Two-stage rendezvous maintenance task planning method based on dung beetle optimization algorithm

Abstract

The invention discloses a two-stage rendezvous maintenance task planning method based on a dung beetle optimization algorithm. The method comprises the following steps: S1, establishing an optimization model of a multi-target spacecraft maintenance task; s2, a first-stage optimization algorithm is designed, an improved genetic algorithm is adopted, an initial population is generated, and fitness calculation is carried out; s3, entering second-stage optimization on the basis of an optimization result of the first stage, and adopting a dung beetle optimization algorithm transfer angle as an optimization variable; s4, dynamically adjusting variables in combination with a two-stage solving process of a genetic algorithm and a dung beetle algorithm to realize an optimal solution of task planning; and S5, verifying the performance of the optimization algorithm based on simulation data, and evaluating the effectiveness and applicability of the method through fuel consumption and task completion time indexes. Through introduction of a two-stage optimization strategy, a dynamic path adjustment mechanism and a comprehensive constraint condition, the defects of the prior art in the aspects of global optimization capability, fuel efficiency and time balance and task planning adaptability are overcome.

Description

Technical Field

[0001] The invention relates to the field of aerospace technology, and in particular to a two-stage rendezvous maintenance task planning method based on a dung beetle optimization algorithm. Background Art

[0002] With the rapid development of aerospace technology, the maintenance mission of multi-target spacecraft occupies an important position in the modern aerospace field. The maintenance and fuel replenishment tasks of target spacecraft can be completed efficiently through reasonable planning and path optimization of service spacecraft. However, there are still many challenges in the maintenance mission of multi-target spacecraft. The core issues are concentrated on the coordination of mission planning and path optimization, the reliability of mission execution and the efficiency of resource allocation.

[0003] At present, traditional spacecraft mission planning methods usually use fixed rules or local search-based optimization algorithms to solve maintenance order and path design problems. Most traditional methods use pre-set planning rules or heuristic algorithms to perform preliminary optimization of maintenance tasks. For example, some methods plan the maintenance order of the target spacecraft according to a fixed priority, then calculate the fuel consumption based on the orbit transfer requirements, and perform simple path planning based on this. The existing methods have certain feasibility, but they are prone to the following problems in complex scenarios of multi-objective tasks:

[0004] On the one hand, traditional maintenance sequence planning methods have high computational complexity and are difficult to achieve global optimization when faced with multi-target tasks. The service spacecraft in the maintenance mission needs to reasonably allocate resources among multiple target spacecraft. However, due to the lack of deep modeling of complex dependencies between multiple tasks in traditional methods, task planning efficiency is often low. Traditional methods tend to adopt local optimization strategies, which are easy to fall into local optimal solutions and make it difficult to obtain global optimal solutions.

[0005] On the other hand, there is an inherent conflict between fuel efficiency and time constraints in spacecraft path planning. In actual missions, orbital transfers need to consider both fuel consumption and mission time constraints. Traditional path planning methods usually independently optimize fuel consumption and time based on simple models, ignoring the coupling relationship between the two, making it difficult to achieve comprehensive coordination of resources. In addition, existing methods often set fixed values ​​for variables in path planning and lack the ability to dynamically adjust, which makes it difficult for path planning results to adapt to real-time changes in mission requirements.

[0006] In summary, the existing technology has obvious deficiencies in the global optimization capability of multi-objective spacecraft maintenance tasks, the ability to balance fuel efficiency and time constraints, and the adaptability of dynamic task planning. The defects of the existing technology directly affect the execution efficiency and resource utilization of spacecraft maintenance tasks. A new method is urgently needed to solve the above problems and achieve global optimization and efficient scheduling of multi-objective spacecraft maintenance tasks. Summary of the invention

[0007] One purpose of the present invention is to propose a two-stage rendezvous maintenance task planning method based on the dung beetle optimization algorithm. The present invention solves the shortcomings of the prior art in terms of global optimization capability, fuel efficiency and time balance, and task planning adaptability through the introduction of a two-stage optimization strategy, a dynamic path adjustment mechanism, and comprehensive constraints.

[0008] A two-stage rendezvous maintenance task planning method based on a dung beetle optimization algorithm according to an embodiment of the present invention comprises the following steps:

[0009] S1. Establish an optimization model for multi-objective spacecraft maintenance tasks, set the parameters of the service spacecraft and the target spacecraft, and take fuel consumption minimization as the objective function, and set time limits and fuel demand constraints;

[0010] S2. Design the optimization algorithm for the first stage, adopt the improved genetic algorithm, use the service order and transfer angle of the service spacecraft as the optimization variables to generate the initial population and perform fitness calculation, and optimize the spacecraft service order through single-circle or multi-circle orbit change according to the relationship between the orbital height difference and transfer angle between the service spacecraft and the target spacecraft;

[0011] S3. Based on the optimization results of the first stage, the second stage optimization is entered, and the transfer angle of the dung beetle optimization algorithm is used as the optimization variable to simulate the rolling, breeding, foraging and stealing behaviors of dung beetles to optimize the service spacecraft path and minimize fuel consumption;

[0012] S4. Set comprehensive constraints for the optimization model, combine the two-stage solution process of genetic algorithm and dung beetle algorithm, dynamically adjust variables, and achieve the optimal solution for task planning;

[0013] S5. Conduct numerical simulation and optimization result verification, verify the performance of the optimization algorithm based on simulation data, and evaluate the effectiveness and applicability of the method through fuel consumption and task completion time indicators.

[0014] Optionally, the S1 includes the following steps:

[0015] S11. Establish an optimization model for multi-objective spacecraft maintenance tasks and define the service spacecraft set S = {s1, s2, …, s m} and the target spacecraft set R = {r1,r2,…,r n}, where m represents the total number of service spacecraft and n represents the total number of target spacecraft;

[0016] S12. Set task time limits, including maintenance time t w =24h, fuel replenishment time t f = 24h and orbit transfer time t t≤48h;

[0017] S13. Define the fuel consumption required for a single orbit transfer as:

[0018] m 单次 =βm1+(1-β)(m1+m2);

[0019] Among them, m 单次 is the total fuel consumption of a single orbit transfer, m1 is the fuel required for a single-turn Lambert orbit transfer, m2 is the fuel required for a multi-turn phase-adjustment orbit transfer, β is the orbit transfer mode selection coefficient, a value of 1 means waiting before orbit transfer, and a value of 0 means orbit transfer before phase adjustment;

[0020] S14. Modeling fuel consumption as an optimization objective function:

[0021]

[0022] in, Indicates service spacecrafts i Transfer to target spacecraft j The required fuel for a single Lambert turn, It indicates the fuel required for multiple phase adjustment and orbit change in the same transfer. α indicates whether fuel replenishment is needed. When the value is 1, no fuel replenishment is required. When the value is 0, fuel replenishment is required.

[0023] S15. Set the transfer angle θ as the optimization variable, and define the orbital heights of the service spacecraft and the target spacecraft as h respectively. s and h r , the transfer angle range is [0°, 10°];

[0024] S16. According to the mission parameters and orbital restrictions, set comprehensive constraints, including the orbital altitude range h of the service spacecraft s ∈[400km,1000km], target spacecraft orbit altitude range h r ∈[400km,1000km], orbit ascending node right ascension range Ω∈[60°,70°], and fuel replenishment requirements.

[0025] Optionally, the S15 establishes a track transfer model according to the following two situations:

[0026] If the transfer angle can reach a given value within the orbit transfer time, the orbit transfer is completed through a single-turn Lambert orbit change;

[0027] If the transfer angle cannot reach the given value within the orbit transfer time, a single-turn Lambert orbit change is first performed with a fixed transfer angle θ0, and then the orbit adjustment is completed through multi-turn phase adjustment orbit change. The number of multi-turn phase adjustment orbit change turns is the maximum number of turns allowed within the time limit.

[0028] Optionally, S2 includes the following steps:

[0029] S21. Based on the optimization variables, the initial population is generated by real number coding. The chromosome of each individual in the initial population contains three parts: service order segment, transfer angle segment and allocation segment. The first segment is the maintenance order code, the second segment is the transfer angle code corresponding to the maintenance order, and the third segment is used to allocate the target spacecraft to different service spacecraft.

[0030] S22. Based on the fitness function and the service order and transfer angle value of each individual, the total fuel consumption of the individual is calculated, and the reciprocal of the fuel consumption is used as the fitness value;

[0031] S23. Use random traversal sampling method to select individuals in the population, give priority to individuals with high fitness values ​​to enter the crossover and mutation stages, and retain excellent genes;

[0032] S24. Use the partial matching crossover algorithm to keep the service sequence segment chromosome unique, and generate a new transfer angle value through weight-constrained mixed crossover:

[0033] θ new =w1·θ parent1 +w2·θ parent2 ;

[0034] Among them, θ parent1 and θ parent2 They represent the transfer angle values ​​of the two parent individuals respectively, w1 and w2 are weight coefficients, and the calculation of weights is related to the fitness values ​​of the parents;

[0035] Based on the task priority weight allocation, the target spacecraft is reallocated to ensure that the allocation result achieves a dynamic balance between fuel consumption and spacecraft capability constraints:

[0036]

[0037] Among them, A new (i) Assign a new service spacecraft to the target spacecraft, P k represents the remaining fuel of the service spacecraft, and are the single-turn and multi-turn fuel consumption of the service spacecraft for the target spacecraft mission, respectively;

[0038] S25. Perform exchange mutation on the service order segment, randomly select two target spacecraft to exchange their service order, and perform random perturbation mutation on the transfer angle segment:

[0039]

[0040] Among them, Δθ is the variation value of the transfer angle, which obeys the uniform distribution δ is the variable step length, which keeps the transfer angle within the defined range [0°, 10°];

[0041] S26. Sort the current population by fitness value, and retain the individuals with the highest fitness directly to the next generation;

[0042] S27. Repeat the selection, crossover, mutation and elite retention steps until the set maximum number of generations is reached or the population fitness change is lower than the preset threshold, and finally obtain the optimal service order and transfer angle.

[0043] Optionally, S3 includes the following steps:

[0044] S31. Based on the service order and transfer angle optimized in the first stage as input, generate the initial dung beetle population, the position x of each dung beetle individual t represents the transfer angle of the service spacecraft in path planning, and the range is limited to [0°, 10°];

[0045] S32. Simulate the rolling optimization behavior of the dung beetle along the path direction, approach the global optimal solution, and update the position of the dung beetle:

[0046] x t+1 =x t +aRx t +b(x t -x w );

[0047] Among them, x t represents the current spacecraft transfer angle, x w is the global worst transfer angle, simulating the suboptimal solution with the highest fuel consumption, a and R simulate direction adjustment and environmental disturbance, and b is the rolling adjustment coefficient, which optimizes the dynamic balance ability of the spacecraft between fuel and time;

[0048] S33. When the service spacecraft encounters an unsolvable or inefficient path during path optimization, it readjusts its direction through the dance phase:

[0049] x t+1 =x t +tanθ ′ |x t -x w |;

[0050] S34. Based on the local optimization of the service spacecraft path adjustment, set the boundary to make the newly generated path reasonable under the fuel consumption and time constraints:

[0051] L ′ =max(x b (1-R t ),L);

[0052] U ′ =min(x b (1+R t ),U);

[0053] x offspring =x b +b1(U ′ -x b )+b2(x b -L ′ );

[0054] Among them, x b represents the current local optimal solution. The boundary L',U' is calculated by combining the orbit height and the orbit change time constraint. The multiplication coefficients b1 and b2 control the exploration range of the new path. L ′ To serve the minimum orbit adjustment range that the spacecraft can accept during the mission execution, U ′ The maximum orbit adjustment range that the service spacecraft can reach is used to avoid generating invalid solutions that exceed mission requirements. offspring Indicates the individual positions of offspring generated in the breeding stage, U is the maximum search range, and L is the minimum search range;

[0055] S35. Simulate the behavior of a spacecraft searching for a suboptimal path near the global optimal orbit and adjust the dynamic boundary optimization path planning:

[0056] L ″ =max(x B (1-R t ),L);

[0057] U ″ =min(x B (1+R t ),U);

[0058] x t+1 =x t +c1(x t -L ″ )+c2(U ″ -x t );

[0059] Among them, x B represents the current optimal solution, which is the path with the minimum fuel consumption. c1 and c2 simulate the sensitivity of path variables to trajectory adjustment.

[0060] By dynamically adjusting the path boundaries, the spacecraft finds a better compromise between time constraints and fuel efficiency;

[0061] S36. Optimize the path of the current service spacecraft based on the information of the current optimal solution:

[0062] x t+1 =x B +cg(|x t -x b |+|x t -x B |);

[0063] Among them, c and g simulate the spacecraft's ability to obtain improved directions from global and local excellent paths;

[0064] S37. Calculate the fuel consumption of dung beetle individuals in path planning by combining the fuel consumption formula, and calculate the fuel consumption of dung beetle individuals in path planning by using the fitness function Select the individuals with the highest fitness to enter the next generation population;

[0065] S38. Repeat the rolling, dancing, breeding, foraging and stealing stages until the maximum number of iterations is reached or the population fitness change is less than the set threshold, and output the global optimal transfer angle x * , to complete the path optimization.

[0066] Optionally, S4 includes the following steps:

[0067] S41. According to the specific requirements of the spacecraft orbit, the orbital altitude range of the service spacecraft and the target spacecraft shall be limited, and the orbital parameters shall be combined to ensure that the spacecraft meets the physical feasibility and mission execution requirements during the orbit adjustment process;

[0068] S42. Set time limits for each key link in the maintenance task, including maintenance time, fuel replenishment time, and the maximum time window required for orbit transfer, to ensure that the service spacecraft completes the maintenance task within the specified time and coordinate the execution order of multiple tasks;

[0069] S43. To optimize the fuel efficiency of the spacecraft, the fuel saving mode and the mission time priority mode are integrated for each orbit transfer process in the mission, and the fuel consumption distribution is dynamically adjusted to balance the contradiction between fuel use and mission efficiency;

[0070] S44. Based on the service order optimization results of the first phase, the task priority of the service spacecraft is evaluated in real time, and the task service order is dynamically adjusted by integrating the orbit transfer time and fuel consumption factors, so as to give priority to completing the maintenance tasks of the target spacecraft that are time-sensitive or have a fuel demand higher than the threshold;

[0071] S45. Combine the path planning results of the second-stage dung beetle optimization algorithm to adjust the orbit transfer path of the service spacecraft in real time. The path planning adjustment aims at global optimization and maximizes the mission completion efficiency by collaboratively optimizing the service sequence and path variables of the service spacecraft.

[0072] S46. Comprehensively analyze the constraints and mission objectives of the optimization model, combine the results of the two-stage algorithm, generate a global optimization solution for multi-task planning, verify the practical applicability of the optimization scheme based on fuel consumption, mission completion time and priority indicators, and flexibly adjust key parameters to meet complex mission requirements;

[0073] S47. In the dynamic optimization process, by continuously adjusting the task service order and path planning scheme, the global optimal solution is gradually approached. The optimization process is based on the set termination conditions. When the maximum number of iterations is reached or the optimization effect is no longer significant, the final optimization plan is output, including the optimal task execution order and the corresponding path planning plan.

[0074] The beneficial effects of the present invention are:

[0075] (1) The present invention proposes a two-stage task planning strategy that combines a genetic algorithm with a dung beetle optimization algorithm. By optimizing the task sequence of the service spacecraft in the first stage and further optimizing the path variables in the second stage, the strategy realizes an effective combination of global search and local fine optimization. In the first stage, an improved genetic algorithm is used to design a multi-objective fitness function and a dynamic service sequence generation mechanism, which significantly improves the global scheduling capability of the maintenance task. In the second stage, a dung beetle optimization algorithm is introduced to efficiently optimize the path variables by simulating the behavior pattern of dung beetles, so that the path planning finds a dynamic balance between fuel consumption and time constraints.

[0076] (2) In order to solve the conflict between fuel efficiency and time limit in spacecraft path planning, the present invention designs a dynamic path adjustment mechanism based on dung beetle behavior simulation. The transfer angle variables in path planning are dynamically adjusted through the rolling, dancing, breeding, foraging and stealing behavior stages. The fuel consumption model and time constraints are combined to achieve real-time optimization of path planning. Through dynamic path boundary adjustment and multi-stage behavior optimization, the problem of resource allocation and scheduling of spacecraft in a complex orbital environment is effectively solved.

[0077] (3) The present invention realizes the dynamic adaptability of multi-objective tasks by setting comprehensive constraints of the multi-objective optimization model and combining it with a dynamic variable adjustment mechanism. By combining orbital altitude restrictions with the task priority model, the task allocation order of the service spacecraft and the target spacecraft is dynamically adjusted. At the same time, the multi-stage behavior simulation of the dung beetle optimization algorithm is used to optimize the path variables, which significantly improves the adaptability of task planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0079] Figure 1 The present invention provides a flowchart of a two-stage rendezvous maintenance task planning method based on a dung beetle optimization algorithm. DETAILED DESCRIPTION

[0080] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0081] refer to Figure 1 A two-stage rendezvous maintenance task planning method based on a dung beetle optimization algorithm comprises the following steps:

[0082] S1. Establish an optimization model for multi-objective spacecraft maintenance tasks, set the parameters of the service spacecraft and the target spacecraft, and take fuel consumption minimization as the objective function, and set time limits and fuel demand constraints;

[0083] S2. Design the optimization algorithm for the first stage, adopt the improved genetic algorithm, use the service order and transfer angle of the service spacecraft as the optimization variables to generate the initial population and perform fitness calculation, and optimize the spacecraft service order through single-circle or multi-circle orbit change according to the relationship between the orbital height difference and transfer angle between the service spacecraft and the target spacecraft;

[0084] S3. Based on the optimization results of the first stage, the second stage optimization is entered, and the transfer angle of the dung beetle optimization algorithm is used as the optimization variable to simulate the rolling, breeding, foraging and stealing behaviors of dung beetles to optimize the service spacecraft path and minimize fuel consumption;

[0085] S4. Set comprehensive constraints for the optimization model, combine the two-stage solution process of genetic algorithm and dung beetle algorithm, dynamically adjust variables, and achieve the optimal solution for task planning;

[0086] S5. Conduct numerical simulation and optimization result verification, verify the performance of the optimization algorithm based on simulation data, and evaluate the effectiveness and applicability of the method through fuel consumption and task completion time indicators.

[0087] In this implementation, S1 includes the following steps:

[0088] S11. Establish an optimization model for multi-objective spacecraft maintenance tasks and define the service spacecraft set S = {s1, s2, …, s m} and the target spacecraft set R = {r1,r2,…,r n}, where m represents the total number of service spacecraft and n represents the total number of target spacecraft;

[0089] S12. Set task time limits, including maintenance time t w=24h, fuel replenishment time t f = 24h and orbit transfer time t t ≤48h;

[0090] S13. Define the fuel consumption required for a single orbit transfer as:

[0091] m 单次 =βm1+(1-β)(m1+m2);

[0092] Among them, m 单次 is the total fuel consumption of a single orbit transfer, m1 is the fuel required for a single-turn Lambert orbit transfer, m2 is the fuel required for a multi-turn phase-adjustment orbit transfer, β is the orbit transfer mode selection coefficient, a value of 1 means waiting before orbit transfer, and a value of 0 means orbit transfer before phase adjustment;

[0093] S14. Modeling fuel consumption as an optimization objective function:

[0094]

[0095] in, Indicates service spacecrafts i Transfer to target spacecraft j The required fuel for a single Lambert turn, It indicates the fuel required for multiple phase adjustment and orbit change in the same transfer. α indicates whether fuel replenishment is needed. When the value is 1, no fuel replenishment is required. When the value is 0, fuel replenishment is required.

[0096] S15. Set the transfer angle θ as the optimization variable, and define the orbital heights of the service spacecraft and the target spacecraft as h respectively. s and h r , the transfer angle range is [0°, 10°];

[0097] S16. According to the mission parameters and orbital restrictions, set comprehensive constraints, including the orbital altitude range h of the service spacecraft s ∈[400km,1000km], target spacecraft orbit altitude range h r ∈[400km,1000km], orbit ascending node right ascension range Ω∈[60°,70°], and fuel replenishment requirements.

[0098] In this implementation, S15 establishes a track transfer model according to the following two situations:

[0099] If the transfer angle can reach a given value within the orbit transfer time, the orbit transfer is completed through a single-turn Lambert orbit change;

[0100] If the transfer angle cannot reach the given value within the orbit transfer time, a single-turn Lambert orbit change is first performed with a fixed transfer angle θ0, and then the orbit adjustment is completed through multi-turn phase adjustment orbit change. The number of multi-turn phase adjustment orbit change turns is the maximum number of turns allowed within the time limit.

[0101] In this implementation, S2 includes the following steps:

[0102] S21. Based on the optimization variables, the initial population is generated by real number coding. The chromosome of each individual in the initial population contains three parts: service order segment, transfer angle segment and allocation segment. The first segment is the maintenance order code, the second segment is the transfer angle code corresponding to the maintenance order, and the third segment is used to allocate the target spacecraft to different service spacecraft.

[0103] S22. Based on the fitness function and the service order and transfer angle value of each individual, the total fuel consumption of the individual is calculated, and the reciprocal of the fuel consumption is used as the fitness value;

[0104] S23. Use random traversal sampling method to select individuals in the population, give priority to individuals with high fitness values ​​to enter the crossover and mutation stages, and retain excellent genes;

[0105] S24. Use the partial matching crossover algorithm to keep the service sequence segment chromosome unique, and generate a new transfer angle value through weight-constrained mixed crossover:

[0106] θ new =w1·θ parent1 +w2·θ parent2 ;

[0107] Among them, θ parent1 and θ parent2 They represent the transfer angle values ​​of the two parent individuals respectively, w1 and w2 are weight coefficients, and the calculation of weights is related to the fitness values ​​of the parents;

[0108] Based on the task priority weight allocation, the target spacecraft is reallocated to ensure that the allocation result achieves a dynamic balance between fuel consumption and spacecraft capability constraints:

[0109]

[0110] Among them, A new (i) Assign a new service spacecraft to the target spacecraft, P k represents the remaining fuel of the service spacecraft, and are the single-turn and multi-turn fuel consumption of the service spacecraft for the target spacecraft mission, respectively;

[0111] S25. Perform exchange mutation on the service order segment, randomly select two target spacecraft to exchange their service order, and perform random perturbation mutation on the transfer angle segment:

[0112]

[0113] Among them, Δθ is the variation value of the transfer angle, which obeys the uniform distribution δ is the variable step length, which keeps the transfer angle within the defined range [0°, 10°];

[0114] S26. Sort the current population by fitness value, and retain the individuals with the highest fitness directly to the next generation;

[0115] S27. Repeat the selection, crossover, mutation and elite retention steps until the set maximum number of generations is reached or the population fitness change is lower than the preset threshold, and finally obtain the optimal service order and transfer angle.

[0116] In this implementation, S3 includes the following steps:

[0117] S31. Based on the service order and transfer angle optimized in the first stage as input, generate the initial dung beetle population, the position x of each dung beetle individual t represents the transfer angle of the service spacecraft in path planning, and the range is limited to [0°, 10°];

[0118] S32. Simulate the rolling optimization behavior of the dung beetle along the path direction, approach the global optimal solution, and update the position of the dung beetle:

[0119] x t+1 =x t +aRx t +b(x t -x w );

[0120] Among them, x t represents the current spacecraft transfer angle, x w is the global worst transfer angle, simulating the suboptimal solution with the highest fuel consumption, a and R simulate direction adjustment and environmental disturbance, and b is the rolling adjustment coefficient, which optimizes the dynamic balance ability of the spacecraft between fuel and time;

[0121] S33. When the service spacecraft encounters an unsolvable or inefficient path during path optimization, it readjusts its direction through the dance phase:

[0122] x t+1 =x t +tanθ ′ |x t -x w |;

[0123] S34. Based on the local optimization of the service spacecraft path adjustment, set the boundary to make the newly generated path reasonable under the fuel consumption and time constraints:

[0124] L ′ =max(x b (1-R t ),L);

[0125] U ′ =min(x b (1+R t ),U);

[0126] x offspring =x b +b1(U ′ -x b )+b2(x b -L ′ );

[0127] Among them, x b represents the current local optimal solution. The boundary L',U' is calculated by combining the orbit height and the orbit change time constraint. The multiplication coefficients b1 and b2 control the exploration range of the new path. L ′ To serve the minimum orbit adjustment range that the spacecraft can accept during the mission execution, U ′ The maximum orbit adjustment range that the service spacecraft can reach is used to avoid generating invalid solutions that exceed mission requirements. offspring Indicates the individual positions of offspring generated in the breeding stage, U is the maximum search range, and L is the minimum search range;

[0128] S35. Simulate the behavior of a spacecraft searching for a suboptimal path near the global optimal orbit and adjust the dynamic boundary optimization path planning:

[0129] L ″ =max(x B (1-R t ),L);

[0130] U ″ =min(x B (1+R t ),U);

[0131] x t+1 =x t +c1(x t -L ″ )+c2(U ″ -x t );

[0132] Among them, x Brepresents the current optimal solution, which is the path with the minimum fuel consumption. c1 and c2 simulate the sensitivity of path variables to trajectory adjustment.

[0133] By dynamically adjusting the path boundaries, the spacecraft finds a better compromise between time constraints and fuel efficiency;

[0134] S36. Optimize the path of the current service spacecraft based on the information of the current optimal solution:

[0135] x t+1 =x B +cg(|x t -x b |+|x t -x B |);

[0136] Among them, c and g simulate the spacecraft's ability to obtain improved directions from global and local excellent paths;

[0137] S37. Calculate the fuel consumption of dung beetle individuals in path planning by combining the fuel consumption formula, and calculate the fuel consumption of dung beetle individuals in path planning by using the fitness function Select the individuals with the highest fitness to enter the next generation population;

[0138] S38. Repeat the rolling, dancing, breeding, foraging and stealing stages until the maximum number of iterations is reached or the population fitness change is less than the set threshold, and output the global optimal transfer angle x * , to complete the path optimization.

[0139] In this implementation, S4 includes the following steps:

[0140] S41. According to the specific requirements of the spacecraft orbit, the orbital altitude range of the service spacecraft and the target spacecraft shall be limited, and the orbital parameters shall be combined to ensure that the spacecraft meets the physical feasibility and mission execution requirements during the orbit adjustment process;

[0141] S42. Set time limits for each key link in the maintenance task, including maintenance time, fuel replenishment time, and the maximum time window required for orbit transfer, to ensure that the service spacecraft completes the maintenance task within the specified time and coordinate the execution order of multiple tasks;

[0142] S43. To optimize the fuel efficiency of the spacecraft, the fuel saving mode and the mission time priority mode are integrated for each orbit transfer process in the mission, and the fuel consumption distribution is dynamically adjusted to balance the contradiction between fuel use and mission efficiency;

[0143] S44. Based on the service order optimization results of the first phase, the task priority of the service spacecraft is evaluated in real time, and the task service order is dynamically adjusted by integrating the orbit transfer time and fuel consumption factors, so as to give priority to completing the maintenance tasks of the target spacecraft that are time-sensitive or have a fuel demand higher than the threshold;

[0144] S45. Combine the path planning results of the second-stage dung beetle optimization algorithm to adjust the orbit transfer path of the service spacecraft in real time. The path planning adjustment aims at global optimization and maximizes the mission completion efficiency by collaboratively optimizing the service sequence and path variables of the service spacecraft.

[0145] S46. Comprehensively analyze the constraints and mission objectives of the optimization model, combine the results of the two-stage algorithm, generate a global optimization solution for multi-task planning, verify the practical applicability of the optimization scheme based on fuel consumption, mission completion time and priority indicators, and flexibly adjust key parameters to meet complex mission requirements;

[0146] S47. In the dynamic optimization process, by continuously adjusting the task service order and path planning scheme, the global optimal solution is gradually approached. The optimization process is based on the set termination conditions. When the maximum number of iterations is reached or the optimization effect is no longer significant, the final optimization plan is output, including the optimal task execution order and the corresponding path planning plan.

[0147] Embodiment 1:

[0148] In this embodiment, for the rendezvous and maintenance tasks of 30 target spacecraft and 3 service spacecraft, the two-stage rendezvous and maintenance task planning method based on the dung beetle optimization algorithm of the present invention is applied to optimize the task sequence and path planning of the service spacecraft, which significantly improves the fuel efficiency and task completion rate. All spacecraft are located in a sun-synchronous orbit (SSO) with an orbital altitude ranging from 400 km to 1000 km and an ascending node right ascension ranging from 60° to 70°. The mission goal is to complete all maintenance tasks within 72 hours while minimizing fuel consumption and meeting time constraints.

[0149] The dry weight of each target spacecraft is 500 kg, and the initial fuel it carries is 3,000 kg. When the fuel is less than 1,000 kg, it needs to go to the integrated service station to refuel. At the beginning of the mission, the service spacecraft are located at orbital altitudes of 7,207 km, 7,006 km and 6,846 km respectively. The maximum maintenance time is limited to 24 hours, and the single orbit transfer time is limited to 48 hours. The orbital altitudes of the target spacecraft are between 6,985 km and 7,351 km, and the maintenance time requirement is 3 to 8 hours. The mission requires the service spacecraft to visit the target spacecraft in turn to complete maintenance and return to the integrated service station to refuel.

[0150] The method of the present invention is divided into two stages of optimization: the first stage uses an improved genetic algorithm to optimize the service sequence, and the second stage uses a dung beetle optimization algorithm to optimize the path planning.

[0151] In the first stage, the optimization variables are the random arrangement of 30 target spacecraft and the transfer angle of the service spacecraft. The initial population size is 50, the maximum number of generations is set to 100, and the genetic operation with a recombination probability of 0.6 and a mutation probability of 0.6 is used. The service order is evaluated by the fitness function, in which fuel consumption, orbital height difference and mission completion time are the main evaluation indicators. The optimization results of the first stage are as follows:

[0152] The service order for servicing spacecraft 1 is: 12→24→18→11→23→19→5→21→14→10;

[0153] The service order for servicing spacecraft 2 is: 27→17→2→4→20→16→9→30→28→3;

[0154] The service order for servicing spacecraft 3 is: 7→6→13→29→25→26→15→1→8→22.

[0155] In the second stage, based on the optimization results of the first stage, the dung beetle optimization algorithm was used to dynamically adjust the path variables, including the orbital transfer angle and orbit change strategy. The path optimization process of the spacecraft was simulated through rolling, dancing, reproduction, foraging and stealing behaviors. When the service spacecraft 1 was maintaining the target spacecraft 12, the transfer angle was adjusted in the rolling stage, which reduced the fuel consumption by 8%. When the service spacecraft 2 was maintaining the target spacecraft 27, the path was optimized through the foraging stage, which reduced the fuel consumption by 12%.

[0156] The comparison of optimized fuel consumption and task completion time with the traditional method is shown in Table 1:

[0157] Table 1 Comparison results between the method of the present invention and the traditional method in multi-target spacecraft maintenance tasks

[0158] Comparison Items Traditional methods Method of the present invention Improved results Average fuel consumption (kg) 280 212 24.3% reduction Average task completion time (hours) 73 68 6.85% shorter Mission completion rate of serviced spacecraft 86％ 100％ 16.3% improvement Track adjustment success rate 87％ 98％ 12.6% improvement

[0159] The fuel consumption data is as follows:

[0160] The total fuel consumption of the service spacecraft 1 was 2880.12 kg, which was 18.6% less than the traditional method;

[0161] The total fuel consumption of Service Spacecraft 2 was 3054.78 kg, which was 20.4% less than the traditional method;

[0162] The total fuel consumption of the service spacecraft 3 was 2962.05 kg, which was 22.1% less than the traditional method.

[0163] In terms of task completion time, the traditional method has an average task completion time of 73 hours due to the limitations of path planning, while the method of the present invention shortens the average task completion time to 68 hours by dynamically adjusting the path and task sequence. In addition, all maintenance tasks can be completed within the time limit, and the task completion rate reaches 100%.

[0164] This example shows that the method of the present invention significantly improves the efficiency of multi-objective spacecraft maintenance tasks through a two-stage optimization strategy. Compared with the traditional method, fuel consumption is reduced by 24.3%, the task completion time is shortened by 6.85%, and the task completion rate and orbit adjustment success rate are significantly improved. This method is suitable for complex scenarios of multi-objective tasks and can effectively solve the shortcomings of traditional methods in terms of fuel efficiency, time constraints and global optimization capabilities.

[0165] The present invention proposes a two-stage task planning strategy combining a genetic algorithm and a dung beetle optimization algorithm. By optimizing the task sequence of servicing spacecraft in the first stage and further optimizing the path variables in the second stage, an effective combination of global search and local fine optimization is achieved. In the first stage, an improved genetic algorithm is used to design a multi-objective fitness function and a dynamic service sequence generation mechanism, which significantly improves the global scheduling capability of maintenance tasks. In the second stage, a dung beetle optimization algorithm is introduced to efficiently optimize the path variables by simulating the behavior pattern of dung beetles, so that path planning finds a dynamic balance between fuel consumption and time constraints.

[0166] In order to solve the conflict between fuel efficiency and time limit in spacecraft path planning, the present invention designs a path dynamic adjustment mechanism based on dung beetle behavior simulation, dynamically adjusts the transfer angle variables in path planning through the rolling, dancing, breeding, foraging and stealing behavior stages, and realizes real-time optimization of path planning by combining the fuel consumption model and time constraints. Through dynamic path boundary adjustment and multi-stage behavior optimization, the difficult problem of resource allocation and scheduling of spacecraft in a complex orbital environment is effectively solved.

[0167] The present invention realizes the dynamic adaptability of multi-objective tasks by setting comprehensive constraints of the multi-objective optimization model and combining it with a dynamic variable adjustment mechanism. By combining orbital altitude restrictions with the task priority model, the task allocation order of the service spacecraft and the target spacecraft is dynamically adjusted. At the same time, the multi-stage behavior simulation of the dung beetle optimization algorithm is used to optimize the path variables, which significantly improves the adaptability of task planning.

[0168] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A two-stage rendezvous maintenance task planning method based on dung beetle optimization algorithm, characterized in that: The steps include: S1. Establish an optimization model for multi-objective spacecraft maintenance tasks, set the parameters of the service spacecraft and the target spacecraft, and take fuel consumption minimization as the objective function, and set time limits and fuel demand constraints; S2. Design the optimization algorithm for the first stage, adopt the improved genetic algorithm, use the service order and transfer angle of the service spacecraft as the optimization variables to generate the initial population and perform fitness calculation, and optimize the spacecraft service order through single-circle or multi-circle orbit change according to the relationship between the orbital height difference and transfer angle between the service spacecraft and the target spacecraft; S3. Based on the optimization results of the first stage, the second stage optimization is entered, and the transfer angle of the dung beetle optimization algorithm is used as the optimization variable to simulate the rolling, breeding, foraging and stealing behaviors of dung beetles to optimize the service spacecraft path and minimize fuel consumption; S4. Set comprehensive constraints for the optimization model, combine the two-stage solution process of genetic algorithm and dung beetle algorithm, dynamically adjust variables, and achieve the optimal solution for task planning; S5. Conduct numerical simulation and optimization result verification, verify the performance of the optimization algorithm based on simulation data, and evaluate the effectiveness and applicability of the method through fuel consumption and task completion time indicators.

2. The two-stage rendezvous maintenance task planning method based on the dung beetle optimization algorithm according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Establish an optimization model for multi-objective spacecraft maintenance tasks and define the service spacecraft set S = {s1, s2, …, s m } and the target spacecraft set R = {r1,r2,…,r n }, where m represents the total number of service spacecraft and n represents the total number of target spacecraft; S12. Set task time limits, including maintenance time t w =24h, fuel replenishment time t f = 24h and orbit transfer time t t ≤48h; S13. Define the fuel consumption required for a single orbit transfer as: m 单次 =βm1+(1-β)(m1+m2); Among them, m 单次 is the total fuel consumption of a single orbit transfer, m1 is the fuel required for a single-turn Lambert orbit transfer, m2 is the fuel required for a multi-turn phase-adjustment orbit transfer, β is the orbit transfer mode selection coefficient, a value of 1 means waiting before orbit transfer, and a value of 0 means orbit transfer before phase adjustment; S14. Modeling fuel consumption as an optimization objective function: in, Indicates service spacecrafts i Transfer to target spacecraft j The fuel required for a single Lambert turn, It indicates the fuel required for multiple phase adjustment and orbit change in the same transfer. α indicates whether fuel replenishment is needed. When the value is 1, no fuel replenishment is required. When the value is 0, fuel replenishment is required. S15. Set the transfer angle θ as the optimization variable, and define the orbital heights of the service spacecraft and the target spacecraft as h respectively. s and h r , the transfer angle range is [0°, 10°]; S16. According to the mission parameters and orbital restrictions, set comprehensive constraints, including the orbital altitude range h of the service spacecraft s ∈[400km,1000km], target spacecraft orbit altitude range h r ∈[400km,1000km], orbit ascending node right ascension range Ω∈[60°,70°], and fuel replenishment requirements.

3. The two-stage rendezvous maintenance task planning method based on the dung beetle optimization algorithm according to claim 2 is characterized in that: The S15 establishes an orbit transfer model according to the following two situations: If the transfer angle can reach a given value within the orbit transfer time, the orbit transfer is completed through a single-turn Lambert orbit change; If the transfer angle cannot reach the given value within the orbit transfer time, a single-turn Lambert orbit change is first performed with a fixed transfer angle θ0, and then the orbit adjustment is completed through multi-turn phase adjustment orbit change. The number of multi-turn phase adjustment orbit change turns is the maximum number of turns allowed within the time limit.

4. The two-stage rendezvous maintenance task planning method based on the dung beetle optimization algorithm according to claim 1 is characterized in that: The S2 comprises the following steps: S21. Based on the optimization variables, the initial population is generated by real number coding. The chromosome of each individual in the initial population contains three parts: service order segment, transfer angle segment and allocation segment. The first segment is the maintenance order code, the second segment is the transfer angle code corresponding to the maintenance order, and the third segment is used to allocate the target spacecraft to different service spacecraft. S22. Based on the fitness function and the service order and transfer angle value of each individual, the total fuel consumption of the individual is calculated, and the reciprocal of the fuel consumption is used as the fitness value; S23. Use random traversal sampling method to select individuals in the population, give priority to individuals with high fitness values ​​to enter the crossover and mutation stages, and retain excellent genes; S24. Use the partial matching crossover algorithm to keep the service sequence segment chromosome unique, and generate a new transfer angle value through weight-constrained mixed crossover: i new =w1·θ parent1 +w2·θ parent2 ; Among them, θ parent1 and θ parent2 They represent the transfer angle values ​​of the two parent individuals respectively, w1 and w2 are weight coefficients, and the calculation of weights is related to the fitness values ​​of the parents; Based on the task priority weight allocation, the target spacecraft is reallocated to ensure that the allocation result achieves a dynamic balance between fuel consumption and spacecraft capability constraints: Among them, A new (i) Assign a new service spacecraft to the target spacecraft, P k represents the remaining fuel of the service spacecraft, and are the single-turn and multi-turn fuel consumption of the service spacecraft for the target spacecraft mission, respectively; S25. Perform exchange mutation on the service order segment, randomly select two target spacecraft to exchange their service order, and perform random perturbation mutation on the transfer angle segment: Among them, Δθ is the variation value of the transfer angle, which obeys the uniform distribution δ is the variable step length, which keeps the transfer angle within the defined range [0°, 10°]; S26. Sort the current population by fitness value, and retain the individuals with the highest fitness directly to the next generation; S27. Repeat the selection, crossover, mutation and elite retention steps until the set maximum number of generations is reached or the population fitness change is lower than the preset threshold, and finally obtain the optimal service order and transfer angle.

5. The two-stage rendezvous maintenance task planning method based on the dung beetle optimization algorithm according to claim 1 is characterized in that: The S3 comprises the following steps: S31. Based on the service order and transfer angle optimized in the first stage as input, generate the initial dung beetle population, the position x of each dung beetle individual t represents the transfer angle of the service spacecraft in path planning, and the range is limited to [0°, 10°]; S32. Simulate the rolling optimization behavior of the dung beetle along the path direction, approach the global optimal solution, and update the position of the dung beetle: x t+1 =x t +aRx t +b(x t -x w ); Among them, x t represents the current spacecraft transfer angle, x w is the global worst transfer angle, simulating the suboptimal solution with the highest fuel consumption, a and R simulate direction adjustment and environmental disturbance, and b is the rolling adjustment coefficient, which optimizes the dynamic balance ability of the spacecraft between fuel and time; S33. When the service spacecraft encounters an unsolvable or inefficient path during path optimization, it readjusts its direction through the dance phase: x t+1 =x t +tanθ ′ |x t -x w |; S34. Based on the local optimization of the service spacecraft path adjustment, set the boundary to make the newly generated path reasonable under the fuel consumption and time constraints: L ′ =max(x b (1-R t ),L); U ′ =min(x b (1+R t ),U); x offspring =x b +b1(U ′ -x b )+b2(x b -L ′ ); Among them, x b represents the current local optimal solution. The boundary L',U' is calculated by combining the orbit height and the orbit change time constraint. The multiplication coefficients b1 and b2 control the exploration range of the new path. L ′ To serve the minimum orbit adjustment range that the spacecraft can accept during the mission execution, U ′ The maximum orbit adjustment range that the service spacecraft can reach is used to avoid generating invalid solutions that exceed mission requirements. offspring Indicates the individual positions of offspring generated in the breeding stage, U is the maximum search range, and L is the minimum search range; S35. Simulate the behavior of a spacecraft searching for a suboptimal path near the global optimal orbit and adjust the dynamic boundary optimization path planning: L ″ =max(x B (1-R t ),L); U ″ =min(x B (1+R t ),U); x t+1 =x t +c1(x t -L ″ )+c2(U ″ -x t ); Among them, x B represents the current optimal solution, which is the path with the minimum fuel consumption. c1 and c2 simulate the sensitivity of path variables to trajectory adjustment. By dynamically adjusting the path boundaries, the spacecraft finds a better compromise between time constraints and fuel efficiency; S36. Optimize the path of the current service spacecraft based on the information of the current optimal solution: x t+1 =x B +cg(|x t -x b |+|x t -x B |); Among them, c and g simulate the spacecraft's ability to obtain improved directions from global and local excellent paths; S37. Calculate the fuel consumption of dung beetle individuals in path planning by combining the fuel consumption formula, and calculate the fuel consumption of dung beetle individuals in path planning by using the fitness function Select the individuals with the highest fitness to enter the next generation population; S38. Repeat the rolling, dancing, breeding, foraging and stealing stages until the maximum number of iterations is reached or the population fitness change is less than the set threshold, and output the global optimal transfer angle x * , to complete the path optimization.

6. The two-stage rendezvous maintenance task planning method based on the dung beetle optimization algorithm according to claim 1 is characterized in that: The S4 comprises the following steps: S41. According to the specific requirements of the spacecraft orbit, the orbital altitude range of the service spacecraft and the target spacecraft shall be limited, and the orbital parameters shall be combined to ensure that the spacecraft meets the physical feasibility and mission execution requirements during the orbit adjustment process; S42. Set time limits for each key link in the maintenance task, including maintenance time, fuel replenishment time, and the maximum time window required for orbit transfer, to ensure that the service spacecraft completes the maintenance task within the specified time and coordinate the execution order of multiple tasks; S43. To optimize the fuel efficiency of the spacecraft, the fuel saving mode and the mission time priority mode are integrated for each orbit transfer process in the mission, and the fuel consumption distribution is dynamically adjusted to balance the contradiction between fuel use and mission efficiency; S44. Based on the service order optimization results of the first phase, the task priority of the service spacecraft is evaluated in real time, and the task service order is dynamically adjusted by integrating the orbit transfer time and fuel consumption factors, so as to give priority to completing the maintenance tasks of the target spacecraft that are time-sensitive or have a fuel demand higher than the threshold; S45. Combine the path planning results of the second-stage dung beetle optimization algorithm to adjust the orbit transfer path of the service spacecraft in real time. The path planning adjustment aims at global optimization and maximizes the mission completion efficiency by collaboratively optimizing the service sequence and path variables of the service spacecraft. S46. Comprehensively analyze the constraints and mission objectives of the optimization model, combine the results of the two-stage algorithm, generate a global optimization solution for multi-task planning, verify the practical applicability of the optimization scheme based on fuel consumption, mission completion time and priority indicators, and flexibly adjust key parameters to meet complex mission requirements; S47. In the dynamic optimization process, by continuously adjusting the task service order and path planning scheme, the global optimal solution is gradually approached. The optimization process is based on the set termination conditions. When the maximum number of iterations is reached or the optimization effect is no longer significant, the final optimization plan is output, including the optimal task execution order and the corresponding path planning plan.

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