A method and system for stratospheric airship trajectory rolling planning based on environmental forecast information

Abstract

This application discloses a rolling trajectory planning method and system for stratospheric airships based on environmental forecast information, relating to the field of automatic control technology. The method includes acquiring stratospheric airship swarm and environmental forecast information, and based on this information, constructing kinematic models, energy cycle models, multi-cooperative coverage models, and wind field perception and dwell models for the airships. Based on the initial state information of these models combined with the latest environmental forecast data, an objective function to be optimized is established. During the rolling planning strategy, at each step of multi-airship waypoint planning, the environmental forecast information is checked for updates. If updates are found, the step of establishing the objective function to be optimized is repeated based on the new environmental forecast information. If no changes are found, multi-airship waypoint planning is performed according to the determined optimal control input. This application can plan flight trajectories that better meet actual flight and mission requirements.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, and in particular to a method and system for rolling trajectory planning of stratospheric airships based on environmental forecast information. Background Technology

[0002] Stratospheric airships are high-altitude unmanned aerial vehicles that achieve controllable flight by using low-density gas buoyancy. They feature long-duration loiter time, precise controllability, high-altitude observation capabilities, and large payload capacity, making them promising for a wide range of applications. Airship swarms represent the future trend in stratospheric airship deployment. Long-endurance, large-area, continuous regional coverage missions are fundamental to airship effectiveness. However, facing dynamic wind fields, improving the long-endurance loiter time and autonomous flight capabilities of airship swarms is a prerequisite for ensuring the mission capabilities of stratospheric airships. On the one hand, stratospheric airships need to consider the constraints imposed by the dynamic flight environment, such as no-fly zones, mission area boundaries, and dynamically non-uniform wind fields. On the other hand, to improve the long-endurance loiter time of stratospheric airships, energy consumption during mission execution must be minimized, and constraints related to the energy cycle system of multiple airships must be considered, such as the daily energy storage of airships must exceed a minimum limit. Finally, the real-time mission performance of the airship swarm needs to be considered, such as the area coverage rate. To improve the performance and reliability of long-endurance regional coverage missions, it is necessary to enhance the autonomy and real-time performance of stratospheric airship trajectory planning. A multi-airship trajectory planning method that simultaneously considers dynamic wind fields, optimizes energy storage consumption, and regional coverage is proposed.

[0003] Currently, some scholars have conducted relevant research on trajectory planning for unmanned aerial vehicle swarms in dynamic environments. However, stratospheric airships are weakly powered aircraft, and dynamic wind fields have a significant impact on their flight. It is necessary to calculate reachable waypoints with low wind speeds and energy consumption through trajectory planning. Furthermore, the continuous area coverage missions performed have unique characteristics and do not have a designated route endpoint. Therefore, current trajectory planning methods are not applicable to stratospheric airships. Summary of the Invention

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

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

[0006] Firstly, this application provides a method for rolling planning of stratospheric airship trajectories based on environmental forecast information, including:

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

[0008] Step 2: Based on the position and attitude information of the stratospheric airship, the energy information, the coverage information, and the potential energy information, establish the stratospheric airship kinematic model, the energy cycle model, the multi-cooperative coverage model, and the wind field perception and dwell model, respectively.

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

[0010] Step 4: Based on the objective function to be optimized within a single step, solve for the optimal control input within a single step using the improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm with improved inertia weight.

[0011] Step 5: Based on the rolling planning strategy, determine whether the environmental forecast information has changed when planning the multi-vessel track points for each step.

[0012] If so, then based on the changed environmental forecast information, repeat steps 3-4;

[0013] If not, then multi-vessel waypoint planning will be performed according to the optimal control input until the area coverage mission is completed.

[0014] Secondly, this application provides a stratospheric airship trajectory rolling planning system based on environmental forecast information, including:

[0015] The information acquisition module is 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] The model building module is used to build a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model, and a wind field perception and dwell model based on the position and attitude information, energy information, coverage information, and potential energy information of the stratospheric airship, respectively.

[0017] The objective function establishment module is used to establish the objective function to be optimized within a single step based on the initial state information of the stratospheric airship kinematic model, the energy cycle model, the multi-cooperative coverage model and the wind field sensing and dwelling model, as well as the latest environmental forecast information.

[0018] The control input solving module is used to solve for the optimal control input within a single step based on the objective function to be optimized within a single step, using an improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm with improved inertia weight.

[0019] The rolling planning module is used to determine whether the environmental forecast information has changed when planning multi-vessel track points for each step based on the rolling planning strategy. If it has, the objective function to be optimized within a single step is re-established based on the changed environmental forecast information. If not, multi-vessel track point planning is performed according to the optimal control input until the regional coverage task is completed.

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

[0021] This application provides a rolling planning method and system for stratospheric airship trajectories based on environmental forecast information. The first step involves collecting detailed information on stratospheric airship swarms and environmental forecast data. This information specifically covers key parameters such as their state, energy, coverage area, and wind potential energy. The second step involves constructing kinematic models, energy cycle models, multi-cooperative coverage models, and wind field perception and dwell models for the stratospheric airships based on the information collected in the first step. These models aim to comprehensively reflect the airships' flight characteristics, energy management, cooperative operation capabilities, and adaptability to wind fields. The third step integrates the initial state information of each model constructed in the second step with the latest environmental forecast information to construct an objective function to be optimized within a single time step. This function aims to evaluate and optimize the airship's flight trajectory within a given time step. The fourth step uses an improved particle swarm optimization algorithm (especially one with improved inertia weights) to solve the objective function from the third step to determine the optimal control input within a single time step. The fifth step involves implementing the rolling planning strategy. When planning multi-ship trajectories at each time step, the system first checks whether the environmental forecast information has changed. If the environmental forecast information has been updated, steps three and four are re-executed based on the new environmental forecast information; if the environmental forecast information has not changed, multi-ship trajectory point planning is performed 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 environmental forecast information and airship swarm system status in real time, thereby solving for target trajectory points that meet real-time environmental conditions. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a stratospheric airship trajectory rolling planning method based on environmental forecast information in one embodiment of this application;

[0024] Figure 2 A schematic diagram of a wind field provided in an embodiment of this application;

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

[0026] Figure 4 This is a schematic diagram of the coverage effect at a waypoint provided in an embodiment of this application;

[0027] Figure 5 This is a diagram illustrating the multi-boat trajectory planning effect provided in one embodiment of this application.

[0028] Figure 6 A rolling planning process provided in one embodiment of this application;

[0029] Figure 7 This application provides a control quantity optimization process according to an embodiment of the present application;

[0030] Figure 8 This is a schematic diagram of the structure of a stratospheric airship trajectory rolling planning system based on environmental forecast information, provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0032] Example 1

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

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

[0035] Step 2: Based on the position and attitude information of the stratospheric airship, the energy information, the coverage information, and the potential energy information, establish the stratospheric airship kinematic model, the energy cycle model, the multi-cooperative coverage model, and the wind field perception and dwell model, respectively.

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

[0037] Step 4: Based on the objective function to be optimized within a single step, solve for the optimal control input within a single step using the improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm with improved inertia weight.

[0038] Step 5: Based on the rolling planning strategy, determine whether the environmental forecast information has changed when planning the multi-vessel track points for each step.

[0039] If so, then based on the changed environmental forecast information, repeat steps 3-4;

[0040] If not, then multi-vessel waypoint planning will be performed according to the optimal control input until the area coverage mission is completed.

[0041] In some embodiments, step 1 can be performed as follows:

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

[0043] Specifically, the status information includes the airship's location coordinates, airspeed, and wind speed at its location; the energy information includes the energy consumed by the propulsion system, payload, and avionics systems, as well as the energy shortage in each airship's storage batteries; the coverage information includes indicators for whether a grid is covered, the total number of covered grids, and the side length of the mission area; and the potential energy information includes the x-axis component from the expected location to the original location, the y-axis component from the expected location to the original location, and the angle between the wind direction at the expected location and the x-axis of the ground coordinate system.

[0044] In some embodiments, step 2 can be performed as follows:

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

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

[0047]

[0048] In the formula, [x,y] TV represents the coordinates of each airship's location; V represents the airship's airspeed in m / s; ψ represents the airship's yaw angle relative to the positive x-axis, where ψ ∈ [-π, π]. wind β is the wind speed at the location of the airship, in m / s; V is the wind speed. wind The angle between the airship and the positive x-axis can be used to determine the magnitude of the meridional and zonal winds at the airship's location using β.

[0049] The airship's position is influenced by both its airspeed and the wind speed at its location; therefore, constraints are established regarding speed and maximum turning angle.

[0050] V min ≤V≤V max .

[0051]

[0052] Where γ is the turning angle, γ max This is the maximum turning angle.

[0053] Then, an energy cycle model is established, where, within Δt, E prop To propel the system, energy is consumed, E load Energy consumed by the payload, E av The energy consumed by the avionics system, the sum of the three is the total energy consumption, E em,i The energy cycle model expression for the energy gap in the energy storage batteries of each airship is as follows:

[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 absorbed and converted power; F T η is the thrust, V is the airship speed, and η is the velocity. mot For motor efficiency, η prop For propeller efficiency; P ijThe power of solar energy absorbed and converted by the solar panels in the i-th row and j-th column can be calculated from the position, attitude, and time of the airship; η is the photovoltaic array conversion efficiency, P prop The propulsion power of the airship can be calculated from its current speed V, E prop This represents the energy consumed by each boat at a step length Δt.

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

[0060]

[0061]

[0062] In the formula, E ea To represent the average energy not fully stored, count1 and count2 represent the minimum energy the airship must have, which must not be 0, and the energy absorbed and stored in a day must be greater than or equal to the energy consumed.

[0063] Among them, such as Figure 4 The aforementioned multi-cooperative coverage model uses regional rasterization to calculate regional coverage. The airship's observation coverage area is defined as a circular region, and the ratio of the number of grid cells covering the circular region to the total number of grid cells is the regional area coverage rate, which is used as an evaluation metric. Specifically, it can be as follows:

[0064]

[0065] Among them, c j This is a flag used to determine whether a raster is covered, where C represents the total number of covered raster cells, and L... x L y The side length of the task region is represented by d, where d is the fine-grained grid size, and S is the side length of the task region. d S represents the total number of grid cells. r This represents the uncovered portion of the area.

[0066] The wind field sensing and dwell model is inspired by the artificial potential field method. The gravitational potential field is used for the wind field sensing model, and the repulsive potential field is used for the dwell model, as shown in the following formula:

[0067]

[0068] U att (x)=P propw ·Δt w .

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

[0070]

[0071] In the formula, d(q,q0) i Let d be the set of distances between each airship's position and the region boundary, where q and q0 represent the points closest to the boundary between the airship's position and the boundary, respectively. ox With d oy The values ​​of D are equal to x and y respectively; t1 The threshold value of the repulsive force function at the region boundary represents the range of action of the boundary repulsive force. This represents the repulsive potential field value at the location of each airship, where k1 is a constant. In D... t1 Within the range, the closer to the boundary, the greater the repulsive force; in D t1 Outside the range, the boundary repulsive potential energy function is 0. The distance between each airship and the nearest point on the no-fly zone boundary. D represents the repulsive field at the location of each airship. t2 This represents the range of the repulsive force field at the boundary of the no-fly zone. Similar to the repulsive force function at the boundary of a region, the closer to the no-fly zone boundary, the greater the repulsive force; the greater the distance, the stronger the repulsive force. t2 At this time, the repulsive force is 0, and the potential energy is 0.

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

[0073]

[0074] In the formula, U rep (x) represents the repulsive potential field state; E rep It is the sum of repulsive potential energy.

[0075] Then, establish the maximum wind resistance constraint, regional boundary constraint, and no-fly zone constraint, with the specific formulas as follows:

[0076]

[0077] This means that the wind speed at the location of the airship's track point must not exceed the airship's maximum wind resistance speed, and it must not be outside the mission area or within the no-fly zone.

[0078] In some embodiments, step 3 can be performed as follows:

[0079] Obtain the initial state x0 of the airship cluster, and load the wind field data at time t through the flight control computer, which is denoted as the global wind field state w(t).

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

[0081] Setting a planning step size Δt, and based on the latest forecasted external environmental information and airship swarm system information, multiple objective functions and constraints are processed using weighted summation and static penalty function methods, and integrated into an objective function to be optimized. The specific formula is as follows:

[0082]

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

[0084] In some embodiments, step 4 can be performed as follows:

[0085] This embodiment designs an improved particle swarm optimization algorithm with enhanced inertia weight to solve for the single-step length Δ. t To determine the optimal cooperative flight mode, a dynamic adaptive weighting method based on an exponential function and changes in population evolution is proposed.

[0086] like Figure 7 As shown, to enhance the algorithm's ability to adjust population diversity by addressing the degree of population evolution, i.e., whether particles exhibit convergence trends, the ratio of fitness values ​​between every two rounds, i.e., the population evolutionary dispersion Fit(k), is used to describe the current state of the particle swarm evolution. Fit(k) can be expressed as:

[0087]

[0088] Where fitness(k) represents the fitness function value of the kth generation.

[0089] Simultaneously, an exponential function is introduced to balance the evolutionary dispersion of the population, making it exhibit linear characteristics, and the inertia weight is improved to... Where k is the generation number; T is the total number of generations; W max The maximum inertia weight is typically set to 0.9; W min b1 is the minimum inertia weight, usually taken as 0.4; b2 are exponential constants, usually taken as -5 and 0.1.

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

[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 For particle update rate, X ij The position of the particle represents a potential solution. C1 represents the self-learning factor, and C2 represents the social learning factor. P ij G represents the optimal position during the particle's self-search process. ij The optimal position in the global autonomous search process is represented by , and w represents the inertia weight of the population. r1 and r2 are random numbers that provide randomness to the particle's motion direction, enhancing the particle's global optimization ability.

[0094] The specific encoding method is as follows: Candidate control variables, namely the airship swarm speed and turning angle, are randomly selected within the speed range. The resulting track point after Δt can be any point within the mission area. Through population evolution optimization, the optimal flight mode within the planned step size is found.

[0095] In some embodiments, step 5 can be performed as follows:

[0096] like Figure 6 As shown, a rolling planning framework is designed. If the forecasted environmental information is updated at this time, return to step 3 to update the forecasted external environmental information and then perform rolling planning. If the forecasted external environmental information is not updated at this time, update the airship position and related information according to the optimal cooperative flight mode, and proceed to the next step of optimal control quantity planning until the total mission time T ends.

[0097] Example 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 and attitude information, energy information, coverage information and potential energy information.

[0100] The model building module 802 is used to build a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model, and a wind field perception and dwell model based on 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 an objective function to be optimized within a single step based on the initial state information of the stratospheric airship kinematic model, the energy cycle model, the multi-cooperative coverage model and the wind field sensing and dwelling model, as well as the latest environmental forecast information.

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

[0103] The rolling planning module 805 is used to determine whether the environmental forecast information has changed when planning multi-vessel track points for each step based on the rolling planning strategy. If it has, the objective function to be optimized within a single step is re-established based on the changed environmental forecast information. If not, multi-vessel track point planning is performed according to the optimal control input until the area coverage task is completed.

[0104] 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] Where w1 and w2 represent the weights of the objective functions for covering the gap and energy gap, respectively, p is the penalty coefficient for each constraint, and w1S r To cover the objective function term, w2E ea For the energy consumption optimization objective function term, w3E att To represent the gravitational potential energy term for the airship's wind resistance, w4E rep The repulsive potential energy term related to the airship's regional loitering ability, where 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 of stratospheric airships, and considers the impact of the external environment on airship cluster missions. The planned trajectory is more in line with actual flight requirements and mission requirements.

[0108] (2) This application considers the characteristics of the wind field where the airship is located being non-uniform and changing over time, and designs a rolling planning strategy that can update environmental forecast information and airship cluster system status in real time, establish and solve the objective function of each airship in steps, and solve the target track point that conforms to the real-time environmental conditions. The resulting trajectory has higher practicality.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for rolling trajectory planning of stratospheric airships based on environmental forecast information, characterized in that, The stratospheric airship trajectory rolling planning method includes: Step 1: Obtain stratospheric airship swarm information and environmental forecast information; the stratospheric airship swarm information includes position and attitude information, energy information, coverage information, and potential energy information; Step 2: Based on the position and attitude information of the stratospheric airship, the energy information, the coverage information, and the potential energy information, establish the stratospheric airship kinematic model, energy cycle model, multi-cooperative coverage model, and wind field perception and dwell model, respectively. Step 3: Based on the initial state information of the stratospheric airship kinematic model, energy cycle model, multi-cooperative coverage model, and wind field sensing and dwelling model, as well as the latest environmental forecast information, establish the objective function to be optimized within a single step. Step 4: Based on the objective function to be optimized within a single step, solve for the optimal control input within a single step using the improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm is a particle swarm optimization algorithm with improved inertia weight. Step 5: Based on the rolling planning strategy, determine whether the environmental forecast information has changed when planning the multi-vessel track points for each step. If so, then based on the changed environmental forecast information, repeat steps 3-4; If not, then plan the multi-vessel waypoints according to the optimal control input until the area coverage mission is completed; Based on the initial state information of the stratospheric airship kinematic model, energy cycle model, multi-cooperative coverage model, and wind field sensing and dwell model, as well as the latest environmental forecast information, an 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; Where w1 and w2 represent the weights of the objective functions for covering the gap and energy gap, respectively, p is the penalty coefficient for each constraint, and w1S r To cover the objective function term, w2E ea For the energy consumption optimization objective function term, w3E att To represent the gravitational potential energy term for the airship's wind resistance, w4E rep The repulsive potential energy term related to the airship's regional loiterness, where m is the number of constraints; The constraints include minimum energy storage constraints, maximum wind resistance constraints, regional boundary constraints, and no-fly zone constraints.

2. The method for rolling planning of stratospheric airship trajectory based on environmental forecast information according to claim 1, characterized in that, The kinematic model of the stratospheric airship is as follows: Where, [x,y] T This represents the coordinates of each airship's location, where V represents the airship's airspeed, and ψ represents the airship's yaw angle relative to the positive x-axis. wind Let V be the wind speed at the location of the airship, and β be the wind speed V. wind The angle between the x-axis and the positive x-axis.

3. The method for rolling planning of stratospheric airship trajectory based on environmental forecast information according to claim 1, characterized in that, The energy cycle model is specifically as follows: AND em,i =And full -AND prop,i -AND av -AND load +P solar,i ·Δt; Among them, E full For the maximum energy storage of each airship, E prop,i E consumes energy for the propulsion system of the i-th airship. load Energy consumed by the payload, E av For the energy consumed by the avionics system, E em,i For the energy shortage in the energy storage battery of the i-th airship, P solar,i Let V represent the solar power that the i-th airship can currently absorb and convert, V represent the airship's airspeed, and Δt represent the time interval.

4. The method for rolling planning of stratospheric airship trajectory based on environmental forecast information according to claim 1, characterized in that, The multi-cooperative coverage model is specifically as follows: Among them, c j This is a flag used to determine whether a raster is covered, where C represents the total number of covered raster cells, and L... x L y The side length of the task region is represented by d, where d is the fine-grained grid size, and S is the side length of the task region. d S represents the total number of grid cells. r This represents the uncovered portion of the area.

5. The method for rolling planning of stratospheric airship trajectory based on environmental forecast information according to claim 1, characterized in that, The wind field sensing and dwell model is specifically as follows: U att (x)=P propw ·Δt w ; U rep (x)=U rep1 (x)+U rep2 (x); In the formula, Δd x Δd represents the x-axis component from the predicted position to the original position. y U represents the y-axis component from the predicted location to the original location, β represents the angle between the wind direction at the predicted location and the x-axis of the ground coordinate system, and U represents the y-axis component from the predicted location to the original location. att (x) represents the average wind potential energy of the airship cluster in the cooperative state x, d(q,q0). i Let d be the set of distances between each airship's position and the region boundary, where q and q0 represent the points closest to the boundary between the airship's position and the boundary, respectively. ox With d oy The values ​​of D are equal to x and y respectively. t1 The threshold value for the repulsive force function at the region boundary. Here, k1 represents the repulsive potential field value at the location of each airship, and k1 is a constant. The distance between each airship and the nearest point on the no-fly zone boundary. D represents the repulsive field at the location of each airship. t2 U represents the range of the repulsive field at the boundary of the no-fly zone. rep (x) represents the repulsive potential field state, E rep It is the sum of repulsive potential energy.

6. The method for rolling planning of stratospheric airship trajectory based on environmental forecast information according to claim 1, characterized in that, The updated formula for the improved particle swarm optimization algorithm is as follows: 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 For particle update rate, X ij P represents the particle position; C1 represents the self-learning factor, C2 represents the social learning factor, and P... ij G represents the optimal position during the particle's self-search process. ij The optimal position in the global autonomous search process is represented by w, which represents the inertial weight of the population. r1 and r2 are random numbers that provide randomness to the particle's motion direction and enhance the particle's global optimization ability.

7. The method for rolling trajectory planning of a stratospheric airship based on environmental forecast information according to claim 1, characterized in that, The expression for the improved inertia weight in the particle swarm optimization algorithm with improved inertia weight is as follows: Where k is the generation number, T is the total number of generations, and W max For the maximum inertia weight, W min b1 and b2 are exponential constants, representing the minimum inertia weight.

8. A stratospheric airship trajectory rolling planning system based on environmental forecast information, used to implement the stratospheric airship trajectory rolling planning method based on environmental forecast information as described in any one of claims 1-7, characterized in that, include: The information acquisition module is used to acquire information on stratospheric airship clusters and environmental forecast information; The stratospheric airship cluster information includes position and attitude information, energy information, coverage information, and potential energy information; The model building module is used to build a stratospheric airship kinematic model, an energy cycle model, a multi-cooperative coverage model, and a wind field perception and dwell model based on the position and attitude information, energy information, coverage information, and potential energy information of the stratospheric airship, respectively. The objective function establishment module is used to establish the objective function to be optimized within a single step based on the initial state information of the stratospheric airship kinematic model, energy cycle model, multi-cooperative coverage model and wind field sensing and dwelling model, as well as the latest environmental forecast information. The control input solving module is used to solve for the optimal control input within a single step based on the improved particle swarm optimization algorithm, according to the objective function to be optimized within a single step. The improved particle swarm optimization algorithm is a particle swarm optimization algorithm with improved inertia weight; The rolling planning module is used to determine whether the environmental forecast information has changed when planning multi-vessel track points for each step based on the rolling planning strategy. If it has, the objective function to be optimized within a single step is re-established based on the changed environmental forecast information. If not, multi-vessel track point planning is performed according to the optimal control input until the regional coverage task is completed.

9. A stratospheric airship trajectory rolling planning system based on environmental forecast information according to claim 8, 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; Where w1 and w2 represent the weights of the objective functions for covering the gap and energy gap, respectively, p is the penalty coefficient for each constraint, and w1S r To cover the objective function term, w2E ea For the energy consumption optimization objective function term, w3E att To represent the gravitational potential energy term for the airship's wind resistance, w4E rep The repulsive potential energy term related to the airship's regional loitering ability, where m is the number of constraints.

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