Satellite emergency task planning method, device and equipment based on differential evolution algorithm

Abstract

The invention provides a satellite emergency task planning method, device and equipment based on a differential evolution algorithm, and the method comprises the steps: building a satellite resource model for a satellite, and building an emergency task model for an emergency task to be executed by the satellite; generating an emergency task planning strategy population based on the satellite resource model and the emergency task model in combination with task attribute constraints, resource attribute constraints, target observation constraints and data transmission constraints; determining a fitness value corresponding to an emergency task sequence contained in the emergency task planning strategy population, and performing adaptive variation and crossover operation on the emergency task sequence according to the fitness value to obtain a new emergency task planning strategy population, and continuing to determine the fitness value corresponding to the emergency task sequence included in the new emergency task planning strategy population, and obtaining a target emergency task sequence until a preset stop condition is met. Under the scene of emergency task planning, more efficient and more reasonable resource allocation and task scheduling can be realized.

Description

Technical Field

[0001] The present invention relates to the field of satellite technology, and in particular, to a satellite emergency mission planning method, device and equipment based on a differential evolution algorithm. Background Art

[0002] The tasks of Earth observation satellites are mainly divided into two categories: one is regular observation, and the other is emergency observation. Regular observation tasks are usually pre-arranged, and the time points and observation targets are known or predictable, and they are mainly carried out in non-emergency situations. In contrast, emergency observation is carried out in special situations, such as military operations or disaster relief. Such tasks often require rapid response, and the target area may be limited to specific locations (such as local conflict areas or disaster-stricken areas), and their observation results may play a decisive role in the success of the entire operation. Therefore, emergency observation has the characteristics of unpredictability, urgency, concentration and criticality.

[0003] Planning satellite observation tasks is a complex task under the condition of limited resources. It requires reasonable arrangement of the priorities of task execution and allocation of corresponding resources and time. Its core goal is to reduce conflicts between tasks and meet user needs as much as possible. This problem has been proven to have the NP-hard property, that is, it is difficult to find the optimal solution. To address this challenge, the academic community has proposed a variety of models and algorithms. It should be noted that a complete Earth observation task usually includes two key links: target observation and data transmission. Although many studies focus on target observation, the data transmission link is often overlooked. In regular observation tasks, this bias may be feasible. However, in emergency observation tasks, the urgency of time and the concentration of space may trigger fierce competition for resources between tasks, especially observation satellites and communication systems. Insufficient resources, whether it is observation equipment or transmission facilities, may have a significant impact on the success of the task. Therefore, it is necessary to re-evaluate and optimize the task planning strategy to ensure the effective use of limited resources in emergency situations. In addition, most researchers have considered a unique planning goal to maximize the profit of task execution, but in emergency observation, task timeliness should also be considered as a key goal.

[0004] Population intelligence optimization technology has become the focus of research in many disciplines due to its remarkable effect in solving complex and large-scale optimization problems. Such methods are usually model-based, including genetic algorithms, differential evolution algorithms and particle swarm optimization algorithms, etc. They adopt a unified parameter configuration and structure to deal with various optimization problems, and have the advantages of convenience in implementation and low requirements for problem background knowledge. However, these methods have limitations in accuracy and are difficult to ensure the best performance in specific problems. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a satellite emergency mission planning method, device and equipment based on the differential evolution algorithm, which can achieve more efficient and reasonable resource allocation and task scheduling in the scenario of emergency mission planning.

[0006] In the first aspect, the present invention provides a satellite emergency mission planning method based on the differential evolution algorithm, including:

[0007] Establish a satellite resource model for the satellite and an emergency mission model for the emergency missions to be executed by the satellite;

[0008] Based on the satellite resource model and the emergency mission model, combined with task attribute constraints, resource attribute constraints, target observation constraints and data transmission constraints, generate an emergency mission planning strategy population, and the emergency mission planning strategy population includes multiple emergency mission sequences;

[0009] Determine the fitness values corresponding to the emergency mission sequences included in the emergency mission planning strategy population, and perform adaptive mutation and crossover operations on the emergency mission sequences according to the fitness values to obtain a new emergency mission planning strategy population, and continue to determine the fitness values corresponding to the emergency mission sequences included in the new emergency mission planning strategy population until the target emergency mission sequence is obtained when the preset stop condition is met.

[0010] In one implementation, the task attribute constraint is: the emergency mission is only executed once, and the satellite only executes one emergency mission at the same time;

[0011] The resource attribute constraint is: the satellite resources of the satellite are in an available state, and the available storage space of the satellite is greater than the data size included in the emergency mission;

[0012] The target observation constraint is: the duration of the target observation task is greater than the necessary duration of the target observation, and the end time of the target observation task is less than the target observation deadline;

[0013] The data transmission constraint is: the data transmission duration is greater than the required transmission time of the data, and the end time of the data transmission is less than the data transmission deadline.

[0014] In one implementation, determining the fitness values corresponding to the emergency mission sequences included in the emergency mission planning strategy population includes:

[0015] Based on a fitness function that comprehensively considers benefits and resource waiting time, determine the fitness values corresponding to the emergency mission sequences included in the emergency mission planning strategy population.

[0016] In one implementation, the expression of the fitness function that comprehensively considers benefits and resource waiting time is as follows:

[0017]

[0018] Among them, f 1 is the fitness function considering the revenue; f 2 is the fitness function considering the resource waiting time; X ij is the decision variable describing whether the emergency task T j is assigned to the satellite S i . If the emergency task T j is assigned to the satellite S i , then X ij = 1. If the emergency task T j is not assigned to the satellite S i , then X ij = 0; w j is the task revenue of the emergency task T j ; is the start time for the satellite to obtain the emergency task T j ; is the demand arrival time of the emergency task T j , is the target observation deadline, is the target observation duration, is the start time when the emergency task T j obtained by the satellite is downloaded to the ground station, is the end time for the satellite to obtain the emergency task T j ; is the data transmission deadline, d j represents the target data volume, r is the data transmission rate in space, m is the total number of satellites, n is the total number of emergency tasks, λ 1 , λ 2 are the weights of f 1 , f 2 .

[0019] In one implementation, according to the fitness values, adaptive mutation and crossover operations are performed on the emergency task sequence to obtain a new population of emergency task planning strategies, including:

[0020] In the current iteration process, the following operations are performed on the population of emergency task planning strategies:

[0021] Based on the set of mutation operators, adaptive mutation operations are performed on the emergency task sequences included in the population of emergency task planning strategies, and the set of mutation operators is updated during the process of performing the adaptive mutation operations on the emergency task sequences until the adaptive mutation operations are completed;

[0022] ​​​​​​​​​​Based on the population of emergency task planning strategies after the cross probability set and adaptive mutation operation, perform an adaptive crossover operation on the emergency task sequences included in the original population of emergency task planning strategies during the current iteration, and update the cross probability set during the process of performing the adaptive crossover operation on the emergency task sequences until the adaptive mutation operation is completed, obtaining a new population of emergency task planning strategies.

[0023] In one implementation, based on the set of mutation operators, perform an adaptive mutation operation on the emergency task sequences included in the population of emergency task planning strategies, including:

[0024] Perform the following operations on the emergency task sequences included in the population of emergency task planning strategies:

[0025] Calculate the mean of the mutation operators included in the set of mutation operators to obtain the mean mutation operator;

[0026] Based on the mean mutation operator and the adaptive mutation control parameter used when performing the adaptive mutation operation on the previous emergency task sequence, determine a new adaptive mutation control parameter;

[0027] Determine a new mutation operator based on the new adaptive mutation control parameter;

[0028] Randomly determine a target mutation rule from the pre-configured set of mutation rules, and based on the target mutation rule, perform an adaptive mutation operation on the current emergency task sequence using the new mutation operator.

[0029] In one implementation, denote the emergency task sequences included in the original population of emergency task planning strategies during the current iteration as the original emergency task sequences, and denote the emergency task sequences included in the population of emergency task planning strategies after the adaptive mutation operation as the mutated emergency task sequences;

[0030] Based on the cross probability set and the population of emergency task planning strategies after the adaptive mutation operation, perform an adaptive crossover operation on the emergency task sequences included in the original population of emergency task planning strategies during the current iteration, including:

[0031] Perform the following operations on the emergency task sequences included in the original population of emergency task planning strategies during the current iteration:

[0032] Calculate the mean of the cross probabilities included in the cross probability set to obtain the mean cross probability;

[0033] Based on the mean cross probability and the adaptive crossover control parameter used when performing the adaptive crossover operation on the previous emergency task sequence, determine a new adaptive crossover control parameter;

[0034] Determine a new crossover probability based on a new adaptive crossover control parameter;

[0035] In the case where the value generated by the random function is less than or equal to the new crossover probability, perform an adaptive crossover operation on the original emergency task sequence and its mutated emergency task sequence that matches it.

[0036] In a second aspect, the present invention also provides a satellite emergency mission planning device based on a differential evolution algorithm, including:

[0037] A model establishment module for establishing a satellite resource model for a satellite and an emergency mission model for the emergency missions to be executed by the satellite;

[0038] A population planning module for generating an emergency mission planning strategy population based on the satellite resource model and the emergency mission model, in combination with task attribute constraints, resource attribute constraints, target observation constraints, and data transmission constraints, where the emergency mission planning strategy population includes multiple emergency mission sequences;

[0039] A task sequence iteration module for determining the fitness value corresponding to each emergency mission sequence included in the emergency mission planning strategy population, so as to perform an adaptive mutation and crossover operation on each emergency mission sequence according to the fitness value to obtain a new emergency mission planning strategy population, and continue to determine the fitness value corresponding to each emergency mission sequence included in the new emergency mission planning strategy population until a target emergency mission sequence is obtained when a preset stop condition is met.

[0040] In a third aspect, the present invention also provides an electronic device, including a processor and a memory, where the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of the first aspect.

[0041] In a fourth aspect, the present invention also provides a computer-readable storage medium, where the computer-readable storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by the processor, the computer executable instructions cause the processor to implement the method according to any one of the first aspect.

[0042] A satellite emergency mission planning method, device and equipment based on differential evolution algorithm provided by the present invention first establishes a satellite resource model for the satellite and an emergency mission model for the emergency missions to be executed by the satellite; then, based on the satellite resource model and the emergency mission model, combined with task attribute constraints, resource attribute constraints, target observation constraints and data transmission constraints, an emergency mission planning strategy population is generated, and the emergency mission planning strategy population includes multiple emergency mission sequences; finally, the fitness values corresponding to the emergency mission sequences included in the emergency mission planning strategy population are determined, so as to perform adaptive mutation and crossover operations on the emergency mission sequences according to the fitness values to obtain a new emergency mission planning strategy population, and continue to determine the fitness values corresponding to the emergency mission sequences included in the new emergency mission planning strategy population until the target emergency mission sequence is obtained when the preset stop condition is met. The above method generates an initial emergency mission planning strategy population for the satellite resource model and the emergency mission model, combined with various constraints such as task attribute constraints, resource attribute constraints, target observation constraints and data transmission constraints, and on this basis, processes the emergency mission sequences in the emergency mission planning strategy population by using adaptive mutation and crossover operations, effectively improving the limitations of the existing technology in solving accuracy, and further performing iterative optimization in combination with the fitness of the emergency mission sequences, so as to obtain the target emergency mission sequence. Therefore, the present invention can achieve more efficient and reasonable resource allocation and task scheduling in the scenario of emergency mission planning.

[0043] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims and drawings.

[0044] In order to make the above objectives, features and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic flow chart of a satellite emergency mission planning method based on differential evolution algorithm provided by an embodiment of the present invention;

[0047] Figure 2The technical framework diagram of a satellite emergency mission planning method based on the differential evolution algorithm provided by an embodiment of the present invention;

[0048] Figure 3 The structural schematic diagram of a satellite emergency mission planning device based on the differential evolution algorithm provided by an embodiment of the present invention;

[0049] Figure 4 The structural schematic diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Currently, there are limitations in accuracy in related technologies, and it is difficult to ensure the best performance in specific problems. Based on this, the embodiments of the present invention provide a satellite emergency mission planning method, device, and equipment based on the differential evolution algorithm. In the scenario of emergency mission planning, it has the characteristics of clear problem model establishment, fast testing speed, and reasonable planning scheme, and can achieve more efficient and reasonable resource allocation and task scheduling.

[0052] For the convenience of understanding this embodiment, first, a satellite emergency mission planning method based on the differential evolution algorithm disclosed in the embodiments of the present invention will be introduced in detail. Refer to Figure 1 The flow schematic diagram of a satellite emergency mission planning method based on the differential evolution algorithm shown, and this method mainly includes the following steps S102 to step S106:

[0053] Step S102, establish a satellite resource model for the satellite and an emergency mission model for the emergency mission to be executed by the satellite.

[0054] Among them, the satellite resource model is used to describe the parameters of the satellite state, available storage space, observation mission time period, and data transmission mission time period; the emergency mission model is used to describe the task target location, task priority, task arrival time, target observation period, data transmission period, observation mission duration, and the amount of data included in the emergency mission.

[0055] Step S104, based on the satellite resource model and the emergency mission model, combined with task attribute constraints, resource attribute constraints, target observation constraints, and data transmission constraints, generate an emergency mission planning strategy population, and the emergency mission planning strategy population includes multiple emergency mission sequences.

[0056] Among them, the task attribute constraints are: the emergency task is only executed once, and the satellite only executes one emergency task at the same time; the resource attribute constraints are: the satellite resources of the satellite are in an available state, and the available storage space of the satellite is greater than the data size included in the emergency task; the target observation constraints are: the duration of the target observation task is greater than the necessary duration of the target observation, and the end time of the target observation task is less than the target observation deadline; the data transmission constraints are: the data transmission duration is greater than the required transmission time of the data, and the end time of the data transmission is less than the data transmission deadline. In one example, an adaptive differential evolution algorithm can be used to generate an initial population of emergency task planning strategies. This population consists of N individuals, each of which is randomly generated within the given variable boundaries, and an individual is also an emergency task sequence.

[0057] Step S106: Determine the fitness values corresponding to the emergency task sequences included in the emergency task planning strategy population, and perform adaptive mutation and crossover operations on the emergency task sequences according to the fitness values to obtain a new emergency task planning strategy population. Continue to determine the fitness values corresponding to the emergency task sequences included in the new emergency task planning strategy population until the target emergency task sequence is obtained when the preset stop condition is met.

[0058] In one example, based on a fitness function that comprehensively considers the benefit and the resource waiting time, the fitness values corresponding to the emergency task sequences included in the emergency task planning strategy population can be determined. The parameter values used in the adaptive mutation and crossover operations are determined using the fitness values, so as to perform adaptive mutation and crossover operations on the emergency task sequences in the emergency task planning strategy population on this basis to obtain a new emergency task planning strategy population. Repeat this operation until the preset stop condition (such as the number of iterations) is met, and use the emergency task sequence with the highest fitness value in the new emergency task planning strategy population obtained at this time as the target emergency task sequence.

[0059] The satellite emergency task planning method based on the differential evolution algorithm provided by the embodiments of the present invention, aiming at the satellite resource model and the emergency task model, combines various constraints such as task attribute constraints, resource attribute constraints, target observation constraints, and data transmission constraints to generate an initial population of emergency task planning strategies, and on this basis, uses adaptive mutation and crossover operations to process the emergency task sequences in the emergency task planning strategy population, effectively improving the limitations of the existing technology in terms of solution accuracy. Further, iterative optimization is performed in combination with the fitness of the emergency task sequences to obtain the target emergency task sequence. Therefore, the present invention can achieve more efficient and reasonable resource allocation and task scheduling in the scenario of emergency task planning.

[0060] The embodiment of the present invention adopts an Adaptive Differential Evolution algorithm (ADE). ADE is an optimization algorithm based on differential evolution, which can adaptively adjust algorithm parameters to improve optimization performance and adaptability. During the operation, it automatically adjusts key parameters according to the progress of the current optimization process, iteratively searches the solution space, and completes mutation and crossover selection operations. Aiming at the characteristics of the emergency mission planning problem of earth observation satellites, it is crucial to improve the solution accuracy of the method and ensure the rationality of the output. Refer to Figure 2 the technical framework diagram of a satellite emergency mission planning method based on the differential evolution algorithm shown in rand . After initializing the population, if the preset number of iterations is not reached, three mutation rules are randomly adopted to perform adaptive mutation operations on the emergency mission strategies in the population, and when rand(0,1) ≤ CR or j = j rand , an adaptive crossover operation is performed on the experimental individuals to become new individuals. When rand(0,1) ≤ CR or j = j

[0061] is not satisfied, the experimental individuals are used as new individuals. After evaluating the individuals, the individuals with higher fitness are selected from the old and new individuals; continue the next round of iterative optimization until the preset number of iterations is reached.

[0062] For the aforementioned step S102, the embodiment of the present invention provides a method for establishing a satellite resource model for a satellite and an emergency mission model for the emergency mission to be executed by the satellite. It includes:

[0063] (1A) Establishing resource and task models

[0064] The vector S = (s 1 , s,..., s m ) represents the resource set, where m is the number of EOSs, and the vector T = (t 1 , t 2 ,..., t N ) represents the task set, where n is the number of tasks, and C is the entire cycle of task execution.

[0065] (1B) Establishing a satellite resource model based on parameters such as satellite status, available satellite storage space, observation task time period, and data transmission task time period:

[0066] The satellite resource S i ∈{A i , C i , tw i,j , gw i,j};

[0067] A i represents the state of S i . A i = 1 means S i is in an available state; otherwise A i = 0;

[0068] C i represents the on - board storage of S i , representing the currently available storage space of S i ;

[0069] tw i,j represents the Target Observation Time Window (TOTW), representing S i can observe the mission T j during the time period, where is the k - th sub - window, represents the start time and end time of the observation, p i,j represents the total number of sub - windows. Since T j cannot be determined in advance, so tw i,j is random;

[0070] gw i represents the Data Transmission Time Window (DTTW), representing S i can transmit data to the ground station during the time period, where is the l - th sub - window, represents the start time and end time of the data transmission, q i represents the total number of sub - windows. Since the orbit of S i and the positions of the available ground stations are known, so gw i can be calculated.

[0071] (1C) Establish a mission model based on the mission target location, mission priority, mission arrival time, target observation period, data transmission period, observation mission duration, and the amount of data included in the emergency mission:

[0072] represents the Target Observation Deadline (TOD). Before , complete data about the target must be obtained; otherwise T j fails;

[0073] represents the Data Transmission Deadline (DTD). The data obtained must be downloaded before ; otherwise T j will also fail due to data expiration;

[0074] Emergency mission model

[0075] tar j Indicates the target position, representing the position of T j ;

[0076] w j ∈(0,1) represents the task benefit, representing the importance of T j , which is determined by the commander and decision maker;

[0077] Indicates the arrival time, representing the required arrival time of T j Since T j is sudden, it is random;

[0078] Indicates the Target Observation Period (TOP), representing the data period during which EOS acquires data of T j , is the start time, is the end time;

[0079] Indicates the Data Transmission Period (DTP), representing the period during which the data acquired by EOS is downloaded to the ground station, is the start time, is the end time;

[0080] Indicates the target observation duration, representing the necessary duration for observing the target;

[0081] d j Indicates the target data volume, representing the data size included in T j .

[0082] For the aforementioned step S104, the embodiments of the present invention respectively explain the constraints used:

[0083] (2A) The task attribute constraint is: The emergency task is only executed once, and the satellite only executes one emergency task at the same time. The expression of the task attribute constraint is as follows:

[0084]

[0085] X ij is the decision variable for describing whether the emergency task T j is assigned to the satellite S i ; Indicates the end time of the target observation period; Indicates the start time of the target observation period. Among them, the decision variable X ij of the satellite mission planning is as follows:

[0086]

[0087] (2B) The resource attribute constraint is that the satellite resources of the satellite are in an available state, and the available storage space of the satellite is greater than the data size included in the emergency mission. The expression of the resource attribute constraint is as follows:

[0088]

[0089] Among them, S i represents the i-th resource, that is, the i-th satellite, and there are m resources in total. C i represents the on-board storage of S i and represents the currently available storage space of S i ; d j represents the target data volume, which represents the data size included in the mission.

[0090] (2C) The target observation constraint is that the duration of the target observation mission is greater than the necessary duration of the target observation, and the end time of the target observation mission is less than the target observation deadline. The expression of the target observation constraint:

[0091]

[0092] Among them, represents the start time of the emergency observation mission; the end time of the emergency observation mission; tw i,j represents the target observation time window (TOTW); represents the target observation duration; represents the target observation deadline (TOD).

[0093] (2D) The data transmission constraint is that the data transmission duration is greater than the required transmission time of the data, and the end time of the data transmission is less than the data transmission deadline. The expression of the data transmission constraint is as follows:

[0094]

[0095] Among them, represents the start time of the data transmission, represents the end time of the data transmission; gw i represents the data transmission time window (DTTW); d j represents the target data volume; r represents the data transmission rate in space; represents the data transmission deadline (DTD).

[0096] Based on the above constraints, the population of emergency mission planning strategies is randomly initialized, and the individuals in the population represent the emergency mission sequences.

[0097] For the foregoing step S106, an embodiment of the present invention provides a specific implementation manner for determining the fitness value corresponding to the emergency task sequence included in the emergency task planning strategy population, adaptively mutating and cross - operating the emergency task sequence according to the fitness value to obtain a new emergency task planning strategy population, and continuing to determine the fitness value corresponding to the emergency task sequence included in the new emergency task planning strategy population until the target emergency task sequence is obtained when a preset stop condition is met.

[0098] In one example, the process of determining the fitness value is as follows: Based on a fitness function that comprehensively considers benefits and resource waiting time, determine the fitness value corresponding to the emergency task sequence included in the emergency task planning strategy population. An embodiment of the present invention considers the observation priority of each target and the resource waiting time, and proposes a fitness function that comprehensively obtains the highest benefit and the shortest waiting time. Calculate the fitness value of each individual according to the fitness function. The higher the fitness of an individual, the higher the benefit of the observation sequence. The expression of the fitness function that comprehensively considers benefits and resource waiting time is as follows:

[0099]

[0100] where, f 1 is the fitness function considering benefits, which means helping more important tasks to be successfully executed to generate better profits. In many studies, only f 1 is considered; f 2 is the fitness function considering resource waiting time, which means reducing the time of waiting for available resources to make the task more timely and improve its timeliness; X ij is a decision variable describing whether the emergency task T j is assigned to the satellite S i . If the emergency task T j is assigned to the satellite S i , then X ij = 1; if the emergency task T j is not assigned to the satellite S i , then X ij = 0; w j is the task benefit of the emergency task T j ; is the start time for the satellite to obtain the emergency task T j ; is the demand arrival time of the emergency task T j ; is the target observation deadline; is the target observation duration; is the start time for the emergency task T j obtained by the satellite to be downloaded to the ground station; is the start time for the satellite to obtain the emergency task Tj The end time, is the data transmission deadline, d j represents the target data volume, r is the data transmission rate in space, m is the total number of satellites, n is the total number of emergency tasks, λ 1 , λ 2 is f 1 , f 2 's weight, λ 1 +λ 2 = 1; represents the waiting time before reducing the emergency task T j ; represents the maximum allowable time of the emergency task T j ; represents the waiting time for data transmission of the emergency task T j ; represents the maximum allowable time for data transmission.

[0101] In one example, the process of adaptive mutation and crossover operations is as follows: In the current iteration process, the following operations are performed on the emergency task planning strategy population:

[0102] (3A) Based on the set of mutation operators, perform an adaptive mutation operation on the emergency task sequences included in the emergency task planning strategy population, and update the set of mutation operators during the process of performing the adaptive mutation operation on the emergency task sequences until the adaptive mutation operation is completed.

[0103] The following operations are performed on the emergency task sequences included in the emergency task planning strategy population: Calculate the mean of the mutation operators included in the set of mutation operators to obtain the mean mutation operator; Based on the mean mutation operator and the adaptive mutation control parameter used when performing the adaptive mutation operation on the previous emergency task sequence, determine a new adaptive mutation control parameter; Determine a new mutation operator based on the new adaptive mutation control parameter; Randomly determine a target mutation rule from the pre-configured set of mutation rules, and based on the target mutation rule, perform an adaptive mutation operation on the current emergency task sequence using the new mutation operator.

[0104] Specifically: Three types of adaptive mutation operations complete the mutation of individuals. Each iteration randomly selects one type for mutation, as shown below:

[0105] The three mutation rules are:

[0106] DE / Rand / 1:

[0107] DE / Best / 1:

[0108] DE / Current-to-rand / 1:

[0109] where r 1 , r 2 , r 3 ∈(1, 2, …, N); represents the emergency task sequence with the highest fitness in the current emergency task planning strategy population; represents three random emergency task sequences in the current emergency task planning strategy population; represents the current emergency task sequence; F i represents the mutation operator of the i-th original vector, which will be re-initialized before each iteration to complete the adaptive process, as follows:

[0110] F i = rand c i (μF, 0.1);

[0111] where μF represents the adaptive mutation control parameter, and rand c i (μ, σ 2 ) represents a value randomly selected from the normal distribution and Cauchy distribution with mean μ and variance σ 2 .

[0112] If the benefit of the mutated emergency task sequence is greater than that of the original emergency task sequence, it proves that this is a successful mutation operation. At this time, F i is stored in a set, denoted by S F , and the update of μF in the next iteration is as follows:

[0113] μF = (1 - c) * μF + c * mean(S F );

[0114] where c is the weight and mean represents the arithmetic mean. At the beginning of each iteration, S F is cleared.

[0115] (3B) Based on the emergency task planning strategy population after the cross probability set and adaptive mutation operation, perform an adaptive crossover operation on the emergency task sequences included in the original emergency task planning strategy population in the current iteration process, and update the cross probability set during the process of performing the adaptive crossover operation on the emergency task sequences until the adaptive mutation operation is completed, to obtain a new emergency task planning strategy population.

[0116] Denote the emergency task sequence contained in the original emergency task planning strategy population during the current iteration as the original emergency task sequence, and denote the emergency task sequence contained in the emergency task planning strategy population after the adaptive mutation operation as the mutated emergency task sequence. Based on this, perform the following operations on the emergency task sequence contained in the original emergency task planning strategy population during the current iteration: Calculate the mean of the crossover probabilities contained in the crossover probability set to obtain the mean crossover probability; Based on the mean crossover probability and the adaptive crossover control parameter used for the adaptive crossover operation on the previous emergency task sequence, determine the new adaptive crossover control parameter; Determine the new crossover probability based on the new adaptive crossover control parameter; In the case where the value generated by the random function is less than or equal to the new crossover probability, perform the adaptive crossover operation on the original emergency task sequence and its matching mutated emergency task sequence.

[0117] Specifically: The adaptive crossover operation generates experimental individuals as follows:

[0118]

[0119] Among them, is the emergency task sequence obtained by adaptive crossover, is the mutated emergency task sequence, is the original emergency task sequence, j rand ∈{1,2,…,D}, is a randomly selected integer to ensure that at least one gene of the mutated emergency task sequence is inherited from the mutant individual. CR i represents the crossover probability of the i-th original vector, and will be re-initialized before each iteration. The adaptive process is completed as follows:

[0120] CR i =rand n i (μCR,0.1);

[0121] Among them, μCR represents the adaptive control parameter, and rand n i (μ,σ 2 ) represents a value randomly selected from the normal distribution and Cauchy distribution with mean μ and variance σ 2 .

[0122] If the benefit of the mutated emergency task sequence is greater than that of the original emergency task sequence, it proves that this is a successful crossover operation. At this time, CR i is stored in a set, denoted by S CR , and the update of μCR in the next iteration is as follows:

[0123] μCR=(1 - c)*μCR + c*mean(S CR );

[0124] Among them, c is the weight, and mean represents the arithmetic mean. At the beginning of each iteration, S is cleared CR .

[0125] Furthermore, a new emergency task sequence is generated according to the adaptive mutation and crossover operations Denoted as the experimental individual, and compare the fitness values of the experimental individual and the original individual (i.e., the original emergency task sequence). If the fitness value of the experimental individual is greater than that of the original individual, the new individual replaces the original individual until the number of updates is not less than pop_size; judge whether the number of updates is less than pop_size. If the number of updates is less than pop_size, where pop_size is the maximum number of updates, then start the mutation and crossover operations; compare and generate the individual with greater fitness among the experimental individual and the original individual; if the number of updates is not less than pop_size, output the planning sequence.

[0126] Round up the updated individuals using the upward rounding strategy. After rounding up, traverse whether there are the same emergency tasks in the updated individuals. If so, perform the duplicate removal operation, and randomly generate an unappeared emergency to replace the repeated one, ensuring that each emergency task is executed only once.

[0127] In summary, the embodiment of the present invention adopts an adaptive differential evolution algorithm to optimize the execution order of emergency tasks by adaptively adjusting the mutation and crossover operations. Each population member represents a potential task sequence, and its performance is evaluated by a fitness function that comprehensively evaluates the benefit and resource waiting time. During the iteration process, new population members are generated by adaptively applying the mutation and crossover operations. If the newly generated experimental individual has a higher fitness, it will replace the original individual. Until the number of updates is not less than pop_size. Finally, when the iteration reaches the predetermined termination condition, the task sequences will be sorted according to their fitness, and the sequence with the highest fitness will be selected as the final output. The advantages of the embodiment of the present invention are that it not only considers the multi-faceted complexity of satellite task planning, but also makes full use of the advantages of the adaptive differential evolution algorithm, including its high efficiency in global optimization and the pursuit of fast convergence. Through simulation experiments, the method of the present invention has been proven to be feasible and effective, providing a new perspective for the task planning of earth observation satellites in the face of emergencies, helping to improve the rationality and efficiency of planning, and ensuring that tasks can be quickly and accurately responded to at critical moments.

[0128] The embodiment of the present invention has at least the following characteristics:

[0129] (1) By constructing a resource model and an emergency task constraint model, the present invention proposes an innovative solution to address the deficiency in the prior art that fails to fully consider the observation benefits and resource waiting time in the observation task planning. The present invention adopts an adaptive differential evolution algorithm, effectively improving the limitations of the prior art in solving accuracy, and has the characteristics of comprehensiveness, fast calculation, and reasonable planning, providing a new method for the emergency task planning of earth observation satellites. Through in-depth analysis of the model, the present invention clarifies the interactions and influences among the components, which helps to develop targeted algorithms to more effectively solve the problem of ground observation satellite task planning.

[0130] (2) The embodiment of the present invention deepens the strategy of satellite task planning and introduces a new fitness function that not only evaluates the benefits of each observation target but also considers the waiting time of resources. This method aims to go beyond the traditional task priority ranking to more comprehensively reflect the complexity of satellite task planning. Through this innovative fitness function, the research can more effectively balance the observation benefits and resource utilization, avoiding simply determining the execution order based on the task priority, which may lead to overly simplified and single planning results. This method is particularly suitable for dealing with satellite task planning problems with tight time and concentrated space, and can better meet the special needs in these situations.

[0131] (3) The embodiment of the present invention also fully considers the actual operating environment of satellite task planning, such as the finiteness of resources, the urgency of tasks, and the uncertainty of the environment, making the planning results closer to the actual application scenarios. This comprehensive consideration method provides a new perspective for satellite task planning, which helps to achieve more efficient and reasonable resource allocation and task scheduling.

[0132] Based on the foregoing embodiments, the embodiment of the present invention provides a satellite emergency task planning device based on a differential evolution algorithm. Refer to Figure 3 the structural schematic diagram of a satellite emergency task planning device based on a differential evolution algorithm as shown. The device mainly includes the following parts:

[0133] A model establishment module 302, configured to establish a satellite resource model for the satellite and an emergency task model for the emergency tasks to be executed by the satellite;

[0134] A population planning module 304, configured to generate an emergency task planning strategy population based on the satellite resource model and the emergency task model, in combination with task attribute constraints, resource attribute constraints, target observation constraints, and data transmission constraints. The emergency task planning strategy population includes multiple emergency task sequences;

[0135] The task sequence iteration module 306 is used to determine the fitness value corresponding to each emergency task sequence included in the emergency task planning strategy population, so as to perform adaptive mutation and crossover operations on each emergency task sequence according to the fitness value, obtain a new emergency task planning strategy population, and continue to determine the fitness value corresponding to each emergency task sequence included in the new emergency task planning strategy population until the target emergency task sequence is obtained when the preset stop condition is met.

[0136] The satellite emergency task planning device provided by the embodiment of the present invention is directed to the satellite resource model and the emergency task model, combines various constraints such as task attribute constraints, resource attribute constraints, target observation constraints, and data transmission constraints, generates an initial emergency task planning strategy population, and on this basis, processes the emergency task sequences in the emergency task planning strategy population by using adaptive mutation and crossover operations, effectively improving the limitations of the existing technology in solving accuracy, and further performing iterative optimization in combination with the fitness of the emergency task sequences, so as to obtain the target emergency task sequence. Therefore, the present invention can achieve more efficient and reasonable resource allocation and task scheduling in the scenario of emergency task planning.

[0137] In one implementation manner, the task attribute constraint is that the emergency task is executed only once, and the satellite executes only one emergency task at the same time;

[0138] The resource attribute constraint is that the satellite resources of the satellite are in an available state, and the available storage space of the satellite is greater than the data size included in the emergency task;

[0139] The target observation constraint is that the target observation task duration is greater than the necessary duration of the target observation, and the end time of the target observation task is less than the target observation deadline;

[0140] The data transmission constraint is that the data transmission duration is greater than the required transmission time of the data, and the end time of the data transmission is less than the data transmission deadline.

[0141] In one implementation manner, the task sequence iteration module 306 is specifically used for:

[0142] Based on the fitness function that comprehensively considers the benefit and the resource waiting time, determine the fitness value corresponding to the emergency task sequence included in the emergency task planning strategy population.

[0143] In one implementation manner, the expression of the fitness function that comprehensively considers the benefit and the resource waiting time is as follows:

[0144]

[0145] Among them, f 1 is the fitness function considering the benefit; f2 is a fitness function considering resource waiting time; X ij is used to describe whether the emergency task T j is assigned to the satellite S i is a decision variable. If the emergency task T j is assigned to the satellite S i then X ij = 1. If the emergency task T j is not assigned to the satellite S i then X ij = 0; w j is the task benefit of the emergency task T j ; is the start time for the satellite to obtain the emergency task T j ; is the demand arrival time of the emergency task T j , is the target observation deadline, is the target observation duration, is the start time for the emergency task T obtained by the satellite to be downloaded to the ground station, j ; is the end time for the satellite to obtain the emergency task T j ; is the data transmission deadline, d j represents the target data volume, r is the data transmission rate in space, m is the total number of satellites, n is the total number of emergency tasks, λ 1 、λ 2 are the weights of f 1 、f 2 ;

[0146] In one implementation, the task sequence iteration module 306 is specifically configured to:

[0147] During the current iteration process, perform the following operations on the emergency task planning strategy population:

[0148] Based on the mutation operator set, perform an adaptive mutation operation on the emergency task sequences included in the emergency task planning strategy population, and update the mutation operator set during the process of performing the adaptive mutation operation on the emergency task sequences until the adaptive mutation operation ends;

[0149] Based on the crossover probability set and the emergency task planning strategy population after the adaptive mutation operation, perform an adaptive crossover operation on the emergency task sequences included in the original emergency task planning strategy population during the current iteration process, and update the crossover probability set during the process of performing the adaptive crossover operation on the emergency task sequences until the adaptive mutation operation ends, to obtain a new emergency task planning strategy population.

[0150] In one embodiment, the task sequence iteration module 306 is specifically configured to:

[0151] Perform the following operations on the emergency task sequences included in the emergency task planning strategy population:

[0152] Calculate the mean value of the mutation operators included in the mutation operator set to obtain the mean mutation operator;

[0153] Based on the mean mutation operator and the adaptive mutation control parameter used for the adaptive mutation operation on the previous emergency task sequence, determine a new adaptive mutation control parameter;

[0154] Determine a new mutation operator based on the new adaptive mutation control parameter;

[0155] Randomly determine a target mutation rule from the pre-configured mutation rule set, and perform an adaptive mutation operation on the current emergency task sequence based on the target mutation rule and the new mutation operator.

[0156] In one embodiment, the task sequence iteration module 306 is specifically configured to;

[0157] Based on the crossover probability set and the emergency task planning strategy population after the adaptive mutation operation, perform an adaptive crossover operation on the emergency task sequences included in the original emergency task planning strategy population during the current iteration process, including:

[0158] Perform the following operations on the emergency task sequences included in the original emergency task planning strategy population during the current iteration process:

[0159] Calculate the mean value of the crossover probabilities included in the crossover probability set to obtain the mean crossover probability;

[0160] Based on the mean crossover probability and the adaptive crossover control parameter used for the adaptive crossover operation on the previous emergency task sequence, determine a new adaptive crossover control parameter;

[0161] Determine a new crossover probability based on the new adaptive crossover control parameter;

[0162] In the case where the value generated by the random function is less than or equal to the new crossover probability, perform an adaptive crossover operation on the original emergency task sequence and the mutated emergency task sequence matched with it.

[0163] The device provided by the embodiments of the present invention has the same implementation principle and the same technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding contents in the foregoing method embodiments.

[0164] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device; a computer program is stored on the storage device, and when the computer program is run by the processor, it executes the method described in any one of the above-described embodiments.

[0165] Figure 4 FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device 100 includes: a processor 40, a memory 41, a bus 42, and a communication interface 43. The processor 40, the communication interface 43, and the memory 41 are connected through the bus 42; the processor 40 is configured to execute an executable module stored in the memory 41, such as a computer program.

[0166] Among them, the memory 41 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 43 (which may be wired or wireless), a communication connection is established between the system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0167] The bus 42 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 4 only a bidirectional arrow is used in FIG. 4, but it does not mean that there is only one bus or one type of bus.

[0168] Among them, the memory 41 is used to store a program. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device defined by the flow process disclosed in any one of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0169] The processor 40 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 40 or the instructions in the form of software. The above-mentioned processor 40 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and combines its hardware to complete the steps of the above method.

[0170] The computer program product of the readable storage medium provided by the embodiments of the present invention includes a computer-readable storage medium storing program codes, and the instructions included in the program codes can be used to execute the methods described in the foregoing method embodiments. For the specific implementation, reference can be made to the foregoing method embodiments, which will not be elaborated here.

[0171] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0172] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A satellite emergency mission planning method based on differential evolution algorithm, characterized in that: include: Establishing a satellite resource model for a satellite, and establishing an emergency task model for an emergency task to be performed by the satellite; Based on the satellite resource model and the emergency task model, combined with task attribute constraints, resource attribute constraints, target observation constraints and data transmission constraints, an emergency task planning strategy population is generated, wherein the emergency task planning strategy population includes a plurality of emergency task sequences; Determine the fitness value corresponding to the emergency task sequence contained in the emergency task planning strategy population, perform adaptive mutation and crossover operations on the emergency task sequence according to the fitness value, and obtain a new emergency task planning strategy population, and continue to determine the fitness value corresponding to the emergency task sequence contained in the new emergency task planning strategy population until the target emergency task sequence is obtained when the preset stop condition is met.

2. The satellite emergency mission planning method based on differential evolution algorithm according to claim 1, characterized in that: The task attribute constraint is: the emergency task is only executed once, and the satellite only executes one emergency task at the same time; The resource attribute constraint is: the satellite resource of the satellite is in an available state, and the available storage space of the satellite is greater than the data size included in the emergency task; The target observation constraint is: the target observation task duration is greater than the necessary duration of the target observation, and the target observation task end time is less than the target observation deadline; The data transmission constraint is that the data transmission duration is greater than the data transmission time required, and the data transmission end time is less than the data transmission deadline time.

3. The satellite emergency mission planning method based on differential evolution algorithm according to claim 1, characterized in that: Determining the fitness value corresponding to the emergency task sequence included in the emergency task planning strategy population includes: Based on a fitness function that comprehensively considers benefits and resource waiting time, a fitness value corresponding to the emergency task sequence included in the emergency task planning strategy population is determined.

4. The satellite emergency mission planning method based on differential evolution algorithm according to claim 3 is characterized in that: The expression of the fitness function based on comprehensive consideration of benefits and resource waiting time is as follows: Among them, f1 is the fitness function considering the benefit; f2 is the fitness function considering the resource waiting time; X ij To describe the emergency task T j Is it assigned to satellite S? i The decision variables of the emergency task T j Assigned to satellite S i Then X ij =1, if the emergency task T j Not assigned to satellite S i Then X ij =0; w j For emergency tasks j The mission benefits; Get emergency mission T for satellite j The start time of For emergency tasks j The demand arrival time, is the target observation cut-off time, is the target observation duration, Emergency missions acquired for satellites T j The start time of downloading to the ground station, Get emergency mission T for satellite j The end time of is the data transmission deadline, d j represents the target data volume, r is the data transmission rate in space, m is the total number of satellites, n is the total number of emergency tasks, λ1 and λ2 are the weights of f1 and f2.

5. The satellite emergency mission planning method based on differential evolution algorithm according to claim 1, characterized in that: According to the fitness value, the emergency task sequence is adaptively mutated and crossover operated to obtain a new emergency task planning strategy population, including: During the current iteration, the following operations are performed on the emergency task planning strategy population: Based on a mutation operator set, an adaptive mutation operation is performed on the emergency task sequence included in the emergency task planning strategy population, and the mutation operator set is updated in the process of the adaptive mutation operation on the emergency task sequence until the adaptive mutation operation is completed; Based on the crossover probability set and the emergency task planning strategy population after the adaptive mutation operation, an adaptive crossover operation is performed on the emergency task sequence contained in the original emergency task planning strategy population in the current iteration process, and the crossover probability set is updated during the process of performing the adaptive crossover operation on the emergency task sequence until the adaptive mutation operation is completed, thereby obtaining a new emergency task planning strategy population.

6. The satellite emergency mission planning method based on differential evolution algorithm according to claim 5 is characterized in that: Based on a mutation operator set, an adaptive mutation operation is performed on the emergency task sequence contained in the emergency task planning strategy population, including: The following operations are performed on the emergency task sequence included in the emergency task planning strategy population: averaging the mutation operators included in the mutation operator set to obtain a mutation operator mean; Determine the new adaptive mutation control parameter based on the mutation operator mean and the adaptive mutation control parameter used when the adaptive mutation operation was performed on the previous emergency task sequence; Determining a new mutation operator based on the new adaptive mutation control parameter; A target mutation rule is randomly determined from a pre-configured mutation rule set, and an adaptive mutation operation is performed on the current emergency task sequence based on the new mutation operator according to the target mutation rule.

7. The satellite emergency mission planning method based on differential evolution algorithm according to claim 5, characterized in that: The emergency task sequence contained in the original emergency task planning strategy population in the current iteration process is recorded as the original emergency task sequence, and the emergency task sequence contained in the emergency task planning strategy population after the adaptive mutation operation is recorded as the mutated emergency task sequence; Based on the crossover probability set and the emergency task planning strategy population after the adaptive mutation operation, an adaptive crossover operation is performed on the emergency task sequence contained in the original emergency task planning strategy population in the current iteration process, including: The following operations are performed on the emergency task sequence contained in the original emergency task planning strategy population in the current iteration process: averaging the crossover probabilities contained in the crossover probability set to obtain a crossover probability mean; Determine new adaptive crossover control parameters based on the crossover probability mean and the adaptive crossover control parameters used when the adaptive crossover operation is performed on the previous emergency task sequence; Determining a new crossover probability based on the new adaptive crossover control parameter; In the case that the value generated by the random function is less than or equal to the new crossover probability, an adaptive crossover operation is performed on the original emergency task sequence and the matched mutated emergency task sequence.

8. A satellite emergency mission planning device based on differential evolution algorithm, characterized in that: include: A model building module, used to build a satellite resource model for a satellite, and to build an emergency task model for an emergency task to be performed by the satellite; A population planning module is used to generate an emergency task planning strategy population based on the satellite resource model and the emergency task model, in combination with task attribute constraints, resource attribute constraints, target observation constraints and data transmission constraints, wherein the emergency task planning strategy population includes multiple emergency task sequences; The task sequence iteration module is used to determine the fitness value corresponding to each emergency task sequence contained in the emergency task planning strategy population, so as to perform adaptive mutation and crossover operations on each emergency task sequence according to the fitness value to obtain a new emergency task planning strategy population, and continue to determine the fitness value corresponding to each emergency task sequence contained in the new emergency task planning strategy population until the target emergency task sequence is obtained when the preset stop condition is met.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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