Urban unmanned aerial vehicle distribution network reliability evaluation method

Abstract

The invention relates to an urban unmanned aerial vehicle distribution network reliability evaluation method, and belongs to the technical field of unmanned aerial vehicle reliability. The method comprises the following steps: inputting basic data of an urban unmanned aerial vehicle distribution network; calculating the number of unmanned aerial vehicles required by commodity orders of a delivery unit and the number of orders loaded when the unmanned aerial vehicles are fully loaded and not fully loaded; calculating the load of the unmanned aerial vehicle; determining an unmanned aerial vehicle number distribution matrix; calculating an effective airline load state matrix; searching a set of feasible power consumption state vectors; and calculating the reliability of the urban unmanned aerial vehicle distribution network. According to the method for evaluating the reliability of the service capability of the urban unmanned aerial vehicle distribution network in the uncertain weather environment, the reliability level of completing a commodity order distribution task by the urban unmanned aerial vehicle distribution network can be accurately evaluated.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicle reliability and relates to a reliability assessment method for a city unmanned aerial vehicle delivery network. Background Art

[0002] With the vigorous development of low-altitude economic industries and urban low-altitude logistics industries, drones have begun to be used in urban logistics services. As an effective supplement to urban ground transportation capacity, small drones have gradually been widely used in urban logistics distribution, forming an urban drone delivery network. Drone delivery has the advantages of low cost, fast speed, green and low carbon, and is not easily affected by ground obstacles, and will not cause delivery delays due to congestion in urban ground transportation networks. Although the use of drones for package delivery has the above advantages, due to the limited battery capacity of drones, the payload is limited, the navigation range and flight time are limited, and in the actual delivery process, it will inevitably be affected by the uncertain weather environment outside. This makes it necessary to study whether the urban drone delivery network can complete the delivery of a certain number of orders as scheduled. Therefore, it is necessary to comprehensively consider the influence of uncertain weather environment and battery capacity constraints to study the reliability of the urban drone delivery network.

[0003] The urban drone delivery network consists of different flight segments and routes. A flight segment refers to a fixed section between two points in the delivery network for drones to fly. A route refers to a flight route from the supply point to the intermediate node and then to the demand point, which is composed of several interconnected flight segments. Due to the existence of no-fly zones and obstacles, the drone route is not a single segment from the supply point to the demand point. Considering the route safety (ground personnel safety, ground facility safety, etc.), the reliability of the operating environment (electromagnetic environment, meteorological environment, etc.), the reliability of the operating technology (navigation capability, communication performance, etc.) and the public acceptability (privacy factors, noise factors, etc.), the route of the urban drone delivery network is determined after scientific demonstration and careful evaluation, and is usually composed of several fixed flight segments. In addition, during the actual flight of the drone, due to the influence of various factors such as wind direction, wind speed, air humidity, and ambient temperature, there is a certain difference between the actual power consumption and the ideal situation. The power consumption coefficient is used to characterize the difference between the actual power consumption and the ideal power consumption of the drone during the flight. In particular, climate factors such as wind direction, wind speed, air humidity, and ambient temperature are constantly changing with the flight segment and time (different flight segments have different weather environments at the same time, and the same flight segment has different weather environments at different times). As a result, the power consumption of the drone during flight (characterized by the power consumption coefficient) is not a fixed value, but a random variable, that is, the power consumption coefficient has multiple states and obeys a certain probability distribution. Therefore, the urban drone delivery network can be regarded as a multi-state network. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a reliability assessment method for urban drone delivery network.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A reliability assessment method for urban drone delivery network, comprising the following steps:

[0007] S1. Input basic data of the urban drone delivery network, including at least order information, drone information and route information, where the route information at least includes the power consumption state vector of each flight segment;

[0008] S2. Calculate the number of drones required for delivery and the number of orders loaded when the drones are fully loaded and partially loaded according to the order information and drone information;

[0009] S3, calculating the load of the drone when it is fully loaded and when it is not fully loaded, respectively, according to the number of orders loaded when the drone is fully loaded and when it is not fully loaded;

[0010] S4, determining the number of drones allocation matrix based on the number of drones and route information;

[0011] S5. Determine the effective route load state matrix according to the UAV quantity allocation matrix and the load of the UAV when fully loaded and not fully loaded;

[0012] S6. Verify the effective route load state vector in the effective route load state matrix according to the power consumption state vector of each flight segment, and place the feasible power consumption state vector into the feasible power consumption state vector set;

[0013] S7. Calculate the reliability level of the delivery task based on the final feasible power consumption state vector set.

[0014] Further, in step S1, the order information includes: the number of commodity orders d delivered from the supply location s to the demand location t, and the average weight η of each unit order;

[0015] Drone information includes: drone weight m, drone maximum load Q load , UAV battery capacity W battery , average flight speed v of the UAV, number of UAV rotors τ, and rotor area δ of the UAV;

[0016] The route information includes: gravitational acceleration g, air fluid density ρ, and the definition B(N,A,E) represents an urban drone delivery network with a single supply location s and a single demand location t, where N = {s,1,2,…n,t} represents the set of network nodes, 1,2,…n represents the intermediate nodes between the supply location s and the demand location t; define A = {a1,a2,…,a I} represents the set of all flight segments in the network, a i represents the i-th flight segment, i = 1, 2, ..., I, where I represents the total number of flight segments in the network, and a route is a valid flight route consisting of several flight segments from s to t. The j-th route in the network is represented by G j ,j=1,2,…,J, J represents the total number of delivery network routes; define i=(e1,e2,…,e I ) represents the power consumption state vector composed of the power consumption coefficients of each flight segment, e i Indicates that the drone is in the i-th flight segment a i The power consumption coefficient on the , i = 1, 2, ..., I; e i The value of k i state, take h i1 arrive A value between where h ig Represents e i The g-th state, g=1,2,…,k i , e i The corresponding state probability is expressed as pr(e i ), the set of all power consumption state vectors E, expressed as

[0017] Further, in step S2, the number of drones K required to deliver d units of commodity orders, as well as the number of orders d loaded when the drone is fully loaded and partially loaded are calculated. 0 and d 1 ,in,

[0018] First calculate the maximum number of orders that a drone in a "fully loaded state" can carry Where Q load represents the maximum load of the drone, η represents the average weight of each unit order, Indicates rounding down;

[0019] Then according to the maximum number of orders that the drone in the "full load state" can carry, d 0 Calculate the number of drones needed to deliver d units of orders in, Indicates rounding up;

[0020] Finally, calculate the number of orders that the drone in the "not fully loaded state" can carry d 1 =d-(K-1)d 0 .

[0021] Further, in step S3, the order weight q of the drone in the "fully loaded state" is calculated first. 0 =ηd 0 , where η represents the average weight of each unit order, d 0 Indicates the number of orders loaded by the drone in "full load state";

[0022] Then calculate the order weight q of the drone in the "not fully loaded state" 1 =ηd 1 , where d 1 Indicates the number of orders that are loaded by the drone in "not fully loaded state".

[0023] Further, in step S4, first calculate the condition satisfying f1+f2+…+f J =K UAV quantity allocation vector F = (f1,f2,…,f J ), where f j represents the number of drones on the jth route, j = 1, 2, ..., J; all drone quantity allocation vectors F form a matrix, that is, the drone quantity allocation matrix, which is expressed as Δ = [F1, F2, ..., F L ] T , where F l represents the l-th UAV quantity allocation vector, l = 1, 2, …, L, and L represents the number of vectors in the matrix Δ.

[0024] Further, in step S5, the number of drones is allocated to the matrix Δ = [F1, F2, ..., F L ] T Transformed into the route load state matrix Ω = [Q1, Q2, ..., Q R ] T , where F l represents the l-th UAV quantity allocation vector, l=1,2,…,L, L represents the number of vectors in the matrix Δ, Q r represents the r-th route load state vector in the matrix Ω, r=1,2,…,R, R represents the number of vectors in the matrix Ω;

[0025] A drone quantity allocation vector F = (f1, f2, ..., f J ) corresponds to one or more route load state vectors Q = (q1, q2, ..., q J ), use q j represents the load status of the key UAV on the jth route, and:

[0026]

[0027] Among them, when f j = 0, there is no UAV on the route, that is, the UAV load is zero, then q j =0; when f j >1, the number of drones on the route is greater than 1, and the key drone on the route is the fully loaded drone, that is, q j =q 0 When f j = 1, there is only one drone on the route. If d 1 = 0, the drone is fully loaded and its load is q j =q 0 If d 1 >0, the drone is not fully loaded and its load is q j =q 1 ;

[0028] The route load state matrix Ω = [Q1, Q2, ..., Q R ] T Finally, compare the size of each row vector in the matrix Ω, find the smallest vector Q in the matrix Ω, and form the effective route load state matrix Ω * ,Ω * is a square matrix with J rows and J columns, where J represents the matrix Ω * China Route G j The number of, also referred to as the matrix Ω * The effective route load state vector The number of, j = 1, 2, ..., J, matrix Ω * Each row in represents a valid route load state vector Each column in represents the load status of the key UAV on a route, where the jth route G j The load status of the key UAV q j Value q 0 or q 1 ; Except for the jth route G j In addition, the load of the key drone on the rest of the routes is 0. If there is only one drone in the distribution network and it is not fully loaded, the effective route load matrix is:

[0029]

[0030] Otherwise, the effective route load matrix is:

[0031]

[0032] Further, in step S6, for each E in the set s, in the matrix Ω *Search so that on the jth route G j The power consumption of the key drone meets The row vector of If the matrix Ω * If there is no row vector that satisfies the condition, then E is an infeasible power consumption state vector; if the matrix Ω * If there is a row vector that satisfies the conditions, then E is a feasible power consumption state vector, and E is placed in the set U, that is:

[0033]

[0034] in:

[0035]

[0036] Where D i Indicates flight segment a i distance, Indicates that the drone is in segment D i Flight time on.

[0037] Further, in step S7, for all feasible power consumption state vectors E=(e1, e2, …, e I ), let Rel d =∑ E∈U Pr(E), where That is, the reliability level Rel of the drone delivery network to complete the delivery task of d units of commodity orders is obtained d .

[0038] The beneficial effects of the present invention are:

[0039] The present invention regards the drone delivery network as a multi-state network and provides a method for evaluating the reliability of the service capabilities of the urban drone delivery network under uncertain weather environments. By using the technology provided by the present invention, the different power consumption caused by the influence of various factors such as wind direction, wind speed, air humidity, and ambient temperature on different sections of the delivery drone route is taken into account, and then energy consumption analysis is performed in combination with the number of orders, the number of drones, and the load, so as to accurately evaluate the reliability level of the urban drone delivery network in completing the commodity order delivery task.

[0040] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0042] Figure 1 It is an overall flow chart of a reliability assessment method for a city UAV delivery network of the present invention;

[0043] Figure 2 The figure is a schematic diagram of a network structure of a specific example of the present invention. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0045] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0046] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0047] See also Figure 1-2 , which is a reliability assessment method for urban drone delivery networks.

[0048] like Figure 1 The overall flow chart of a reliability assessment method for a city drone delivery network shown in FIG. 1 includes the following steps:

[0049] S1. Input basic data of the urban drone delivery network, including at least order information, drone information and route information, where the route information at least includes the power consumption state vector of each flight segment;

[0050] S2. Calculate the number of drones required for delivery and the number of orders loaded when the drones are fully loaded and partially loaded according to the order information and drone information;

[0051] S3, calculating the load of the drone when it is fully loaded and when it is not fully loaded, respectively, according to the number of orders loaded when the drone is fully loaded and when it is not fully loaded;

[0052] S4, determining the number of drones allocation matrix based on the number of drones and route information;

[0053] S5. Determine the effective route load state matrix according to the UAV quantity allocation matrix and the load of the UAV when fully loaded and not fully loaded;

[0054] S6. Verify the effective route load state vector in the effective route load state matrix according to the power consumption state vector of each flight segment, and place the feasible power consumption state vector into the feasible power consumption state vector set;

[0055] S7. Calculate the reliability level of the delivery task based on the final feasible power consumption state vector set.

[0056] Example

[0057] This embodiment describes in detail a method for evaluating the reliability of a city drone delivery network according to the present invention with reference to a specific example:

[0058] A specific example is Figure 2 As shown, Figure 2 It is a network diagram obtained by abstracting a city drone delivery network. There are 4 nodes in the network, namely N = {s, 1, 2, t}, where node s represents the commodity supply location, node 1 and node 2 represent the intermediate nodes, and node t represents the commodity demand location. There are 5 flight segments in the network, and the set of flight segments is represented by A = {a1, a2, a3, a4, a5}, a i Indicates the i-th flight segment, route G j The network consists of several interconnected flight segments. There are three routes in the network, namely G1 = {a1, a4}, G2 = {a2, a3, a4}, and G3 = {a2, a5}. Table 1 gives the distance, power consumption coefficient and corresponding probability distribution of each flight segment in the network. Among them, the power consumption coefficient e i It represents the difference between the actual power consumption and the ideal power consumption of the drone during flight, then e i =(w 实际 -w 理想 ) / w 理想, assuming that the power consumption under ideal conditions is w 理想 , then in the actual flight process, the actual power consumption is (1+e i ) 理想 The extra power consumed is the extra work done by the drone to resist the influence of weather and environment. i The value of is based on the UAV’s flight segment a i The historical data of actual work done is estimated through statistical analysis. Similarly, the probability distribution corresponding to the power consumption coefficient is also obtained based on the statistical analysis of historical data.

[0059] Table 1

[0060]

[0061] Next, the reliability of the city’s UAV delivery network is calculated using the method of the present invention to solve Rel 10 Here are the steps:

[0062] 1) Input basic data of urban drone delivery network:

[0063] The number of commodity orders delivered from the supply location s to the demand location t is d = 10, the average weight of each unit order is η = 0.4 kg, the drone's own weight is m = 3 kg, and the drone's maximum load is Q load =2.5kg, drone battery capacity W battery = 0.27 kWh, average flight speed of the drone v = 36 km / h, number of drone rotors τ = 6, and rotor area of ​​the drone δ = 0.0064 m 2 , gravitational acceleration g = 9.81N / kg, air fluid density ρ = 1.204kg / m 3 , the set of all power consumption coefficients E = (e1, e2, e3, e4, e5) S = {(e1, e2, e3, e4, e5) | h i1 ≤e i ≤h i3 ,1≤i≤5}, the specific data are shown in Table 1;

[0064] 2) Calculate the number of drones K required to complete the delivery of d units of commodity orders, as well as the number of orders d loaded when the drone is fully loaded and partially loaded 0 and d 1 :First calculate the number of orders loaded by the drone in the "full load state" Then calculate the number of drones needed to complete d = 10 units of orders Finally, calculate the number of orders d loaded by the drone in the "not fully loaded state" 1 =d-(K-1)d 0 =4;

[0065] 3) Calculate the load of the drone: First calculate the order weight q of the drone in the "fully loaded state" 0 =ηd 0 =0.4×6=2.4kg, and then calculate the order weight q of the drone in the "not fully loaded state" 1 =ηd 1 =0.4×4=1.6kg;

[0066] 4) Determine the drone quantity allocation matrix Δ: First calculate the drone quantity allocation vector F = (f1, f2, f3) that satisfies f1 + f2 + f3 = 2, a total of 6, and combine all drone quantity allocation vectors F to form a drone quantity allocation matrix:

[0067]

[0068] 5) Calculate the effective route load state matrix Ω * :According to the conversion relationship The number of drones on each route f j Transformed into the load status q of key UAVs on the route j , that is, convert the UAV quantity allocation vector F = (f1, f2,, f3) into the route load state vector Q = (q1, q2, q3), and convert the UAV quantity allocation matrix Converted into route load state matrix Then compare the size of each row vector Q in the matrix Ω, find the smaller vector in the matrix to form a new matrix, that is, the effective route load state matrix

[0069] 6) Find the set U of feasible power consumption state vectors:

[0070] For the set S = {(e1,e2,e3,e4,e5)|h i1 ≤e i ≤h i3 ,1≤i≤5}, each E=(e1,e2,…,e5), verification matrix Ω * Is there a row vector in Make sure that the load is in the effective route Next, the jth (j=1,2,3) route G j The power consumption of the key drone meets If it holds, then E is a feasible power consumption state vector, and E is put into the set U, that is, Where D i Indicates flight segment a i distance, v represents the average flight speed of the drone, given by Calculate the drone’s flight segment Di The flight time on the ground, g represents the acceleration due to gravity, ρ represents the air fluid density, δ represents the rotor area of ​​the drone, τ represents the number of rotors of the drone, m represents the weight of the drone, and q j represents the load status of the UAV on the jth route. Since the load of the UAV remains unchanged during the flight, the flight segments a of the UAV on the jth route i The load on the same ij =q j , so according to Calculate the power of the drone in each flight segment, and then use w ij =p ij ×t calculates the ideal work done by the UAV in each flight segment, and finally Calculate the actual work done by the drone on each route, that is, the power consumption. For example, the steps to verify whether E1 = (0.1, 0.1, 0.1, 0.1, 0.1) is a feasible power consumption state vector are as follows: The first row vector in Calculate the load state vector of the key UAV in the effective route In the case of the first route G1 = {a1, a4}, the power consumption is q1 = 2.4.

[0071]

[0072] Therefore, E1 = (0.1, 0.1, 0.1, 0.1, 0.1) is a feasible power consumption state vector. If Then calculate Is it less than or equal to W? battery Established until one is found Then E1 is a feasible power consumption state vector, otherwise E1 is not a feasible power consumption state vector, repeat the above steps, and then verify E2, E3, …, E 243 Whether it is a feasible power consumption state vector, we can find all feasible power consumption state vectors, a total of 135, forming a set U;

[0073] 7) Calculate the reliability of the urban drone delivery network Rel 10 :For the 135 feasible power consumption state vectors E=(e1,e2,…,e5) in the set U, let Rel 10 =∑ E∈U Pr(E), where Then calculate Rel 10The specific steps are as follows: first calculate Pr(E1)=pr(e1)×pr(e2)×pr(e3)×pr(e4)×pr(e5)=0.70×0.82×0.75×0.72×0.80=0.247968, then calculate Pr(E2)=pr(e1)×pr(e2)×pr(e3)×pr(e4)×pr(e5)=0.20×0.82×0.75×0.72×0.80=0.070848, until the probability Pr(E) corresponding to 135 E=(e1, e2,…, e5) is calculated, and all Pr(E) are accumulated to get Rel 10 =0.9680, that is, the reliability level of the drone delivery network in completing the delivery task of 10 units of commodity orders is 0.9680.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A reliability assessment method for urban drone delivery network, characterized by: It includes the following steps: S1. Input basic data of the urban drone delivery network, including at least order information, drone information and route information, where the route information at least includes the power consumption state vector of each flight segment; S2. Calculate the number of drones required for delivery and the number of orders loaded when the drones are fully loaded and partially loaded according to the order information and drone information; S3, calculating the load of the drone when it is fully loaded and when it is not fully loaded, respectively, according to the number of orders loaded when the drone is fully loaded and when it is not fully loaded; S4, determining the number of drones allocation matrix based on the number of drones and route information; S5. Determine the effective route load state matrix according to the UAV quantity allocation matrix and the load of the UAV when fully loaded and not fully loaded; S6. Verify the effective route load state vector in the effective route load state matrix according to the power consumption state vector of each flight segment, and place the feasible power consumption state vector into the feasible power consumption state vector set; S7. Calculate the reliability level of the delivery task based on the final feasible power consumption state vector set.

2. A reliability assessment method for urban drone delivery network according to claim 1, characterized in that: In step S1, the order information includes: the quantity d of commodity orders delivered from the supply location s to the demand location t, and the average weight η of each unit order; Drone information includes: drone weight m, drone maximum load Q load , UAV battery capacity W battery , average flight speed v of the UAV, number of UAV rotors τ, and rotor area δ of the UAV; The route information includes: gravitational acceleration g, air fluid density ρ, and the definition B(N,A,E) represents an urban drone delivery network with a single supply location s and a single demand location t, where N = {s,1,2,…n,t} represents the set of network nodes, 1,2,…n represents the intermediate nodes between the supply location s and the demand location t; define A = {a1,a2,…,a I } represents the set of all flight segments in the network, a i represents the i-th flight segment, i = 1, 2, ..., I, where I represents the total number of flight segments in the network, and a route is a valid flight route consisting of several flight segments from s to t. The j-th route in the network is represented by G j ,j=1,2,…,J, J represents the total number of delivery network routes; define e=(e1,e2,…,e I ) represents the power consumption state vector composed of the power consumption coefficients of each flight segment, e i Indicates that the drone is in the i-th flight segment a i The power consumption coefficient on, i = 1, 2, ..., i; e i The value of k i state, take h i1 arrive A value between where h ig Represents e i The g-th state, g=1,2,…,k i , e i The corresponding state probability is expressed as pr(e i ), the set of all power consumption state vectors E, expressed as 3. A reliability assessment method for urban drone delivery network according to claim 2, characterized in that: In step S2, the number of drones K required to deliver d units of commodity orders, as well as the number of orders d loaded when the drone is fully loaded and partially loaded, are calculated. 0 and d 1 ,in, First calculate the maximum number of orders that can be loaded on a drone in a "fully loaded state" Where Q load represents the maximum load of the drone, η represents the average weight per unit order, Indicates rounding down; Then according to the maximum number of orders that the drone in "full load state" can carry, d 0 Calculate the number of drones needed to deliver d units of orders in, Indicates rounding up; Finally, calculate the number of orders that the drone in the "not fully loaded state" can carry d 1 =d-(K-1)d 0 .

4. A reliability assessment method for urban drone delivery network according to claim 3, characterized in that: In step S3, the order weight q of the drone in the "full load state" is calculated first. 0 =ηd 0 , where η represents the average weight of each unit order, d 0 Indicates the number of orders loaded by the drone in "full load state"; Then calculate the order weight q of the drone in the "not fully loaded state" 1 =ηd 1 , where d 1 Indicates the number of orders that are loaded on the drone in "not fully loaded" state.

5. A reliability assessment method for urban drone delivery network according to claim 4, characterized in that: In step S4, first calculate the condition that satisfies f1+f2+…+f J =K UAV quantity allocation vector F = (f1,f2,…,f J ), where f j represents the number of drones on the jth route, j = 1, 2, ..., J; all drone quantity allocation vectors F form a matrix, that is, the drone quantity allocation matrix, which is expressed as Δ = [F1, F2, ..., F L ] T , where F l represents the l-th UAV quantity allocation vector, l = 1, 2, …, L, and L represents the number of vectors in the matrix Δ.

6. A reliability assessment method for urban drone delivery network according to claim 5, characterized in that: In step S5, the number of drones is allocated to the matrix Δ = [F1, F2, ..., F L ] T Transformed into the route load state matrix Ω = [Q1, Q2, ..., Q R ] T , where F l represents the l-th UAV quantity allocation vector, l=1,2,…,L, L represents the number of vectors in the matrix α, Q r represents the r-th route load state vector in the matrix Ω, r=1,2,…,R, R represents the number of vectors in the matrix Ω; A drone quantity allocation vector F = (f1, f2, ..., f J ) corresponds to one or more route load state vectors Q = (q1, q2, ..., q J ), use q j represents the load status of the key UAV on the jth route, and: j=1,2,…,J Among them, when f j = 0, there is no UAV on the route, that is, the UAV load is zero, then q j =0; when f j >1, the number of drones on the route is greater than 1, and the key drone on the route is the fully loaded drone, that is, q j =q 0 When f j = 1, there is only one drone on the route. If d 1 = 0, the drone is fully loaded and its load is q j =q 0 If d 1 >0, the drone is not fully loaded and its load is q j =q 1 ; The route load state matrix Ω = [Q1, Q2, ..., Q R ] T Finally, compare the size of each row vector in the matrix Ω, find the smallest vector Q in the matrix Ω, and form the effective route load state matrix Ω * ,Ω * is a square matrix with J rows and J columns, where J represents the matrix Ω * China Route G j The number of, also referred to as the matrix Ω * The effective route load state vector The number of, j = 1, 2, ..., J, matrix Ω * Each row in represents a valid route load state vector Each column in represents the load status of the key UAV on a route, where the jth route G j The load status of the key UAV q j Value q 0 or q 1 ; Except for the jth route G j In addition, the load of the key drone on the rest of the routes is 0. If there is only one drone in the distribution network and it is not fully loaded, the effective route load matrix is: Otherwise, the effective route load matrix is:

7. A reliability assessment method for urban drone delivery network according to claim 6, characterized in that: In step S6, for each E in the set S, in the matrix Ω * Search so that on the jth route G j The power consumption of the key drone meets The row vector of If the matrix Ω * If there is no row vector that satisfies the condition, then E is an infeasible power consumption state vector; if the matrix Ω * If there is a row vector that satisfies the conditions, then E is a feasible power consumption state vector, and E is placed in the set U, that is: in: Where D i Indicates flight segment a i distance, Indicates that the drone is in segment D i Flight time on.

8. The reliability assessment method of a city drone delivery network according to claim 7 is characterized by: In step S7, for all feasible power consumption state vectors E=(e1, e2, …, e I ), let Rel d =∑ E∈U Pr(E), where That is, the reliability level Rel of the drone delivery network to complete the delivery task of d units of commodity orders is obtained d .

Images