Cooperative processing method for observation tasks of multi-source remote sensing satellites

Abstract

The invention provides a cooperative processing method for observation tasks of a multi-source remote sensing satellite. Firstly, a multi-source remote sensing satellite is configured, and remote sensing satellite parameters are obtained. Meanwhile, a satellite ground station is configured, and ground station target information is extracted. On the basis, satellite data receiving available stripes are obtained according to remote sensing satellite model parameters and ground station target information, and satellite observation available stripes are obtained according to the remote sensing satellite model parameters and observation demand target information. According to the multi-satellite task planning method, stripe segmentation processing is carried out on a satellite data receiving available stripe and a satellite observation available stripe to obtain multi-stripe resource data, and then multi-stripe resource allocation is carried out on the multi-stripe resource data, so that a multi-satellite task planning result is obtained. Through task resource matching, a plurality of remote sensing satellites are planned, it is ensured that observation tasks of all the satellites are reasonably planned and executed, resource idling and waste are avoided, resource configuration is optimized, and efficient cooperative processing of the observation tasks is achieved.

Description

Technical Field

[0001] The present invention relates to the field of satellite observation, and in particular to a collaborative processing method for multi-source remote sensing satellite observation tasks. Background Art

[0002] At present, in the existing technology, with the continuous increase in demand for satellite remote sensing data in disaster emergency response, environmental monitoring, urban construction and other fields, how to plan imaging tasks for diverse monitoring needs and make full use of on-orbit satellite resources for monitoring has become an urgent problem to be solved. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0004] To this end, a first aspect of the present invention proposes a collaborative processing method for multi-source remote sensing satellite observation tasks.

[0005] In view of this, a first aspect of the present invention provides a collaborative processing method for multi-source remote sensing satellite observation tasks, including: configuring multi-source remote sensing satellites to obtain remote sensing satellite parameters; configuring satellite ground stations to extract ground station target information; receiving observation requirement target information, and obtaining satellite available strips based on remote sensing satellite model parameters, ground station target information and observation requirement target information; performing strip segmentation processing on the satellite available strips to obtain multi-strip resource data; performing multi-strip resource allocation on the multi-strip resource data to obtain multi-satellite mission planning results; wherein the satellite available strips include satellite data reception available strips and satellite observation available strips.

[0006] In addition, the collaborative processing method of multi-source remote sensing satellite observation tasks in the above technical solution provided by the present invention may also have the following additional technical features: In some technical solutions of the present invention, optionally, the available strips of the satellite are processed by strip segmentation to obtain multi-strip resource data, including: obtaining the sub-satellite point trajectory equation based on the satellite orbit parameters and the monitoring area; performing segmentation processing on the regional target based on the sub-satellite point trajectory equation to obtain segmentation result data; and filtering the segmentation result data to obtain multi-strip resource data.

[0007] In some technical solutions of the present invention, optionally, based on satellite orbit parameters and the monitoring area, the sub-satellite point trajectory equation is obtained, including: screening based on the image type of the monitoring area to obtain the first sensor resource; performing secondary screening based on the resolution requirement of the monitoring area to obtain the second sensor resource; obtaining the first time window data based on the sunlight parameters, the first sensor resources, the second sensor resources and the location of the monitoring area; and obtaining the sub-satellite point trajectory equation based on the satellite orbit parameters and the first time window data.

[0008] In some technical schemes of the present invention, optionally, based on the sub-satellite point trajectory equation, the regional target is segmented and processed to obtain segmentation result data, including: selecting unprocessed first time window data, and obtaining the sensor observation range based on the unprocessed first time window data, the satellite trajectory equation and the sensor technical parameters; selecting a working mode, if the manual intervention mode is selected, importing a predefined scene reference library, based on the sensor observation range and the regional target, screening out candidate scenes in the predefined scene reference library, and obtaining segmentation result data; if the single scene mode is selected, performing strip segmentation on the regional target, and performing single scene segmentation on the single scene imaging after strip segmentation, and obtaining segmentation result data.

[0009] In some technical solutions of the present invention, optionally, the segmentation result data is filtered to obtain multi-band resource data, including: selecting an observation scene from the segmentation result data; obtaining continuous observation time based on satellite platform parameters and the coverage of the observation scene; obtaining second time window data based on the continuous observation time and observation requirements; based on the second time window data, filtering the observation scene, if there is second time window data corresponding to the observation scene, the observation scene is added to the multi-band resource data, otherwise, the observation scene is deleted.

[0010] In some technical schemes of the present invention, optionally, multi-band resource allocation is performed on multi-band resource data to obtain multi-satellite mission planning results, including: performing task resource matching on multi-band resource data to obtain available satellite orbital arcs; performing multi-task conflict degree evaluation on multiple satellite available orbital arcs to obtain the possibility of task conflict, and obtaining the maximum monitoring benefit of the satellite based on target monitoring benefit and task monitoring cost; obtaining task conflict degree and monitoring resource consumption degree based on space target monitoring opportunity parameters, scheduling conflict degree parameters and resource consumption degree parameters; performing task allocation scheduling based on task conflict degree and monitoring resource consumption degree to obtain multi-satellite mission planning results.

[0011] In some technical solutions of the present invention, optionally, task resource matching is performed on multi-band resource data to obtain available satellite orbital arc segments, including: obtaining a service time window, expected service duration parameters and visible arc segment parameters based on a visible time window of the multi-band resource data; and obtaining available satellite orbital arc segments based on the service time window, expected service duration parameters and visible arc segment parameters.

[0012] In some technical schemes of the present invention, optionally, a multi-task conflict degree evaluation is performed on multiple satellite available orbital arcs to obtain the possibility of task conflict, including: respectively bringing two satellite available orbital arcs into the two-dimensional coordinate axis to obtain the execution interval area and the conflict area; based on the execution interval area and the conflict area, obtaining the possibility of task conflict; obtaining the target monitoring benefit and the task monitoring cost.

[0013] In some technical solutions of the present invention, optionally, based on the space target monitoring opportunity parameters, scheduling conflict parameters and resource consumption parameters, task conflict and monitoring resource consumption are obtained, including: obtaining a scheduling conflict set based on the space target monitoring opportunity parameters and the scheduling conflict parameters; obtaining task weight parameters based on time sensitivity, preset important parameters and preset value parameters; obtaining the sum of task weights based on the scheduling conflict set and task weight parameters; obtaining task conflict based on the sum of task weights; and obtaining monitoring resource consumption based on the resource continuous monitoring time, the resource remaining monitoring time, the storage space required for the resource and the resource remaining storage space.

[0014] In some technical solutions of the present invention, optionally, based on the space target monitoring opportunity parameters, scheduling conflict parameters and resource consumption parameters, task conflict and monitoring resource consumption are obtained, including: obtaining a scheduling conflict set based on the space target monitoring opportunity parameters and the scheduling conflict parameters; obtaining task weight parameters based on time sensitivity, preset important parameters and preset value parameters; obtaining the sum of task weights based on the scheduling conflict set and task weight parameters; obtaining task conflict based on the sum of task weights; and obtaining monitoring resource consumption based on the resource continuous monitoring time, the resource remaining monitoring time, the storage space required for the resource and the resource remaining storage space.

[0015] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 One of the flow charts of a collaborative processing method for multi-source remote sensing satellite observation tasks according to an embodiment of the present invention; Figure 2 A schematic diagram of strip segmentation based on the Lemaître method according to an embodiment of the present invention; Figure 3 FIG. 1 is a schematic diagram of the possibility of task conflict according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0019] Refer to the following Figure 1 A collaborative processing method for multi-source remote sensing satellite observation tasks according to some embodiments of the present invention is described.

[0020] like Figure 1 As shown, the first aspect of the present invention provides a collaborative processing method for multi-source remote sensing satellite observation tasks, comprising: Step 102, configuring multi-source remote sensing satellites and obtaining remote sensing satellite parameters; Step 104, configuring the satellite ground station and extracting ground station target information; Step 106, receiving observation requirement target information, and acquiring satellite available bands based on remote sensing satellite model parameters, ground station target information and observation requirement target information; Step 108, performing strip segmentation processing on the satellite available strips to obtain multi-strip resource data; Step 110, performing multi-strip resource allocation on the multi-strip resource data to obtain a multi-satellite mission planning result; Among them, the satellite available bands include satellite data reception available bands and satellite observation available bands.

[0021] The present invention provides a collaborative processing method for multi-source remote sensing satellite observation tasks. First, configure multi-source remote sensing satellites to obtain remote sensing satellite parameters. At the same time, configure satellite ground stations to extract ground station target information. On this basis, obtain available strips for satellite data reception according to remote sensing satellite model parameters and ground station target information, and obtain available strips for satellite observation according to remote sensing satellite model parameters and observation demand target information. Perform strip segmentation processing on available strips for satellite data reception and available strips for satellite observation, obtain multi-strip resource data, and then perform multi-strip resource allocation on the multi-strip resource data, thereby obtaining multi-satellite task planning results. By matching task resources, multiple remote sensing satellites are planned to ensure that the observation tasks of each satellite are reasonably planned and executed, avoid idle and waste of resources, optimize resource allocation, achieve efficient collaborative processing of observation tasks, and improve observation efficiency and accuracy.

[0022] Specifically, configure multi-source remote sensing satellites and obtain remote sensing satellite parameters, including: establishing a unified model of remote sensing satellites based on satellite platform characteristics, satellite payloads and satellite usage constraints; obtaining two rows of satellite parameters through a timer; bringing the two rows of satellite parameters into the unified model of remote sensing satellites to obtain remote sensing satellite parameters. Among them, satellite platform characteristics include basic satellite properties, satellite orbit properties, satellite antenna properties, platform constraints and solar constraints. Satellite payloads include reconnaissance methods, relay support, orbital maneuvers, imaging reconnaissance parameters and electronic reconnaissance parameters. Satellite usage constraints include general constraints and specific constraints. Specific constraints include side swing constraints of visible light reconnaissance payloads, sunlight angle constraints, cloud impact parameters, etc. General constraints include storage capacity, single-circuit working time, number of orbital maneuvers, maximum number of daily startups, instruction interval time, etc.

[0023] Furthermore, in some embodiments of the present invention, the satellite available strips are subjected to strip segmentation processing to obtain multi-strip resource data, including: determining the monitoring area based on the satellite available strips; obtaining the sub-satellite point trajectory equation based on the satellite orbit parameters and the monitoring area; performing segmentation processing on the regional target based on the sub-satellite point trajectory equation to obtain segmentation result data; and filtering the segmentation result data to obtain multi-strip resource data.

[0024] In this embodiment, first, the monitoring area is determined according to the available satellite strips, and then the sub-satellite point trajectory equation is determined according to the satellite orbit parameters and the monitoring area. On this basis, the regional target for the observation range boundary detection can be determined according to the sub-satellite point trajectory equation for segmentation processing, thereby obtaining segmentation result data, and finally filtering the segmentation result data to obtain multi-strip resource data. The above method obtains multi-strip resource data, improves monitoring efficiency, and enhances data accuracy.

[0025] It should be noted that both the satellite data reception available strip and the satellite observation available strip can obtain multi-strip resource data through the above method. The multi-strip resource data obtained through the satellite data reception available strip is the strip that can receive data, and the multi-strip resource data obtained through the satellite observation available strip is the strip that can download data.

[0026] Furthermore, in some embodiments of the present invention, based on satellite orbit parameters and the monitoring area, the sub-satellite point trajectory equation is obtained, including: screening based on the image type of the monitoring area to obtain the first sensor resource; performing secondary screening based on the resolution requirement of the monitoring area to obtain the second sensor resource; obtaining the first time window data based on the sunlight parameters, the first sensor resources, the second sensor resources and the location of the monitoring area; and obtaining the sub-satellite point trajectory equation based on the satellite orbit parameters and the first time window data.

[0027] In this embodiment, first, screening is performed based on the image type requirements of the monitoring area to obtain the first sensor resources, and then the sensor resources are further screened based on this. A second screening is performed based on the resolution requirements of the monitoring area to obtain the second sensor resources.

[0028] Then, the first time window data is obtained according to the sunlight parameters, the first sensor resources, the second sensor resources and the location of the monitoring area. Then, the sub-satellite point trajectory equation is obtained according to the satellite orbit parameters and the first time window data. Through the sub-satellite point trajectory equation, the detectable area can be confirmed, the monitoring requirements can be accurately matched, and the monitoring accuracy and flexibility can be improved.

[0029] Specifically, based on the satellite orbit parameters and the first time window data, the sub-satellite point trajectory equation is obtained, including: based on the first time window data, the orbital circles and time periods of the satellite orbit in the observable area target are obtained, and the satellite observation range is deduced from the orbital circles and time periods. When the satellite observation range is known, the satellite position is calculated by the Kepler equation through the satellite orbit parameters and the first time window data, and the sub-satellite point coordinates are further calculated based on the satellite position, and the sub-satellite point trajectory equation is determined based on the sub-satellite point coordinates.

[0030] Furthermore, in some embodiments of the present invention, based on the sub-satellite point trajectory equation, the regional target is segmented and processed to obtain segmentation result data, including: selecting unprocessed first time window data, and obtaining the sensor observation range based on the unprocessed first time window data, the satellite trajectory equation and the sensor technical parameters; selecting a working mode, if the manual intervention mode is selected, importing a predefined scene reference library, based on the sensor observation range and the regional target, screening out candidate scenes in the predefined scene reference library, and obtaining segmentation result data; if the single scene mode is selected, performing strip segmentation on the regional target, and performing single scene segmentation on the single scene imaging after strip segmentation, and obtaining segmentation result data.

[0031] In this embodiment, first, unprocessed first time window data is selected, and the sensor observation range is acquired according to the unprocessed first time window data, the satellite trajectory equation and the sensor technical parameters.

[0032] On this basis, select the working mode. If the manual intervention mode is selected, it is necessary to import the predefined scene reference library and filter out the candidate scenes in the predefined scene reference library according to the sensor observation range and regional targets to obtain the segmentation result data; if the single scene mode is selected, the regional targets are segmented into strips, and the single scene imaging after strip segmentation is segmented into single scenes to obtain the segmentation result data. By providing two options, the manual intervention mode and the single scene mode, the system can be flexibly adjusted according to actual needs, which improves the applicability and flexibility of the system.

[0033] Specifically, based on the sensor observation range and the regional target, candidate scenes in the predefined scene reference library are screened out, including: according to the overlap between the regional target and the sensor observation range, suitable scenes are screened from the reference library, and the sensor side swing angle corresponding to each scene is calculated.

[0034] Specifically, an unprocessed first time window data is selected, and the observation range of the sensor in the plane rectangular coordinate system is calculated based on the local trajectory equation, the start and end points and the parameters of the satellite-borne sensor corresponding to the unprocessed first time window data.

[0035] Specifically, Figure 2 As shown, the scene must intersect with the regional target; the scene must be within the observation range of the onboard sensor; the scene must be able to be completed by the onboard sensor in one go; and when the satellite operates in the orbital circle corresponding to the time window, the onboard sensor must have the opportunity to observe the selected scene.

[0036] Specifically, strip segmentation is based on the Lemaître method, which divides part or all of the regional target into a set of several adjacent strips of equal width. The observation range is used as the reference boundary of the segmentation, and the unobservable area is excluded in advance to avoid the situation where the segmentation result has no visible time window. By introducing the offset parameter, the offset parameter can be selected according to the specific situation to adjust the overlap degree of adjacent strips. For any strip, the strip is divided into adjacent single scenes from north to south with reference to the single scene coverage range, and the strip is completely covered.

[0037] Furthermore, in some embodiments of the present invention, the segmentation result data is filtered to obtain multi-band resource data, including: selecting an observation scene from the segmentation result data; obtaining continuous observation time based on satellite platform parameters and the coverage of the observation scene; obtaining second time window data based on the continuous observation time and observation requirements; based on the second time window data, filtering the observation scene, if there is second time window data corresponding to the observation scene, the observation scene is added to the multi-band resource data, otherwise, the observation scene is deleted.

[0038] In this embodiment, firstly, based on the satellite platform parameters and the coverage of the observation scene, the continuous observation time is obtained, and further according to the continuous observation time and the observation requirement, the second time window data is obtained.

[0039] On this basis, based on the second time window data, the observation scene is screened, if there is second time window data corresponding to the observation scene, the observation scene is added to the multi-strip resource data, otherwise, the observation scene is deleted.

[0040] Furthermore, in some embodiments of the present invention, multi-band resource allocation is performed on multi-band resource data to obtain multi-satellite mission planning results, including: performing task resource matching on multi-band resource data to obtain available satellite orbital arcs; performing multi-task conflict degree evaluation on multiple satellite available orbital arcs to obtain the possibility of task conflict, and obtaining the maximum monitoring benefit of the satellite based on the target monitoring benefit and the task monitoring cost; on the premise of meeting the maximum monitoring benefit, obtaining the task conflict degree and monitoring resource consumption degree based on the space target monitoring opportunity parameters, scheduling conflict degree parameters and resource consumption degree parameters; performing task allocation scheduling based on the task conflict degree and monitoring resource consumption degree to obtain multi-satellite mission planning results.

[0041] In this embodiment, first, task resource matching is performed on multiple strip resource data to obtain the satellite's available orbital arc segment. Then, multi-task conflict degree evaluation is performed on multiple satellite available orbital arc segments to obtain the possibility of task conflict, and the maximum monitoring benefit of the satellite is obtained based on the target monitoring benefit and the task monitoring cost.

[0042] On the premise of meeting the maximum monitoring benefit, based on the space target monitoring opportunity parameters, scheduling conflict parameters and resource consumption parameters, the task conflict degree and monitoring resource consumption degree are obtained, and then task allocation and scheduling are carried out to obtain the multi-satellite mission planning results.

[0043] By matching multi-band resource data with mission resources, we can ensure that satellite resources are used most effectively, thereby resolving mission conflicts and improving monitoring efficiency.

[0044] Specifically, the calculation formula for the maximum monitoring benefit is: .in, for The monitoring target income, is the mission cost, which is represented by the energy consumption of the satellite during the monitoring process. For each satellite planning, a single satellite only solves a local optimization problem. for Maximum monitoring benefit at minimum execution cost.

[0045] Furthermore, in some embodiments of the present invention, task resource matching is performed on multi-band resource data to obtain available satellite orbital arcs, including: obtaining a service time window, expected service duration parameters and visible arc parameters based on a visible time window of the multi-band resource data; and obtaining available satellite orbital arcs based on the service time window, expected service duration parameters and visible arc parameters.

[0046] In this embodiment, first, based on the visible time window of the multi-strip resource data, the service time window, the expected service duration parameter and the visible arc parameter are obtained. Based on the service time window, the expected service duration parameter and the visible arc parameter, the satellite available orbit arc is obtained. Among them, when the difference between "the maximum value of the start time of the service time window and the start time of the visible arc parameter" and "the minimum value of the end time of the service time window and the end time of the visible arc parameter" is greater than or equal to the expected service duration parameter, the satellite available orbit arc can be obtained.

[0047] Furthermore, in some embodiments of the present invention, a multi-task conflict degree evaluation is performed on multiple satellite available orbital arcs to obtain the possibility of mission conflict, including: respectively bringing two satellite available orbital arcs into the two-dimensional coordinate axis to obtain the execution interval area and the conflict area; based on the execution interval area and the conflict area, obtaining the possibility of mission conflict; and obtaining the target monitoring benefit and the mission monitoring cost.

[0048] In this embodiment, first, Figure 3 As shown, the two satellite available orbit arcs are respectively brought into the two-dimensional coordinate axis, the horizontal axis and the vertical axis represent the duration of any two available arcs respectively, and the dotted lines represent the actual start and end time. (a1, b1) and (a2, b2) represent the start and end time of the arcs with a cross relationship. The quadrilateral surrounded by ABCD is the intersection of the two arcs where the conflict may occur. The relationship between the area of ​​the shaded part and the area of ​​the quadrilateral is calculated to quantify the conflict degree of the arcs. When the execution interval of the two arcs is located in the shaded part, a conflict will inevitably occur. When it is located outside the shaded part, there will be no conflict. The ratio of the area of ​​the quadrilateral surrounded by ABCD, that is, the execution interval area, to the area of ​​the shaded part surrounded by the two boundary lines, that is, the conflict area, is the possibility of task conflict.

[0049] Furthermore, in some embodiments of the present invention, based on space target monitoring opportunity parameters, scheduling conflict parameters and resource consumption parameters, task conflict and monitoring resource consumption are obtained, including: obtaining a scheduling conflict set based on space target monitoring opportunity parameters and scheduling conflict parameters; obtaining task weight parameters based on time sensitivity, preset important parameters and preset value parameters; obtaining the sum of task weights based on the scheduling conflict set and task weight parameters; obtaining task conflict based on the sum of task weights; and obtaining monitoring resource consumption based on resource continuous monitoring time, resource remaining monitoring time, resource required storage space and resource remaining storage space.

[0050] In this embodiment, first, based on the space target monitoring opportunity parameter and the scheduling conflict degree parameter, a scheduling conflict set is obtained. At the same time, based on the time sensitivity, the preset important parameter and the preset value parameter, a task weight parameter is obtained. On this basis, based on the scheduling conflict set and the task weight parameter, the total task weight is obtained.

[0051] The calculation formula for the total task weight is: .in, is the scheduling conflict set, is the task weight parameter, is the sum of task weights.

[0052] Then, based on the sum of task weights, the task conflict degree is obtained. It can be understood that one time window corresponds to one surveillance opportunity, space target surveillance opportunity For the task , Sub-dispatching center , and the monitoring opportunity corresponding to the monitoring resource r. Scheduling conflict degree: Representation Task Hezi Dispatch Center The set of monitoring opportunities between yes The number of space target surveillance opportunities in . Let the set Indicates monitoring opportunities On The set of conflicting scheduled tasks. Just a task A surveillance opportunity The sum of the weights of the conflicting tasks calculated above. Then for Collection For example, existence Each value represents the conflict value between the monitoring opportunity and the task assigned by the dispatch center. In order to relatively quantify the size of this conflict value, The value is the ratio of the conflict value to the maximum value of the conflict value set.

[0053] Based on the resource continuous monitoring time, resource remaining monitoring time, resource required storage space and resource remaining storage space, the monitoring resource consumption is obtained. Specifically, It is a monitoring task The continuous monitoring time of the required monitoring resource r, is the remaining monitoring time of resource r, It is a monitoring task The storage space required to monitor resource r, is the remaining storage space of resource r. The calculation formula for monitoring resource consumption is: .in, To monitor resource consumption.

[0054] Furthermore, in some embodiments of the present invention, task allocation and scheduling are performed based on the task conflict degree and the monitoring resource consumption degree to obtain multi-satellite task planning results, including: obtaining comprehensive utility based on the task conflict degree and the monitoring resource consumption degree; performing task allocation based on a simulated annealing algorithm and the sum of the comprehensive utility; performing task scheduling based on a variable neighborhood search descent algorithm to obtain multi-satellite task planning results.

[0055] In this embodiment, first, the comprehensive utility is obtained based on the task conflict degree and the monitoring resource consumption degree, task allocation is performed based on the simulated annealing algorithm and the sum of the comprehensive utility, and task scheduling is performed based on the variable neighborhood search descent algorithm to obtain the multi-satellite task planning results.

[0056] Specifically, assuming Representation Task Hezi Dispatch Center The set of monitoring opportunities between yes Based on the above analysis, the conflict degree can be calculated for each space target surveillance opportunity. and resource consumption , that is, the comprehensive utility of a space target surveillance opportunity can be described as: .in, and are the weights of conflict degree and monitoring resource consumption respectively. Assuming that the task In the sub-dispatch center There are monitoring opportunities, then the sub-dispatching center To the task The comprehensive effect is: , the goal of task allocation is to maximize the total utility of tasks assigned to each sub-dispatching center. In the task collaborative allocation stage, based on the heuristic criterion of task allocation, a parameter adaptive simulated annealing algorithm combined with a taboo table is proposed. The simulated annealing algorithm combines the cooling strategy and the state probability random transition mechanism, and has the ability to jump out of the local optimum in the process of searching for the optimal solution of the problem. However, one of its shortcomings is that it lacks memory of the historical search process. The taboo search algorithm can remember the search operations executed recently, and by prohibiting certain search operations (taboo table), the algorithm can avoid short-term revisits or cyclic revisits to certain solution components, thereby avoiding the increase in computational overhead caused by cycles and revisits. Parameter adaptation can dynamically adjust the exploration and mining capabilities of the simulated annealing algorithm and improve the algorithm's optimization performance.

[0057] In each iteration of the adaptive simulated annealing algorithm, if a newly generated solution is of higher quality (higher fitness) than the current solution, the new solution will be accepted; if the new solution is worse than the current solution, the new solution will be accepted with a certain probability, and the acceptance probability is determined by the difference between the current temperature and the fitness of the new and old solutions (i.e., the objective function value). Once the new solution is accepted, the corresponding reallocated task generated by the new solution will be added to the taboo list. If the taboo list is full, the task that was first added to the taboo list will be released first. Tasks in the taboo list are prohibited from being reallocated until they are released. A simple and intuitive task redistribution neighborhood structure is designed. In each iteration, a previously assigned task is randomly selected and reallocated to another sub-scheduling center under the condition that the constraints are met.

[0058] It is understandable that the taboo table can be simply regarded as a table that changes dynamically during the calculation process if a better solution is obtained when using simulated annealing and variable neighborhood search algorithms to solve the optimal solution. The first is to avoid excessive calculations, and the second is to prevent the destruction of the better solution that has been obtained.

[0059] The specific algorithm flow is as follows: set algorithm parameters such as initial temperature, maximum annealing times, temperature drop ratio, initialize taboo table and neighborhood structure set, etc.; generate initial task allocation results based on task collaborative allocation heuristic criteria and plan scheduling scheme; calculate the objective function fitness value of the initial solution and record the global optimal solution ; The initial solution is taken as ,exist The neighborhood of updates the task allocation location with a probability of 0.5 and generates a neighborhood solution ; Judge the fitness value of the neighborhood solution, and use the Metropolis criterion to determine whether to accept the neighborhood solution as the current solution; add the assigned task corresponding to the updated current solution to the taboo table. If the taboo table is full, the task that was first added to the taboo table will be released first; insert the released task and calculate the optimal solution . Return to Step 3; Annealing is performed as a cooling function to determine whether the maximum number of iterations of the algorithm has been reached. If not, return to Step 4 and continue the loop iteration. If it is met, output the current optimal solution.

[0060] After the tasks are assigned, the satellite monitoring resources need to be scheduled. Each sub-scheduling center uses the adaptive variable neighborhood search descent algorithm combined with the taboo table to schedule the monitoring resources for the assigned monitoring tasks. The variable neighborhood search descent algorithm uses multiple neighborhood structures for alternating searches, which greatly enhances the algorithm's search ability for the solution space. However, due to its lack of memory of the historical search process, it will repeatedly search some solution spaces, which is not conducive to improving the search efficiency. By integrating the advanced mechanisms of classic heuristic algorithms and intelligent optimization algorithms, an adaptive variable neighborhood search descent algorithm combined with the taboo table strategy is designed, and the final monitoring resource scheduling plan is output through continuous iterative optimization.

[0061] According to the characteristics of regional target monitoring resource scheduling problems, a variety of neighborhood structures are designed for alternating search. Different neighborhood structures will produce different disturbances to the current solution, thereby generating different neighborhood solutions. In order to enhance the diversity of neighborhood solutions during the search process, three neighborhood structures are mainly designed, namely, the insertion neighborhood structure, the replacement neighborhood structure, and the reallocation neighborhood structure. The first two neighborhood structures mainly operate on tasks that have not been successfully scheduled, and the third neighborhood structure mainly perturbs tasks that have been scheduled. The specific operation task object is determined by some heuristic rules, including: the greedy rule, that is, directly selecting the task with the largest benefit value from the set of tasks to be selected; the random rule, that is, randomly selecting tasks from the set of tasks to be selected.

[0062] Inserting a neighborhood structure means adding unsuccessfully scheduled tasks into the scheduling scheme to improve task completion rate and resource utilization. First, determine the task to be inserted, then randomly determine the monitoring resource to be allocated in the set of available monitoring resources corresponding to the task, and determine whether the insertion opportunity conflicts with the scheduling scheme. If not, insert it into the scheduling sequence.

[0063] The replacement neighborhood structure means that by adding tasks with higher priority or larger calculation weight to the scheduling plan, the allocated resources are arranged for tasks that contribute more to the objective function, thereby improving resource utilization. First, the task to be inserted is determined, and then the monitoring resource to be inserted is randomly determined from the set of available monitoring resources corresponding to the task, and the sum of the weight values ​​of the scheduled tasks that conflict with it is calculated. If it is less than the task to be replaced, the conflicting task will be deleted from the scheduling plan, and then the task to be replaced will be added to the scheduling sequence.

[0064] The reallocation neighborhood structure means that since the available monitoring resources and time windows corresponding to the tasks may not be unique, in order to achieve the reallocation of monitoring resources, after the scheduled tasks are transferred from the current scheduled location to another location, the currently occupied resources may be released so that other tasks can be scheduled. First, a scheduled task is randomly selected from the scheduling scheme, and then it is tried to be rescheduled to another available monitoring resource.

[0065] The specific algorithm flow is as follows: preprocessing, based on the task allocation heuristic rules, according to the resource information of each sub-dispatching center and the assigned monitoring task information, a greedy algorithm is used to generate a better initial solution; initialize the algorithm parameters, set the number of searches I and the maximum number of iterations S in each neighborhood; select the neighborhood structure in a roulette manner, and search for neighborhood solutions in the selected neighborhood structure; calculate the objective function value corresponding to the neighborhood solution, if the searched neighborhood solution is better than the current optimal solution, then update the current optimal solution and add the corresponding search operation to the taboo table; if the neighborhood structure selection probability update condition is met: update the neighborhood selection probability every 100 searches; when the taboo duration of a search operation is equal to the length of the taboo table, unban the related operation; repeat Step 4 to Step 6 to determine whether the number of searches in each neighborhood has been reached. If reached, change the neighborhood and return to Step 4 until the maximum number of iterations S of the algorithm is reached, the algorithm ends and outputs the current optimal solution.

[0066] In the claims, specification and drawings of the present invention, the term "multiple" refers to two or more than two. Unless otherwise clearly defined, the orientation or positional relationship indicated by the terms "upper" and "lower" is based on the orientation or positional relationship shown in the drawings, which is only for the purpose of more conveniently describing the present invention and making the description process simpler, rather than indicating or implying that the device or element referred to must have the specific orientation described, be constructed and operated in a specific orientation, so these descriptions cannot be understood as limiting the present invention; the terms "connect", "install", "fix" and the like should be understood in a broad sense. For example, "connection" can be a fixed connection between multiple objects, or a detachable connection between multiple objects, or an integral connection; it can be a direct connection between multiple objects, or an indirect connection between multiple objects through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood based on the specific circumstances of the above data.

[0067] In the claims, specification and drawings of the present invention, the description of the terms "one embodiment", "some embodiments", "specific embodiments" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In the claims, specification and drawings of the present invention, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A collaborative processing method for multi-source remote sensing satellite observation tasks, characterized in that: include: Configure multi-source remote sensing satellites and obtain remote sensing satellite parameters; Configure satellite ground stations and extract ground station target information; Receiving observation requirement target information, and acquiring satellite available bands based on the remote sensing satellite parameters, the ground station target information and the observation requirement target information; Performing strip segmentation processing on the satellite available strip to obtain multi-strip resource data; Performing multi-strip resource allocation on the multi-strip resource data to obtain a multi-satellite mission planning result; The satellite available bands include satellite data reception available bands and satellite observation available bands.

2. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 1, characterized in that: The available satellite strips are subjected to strip segmentation processing to obtain multi-strip resource data, including: Based on the satellite orbit parameters and monitoring area, obtain the sub-satellite point trajectory equation; Based on the sub-satellite point trajectory equation, segment the regional target to obtain segmentation result data; The segmentation result data is filtered to obtain the plurality of strip resource data.

3. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 2 is characterized in that: The obtaining of the sub-satellite point trajectory equation based on the satellite orbit parameters and the monitoring area includes: Filtering based on the image type of the monitoring area to obtain a first sensor resource; Performing a secondary screening on the first sensor resources based on the resolution requirement of the monitoring area to obtain second sensor resources; Acquire first time window data based on sunlight parameters, the second sensor resources and the location of the monitoring area; Based on the satellite orbit parameters and the first time window data, a sub-satellite point trajectory equation is obtained.

4. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 3 is characterized in that: The segmentation process of the regional target based on the sub-satellite point trajectory equation to obtain segmentation result data includes: Selecting unprocessed first time window data, and acquiring a sensor observation range based on the unprocessed first time window data, a satellite trajectory equation, and sensor technical parameters; Select a working mode. If the manual intervention mode is selected, import a predefined scene reference library, filter out candidate scenes in the predefined scene reference library based on the sensor observation range and the regional target, and obtain the segmentation result data. If the single-view mode is selected, the regional target is segmented into strips, and the single-view imaging after the strip segmentation is segmented into single scenes to obtain the segmentation result data.

5. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 4 is characterized in that: The filtering of the segmentation result data to obtain the plurality of stripe resource data includes: Selecting an observation scene from the segmentation result data; Obtaining continuous observation time based on satellite platform parameters and coverage of the observation scene; Based on the continuous observation time and the observation requirements, obtain the second time window data; Based on the second time window data, the observation scene is screened, and if the second time window data corresponding to the observation scene exists, the observation scene is added to the multiple strips of resource data, Otherwise, the observation scene is deleted.

6. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 5 is characterized in that: The performing multi-strip resource allocation on the multi-strip resource data to obtain a multi-satellite mission planning result includes: Performing task resource matching on the plurality of strips of resource data to obtain available orbital arc segments of the satellite; Performing multi-task conflict evaluation on the available orbital arcs of the plurality of satellites to obtain the possibility of mission conflict, and obtaining the maximum monitoring benefit of the satellite based on the target monitoring benefit and the mission monitoring cost; Based on the space target monitoring opportunity parameters, scheduling conflict parameters and resource consumption parameters, the task conflict degree and monitoring resource consumption degree are obtained; Based on the task conflict degree and the monitoring resource consumption degree, task allocation and scheduling are performed to obtain the multi-satellite task planning result.

7. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 6 is characterized in that: The performing task resource matching on the plurality of strips of resource data to obtain the available orbital arc of the satellite includes: Based on the visible time windows of the plurality of strip resource data, obtaining a service time window, an expected service duration parameter and a visible arc segment parameter; Based on the service time window, the expected service duration parameter and the visible arc parameter, the available orbital arc of the satellite is obtained.

8. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 7, characterized in that: The step of performing multi-task conflict degree evaluation on the plurality of satellite available orbital arcs to obtain the possibility of mission conflict includes: Bring the two satellite available orbital arcs into the two-dimensional coordinate axis respectively to obtain the execution interval area and the conflict area; The task conflict possibility is obtained based on the execution interval area and the conflict area.

9. The collaborative processing method for multi-source remote sensing satellite observation tasks according to claim 8, characterized in that: The obtaining of task conflict and monitoring resource consumption based on space target monitoring opportunity parameters, scheduling conflict parameters and resource consumption parameters includes: Acquire a scheduling conflict set based on the space target monitoring opportunity parameter and the scheduling conflict degree parameter; Obtain task weight parameters based on time sensitivity, preset important parameters and preset value parameters; Based on the scheduling conflict set and the task weight parameter, obtaining a total task weight; Based on the sum of the task weights, obtaining the task conflict degree; The monitoring resource consumption is obtained based on the resource continuous monitoring time, the resource remaining monitoring time, the resource required storage space and the resource remaining storage space.

10. The collaborative processing method of multi-source remote sensing satellite observation tasks according to claim 9, characterized in that: The performing task allocation and scheduling based on the task conflict degree and the monitoring resource consumption degree to obtain the multi-satellite task planning result includes: Based on the task conflict degree and the monitoring resource consumption degree, obtaining a comprehensive utility; Performing task allocation based on a simulated annealing algorithm and the sum of the comprehensive utilities; Based on the variable neighborhood search descent algorithm, task scheduling is performed to obtain the multi-satellite task planning result.

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