Rapid evaluation system and method for flight airspace capacity of unmanned aerial vehicle

Abstract

The invention provides a system and a method for rapidly evaluating the flight airspace capacity of an unmanned aerial vehicle. The method comprises the following steps: step 1, receiving the route of the flight airspace of the unmanned aerial vehicle, the type of the unmanned aerial vehicle, the flight interval, the weather and the airspace limitation information; step 2, establishing a route network model of the flight airspace of the unmanned aerial vehicle; 3, according to the flight route network model of the flight airspace of the unmanned aerial vehicle established in the step 2, establishing a mathematical model for evaluating the flight airspace capacity of the unmanned aerial vehicle; 4, solving the mathematical model for evaluating the flight airspace capacity of the unmanned aerial vehicle established in the step 3, and respectively generating a theoretical capacity and an operation capacity of the flight airspace of the unmanned aerial vehicle; and step 5, publishing the flight airspace capacity of the unmanned aerial vehicle generated in the step 4, wherein the flight airspace capacity comprises theoretical capacity and operation capacity. The technical basis is provided for low-altitude and unmanned aerial vehicle airspace evaluation and unmanned aerial vehicle route management, and the implementation method is simple, rapid and easy to operate.

Description

Technical Field

[0001] The present invention belongs to the field of air traffic management and command control, and in particular relates to a system and method for quickly evaluating the flying airspace capacity of unmanned aerial vehicles. Background Art

[0002] UAV airspace capacity assessment is the main method for assessing the capacity of UAV route networks or flight airspaces. Traditional airspace capacity assessment methods include computer simulation, based on controller load, historical statistical data analysis, mathematical models, etc. Among them, the computer simulation method establishes simulation models such as the operating environment and operating process of the airspace to be assessed, simulates the operating characteristics of the flight flow in the airspace, and evaluates the capacity by statistically analyzing indicators such as flight delay time. It has the advantages of accurate assessment results and good assessment flexibility, and has formed commercial software, but requires more parameters and a longer assessment cycle. The method based on controller load mainly collects actual or simulated control operation data based on conditions such as the controller's working status and control means, quantifies the controller's workload under a certain flight volume, and statistically analyzes the capacity. The historical statistical data analysis method evaluates the actual operating capacity of the airspace by statistically analyzing actual historical data such as flight volume, flight delay time, and flight delay sorties. The mathematical model method mainly focuses on factors such as route layout and operation mode, establishes corresponding mathematical analytical models, and obtains capacity through model solution. It has the advantages of fewer required parameters and fast calculation.

[0003] UAV flight is relatively flexible, airspace is difficult to simulate accurately, and flight management methods are not yet perfect. The above methods are difficult to directly apply to the assessment of UAV airspace capacity. There are a few studies on low-altitude and UAV airspace capacity assessment methods at home and abroad, such as threshold-based airspace capacity assessment methods, geometric topology-based airspace capacity assessment methods, and control variable-based airspace capacity assessment methods. These methods require many parameters and a large amount of calculation or simulation work. At present, there is still a lack of a simple method suitable for quickly assessing the capacity of a specified UAV airspace.

[0004] In terms of system tools, there are few specialized low-altitude and UAV airspace capacity assessment systems, and the capacity assessment function in the UAV operation management system or low-altitude airspace management system needs to be improved. At present, there is still a lack of a universal, portable, and embeddable UAV flight airspace capacity rapid assessment tool. Summary of the invention

[0005] Purpose of the invention: The technical problem to be solved by the present invention is to provide a system and method for rapid assessment of the capacity of UAV flight airspace in view of the deficiencies of the prior art. According to the conditions of the UAV flight airspace, such as the route, flight interval, weather, and airspace restrictions, the route network of the UAV flight airspace is abstractly established, and the airspace capacity is mathematically analyzed and calculated to achieve rapid assessment of the capacity of the UAV flight airspace with fixed routes, providing a basis for low-altitude and UAV airspace management.

[0006] In order to achieve the purpose of the present invention, the present invention discloses a UAV flight airspace capacity rapid assessment system, including an information input module, a flight airspace route network modeling module, a flight airspace capacity assessment modeling module, a flight airspace capacity calculation module, a flight airspace capacity publishing module, a server, a workstation and a comprehensive information access device;

[0007] The information input module is used to receive the route, type of drone, flight interval, weather and airspace restriction information of the drone flight airspace;

[0008] The flight airspace route network modeling module is used to establish a route network model of the UAV flight airspace according to the route, UAV type, flight interval, weather and airspace restriction information of the UAV flight airspace received by the information input module;

[0009] The flight airspace capacity assessment modeling module is used to establish a mathematical model for evaluating the flight airspace capacity of the drone according to the route network model of the drone flight airspace;

[0010] The flight airspace capacity calculation module is used to solve the mathematical model of the UAV flight airspace capacity assessment, and generate the theoretical capacity and operating capacity of the UAV flight airspace respectively;

[0011] The flight airspace capacity publishing module is used to publish the UAV flight airspace capacity generated by the flight airspace capacity calculation module, including theoretical capacity and operating capacity.

[0012] The server is used to calculate, store and manage the information received, transmitted and generated by the system, and complete the functional response of each module;

[0013] The workstation is used for various types of graphic image processing, data calculation and human-computer interaction;

[0014] The comprehensive information access device is used to provide an interface for information input.

[0015] The present invention also provides a method for quickly evaluating the flying airspace capacity of a UAV based on the system, comprising the following steps:

[0016] Step 1, receiving the route, drone type, flight interval, weather and airspace restriction information of the drone flight airspace;

[0017] Step 2, establishing a route network model of the UAV flight airspace based on the route, UAV type, flight interval, weather and airspace restriction information of the UAV flight airspace received in step 1;

[0018] Step 3, establishing a mathematical model for evaluating the capacity of the UAV flight airspace based on the route network model of the UAV flight airspace established in step 2;

[0019] Step 4, solving the mathematical model for evaluating the UAV flight airspace capacity established in step 3, generating the theoretical capacity of the UAV flight airspace, and the operating capacity of the UAV flight airspace is calculated at 85% to 90% of the theoretical capacity;

[0020] Step 5: Publish the UAV flight airspace capacity generated in step 4, including theoretical capacity and operational capacity.

[0021] Step 2 includes the following steps:

[0022] Step 2.1, based on the route, drone type, flight interval, weather and airspace restriction information of the drone flight airspace received in step 1, delete the flight segment or flight segment altitude layer that is unavailable due to weather and airspace restrictions;

[0023] Step 2.2, according to the routes and flight intervals of the drone flight airspace processed in step 2.1, routes with true route angles in the range of 0 degrees to 179 degrees and routes with true route angles in the range of 180 degrees to 359 degrees are respectively formed into a one-way network, the starting point of the network is set to the source airspace outside the drone flight airspace of the network route, the end point of the network is set to the destination airspace outside the drone flight airspace of the network route, and the segments with more than two altitude layers overlap into a single altitude layer segment;

[0024] Step 2.3, according to step 2.2, the one-way network composed of routes with true route angles ranging from 0 degrees to 179 degrees is established to establish the UAV route network model G est = {V est ,E est},V est Represents the UAV route network model G est The route point set, E est Represents the UAV route network model G est The flight segment set, v i est Represents the UAV route network model G est The i-th route point in k est Represents the UAV route network model G est The kth flight segment in k est =(vi est ,v j est );v i est ,v j est ∈V est ; i is a positive integer; k is a positive integer;

[0025] Step 2.4, according to the UAV route network model G est , establish the kth flight segment e k est Capacity C ij est , expressed as:

[0026]

[0027] Among them, C ijl est represents the kth flight segment e k est The capacity of the altitude layer l, l = 1, 2, ..., L k est , L k est is the kth flight segment e k est Number of altitude levels;

[0028] UAV route network model G est The average flight interval S est It is expressed as:

[0029]

[0030] Among them, S pq represents the minimum flight interval when the leading aircraft is a class p drone and the following aircraft is a class q drone, α pq est Represents the UAV route network model G est The ratio of the front aircraft being a UAV of type p and the rear aircraft being a UAV of type q; p,q∈P, P is the set of UAV types in the UAV flight airspace;

[0031] The kth flight segment e k est The capacity C of the altitude layer l ijl est It is expressed as:

[0032]

[0033] Among them, D ijlest represents the kth flight segment e k est The length of the height layer l;

[0034] Step 2.5, according to step 2.2, the one-way network composed of routes with true route angles ranging from 180 degrees to 359 degrees is established to establish the UAV route network model G wst = {V wst ,E wst},V wst Represents the UAV route network model G wst The route point set, E wst Represents the UAV route network model G wst The flight segment set, v u wst Represents the UAV route network model G wst The u-th route point in x wst Represents the UAV route network model G wst The xth flight segment in e x wst =(v u wst ,v w wst ), v u wst ,v w wst ∈V wst ; u is a positive integer; x is a positive integer;

[0035] Step 2.6, according to the UAV route network model G wst , establish the xth flight segment e x wst Capacity C uw wst , expressed as:

[0036]

[0037] Among them, C uwy wst Indicates the xth flight segment e x wst The capacity of the altitude layer y, y=1,2,…,L x wst , L x wst For the xth flight segment e x wst Number of altitude levels;

[0038] UAV route network model Gwst The average flight interval S wst It is expressed as:

[0039]

[0040] Among them, α pq wst Represents the UAV route network model G wst The ratio of the front aircraft being a P-type UAV and the rear aircraft being a Q-type UAV;

[0041] The xth flight segment e x wst The capacity C of the altitude layer y uwy wst It is expressed as:

[0042]

[0043] Among them, D uwy wst Indicates the xth flight segment e x wst The length of the height layer y;

[0044] Step 2.7, based on the drone route network model G established in step 2.4 est The flight segment capacity C ij est and the UAV route network model G established in step 2.6 wst The flight segment capacity C uw wst , establish the capacity C of the overlapping route points of the two UAV route networks h est / wst .

[0045] In step 2.7, the capacity C h est / wst It is expressed as:

[0046]

[0047] Among them, α p represents the proportion of p-type drones in the drone flight airspace, V p is the flight speed of the p-type UAV in the UAV flight airspace, α pq It indicates the ratio of the leading drone to the following drone in the drone flight airspace.

[0048] Step 3 includes the following steps:

[0049] Step 3.1: Based on the route network model of the UAV flight airspace established in step 2, with the maximum flow rate of the UAV flight airspace as the goal, establish the following objective function:

[0050]

[0051] Among them, f est (i,j),f wst (u,w) represent the flight segment e respectively k est Traffic and flight segments x wst The flow rate of the flight segment is calculated as follows:

[0052]

[0053] Among them, f l est (i,j) represents flight segment e k est The flow rate at altitude level l, Indicates flight segment e x wst The flow rate at altitude layer y;

[0054] Step 3.2, establishing constraints based on the objective function established in step 3.1;

[0055] Step 3.3, based on the objective function of UAV flight airspace flow established in step 3.1 and the constraints established in step 3.2, a mathematical model for UAV flight airspace capacity assessment is established.

[0056] In step 3.2, the constraints include:

[0057] It is the segment capacity constraint, which means the segment flow does not exceed the segment capacity;

[0058]

[0059] is the route point flow conservation constraint, which means that the inflow and outflow of any route point in the UAV route network are equal;

[0060]

[0061] is the route network flow conservation constraint, which means that the total outflow from the starting point in the UAV route network is equal to the total inflow from the end point, s est 、s wst Respectively represent G est The starting point, G wst The starting point, t est ,t wst Respectively represent G est The end point, G wst The end point, s e ,te Respectively in f est (s e ,i),f est (j,t e ) indicates G est The starting point and end point, s w ,t w Respectively in f wst (s w ,u)、f wst (w,t w ) indicates G wst The starting point and end point of

[0062] is the capacity constraint of the overlapping route points, which means that the flow of the overlapping route points of the two UAV route networks does not exceed the capacity of the overlapping route points.

[0063] In step 3.3, the mathematical model for evaluating the UAV flight airspace capacity is expressed as:

[0064]

[0065] Where st means constrained by.

[0066] The present invention also provides an electronic device, comprising a processor and a memory, wherein the memory stores program code, and when the program code is executed by the processor, the processor executes the steps of the described method.

[0067] The present invention also provides a storage medium storing a computer program or instruction. When the computer program or instruction is run on a computer, the steps of the method described are executed.

[0068] The present invention has the following beneficial effects:

[0069] 1. It provides an implementation tool for low-altitude airspace management, urban air traffic management, and drone flight services, which is easy to embed into air traffic control systems, low-altitude flight service systems, and drone-related command and control systems, and provides technical support for the development and upgrading of air traffic control systems, drone-related command and control systems, low-altitude airspace management systems, and low-altitude flight service systems;

[0070] 2. It provides a technical basis for low-altitude and UAV airspace assessment and UAV route management, and the implementation method is simple, fast and easy to operate;

[0071] 3. It provides a technical basis for low-altitude airspace management and drone traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more clear.

[0073] Figure 1 The module composition and internal information relationship diagram of the UAV flight airspace capacity rapid assessment system of the present invention;

[0074] Figure 2 A flowchart of a method for quickly assessing the flying airspace capacity of a UAV according to the present invention;

[0075] Figure 3 A schematic diagram of a drone flight airspace route network for a specific implementation method;

[0076] Figure 4 A flow chart of a method for establishing a route network model for UAV flight airspace of the present invention;

[0077] Figure 5 A flow chart of a method for establishing a mathematical model for evaluating the capacity of UAV flight airspace of the present invention;

[0078] Figure 6 Schematic diagram of a route network of a certain drone flight airspace in an embodiment. DETAILED DESCRIPTION

[0079] like Figure 1 As shown, an embodiment of the present invention discloses a system for rapid assessment of the flight airspace capacity of an unmanned aerial vehicle, including software and hardware; the software includes an information input module, a flight airspace route network modeling module, a flight airspace capacity assessment modeling module, a flight airspace capacity calculation module, and a flight airspace capacity publishing module; the hardware includes a server, a workstation, and a comprehensive information access device.

[0080] The information input module is used to receive information such as the route of the drone flight airspace, drone type, flight interval, weather, airspace restrictions, etc.

[0081] The flight airspace route network modeling module is used to establish a route network model of the UAV flight airspace according to the route, UAV type, flight interval, weather, airspace restrictions and other information of the UAV flight airspace received by the information input module;

[0082] The flight airspace capacity assessment modeling module is used to establish a mathematical model for evaluating the flight airspace capacity of the drone based on the route network model of the drone flight airspace established by the flight airspace route network modeling module;

[0083] The flight airspace capacity calculation module is used to solve the mathematical model of the UAV flight airspace capacity assessment established by the flight airspace capacity assessment modeling module, and generate the theoretical capacity and operating capacity of the UAV flight airspace respectively;

[0084] The flight airspace capacity publishing module is used to publish the UAV flight airspace capacity generated by the flight airspace capacity calculation module, including theoretical capacity and operating capacity.

[0085] The server is used to calculate, store and manage various types of information of a UAV flight airspace capacity rapid assessment system, and complete the functional response of each software module;

[0086] The workstation is used for various graphics and image processing, data calculation and human-computer interaction of a UAV flight airspace capacity rapid assessment system;

[0087] The comprehensive information access device is used as an interface for information input of a rapid assessment system of the flying airspace capacity of unmanned aerial vehicles.

[0088] like Figure 2 As shown, the present invention discloses a method for quickly evaluating the flying airspace capacity of a UAV, comprising the following steps:

[0089] Step 1: Receive Figure 3 The information of the UAV flight airspace shown, such as the route, UAV type, flight interval, weather, airspace restrictions, etc., where the spatial range of the UAV flight airspace can generally meet the flight range of UAV transportation, operation and other types of missions, such as the low-altitude airspace of a certain administrative area or test area, etc. The route information includes the true heading of the route, the length and altitude of each segment of the route, etc. The types of UAVs include micro, light, small, medium, large, etc.;

[0090] Step 2: Based on the information of the UAV flight airspace received in step 1, such as the route, UAV type, flight interval, weather, airspace restrictions, etc., a route network model of the UAV flight airspace is established;

[0091] Step 3: Based on the route network model of the UAV flight airspace established in step 2, a mathematical model for evaluating the capacity of the UAV flight airspace is established;

[0092] Step 4: Use the Ford-Fulkerson labeling algorithm and other methods to solve the mathematical model of drone flight airspace capacity assessment established in step 3 to generate the theoretical capacity of drone flight airspace. According to the experience of traditional civil aviation airspace capacity assessment, the operating capacity is about 85% to 90% of the capacity calculated by the theoretical model. The operating capacity of drone flight airspace is calculated based on 85% to 90% of the theoretical capacity.

[0093] Step 5: Publish the drone flight airspace capacity generated in step 4, including theoretical capacity and operational capacity. The recipients are generally air traffic control, low-altitude flight service agencies, low-altitude airspace users and other relevant parties.

[0094] like Figure 4 As shown, the method for quickly evaluating the capacity of a UAV flight airspace disclosed in the present invention, step 2 proposes a method for establishing a route network model of a UAV flight airspace, including the following steps:

[0095] Step 2.1: Based on the information of the flight route, drone type, flight interval, weather, airspace restrictions, etc. of the drone flight airspace received in step 1, delete the flight segment or flight segment altitude layer that is unavailable due to weather and airspace restrictions;

[0096] Step 2.2: According to the routes and flight intervals of the drone flight airspace processed in step 2.1, routes with true route angles in the range of 0 to 179 degrees and routes with true route angles in the range of 180 to 359 degrees are respectively formed into a one-way network. The starting point of the network is set to the source airspace outside the drone flight airspace of the network route, and the end point of the network is set to the destination airspace outside the drone flight airspace of the network route. The segments with two or more altitude layers overlap into a single altitude layer segment;

[0097] Step 2.3, according to step 2.2, the one-way network composed of routes with true route angles ranging from 0 degrees to 179 degrees is established to establish the UAV route network model G est = {V est ,E est},V est Represents the UAV route network model G est The route point set, E est Represents the UAV route network model G est The flight segment set, v i est Represents the UAV route network model G est The i-th route point in k est Represents the UAV route network model G est The kth flight segment in k est =(v i est ,v j est );v i est ,v j est ∈V est ; i is a positive integer; k is a positive integer;

[0098] Step 2.4: The UAV route network model G established according to step 2.3 est , create segment e k est Capacity C ijest , which is the sum of the capacities of each altitude layer in this segment, expressed as:

[0099]

[0100] Among them, C ijl est Indicates flight segment e k est The altitude layer l (l=1,2,…,L k est , L k est For segment e k est The capacity of the UAV route network model G est The average flight interval S est , expressed as:

[0101]

[0102] Among them, S pq represents the minimum flight interval when the leading aircraft is a drone of type p (p,q∈P, P is the set of drone types in the drone flight airspace) and the following aircraft is a drone of type q, α pq est Represents the UAV route network model G est The ratio of the front drone being a p-type drone and the rear drone being a q-type drone, C ijl est It is expressed as:

[0103]

[0104] Among them, D ijl est Indicates flight segment e k est The length of the height layer l;

[0105] Step 2.5, according to step 2.2, the one-way network composed of routes with true route angles ranging from 180 degrees to 359 degrees is established to establish the UAV route network model G wst = {V wst ,E wst},V wst Represents the UAV route network model G wst The route point set, E wst Represents the UAV route network model G wst The flight segment set, v u wst Represents the UAV route network model G wst The u-th route point in x wstRepresents the UAV route network model G wst The xth flight segment in e x wst =(v u wst ,v w wst ), v u wst ,v w wst ∈V wst ; u is a positive integer; x is a positive integer;

[0106] Step 2.6, according to the UAV route network model G wst , establish the xth flight segment e x wst Capacity C uw wst , expressed as:

[0107]

[0108] Among them, C uwy wst Indicates the xth flight segment e x wst The capacity of the altitude layer y, y=1,2,…,L x wst , L x wst For the xth flight segment e x wst Number of altitude levels;

[0109] UAV route network model G wst The average flight interval S wst It is expressed as:

[0110]

[0111] Among them, α pq wst Represents the UAV route network model G wst The ratio of the front aircraft being a P-type UAV and the rear aircraft being a Q-type UAV;

[0112] The xth flight segment e x wst The capacity C of the altitude layer y uwy wst It is expressed as:

[0113]

[0114] Among them, D uwywst Indicates the xth flight segment e x wst The length of the height layer y;

[0115] Step 2.7: The drone route network model G established according to step 2.4 est The flight segment capacity C ij est and the UAV route network model G established in step 2.6 wst The flight segment capacity C ij wst , establish the capacity C of the overlapping route points of the two UAV route networks h est / wst , expressed as:

[0116]

[0117] Among them, α p represents the proportion of p-type drones in the drone flight airspace, V p is the flight speed of the p-type UAV in the UAV flight airspace, α pq It indicates the ratio of the leading drone to the following drone in the drone flight airspace.

[0118] like Figure 5 As shown, the method for quickly evaluating the flying airspace capacity of unmanned aerial vehicles disclosed in the present invention, step 3 proposes a method for establishing a mathematical model for evaluating the flying airspace capacity of unmanned aerial vehicles, including the following steps:

[0119] Step 3.1: Based on the route network model of the drone flight airspace established in step 2, with the maximum flow rate of the drone flight airspace as the goal, establish the objective function, which is expressed as:

[0120]

[0121] Among them, f est (i,j),f wst (u,w) represent the flight segment e respectively k est Traffic and flight segments x wst The flow rate of the flight segment is calculated as follows:

[0122]

[0123] Among them, f l est (i,j) represents flight segment e k est The flow rate at altitude level l, Indicates flight segment e xwst The flow rate at altitude layer y;

[0124] Step 3.2: According to the objective function established in step 3.1, establish the constraint conditions, expressed as:

[0125] It is the segment capacity constraint, which means the segment flow does not exceed the segment capacity;

[0126]

[0127] is the route point flow conservation constraint, which means that the inflow and outflow of any route point in the UAV route network are equal;

[0128]

[0129] is the route network flow conservation constraint, which means that the total outflow from the starting point in the UAV route network is equal to the total inflow from the end point, s est 、s wst Respectively represent G est The starting point, G wst The starting point, t est ,t wst Respectively represent G est The end point, G wst The end point, s e ,t e Respectively in f est (s e ,i),f est (j,t e ) indicates G est The starting point and end point, s w ,t w Respectively in f wst (s w ,u)、f wst (w,t w ) indicates G wst The starting point and end point of

[0130] is the capacity constraint of the overlapping route points, which means that the flow of the overlapping route points of the two UAV route networks does not exceed the capacity of the overlapping route points.

[0131] Step 3.3: Based on the objective function established in step 3.1 and the constraints established in step 3.2, a mathematical model for evaluating the UAV flight airspace capacity is established, which can be expressed as:

[0132]

[0133] In another specific embodiment of the present invention, a method for quickly evaluating the flying airspace capacity of a UAV is provided, comprising:

[0134] Step 1: Receive Figure 6 The route, drone type, flight interval, weather, airspace restrictions and other information of a drone flight airspace are shown in Table 1. The route information is shown in Table 1. The drone types include small, medium and large types, as shown in Table 2. The flight interval information (in kilometers) is shown in Table 3.

[0135] Table 1 Route information

[0136]

[0137]

[0138] Table 2 UAV type information

[0139]

[0140] Table 3 Flight interval information

[0141]

[0142] Step 2: Based on the information of the UAV flight airspace received in step 1, such as the route, UAV type, flight interval, weather, and airspace restrictions, a route network model of the UAV flight airspace is established.

[0143] The UAV route network model G is calculated est , G wst The average flight interval S est , S wst They are 9.17 km and 8.35 km respectively. est , G wst The segment capacity is shown in Table 4.

[0144] Table 4 Segment Capacity

[0145]

[0146]

[0147] The average cruising speeds of small, medium and large drones are set to 50 km / h, 80 km / h and 100 km / h respectively. The calculated capacity of the overlapping route points B, C, E, F, G and I of the two drone route networks is 9.15 flights / hour.

[0148] Step 3: Based on the route network model of the UAV flight airspace established in step 2, a mathematical model for evaluating the capacity of the UAV flight airspace is established.

[0149] Step 4, use the Ford-Fulkerson labeling algorithm and other methods to solve the mathematical model of UAV flight airspace capacity assessment established in step 3, and generate a theoretical capacity of 60.57 flights / hour for UAV flight airspace. According to the experience of traditional civil aviation airspace capacity assessment, the operating capacity is approximately 85% to 90% of the capacity calculated by the theoretical model. The operating capacity of UAV flight airspace is calculated based on 85% to 90% of the theoretical capacity, which is 51.48 flights / hour to 54.51 flights / hour.

[0150] Step 5: Publish the drone flight airspace capacity generated in step 4, including theoretical capacity and operational capacity. The recipients are generally air traffic control, low-altitude flight service agencies, low-altitude airspace users and other relevant parties.

[0151] The present invention provides a system and method for quickly assessing the flying airspace capacity of a UAV. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A rapid assessment system for UAV flight airspace capacity, characterized in that: It includes information input module, flight airspace route network modeling module, flight airspace capacity assessment modeling module, flight airspace capacity calculation module, flight airspace capacity publishing module, server, workstation and comprehensive information access equipment; The information input module is used to receive the route, type of drone, flight interval, weather and airspace restriction information of the drone flight airspace; The flight airspace route network modeling module is used to establish a route network model of the UAV flight airspace according to the route, UAV type, flight interval, weather and airspace restriction information of the UAV flight airspace received by the information input module; The flight airspace capacity assessment modeling module is used to establish a mathematical model for evaluating the flight airspace capacity of the drone according to the route network model of the drone flight airspace; The flight airspace capacity calculation module is used to solve the mathematical model of the UAV flight airspace capacity assessment, and generate the theoretical capacity and operating capacity of the UAV flight airspace respectively; The flight airspace capacity publishing module is used to publish the UAV flight airspace capacity generated by the flight airspace capacity calculation module, including theoretical capacity and operating capacity.

2. The system according to claim 1, characterized in that The server is used to calculate, store and manage the information received, transmitted and generated by the system, and complete the functional response of each module; The workstation is used for various types of graphic image processing, data calculation and human-computer interaction; The comprehensive information access device is used to provide an interface for information input.

3. A method for quickly assessing the flying airspace capacity of an unmanned aerial vehicle based on the system according to any one of claims 1 to 2, characterized in that: The following steps are involved: Step 1, receiving the route, drone type, flight interval, weather and airspace restriction information of the drone flight airspace; Step 2, establishing a route network model of the UAV flight airspace based on the route, UAV type, flight interval, weather and airspace restriction information of the UAV flight airspace received in step 1; Step 3, establishing a mathematical model for evaluating the capacity of the UAV flight airspace based on the route network model of the UAV flight airspace established in step 2; Step 4, solving the mathematical model for evaluating the UAV flight airspace capacity established in step 3, and generating the theoretical capacity and operational capacity of the UAV flight airspace respectively; Step 5: Publish the UAV flight airspace capacity generated in step 4, including theoretical capacity and operational capacity.

4. The method according to claim 3, characterized in that Step 2 includes the following steps: Step 2.1, based on the route, drone type, flight interval, weather and airspace restriction information of the drone flight airspace received in step 1, delete the flight segment or flight segment altitude layer that is unavailable due to weather and airspace restrictions; Step 2.2, according to the routes and flight intervals of the drone flight airspace processed in step 2.1, routes with true route angles in the range of 0 degrees to 179 degrees and routes with true route angles in the range of 180 degrees to 359 degrees are respectively formed into a one-way network, the starting point of the network is set to the source airspace outside the drone flight airspace of the network route, the end point of the network is set to the destination airspace outside the drone flight airspace of the network route, and the segments with more than two altitude layers overlap into a single altitude layer segment; Step 2.3, according to step 2.2, the one-way network composed of routes with true route angles ranging from 0 degrees to 179 degrees is established to establish the UAV route network model G est = {V est ,E est },V est Represents the UAV route network model G est The route point set, E est Represents the UAV route network model G est The flight segment set, v i est Represents the UAV route network model G est The i-th route point in est e k Represents the UAV route network model G est The kth flight segment in k est =(v i est ,v j est ); i is a positive integer; k is a positive integer; Step 2.4, according to the UAV route network model G est , establish the kth flight segment e k est Capacity C ij est , expressed as: Among them, C ijl est represents the kth flight segment e k est The capacity of the altitude layer l, l = 1, 2, ..., L k est , L k est is the kth flight segment e k est Number of altitude levels; UAV route network model G est The average flight interval S est It is expressed as: Among them, S pq represents the minimum flight interval when the leading aircraft is a class p drone and the following aircraft is a class q drone, α pq est Represents the UAV route network model G est The ratio of the front aircraft being a UAV of type p and the rear aircraft being a UAV of type q; p,q∈P, P is the set of UAV types in the UAV flight airspace; The kth flight segment e k est The capacity C of the altitude layer l ijl est It is expressed as: Among them, D ijl est represents the kth flight segment e k est The length of the height layer l; Step 2.5, according to step 2.2, the one-way network composed of routes with true route angles ranging from 180 degrees to 359 degrees is established to establish the UAV route network model G wst = {V wst ,E wst },V wst Represents the UAV route network model G wst The route point set, E wst Represents the UAV route network model G wst The flight segment set, v u wst Represents the UAV route network model G wst The u-th route point in x wst Represents the UAV route network model G wst The xth flight segment in e x wst =(v u wst ,v w wst ), v u wst ,v w wst ∈V wst ; u is a positive integer; x is a positive integer; Step 2.6, according to the UAV route network model G wst , establish the xth flight segment e x wst Capacity C uw wst , expressed as: Among them, C uwy wst Indicates the xth flight segment e x wst The capacity of the altitude layer y, y=1,2,…,L x wst , L x wst For the xth flight segment e x wst Number of altitude levels; UAV route network model G wst The average flight interval S wst It is expressed as: Among them, α pq wst Represents the UAV route network model G wst The ratio of the front aircraft being a P-type UAV and the rear aircraft being a Q-type UAV; The xth flight segment e x wst The capacity C of the altitude layer y uwy wst It is expressed as: Among them, D uwy wst Indicates the xth flight segment e x wst The length of the height layer y; Step 2.7, based on the drone route network model G established in step 2.4 est The flight segment capacity C ij est and the UAV route network model G established in step 2.6 wst The flight segment capacity C uw wst , establish the capacity C of the overlapping route points of the two UAV route networks h est / wst .

5. The method according to claim 4, characterized in that In step 2.7, the capacity C h est / wst It is expressed as: Among them, α p represents the proportion of p-type drones in the drone flight airspace, V p is the flight speed of the p-type UAV in the UAV flight airspace, α pq It indicates the ratio of the leading drone to the following drone in the drone flight airspace.

6. The method according to claim 5, characterized in that Step 3 includes the following steps: Step 3.1: Based on the route network model of the UAV flight airspace established in step 2, with the maximum flow rate of the UAV flight airspace as the goal, establish the following objective function: Among them, f est (i,j),f wst (u,w) represent the flight segment e respectively k est Traffic and flight segments x wst The flow rate of the flight segment is calculated as follows: in, Indicates flight segment e k est The flow rate at altitude level l, Indicates flight segment e x wst The flow rate at altitude layer y; Step 3.2, establishing constraints based on the objective function established in step 3.1; Step 3.3, based on the objective function established in step 3.1 and the constraints established in step 3.2, a mathematical model for evaluating the UAV flight airspace capacity is established.

7. The method according to claim 6, characterized in that In step 3.2, the constraints include: It is the segment capacity constraint, which means the segment flow does not exceed the segment capacity; is the route point flow conservation constraint, which means that the inflow and outflow of any route point in the UAV route network are equal; is the route network flow conservation constraint, which means that the total outflow from the starting point in the UAV route network is equal to the total inflow from the end point, s est 、s wst Respectively represent G est The starting point, G wst The starting point, t est ,t wst Respectively represent G est The end point, G wst The end point, s e ,t e Respectively in f est (s e ,i),f est (j,t e ) indicates G est The starting point and end point, s w ,t w Respectively in f wst (s w ,u)、f wst (w,t w ) indicates G wst The starting point and end point of is the capacity constraint of the overlapping route points, which means that the flow of the overlapping route points of the two UAV route networks does not exceed the capacity of the overlapping route points.

8. The method according to claim 7, characterized in that In step 3.3, the mathematical model for evaluating the UAV flight airspace capacity is expressed as: Where st means constrained by.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the method according to any one of claims 3 to 8.

10. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is run on a computer, the steps of the method according to any one of claims 3 to 8 are executed.

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