CN110210700A - More star dynamic task planing methods of task based access control priority towards emergency response - Google Patents
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Abstract
The more star dynamic task planing methods for the task based access control priority towards emergency response that the invention discloses a kind of, for in emergency circumstances earth observation satellite integrated dispatch problem, on the basis of analysis task priority, a kind of integrated dispatch model of mission requirements is proposed.First against contingency tasks design objective priority interpretational criteria, a set of reasonable task priority calculation method is proposed based on seven kinds of influence factors.Secondly, constraint condition in detailed analysis scheduling process, there is the waiting time of urgent desired task as specific item scalar functions to maximize observation mission priority, maximize task income and minimize execution time, initial satellite scheduling scheme is obtained by genetic tabu hybrid algorithm.For the case where contingency tasks dynamic reaches in initial scheme implementation procedure, a kind of dynamic dispatching algorithm priority-based is proposed to solve the scheduling problem of newly-increased task, to obtain the scheduling scheme of newly-increased task.
Description
Technical Field
The invention relates to a multi-satellite dynamic task planning method, in particular to an emergency response-oriented multi-satellite dynamic task planning method based on task priority.
Background
The task planning of the earth observation satellite is to determine the execution sequence of the tasks and the corresponding relation between the tasks and the resources and time under the conditions that the number of the satellites is fixed, the capacity of the sensors is limited, and the tasks conflict with each other, so as to eliminate the conflict among different tasks, maximally meet the requirements of users, improve the utilization rate of the satellite resources and optimize the execution scheme of the observation tasks. In recent years, with the wide application of earth observation satellites under emergency conditions such as natural disaster monitoring, accident disaster, public health events and the like, the earth observation satellite scheduling presents some new characteristics. There are many uncertain factors including user requirements, weather conditions, satellite states, etc. in earth observation satellite scheduling under emergency conditions. In addition, the emergency tasks submitted by the users are usually dynamically arrived, and the number of the tasks and the arrival time are uncertain. Due to the characteristics of dynamic real-time scheduling, multiple targets, such as scheduling benefits, stability and energy conservation, need to be considered simultaneously during scheduling. All of these constraints and uncertainties make emergency scheduling an NP-hard combinatorial optimization problem. Therefore, the rapid and efficient multi-satellite dynamic emergency scheduling strategy is of great significance.
At present, scholars at home and abroad carry out a great deal of research aiming at the satellite scheduling problem, and reasonably distribute satellite resources and observation time periods for imaging observation tasks under the condition of meeting various constraints so as to improve the efficiency of task execution. However, allocating multiple interactive satellites on a target is more complex and challenging when resource contention is considered. Methods to solve this problem include priority-based heuristics, local search, tabu search, simulated annealing, genetic algorithms, and ant colony algorithms. The satellite scheduling strategy based on the method is mostly put forward under a static scheduling framework and is not suitable for the multi-satellite dynamic scheduling problem under the emergency condition. The method has the remarkable characteristic that a new emergency imaging task comes, some scholars use a full-re-planning algorithm to re-model and solve the original problem, and the generated new task planning scheme is greatly different from the initial scheme, so that the satellite re-scheduling difficulty is high. However, the above methods are mostly based on conventional tasks, and are difficult to be applied to the scheduling of emergency tasks. For emergency observation task planning, most research methods are focused on obtaining satellite images under emergency conditions, and the satellite planning and scheduling problem is not described in detail. In addition, due to the fact that the importance of the tasks is different, the execution sequence is different, and a reasonable and effective priority algorithm has significance for improving the task execution efficiency.
Disclosure of Invention
In consideration of the particularity of the emergency observation problem, a set of complete model is constructed to research the multi-satellite dynamic scheduling problem under the emergency condition. Firstly, designing a task priority evaluation criterion aiming at an emergency task, and considering factors including imaging task level, observation image type, availability of satellite resources, execution urgency degree, execution task type, conflict degree between tasks and imaging task income to obtain the importance degree of the task and use the importance degree as heuristic information when solving a constraint planning problem. Secondly, main constraint conditions in the problem are analyzed in detail, the waiting time of tasks with the maximum observation task priority, the maximum task income and the minimum execution time urgency requirement is taken as a sub-target function, and an initial satellite scheduling scheme is obtained by combining a genetic tabu hybrid algorithm, so that a large amount of useless iteration is avoided when the algorithm approaches to the optimal solution in the later period. Based on the above, under the condition that a new urgent task is inserted, a dynamic scheduling algorithm based on priority is provided to solve the scheduling problem of the newly added task. And finally, verifying the effectiveness of the algorithm through experimental simulation, and verifying the feasibility of the emergency observation task planning model through a model performance evaluation function.
The invention realizes the above thought and adopts the specific technical scheme that:
the invention constructs a multi-satellite cooperative emergency scheduling model (figure 1) based on task priority, and further divides the model into two parts, namely a task ordering subproblem and a satellite resource allocation subproblem.
1 task prioritization
1.1 task priority impact factor
Determining the following main influence factors mainly considered by task priority setting through analyzing the application requirements of earth observation satellite resources and emergency tasks
1.2 task priority computation model
Quantifying qualitative factors in the task priority influence factors based on different task priority influence factors, and performing comprehensive calculation by adopting TOPSIS (technique for Order Preference by Similarity to Ideal solution) to obtain a quantitative index of the task priority.
2. Emergency observation task planning modeling
Unlike the traditional scheduling mode, the dynamic conforming emergency is mainly aimed at the non-periodic task with expected completion time and uncertain arrival time. Under the condition of meeting satellite observation constraints and task requirements, the multi-satellite dynamic emergency scheduling multi-objective mathematical programming model is established, and an observation scheme is optimized so as to maximally complete the objective of task programming.
2.1 satellite mission planning constraint analysis
A task is defined herein as a point target task that can be observed by a satellite sensor within a field of view. And (3) considering various constraint conditions of satellite imaging, establishing a mathematical programming model to describe the problem, and determining specified main constraint conditions in an observation scheduling stage.
2.2 optimizing the objective function
The multi-satellite emergency scheduling model based on task priority is regarded as a multi-objective optimization solving problem, the completion rate of key emergency tasks in a planning scheme is required to be high, imaging task benefits are high, and the planning efficiency of each task is required to be high as much as possible to ensure that the reservation rate of satellite resources is high. Therefore, the optimization objective function is constructed for the multi-satellite resource scheduling model by adopting a weighted sum method, the priority execution of more important, more urgent and higher-income tasks is ensured, the allocation of resources is reasonably carried out, and the global optimization is realized. The method for constructing the optimization objective function comprises the following steps: the method comprises the steps of firstly constructing a sub-objective function which maximizes the sum of priorities of observation tasks, maximizes the sum of benefits of the tasks and minimizes the waiting time of the tasks with urgent requirements on execution time, and then obtaining a global optimization objective function by using a weighting method based on the sub-optimization objective function.
3 satellite resource dynamic matching for emergency scheduling
The purpose of the emergency observation task planning is to determine the execution sequence of tasks and reasonably distribute satellite resources and execution time for each task, and as new emergency tasks are continuously added, the complexity of the problem is further increased. Because the multi-satellite cooperative scheduling involves more constraints, and the arrival of a new task can affect a task set, a satellite resource set, a time window and a constraint set in an original problem model, the research provides a dynamic scheduling algorithm based on priority to solve the scheduling problem of the newly added task. The method comprises the steps of firstly realizing initial scheduling of tasks through a genetic tabu hybrid algorithm, searching satellites matched with task requirements in a resource set on the basis of an initial scheduling result when new tasks are added, and respectively carrying out direct insertion, redistribution, replacement and deletion operations on the satellites so as to meet a highest priority completion principle, a principle of minimum change of an initial scheduling scheme and a fastest scheduling principle in dynamic scheduling.
3.1 initial scheduling scheme for satellite resources
A genetic tabu hybrid algorithm is designed, satellite resource allocation is carried out on the obtained task set, and the algorithm is used for solving the combined optimization model to obtain an initial scheduling scheme. The genetic algorithm is adopted to search in a large range in a global space, namely, an initial population quickly traverses most of the region of a solution space in a parallel mode, the result is stabilized in a region with a better solution space part when iteration is terminated, and then a tabu search algorithm is adopted to search in a small range in the local space from each individual in an optimization region, so that the genetic algorithm is delayed or prevented from falling into local optimization, and the optimization capability of the algorithm is improved. The hybrid algorithm effectively makes up the defects that the local search capability of the genetic algorithm is weak, the tabu search algorithm is strongly dependent on the initial solution and the like.
4.2 dynamic scheduling scheme for newly-added emergency tasks
In the newly-added emergency task dynamic scheduling scheme, the research provides a priority-based dynamic scheduling algorithm to solve the scheduling problem of the newly-added task, and the method inserts the newly-arrived task into the initial scheduling scheme based on rules to obtain a new scheduling scheme. The tasks in the newly added task set are sorted according to the priority from high to low, the tasks with high priority are scheduled preferentially, and the adding of the new tasks can be divided into a direct insertion process, a task redistribution process, a task replacement process and a task deletion process. The direct insertion of the new task refers to that on the premise of not changing the resources, time windows and scheduling sequences of the task arrangement of the current scheduling scheme, whether the tasks in the new task set can be directly inserted into the task scheduling arrangement of the available satellite resources of the new task set under the condition that all scheduling constraints are met is sequentially judged according to the sequence of the priority of the new task from high to low. When inserting the task, determining the sequence of the insertion points according to the starting time of the task visible time window and the completion time of the scheduled scheduling task. Task reallocation refers to inserting an original task which conflicts with a new task into a time window of other resources under the condition that the task in an original scheduling scheme is not deleted, and inserting the new task into the time window of the original task, wherein the mode can cause that the resources and time for scheduling part of tasks in a planning scheduling scheme are changed. Task replacement means that in order to further improve the performance of a planning scheduling scheme under the condition that direct insertion and redistribution cannot be performed, a new task with a high priority is used for replacing a task with a low priority in an original scheduling scheme, and the task in the original scheduling scheme is deleted in the mode, so that the stability of the original scheduling scheme is influenced to a certain extent. Task deletion refers to deleting a new atomic task and giving up resource arrangement under the condition that the former three ways cannot be met. And in the dynamic planning under the condition that a new task arrives, sequentially performing the four operations, and performing iteration according to the local search idea to obtain a relatively ideal dynamic task adjustment scheme.
4 evaluation of model Properties
The multi-satellite dynamic emergency scheduling model based on task priority not only requires a high task completion rate and a maximum total profit of a planning scheme, but also makes a planning period of each task better as much as possible and ensures an execution rate of a task with a high priority. Therefore, according to the characteristics of the model, a task completion rate index (TCR), a task priority execution rate index (TPR) and a scheduling scheme change rate index sc (scheme change rate) are constructed, and finally an evaluation function is obtained to verify the performance of the algorithm.
Wherein, TCR is n/n ', n is the total number of the actual scheduling tasks after the scheme adjustment, n' is the total number of the scheduling tasks,num(Tc) Number of tasks for scheduling scheme change, num (T)q) Is the initial number of tasks.
After the technical scheme is adopted, the invention has the beneficial effects that: a set of complete models is constructed to research the multi-satellite dynamic scheduling problem under the emergency condition. Firstly, designing a task priority evaluation criterion aiming at an emergency task, and considering factors including imaging task level, observation image type, availability of satellite resources, execution urgency degree, execution task type, conflict degree between tasks and imaging task income to obtain the importance degree of the task and use the importance degree as heuristic information when solving a constraint planning problem. Secondly, main constraint conditions in the problem are analyzed in detail, the waiting time of tasks with the maximum observation task priority, the maximum task income and the minimum execution time urgency requirement is taken as a sub-target function, and an initial satellite scheduling scheme is obtained by combining a genetic tabu hybrid algorithm, so that a large amount of useless iteration is avoided when the algorithm approaches to the optimal solution in the later period. Based on the above, under the condition that a new urgent task is inserted, a dynamic scheduling algorithm based on priority is provided to solve the scheduling problem of the newly added task. The invention can support some emergency ground actions, and has great significance especially for tasks with high dynamic and high time sensitive observation requirements, such as disaster monitoring and early warning.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a flow chart of multi-satellite dynamic emergency scheduling based on task priority.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention. The invention provides a multi-satellite dynamic task planning method based on task priority in emergency response, which adopts the technical scheme that:
the invention realizes the above thought and adopts the specific technical scheme that:
the invention constructs a multi-satellite cooperative emergency scheduling model (figure 1) based on task priority, and further divides the model into two parts, namely a task ordering subproblem and a satellite resource allocation subproblem.
1 task prioritization
1.1 task priority impact factor
By analyzing the application requirements of earth observation satellite resources and emergency tasks, the following 7 influence factors are mainly considered for setting the task priority (table 1).
TABLE 1 task priority impact factors (evaluation indexes of the priority of the task)
1.2 task priority computation model
Quantifying qualitative factors in the task priority influence factors based on different task priority influence factors, and comprehensively calculating by adopting TOPSIS (technique for order preference by similarity Ideal solution) 35 to obtain a quantitative index of the task priority, wherein the method mainly comprises the following steps.
(1) For a task set T ═ T1,t2,...,tmCalculating priority influence factors respectively to form an influence factor matrix X ═ Xij]m×7Wherein x isijIs tiM is the number of tasks.
(2) Changing the influence factor matrix X into [ X ] by using range transform methodij]m×7Conversion to the standard matrix Y ═ Yij]m×7，0≤yij≤1。
(3) Setting ideal solution I ═ I ≦ I+,I-Is disclosed in+1, (1, 1., 1) is a positive ideal solution, I-A negative ideal solution is (0, 0.., 0).
(4) Computing task tiCloseness to ideal solution Ci：
Wherein,andas task tiThe distance from the ideal solution I is,
(5) task tiHas a priority of Pi:
Through the steps, the priority quantitative numerical value of each task can be obtained and is taken as [0,10 ]]Between, PiThe higher the value the higher the priority of the task.
2. Emergency observation task planning modeling
Unlike the traditional scheduling mode, the dynamic conforming emergency is mainly aimed at the non-periodic task with expected completion time and uncertain arrival time. Under the condition of meeting satellite observation constraints and task requirements, the multi-satellite dynamic emergency scheduling multi-objective mathematical programming model is established, and an observation scheme is optimized so as to maximally complete the objective of task programming.
2.1 satellite mission planning constraint analysis
The task defined by the invention refers to a point target task which can be observed in a visual field by a satellite sensor. And (3) considering various constraint conditions of satellite imaging, establishing a mathematical programming model to describe the problem, wherein in an observation scheduling stage, the specified main constraint conditions are as follows. The variables in the definition constraints are shown in table 2 and the function definitions are shown in table 3.
TABLE 2Variable definition
TABLE 3Function definition
(1) The satellite loads and the observation targets must be visible, with the following visible window constraints:
estij-bstij≥CapLen(ti)
(2) and (4) restricting the starting time. The boot time must be at satellite sjSensor s 'of'jMinimum boot time MinTjAnd maximum boot time MaxTjWithin the time interval of two startup and shutdown, the minimum time interval MinPeriod of the resource preset cannot be exceededj。
CS(OSij)-IS(OSij)≥MinTj,
CS(OSij)-IS(OSij)≤MaxTj,
IS(OSij)-CS(OS(i-1)j)≥MinPeriodj
(3) Taiwan (Chinese character of 'tai')The elevation angle of the sun. For visible light load, sensor s'jMust satisfy the solar altitude limit Sun thetai:
(4) And (5) observing angle constraint. Satellite loads and observation targets must meet a user-specified minimum view MinObser θiThe demand, can be expressed as:
(5) storage capacity constraints. For satellite sjObserving with multiple sensors, any two adjacent data transmission windows w1,w2The storage capacity occupied by the tasks observed between cannot exceed that of the satellite sjStorage capacity Mj。vjRepresenting a satellite sjThe read and write rates of the memory.
(6) And (4) energy constraint. The satellite activity consumes energy, expressed as a function of the observation time, the sensor yaw, the power on/off, without the consumption exceeding the maximum limit Pj。Pj' is the energy consumed per unit time observed; qjEnergy consumed by a unit angle of side swing; o isjConsumed for power on and off.
(7) And (4) load action constraint. For satellite sjA sensor of (1), which continuously observesThe time interval of the service cannot be smaller than the time for adjusting the side sway and the stabilization time after the side sway. VjFor yaw rate, temporarily using uniform motion as a simplified substitute, tsjThe roll stability time:
(8) and (4) radar satellite imaging mode constraint. For radar loads, one radar imaging cannot switch the working wave position constraint.
WP(mtia)＝WP(mtib)
2.2 optimizing the objective function
The multi-satellite emergency scheduling model based on task priority is regarded as a multi-objective optimization solving problem, the completion rate of key emergency tasks in a planning scheme is required to be high, imaging task benefits are high, and the planning efficiency of each task is required to be high as much as possible to ensure that the reservation rate of satellite resources is high. Therefore, the optimization objective function is constructed for the multi-satellite resource scheduling model by adopting a weighted sum method, the priority execution of more important, more urgent and higher-income tasks is ensured, the allocation of resources is reasonably carried out, and the global optimization is realized.
(1) Sub-goals 1: the sum of the observed task priorities is maximized.
In the formula,scheduling a decision matrix, x, for a taskijk1 denotes task tiDistribution to satellite sjThe k-th observation opportunity of (1), otherwise xijk＝0。
(2) Sub-goals 2: maximizing the sum of the benefits of the tasks.
(3) Sub-goal 3: the latency of tasks with urgent requirements on execution time is minimized.
Based on the sub-optimization objective function, a global optimization objective function is obtained by using a weighting method:
Maximize f＝α·f1+β·f2+γ·(1-f3)
in the formula, α, γ is a weighting factor, α + β + γ is 1, the weighting value determines the importance degree of each sub-target in planning scheme optimization, and the invention takes task priority as an important consideration in emergency response, so that α is 0.6, β is 0.2, and λ is 0.2.
3 satellite resource dynamic matching for emergency scheduling
The purpose of the emergency observation task planning is to determine the execution sequence of tasks and reasonably distribute satellite resources and execution time for each task, and as new emergency tasks are continuously added, the complexity of the problem is further increased. Because the multi-satellite cooperative scheduling involves more constraints and the arrival of a new task can affect a task set, a satellite resource set, a time window and a constraint set in an original problem model, the invention provides a dynamic scheduling algorithm based on priority to solve the scheduling problem of the newly added task. The method comprises the steps of firstly realizing initial scheduling of tasks through a genetic tabu hybrid algorithm, searching satellites matched with task requirements in a resource set on the basis of an initial scheduling result when new tasks are added, and respectively carrying out direct insertion, redistribution, replacement and deletion operations on the satellites so as to meet a highest priority completion principle, a principle of minimum change of an initial scheduling scheme and a fastest scheduling principle in dynamic scheduling.
3.1 initial scheduling scheme for satellite resources
Genetic algorithms and tabu search are common algorithms for solving combinatorial optimization problems. The genetic algorithm is a highly parallel, random and self-adaptive optimization algorithm based on biological evolution and selection mechanisms, has strong robustness and global search capability, but is low in later-stage convergence speed and calculation efficiency and easy to fall into local optimization. The tabu search algorithm is a neighborhood search algorithm based on the tabu technology, and is characterized in that the tabu technology is adopted, and the repeated work is forbidden, so that the local optimum is skipped. However, the tabu search algorithm also has certain defects, such as strong dependence on initial solutions, serial operation on only one solution, and low search efficiency. Based on the characteristics, the invention designs a genetic tabu hybrid algorithm, performs satellite resource allocation on the obtained task set, and solves the combined optimization model by using the algorithm to obtain an initial scheduling scheme. The genetic algorithm is adopted to search in a large range in a global space, namely, an initial population quickly traverses most of the region of a solution space in a parallel mode, the result is stabilized in a region with a better solution space part when iteration is terminated, and then a tabu search algorithm is adopted to search in a small range in the local space from each individual in an optimization region, so that the genetic algorithm is delayed or prevented from falling into local optimization, and the optimization capability of the algorithm is improved. The hybrid algorithm effectively makes up the defects that the local search capability of the genetic algorithm is weak, the tabu search algorithm is strongly dependent on the initial solution and the like. The flow of the genetic tabu mixing algorithm is as follows:
step 1: and setting initial parameters. Determining maximum iteration number i of genetic algorithmga-maxThe maximum iteration number of tabu search is its-maxPopulation size N, crossover probability PcProbability of variation PmLength l of the tabu table, neighborhood scale n, neighborhood function F (x), and the tabu table is empty.
Step 2: determining the coding mode of the chromosome. Chromosome coding is divided into segments, each representing a mission plan for one satellite payload type. In each segment (i.e., the same type of load plan), the execution windows of the tasks are arranged in the order of the numbers to form the chromosome code of the segment. Let the chromosome coding template beNamely task tiCorresponding visible window ofAnd the initial execution time st of the taskiThe scheme coding template is expressed as a vector v ═ { u ═ u1,u2,...,un}。
And step 3: and initializing the population. An initialization population of size N is randomly generated. Assuming that the initial algebra t is 0, m chromosomes are generated randomlySetting the optimum value as Xbest。
And 4, step 4: and calculating a fitness function value. Calculating the fitness function value of each chromosome in the population by taking the target function as the fitness function of the chromosomeIf there isThen order
And 5: if t < imaxLet t be t + 1; otherwise, the iteration is ended;
step 6, carrying out selection operation on the population, selecting m chromosomes from the population as parents of the next generation, and using a roulette wheel selection method, wherein the probability of each chromosome being selected isAnd meanwhile, a guarantee principle is used in the selection process, namely the optimal in the previous generation is reserved in the next generation.
And 7: and performing cross operation on the population. According to cross probability PcThe chromosomes were crossed with a single point cross.
And 8: and performing mutation operation on the population. According to the mutation probability PmIndividuals in the population are mutated to generate a new generation of population.
And step 9: carrying out tabu search on each generation of individuals of the new generation of population, judging whether the termination criterion is met, and if so, outputting an optimal solution result; if not, a candidate solution is determined by the neighborhood function F (x) to produce a neighborhood solution from the current solution.
Step 10: judging whether the candidate solution meets the scofflaw criterion or not according to the fitness function; if yes, assigning the current solution and the historical optimal solution, resetting the taboo length for the current solution and the historical optimal solution, and updating the taboo lengths of other elements in the taboo table; if not, the non-taboo sub-optimal solution is taken as the current solution and is placed into the taboo table, the object which enters the taboo table at the earliest is replaced, and the taboo length of other elements in the taboo table is updated.
Step 11: until reaching the maximum number of iterations its-maxAnd outputting the optimal solution.
3.2 dynamic scheduling scheme for newly-added emergency tasks
In the newly-added emergency task dynamic scheduling scheme, the invention provides a priority-based dynamic scheduling algorithm to solve the scheduling problem of the newly-added task. The tasks in the newly added task set are sorted according to the priority from high to low, the tasks with high priority are scheduled preferentially, and the adding of the new tasks can be divided into a direct insertion process, a task redistribution process, a task replacement process and a task deletion process. The direct insertion of the new task refers to that on the premise of not changing the resources, time windows and scheduling sequences of the task arrangement of the current scheduling scheme, whether the tasks in the new task set can be directly inserted into the task scheduling arrangement of the available satellite resources of the new task set under the condition that all scheduling constraints are met is sequentially judged according to the sequence of the priority of the new task from high to low. When inserting the task, determining the sequence of the insertion points according to the starting time of the task visible time window and the completion time of the scheduled scheduling task. Task reallocation refers to inserting an original task which conflicts with a new task into a time window of other resources under the condition that the task in an original scheduling scheme is not deleted, and inserting the new task into the time window of the original task, wherein the mode can cause that the resources and time for scheduling part of tasks in a planning scheduling scheme are changed. Task replacement means that in order to further improve the performance of a planning scheduling scheme under the condition that direct insertion and redistribution cannot be performed, a new task with a high priority is used for replacing a task with a low priority in an original scheduling scheme, and the task in the original scheduling scheme is deleted in the mode, so that the stability of the original scheduling scheme is influenced to a certain extent. Task deletion refers to deleting a new atomic task and giving up resource arrangement under the condition that the former three ways cannot be met. And in the dynamic planning under the condition that a new task arrives, sequentially performing the four operations, and performing iteration according to the local search idea to obtain a relatively ideal dynamic task adjustment scheme. The algorithm comprises the following specific steps:
step 1: initialization: and (4) sorting the tasks in the new task set T ' from high priority to low priority, and enabling the task with the highest priority in the T ' to be the current scheduling task T ', wherein the available satellite resource set is S.
Step 2: insert operation: and traversing each available resource of the S for the task t ', judging whether a visible time window on the current resource S ' is available, judging whether a task arranged and scheduled in the current time window in the tasks t ' and S ' conflicts, and directly inserting the task t ' into the current time window if no conflict exists. And if the new task cannot be inserted, placing the task T which conflicts with the task T' in the task conflict set T ", and turning to the step 3.
And step 3: traversing all tasks which conflict with the current task T 'in the conflict set T', setting the 1 st task in the conflict set as the current conflict task T, and searching the available time window of the current conflict task T again; and if the current conflict task t can be moved to a new window, inserting the task t' into the current time window, and moving the conflict task t to another time window for execution. And if the current conflict task t can not be moved to a new window, turning to the step 4.
And 4, step 4: judging the priority of the task t ' and the current conflict task t, if the priority of the task t ' is higher than that of the task t, deleting the current conflict task t, and then inserting the new task t ' into the visible time window; otherwise, the task replacement is unsuccessful, and then the step 5 is carried out.
And 5: in case none of steps 2, 3 and 4 can be fulfilled, the new task t' is deleted and the resource scheduling is abandoned.
Step 6: and judging whether the available satellite resources traverse. If the evaluation solution set is traversed, selecting a solution corresponding to the maximum value in the evaluation solution set as a final solution, finishing scheduling the current task t', and turning to the step 1 until all new tasks in the set are scheduled; otherwise, the satellite resource serial number is added with 1 and the step 2 is carried out.
4 evaluation of model Properties
The multi-satellite dynamic emergency scheduling model based on task priority not only requires a high task completion rate and a maximum total profit of a planning scheme, but also makes a planning period of each task better as much as possible and ensures an execution rate of a task with a high priority. Therefore, according to the characteristics of the model, a task completion rate index (TCR), a task priority execution rate index (TPR) and a scheduling scheme change rate index (SC) (scheduling performance) are constructed, and finally an evaluation function is obtained to verify the performance of the algorithm.
Wherein, TCR is n/n ', n is the total number of the actual scheduling tasks after the scheme adjustment, n' is the total number of the scheduling tasks,num(Tc) Number of tasks for scheduling scheme change, num (T)q) Is the initial number of tasks.
After the technical scheme is adopted, the invention has the beneficial effects that: a set of complete models is constructed to carry out research on multi-satellite dynamic scheduling problems under emergency conditions. Firstly, designing a task priority evaluation criterion aiming at an emergency task, and considering factors including imaging task level, observation image type, availability of satellite resources, execution urgency degree, execution task type, conflict degree between tasks and imaging task income to obtain the importance degree of the task and use the importance degree as heuristic information when solving a constraint planning problem. Secondly, main constraint conditions in the problem are analyzed in detail, the waiting time of tasks with the maximum observation task priority, the maximum task income and the minimum execution time urgency requirement is taken as a sub-target function, and an initial satellite scheduling scheme is obtained by combining a genetic tabu hybrid algorithm, so that a large amount of useless iteration is avoided when the algorithm approaches to the optimal solution in the later period. Based on the above, under the condition that a new urgent task is inserted, a dynamic scheduling algorithm based on priority is provided to solve the scheduling problem of the newly added task. The invention can support some emergency ground actions, and has great significance especially for tasks with high dynamic and high time sensitive observation requirements, such as disaster monitoring and early warning.
Claims (8)
1. A multi-satellite dynamic task planning method based on task priority facing emergency response comprises a comprehensive scheduling model of task requirements, a set of reasonable task priority calculation model and a dynamic scheduling scheme based on priority; aiming at the problem of comprehensive scheduling of earth observation satellites in emergency, the comprehensive scheduling model of the task demands is used for determining the specific execution sequence, execution time and execution mode of each task under the condition of facing a large number of point target imaging tasks (which refer to targets with smaller range and can be covered by a remote sensor at a time) with different priorities and adding new tasks, and finally generating a satellite earth observation plan;
the task priority computing method designs a task priority evaluation criterion aiming at an emergency task, and integrates a quantitative task priority influence factor and a task priority computing model to obtain a quantitative index of the task priority so as to determine the priority degree of each task;
the dynamic scheduling model based on the priority utilizes a genetic tabu hybrid algorithm to perform satellite resource allocation on the obtained task set, utilizes the algorithm to solve the combined optimization model to obtain an initial scheduling scheme, and then utilizes the dynamic scheduling algorithm based on the priority to solve the scheduling problem of the newly added task.
2. The comprehensive scheduling model of task requirements according to claim 1, characterized in that, aiming at the problem of comprehensive scheduling of earth observation satellites in emergency, under the condition that a large number of target imaging tasks (which refer to targets with a small range and can be covered by a remote sensor at a time) with different priority points and new task addition are faced, the capabilities of the satellites, the remote sensor and other resources, task priority influence factors and various constraint conditions are comprehensively considered, constraint check is performed, task priority ordering and conflict elimination among the tasks are performed, the imaging tasks are reasonably distributed for each satellite, the specific execution order, execution time and execution mode of each task are determined, and finally a satellite earth observation plan is generated; the model is further divided into a task ordering subproblem and a satellite resource allocation subproblem.
3. The task priority computing model according to claim 1, wherein 7 main influence factors are set for task priority, and the importance of satellite resource receiving task requirements is determined by quantitatively analyzing application requirements of earth observation satellite resources and emergency tasks through the task priority computing model.
4. The 7 main impact factors according to claim 3 are:
(1) imaging task level. In the process of emergency response, different emergencies are classified into 4 levels (I-IV) according to factors such as the nature, the severity, the controllability and the influence range of the emergencies.
(2) The image type is observed. For example, suppose the image types satisfy the following ordering visible > microwave > infrared.
(3) Availability of satellite resources. When the priority is set, a task with high satellite resource availability is given higher priority, so that the task is guaranteed to be executed.
(4) The degree of urgency is enforced. The more urgent a task is, the more likely it is to miss an execution period and fail, and it should be executed with priority.
(5) The type of task to be performed. Defining the priorities of different emergency task types satisfies the following ordering: marine moving target > marine static target > terrestrial moving target > terrestrial static target.
(6) The degree of conflict between tasks. The task conflict degree is defined as the number of tasks competing for the same satellite resource with the task, and the task with small task conflict is defined to have high priority.
(7) And (6) imaging task benefits. The imaging task gain, which is determined by the importance of the observation task, is taken as the basic gain. Furthermore, the actual yield of the imaging task varies due to the cloud coverage. Therefore, the overall imaging task profit needs to consider the two factors, and the task with high imaging profit is preferentially selected to allocate the satellite resources.
TABLE 1 task priority impact factors (evaluation indexes of task priority)
5. The task priority computation model of claim 3, comprising in particular
Quantifying qualitative factors in the task priority influence factors based on different task priority influence factors, and comprehensively calculating by adopting TOPSIS (technique for Order Preference by Similarity to Ideal solution) 35 to obtain a quantitative index of the task priority, wherein the method mainly comprises the following steps.
(1) For a task set T ═ T1,t2,...,tmCalculating priority influence factors respectively to form an influence factor matrix X ═ Xij]m×7Wherein x isijIs tiM is the number of tasks.
(2) Changing the influence factor matrix X into [ X ] by using range transform methodij]m×7Conversion to the standard matrix Y ═ Yij]m×7，0≤yij≤1。
(3) Setting ideal solution I ═ I ≦ I+,I-Is disclosed in+1, (1, 1., 1) is a positive ideal solution, I-A negative ideal solution is (0, 0.., 0).
(4) Computing task tiCloseness to ideal solution Ci：
Wherein,andas task tiThe distance from the ideal solution I is,
(5) task tiHas a priority of Pi:
Through the steps, the priority quantification numerical value of each task can be obtained,and takes on a value of [0,10]Between, PiThe higher the value the higher the priority of the task.
6. The scheme is characterized in that the initial scheduling of tasks is realized through a genetic tabu mixing algorithm, and when a new task is added, the scheduling problem of the newly added task is solved by the priority-based dynamic scheduling algorithm on the basis of the initial scheduling result. And searching the satellite matched with the task requirement in the resource set, and performing direct insertion, redistribution, replacement and deletion operation on the task respectively so as to meet the highest priority completion principle, the minimum principle of initial scheduling scheme change and the fastest scheduling principle in dynamic scheduling.
7. The genetic tabu hybrid algorithm according to claim 6, characterized in that the hybrid algorithm effectively makes up for the defects of the genetic algorithm, such as weak local search capability and the tabu search algorithm, strong dependence on the initial solution. The flow of the genetic tabu mixing algorithm is as follows:
step 1: and setting initial parameters. Determining maximum iteration number i of genetic algorithmga-maxThe maximum iteration number of tabu search is its-maxPopulation size N, crossover probability PcProbability of variation PmLength l of the tabu table, neighborhood scale n, neighborhood function F (x), and the tabu table is empty.
Step 2: determining the coding mode of the chromosome. Chromosome coding is divided into segments, each representing a mission plan for one satellite payload type. In each segment (i.e., the same type of load plan), the execution windows of the tasks are arranged in the order of the numbers to form the chromosome code of the segment. Let the chromosome coding template beNamely task tiCorresponding visible window ofAnd the initial execution time st of the taskiThe scheme coding template is expressed as a vector v ═ { u ═ u1,u2,...,un}。
And step 3: and initializing the population. An initialization population of size N is randomly generated. Assuming that the initial algebra t is 0, m chromosomes are generated randomlySetting the optimum value as Xbest。
And 4, step 4: and calculating a fitness function value. Calculating the fitness function value of each chromosome in the population by taking the target function as the fitness function of the chromosomeIf there isThen order
And 5: if t < imaxLet t be t + 1; otherwise, the iteration is ended;
step 6, carrying out selection operation on the population, selecting m chromosomes from the population as parents of the next generation, and using a roulette wheel selection method, wherein the probability of each chromosome being selected isAnd meanwhile, a guarantee principle is used in the selection process, namely the optimal in the previous generation is reserved in the next generation.
And 7: and performing cross operation on the population. According to cross probability PcThe chromosomes were crossed with a single point cross.
And 8: and performing mutation operation on the population. According to the mutation probability PmIndividuals in the population are mutated to generate a new generation of population.
And step 9: carrying out tabu search on each generation of individuals of the new generation of population, judging whether the termination criterion is met, and if so, outputting an optimal solution result; if not, a candidate solution is determined by the neighborhood function F (x) to produce a neighborhood solution from the current solution.
Step 10: judging whether the candidate solution meets the scofflaw criterion or not according to the fitness function; if yes, assigning the current solution and the historical optimal solution, resetting the taboo length for the current solution and the historical optimal solution, and updating the taboo lengths of other elements in the taboo table; if not, the non-taboo sub-optimal solution is taken as the current solution and is placed into the taboo table, the object which enters the taboo table at the earliest is replaced, and the taboo length of other elements in the taboo table is updated.
Step 11: until reaching the maximum number of iterations its-maxAnd outputting the optimal solution.
8. The priority-based dynamic scheduling algorithm according to claim 6, wherein the tasks in the newly added task set are sorted according to the priority from high to low, and the task with high priority is scheduled preferentially, and the addition of the new task can be divided into a direct insertion process, a task reallocation process, a task replacement process and a task deletion process. And sequentially carrying out the four operations, and carrying out iteration according to the local search idea to obtain a relatively ideal dynamic task adjustment scheme. The algorithm comprises the following specific steps:
step 1: initialization: and (4) sorting the tasks in the new task set T ' from high priority to low priority, and enabling the task with the highest priority in the T ' to be the current scheduling task T ', wherein the available satellite resource set is S.
Step 2: insert operation: and traversing each available resource of the S for the task t ', judging whether a visible time window on the current resource S ' is available, judging whether a task arranged and scheduled in the current time window in the tasks t ' and S ' conflicts, and directly inserting the task t ' into the current time window if no conflict exists. And if the new task cannot be inserted, placing the task T which conflicts with the task T' in the task conflict set T ", and turning to the step 3.
And step 3: traversing all tasks which conflict with the current task T 'in the conflict set T', setting the 1 st task in the conflict set as the current conflict task T, and searching the available time window of the current conflict task T again; and if the current conflict task t can be moved to a new window, inserting the task t' into the current time window, and moving the conflict task t to another time window for execution. And if the current conflict task t can not be moved to a new window, turning to the step 4.
And 4, step 4: judging the priority of the task t ' and the current conflict task t, deleting the current conflict task t if the priority of the task t ' is higher than that of the task t, and then inserting the new task t ' into the visible time window; otherwise, the task replacement is unsuccessful, and then the step 5 is carried out.
And 5: in case none of steps 2, 3 and 4 can be fulfilled, the new task t' is deleted and the resource scheduling is abandoned.
Step 6: and judging whether the available satellite resources traverse. If the evaluation solution set is traversed, selecting a solution corresponding to the maximum value in the evaluation solution set as a final solution, finishing scheduling the current task t', and turning to the step 1 until all new tasks in the set are scheduled; otherwise, the satellite resource serial number is added with 1 and the step 2 is carried out.
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