehensive evaluation indexes of all airway networks are calculated to quantitatively reflect the good and bad performances of the airway networks.
Therefore, the urban low-altitude unmanned aerial vehicle route network evaluation method has the following beneficial effects:
(1) The invention is based on multi-source information data of unmanned aerial vehicle airway network system, comprehensively considers two layers of unmanned aerial vehicle airway network structure and network operation, establishes airway network evaluation index set, screens key indexes by calculating various index variation coefficients and correlation coefficients, and adopts an approximate ideal solution ordering method based on entropy weight to comprehensively and quantitatively evaluate airway network.
(2) The unmanned aerial vehicle route network performance characteristic comprehensive consideration method is beneficial to comprehensive consideration of unmanned aerial vehicle route network performance characteristics, improves unmanned aerial vehicle flight safety and efficiency, and plays an important role in perfecting unmanned aerial vehicle route management flow.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a flow chart of the overall implementation of the present invention;
FIG. 2 is a schematic diagram of a low-altitude unmanned aerial vehicle airway network in a city according to the present invention;
fig. 3 is a set of evaluation indicators for the unmanned aerial vehicle airway network of the present invention.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
Examples
As shown in fig. 1-3, the method for evaluating the urban low-altitude unmanned aerial vehicle route network comprises the following steps:
step one, acquiring multisource information data of an unmanned aerial vehicle route network system based on communication, navigation and monitoring equipment.
Based on communication, navigation and monitoring equipment of the unmanned aerial vehicle and the ground, multi-source information data of an unmanned aerial vehicle airway network system is obtained, wherein the multi-source information data comprise service range data, airway network coordinate data, unmanned aerial vehicle flight plan data and unmanned aerial vehicle track data.
And secondly, constructing a course network evaluation index set from two layers of unmanned aerial vehicle course network structure and network operation.
The airway network is expressed asFor the set of all target nodes in the airway network, < > for>A= (a) for the set of all edges in the airway network ij ) n×n For the adjacency matrix of the airway network, if a ij =1, representing node v i And v j There is one in betweenStrip direct connection section, otherwise a ij ＝0。
The urban low-altitude airspace adopts a layered airway strategy, namely all the airway segments in each airway network are positioned at the same altitude layer, the unmanned aerial vehicle only performs vertical ascending or descending operation at a destination node taking-off and landing airport, and based on the operation strategy, an airway network evaluation index set is constructed from two layers of the airway network structure and network operation of the unmanned aerial vehicle.
The channel network structure indexes comprise network total length, network average degree, network efficiency, average clustering coefficient, characteristic channel length, degree distribution entropy, average nonlinear coefficient and network connectivity, and are respectively expressed as:
(1) The total network length D, defined as the sum of the lengths of all the direct-connected legs in the road network, is expressed as:
wherein d ij V is i And v j The actual shortest path length between nodes;
(2) The network average degree K is defined as the ratio of the sum of all target node degrees to the number of target nodes in the route network, and is expressed as:
wherein a is ij And the element value of the ith row and the jth column of the adjacent matrix of the airway network is obtained, and n is the number of target nodes.
(3) Network efficiency E, which is defined as the average value of the reciprocal of the actual route length among all target nodes of the route network, is positively correlated with network transport performance and is expressed as:
(4) The average clustering coefficient C is defined as the average value of all target node clustering coefficients in the airway network, reflects the aggregation degree between the target node and the neighbor nodes, and is expressed as:
wherein a is ij For the element value of row j column of the route network adjacency matrix, a ik For the element value of row k column of the route network adjacency matrix, a jk The values of the elements in the j-th row and k-th column of the adjacency matrix for the airway network.
(5) The characteristic route length L is defined as the average value of the shortest route between any two nodes in the route network, reflects the dispersion degree among the nodes, and is expressed as:
(6) The degree distribution entropy H reflects the heterogeneity of the degree distribution of the airway network, and can also be used for evaluating the robustness of the airway network, and the smaller the degree distribution entropy is, the more stable the airway network is, expressed as:
in the formula g i For node v i The ratio of the degree of (c) to the degree of all target nodes is expressed as:
(7) The average nonlinear coefficient I is defined as the average value of nonlinear coefficients among all nodes in the airway network, reflects the convenience degree of unmanned aerial vehicle operation among the nodes of the airway network, and is expressed as:
wherein s is ij V is i And v j The space linear length between the nodes;
(8) The network connectivity J is defined as the intensity of planning the mutual communication of each target node depending on the route in the low-altitude space, reflects the structural layout characteristics of the route network, and is expressed as follows:
wherein D is the total length of the route network, lambda is the ratio of the total length of the route between nodes to the total length of the space straight line, and A is the horizontal area of the planned low-altitude airspace.
The route network operation indexes comprise network saturation, network reachability, operation cost, flow entropy, traffic density, delay time, conflict times and conflict occurrence rate, which are respectively expressed as:
(1) The network saturation S is defined as the ratio of the traffic volume to the capacity of the airway network at a certain moment, the value range is 0 to 1, the saturation degree of the airway network is reflected, and the method is expressed as follows:
wherein V is net C, setting up times for unmanned aerial vehicle running in airway network net Is the capacity of the airway network;
(2) Network reachability G is defined as the average shortest route length of the unmanned aerial vehicle flying in the route network in unit time, reflects the accessibility degree of the unmanned aerial vehicle among target nodes, and is expressed as:
wherein f ij Node v is unit time i And v j The flying flow between the unmanned frames is the unmanned frame number;
(3) The running cost M, if the running cost of the unit mileage of the unmanned aerial vehicle is approximately equal, the running cost of the unit time airway network can be measured through the total flying mileage of the unmanned aerial vehicle, and the running cost is expressed as:
(4) The flow entropy F represents the equilibrium state of the air route network flight flow distribution in unit time, and is expressed as:
wherein k is i Node v is unit time i The ratio of the flying flow to the entire course network flow is expressed as:
wherein t is i Node v is unit time i Unmanned aerial vehicle flight flow rate;
(5) The traffic density T is defined as unmanned aerial vehicle number in the air route network in unit time, reflects the congestion degree of the air route network, and is expressed as follows:
(6) Delay time P, because of random factors such as special activities, bad weather, etc., delay may occur when the unmanned aerial vehicle executes the task according to the initial flight plan, so the average delay time of the unmanned aerial vehicle per unit time can be expressed as:
wherein m is the flying flow of the unmanned aerial vehicle in the air route network in unit time, t i real T is the actual moment when the ith unmanned aerial vehicle arrives at the target node i plan The planned moment when the ith unmanned aerial vehicle arrives at the target node.
(7) The number of collision times R, each time the distance between two unmanned aerial vehicles is smaller than the minimum safety interval, the unmanned aerial vehicles are considered to generate a flight collision, the index is defined as the total number of flight collision of the unmanned aerial vehicles in the air route network in unit time, and the index is expressed as:
wherein r is i The number of collisions for the ith unmanned aerial vehicle.
(8) The conflict occurrence rate Q is defined as the ratio of the number of times of conflict of the unmanned aerial vehicle in the unit time airway network to the flight flow, reflects the whole safety level of the low-altitude airspace in the time period, and is expressed as follows:
and thirdly, screening the road network evaluation key indexes by calculating various index variation coefficients and correlation coefficients based on the basic information data.
Based on basic information data, N groups of low-altitude unmanned aerial vehicle route network experiments are designed, route network structures and network operation indexes are calculated, in order to eliminate dimensional influence among indexes, original positive index data and negative index data are normalized through a range method, and the formula is as follows:
wherein x 'is' ik Normalized value, x, for the kth index in the ith set of experiments ik Is the original value of the kth index in the ith set of experiments, min (x ik ) And max (x) ik ) The minimum value and the maximum value of the index in the N groups of experiments are respectively, the larger the positive index value is, the better the positive index value is, and the smaller the negative index value is, the better the negative index value is.
To screen for more informationThe evaluation index adopts the variation coefficient to quantify the index information quantity, wherein the variation coefficient is in direct proportion to the information quantity, and the variation coefficient v of the kth index k Expressed as:
wherein N is the total experiment times.
Sorting the variation coefficients of all indexes in descending order, if the sorted index sequence is v r1 ,v r2 ,...,v r16 The cumulative coefficient of variation ratio q of the first w indexes is obtained through the cumulative coefficient of variation ratio screening indexes w Expressed as:
in order to screen out the index with stronger independence, the index correlation is analyzed by calculating the pearson coefficient between the indexes, and the pearson coefficient p between the ith index and the jth index ij Expressed as:
if the number of the indexes after screening is required to be not more than 30% of the number of the original indexes, the contained information is not less than 95%, namely the cumulative variation coefficient ratio is not less than 0.95, the t indexes screened are key indexes.
And step four, evaluating the airway network by adopting an approximation ideal solution ordering method based on entropy weight according to the screened key indexes, and calculating to obtain a comprehensive evaluation index.
Based on the screened t unmanned aerial vehicle airway network key indexes, comprehensive evaluation is carried out on a plurality of airway networks under different running states by adopting an approximate ideal solution ordering method based on entropy weight, and the quality performance of the airway network is quantitatively reflected by calculating the comprehensive evaluation index of each airway network.
Entropy of kth key indexValue e k Expressed as:
in the formula, h ik The probability value of the kth key index of the evaluation object for the ith airway network is z, which is the total number of the evaluated airway networks;
expressed as:
entropy weight w of kth index k The method comprises the following steps:
after weighting, the kth key index of the ith route network evaluation object is z ik ＝w k x' ik Can calculate and obtain a positive ideal solution Z + ＝{maxz ik I=1, 2,..z } and negative ideal solution Z - ＝{minz ik I=1, 2,..z }, at which point the perpendicular distance between each evaluation object and the positive and negative ideal solutions can be calculated:
comprehensive evaluation index of ith route network evaluation object i Can be expressed as:
in the formula, index i ∈[0,1]The more the comprehensive evaluation index value isThe greater the road network evaluation object, the better.
Therefore, the urban low-altitude unmanned aerial vehicle airway network evaluation method is adopted, the urban low-altitude unmanned aerial vehicle airway network structure and network operation characteristics are comprehensively considered, network evaluation key indexes are screened based on multi-source information data of an unmanned aerial vehicle airway network system, an approximate ideal solution ordering method based on entropy weight is established to comprehensively and quantitatively evaluate the airway network, and method support is provided for guaranteeing stable and orderly operation of large-scale unmanned aerial vehicles.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.
Claims (4)
1. A city low-altitude unmanned aerial vehicle route network evaluation method is characterized in that: the method comprises the following steps:
step one, acquiring multisource information data of an unmanned aerial vehicle route network system based on communication, navigation and monitoring equipment;
step two, constructing a course network evaluation index set from two levels of unmanned aerial vehicle course network structure and network operation;
step three, screening the channel network evaluation key indexes by calculating various index variation coefficients and correlation coefficients based on basic information data;
step four, evaluating the airway network by adopting an approximation ideal solution ordering method based on entropy weight according to the screened key indexes, and calculating to obtain a comprehensive evaluation index;
in step two, the airway network is expressed as For the set of all target nodes in the airway network, < > for>A= (a) for the set of all edges in the airway network ij ) n×n A adjacency matrix for the airway network;
the method comprises the steps that a layering airway strategy is adopted in a low-altitude airspace of a city, all airlines in each airway network are located at the same altitude layer, an unmanned aerial vehicle performs vertical ascending or descending operation at an airport taking off and landing target node, and based on the operation strategy, an airway network evaluation index set is constructed from two layers of an airway network structure and network operation of the unmanned aerial vehicle;
the channel network structure indexes comprise network total length, network average degree, network efficiency, average clustering coefficient, characteristic channel length, degree distribution entropy, average nonlinear coefficient and network connectivity, and are respectively expressed as:
the total network length D, defined as the sum of the lengths of the direct-connect legs in the airway network, is expressed as:
wherein d ij V is i And v j The actual shortest path length between nodes;
the network average degree K is defined as the ratio of the sum of the target node degrees to the number of target nodes in the route network, and is expressed as:
wherein a is ij The element value of the ith row and the jth column of the adjacent matrix of the airway network is given, and n is the number of target nodes;
network efficiency E, defined as the mean value of the reciprocal of the actual course length between the target nodes of the course network, is positively correlated with network transport performance, expressed as:
the average clustering coefficient C is defined as the average value of the clustering coefficients of the target nodes in the airway network and expressed as:
wherein a is ij For the element value of row j column of the route network adjacency matrix, a ik For the element value of row k column of the route network adjacency matrix, a jk The element value of the j row and k column of the adjacent matrix of the airway network;
the characteristic route length L is defined as the average value of the shortest route between two nodes in the route network, and is expressed as:
the degree distribution entropy H, which represents the heterogeneity of the road network degree distribution, is expressed as:
in the formula g i For node v i The ratio of the degree of (c) to the degree of all target nodes is expressed as:
the average nonlinear coefficient I, defined as the average of nonlinear coefficients between nodes in the airway network, is expressed as:
wherein s is ij V is i And v j The space linear length between the nodes;
the network connectivity J is defined as the intensity of mutual communication of all target nodes in the planning low-altitude space depending on the route, and is expressed as:
wherein D is the total length of the route network, lambda is the ratio of the total length of the route between nodes to the total length of the space straight line, A is the horizontal area of the planned low-altitude airspace;
the route network operation indexes comprise network saturation, network reachability, operation cost, flow entropy, traffic density, delay time, conflict times and conflict occurrence rate, which are respectively expressed as:
the network saturation S is defined as the ratio of the traffic volume to the capacity of the airway network at a certain moment, and the value range is 0 to 1, and is expressed as:
wherein V is net C, setting up times for unmanned aerial vehicle running in airway network net Is the capacity of the airway network;
network reachability G, defined as the average shortest path length per unit time for an unmanned aerial vehicle to fly in a path network, expressed as:
wherein f ij Node v is unit time i And v j The flying flow rate between;
the running cost M is expressed as:
the flow entropy F represents the equilibrium state of the air route network flight flow distribution in unit time, and is expressed as:
wherein k is i Node v is unit time i The ratio of the flying flow to the airway network flow is expressed as:
wherein t is i Node v is unit time i Unmanned aerial vehicle flight flow rate;
traffic density T, defined as the number of unmanned aerial vehicle frames in the course network per unit time, is expressed as:
delay time P, the average delay time of unmanned aerial vehicle in unit time is expressed as:
wherein m is the flying flow of the unmanned aerial vehicle of the airway network in unit time,for the actual moment when the ith unmanned aerial vehicle arrives at the target node,/-or->Ith unmanned aerial vehicle reaching targetPlanning time of the node;
the number of conflict R is defined as the total number of times that the unmanned aerial vehicle of the air route network generates flight conflict in unit time, and is expressed as:
wherein r is i The number of times of collision for the ith unmanned aerial vehicle;
the conflict occurrence rate Q is defined as the ratio of the number of times of conflict of the unmanned aerial vehicle in the unit time route network to the flight flow, and is expressed as:
2. the urban low-altitude unmanned aerial vehicle route network evaluation method according to claim 1, wherein the method comprises the following steps: in the first step, based on communication, navigation and monitoring equipment of the unmanned aerial vehicle and the ground, multi-source information data of an unmanned aerial vehicle airway network system are obtained, wherein the multi-source information data of the unmanned aerial vehicle airway network system comprises service range data, airway network coordinate data, unmanned aerial vehicle flight plan data and unmanned aerial vehicle track data.
3. The urban low-altitude unmanned aerial vehicle route network evaluation method according to claim 2, wherein the method comprises the following steps: step three, designing N groups of low-altitude unmanned aerial vehicle route network experiments based on basic information data, calculating route network structures and network operation indexes, and carrying out normalization processing on original positive index and negative index data by a range method;
and quantizing the index information quantity by using the variation coefficient, wherein the variation coefficient is in direct proportion to the information quantity, sorting the variation coefficients of all indexes in descending order, and screening the indexes by accumulating the variation coefficient ratio, wherein the screened t indexes are key indexes.
4. A method for evaluating a low-altitude unmanned aerial vehicle airway network in a city according to claim 3, wherein: and step four, comprehensively evaluating a plurality of route networks under different running states by adopting an approximate ideal solution sequencing method based on entropy weight based on the key indexes of the screened t unmanned aerial vehicle route networks, and quantitatively reflecting the good and bad performances of the route networks by calculating the comprehensive evaluation indexes of the route networks.
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