Planning method for searching resident path of stratospheric airship area based on two-dimensional interval prediction

Abstract

The invention discloses a planning method for predicting and searching a resident path of a stratospheric airship area based on a two-dimensional interval, and the method comprises the following steps: building an energy consumption model of a stratospheric airship, and expressing an optimization problem according to the flight airspeed and energy consumption of the stratospheric airship; with reference to an interval prediction algorithm and a simulated annealing algorithm, an optimization problem is solved according to a kinetic model and an energy consumption model of the stratospheric airship, and an optimal flight airspeed and an airship course angle are obtained; secondly, the current state quantity of the airship is updated, and task completion judgment is carried out; in the practical application, the initial energy reserve, the task initial point, the target point and other state variables of the flight task are input into the controller, and then the flight task optimization target priority parameters are adjusted, so that the stratospheric airship plans a route meeting the requirements to complete the task. When the method is applied to the stratospheric airship regional residence task, the situation that the airship is driven into the airspace with the target point outside the airspace as the starting point and is subjected to the regional residence task is achieved.

Description

Technical Field

[0001] The invention provides a method for planning a stratospheric airship regional residence path based on a two-dimensional interval prediction algorithm and a simulated annealing algorithm. The invention provides a method for planning a stratospheric airship path taking into account airspace range, wind field conditions, target point positions and energy consumption, and belongs to the field of automatic control technology. Background Art

[0002] A stratospheric airship is an aircraft that can stay in the stratosphere for a long time and perform tasks. It combines the buoyancy of an airship with the propulsion of an aircraft, supplemented by solar cells or other energy sources. Since the atmospheric conditions in the stratosphere are relatively stable, the wind speed is low, and it is far away from various interference sources on the ground, stratospheric airships can perform long-term monitoring, communication relay, meteorological observation and other tasks.

[0003] In recent years, with the development of materials science, energy storage, aerodynamics and automatic control technology, the design and manufacturing technology of stratospheric airships has gradually matured, and stratospheric airships are gradually transitioning from the experimental stage to practical application. Despite this, stratospheric airships still face some technical challenges, such as energy supply and aircraft control. In terms of aircraft control, the flight control constraints of stratospheric airships mainly come from the control capability constraints caused by coupling with other systems and the environmental constraints brought by the flight airspace and mission requirements. At the same time, in order to ensure the accessibility of the flight mission and extend the time of regional residence, under the constraints of energy storage batteries and power systems, the stratospheric airship should select the corresponding heading angle and navigation airspeed according to the wind field environment and the target point orientation; on the other hand, as an aircraft that stays in the stratosphere for a long time, its position needs to be kept within a certain airspace range. Therefore, it is necessary to propose a regional residence path planning method that can take into account the flight airspeed, energy loss and position constraints of the stratospheric airship. The present invention "A method for regional residence path planning of stratospheric airship based on two-dimensional interval prediction search algorithm" takes the above problems as the starting point, and proposes a targeted solution to the regional residence path planning problem of stratospheric airship based on the wind field environment and the size of the airspace. First, taking the minimum task completion time and energy consumption as the optimization goal, considering the airspace range and flight capability, an optimization problem model for regional residence path planning of stratospheric airship is established; then, based on the idea of ​​two-dimensional interval prediction, the optimization problem is solved to obtain the heading angle and navigation airspeed that can meet the energy consumption and task completion time; finally, through simulation verification, it is proved that the control method involved can achieve the path planning task of the regional residence of the stratospheric airship with faster task completion time and lower energy consumption under the conditions of satisfying the position constraints and energy constraints. Summary of the invention

[0004] The purpose of the present invention is to provide a method for path planning in the case of regional residence of a stratospheric airship based on a two-dimensional interval prediction algorithm, which can provide a flight trajectory that meets the requirements under the premise of judging the mission accessibility and having both low energy consumption and rapid completion of the flight mission. As a multi-optimization target task, airship path planning needs to take into account both task completion time and energy consumption. Control engineers can adjust parameters in combination with actual application scenarios and adjust the optimization target priority during airship path planning.

[0005] The technical solution adopted by the present invention is a method for regional residence of stratospheric airships based on a two-dimensional interval prediction search algorithm. First, an energy consumption model of a stratospheric airship is established, and an optimization problem is expressed according to the flight airspeed and energy consumption of the stratospheric airship; then, the optimization problem is solved according to the dynamic model and energy consumption model of the stratospheric airship with reference to the interval prediction algorithm and the simulated annealing algorithm, and the optimal flight airspeed and the heading angle of the airship are obtained; secondly, the current state quantity of the airship is updated, and the task completion judgment is performed. In practical applications, the state quantities such as the initial energy reserve quantity of the flight mission, the mission starting point, and the target point are input into the controller, and then the flight mission optimization target priority parameters are adjusted, so that the stratospheric airship plans a route that meets the requirements to complete the task.

[0006] The specific steps are as follows:

[0007] Step 1: According to the flight mission requirements, there are two optimization goals, and the corresponding weights of the two optimization goals are set to adjust the flight mission optimization goal priority. According to the two optimization goals of faster airspeed and less energy consumption, the optimization objective function is constructed.

[0008] Step 2: Construct a solver based on the interval prediction algorithm and the simulated annealing algorithm. Take the flight speed and heading angle of the stratospheric airship as two decision variables to perform two-dimensional interval prediction; adopt the simulated annealing algorithm to retain the suboptimal solution with probability to improve the reliability of the algorithm, and the probability calculation refers to the Metropolis criterion. In this way, the solver solves the objective function and obtains the optimal flight speed and heading angle for the next step.

[0009] Step 3: Calculate various parameters of the airship after the airspeed and heading angle of the stratospheric airship output in step 2 have been used for one step of navigation, including airspeed, heading angle, remaining energy, navigation time, etc. The current state of the airship is updated with the calculated airship parameters to determine whether the mission is completed. If the mission is not completed, jump to step 3 to continue iterating the state of the airship; if the mission is completed, output the planned path and the flight-related parameters of the stratospheric airship.

[0010] Among them, in terms of the optimization goal design described in step 1, when achieving regional residence, the optimization goal needs to be designed in two parts. First, it is necessary to determine whether the starting position is within the airspace range. If the starting position is outside the airspace range, it is necessary to quickly fly into the airspace range at the beginning of the regional residence mission and then continue the regional residence mission; if the starting position is within the airspace range, the regional residence is directly carried out within this range. Therefore, the two parts of the optimization goals are different.

[0011] (1) When the starting position is outside the area, the regional residence target point needs to be set as the navigation end point. Since the navigation task at this time is to fly to the target point, the flight distance and energy consumption are the optimization targets that need to be considered. Therefore, the optimization target at this time consists of these two parts. Set the current stratospheric airship position to X now , Y now It means that after one step, the position of the stratospheric airship is X next , Y next Indicates that the target point position is expressed in X target , Y target express.

[0012] The flight distance consists of two parts. One part is the flight distance D within one step. step The other part is the distance D between the airship position and the target point after one step. end .

[0013] First is D step , we can get from the formula:

[0014]

[0015] Next is D target , expressed as the horizontal distance from the current point to the target point, the formula is:

[0016] D t =|X target -X now |+|Y target -Y now | (2)

[0017] From the above formula, we can get D step With D target Then, the flight distance is obtained:

[0018] D=D step +D target (3)

[0019] In terms of energy consumption, the energy consumption within one step is used as a reference object, and the remaining energy after the stratospheric airship flies for one step is used to express the energy consumption of the flight strategy. Therefore, the remaining energy of the airship needs to be calculated. The remaining energy is expressed by the remaining energy percentage. The calculation method is as follows:

[0020]

[0021] Among them, W b is the current remaining battery energy, W bm The maximum capacity of the battery.

[0022] Calculate the remaining battery energy W b The method is as follows:

[0023]

[0024] Where W b0 is the remaining battery energy a step ahead, P b is the energy increased or consumed in a step time, and δ is the step time span in minutes.

[0025] Since the task has multiple optimization objectives, two coefficients a1 and a2 are designed in terms of the focus of the optimization objectives, which respectively represent the distance priority and energy consumption priority in this path planning. From the above formula, it can be concluded that when the starting point is outside the airspace range, the cost expression of the optimization function is as follows:

[0026] J1=a1D+a2N (6)

[0027] Therefore, the optimization objective in this design can be expressed as:

[0028] minJ1=a1D+a2N (7)

[0029] During the flight, it is necessary to continuously check whether the current position is within the airspace range. If the current position is within the airspace range, the optimization target needs to be changed.

[0030] (2) When the airship is within the airspace, the optimization objective consists of two parts: the distance between the stratospheric airship and the airspace boundary and the energy consumption.

[0031] First, the distance D between the airship and the airspace boundary edge Since there are many airspace boundaries and the specific number of boundaries is uncertain, we first need to determine the number of airspace boundaries. After determining the number of boundaries, each target point is tested with this boundary distance, and the minimum distance between each target point and all boundaries is taken as the boundary distance D corresponding to the target point. edge , and then get the boundary distance D edgeFinally, the inverse of the minimum distance between the target point and all boundaries is taken as the distance level optimization target, so as to filter out the P points that are too close to the boundary. n effect.

[0032] The method for calculating the boundary distance is as follows: First, the current point P needs to be represented n The foot point of the boundary line segment P(X p ,Y p ), the expression of the foot of the perpendicular P is as follows:

[0033]

[0034] And calculate P next The distance D from P is calculated as follows:

[0035]

[0036] Thus, the distance level optimization target D e Expressed as follows:

[0037] D e =-D edge (11)

[0038] In terms of energy consumption, the energy consumption within one step is used as a reference object, and the remaining energy after the stratospheric airship flies for one step is used to express the energy consumption of the flight strategy. Therefore, the remaining energy of the airship needs to be calculated. The remaining energy is expressed by the remaining energy percentage, and the calculation method is the same as shown above:

[0039]

[0040] Among them, W b is the current remaining battery energy, W bm The maximum capacity of the battery.

[0041] Calculate the remaining battery energy W b The method is as follows:

[0042]

[0043] Where W b0 is the remaining battery energy a step ahead, P b is the energy increased or consumed in a step time, and δ is the step time span in minutes.

[0044] Similarly, since the regional residence task still has multiple optimization objectives, it is also necessary to design optimization target parameters to express the emphasis on the optimization objectives. e1 , a e2The two coefficients represent the priority of boundary distance and energy consumption in this path planning. Therefore, it can be concluded that when the stratospheric airship is located inside the airspace, the optimization function can be expressed as follows:

[0045] J2=a e1 D e +a e2 N (14)

[0046] Therefore, the optimization objective is expressed as:

[0047] minJ2=a e1 D e +a e2 N (15)

[0048] The specific solution method of the solver constructed based on two-dimensional interval prediction and simulated annealing algorithm described in step 2 is as follows:

[0049] Step 1: Since the dimensions of the two-dimensional interval prediction are the airspeed of the stratospheric airship and the heading angle of the stratospheric airship, a two-dimensional coordinate system with the airspeed of the stratospheric airship and the flight heading angle as the coordinate axes is constructed. The heading angle is divided into m equal parts, and the maximum airspeed and the minimum airspeed are divided into n equal parts. Each equal part of the two coordinate axes is regarded as a unit distance. The heading angle and the airspeed have an intersection every unit distance, and a total of m×n intersections can be obtained. These discrete intersections are used as the initial search range of the two-dimensional interval prediction, and subsequent two-dimensional interval predictions are performed.

[0050] Step 2: After obtaining the above m×n discrete coordinate points, select the corresponding objective function according to the current position of the stratospheric airship. If the airship is outside the airspace at this time, substitute the airspeed and heading angle corresponding to the coordinate point into formula (6); if the airship is within the airspace at this time, substitute the airspeed and heading angle corresponding to the coordinate point into formula (14). In this way, the navigation cost corresponding to each coordinate point is obtained.

[0051] Step 4: Among the m×n discrete coordinate points mentioned above, according to the navigation mission requirements, select the coordinate point corresponding to the optimal cost and record this coordinate point in the set to be tested. The central idea in this step is to use the coordinate point corresponding to the optimal cost to represent the midpoint of the interval, so as to predict the interval where the optimal solution is located, and narrow the prediction range of airspeed and heading angle, so as to achieve the purpose of narrowing the prediction range of the subsequent two-dimensional interval.

[0052] Step 5: Determine whether there is a part exceeding the maximum airspeed and heading angle in the area surrounded by the unit airspeed and unit heading angle of the upper left, upper right, lower left, and lower right points contained in the set to be detected. If there is a part exceeding the maximum value, it is deemed that the area beyond the range does not exist; otherwise, it is deemed that the area exists as a whole. If the area exists, the unit airspeed and unit heading angle are divided equally, set as the new unit airspeed and unit heading angle, and the cost of the midpoint of the area is calculated, and the midpoint with a better cost than the optimal coordinate point is retained. At the same time, since this design provides a set of solutions and searches for the optimal solution in the set, it is different from the idea of ​​adding perturbations on the basis of the solution of the simulated annealing algorithm to find a better solution, but since the purpose of this design is still to find the optimal solution, and it is hoped to avoid the search from falling into the local optimal solution in a way. Therefore, referring to the idea of ​​probabilistic retention of non-optimal solutions in the simulated annealing algorithm, and referring to the Metropolis criterion, the midpoint of the non-optimal coordinate point is probabilistically retained, and the retained points are recorded in the set to be detected.

[0053] Referring to the Metropolis criterion, the calculation formula for the retention probability P is as follows: In the formula, J is the cost corresponding to the current solution, J best is the cost of the current optimal solution:

[0054]

[0055] In this way, the effect of probabilistically retaining the suboptimal solution can be achieved based on the cost value of the non-optimal solution.

[0056] Step 6: Determine whether there is a point to be detected in the set to be detected. If the set to be detected is an empty set, output the airspeed and heading angle corresponding to the optimal coordinate point; if the set to be detected is not an empty set, it means that the current optimal solution may not be the global optimal solution, and it is still necessary to continue to perform two-dimensional interval prediction to try to find a better solution. In this case, jump to step 5 and continue to solve until the set to be detected is an empty set or the set number of cycles is completed.

[0057] Among them, the updating of the stratospheric airship state in step 3 is to substitute the optimal heading angle and optimal airspeed solved by the solver constructed in the second step into the stratospheric airship dynamics model, and obtain the latitude and longitude of the airship after sailing at the optimal heading angle and optimal airspeed for a long time, the remaining energy of the airship, the total navigation time and other parameters based on the current relevant parameters of the airship. After the stratospheric airship state is updated, the mission completion of the stratospheric airship is judged. If the stratospheric airship successfully stays in the designated airspace for the set residence time, it is considered that the mission is completed and the loop is exited; if it does not stay for the set residence time, it returns to step 3 and substitutes the new stratospheric airship state into the solver constructed in the second step for solution; if the airship leaves the residence area or runs out of energy due to environmental factors such as wind speed, it is considered that the airship cannot complete the designated mission.

[0058] The technical breakthrough and performance advantages of the present invention are:

[0059] The invention has high interval search efficiency and accurate optimal point search results. When applied to the regional residence mission of a stratospheric airship, the invention can realize the mission of entering the airspace and performing regional residence with a target point outside the airspace as the starting point.

[0060] When the present invention is applied to the regional residence mission of the stratospheric airship, it is first determined whether the starting point is within the airspace range. If it is within the airspace range, the path planning is directly performed in the regional residence mode; if the starting point is outside the airspace range, entering the airspace is the first task, and the regional residence task is performed after entering the airspace. Since the present invention is applied to the regional residence mission of the stratospheric airship, the two dimensions of the two-dimensional interval prediction can correspond to the airspeed and heading angle of the stratospheric airship, and these two determine the path planning during regional residence. Therefore, the two-dimensional interval prediction search algorithm can directly solve the optimal airspeed and heading angle in the next time interval through the input of external parameters, thereby improving the efficiency of path planning. At the same time, since the interval prediction search method is adopted, the search mode of the global search is avoided, and the search efficiency of the optimal point is improved. Finally, the idea of ​​the simulated annealing algorithm is introduced to retain the suboptimal solution with a certain probability, prevent the two-dimensional interval prediction search algorithm from falling into the local optimal solution, and improve the search accuracy of the optimal point.

[0061] The symbols are explained as follows:

[0062] D step is the distance traveled in one step;

[0063] X now is the X-axis coordinate of the current position;

[0064] X next is the X-axis coordinate of the next position;

[0065] Y now is the Y-axis coordinate of the current position;

[0066] Y next is the Y-axis coordinate of the next position;

[0067] D target is the distance to the target point;

[0068] D end The distance between the airship and the target point after one step of flight;

[0069] X target is the X-axis coordinate of the target point;

[0070] Y target is the Y-axis coordinate of the target point;

[0071] m is the equal fraction of the heading angle of the stratospheric airship;

[0072] n is the fraction of the airspeed of the stratospheric airship;

[0073] D is the optimization target in terms of distance;

[0074] N is the optimization target in terms of energy;

[0075] W b The remaining energy of the battery;

[0076] W bm Maximum energy storage for the battery;

[0077] W b0 The remaining energy of the battery at the last moment;

[0078] P b The battery consumption / increase energy in a long period of time;

[0079] δ is the step time;

[0080] J1 is the optimization target in navigation mode;

[0081] a1 is the distance target parameter in navigation mode;

[0082] a2 is the energy target parameter in navigation mode;

[0083] X p is the X-axis coordinate of the perpendicular foot of the airship position and the boundary position;

[0084] Y p is the Y-axis coordinate of the perpendicular foot of the airship position and the boundary position;

[0085] X1 is the X-axis coordinate of the boundary;

[0086] X2 is the X-axis coordinate of the boundary;

[0087] X3 is the X-axis coordinate of the airship’s position;

[0088] Y1 is the Y-axis coordinate of the boundary;

[0089] Y2 is the Y-axis coordinate of the boundary;

[0090] Y3 is the Y-axis coordinate of the airship’s position;

[0091] D edge is the distance between the airship and the boundary;

[0092] D e Optimize goals for airships and boundaries;

[0093] a e1It is the boundary distance parameter in the dwell mode;

[0094] a e2 Parameters for boundary energy consumption in resident mode;

[0095] J2 is the optimization target in the dwell mode;

[0096] J is the cost corresponding to the current solution;

[0097] J best is the cost of the current optimal solution;

[0098] P is the probability of retaining a non-optimal solution; BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 It is a flow chart for implementing the method of the present invention. Figure 2 It is a speed line graph. Figure 3 It is a line graph of heading angle. Figure 4 It is a trajectory planning map. DETAILED DESCRIPTION The following is a further description of the design methods of each part of the present invention:

[0100] The present invention "a method for regional residence of stratospheric airships based on a two-dimensional interval prediction search algorithm" has the following specific steps:

[0101] Step 1: Select the working mode according to the location of the airship and construct the optimization objective function

[0102] The location of the airship may be within the required airspace range, or it may be outside the required airspace, so a position judgment is required. If the airship is within the airspace range, it can directly perform the regional residence task when it is stationed in the area; if the airship is outside the airspace range, it needs to fly to the airspace range first, and then perform the regional residence task. Therefore, the first step of the present invention needs to determine the working mode and construct the corresponding optimization objective function according to the position of the stratospheric airship when the mission starts. At the same time, the optimization target tendency can be adjusted according to the two-dimensional optimization target weight input externally, so that the algorithm is more inclined to speed priority or energy saving priority.

[0103] If you need to fly from outside the airspace to inside the airspace, the optimization goal can be set as:

[0104] minJ1=a1D+a2N

[0105] If it is already inside the airspace, the optimization objective can be set as:

[0106] minJ2=ae1 D e +a e2 N

[0107] Step 2: Construct an optimization objective function solver based on the two-dimensional interval prediction algorithm

[0108] Taking the airspeed and heading angle as the interval coordinate axes, the optimization objective function solver is designed according to the two-dimensional interval prediction algorithm proposed in this paper. Referring to the idea of ​​retaining non-optimal solutions in the simulated annealing algorithm, the range of the search solution set is slightly broadened to improve the reliability of the obtained solution and avoid falling into the situation of local optimal solution. On this basis, the interval prediction estimation is carried out, the prediction interval is gradually narrowed, and the interval where the optimal solution is located is found, so as to find the global optimal airspeed and heading angle. After finding the optimal airspeed and heading angle, the obtained speed and heading angle are used as the navigation speed and heading angle for the next long time.

[0109] Step 3: Update the real-time position of the airship based on the parameters obtained by the solver

[0110] Substitute the airspeed and heading angle of the next long time obtained in step 2 into the solver constructed in the second step, and find the interval where the optimal airspeed and heading angle are located by continuously narrowing the prediction interval, so as to find the optimal airspeed and heading angle corresponding to the interval. Substitute the solved optimal airspeed and heading angle into the execution layer to solve various indicators of the airship at the next moment, including the airship speed, heading angle, position coordinates, and remaining energy in the stratosphere, and use the airship area residence time as the standard to judge the completion of the task. If the residence time preset by the program is successfully retained, the loop ends; if the residence time preset by the program is not completed, return to step 2 and continue to predict the next long interval.

[0111] The flow chart of the present invention is as follows Figure 1 As shown:

[0112] Experimental charts and related data:

[0113] After simulation, the present invention has been verified to be reliable and feasible. The present invention will be described below from the perspective of the trajectory planning diagram of the regional residence, the speed planning line diagram, and the heading angle planning line diagram.

[0114] Speed ​​line graph Figure 2 As shown:

[0115] The heading angle line graph is as follows Figure 3 As shown:

[0116] Trajectory planning diagram Figure 4 As shown:

[0117] It can be seen from the line graph that when the present invention is applied to the problem of regional residence of stratospheric airships, the stratospheric airship can be made to fly into the airspace, and based on environmental factors such as wind direction and wind speed and the established optimization objective function, the heading angle and airspeed most suitable for regional residence in the next time interval can be found, and trajectory planning can be performed accordingly.

[0118] When applied to the regional residence mission of the stratospheric airship, the present invention has the characteristic of being able to flexibly set the maximum airspeed and heading angle. At the same time, the present invention can also find the airspeed and heading angle that are most suitable for the next time interval with higher accuracy and faster search speed, thereby guiding the airship to perform the flight mission.

Claims

1. A method for planning a stratospheric airship regional residence path based on two-dimensional interval prediction and search, characterized in that: Firstly, the energy consumption model of the stratospheric airship is established, and the optimization problem is expressed according to the flight airspeed and energy consumption of the stratospheric airship; then, the optimization problem is solved according to the dynamic model and energy consumption model of the stratospheric airship by referring to the interval prediction algorithm and the simulated annealing algorithm, and the optimal flight airspeed and heading angle of the airship are obtained; secondly, the current state of the airship is updated, and the task completion judgment is made; In practical applications, the initial energy reserve of the flight mission, the mission starting point, and the target point state are input into the controller, and then the flight mission optimization target priority parameters are adjusted, so that the stratospheric airship plans a route that meets the requirements to complete the mission; The specific steps are as follows: Step 1: According to the flight mission requirements, there are two optimization goals, and the corresponding weights of the two optimization goals are set to adjust the flight mission optimization goal priority; according to the two optimization goals of faster airspeed and less energy consumption, the optimization objective function is constructed; Step 2: Construct a solver based on the interval prediction algorithm and the simulated annealing algorithm; use the flight airspeed and flight heading angle of the stratospheric airship as two decision variables to perform two-dimensional interval prediction; adopt the simulated annealing algorithm to retain the suboptimal solution with probability, and the probability calculation adopts the Metropolis criterion; thereby, the solver solves the objective function and obtains the optimal flight airspeed and heading angle for the next step; Step three, calculate various parameters of the airship after one step of navigation using the flight airspeed and flight heading angle of the stratospheric airship output in step two, including the flight airspeed, heading angle, remaining energy of the airship, and navigation time data of the airship; and update the current state of the airship with the calculated various parameters of the airship to determine whether the mission is completed. If the mission is not completed, jump to step three to continue iterating the state of the airship; if the mission is completed, output the planned path and the flight-related parameters of the stratospheric airship.

2. The method for planning a resident path for a stratospheric airship region based on two-dimensional interval prediction and search according to claim 1, characterized in that: In terms of the optimization target design described in step 1, when implementing regional residence, the optimization target needs to be designed in two parts; first, determine whether the starting position is within the airspace range. If the starting position is outside the airspace range, then at the beginning of the regional residence mission, fly into the airspace range and continue the regional residence mission; if the starting position is within the airspace range, then directly perform regional residence within this range; (1) When the starting position is outside the area, the regional residence target point needs to be set as the navigation end point. Since the navigation task is to fly to the target point, the flight distance and energy consumption are the optimization targets that need to be considered. Therefore, the optimization targets at this time are flight distance and energy consumption. Set the current stratospheric airship position to X now , Y now It means that after one step, the position of the stratospheric airship is X next , Y next Indicates that the target point position is expressed in X target , Y target express; The flight distance consists of two parts. One part is the flight distance D within one step. step The other part is the distance D between the airship position and the target point after one step. end ; First is D step , we can get from the formula: Next is D target , expressed as the horizontal distance from the current point to the target point, the formula is: D t =|X target -X now |+|Y target -Y now | (2) From the above formula, we can get D step With D target Then, the flight distance is obtained: D=D step +D target (3) In terms of energy consumption, the energy consumption within one step is used as a reference object, and the remaining energy after the stratospheric airship flies for one step is used to express the energy consumption of the flight strategy; the remaining energy of the airship is calculated, and the remaining energy is expressed by the remaining energy percentage. The calculation method is as follows: Among them, W b is the current remaining battery energy, W bm is the maximum capacity of the battery; Calculate the remaining battery energy W b The method is as follows: Where W b0 is the remaining battery energy a step ahead, P b is the energy increased or consumed in a step time, δ is the step time span in minutes; In terms of the focus of the optimization goal, two coefficients a1 and a2 are designed to represent the distance priority and energy consumption priority in path planning respectively; from the above formula, when the starting point is outside the airspace range, the cost expression of the optimization function is as follows: J1=a1D+a2N (6) Therefore, the optimization objective is expressed in the design as: minJ1=a1D+a2N (7) During the flight, it is necessary to constantly check whether the current position is within the airspace range. If the current position is within the airspace range, the optimization target needs to be changed; (2) When the airship is within the airspace, the optimization objective consists of the distance between the stratospheric airship and the airspace boundary and the energy consumption; First, the distance D between the airship and the airspace boundary edge Since there are many airspace boundaries and the specific number of boundaries is uncertain, we first need to determine the number of airspace boundaries. After determining the number of boundaries, each target point is tested with this boundary distance, and the minimum distance between each target point and all boundaries is taken as the boundary distance D corresponding to the target point. edge , and then get the boundary distance D edge Finally, the inverse of the minimum distance between the target point and all boundaries is taken as the distance level optimization target, so as to filter out the P points that are too close to the boundary. n effect; The method for calculating the boundary distance is as follows: First, the current point P needs to be represented n The foot point of the boundary line segment P(X p ,Y p ), the expression of the foot of the perpendicular P is as follows: And calculate P next The distance D from P is calculated as follows: Thus, the distance level optimization target D e Expressed as follows: D e =-D edge (11) In terms of energy consumption, the energy consumption within one step is used as a reference object, and the remaining energy after the stratospheric airship flies for one step is used to express the energy consumption of the flight strategy. Therefore, the remaining energy of the airship needs to be calculated. The remaining energy is expressed by the remaining energy percentage, and the calculation method is the same as shown above: Among them, W b is the current remaining battery energy, W bm is the maximum capacity of the battery; Calculate the remaining battery energy W b The method is as follows: Where W b0 is the remaining battery energy a step ahead, P b is the energy increased or consumed in a step time, δ is the step time span in minutes; Since the regional residence task still has multiple optimization objectives, the optimization target parameters are designed to express the emphasis on the optimization target. e1 , a e2 The two coefficients represent the priority of boundary distance and energy consumption in this path planning. When the stratospheric airship is located inside the airspace, the optimization function is expressed as follows: J2=a e1 D e +a e2 N (14) The optimization objective is expressed as: minJ2=a e1 D e +a e2 N (15) D step is the distance traveled in one step; X now is the X-axis coordinate of the current position; X next is the X-axis coordinate of the next position; Y now is the Y-axis coordinate of the current position; Y next is the Y-axis coordinate of the next position; D target is the distance to the target point; D end The distance between the airship and the target point after one step of flight; X target is the X-axis coordinate of the target point; Y target is the Y-axis coordinate of the target point; m is the equal fraction of the heading angle of the stratospheric airship; n is the fraction of the airspeed of the stratospheric airship; D is the optimization target in terms of distance; N is the optimization target in terms of energy; W b The remaining energy of the battery; W bm Maximum energy storage for the battery; W b0 The remaining energy of the battery at the last moment; P b The battery consumption / increase energy in a long period of time; δ is the step time; J1 is the optimization target in navigation mode; a1 is the distance target parameter in navigation mode; a2 is the energy target parameter in navigation mode; X p is the X-axis coordinate of the perpendicular foot of the airship position and the boundary position; Y p is the Y-axis coordinate of the perpendicular foot of the airship position and the boundary position; X1 is the X-axis coordinate of the boundary; X2 is the X-axis coordinate of the boundary; X3 is the X-axis coordinate of the airship’s position; Y1 is the Y-axis coordinate of the boundary; Y2 is the Y-axis coordinate of the boundary; Y3 is the Y-axis coordinate of the airship’s position; D edge is the distance between the airship and the boundary; D e Optimize goals for airships and boundaries; a e1 It is the boundary distance parameter in the dwell mode; a e2 Parameters for boundary energy consumption in resident mode; J2 is the optimization target in the dwell mode; J is the cost corresponding to the current solution; J best is the cost of the current optimal solution; P is the probability of retaining a non-optimal solution.

3. The method for planning a resident path for a stratospheric airship region based on two-dimensional interval prediction and search according to claim 2, characterized in that: The specific solution method of the solver constructed based on two-dimensional interval prediction and simulated annealing algorithm described in step 2 is as follows: Step 1: Since the dimensions of the two-dimensional interval prediction are the airspeed of the stratospheric airship and the heading angle of the stratospheric airship, a two-dimensional coordinate system with the airspeed of the stratospheric airship and the flight heading angle as the coordinate axes is constructed. The heading angle is divided into m equal parts, and the maximum airspeed and the minimum airspeed are divided into n equal parts. Each equal part of the two coordinate axes is regarded as a unit distance. An intersection point is obtained every unit distance between the heading angle and the airspeed, and a total of m×n intersection points can be obtained. These discrete intersection points are used as the initial search range of the two-dimensional interval prediction, and subsequent two-dimensional interval prediction is performed; Step 2: After obtaining the above m×n discrete coordinate points, select the corresponding objective function according to the current position of the stratospheric airship. If the airship is outside the airspace at this time, substitute the airspeed and heading angle corresponding to the coordinate point into formula (6); if the airship is within the airspace at this time, substitute the airspeed and heading angle corresponding to the coordinate point into formula (14); thereby obtaining the navigation cost corresponding to each coordinate point; Step 4: Among the m×n discrete coordinate points mentioned above, according to the navigation mission requirements, select the coordinate point corresponding to the optimal cost and record this coordinate point in the set to be tested; the central idea in this step is to use the coordinate point corresponding to the optimal cost to represent the midpoint of the interval, so as to predict the interval where the optimal solution is located, and narrow the prediction range of airspeed and heading angle, so as to achieve the purpose of narrowing the prediction range of the subsequent two-dimensional interval; Step 5: Determine whether there is a part exceeding the maximum airspeed and heading angle in the area enclosed by the unit airspeed and unit heading angle in the upper left, upper right, lower left and lower right of the points contained in the set to be detected. If there is a part exceeding the maximum value, it is deemed that the area beyond the range does not exist; otherwise, it is deemed that the entire area exists; if the area exists, the unit airspeed and unit heading angle are equally divided and set as the new unit airspeed and unit heading angle, and the cost of the midpoint in the area is calculated, and the midpoint with a better cost than the corresponding cost of the optimal coordinate point is retained; Referring to the Metropolis criterion, the calculation formula for the retention probability P is as follows: In the formula, J is the cost corresponding to the current solution, J best is the cost of the current optimal solution: In this way, the suboptimal solution can be retained with probability according to the cost value of the non-optimal solution; Step 6: Determine whether there is a point to be detected in the set to be detected. If the set to be detected is an empty set, output the airspeed and heading angle corresponding to the optimal coordinate point; if the set to be detected is not an empty set, it means that the current optimal solution may not be the global optimal solution, and continue to perform two-dimensional interval prediction to find a better solution; Jump to step 5 and continue solving until the set to be tested is empty or the set number of cycles is completed.

4. The method for planning a resident path for a stratospheric airship region based on two-dimensional interval prediction and search according to claim 3, characterized in that: The updating of the stratospheric airship state described in step 3 is achieved by substituting the optimal heading angle and optimal airspeed solved by the solver constructed in step 2 into the stratospheric airship dynamics model, and based on the current relevant parameters of the airship, obtaining the longitude and latitude of the airship after sailing for one step at the optimal heading angle and optimal airspeed, the remaining energy of the airship, and the total flight time; After the state of the stratospheric airship is updated, the mission completion of the stratospheric airship is judged. If the stratospheric airship successfully stays in the specified airspace for the set residence time, the mission is considered completed and the loop is exited; if it does not stay for the set residence time, it returns to step three and substitutes the new stratospheric airship state into the solver constructed in the second step for solution; if the airship leaves the residence area or runs out of energy due to environmental factors such as wind speed, it is considered that the airship cannot complete the specified mission.

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