CN116300990A

CN116300990A - Time planning method for collaborative search and rescue of helicopter and unmanned aerial vehicle in low-altitude environment

Info

Publication number: CN116300990A
Application number: CN202211444303.3A
Authority: CN
Inventors: 张明; 孙丽超; 刘旭
Original assignee: Nanjing University of Aeronautics and Astronautics
Current assignee: Nanjing University of Aeronautics and Astronautics
Priority date: 2022-11-18
Filing date: 2022-11-18
Publication date: 2023-06-23
Anticipated expiration: 2042-11-18
Also published as: CN116300990B

Abstract

The embodiment of the invention discloses a time planning method for collaborative search and rescue of a helicopter and an unmanned aerial vehicle in a low-altitude environment, relates to the technical field of unmanned aerial vehicle track planning, and can optimize an address selection mode under the cooperation of a carrier helicopter and the unmanned aerial vehicle so as to reduce address selection cost. The invention comprises the following steps: acquiring site selection condition parameters, and establishing an unmanned aerial vehicle take-off and landing point site selection model in a collaborative search and rescue scene according to the site selection condition parameters; acquiring an address selection result of the unmanned aerial vehicle take-off and landing point by using the unmanned aerial vehicle take-off and landing point address selection model; obtaining subtasks corresponding to the address selection result through a task distribution model; and determining the task completion time of the unmanned aerial vehicle through the search and rescue time model. The invention is suitable for the collaborative search and rescue of the helicopter and the unmanned aerial vehicle in the low-altitude environment.

Description

Time planning method for collaborative search and rescue of helicopter and unmanned aerial vehicle in low-altitude environment

Technical Field

The invention relates to the technical field of unmanned aerial vehicle track planning, in particular to a time planning method for collaborative search and rescue of a helicopter and an unmanned aerial vehicle in a low-altitude environment.

Background

The existing modes under the cooperation of the carrier and the unmanned aerial vehicle can be roughly divided into two modes: the cooperation of the carrier and the single unmanned aerial vehicle is in cooperation with the carrier and the multiple unmanned aerial vehicles. However, whichever collaboration method has more or less drawbacks, such as: in the past modeling to unmanned aerial vehicle take-off and landing point site selection, in order to simplify the problem and conveniently solve, take-off and landing points of unmanned aerial vehicle are mostly set to the same position, and the situation that the unmanned aerial vehicle can land nearby after the search and rescue task is executed in actual flight is not considered, so that site selection cost is high is avoided. And the scheme that the flying spot and the landing spot are arranged at the same position can also lead to that the unmanned aerial vehicle must return to the flying spot to perform the next task after executing the task each time, and the execution efficiency of the continuous task is low.

In general, studies on the problem of the efficiency of the cooperation of the drone and the carrier have led to some preliminary solutions, but there are also some problems, such as: in the existing collaborative mode, only a single unmanned aerial vehicle or a few unmanned aerial vehicles are considered to execute tasks, and in the low-altitude search and rescue mode, more unmanned aerial vehicles are often needed to participate, so that the site selection cost for realizing collaborative search and rescue of a helicopter and the unmanned aerial vehicles is high;

therefore, how to optimize the site selection mode under the cooperation of the carrier helicopter and the unmanned aerial vehicle in the low-altitude environment and in the process of executing the collaborative search and rescue of the helicopter and the unmanned aerial vehicle, so that the site selection cost is reduced, and the problem of further research is solved.

Disclosure of Invention

The embodiment of the invention provides a time planning method for collaborative search and rescue of a helicopter and an unmanned aerial vehicle in a low-altitude environment, which can optimize a site selection mode under the cooperation of the carrier helicopter and the unmanned aerial vehicle so as to reduce site selection cost.

In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:

s1, acquiring address selection condition parameters, and establishing an unmanned aerial vehicle take-off and landing point address selection model in a collaborative search and rescue scene according to the address selection condition parameters, wherein the unmanned aerial vehicle take-off and landing point address selection model is established with the lowest address selection cost as a target;

S2, acquiring an address selection result of the unmanned aerial vehicle take-off and landing point by using the unmanned aerial vehicle take-off and landing point address selection model;

s3, obtaining subtasks corresponding to the address selection result through a task allocation model;

s4, determining task completion time of the unmanned aerial vehicle through the search and rescue time model.

According to the time planning method for collaborative search and rescue of the helicopter and the unmanned aerial vehicle in the low-altitude environment, which is provided by the embodiment of the invention, aiming at the problem of high site selection cost in collaborative search and rescue of the helicopter and the unmanned aerial vehicle, an unmanned aerial vehicle take-off and landing site selection model for collaborative search and rescue is established, the influence of environmental temperature change, the load of the unmanned aerial vehicle and the flight state of the unmanned aerial vehicle on the endurance of the unmanned aerial vehicle is considered in the model, and the unmanned aerial vehicle can not be required to take off and land on the same point in constraint. The improved model has reduced site selection cost compared with the prior research.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of a local take-off and landing mode of an unmanned aerial vehicle for collaborative search and rescue;

fig. 2 is a schematic diagram of a landing mode of an unmanned aerial vehicle for collaborative search and rescue;

FIG. 3 is a schematic diagram of a temperature-battery actual capacity fit curve (measured scatter plot versus three types of fit curves);

FIG. 4 is a schematic diagram of the result of setting weighted distance allocation;

FIG. 5 is a schematic diagram of the result of distance allocation without weights;

FIG. 6 is a diagram of weighted distance calculation;

FIG. 7 is a schematic diagram of case 1;

FIG. 8 is a schematic diagram of case 2;

FIG. 9 is a schematic diagram of case 3;

FIG. 10 is a schematic diagram of two chromosome crossover;

FIG. 11 is a schematic representation of chromosomal variations;

FIG. 12a is a diagram showing the comparison of the addressing costs of 30 points to be searched in one example;

FIG. 12b is a diagram showing the comparison of the addressing costs of 50 points to be searched in one example;

FIG. 12c is a diagram showing the comparison of the location costs of 80 points to be searched in one example;

FIG. 12d is a diagram showing the comparison of the costs of 100 candidate search and rescue addresses in one example;

FIG. 13 is a diagram illustrating a sixth set of subtask assignments for a 100-point to be searched in one example;

FIG. 14 is a schematic diagram of collaborative search and rescue at a scale of 100 points to be searched and rescue in one example;

fig. 15 and 16 are schematic flow diagrams of the method provided in this embodiment.

Detailed Description

The present invention will be described in further detail below with reference to the drawings and detailed description for the purpose of better understanding of the technical solution of the present invention to those skilled in the art. Embodiments of the present invention will hereinafter be described in detail, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The embodiment of the invention provides a time planning method for collaborative search and rescue of a helicopter and an unmanned aerial vehicle in a low-altitude environment, which is used for a scene of collaborative search and rescue of the helicopter and the unmanned aerial vehicle in the low-altitude environment, wherein the search and rescue process is approximately as follows: the helicopter carries the unmanned aerial vehicle and starts from the starting point, and when the helicopter flies to the unmanned aerial vehicle take-off and landing point planned in advance, the helicopter releases the unmanned aerial vehicle to execute the search and rescue task under the group of subtasks, and when the unmanned aerial vehicle executes the search and rescue task, the helicopter continuously flies according to the planned flight path, so that the unmanned aerial vehicle is alternately released and recovered until the task is completed. The time planning in the search and rescue process is the main direction of design and improvement in the embodiment. The main improvement thought is to optimize the unmanned aerial vehicle take-off and landing point location model, and introduce parameters such as environmental temperature, unmanned aerial vehicle load, flight state and the like related to the endurance capacity and flight time of the unmanned aerial vehicle into the model so that the location model can be more accurate and fit with reality. The secondary is to improve the task planning of the unmanned aerial vehicle cluster after the site selection is completed, especially to realize that the cluster can simultaneously execute a plurality of tasks and estimate the execution time of two adjacent subtasks. Specifically, as shown in fig. 15 and 16, the method includes:

S1, acquiring site selection condition parameters, and establishing an unmanned aerial vehicle take-off and landing point site selection model in a collaborative search and rescue scene according to the site selection condition parameters.

And establishing the unmanned aerial vehicle take-off and landing point site selection model by taking the lowest site selection cost as a target.

S2, obtaining an address selection result of the unmanned aerial vehicle take-off and landing point by using the unmanned aerial vehicle take-off and landing point address selection model.

S3, obtaining the subtasks corresponding to the address selection result through a task distribution model.

In this embodiment, the influence of the flight state of the unmanned aerial vehicle on the endurance of the unmanned aerial vehicle needs to be considered, for example: in the process of low-altitude search and rescue by using the unmanned aerial vehicle, the unmanned aerial vehicle releases from the helicopter to fly to the position above a to-be-searched and rescue point along a planned route to hover and detect the task, and the unmanned aerial vehicle must complete the search and rescue and detect the task within the range of cruising ability to fly back to the helicopter to supplement energy. In the unmanned aerial vehicle search and rescue process, due to mountain terrain and search and rescue requirements, the unmanned aerial vehicle is required to carry detection equipment to hover above a point to be searched and rescue in order to meet detection requirements. The unmanned aerial vehicle can also generate energy loss in the hovering flight process, and the carrying equipment increases the total flight weight of the unmanned aerial vehicle and can influence the energy consumption of the unmanned aerial vehicle. Only the theoretical unmanned aerial vehicle endurance can not be considered when task planning is carried out, and the factors such as the weight of detection equipment carried by the unmanned aerial vehicle, the flight state of the unmanned aerial vehicle and the like are added into the constraint of the model. The planned result can meet the search and rescue requirements better on the basis, and the search and rescue purpose is achieved. Therefore, the power consumption of the unmanned aerial vehicle needs to be predicted in planning, wherein a power consumption model is designed as follows:

The power equation when the unmanned aerial vehicle hovers is represented by equation (1):

the power equation when the unmanned aerial vehicle flies horizontally is represented by formula (2):

wherein ρ is the air density, W (N) is the total weight,

is the rotor area, V _hor Is the speed of the horizontal flight of the unmanned aerial vehicle, alpha (V) _hor ) And eta _hor Is an empirical coefficient and can be calculated by using a least square method. Let the power of unmanned plane in horizontal flight under no-load condition be P _h ' the power during hovering is P _hor '. When the mass of the carried detection equipment is m _l The unmanned aerial vehicle has the mass of m _u W in the formulas (1) and (2) has the total mass of m _l +m _u At the moment, the hovering power of the unmanned aerial vehicle under the load condition is set to be P _h ", unmanned plane horizontal flight power is P _hor ". It can then be further defined that:

S＝vtσ ₁ (5)

S'＝vt'σ ₂ (6)

in formula (3) sigma ₁ The ratio of the power when the unmanned aerial vehicle hovers with a load to the power when the unmanned aerial vehicle flies horizontally without load is set; sigma (4) ₂ And the ratio of the power of the unmanned aerial vehicle when carrying the load to the unmanned aerial vehicle when carrying the unmanned aerial vehicle to the unmanned aerial vehicle. Let unmanned aerial vehicle horizontal flight time be t, hover time be t ', unmanned aerial vehicle's horizontal flight speed be v. And the horizontal flight time t of the unmanned aerial vehicle under the load condition corresponds to the flight distance S of the unmanned aerial vehicle under the standard condition and is shown by (5), the hovering time t 'of the unmanned aerial vehicle under the load condition corresponds to the flight distance S' of the unmanned aerial vehicle under the standard condition and is shown by (6).

On the basis of the power consumption model, an unmanned aerial vehicle take-off and landing point site selection model meeting the actual application requirements needs to be established, and the setting of the model mainly comprises the following steps: the points to be searched and rescued are determined, and each point to be searched and rescued only needs to be detected once; taking the influence caused by actual obstacle avoidance of the unmanned aerial vehicle into consideration, a reserved energy consumption coefficient can be set; the detection time of each point to be searched and rescuing is known, and the hovering time and the flight time of the unmanned aerial vehicle under the carrying load are converted into the range of the plane flight under the standard condition of the unmanned aerial vehicle (the unmanned aerial vehicle is unloaded, and the running environment temperature is 25 ℃); the specific states of the unmanned aerial vehicle during take-off and landing are ignored, and the take-off and landing of each unmanned aerial vehicle can be completed instantly by default; the unmanned aerial vehicle can automatically charge or replace the battery on the helicopter. Wherein, the variables and parameters mainly related to the unmanned aerial vehicle take-off and landing point site selection model can be listed as:

TABLE 1 unmanned aerial vehicle take-off and landing point site selection model parameter and variable table

Specifically, in S1, the obtained site selection condition parameters include: parameters for representing the load conditions of the unmanned aerial vehicle and the flight status of the unmanned aerial vehicle; the unmanned aerial vehicle taking into consideration the load of the unmanned aerial vehicle, the flight state (flying and hovering) of the unmanned aerial vehicle and the influence of the temperature change of the running environment on the endurance of the unmanned aerial vehicle, improves the site selection model in the past research, and establishes an unmanned aerial vehicle taking-off and landing site selection model for collaborative search and rescue of the helicopter and the unmanned aerial vehicle. Wherein, with the lowest addressing cost as an objective function, the addressing objective function in the unmanned aerial vehicle take-off and landing point addressing model is represented by formula (9):

Considering the load of the unmanned aerial vehicle, the energy consumption relation between the plane flight and the hovering and the influence of the environmental temperature on the endurance capacity of the unmanned aerial vehicle, and meeting the range requirement, the hovering of the unmanned aerial vehicle is represented according to formulas (3) and (4), the range of the unmanned aerial vehicle is obtained by solving the load, the range is represented by formula (10), and the formula (10) is a basic model of the total range.

S _b,k,l ＝(d _b,k +d _k,l )σ ₁ +t _k vσ ₂ ，b,l∈M',k∈M (10)

The influence of the environmental temperature on the endurance of the unmanned aerial vehicle can be further considered in the embodiment, for example: the power battery system is a key system in the unmanned aerial vehicle, the performance of the power battery system directly influences the safety of the power characteristics of the unmanned aerial vehicle, and the use performance of the battery can be influenced by the excessively high or excessively low ambient temperature in the use process of the battery. The lithium ion battery is widely used for a power system of the unmanned aerial vehicle due to high energy density, good cycle characteristics and excellent charge and discharge performance, and the adaptability of the lithium ion battery to temperature becomes one of key factors restricting the large-scale application of the unmanned aerial vehicle due to the complexity of the working environment of the unmanned aerial vehicle and the nonlinear characteristics of the lithium ion battery in a low-altitude search and rescue background. In unmanned aerial vehicle winter operation, unmanned aerial vehicle appears the problem that duration declines more, and the key reason that appears this condition is that the discharge volume of battery system itself is lower than discharging under the normal atmospheric temperature state winter, and electrolyte wherein is in between solidification and the semi-solidification state, leads to the lithium ion to carry out the process resistance that migrates great, and then leads to lithium ion activity relatively poor, discharge performance variation.

Therefore, it becomes very important to build a model of the unmanned aerial vehicle endurance and temperature, the performance of the lithium ion battery is affected by many factors, in this embodiment, only the influence of the environmental temperature on the unmanned aerial vehicle endurance is considered when the unmanned aerial vehicle is used for low-altitude search and rescue, in order to build a model of the unmanned aerial vehicle endurance changing with temperature, the actual battery capacity of the lithium ion battery at different operating temperatures is shown in table 2, and the standard battery capacity of the lithium ion battery is the battery capacity of the environmental temperature at 25 ℃. And obtaining the relation between the capacity change of the lithium battery and the temperature by using different fitting methods, and establishing a functional relation between the actual capacity of the lithium ion battery and the ambient temperature.

Table 2 battery capacity with ambient temperature change table

In a preferred embodiment, according to the actual operation result of the SPSS, three linear parameter tables for fitting the ambient temperature and the battery capacity in the actual measurement result are summarized as shown in table 3, and fig. 3 shows a fitting graph, in which the abscissa indicates the ambient temperature and the ordinate indicates the percentage of the battery capacity at the current temperature.

Table 3 temperature-battery actual capacity fitting parameter table

R in Table 3 ² The index reflects the interpretation of the independent variable by the dependent variable, and is usually preferably at least 70%. R is R ² The larger the model that is fitted, the greater the percentage of change in the dependent variable can be explained. Curve fitting R can be performed once from the above table ² 52.4%, quadratic curve fitted R ² 83.3%. Cubic curve fitting R ² 87.0% according to comparison R ² Therefore, the cubic curve fitting effect is best, and the relation of the actual capacity of the battery along with the change of the ambient temperature can be well simulated, so that the result of the cubic curve fitting is selected to represent the relation of the capacity of the battery along with the change of the temperature:

δ _x ＝0.98+6.72×10 ^-4 x+3.11×10 ^-5 x ² -8.27×10 ^-7 x ³ -20≤x≤25 (7)

S _x ”＝vt'δ _x (8)

the three-dimensional functional relation of the actual capacity of the lithium ion battery along with the temperature change is represented by a formula (7), wherein x represents the environmental temperature in the unmanned aerial vehicle search and rescue process, and delta is represented as the unit of the temperature _x The ratio of the actual capacity to the theoretical capacity of the battery at the ambient temperature x is expressed, and the physical meaning of the ratio is the ratio of the lithium ion battery capacity to the standard battery capacity at the current temperature. Setting the horizontal flight speed of the unmanned aerial vehicle as v and the maximum flight time as t', and setting the maximum endurance S of the unmanned aerial vehicle at the temperature x _x "represented by formula (8).

Therefore, according to the basic model of the total range and the running environment temperature of the unmanned aerial vehicle, a total range model related to the temperature can be established, and specifically, the model is further improved by combining the formula (7), and the improved total range model related to the temperature can reflect the requirement of the unmanned aerial vehicle on the endurance capability to be met when the environment temperature is x, as shown in the formula (11).

S _b,k,l ≤Sδ _x (1-β),b,l∈M',k∈M (11)

Wherein M represents a set of points to be searched and rescaled, and the points are alternatively selected as a set M ' of points of flying and falling points of the unmanned aerial vehicle, b represents the flying points, k represents the points to be searched and rescaled, l represents the falling points, c represents the flight cost of the unmanned aerial vehicle, and x ' ' _b,k Indicating whether the search and rescue point k is served by the take-off and landing point b of the alternative unmanned aerial vehicle, if yes, the search and rescue point k is 1, otherwise, the search and rescue point k is 0, p' _k,l Indicating whether the search and rescue point k is served by an alternative unmanned aerial vehicle take-off and landing point l, if yes, the search and rescue point k is 1, otherwise, the search and rescue point k is 0,S _b,k,l And (5) representing the total course required to be consumed by the unmanned aerial vehicle to fly from the point b to search and rescue the kth point to be searched and rescue to drop to the point l. d, d _b,k Representing the three-dimensional distance d from point b to point k of the unmanned aerial vehicle _k,l Representing the three-dimensional distance, sigma, of the unmanned aerial vehicle from point k to point l ₁ For the ratio of the power when the unmanned aerial vehicle hovers with a load to the power when the unmanned aerial vehicle flies horizontally without load, sigma ₂ The ratio of the power of the unmanned aerial vehicle when carrying the load to the unmanned aerial vehicle when carrying the unmanned aerial vehicle, t is t _k And the hovering time of the unmanned aerial vehicle at the point k to be searched and rescuing is represented, and v represents the speed of the unmanned aerial vehicle in constant speed flight. Further, the obtained site selection condition parameters further include: the operation environment temperature of the unmanned aerial vehicle, wherein S represents the maximum driving mileage of the unmanned aerial vehicle, beta represents the energy consumption reservation coefficient, x represents the operation environment temperature of the unmanned aerial vehicle, delta _x The relationship between the battery capacity and the temperature change is shown.

It should be noted that, in this embodiment, the unmanned aerial vehicle adopts a different-place take-off and landing mode, a plurality of groups of take-off and landing points of the unmanned aerial vehicle need to be selected, and according to the service mode setting conditions, if each point to be searched and searched is served by only one take-off and landing point of the unmanned aerial vehicle, the setting conditions can be represented by the formula (12); or if each point to be searched and rescuing is served by only one unmanned aerial vehicle landing point, the set condition can be represented by a formula (13); or if a group of unmanned aerial vehicle take-off and landing points serve a plurality of to-be-searched and rescuing points, and each to-be-searched and rescuing point is served by only one group of unmanned aerial vehicle take-off and landing points, the set condition can be represented by formula (14), and the number of the selected unmanned aerial vehicle take-off and landing points is equal to that of the unmanned aerial vehicle take-off and landing points, as shown in formula (15):

wherein x is _b Indicating whether the take-off and landing point b of the alternative unmanned aerial vehicle is selected, if so, the take-off and landing point b is 1, otherwise, the take-off and landing point b is 0; p is p _l Indicating whether the take-off and landing point l of the alternative unmanned aerial vehicle is selected, if so, the take-off and landing point l is 1, otherwise, the take-off and landing point l is 0; the unmanned aerial vehicle service that the unmanned aerial vehicle can only exist when b is selected as the unmanned aerial vehicle flying point and the unmanned aerial vehicle service that the unmanned aerial vehicle flies from the point is represented by formula (16), and the unmanned aerial vehicle service can only exist when l is selected as the unmanned aerial vehicle landing point and the unmanned aerial vehicle service finishes landing and is represented by formula (17):

x' _b,k ≤x _b ,b∈M',k∈M (16)

p' _k,l ≤p _l ,l∈M',k∈M (17)

And x' _b,k ，x _b ，p' _k,l ，p _l The variables are 0 and 1, and (18) indicates that the unmanned aerial vehicle flies from the point b to the point k to be searched and rescuing, and the variable k is 1, otherwise, the variable k is 0; (19) B is selected as the flying spot of the unmanned aerial vehicle and is 1, otherwise, is 0; equation (20) shows that the unmanned aerial vehicle flies back to the landing point l from the point k to be searched for and rescuing is 1, otherwise, is 0; equation (21) indicates that l is selected as the unmanned landing point and is 1, otherwise, 0.

x _b,k '＝{0,1},b∈M',k∈M (18)

x _b ＝{0,1},b∈M', (19)

p _k,l '＝{0,1},l∈M',k∈M (20)

p _l ＝{0,1},l∈M' (21)

In this embodiment, to the higher problem of site selection cost in helicopter and unmanned aerial vehicle collaborative search and rescue, establish the unmanned aerial vehicle take-off and landing point site selection model of collaborative search and rescue, considered the influence of environment temperature change, unmanned aerial vehicle's load and unmanned aerial vehicle flight status to unmanned aerial vehicle duration in the model to can not require unmanned aerial vehicle's take-off and landing on the constraint in same point. The improved model has reduced site selection cost compared with the prior research.

In practical application, in order to solve the total time of collaborative search and rescue of a helicopter and an unmanned aerial vehicle, unmanned aerial vehicle task allocation is required to be carried out on each group of subtasks in an unmanned aerial vehicle take-off and landing point address selection result, and the time required for completing each group of subtasks and the number of unmanned aerial vehicles required to complete the tasks are determined. The problem of cooperation of the truck and the unmanned aerial vehicle is often solved in the conventional allocation mode, and the track of the unmanned aerial vehicle and the route of the truck are not overlapped in space, but under the background of cooperation of the helicopter and the unmanned aerial vehicle, the unmanned aerial vehicle can frequently pass through the helicopter route in the operation process according to the conventional task allocation mode, so that the unmanned aerial vehicle and the helicopter collide. In order to solve the problem, a weight distance matrix among the points to be searched and rescuing is introduced in the process of solving task allocation, a task allocation model is built by taking the highest task equilibrium completion as an objective function on the basis, and the influence of the running environment temperature, the unmanned aerial vehicle flight state and the unmanned aerial vehicle load on the unmanned aerial vehicle endurance is considered in the model.

Therefore, in this embodiment, the problem of unmanned aerial vehicle task allocation and search and rescue total time estimation in the unmanned aerial vehicle off-site take-off and landing mode is also considered. Before the subtask corresponding to the address selection result is acquired in S3, the method further includes: acquiring weighted distances among the points to be searched and rescuing, and classifying all the points to be searched and rescuing by utilizing the weighted distances, wherein the classification mode comprises the following steps: the point to be searched and rescuing is positioned at the right side in the flight direction of the helicopter, and the point to be searched and rescuing is positioned at the left side in the flight direction of the helicopter; after classification is completed, distances between the to-be-searched and rescuing points of different categories are set to be infinity, and a weighted distance matrix of each category is established, wherein the three-dimensional distance between two points of the to-be-searched and rescuing points of the same category is recorded in the weighted distance matrix. For example: by introducing the weighted distance to classify the points to be searched and rescued in the subtasks, particularly under the background of taking off and landing of unmanned aerial vehicles in different places for collaborative search and rescue, when unmanned aerial vehicle task allocation is carried out on each subtask, the scheme hopes that the execution task sequence of the unmanned aerial vehicle and the helicopter route are consistent as shown in fig. 4, the dotted line in the box represents the points to be searched and rescued allocated to each unmanned aerial vehicle, but because the distribution of the points to be searched and rescued is not on one side of the helicopter route, in order to avoid the unmanned aerial vehicle to shuttle back and forth on the helicopter route as shown in fig. 5 when the unmanned aerial vehicle executes the tasks, the efficiency is influenced, the flight safety is influenced, the allocation result does not meet the safety requirement, and the scheme introduces the weighted distance between the points to be searched and rescued, for example:

Let A (0, 0), B (x) ₁ ,y ₁ ,0)、C(x ₂ ,y ₂ 0), A, B are unmanned aerial vehicle take-off and landing points under the task group, the connecting line of the two points forms the route of the helicopter, and the point C is the point to be searched and rescuing. As shown in fig. 6: AB represents the advancing direction of the helicopter and the vector is recorded

C is the vector of the position of the point to be searched for and rescued +.>

The scheme judges whether a certain point to be searched and resched is on the left side or the right side of a helicopter route by means of the cross multiplication of two vectors (the flight direction of the helicopter is not determined at the moment, and the point to be searched and resched is distinguished only by using the method), and the point to be searched and rescuing is obtained by a vector cross multiplication formula>

If x ₁ y ₂ -x ₂ y ₁ Not less than 0, wherein the vector AC is in the anticlockwise direction of the vector AB, which indicates that the point C to be searched and rescuing is at the left side of the flying direction of the helicopter, and vice versa, x ₁ y ₂ -x ₂ y ₁ And < 0 indicates that the point to be searched and rescuing is at the right side of the flight direction of the helicopter. According to the method, all the points to be searched and rescuing in the task are divided into two types. The processed points to be searched and rescued are divided into two types, so that the points to be searched and rescued on two sides of the helicopter are distinguished, distances between the points to be searched and rescued in different types are set to be infinite, the distances between the points to be searched and rescued in the same type are three-dimensional distances between the two points, a weighted distance matrix between the points to be searched and rescued is generated, and therefore after the distance matrix is set, the same helicopter cannot be allocated to the points to be searched and rescued in different types when unmanned aerial vehicle task allocation is solved.

After classifying the points to be searched, a task allocation model in a different-place take-off and landing mode of the unmanned aerial vehicle can be further established, and the establishment of the model mainly comprises the following steps: the coordinates of the points to be searched and rescuing in each subtask, the flying point of the unmanned aerial vehicle and the descending point of the unmanned aerial vehicle are known, and the three-dimensional distance between the points to be searched and rescuing is calculated according to the generated weighted distance matrix. Each unmanned aerial vehicle to be searched and rescued is only one unmanned aerial vehicle to search, and under the condition that the range of the unmanned aerial vehicle (taking the running environment temperature of the unmanned aerial vehicle, the load of the unmanned aerial vehicle and the flight state of the unmanned aerial vehicle into consideration) does not exceed the maximum range of the unmanned aerial vehicle, the unmanned aerial vehicle can search a plurality of points to be searched and rescued. The unmanned aerial vehicle flies at a constant speed. The detection time of each point to be searched and rescuing is known and is determined according to the disaster grade. The variables and parameters mainly related to the task allocation model can be listed as follows:

table 4 unmanned aerial vehicle task allocation model parameter table

Aggregation	Meaning of
		N	Aggregation of points to be searched and rescuing
N'	A subset of the points N to be searched and rescued, namely a set of points distributed by one unmanned plane to be searched and rescued
		K	Unmanned aerial vehicle path set
T	Aggregation of time to complete a mission for all unmanned aerial vehicles
		Variable(s)	Meaning of
x _i',j',k'	The kth '(K' ∈k) path from point i 'to point j' is 1, otherwise 0
		z _i',k'	If node i ' is on the kth ' (K ' e K) path then 1 is present, otherwise 0 is present
Z ₂	The difference between the maximum and minimum times for completion of a task in a set of tasks(s)
		w _i',j'	Time of flight(s) of the drone between points i' to j
u _k,i'	Representing the ith' node in unmanned path k
		b	Unmanned aerial vehicle flying spot in search and rescue task of group
l	Unmanned aerial vehicle falling point in search and rescue task of group

Specifically in S3, task allocation of the unmanned aerial vehicle is performed based on the address selection result of the take-off and landing points of the unmanned aerial vehicle, so as to establish a task objective function in the task allocation model, where the task objective function is represented by a formula (23) with task equalization completed as an objective function, where max (T _k' ) Indicating the longest time for the unmanned aerial vehicle to complete the search and rescue task, min (T _k' ) The shortest time for the unmanned aerial vehicle to complete search and rescue tasks is represented as T _k' Indicating completion of unmanned aerial vehicleTime of search and rescue task, Z ₂ Representing the difference between the maximum and minimum times for completing a task in a set of tasks;

Z ₂ ＝max(T _k' )-min(T _k' ),max(T _k' ),min(T _k' )∈T (23)

the constraints are: the time for each unmanned aerial vehicle to complete search and rescue is shown as a formula (24), the time is composed of flight time of the unmanned aerial vehicle and hovering time of the unmanned aerial vehicle at each point to be searched and rescue, the time required for the unmanned aerial vehicle to fly from point i 'to point j' is shown as a formula (25), b represents the corresponding unmanned aerial vehicle flying point in the subtask, and l represents the corresponding unmanned aerial vehicle landing point w in the subtask _b,l The time from the departure point to the landing point of the unmanned aerial vehicle is represented, and the time of subtracting the distance is the time for each unmanned aerial vehicle to complete search and rescue because the distance is flown by the helicopter.

Wherein N is a subset of N, N ' is used for representing the set of the to-be-searched and rescuing points distributed by one unmanned aerial vehicle, i ' and j ' respectively represent 2 different to-be-searched and rescuing points, and w _i',j' Representing the flight time of the unmanned aerial vehicle between points i 'to j', t _i' Representing the flight time, w, of the point i' to be searched for _b,l Representing landing points of corresponding unmanned aerial vehicles in subtasks, k' represents a kth search and rescue path, d _i',j' The three-dimensional distance from the point i 'to the point j' of the unmanned aerial vehicle is represented, and v represents the speed of the unmanned aerial vehicle in constant-speed flight; x is x _i',j',k' Indicating whether the kth '(K' e K) path can go from point i 'to point j', if so, 1, otherwise 0; z _i',k' Indicating whether the node i ' is on the kth ' (K ' e K) path, if yes, then 1, otherwise 0;

in addition, in task allocation, each point to be searched and rescued can only be completed by one unmanned aerial vehicle, namely, each point to be searched and rescued only exists in one path and is represented by a formula (26), the selected flying point of the unmanned aerial vehicle and the falling point of the unmanned aerial vehicle are necessarily existed in each path and are represented by formulas (27) and (28), and the starting point and the ending point of the path node are represented by a formula (29), so that a path must exist between the flying point of the unmanned aerial vehicle and the falling point of the unmanned aerial vehicle in a model for achieving the purpose, and the road section is not considered in the process of calculating the flight of the unmanned aerial vehicle and the time for completing search and rescue.

Wherein z is _b,k' Indicating that if the point b to be searched and rescuing is on the kth path, the point b is 1, otherwise, the point b is 0; z _l',k' Indicating that if the alternative unmanned aerial vehicle drop point l is on the kth' path, the alternative unmanned aerial vehicle drop point l is 1, otherwise, the alternative unmanned aerial vehicle drop point l is 0; x is x _b,l,k' And (4) indicating that the distance from the point b to be searched and rescuing in the unmanned aerial vehicle path k' to the unmanned aerial vehicle landing point l is 1, and otherwise, the distance is 0. Specifically, if there is a flight path from i 'to j' in the task allocation, a unmanned plane must be required to complete the task, so constraints are set as shown in equation (30), and to ensure the continuity of the node, i.e. access to the point j 'to be searched must leave the point j' to be searched, as shown in equation (31). Equation (32) indicates that the total range after each path consideration (temperature, load, hover, etc.) is not beyond the maximum flight range of the drone. In order to avoid generating sub-loop solutions, the scheme can also add linear relaxation into a task allocation model and introduce a free variable u _k',i' Representing the i 'th node in the drone path k'. Therefore, the constraints represented by the formulas (33), (34) are generated, N is the number of points contained in the set N'. U.b.l。

u _k',i' -u _k',j' +1≤(n-1)(1-x _i',j',k' )i',j'∈N'∪{b,l},k'∈K (33)

1≤u _k',i' ≤n-1 (34)

Equation (35) shows that only if point i 'exists in the path of k', there exists a route from i 'to j'. Formulas (36) - (37) represent the value ranges of the variables, and formula (36) represents that the point i 'to be searched for and rescuing is set to 1 if in the kth path, otherwise, the point i' to be searched for and rescuing is set to 0; equation (37) indicates that the flight paths for i ' through j ' in the kth ' path are set to 1, otherwise to 0.

Further, in order to improve the problem that only a single subtask can be executed at the same time in collaborative search and rescue of a traditional helicopter and an unmanned aerial vehicle, the search and rescue time model is improved on the basis of meeting the performance of the helicopter, so that the situation that two subtasks are executed simultaneously possibly exists is conveniently applied, and the setting of the model mainly comprises the following steps: after the unmanned aerial vehicle falls onto the helicopter, the time for energy supply is ignored; the unmanned aerial vehicle takes off each time to be full electric quantity; the completion time of each subtask and the number of unmanned aerial vehicles required are known, and the coordinates of the take-off and landing points of the unmanned aerial vehicles for completing each subtask are known; ignoring the dynamic characteristics of a part of unmanned aerial vehicles, wherein the take-off and landing of the unmanned aerial vehicles are completed instantaneously; the helicopter keeps constant speed during flying. The variables and parameters mainly related to the search and rescue time model can be listed as follows:

table 5 solve the search and rescue total time model parameter table

In practical application, how to determine the execution sequence of each subtask on the basis of completing unmanned aerial vehicle take-off and landing point address selection and unmanned aerial vehicle task allocation work, and the shortest total time for completing the whole search and rescue task on the basis of meeting the performance of a helicopter are the key of the whole search and rescue task. Specifically, set s _i Represents the time (i.e., the completion time of sub-task i minus the length of time two groups of sub-tasks intersect on the time axis) that sub-task i alone performs the search and rescue task, |b _i -l _i The i represents the three-dimensional distance between two points. In S4, it includes: and (3) aiming at the shortest time for completing the search and rescue task, establishing a time objective function in the search and rescue time model, wherein the time objective function is shown as a formula (38).

Wherein Z is ₃ Indicating the time for completing all search and rescue tasks, wherein I indicates the set of the search and rescue tasks to be completed, T _i Represents the time, g, required for the unmanned aerial vehicle to complete subtask i _i Representing binary variables s _i The time of the unmanned aerial vehicle to independently execute the search and rescue when completing the search and rescue task of the subtask i is represented,

unmanned plane drop point l for representing current task of slave point of helicopter _i Unmanned plane flying spot b to next task _i+1 Time，b _i Indicating the flying spot position of unmanned aerial vehicle when carrying out search and rescue task of subtask i, l _i And (5) indicating the falling point position of the unmanned aerial vehicle when the unmanned aerial vehicle performs the search and rescue task of the subtask i.

In this embodiment, after the helicopter carries the unmanned aerial vehicle to start, each time the helicopter flies to the point of departure of the unmanned aerial vehicle determined by the site selection in step S2, the helicopter releases the unmanned aerial vehicle to execute the search and rescue task under the group of subtasks, and when the unmanned aerial vehicle executes the search and rescue task, the helicopter continues to fly according to the planned flight path, so that the unmanned aerial vehicle is released and recovered alternately until the task is completed. The subtasks acquired in the step S2 are imported into the released unmanned aerial vehicle, and the subtasks imported into the unmanned aerial vehicle at present correspond to the take-off and landing points of the unmanned aerial vehicle, which the helicopter flies to at present.

The determining the task completion time of the unmanned aerial vehicle comprises the following steps: and determining the task completion time according to the relation between two adjacent subtasks, wherein the relation between the two adjacent subtasks comprises at least three types. Wherein, type 1 is: no crossover between the two adjacent groups of subtasks in time series; the type 2 is: wherein one set of tasks is included in the other set of tasks, and the two sets of subtasks are performed simultaneously; the type 3 is: the adjacent two sets of subtasks cross over in time series, and one set of subtasks has already begun when the other has not yet completed.

One, when the relationship between two adjacent subtasks is type 1, as shown in fig. 7, is the simplest case where the two groups of subtasks do not have any intersection in the time series. The search and rescue time required for completing the two groups of subtasks is C ₁ ，v _h Representing the maximum flight speed of the helicopter, wherein:

the constraints are:

and when the relationship between two adjacent subtasks is of type 2, as shown in fig. 8, one group of tasks is contained in the other group of tasks, the two groups of subtasks are performed simultaneously, and the other group of tasks is completed when the one group of tasks with longer completion time is completed. The search and rescue time required for completing the two groups of subtasks is C ₂ Wherein:

C ₂ ＝T _i (41)

the constraints are:

T _i >T _i+1 (42)

wherein T is _i The time required by the unmanned plane to complete the subtask i is represented; in the case shown in fig. 8, the constraint shown by the formula (42) is satisfied, and the time required to complete the task 1 is higher than the time required to complete the task 2; and to meet the performance requirements of the helicopter, the helicopter is represented by a formula (43); after the unmanned aerial vehicle takes off, the helicopter flies to the landing point of the unmanned aerial vehicle to execute the recovery task under the condition that the maximum flying speed is not exceeded.

Third, when the relationship between two adjacent subtasks is type 3, as shown in fig. 9, this is a more complex case, where there are two sets of subtasks intersecting in time series, where one set of subtasks has not yet been completed, and the other set of tasks has already started. The search and rescue time required for completing the two groups of subtasks is C ₃ Wherein:

C ₃ ＝T _i +s _i+1 (44)

the constraints are:

T _i -s _i ＝T _i+1 -s _i+1 (46)

in this case the constraints of helicopter performance must be met, i.e. each journey must not exceed its maximum speed of flight, indicated by (45), and the simultaneous execution of the two subtasks must take place in equal time, indicated by equation (46).

In conclusion, three conditions form the relation between any two adjacent subtasks, and a binary variable g is defined for the sake of simplicity and convenience in representation _i E {0,1}, defined as g if two adjacent subtasks intersect on the timeline _i =0, and the adjacent two subtasks are defined as g if they do not intersect on the time axis _i =1, then any two subtasks must be completed to meet helicopter performance, meet (47), (48):

T _i -s _i ＝T _i+1 -s _i+1 (48)

the time required for the helicopter to finish the ith subtask and go to the (i+1) th subtask is as follows

Represented by formula (49).

The current modes of cooperation of the carrier and the unmanned aerial vehicle can be roughly divided into two modes: the cooperation of the carrier and the single unmanned aerial vehicle and the cooperation of the carrier and the multiple unmanned aerial vehicles, and the site selection modes under different cooperation modes: (1) Research on unmanned aerial vehicle take-off and landing point site selection problem under carrier and single unmanned aerial vehicle cooperation mode: in the unmanned aerial vehicle heterogeneous mode, a Murray proposed method starts from a distribution center by using an unmanned aerial vehicle and a truck at the same time, returns to the same place after completing a distribution task, and sets an unmanned aerial vehicle to serve only one client because the unmanned aerial vehicle has limited energy, and based on the concept of' truck priority, the unmanned aerial vehicle firstly determines a truck route with the shortest route, and makes a round check on each node in the route, judges whether the node is suitable for the unmanned aerial vehicle to complete the task according to the surrounding client positions, if so, takes the node as a flying spot of the unmanned aerial vehicle, and continuously judges whether the next node meets the navigation constraint of the unmanned aerial vehicle landing, if so, sets the node as a landing spot of the unmanned aerial vehicle; gonzalez-R et al also uses a truck and an unmanned aerial vehicle to complete tasks, and proposes a greedy heuristic on solving to solve the problem of the take-off and landing points of the unmanned aerial vehicle; mbiadou et al improve the collaboration problem, set carrier and unmanned aerial vehicle can both carry out the task, the route problem of the carrier is regarded as classical travel business problem, confirm the flying spot and landing spot of the unmanned aerial vehicle on the basis of the carrier route; mario [15] et al propose a new solving method, firstly, determining the route of a carrier, generating the route of an unmanned aerial vehicle between any two nodes of the route, judging whether the performance requirement of the unmanned aerial vehicle is met, and if so, determining the endpoint on the route as the take-off and landing point of the unmanned aerial vehicle; dayarian et al provide a different solution, where the carrier is responsible for performing tasks and the unmanned aerial vehicle is responsible for replenishing trucks, and where the unmanned aerial vehicle take-off and landing points are determined on the road on which the carrier is running according to the location of the demand points; mathew et al studied logistics distribution problems in urban environments using a truck and an unmanned aerial vehicle, the departure and landing points of the unmanned aerial vehicle were determined at the end points of the street according to the distribution demand, and the efficiency problem of truck and unmanned aerial vehicle distribution was solved under this constraint. (2) Research on taking-off and landing point address selection problems of carrier and multi-unmanned plane cooperative unmanned plane: ferrandez et al propose a new mode of a truck and a plurality of unmanned aerial vehicles, but limit the unmanned aerial vehicle to serve only one customer, set the take-off and landing points of the unmanned aerial vehicle as the same position, and determine the position of the take-off and landing points of the unmanned aerial vehicle by using a K-means clustering algorithm; freitas et al propose a hybrid heuristic algorithm for determining the take-off and landing points of the unmanned aerial vehicle on the basis of a truck and a plurality of unmanned aerial vehicle modes, an initial solution is generated by a TSP solver, and then the take-off and landing points of the unmanned aerial vehicle and the route of the truck are solved by using the hybrid heuristic algorithm; agatz et al propose a new model of multiple trucks and multiple unmanned aerial vehicles, under which the unmanned aerial vehicle is assumed to have to be at the same position for each take-off and landing, and the take-off and landing points of the unmanned aerial vehicle are determined by using a local search method; in order to determine the unmanned aerial vehicle take-off and landing point problem under the cooperation of a carrier and a plurality of unmanned aerial vehicles, bouman provides a precise method for dynamic planning, and a large-scale example can be solved through the method through example verification.

However, the above-described research on the problem of unmanned aerial vehicle take-off and landing site selection in which a carrier and an unmanned aerial vehicle cooperate also has the following disadvantages: 1. in the past modeling to unmanned aerial vehicle take-off and landing point site selection, in order to simplify the problem and conveniently solve, take-off and landing points of unmanned aerial vehicle are mostly set to the same position, and the situation that the unmanned aerial vehicle can land nearby after the search and rescue task is executed in actual flight is not considered, so that site selection cost is high is avoided. 2. The influence of the environment temperature, the load of the unmanned aerial vehicle and the flight attitude of the unmanned aerial vehicle on the cruising ability of the unmanned aerial vehicle is rarely considered in the constraint of the conventional model. Therefore, the embodiment aims at improving the traditional unmanned aerial vehicle take-off and landing point site selection model, takes the influence of the environmental temperature, the load of the unmanned aerial vehicle and the flight attitude of the unmanned aerial vehicle on the endurance of the unmanned aerial vehicle as the constraint of the model, and does not require whether the take-off and landing points of the unmanned aerial vehicle are at the same position in the model, so that the site selection problem of the take-off and landing points of the unmanned aerial vehicle is more practical.

At present, the unmanned aerial vehicle task allocation problem under a cooperative mode is mainly studied from two aspects of a model and a solving algorithm. When solving unmanned aerial vehicle task allocation problems, two traditional schemes mainly comprise a vehicle path problem and a multi-travel business problem. Kota et al directly reduce multi-unmanned aerial vehicle task allocation to assignment problems for solution; guerrioro et al treat the task allocation problem as a multi-traveler problem with time constraints and solve using a particle swarm algorithm. Modeling for task allocation in complex situations can be mainly summarized into the following three types: a (MILP) model proposed by Song et al that can quickly solve the task allocation problem of an unmanned aerial vehicle under complex conditions; the method is characterized by comprising the steps of establishing a multi-dimensional knapsack model based on the knapsack problem thought, wherein the model is suitable for the task allocation problem of a distributed multi-unmanned aerial vehicle; and after the problems are optimally combined, the Ye et al establishes a multi-unmanned aerial vehicle cooperative task allocation problem model, and then solves the problems. In the method for solving task allocation, hu et al propose a multi-unmanned aerial vehicle collaborative monitoring research based on distributed predictive control, and effectively solve a multi-unmanned aerial vehicle collaborative path planning research with multi-objective function; aiming at the path planning problem of multiple unmanned aerial vehicles, the problem is divided into two steps when solving by Li et al: firstly, extracting a boundary diagram which possibly appears, and then selecting a corresponding processing method aiming at the convex-concave property of the boundary diagram; chen et al propose a clustering algorithm for solving the problem of dynamic programming of multiple unmanned aerial vehicles, and the algorithm can realize reasonable programming of the paths of the reconnaissance tasks of the multiple unmanned aerial vehicles; sun et al proposes an auction algorithm based on a Boolean network for solving the task allocation problem among multiple unmanned aerial vehicles, wherein the algorithm processes tasks by utilizing an auction strategy based on cluster combination, so as to solve the allocation conflict problem among unmanned aerial vehicles; wei proposes a centralized-distributed hybrid control framework for solving the task allocation and scheduling problems, utilizing a dynamic data driven system to better accommodate the changing environment and task conditions.

However, the above-described task allocation problem has made many beneficial studies on a model for solving the problem and a method for solving the problem, but has the following disadvantages: 1. in the prior modeling, the flight path idealization processing of the unmanned aerial vehicle does not consider the actual running environment of the unmanned aerial vehicle, the flight attitude of the unmanned aerial vehicle and the influence of the carrying equipment on the cruising ability when the unmanned aerial vehicle executes tasks. 2. Under the unmanned aerial vehicle off-site take-off and landing mode background of the collaborative search and rescue of the helicopter and the unmanned aerial vehicle, the distribution result solved by the traditional distribution mode can enable the unmanned aerial vehicle to frequently pass through a flight path of the helicopter, and safety accidents are extremely easy to cause. Therefore, the method is improved based on the traditional task allocation model, influences of unmanned aerial vehicle environment temperature, unmanned aerial vehicle load and flight attitude on unmanned aerial vehicle endurance are considered in the allocation model, a weighted distance matrix between the points to be searched and rescued is introduced when the task allocation of the unmanned aerial vehicle is solved, distances between the points to be searched and rescued in different categories are set to be infinite, and therefore the same unmanned aerial vehicle can not be allocated to the points to be searched and rescued in different categories to execute search and rescue tasks, and the allocation result meets the actual search and rescue requirements more.

In summary, the embodiment improves the shortcomings of the related problems in the traditional collaborative search and rescue mode of the helicopter and the unmanned aerial vehicle, and solves the search and rescue total time problem display study on the basis of solving the unmanned aerial vehicle taking-off and landing point site selection problem of the collaborative search and rescue of the helicopter and the unmanned aerial vehicle, the task allocation problem in the unmanned aerial vehicle off-site taking-off and landing mode and considering the performance of the helicopter. Perfecting basic theory and key technology of low-altitude collaborative search and rescue, the main advantages are as follows:

1. aiming at the problem of higher site selection cost in collaborative search and rescue of a helicopter and an unmanned aerial vehicle, an unmanned aerial vehicle taking-off and landing site selection model for collaborative search and rescue is established, the influence of environmental temperature change, unmanned aerial vehicle load and unmanned aerial vehicle flight state on unmanned aerial vehicle endurance is considered in the model, and unmanned aerial vehicle take-off and landing are not required to be at the same point on constraint. The Yushu earthquake calculation example shows that the model improved under four different search and rescue modes has obvious reduction in site selection cost compared with the prior study, wherein the site selection cost is reduced by 56.08% under the 50-scale point to be searched and rescue background.

2. Aiming at the simple processing of the flight path idealization of the unmanned aerial vehicle in the prior unmanned aerial vehicle distribution model, the influence of the environment temperature, the unmanned aerial vehicle load and the flight state on the endurance capacity of the unmanned aerial vehicle is not considered; moreover, the traditional distribution mode can lead the unmanned aerial vehicle to frequently pass through the helicopter route, so that the problem of safety accidents is extremely easy to cause. In the embodiment, a weighted distance matrix among the points to be searched and rescued is introduced, the distances among the points to be searched and rescued in different categories are set to be infinite, an unmanned aerial vehicle task allocation model is built on the basis, and an improved ant colony algorithm is utilized for solving.

3. Aiming at the problem that only a single subtask can be executed at the same time in the prior collaborative search and rescue, the collaborative search and rescue efficiency is low, the embodiment analyzes the possible condition of executing two subtasks simultaneously, establishes a search and rescue total time model on the basis of meeting the performance of a helicopter, and solves the problem by utilizing a genetic algorithm. The Jade tree earthquake calculation example shows that the improved model under four different search and rescue scales respectively improves the search and rescue efficiency compared with the prior research, wherein the efficiency of completing 50 points to be searched and rescued is improved by 37.3 percent.

Specific examples are:

1. in practical application of the embodiment, in the process of selecting the landing points, an improved binary particle swarm algorithm can be adopted to solve the problem of the landing points of the unmanned aerial vehicle: and solving the unmanned aerial vehicle take-off and landing point address selection problem by utilizing an improved binary particle swarm algorithm. The object of solving the unmanned aerial vehicle take-off and landing point site selection model is to select a series of take-off and landing points of the unmanned aerial vehicle, and as each set of take-off and landing points only has two states of selected middle and unselected, the thinking of a binary particle swarm algorithm is adopted, each particle represents a site selection sequence, 1 represents that the set of take-off and landing points is selected, and 0 represents that the set of take-off and landing points is unselected. In order to solve the problem that the conventional binary particle swarm algorithm is easy to fall into a local optimal solution in the iterative process, the embodiment adopts the following method for improvement: firstly, introducing a method for dynamically adjusting inertia weight; and secondly, increasing the judgment of the similarity between two particles in the particle updating process, and updating the position and the speed of the particles according to a new rule for the particles judged to be similar. The method comprises the following steps:

1. Coding mode design and population initialization:

the initialization population is that a plurality of groups of alternative unmanned aerial vehicle take-off and landing positions are combined in pairs, the limitation of (15) in the model is met by solving in the mode, and the unmanned aerial vehicle take-off and landing points are selected from the combination. The site selection cost of the unmanned aerial vehicle take-off and landing points is related to the take-off and landing positions of the unmanned aerial vehicle, so that once the take-off and landing positions of the unmanned aerial vehicle are determined, the to-be-searched and rescuing points meeting the unmanned aerial vehicle range requirements (considering the running environment temperature, the unmanned aerial vehicle load and the unmanned aerial vehicle flight state) can be distributed. If 10 alternative unmanned aerial vehicle take-off and landing points exist, then the following steps are generated

The group address selection combination is that the selected unmanned aerial vehicle take-off and landing point combination is set as 1, and the rest is set as 0, and can be coded as [1, 0,1 … … 0 ]]Indicating that the 1,2,4,5 combination is selected, the remaining 41 positions are closed. After the take-off and landing points of the alternative unmanned aerial vehicle are determined, each row represents a to-be-searched point in a generated matrix list, each column represents an unmanned aerial vehicle take-off and landing point combination, the column where the unselected position is located is set to be 0, the to-be-searched point adopts the principle of near distribution, and is added into the column selected as the unmanned aerial vehicle take-off and landing point combination, and the rows are closedThe method is set to 0, which satisfies the requirements of (12) and (13) in the model, and also satisfies the requirements that a group of unmanned aerial vehicle take-off and landing points in the model can serve a plurality of to-be-searched and rescuing points, and judges whether the condition constraint of (11) in the model is satisfied, otherwise, the particle stops the evolution in the direction, and the distribution is completed until all to-be-searched and rescuing points.

2. Definition of the objective function:

Z ₁ representing the site selection cost, namely the formula (50), the smaller the site selection cost of the take-off and landing points of the unmanned aerial vehicle is, the more satisfactory the unmanned aerial vehicle is.

3. Improved particle swarm algorithm:

3.1, dynamic adjustment of inertia weight: the traditional particle swarm algorithm linearly reduces the weight omega in the iterative calculation process, but only reduces omega in the operation, so that the function is easy to converge to a local extreme point. In order to solve the problem, the method for dynamically changing the inertial weight is adopted in the process of solving the unmanned aerial vehicle landing point address selection problem in the embodiment:

in the formula (52): m is the size of the particle group,

the function value corresponding to the ith particle in the nth iteration,

and the function value corresponding to the optimal particle in the nth iteration. However, this method is not applicable in all cases, i.e. when the population size is small, when allWhen the individuals of (a) are all the same solution, alpha is known from the formula (52) ⁿ At the next iteration α =0 ^n-1 As shown in equation (51), the inertial weight ω= infinity is given by =0. The value of the inertia weight does not change as the number of iterations increases, and in order to avoid this, in this embodiment, improvement is made in such a way that the inertia weight ω=k is calculated when the initial parameter is assigned, and when α ⁿ Not equal to 0 and alpha ^n-1 Not equal to 0, the inertial weight is determined according to equation (51) in an iterative process, and once α occurs ⁿ Let ω=k again =0.

3.2, controlling particle similarity: during evolution, the similarity of 2 particle individuals can be described according to the following distance formula

In expression (54), x _a,i And x _b,i The coordinates of two particles, D' is the dimension of the variable, in this embodiment the number of points to be searched. Thus the similarity between particles can be controlled by the distance between particles, the threshold value D of the distance being determined first _ε The threshold is set to half the particle length in this embodiment. In this embodiment, the similarity is controlled as follows: when D is _a,b ≤D _ε When particle a is updated according to the original method, and particle b is updated according to (55).

4. The method for solving the remote site selection problem based on the improved binary particle swarm algorithm comprises the following steps:

4.1, determining particle swarm size m, maximum iteration number n and parameter cognition weight factor c ₁ Social weight factor c ₂ And an inertial weight ω.

4.2, generating the take-off and landing points of the unmanned aerial vehicle in a combined mode, and randomly generating m feasible solutions X according to the rule of population initialization introduced above ₁ ,X ₂ ,X ₃ ,...,X _m The number of matrix rows of each solution is the number of combinations of the points to be searched and rescuing. Finding the corresponding position of the unopened addressing combination in the distance matrix as infinity, finding the minimum position except for non-0 in each row, generating an assignment matrix to meet (14) and (15), and judging whether the solutions in the matrix meet the formulas (10) and (11). If it satisfies the calculation X according to the formula (50) _i And taking the initial fitness as an initial individual extremum P _best,i Since the objective function in the problem of remote site selection is that the search and rescue cost meets the minimum requirement, all P's are used _best,i The minimum value of (2) is assigned to G _best As an initial global extremum, the initial value of the particle velocity is set to 0.

4.3, judging the similarity of the particles according to a formula (54), and updating the particle swarm according to a rule of particle similarity control.

4.4, calculating the unmanned aerial vehicle take-off and landing point location cost corresponding to each individual after updating according to the formula (50), updating the inertia weight omega according to the formulas (51) and (52), and if the unmanned aerial vehicle take-off and landing point location cost of the particle i is lower than the individual extremum P before the unmanned aerial vehicle take-off and landing point location cost _best,i Then put it as P _best,i If the best P _best,i Extremum G superior to previous global extremum G _best It is set to G _best 。

And 4.5, stopping calculation if the convergence condition is met or the maximum iteration number is reached, otherwise, returning to the step (3).

2. In practical application of the embodiment, in the process of subtask allocation, an improved ant colony algorithm can be adopted to solve the task allocation problem in the off-site take-off and landing mode:

on the basis of the obtained site selection position of the take-off and landing point of the unmanned aerial vehicle and the corresponding point to be searched and rescued, after the point to be searched and rescued in the task to be allocated is classified by using the weighted distance, the unmanned aerial vehicle allocation problem is solved by adopting an ant colony algorithm introducing dynamic pheromone update. Aiming at the problem that the conventional ant colony algorithm is easy to obtain a local optimal solution due to continuous accumulation of pheromones, the embodiment improves the problem and introduces a method for dynamically updating the pheromones to solve the problem of unmanned aerial vehicle task allocation in the embodiment. In the prior art, any solution is adopted In the task allocation, the continual accumulation of pheromones in a better task allocation scheme can lead to the calculation of a local optimal solution; however, if the pheromones are uniformly distributed to improve the problem of prematurely sinking into the locally optimal solution, the global searching capability of the algorithm can be improved, but the algorithm performance is seriously affected, and a plurality of iterations are needed to find the globally optimal solution. The following improvement method is adopted in the embodiment: setting m ants to be iterated k times, storing the obtained unmanned aerial vehicle task allocation schemes in an array P, arranging the cost of the maximum time for completing the search and rescue task and the minimum time difference value of each scheme in the array P 'from small to large, storing the sequence number in the array P', taking h=r not equal to m if r allocation schemes are found, and introducing variables Y and d _i,j Representing the three-dimensional distance between the points to be searched and rescuing (taking into account the range of the unmanned aerial vehicle after load conversion), τ _j Representing the conversion of the hovering time of the unmanned aerial vehicle at j to-be-searched and rescuing point (considering the unmanned aerial vehicle load) into the flight course, c _i,j (t) representing the expected degree of the ants transferring from the point i to the point j to be searched and rescuing at the moment t, taking c _i,j (t)＝1/(d _i,j +τ _j )，Tabu _k Indicating that the tabu list stores the search and rescue points accessed by ant k, allowed _k ＝{V-Tabu _k -representing the point to be searched and rescuing, eta, accessible to ants _i,j The residual quantity of pheromones on the paths of the points i and j to be searched and rescaled at the moment t is represented:

the dynamic update rule of the pheromone is as follows:

q ₀ (k)＝e ^R(T/N-1) (58)

in the formula (58): n is the most significant of the algorithmThe iteration times are large; t represents the current iteration number; after the m ants complete the kth iteration, dynamically updating the pheromone according to a formula (57) on the unmanned aerial vehicle task allocation scheme stored in the P array. In solving the unmanned aerial vehicle task allocation problem by utilizing the ant colony algorithm, the dynamic update of the pheromone can be seen to be divided into two stages, and when iteration is just started, namely q is more than q ₀ (k) Has high possibility and is updated according to the normal rule; as the iteration proceeds, because h > 1, the sequence number of the preferred strategy is stored in the P' array to be smaller, the corresponding Y value is increased, and the increment of the pheromone is known to be increased according to the formula (57), so that the preferred strategy is maintained.

The step of solving the task assignment is:

(1) And initializing and setting a desired heuristic factor A, a pheromone heuristic factor B, a pheromone volatilization factor Q, a pheromone volatilization adjustment coefficient epsilon and a maximum iteration number iter_max.

(2) The current path node is set as the departure point of the unmanned aerial vehicle, and thus the requirement satisfying the formula (27) is set.

(3) Selecting the next point to be searched and rescued according to a probability calculation formula (68), adding the selected point to be searched and rescued into the current path, adding the point number of the selected point to the tabu list matrix, and meeting the requirements of formulas (30) and (31).

(4) Judging whether the total range difference value between the current path length (taking the flight state, the ambient temperature and the load into consideration) converted and the unmanned aerial vehicle is larger than the distance between the unmanned aerial vehicle and the unmanned aerial vehicle landing point from the current to-be-searched point according to a formula (32), if the current node is not met, setting the current node as the unmanned aerial vehicle landing point, meeting the requirement of a formula (28), returning to the step (2), setting the node requirement to meet the requirement (33), preventing generation of a solution of a sub-loop, if the current path length is met, continuing, and adding the selected to-be-searched point into Tabu in a Tabu list _k Meets the requirement of (26), and unselected search and rescue points are added into Allowed _k 。

(5) And judging whether the search and rescue points still remain unassigned.

(6) If yes, returning to the step (3) to continue iteration, and if not, continuing iteration.

(7) Setting the current path node as the landing point of the unmanned aerial vehicle, calculating the time required for completing the search and rescue task for each path according to a formula (24), calculating the difference between the maximum time and the minimum time for completing the task, and updating the global pheromone according to a formula (57).

(8) And (3) checking whether an abort condition is met, if so, outputting a difference value between the maximum time and the minimum time of the unmanned aerial vehicle completing the task and the maximum time of the unmanned aerial vehicle completing the task, and if not, returning to the step (3) to continue iteration.

3. In practical application of the embodiment, a genetic algorithm may be used to solve the search and rescue time:

the basis for solving the search and rescue time is to plan a reasonable helicopter flight path, so that the performance requirement of the helicopter is met, and the solved search and rescue time is shorter. In the conventional path planning problem, the shortest path of a helicopter is often used as an objective function, but in the unmanned aerial vehicle off-site take-off and landing mode of collaborative search and rescue of the helicopter and the unmanned aerial vehicle, the solved path cannot only consider the length of the path, and also consider the time for completing search and rescue. And solving the search and rescue time on the basis of the completed unmanned aerial vehicle take-off and landing point address selection work and unmanned aerial vehicle task allocation work. Because the problem of solving the search and rescue time can be regarded as solving the shortest problem, the accurate calculation is more complex when the data scale is larger, and therefore, the problem is solved by utilizing a genetic algorithm. The solving process is as follows:

(1) Initializing: the population size p, the crossover probability pc, the mutation probability pm and the maximum iteration number T are defined. And reading the landing point position data of the unmanned aerial vehicle. And randomly generating an initial population, each chromosome representing a helicopter path.

(2) Judging whether the maximum iteration times are reached, if not, continuing to (3), and if so, outputting a result.

(3) For each individual in the population, a corresponding helicopter path is generated. And numbering the generated paths from small to large according to the sequence of starting points, wherein the numbering result is 1,2,3, …, n and n is the number of the take-off and landing points of the unmanned aerial vehicle. Set the serial number x in the generation path corresponding to the unmanned aerial vehicle take-off and landing point in the ith group of subtasks _i,1 ,x _i.2 。

(4) Judging whether the path meets the requirements. TraversingAny two groups of subtasks i, j, if satisfied (x _i1 -x _j1 )×(x _i2 -x _j2 ) And (3) judging whether the path meets the requirement (42) or not and meeting the requirement (43), and if the path does not meet the requirement, setting the adaptability to be 0.

(5) Generating a matrix g according to the relation between adjacent subtasks for each path, generating a matrix s according to formulas (47) and (48), calculating the completion time of each path according to formula (38), and taking the inverse of the solved time as the fitness f of the current path.

(6) The p chromosomes in the population are ordered from large to small according to fitness value f, the performance of individuals arranged in the front part is optimal, and the individuals are directly added into the population of the next generation. The remaining p-1 chromosomes were selected for individuals according to a round robin method. Therefore, the chromosome with highest fitness can be ensured to survive to the next generation, and the chance that the chromosome is selected to the next generation because of large difference of fitness values among individuals can be avoided.

(7) And (3) performing cross recombination on the individual selected in the step (6) according to the cross probability pc. Randomly selecting a decimal fraction between 0 and 1, crossing the two chromosomes when the decimal fraction is smaller than the crossing probability to obtain two sub-chromosomes, and directly copying the two chromosomes into sub-chromosomes otherwise. When crossing chromosomes, first two crossing bits min and max (min < max) are randomly generated within the chromosome length, and the [ min, max ] regions of the two individuals are interchanged. As shown in fig. 10: min=2, max=5, after copying the mapped portion in parent I (parent II) to child II (child I), the remaining genes of both children are filled by copying the genes of the corresponding parent. The coding method based on path representation in this embodiment requires that each chromosome code is not allowed to have a repeated gene code, that is, the constraint that any unmanned aerial vehicle take-off and landing point must be accessed only once is satisfied. When the 2 nd gene of the progeny I is produced by directly replicating the gene of the parent I to the progeny I, it is 2. But this gene is already present, so this scheme resets the 2 nd gene of offspring I to 4 according to maps 2-4, eliminating this.

(8) And (5) performing mutation operation. Judging whether to perform mutation operation on all individuals in the population according to the preset mutation probability pm. For each chromosome to be subjected to mutation operation, an exchange operator method is adopted, two positions (such as 2 and 5) on the chromosome are randomly generated, and genes at the two positions are exchanged, and a schematic diagram is shown in FIG. 11.

(9) And (4) adding one to the iteration number, judging whether the maximum iteration number is reached, and if not, returning to the step (4).

(10) And when the maximum iteration times are reached, outputting an optimal solution to obtain the time for completing the search and rescue task and the helicopter path.

Specific examples of the present embodiment in the numerical experiments: in order to verify the effectiveness of the model in the collaborative search and rescue mode of the helicopter and the unmanned aerial vehicle, the embodiment takes the Jade tree earthquake of 4 th year and 14 th year as an example for analysis, and the Jade tree county and the county with serious disaster on the earthquake zone are selected. The model of the life detector is DKL LifeGuard TM, the detection distance of the model detector can reach more than 500m in an open space, the water surface reaches more than one kilometer, and the detection range can be reduced when the model detector encounters an obstacle. The model number of the unmanned aerial vehicle is Wei Tai X6L, the model number of the helicopter is Bell-609, the maximum endurance time of the helicopter is 3.6h, the cruising speed of the helicopter is 141m/s, and specific parameters are shown in Table 6.

TABLE 6 parameter Table

For selection of points to be searched and rescuing, actual disaster conditions are considered, unmanned aerial vehicles are used for carrying life detectors to detect areas possibly needing rescue in disaster-affected counties with large population, population is very sparse in some counties, and large-scale detection value of unmanned aerial vehicles is low. According to the embodiment, population data of disaster-stricken areas are obtained from a geographic monitoring cloud platform, more points to be searched and rescshed are selected in disaster-stricken counties with more population according to population quantity conditions of each county, and fewer points to be searched and rescuing are selected in disaster-stricken counties with less population. And determining the number distribution of the points to be searched and rescuing according to the proportion relation of the population of each disaster-stricken county. 100 search and rescue points were selected, wherein the Yushu county (No. 1-80), the Duoxian county (No. 81-100) and part of the data are shown in Table 7.

TABLE 7 specific coordinates of disaster points to be searched and rescuing

Solving the landing point of the unmanned aerial vehicle and the corresponding point to be searched and rescuing:

(1) Parameter setting

In the calculation of this chapter, in order to verify the advantage of the unmanned aerial vehicle off-site take-off and landing mode relative to the unmanned aerial vehicle local take-off and landing mode in the cooperative search and rescue background of the helicopter and the unmanned aerial vehicle, relevant parameters of the particle swarm algorithm are shown in table 8.

Table 8 particle swarm algorithm parameter set table

(2) Analysis of results

In the process of verification of the calculation, in order to verify the advantage of the unmanned aerial vehicle off-site take-off and landing mode on the unmanned aerial vehicle take-off and landing point site selection cost in the collaborative search and rescue of the helicopter and the unmanned aerial vehicle, the influence of different search and rescue scales on the site selection cost is considered in the calculation, four different search and rescue points are selected, 30 search and rescue points are respectively set, 50 search and rescue points are respectively set, 80 search and rescue points are respectively verified, and 100 search and rescue points are respectively verified. In the calculation example, the number of the take-off and landing points of the unmanned aerial vehicle is the same as the number of the points to be searched and rescued, and the take-off and landing positions of the unmanned aerial vehicle are increased by 100m in height on the basis of the positions of the points to be searched and rescued. The unmanned plane landing point which arrives at the result of solving the model is a combination of two points, and corresponds to a group of points to be searched and rescuing. In the site selection stage, the scheme does not require that the unmanned aerial vehicle specifically takes off from which point and lands from which point, and after planning the route of the helicopter on the basis of subsequent unmanned aerial vehicle task allocation, the flying point of the unmanned aerial vehicle and the landing point of the unmanned aerial vehicle are determined according to the sequence of executing each subtask.

(2a) Model comparison

According to the embodiment, the influence of the change of the environmental temperature on the endurance of the unmanned aerial vehicle and the influence of the flight attitude of the unmanned aerial vehicle and the load of the unmanned aerial vehicle on the endurance of the unmanned aerial vehicle are considered, the traditional collaborative search and rescue unmanned aerial vehicle local take-off and landing model is improved, and the unmanned aerial vehicle take-off and landing point location model with the minimum location cost as an objective function is established, so that the advantage and the disadvantage of the model are compared with the traditional study table 9. In the aspect of the construction of the model, the model considers more unmanned aerial vehicle performance constraints, and considers more reasonable take-off and landing modes for the detection and search and rescue of the unmanned aerial vehicle, so that the unmanned aerial vehicle is not constrained to return to the take-off and landing after the unmanned aerial vehicle completes the task.

Table 9 model comparison table

(2b) Comparative analysis of example results

In order to compare the advantages and disadvantages of the addressing cost in different search and rescue modes, in an example, the scheme is verified from three aspects:

1. the improved binary particle swarm algorithm is utilized to solve the model, and the particle swarm algorithm belongs to a heuristic algorithm, so that the method has randomness in the solving process, the address selecting cost and the address selecting result obtained by each solving can not accurately reflect the advantages and disadvantages of the results, and the model is solved for ten times to reduce the contingency in the solving process, so that the contingency error is eliminated.

The comparison of the site-selection costs is shown in fig. 12a, 12b, 12c, 12 d: the result of ten arithmetic operations of the algorithm under the scale of 30, 50 and 80 points to be searched and rescuing is that the addressing cost of the unmanned aerial vehicle local take-off and landing mode is higher than the addressing cost of the unmanned aerial vehicle remote take-off and landing mode; the site selection cost of the unmanned aerial vehicle local take-off and landing mode is eight times higher than the site selection cost of the unmanned aerial vehicle in the off-site take-off and landing mode under the scale of 100 to-be-searched points, and the off-site take-off and landing mode of the unmanned aerial vehicle is more advantageous compared with the unmanned aerial vehicle local take-off and landing mode on the site selection cost. In summary, in the calculation example, the scale of the search and rescue points and possible errors of the algorithm are considered, and the results prove that the search and rescue mode proposed by the embodiment is better.

2. In order to further explore the advantages and disadvantages of the two models, the average value of the site selection cost after 10 times of operation is calculated, and the cost change rate is calculated, the data in table 10 can show that the models provided by the chapter have obvious advantages in site selection cost in collaborative search and rescue, and the cost of the off-site take-off and landing modes of the unmanned aerial vehicle under different numbers of the scale of the points to be searched and rescue is respectively saved by 14.98%,56.08%,31.69% and 31.06% compared with the cost of the local take-off and landing mode of the unmanned aerial vehicle.

Table 10 mean of site selection costs and rate of change of costs for two modes at different scales

In summary, in the practical calculation example, by increasing the algorithm operation times, increasing the comparison of the site selection cost under the condition of different number and scale of points to be searched and rescuing, and the change rate of the site selection cost, the unmanned aerial vehicle off-site take-off and landing mode in the joint search and rescue provided by the chapter is verified to be better in the cost of selecting the unmanned aerial vehicle take-off and landing point, and the situation of real search and rescue is more met.

Solving task allocation under the unmanned aerial vehicle off-site take-off and landing mode:

in order to verify the feasibility and effectiveness of the task allocation model provided by the embodiment, task allocation is the basis for solving the search and rescue efficiency. Considering the actual rescue scene, the sixth group of subtasks in the unmanned aerial vehicle take-off and landing point site selection result under the scale of 100 to-be-searched points are obtained for research, the take-off and landing point combination of the unmanned aerial vehicle is [80,92], and the take-off and landing point combination is responsible for 52, 53, 55, 56, 58, 59, 64, 65, 66, 69, 71, 73, 76, 78, 80, 81, 82, 84, 85, 86, 87, 91, 93 and 95, and the total 24 to-be-searched points are obtained, and the ant colony algorithm parameter setting is shown in table 11.

Table 11 ant colony algorithm parameter settings

Dividing the points to be searched and rescued by using the introduced weighted distances, classifying the points to be searched and rescued 69, 56, 93, 91, 58, 66, 65, 64, 86, 87, 53, 52, 76 and 80 into one type, and classifying the points to be searched and rescued into one type, 95, 78, 81, 55, 71, 85, 84, 82, 59 and 73 into one type according to the weighted distances, and generating weighted distance matrixes among the points to be searched and rescued according to the classification result. And solving a sixth group of subtasks under the 100-scale to-be-searched and rescuing points by utilizing an improved ant colony algorithm, taking task distribution balance into consideration as an objective function, solving the time for completing the group of tasks to be 2979 seconds, and requiring three unmanned aerial vehicles to complete the search and rescue tasks, wherein the execution task of each unmanned aerial vehicle is shown in a table 12, and the distribution result is shown in a schematic diagram of fig. 13.

Table 12 task allocation table for unmanned aerial vehicle

And continuing to solve the remaining five groups of subtasks, wherein the final distribution result is shown in a table 13, and according to the address selection result of the previous chapter, 6 groups of subtasks are shared under the scale of 100 points to be searched and rescued, and the solved result comprises the time required for completing each group of search and rescue tasks, the number of unmanned aerial vehicles required for completing the tasks and the execution sequence of corresponding points of each unmanned aerial vehicle.

Table 13 results of 100-scale solution for task allocation of points to be searched and rescuing

Solving the total search and rescue time: taking 100 to-be-searched and rescuing points as an example, the time required for completing the total search and rescue task is calculated. The parameter settings of the genetic algorithm are shown in table 14.

Table 14 genetic algorithm parameter set table

In order to calculate the total time required for completing all search and rescue tasks, the scheme solves the search and rescue total time on the basis of unmanned aerial vehicle take-off and landing point address selection and unmanned aerial vehicle task distribution, the take-off and landing points of the unmanned aerial vehicle form a flight route of the helicopter, and the result of task distribution determines the total time for completing search and rescue. The execution sequence of each subtask under the 100-scale to-be-searched-and-rescue point is obtained by solving the chapter through a genetic algorithm, and the hovering and cruising time of the helicopter is shown in a table 15.

Table 15 order and time for completion of tasks

The result of calculating the 100-scale to-be-searched-and-rescuing points is shown in fig. 14, a black solid line represents a helicopter flight path, and a dotted line represents an unmanned plane flight path with the longest task completion time in a subtask. After planning, the No. 39 point of the helicopter path is the flight starting point of the helicopter, so that the flight path of the helicopter is 39-34-12-30-44-80-67-61-92-89-74-98. According to the combination planned in advance, the helicopter releases two unmanned aerial vehicles from the flight starting point of the 39 th point to search and rescue all points to be searched and rescue in the second group of subtasks; after the unmanned aerial vehicle is released, the helicopter does not need to wait in situ but continuously flies to the 34 # point, and four unmanned aerial vehicles are released to search and rescue all the points to be searched and rescued in the second group of subtasks; continuing to fly to the point 12 after the release is finished, and after four unmanned aerial vehicles are recovered, completing search and rescue tasks of the first group of subtasks by the unmanned aerial vehicles; continuing to fly along the helicopter route to a No. 30 point to recycle the 2 unmanned aerial vehicle for completing the second group of subtasks for searching and rescuing; continuing to fly to the point 44 after the recovery is finished, and releasing one unmanned aerial vehicle to search and rescue the point to be searched and rescued in the third group of subtasks; after the release is finished, flying to the point 80 immediately, and releasing 3 unmanned aerial vehicles to search and rescue the point to be searched and rescued in the sixth group of subtasks; the helicopter continuously flies to a 67 th point along the route to release one unmanned aerial vehicle to finish a point to be searched and rescuing in the fourth group of subtasks, after the release is finished, the helicopter flies to the 61 th point to recover and search and rescue the 1 unmanned aerial vehicle to finish the third group of subtasks, and after the recovery is finished, the helicopter continuously flies to a 92 th point along the route to recover and finish the three unmanned aerial vehicles of the sixth group of subtasks; after the recovery is finished, flying to the point 89, and recovering the unmanned aerial vehicle for completing the fourth group of search and rescue tasks; after recovery, flying to a point 74 to release a point to be searched for a fifth group of subtasks for searching and rescuing by 2 unmanned aerial vehicles; and after flying to the point 98, recovering two unmanned aerial vehicles for completing the search and rescue task of the fifth group of subtasks, wherein the time spent for completing the whole search and rescue task is 9092s.

Analysis of experimental results

(1) Contrast of search and rescue modes

In the embodiment, the performance of the helicopter and the unmanned aerial vehicle is considered by taking the combined search and rescue of the low-altitude helicopter and the unmanned aerial vehicle as the background, and the shortest total search and rescue time is solved on the basis of completing remote site selection and task allocation. Table 16 shows the results of the previous studies.

Table 16 search and rescue mode comparison table

(2) Comparative analysis of example results

In the past collaborative search and rescue, the execution of single subtasks can only be carried out at the same time, and the unmanned aerial vehicle is incomplete and searches and rescue the task helicopter can only wait in the spot, and under the unmanned aerial vehicle off-site take-off and landing mode of the unmanned aerial vehicle that the helicopter and the unmanned aerial vehicle search and rescue in the collaborative mode that the embodiment provided, after the site selection of unmanned aerial vehicle take-off and landing point and unmanned aerial vehicle task allocation are accomplished, the performance of the helicopter is still considered, the advantage of the helicopter is fully exerted on the basis of meeting the performance of the helicopter, the completion sequence of each subtask is reasonably planned, and finally the total search and rescue time is solved. In order to verify the merits of the two search and rescue modes, the chapter performs analysis and comparison of calculation examples in the following aspects:

and 1, preventing the scale of the number of the points to be searched from influencing the comparison of the total search and rescue time, and selecting 30, 50, 80 and 100 points to be searched and rescue in the example verification to compare the total search and rescue time under four different scales. Because the result of the genetic algorithm used in solving the total search and rescue time has randomness, ten times of average values are respectively solved for different search and rescue scales in order to eliminate the accidental errors, the time change rate is introduced for more visual comparison and is shown in a formula (66), and the calculated result is shown in a table 17:

Table 17 results of solving the search and rescue efficiencies of the local landing mode and the off-site landing mode under different search and rescue scales

It can be seen that under different number scale search and rescue conditions, the search and rescue modes proposed by the embodiment respectively reduce the search and rescue time by 29.7%, 37.3%, 45.2% and 36.5% relative to the total search and rescue time in the previous study. The index of the time change rate reaches more than 25%, and the time change is not obvious along with the increase of the search and rescue scale, so that the search and rescue mode provided by the embodiment is higher in efficiency and more suitable for large-scale search and rescue tasks, which is important for understanding earthquake disaster conditions.

The performances of the unmanned aerial vehicle and the helicopter are fully considered in the unmanned aerial vehicle off-site take-off and landing mode of collaborative search and rescue in the embodiment, the maximum cruising capacity of the bell-609 selected in the embodiment is about 3.6h, but the time for completing the unmanned aerial vehicle local take-off and landing mode of collaborative search and rescue in 80 search and rescue point scales is 13705s (about 3.80 h), and the search and rescue time in 100 search and rescue point scales reaches 14334s (about 3.98 h), which is not in line with the actual situation, namely the search and rescue task cannot be completed. In this embodiment, since the highest-scale computing example is 100, and since the helicopter carrier itself also has range and flight time limitation, under the computing example solving background set in this embodiment, the unmanned aerial vehicle local take-off and landing mode of the joint search and rescue can only complete the search and rescue tasks of 30 and 50 points to be searched and rescue, but more task points are needed for search and rescue in the actual search and rescue task, so that the helicopter and unmanned aerial vehicle joint search and rescue have better expansibility, and can complete the search and rescue task of a larger scale in the actual situation.

The search and rescue scope is enlarged, and the unmanned aerial vehicle is light and handy and convenient, the helicopter has strong endurance and high flying speed, so that the search and rescue task can be efficiently completed. The total time of the collaborative search and rescue of the helicopter and the unmanned aerial vehicle can be divided into two parts: the first is that the helicopter carries the cruising time of the unmanned plane, its value is equal to the ratio of the length of the helicopter route and its speed; secondly, the helicopter hovers and waits for unmanned aerial vehicle recovery time. The longer the helicopter cruising time, the higher the utilization rate of the helicopter in the cooperative mode is, and the utilization rate of the helicopter is introduced in the embodiment as shown in a formula (67).

TABLE 18 Carrier utilization at different search and rescue scales

From table 18, it can be known that under different search and rescue scales, the helicopter utilization rate of the unmanned aerial vehicle in the off-site take-off and landing mode is higher than that of the unmanned aerial vehicle in the local take-off and landing mode, because the unmanned aerial vehicle in the off-site take-off and landing mode of the unmanned aerial vehicle and the helicopter are used for collaborative search and rescue fully utilizes the advantages of the helicopter as a carrier, and the unmanned aerial vehicle flies relatively closely to complete search and rescue tasks.

According to the method, based on the fact that emergency search and rescue of the helicopter and the unmanned aerial vehicle are used as the background after earthquake disasters, the problem that ground vehicles cannot pass rapidly due to inconvenient traffic in disaster areas is solved, the unmanned aerial vehicle which is flexible and portable is used for detection and rescue on search and rescue, the unmanned aerial vehicle is limited in range and is susceptible to the influence of search and rescue environment temperature, and the unmanned aerial vehicle carries detection equipment and the flight state of the unmanned aerial vehicle can influence the cruising ability of the unmanned aerial vehicle, so that the helicopter is introduced to serve as a carrier to carry the unmanned aerial vehicle to complete search and rescue tasks. Under the background of collaborative search and rescue of the prior helicopter and the unmanned aerial vehicle, the used unmanned aerial vehicle must return to a starting point to finish recovery after completing tasks, and the helicopter continues to fly to the next task point, so that search and rescue efficiency is reduced.

The method and the device aim at solving the collaborative search and rescue efficiency of the helicopter and the unmanned aerial vehicle, and respectively solve the unmanned aerial vehicle take-off and landing point address selection problem, the task allocation problem under the unmanned aerial vehicle off-site take-off and landing mode and the search and rescue total time problem. Specifically, the unmanned aerial vehicle taking-off and landing point site selection model for collaborative search and rescue is established aiming at the problem of higher site selection cost in the collaborative search and rescue of a helicopter and an unmanned aerial vehicle, the influence of environmental temperature change, unmanned aerial vehicle load and unmanned aerial vehicle flight state on unmanned aerial vehicle endurance is considered in the model, and the unmanned aerial vehicle is not required to take off and land on the same point in constraint. The Yushu earthquake calculation example shows that the model improved under four different search and rescue modes has obvious reduction in site selection cost compared with the prior study, wherein the site selection cost is reduced by 56.08% under the 50-scale point to be searched and rescue background. After solving the problem of the take-off and landing points of the unmanned aerial vehicle, task allocation is carried out on each group of subtasks, and aiming at the problem that unmanned aerial vehicle flight path idealization is simple to process in the traditional unmanned aerial vehicle allocation model, unmanned aerial vehicles can frequently pass through helicopter flight lines, safety accidents are extremely easy to cause, the embodiment introduces a weighted distance matrix among the points to be searched and rescuing, and establishes an unmanned aerial vehicle task allocation model and solves the problem. After the number of unmanned aerial vehicles and the completion time required for completing tasks are determined, the completion sequence of each group of subtasks is reasonably arranged, and aiming at the problem that only a single subtask can be executed at the same time in collaborative search and rescue in the past to cause lower collaborative search and rescue efficiency, the embodiment analyzes the possible situation that two subtasks are executed simultaneously, and establishes a search and rescue total time model on the basis of meeting the performance of a helicopter. The Jade tree earthquake calculation example shows that the improved model under four different search and rescue scales respectively improves the search and rescue efficiency compared with the prior research, wherein the efficiency of completing 50 points to be searched and rescued is improved by 37.3 percent.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A time planning method for collaborative search and rescue of a helicopter and an unmanned aerial vehicle in a low-altitude environment is characterized by comprising the following steps:

2. The method according to claim 1, wherein in S1, the obtained site selection condition parameters include: parameters for representing the load conditions of the unmanned aerial vehicle and the flight status of the unmanned aerial vehicle;

the addressing objective function in the unmanned aerial vehicle take-off and landing point addressing model is expressed as:

m represents a set of points to be searched and rescaled, and is alternatively replaced by a set M ' of points which are selected as flying points and landing points of the unmanned aerial vehicle, b represents the flying points, k represents the points to be searched and rescaled, l represents the landing points, c represents the flight cost of the unmanned aerial vehicle, and x ' ' _b,k Indicating whether the search and rescue point k is served by the take-off and landing point b of the alternative unmanned aerial vehicle, if yes, the search and rescue point k is 1, otherwise, the search and rescue point k is 0, p' _k,l Indicating whether the search and rescue point k is served by an alternative unmanned aerial vehicle take-off and landing point l, if so, the search and rescue point k is 1Otherwise is 0,S _b,k,l And (5) representing the total course required to be consumed by the unmanned aerial vehicle to fly from the point b to search and rescue the kth point to be searched and rescue to drop to the point l.

3. The method of claim 2, wherein the base model of the total range is S _b,k,l ＝(d _b,k +d _k,l )σ ₁ +t _k vσ ₂ ，b,l∈M',k∈M，d _b,k Representing the three-dimensional distance d from point b to point k of the unmanned aerial vehicle _k,l Representing the three-dimensional distance, sigma, of the unmanned aerial vehicle from point k to point l ₁ For the ratio of the power when the unmanned aerial vehicle hovers with a load to the power when the unmanned aerial vehicle flies horizontally without load, sigma ₂ The ratio of the power of the unmanned aerial vehicle when carrying the load to the unmanned aerial vehicle when carrying the unmanned aerial vehicle, t is t _k The hovering time of the unmanned aerial vehicle at the point k to be searched and rescuing is represented, and v represents the speed of the unmanned aerial vehicle in constant speed flight;

in S1, further comprising: according to the basic model of the total course and the running environment temperature of the unmanned aerial vehicle, a total course model related to the temperature is established: s is S _b,k,l ≤Sδ _x (1-beta), b, l epsilon M', k epsilon M, wherein the obtained site selection condition parameters further comprise: the operation environment temperature of the unmanned aerial vehicle, S represents the maximum driving mileage of the unmanned aerial vehicle, beta represents the energy consumption reservation coefficient, x represents the operation environment temperature of the unmanned aerial vehicle, delta _x The relationship between the battery capacity and the temperature change is shown.

4. A method according to claim 3, wherein the relationship of battery capacity with temperature is expressed as a ratio of actual capacity of the battery to theoretical capacity at an ambient temperature x:

δ _x ＝0.98+6.72×10 ^-4 x+3.11×10 ^-5 x ² -8.27×10 ^-7 x ³ x is more than or equal to 20 and less than or equal to 25, and the maximum endurance time S _x ”＝vt'δ _x T' represents the maximum time of flight.

5. The method according to claim 1 or 2, wherein in S2, the unmanned aerial vehicle landing site selection process using the unmanned aerial vehicle landing site selection model includes:

the number of the selected unmanned aerial vehicle flying points is equal to the number of the unmanned aerial vehicle landing points, and the number of the selected unmanned aerial vehicle flying points is expressed as:

and x' _b,k ≤x _b ,b∈M',k∈M p' _k,l ≤p _l ,l∈M',k∈M，x _b Indicating whether the take-off and landing point b of the alternative unmanned aerial vehicle is selected, if so, the take-off and landing point b is 1, otherwise, the take-off and landing point b is 0; pl represents whether the take-off and landing point l of the alternative unmanned aerial vehicle is selected, if yes, 1 is obtained, otherwise, 0 is obtained;

setting conditions according to the service mode, wherein if each point to be searched and rescuing is served by only one unmanned aerial vehicle take-off and landing point, setting the conditions:

or if each point to be searched and rescuing is served by only one unmanned aerial vehicle landing point, setting the conditions:

or if a group of unmanned aerial vehicle take-off and landing points serve a plurality of to-be-searched and rescuing points, and each to-be-searched and rescuing point is only served by a group of unmanned aerial vehicle take-off and landing points, setting conditions:

6. the method of claim 1, further comprising, prior to acquiring the subtask corresponding to the addressing result in S3:

acquiring weighted distances among the points to be searched and rescuing, and classifying all the points to be searched and rescuing by utilizing the weighted distances, wherein the classification mode comprises the following steps: the point to be searched and rescuing is positioned at the right side in the flight direction of the helicopter, and the point to be searched and rescuing is positioned at the left side in the flight direction of the helicopter;

After classification is completed, distances between the to-be-searched and rescuing points of different categories are set to be infinity, and a weighted distance matrix of each category is established, wherein the three-dimensional distance between two points of the to-be-searched and rescuing points of the same category is recorded in the weighted distance matrix.

7. The method according to claim 1 or 6, characterized in that in S3 it comprises:

establishing a task objective function in the task allocation model, wherein the task objective function is as follows:

Z ₂ ＝max(T _k′ )-min(T _k' ),max(T _k' ),min(T _k' )∈T，max(T _k' ) Indicating the longest time for the unmanned aerial vehicle to complete the search and rescue task, min (T _k' ) Representing the shortest time for the unmanned aerial vehicle to complete the search and rescue task, T represents the set of time for all unmanned aerial vehicles to complete the task, T _k' Time for unmanned aerial vehicle to complete search and rescue task is represented, Z ₂ Representing the difference between the maximum and minimum times for completing a task in a set of tasks;

the constraint conditions of the task objective function are as follows:

wherein N is a subset of N, N ' is used for representing the set of the to-be-searched and rescuing points distributed by one unmanned aerial vehicle, i ' and j ' respectively represent 2 different to-be-searched and rescuing points, and w _i′,j′ Representing the flight time of the unmanned aerial vehicle between points i 'to j', t _i′ Representing the flight time, w, of the point i' to be searched for _b,l Representing landing points of corresponding unmanned aerial vehicles in subtasks, k' represents a kth search and rescue path, d _i′,j′ The three-dimensional distance from the point i 'to the point j' of the unmanned aerial vehicle is represented, and v represents the speed of the unmanned aerial vehicle in constant-speed flight; x is x _{i′,j′,k′} Indicating whether the kth '(K' e K) path can go from point i 'to point j', if so, 1, otherwise 0; z _i′,k′ Indicating whether the node i ' is on the kth ' (K ' e K) path, if yes, then 1, otherwise 0;

and, in addition, the processing unit,

z _b,k′ indicating that 1 is the first point b on the kth path or 0 is the second point b on the kth path, z is the third point b on the kth path _l′,k′ Indicating that if the alternative unmanned aerial vehicle falls down point l is on the kth' path, 1 is otherwise 0, x _b,l,k' And indicating that the distance from the point b to be searched and rescuing in the unmanned aerial vehicle path k' to the unmanned aerial vehicle landing point l is 1, otherwise, the distance is 0.

8. The method according to claim 1, characterized in that in S4 it comprises:

and establishing a time objective function in the search and rescue time model by taking the shortest time for completing the search and rescue task as a target, wherein the time objective function is as follows:

wherein Z is ₃ Indicating the time for completing all search and rescue tasks, wherein I indicates the set of the search and rescue tasks to be completed, T _i Represents the time, g, required for the unmanned aerial vehicle to complete subtask i _i Representing binary variables s _i Indicating the time of independently executing search and rescue when the unmanned aerial vehicle completes the search and rescue task of the subtask i,/->

Unmanned plane drop point l for representing current task of slave point of helicopter _i Unmanned plane flying spot b to next task _i+1 Time, b _i Indicating the flying spot position of unmanned aerial vehicle when carrying out search and rescue task of subtask i, l _i And (5) indicating the falling point position of the unmanned aerial vehicle when the unmanned aerial vehicle performs the search and rescue task of the subtask i.

9. The method as recited in claim 8, further comprising:

when the helicopter carries the unmanned aerial vehicle to leave, and each time the helicopter flies to the unmanned aerial vehicle take-off and landing point determined by the site selection of the step S2, the unmanned aerial vehicle is released by the helicopter, wherein the subtask obtained by the step S2 is imported into the released unmanned aerial vehicle, and the subtask imported into the unmanned aerial vehicle at present corresponds to the unmanned aerial vehicle take-off and landing point flown by the helicopter at present;

the determining the task completion time of the unmanned aerial vehicle comprises the following steps: determining task completion time according to the relation between two adjacent subtasks, wherein the relation between the two adjacent subtasks comprises at least three types, and the type 1 is as follows: no crossover between the two adjacent groups of subtasks in time series; the type 2 is: wherein one set of tasks is included in the other set of tasks, and the two sets of subtasks are performed simultaneously; the type 3 is: the adjacent two sets of subtasks cross over in time series, and one set of subtasks has already begun when the other has not yet completed.

10. The method of claim 9, wherein when the relationship between two adjacent subtasks is type 1, the search and rescue time required to complete the two sets of subtasks is C ₁ Wherein, the method comprises the steps of, wherein,

and->

v _h Representing a maximum flight speed of the helicopter;

when the relation between two adjacent subtasks is of type 2, the search and rescue time required for completing the two groups of subtasks is C ₂ Wherein C ₂ ＝T _i And T is _i >T _i+1 ，

T _i The time required by the unmanned plane to complete the subtask i is represented;

when the relation between two adjacent subtasks is of type 3, the search and rescue time required for completing the two groups of subtasks is C ₃ Wherein C ₃ ＝T _i +s _i+1 And (2) and

T _i -s _i ＝T _i+1 -s _i+1 。