Stratospheric airship trajectory rolling planning method and system based on environment forecast information

Abstract

The invention discloses a stratospheric airship trajectory rolling planning method and system based on environment forecast information, and relates to the technical field of automatic control, and the method comprises the steps: obtaining a stratospheric airship cluster and environment forecast information, and carrying out the planning of the trajectory rolling of a stratospheric airship based on the information; respectively constructing a kinematics model, an energy cycle model, a multi-collaborative coverage model and a wind field perception and residence model of the airship; and establishing a to-be-optimized objective function according to initial state information of the models in combination with latest environment forecast data. When a rolling planning strategy is implemented, whether environment forecast information is updated or not is checked when multi-boat track point planning is carried out in each step length; if an update is found, repeatedly executing the step of establishing the target function to be optimized based on the new environment forecast information; and if not, performing multi-boat track point planning according to the determined optimal control input. According to the method, the flight path which better meets the actual flight requirement and the task requirement can be planned.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a method and system for rolling planning of stratospheric airship trajectories based on environmental forecast information. Background Art

[0002] Stratospheric airships are high-altitude unmanned aerial vehicles that use low-density gas buoyancy to achieve controllable flight. They have the characteristics of long-term airborne stay, precise control, high-altitude vision, and large payload, and have broad application prospects. Airship clusters are the future development trend of stratospheric airship deployment. Long-flight, large-scale continuous regional coverage missions are the basis for airships to exert their effectiveness. In the face of dynamic wind fields, improving the long-flight airborne and autonomous flight capabilities of airship clusters is the prerequisite for ensuring the mission capabilities of stratospheric airships. On the one hand, stratospheric airships need to consider the constraints brought by the dynamic flight environment, such as no-fly zones, mission area boundaries, dynamic uneven wind fields, etc.; on the other hand, in order to improve the long-flight airborne capability of stratospheric airships, it is necessary to reduce the energy consumption during the mission execution as much as possible, and consider the constraints related to the energy circulation system of multiple airships, such as the airship energy storage must be greater than the minimum limit in a day; finally, it is necessary to consider the completion of real-time mission indicators of airship clusters, such as regional area coverage. In order to improve the execution effect and reliability of long-duration regional coverage missions, it is necessary to enhance the autonomy and real-time performance of stratospheric airship trajectory planning. A multi-aircraft trajectory planning method is proposed which simultaneously considers dynamic wind fields, optimizes energy storage and consumption, and regional coverage.

[0003] At present, some scholars have conducted relevant research on the trajectory planning of unmanned aerial vehicle clusters in dynamic environments. However, the stratospheric airship itself is a weakly powered aircraft, and the dynamic wind field has a great impact on the flight of the airship. It is necessary to calculate the energy-saving and reachable track points with low wind speed through trajectory planning. The continuous area coverage mission is special and there is no designated route end point, so the current trajectory planning method is not applicable to stratospheric airships. Summary of the invention

[0004] The purpose of this application is to provide a method and system for stratospheric airship trajectory rolling planning based on environmental forecast information, which can plan a flight trajectory that better meets actual flight requirements and mission requirements.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for rolling trajectory planning of a stratospheric airship based on environmental forecast information, comprising:

[0007] Step 1: Acquire stratospheric airship cluster information and environmental forecast information; the stratospheric airship cluster information includes position attitude information, energy information, coverage information and potential energy information;

[0008] Step 2: According to the position and attitude information of the stratospheric airship, the energy information, the coverage information and the potential energy information, a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model and a wind field perception and residence model are respectively established;

[0009] Step 3: Based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field sensing and residence model, and the latest environmental forecast information, establish the objective function to be optimized within a single step length;

[0010] Step 4: according to the objective function to be optimized within a single step, based on the improved particle swarm algorithm, the optimal control input within the single step is solved; the improved particle swarm algorithm is a particle swarm algorithm with improved inertia weight;

[0011] Step 5: Based on the rolling planning strategy, when planning the multi-boat track points for each step, determine whether the environmental forecast information changes;

[0012] If yes, then re-execute steps 3-4 based on the changed environmental forecast information;

[0013] If not, multi-boat track point planning is performed according to the optimal control input until the area coverage mission is completed.

[0014] In a second aspect, the present application provides a stratospheric airship trajectory rolling planning system based on environmental forecast information, comprising:

[0015] An information acquisition module, used to acquire stratospheric airship cluster information and environmental forecast information; the stratospheric airship cluster information includes position and attitude information, energy information, coverage information and potential energy information;

[0016] A model building module is used to respectively establish a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model, and a wind field perception and residence model according to the position and attitude information of the stratospheric airship, the energy information, the coverage information, and the potential energy information;

[0017] An objective function establishment module is used to establish an objective function to be optimized within a single step length based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field perception and residence model, and the latest environmental forecast information;

[0018] A control input solving module is used to solve the optimal control input within a single step length according to the objective function to be optimized within a single step length based on an improved particle swarm algorithm; the improved particle swarm algorithm is a particle swarm algorithm with improved inertia weight;

[0019] The rolling planning module is used to determine whether the environmental forecast information has changed based on the rolling planning strategy when planning the multi-boat track points for each step; if so, the objective function to be optimized within a single step is re-established based on the changed environmental forecast information; if not, the multi-boat track points are planned according to the optimal control input until the area coverage task is completed.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application provides a method and system for rolling planning of stratospheric airship trajectories based on environmental forecast information. The first step is to collect detailed information and environmental forecast data of stratospheric airship clusters. The stratospheric airship cluster information specifically covers key parameters such as its state, energy, coverage area, and wind potential energy. The second step is to construct a kinematic model, an energy cycle model, a multi-cooperative coverage model, and a wind field perception and residence model of the stratospheric airship based on the information collected in the first step. These models are intended to fully reflect the flight characteristics, energy management, collaborative operation capabilities, and adaptability to wind fields of the airship. The third step is to integrate the initial state information of each model constructed in the second step and the latest environmental forecast information to construct an objective function to be optimized within a single step. This function is intended to evaluate and optimize the flight trajectory of the airship within a given time step. The fourth step is to use an improved particle swarm algorithm (especially an improved inertia weighted particle swarm algorithm) to solve the objective function to be optimized in the third step to determine the optimal control input within a single step. The fifth step is to implement a rolling planning strategy. When performing multi-vessel trajectory planning at each time step, first check whether the environmental forecast information has changed. If the environmental forecast information has been updated, re-execute the third and fourth steps based on the new environmental forecast information; if the environmental forecast information has not changed, perform multi-vessel track point planning according to the solved optimal control input. The trajectory planning method and system proposed in this application can ensure that the planned trajectory is closer to the actual flight and mission requirements, and has the ability to update the environmental forecast information and the status of the airship cluster system in real time, so as to solve the target track points that meet the real-time environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A schematic flow chart of a method for rolling trajectory planning of a stratospheric airship based on environmental forecast information in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a wind farm provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of a rolling planning strategy provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram of the coverage effect at a track point provided by an embodiment of the present application;

[0027] Figure 5 A multi-vessel trajectory planning effect diagram provided in an embodiment of the present application;

[0028] Figure 6 A rolling planning process provided for an embodiment of the present application;

[0029] Figure 7 A control quantity optimization process provided in an embodiment of the present application;

[0030] Figure 8 A schematic diagram of the structure of a stratospheric airship trajectory rolling planning system based on environmental forecast information provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] Embodiment 1

[0033] like Figure 1 As shown, this embodiment provides a stratospheric airship trajectory rolling planning method based on environmental forecast information, and the stratospheric airship trajectory rolling planning method includes:

[0034] Step 1: Acquire stratospheric airship cluster information and environmental forecast information; the stratospheric airship cluster information includes position attitude information, energy information, coverage information and potential energy information;

[0035] Step 2: According to the position and attitude information of the stratospheric airship, the energy information, the coverage information and the potential energy information, a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model and a wind field perception and residence model are respectively established;

[0036] Step 3: Based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field sensing and residence model, and the latest environmental forecast information, establish the objective function to be optimized within a single step length;

[0037] Step 4: according to the objective function to be optimized within a single step, based on the improved particle swarm algorithm, the optimal control input within the single step is solved; the improved particle swarm algorithm is a particle swarm algorithm with improved inertia weight;

[0038] Step 5: Based on the rolling planning strategy, when planning the multi-boat track points for each step, determine whether the environmental forecast information changes;

[0039] If yes, then re-execute steps 3-4 based on the changed environmental forecast information;

[0040] If not, multi-boat track point planning is performed according to the optimal control input until the area coverage mission is completed.

[0041] In some embodiments, when executing step 1, the specific steps may be as follows:

[0042] Acquire stratospheric airship cluster information and environmental forecast information; the stratospheric airship cluster information includes position attitude information, energy information, coverage information and potential energy information.

[0043] Specifically, the status information includes the coordinates of the airship's location, the airspeed of the airship, the wind speed at the airship's location, etc.; the energy information includes the energy consumed by the propulsion system, the energy consumed by the payload, the energy consumed by the avionics system, the energy vacancy of each airship's energy storage battery, etc.; the coverage information includes the judgment mark of whether the grid is covered, the total number of covered grids, the side length of the mission area, etc.; the potential energy information includes the x-axis component from the expected position to the original position, the y-axis component from the expected position to the original position, the angle between the wind direction of the wind field at the expected position and the x-axis of the ground coordinate system, etc.

[0044] In some embodiments, when executing step 2, the specific steps may be as follows:

[0045] When modeling the trajectory planning of a stratospheric airship cluster in a wind field, first determine the wind field resolution. Figure 2 As shown, a kinematic model of the airship in the wind field is established.

[0046] First, the kinematic model of the stratospheric airship is established as follows:

[0047]

[0048] Where [x,y] Trepresents the position coordinates of each airship; V represents the airship airspeed, in m / s; ψ represents the yaw angle of the airship airspeed relative to the positive x-axis, yaw angle ψ∈[-π,π]. V wind is the wind speed at the location of the airship, in m / s; β is the wind speed V wind The angle β with the positive direction of the x-axis can be used to determine the magnitude of the meridional and zonal winds at the location of the airship.

[0049] The position of the airship is affected by the airspeed of the airship and the wind speed at the location. The speed and maximum turning angle constraints are established:

[0050] V min ≤V≤V max .

[0051]

[0052] Among them, γ is the turning angle, γ max is the maximum turning angle.

[0053] Then, an energy cycle model is established, in which, within Δt, E prop Energy consumed by the propulsion system, E load Energy consumed by the payload, E av is the energy consumed by the avionics system, and the sum of the three is the total energy consumption, E em,i is the energy vacancy of the energy storage battery of each airship, and the energy cycle model expression is:

[0054]

[0055] P prop =F T V / (η mot η prop ).

[0056] E prop =P prop ·Δt.

[0057] E em,i =E full -E prop,i -E av -E load +P solar,i ·Δt.

[0058] Among them, P solar The sum of solar energy absorbed and converted by each airship per unit time is the absorption conversion power; F T is the thrust, V is the airship speed, η mot is the motor efficiency, η prop is the propeller efficiency; P ijThe power of solar energy absorbed and converted by the solar panel in the i-th row and j-th column can be calculated from the position, attitude and time of the airship; η is the conversion efficiency of the photovoltaic array, P prop is the propulsion power of the airship, which can be calculated from the current speed V of the airship, E prop is the energy consumed by each boat in step length Δt.

[0059] The energy consumption objective function and the minimum energy storage constraint are established as follows:

[0060]

[0061]

[0062] In the formula, E ea is the average of the energy that is not fully stored. Count1 and Count2 represent that the minimum energy of the airship must not be 0, and the energy absorbed and stored in one day must be greater than or equal to the sum of the consumed energy.

[0063] Among them, Figure 4 The multi-cooperative coverage model is to rasterize the area to calculate the regional coverage rate, set the airship observation coverage area as a circular area, and the ratio of the number of grids covered in the circular area to the total number of grids is the regional area coverage rate, which is used as an evaluation index, specifically as follows:

[0064]

[0065] Among them, c j It is a flag to judge whether the grid is covered. C represents the total number of covered grids. L x , L y represents the side length of the task area, d is the grid granularity, S d is the total number of grids, S r Represents the uncovered portion of the area.

[0066] Among them, the wind field perception and residence model is inspired by the artificial potential field method, the gravitational potential field is the wind field perception model, and the repulsive potential field is the residence model, as shown in the following formula:

[0067]

[0068] U att (x) = P propw ·Δt w .

[0069] In the formula, Δd x is the x-axis component from the predicted position to the original position, Δd y is the y-axis component from the predicted position to the original position; β is the angle between the wind direction of the predicted position and the x-axis of the ground coordinate system; U att(x) is the average wind potential energy of the airship cluster in the coordinated state x, representing the gravitational potential energy state of the airship cluster.

[0070]

[0071] Where, d(q,q0) i is the set of distances between each airship position and the region boundary, q and q0 represent the airship position and the point closest to the boundary, d ox With d oy The values ​​of D are equal to x and y respectively; t1 is the repulsion function threshold of the region boundary, representing the scope of the boundary repulsion force. represents the repulsive potential field value at the location of each airship, and k1 takes a constant value. t1 Within the range, the closer to the boundary, the greater the repulsion; t1 Outside the range, the boundary repulsive potential energy function is 0. is the distance between each airship and the nearest point of the no-fly zone boundary, represents the repulsive field at each airship’s location, D t2 is the range of the repulsive field at the boundary of the no-fly zone. Similar to the regional boundary repulsive function, the closer the distance to the boundary of the no-fly zone, the greater the repulsive force; the distance is greater than D t2 When , the repulsive force is 0 and the potential energy is 0.

[0072] U rep (x) = U rep1 (x)+U rep2 (x).

[0073]

[0074] Where U rep (x) is the state of the repulsive potential field; E rep is the sum of the repulsive potential energy.

[0075] Then, the maximum wind resistance constraint, regional boundary constraint, and no-fly zone constraint are established. The specific formulas are as follows:

[0076]

[0077] Correspondingly, the wind speed at the location of the airship track point must not exceed the maximum wind speed of the airship, and must not be outside the mission area or within the no-fly zone.

[0078] In some embodiments, when executing step 3, the specific steps may be as follows:

[0079] The initial state x0 of the airship cluster is obtained, and the wind field data at time t is loaded through the flight control computer and recorded as the global wind field state w(t).

[0080] Through flight control computer monitoring, the current airship cluster status x(t) is obtained, including the position of each airship, remaining power, local wind field data, current speed and yaw angle, etc.;

[0081] The planning step length Δt is set, and according to the latest forecast external environment information and airship cluster system information, multiple objective functions and constraints are processed by weighted summation and static penalty function method to integrate them into the objective function to be optimized. The specific formula is as follows:

[0082]

[0083] Where w1 and w2 represent the weights of the coverage gap and energy gap objective functions, respectively, p is the penalty coefficient of each constraint, and w1S r To cover the objective function term, w2E ea is the energy consumption optimization objective function term, w3E att To represent the gravitational potential energy term of the airship's wind resistance, w4E rep The repulsive potential energy term related to the regional residence of the airship, and m is the number of constraints.

[0084] In some embodiments, when executing step 4, the specific steps may be as follows:

[0085] In this embodiment, the particle swarm algorithm with improved inertia weight is designed to solve the single step length Δ t In order to find the optimal cooperative flight mode within the system, a dynamic adaptive weight based on the exponential function and the change of population evolution degree is proposed.

[0086] like Figure 7 As shown in the figure, in order to enhance the algorithm's ability to cope with the degree of population evolution, that is, whether the particles have a convergence trend to adjust the diversity of the population, the ratio of the fitness values ​​of each two rounds, that is, the concept of population evolution discreteness Fit(k), is used to describe the current state of particle swarm evolution. Fit(k) can be expressed as:

[0087]

[0088] Among them, fitness(k) represents the fitness function value of the kth generation.

[0089] At the same time, an exponential function is introduced to balance the evolutionary discreteness of the population, making it present a linear characteristic, and the improved inertia weight is Where k is the evolutionary generation; T is the total evolutionary generation; W max is the maximum inertia weight, generally 0.9; W min is the minimum inertia weight, which is usually 0.4; b1 and b2 are exponential constants, which are usually -5 and 0.1.

[0090] The improved particle swarm algorithm update formula is:

[0091] v ij =w·v ij +C1·r1·(P ij -X ij )+C2·r2·(P gj -X ij ).

[0092] X ij =v ij +X ij .

[0093] Among them, v ij is the particle update speed, X ij is the particle position, which is the alternative solution. C1 represents the self-learning factor, and C2 represents the social learning factor. ij is the optimal position in the particle's self-search experience, G ij is the optimal position in the global self-sustaining search experience, and w represents the inertia weight of the population. r1 and r2 are random numbers that provide randomness for the particle movement direction and enhance the particle's global optimization ability.

[0094] The specific encoding method is: randomly select alternative control quantities within the speed range, that is, the airship cluster speed and turning angle. The track point obtained after Δt can be any point in the mission area. Through population evolution optimization, the optimal flight mode within the planning step is found.

[0095] In some embodiments, when executing step 5, the specific steps may be as follows:

[0096] like Figure 6 As shown, a rolling planning framework is designed. If the forecast environment information is updated at this time, return to step 3 to update the forecast external environment information and then perform rolling planning; if the forecast external environment information is not updated at this time, update the airship position and related information according to the optimal coordinated flight method, and perform the optimal control quantity planning for the next step until the total mission time T is completed.

[0097] Embodiment 2

[0098] like Figure 8 As shown, this embodiment provides a stratospheric airship trajectory rolling planning system based on environmental forecast information, including:

[0099] The information acquisition module 801 is used to acquire stratospheric airship cluster information and environmental forecast information; the stratospheric airship cluster information includes position attitude information, energy information, coverage information and potential energy information.

[0100] The model building module 802 is used to respectively build a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model and a wind field perception and residence model according to the position and attitude of the stratospheric airship, the energy information, the coverage information and the potential energy information.

[0101] The objective function establishment module 803 is used to establish the objective function to be optimized within a single step based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field perception and residence model, and the latest environmental forecast information.

[0102] The control input solving module 804 is used to solve the optimal control input within a single step according to the objective function to be optimized within a single step based on the improved particle swarm algorithm; the improved particle swarm algorithm is a particle swarm algorithm with improved inertia weight.

[0103] The rolling planning module 805 is used to determine whether the environmental forecast information has changed based on the rolling planning strategy when performing multi-boat track point planning for each step; if so, re-establish the objective function to be optimized within a single step based on the changed environmental forecast information; if not, perform multi-boat track point planning according to the optimal control input until the area coverage task is completed.

[0104] Among them, the objective function establishment module is used to establish the objective function according to the formula Establish the objective function to be optimized within a single step.

[0105] Among them, w1 and w2 represent the weights of the coverage vacancy and energy vacancy objective functions respectively, p is the penalty coefficient of each constraint, and w1S r To cover the objective function term, w2E ea is the energy consumption optimization objective function term, w3E att To represent the gravitational potential energy term of the airship's wind resistance, w4E rep The repulsive potential energy term related to the regional residence of the airship, and m is the number of constraints.

[0106] In summary, this application has the following technical effects:

[0107] (1) This application takes into account the wind resistance and energy cycle issues of stratospheric airships, and the impact of the external environment on the airship cluster mission. The planned trajectory is more in line with actual flight and mission requirements.

[0108] (2) This application takes into account the characteristics of the wind field where the airship is located, which is spatially uneven and changes over time, and designs a rolling planning strategy that can update environmental forecast information and the status of the airship cluster system in real time, establish and solve the objective function of each airship in steps, and solve the target track points that meet the real-time environmental conditions. The resulting trajectory has higher practicality.

[0109] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A stratospheric airship trajectory rolling planning method based on environmental forecast information, characterized in that: The stratospheric airship trajectory rolling planning method comprises: Step 1: Acquire stratospheric airship cluster information and environmental forecast information; the stratospheric airship cluster information includes position attitude information, energy information, coverage information and potential energy information; Step 2: According to the position and attitude information of the stratospheric airship, the energy information, the coverage information and the potential energy information, a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model and a wind field perception and residence model are respectively established; Step 3: Based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field sensing and residence model, and the latest environmental forecast information, establish the objective function to be optimized within a single step length; Step 4: according to the objective function to be optimized within a single step, based on the improved particle swarm algorithm, the optimal control input within the single step is solved; the improved particle swarm algorithm is a particle swarm algorithm with improved inertia weight; Step 5: Based on the rolling planning strategy, when planning the multi-boat track points for each step, determine whether the environmental forecast information changes; If yes, then re-execute steps 3-4 based on the changed environmental forecast information; If not, multi-boat track point planning is performed according to the optimal control input until the area coverage mission is completed.

2. The method for stratospheric airship trajectory rolling planning based on environmental forecast information according to claim 1, characterized in that: The stratospheric airship kinematic model is specifically: Where [x,y] T represents the position coordinates of each airship, V represents the airspeed of the airship, ψ represents the yaw angle of the airship airspeed relative to the positive x-axis, V wind is the wind speed at the location of the airship, β is the wind speed V wind The angle with the positive x-axis.

3. The method for stratospheric airship trajectory rolling planning based on environmental forecast information according to claim 1, characterized in that: The energy cycle model is specifically established as follows: Eem,i=Efull-Eprop,i-Eav-Eload+Psolar,i·Δt; Among them, E full is the maximum energy storage capacity of each airship, E prop,i is the energy consumed by the propulsion system of the i-th airship, E load Energy consumed by the payload, E av is the energy consumed by the avionics system, E em,i is the energy vacancy of the energy storage battery of the i-th airship, Psolar,i is the solar power that the i-th airship can currently absorb and convert, V represents the airspeed of the airship, and Δt is the time interval.

4. The method for rolling trajectory planning of a stratospheric airship based on environmental forecast information according to claim 1, characterized in that: The multi-cooperative coverage model is specifically: Among them, cj is the judgment mark of whether the grid is covered, C represents the total number of covered grids, L x , L y represents the side length of the task area, d is the grid granularity, S d is the total number of grids, S r Represents the uncovered portion of the area.

5. The method for stratospheric airship trajectory rolling planning based on environmental forecast information according to claim 1, characterized in that: The wind field perception and residence model is specifically as follows: Uatt(x)=Ppropw·Δtw; Urep(x)=Urep1(x)+Urep2(x); In the formula, Δd x is the x-axis component from the predicted position to the original position, Δd y The distance from the estimated position to the original position y axis component, β is the angle between the wind direction of the wind field at the predicted location and the x-axis of the ground coordinate system, U att (x) is the average wind potential energy of the airship cluster in the coordinated state x, d(q,q0) i is the set of distances between each airship position and the boundary of the region, q and q0 represent the position of the airship and the point closest to the boundary, d ox With d oy The values ​​of x and y are equal, D t1 is the repulsion function threshold of the region boundary, is the repulsive potential field value at the location of each airship, k1 is a constant value, is the distance between each airship and the nearest point of the no-fly zone boundary, is the repulsive field at each airship’s location, D t2 is the range of the repulsive field at the boundary of the no-fly zone, Urep(x) is the state of the repulsive potential field, and Erep is the sum of the repulsive potential energy.

6. The method for stratospheric airship trajectory rolling planning based on environmental forecast information according to claim 1, characterized in that: Based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field perception and residence model, and the latest environmental forecast information, the objective function to be optimized within a single step is established, specifically including: According to the formula Establish the objective function to be optimized within a single step; Among them, w1 and w2 represent the weights of the coverage vacancy and energy vacancy objective functions respectively, p is the penalty coefficient of each constraint, and w1S r is the covering objective function term, w2Eea is the energy consumption optimization objective function term, w3Eatt is the gravitational potential energy term representing the wind resistance of the airship, and w4E rep The repulsive potential energy term related to the regional residence of the airship, and m is the number of constraints.

7. The method for stratospheric airship trajectory rolling planning based on environmental forecast information according to claim 1, characterized in that: The update formula of the improved particle swarm algorithm is: v ij =w·v ij +C1·r1·(P ij -X ij )+C2·r2·(P gj -X ij ); X ij =v ij +X ij ; Among them, v ij is the particle update speed, X ij is the particle position; C1 represents the self-learning factor, C2 represents the social learning factor, P ij is the optimal position in the particle's self-search experience, G ij is the optimal position in the global self-sustaining search experience, w represents the inertia weight of the population, r1 and r2 are random numbers, which provide randomness for the direction of particle movement and enhance the global optimization ability of particles.

8. The method for stratospheric airship trajectory rolling planning based on environmental forecast information according to claim 1, characterized in that: The expression of improved inertia weight in the particle swarm algorithm with improved inertia weight is: Among them, k is the evolutionary generation, T is the total evolutionary generation, W max is the maximum inertia weight, W min is the minimum inertia weight, b1 and b2 are exponential constants.

9. A stratospheric airship trajectory rolling planning system based on environmental forecast information, characterized in that: include: An information acquisition module is used to obtain stratospheric airship cluster information and environmental forecast information; The stratospheric airship cluster information includes position and attitude information, energy information, coverage information and potential energy information; A model building module is used to respectively establish a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model, and a wind field perception and residence model according to the position and attitude information of the stratospheric airship, the energy information, the coverage information, and the potential energy information; An objective function establishment module is used to establish an objective function to be optimized within a single step length based on the stratospheric airship kinematic model, the energy cycle model, the initial state information of the multi-cooperative coverage model and the wind field perception and residence model, and the latest environmental forecast information; The control input solving module is used to solve the optimal control input within a single step length based on the objective function to be optimized within a single step length and based on the improved particle swarm algorithm; The improved particle swarm algorithm is a particle swarm algorithm with improved inertia weight; The rolling planning module is used to determine whether the environmental forecast information has changed based on the rolling planning strategy when planning the multi-boat track points for each step; if so, the objective function to be optimized within a single step is re-established based on the changed environmental forecast information; if not, the multi-boat track points are planned according to the optimal control input until the area coverage task is completed.

10. The system for rolling trajectory planning of stratospheric airship based on environmental forecast information according to claim 9, characterized in that: The objective function establishment module is used to establish the objective function according to the formula Establish the objective function to be optimized within a single step; Among them, w1 and w2 represent the weights of the coverage vacancy and energy vacancy objective functions respectively, p is the penalty coefficient of each constraint, and w1S r To cover the objective function term, w2E ea is the energy consumption optimization objective function term, w3E att To represent the gravitational potential energy term of the airship's wind resistance, w4E rep The repulsive potential energy term related to the regional residence of the airship, and m is the number of constraints.

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