CN117978245B - Virtual networking optimization method for multi-source domestic satellite - Google Patents
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Abstract
The invention provides a method for optimizing virtual networking of a multi-source domestic satellite, which comprises the following steps: collecting and sorting multi-source domestic satellite resources and operating parameters for characteristic analysis; the unified management of the observation resources in three dimensions of satellite platform, load and receiving is realized by utilizing satellite resource modeling and dynamic management; virtual networking calculation is carried out on observation influence factors facing to ground targets, and satellite-ground integrated observation capability simulation analysis is carried out; the on-orbit observation capability is reflected by adopting a parameter dynamic updating weighting factor method; establishing a task planning capacity adjustment model to realize self-adaptive dynamic adjustment of the observation capacity; the optimal observation capability is realized through dynamic adjustment of the observation capability; establishing a distributed multi-constellation observation resource overall operation control layered architecture; and dynamically collecting the scheduling result of the single star observation resources to realize the optimization of the networking allocation scheme of the observation resources. The invention realizes the virtual networking of multi-constellation and multi-load observation resources and improves the imaging capability of domestic satellites to the level of monthly space observation.
Description
Technical Field
The invention relates to the technical field of satellite operation management, in particular to a method for optimizing virtual networking of a multi-source domestic satellite.
Background
At present, a domestic satellite for managing an orbit while in orbit is currently used, taking a terrestrial satellite as an example, includes: 2 m-level resolution optical satellites such as ZY3-02, ZY3-03, GF1, GF-1B, GF-1C, GF-1D, CB04A, GF and the like, sub-m-level resolution optical satellites such as GF2, GF7 and GFDM and the like, hyperspectral satellites such as ZY1-02D, ZY1-02E, GF5-01A, GF-5B and the like, L-SAR 01A, L-SAR 01B, GF3, GF-3B, GF-3C radar satellites and the like.
However, since the types of satellites are different, the types of loads are various, and the orbits of satellites are different, for example: the orbit heights are different, the satellite constellations have different running speeds, so that the phase difference between the satellite constellations can not be kept for a long time, the theoretical optimal condition running of ground track equipartition can not be realized, the situation that the ground transit track is repeatedly covered exists, and the satellite in-orbit efficiency is lost.
Dynamic adjustment of orbit resources can be realized aiming at task targets through virtual networking, data redundancy and shooting resource waste caused by repeated track coverage are removed to the maximum extent, and therefore satellite data acquisition efficiency is improved.
In the existing various satellite observation resources, the regression period of a 2-meter-level resolution optical satellite constellation is 10 days, and the revisit period is 1 day; the regression period of the hyperspectral satellite constellation is 28 days, and the revisit period is 1 day; the regression period of the radar satellite constellation is 4 days, and the revisit period is 1 day. Under the condition of not considering weather influences of cloud, fog, rain and the like, the conventional satellite observation resources are comprehensively optimized, and the current domestic remote sensing satellite theoretically has the revisiting capability of the current day at any place within 84 degrees of the north and south latitude of the world.
However, in actual satellite data acquisition, on one hand, the imaging capability of each satellite cannot be completely released due to insufficient ground receiving resources, and a plurality of observation tasks are required to be considered for shooting the acquired ground targets, so that the daily imaging working time is limited; on the other hand, the satellite image data quality is greatly influenced by weather and the topography and landform conditions of an observation target, and the image data which is covered by a single-view cloud cover by not more than 20% is generally used as effective qualified data, so that the actual efficiency of acquiring the satellite effective data is far less than the theoretical imaging capability. The space observation level currently available for satellite imaging capability in the territory of China is quarter effective data coverage.
In order to meet the current situation of the requirement that the space observation level of the satellite imaging capability is continuously improved at the present stage, development and research on key technologies of satellite resources are further needed, virtual networking of multi-constellation and multi-load observation resources is realized, and the space observation level of the domestic satellite imaging capability is improved.
Disclosure of Invention
In view of this, the invention aims to design and realize a method for optimizing virtual networking of a multi-source domestic satellite under the combination of the specific demand current situation and the technical development trend, based on the existing domestic satellite overall image acquisition mechanism, the parameter characteristics of the satellite such as orbit, sensor, acquisition capacity and the like are arranged, virtual networking calculation of multiple satellite constellations, multiple resolutions and multiple sensors is carried out, satellite-ground integrated observation capacity simulation analysis is carried out, the observation efficiency of the domestic satellite in-orbit actual constellation and the different-orbit virtual constellation is compared and analyzed, a distributed multi-constellation observation resource overall operation control layered architecture is established, the lower layer dynamically adjusts the in-orbit observation capacity model towards the ground observation target, the upper layer corresponds to the satellite resource model, the observation capacity model is distributed to different satellites or constellations, a virtual observation network at the target observation time is constructed, and the imaging capacity of the domestic satellite is improved to the lunar space observation level.
The invention provides a method for optimizing virtual networking of a multi-source domestic satellite, which comprises the following steps:
s1, collecting and arranging observation data of multi-source domestic satellite resources and operation parameters of a networked operation satellite constellation, and carrying out feature analysis on the observation data and the operation parameters;
the observation data of the multi-source domestic satellite resource comprises: track, sensor, parameter characteristics of acquisition capability;
The operation parameters of the networking operation satellite constellation comprise: on-orbit satellite load parameters, constellation regression periods, constellation revisit periods and emergency revisit periods;
S2, a standardized modeling and resource topological structure modeling method based on satellite observation and receiving resources is combined with dynamic management, so that unified resource management of various domestic satellite observation resources in three dimensions of a satellite platform, load and receiving is realized; the observation efficiency of the domestic satellite networking operation is improved;
The standardized modeling and resource topological structure modeling method for satellite observation and receiving resources comprises the following steps:
Aiming at on-orbit observation resources and ground receiving resources, a layering modeling and model aggregation mechanism is adopted at a model layer to form a model description specification, and a unified description model about resource characteristics and use constraints is established;
Analyzing the use constraint and restriction rules of various satellite platforms and loads thereof at a resource layer, abstracting the constraints of different types of satellite platforms and loads, and extracting common characteristics in the constraints of the platforms and loads;
Constructing a topological structure under a set space-time condition in real time at a topological layer, analyzing links aiming at information acquisition and transmission links, describing the connectivity relationship among nodes according to a description specification of the connectivity relationship among resource nodes, and describing the topological structure according to a description specification of a topological structure model;
Uniformly building a resource model and a topological structure based on the constraint and space-time relationship of the observation resource at a management layer, expressing the observation resource into a form which can be identified and processed by a computer, and generating various models for other subsystems to call interfaces; according to the characteristic of observing dynamic changes of resources, various resources are managed and maintained, dynamic modeling and real-time processing are supported, and the resource states are enabled to be globally consistent.
S3, observing influence factors facing ground targets, performing virtual networking calculation of multiple satellite constellations, multiple resolutions and multiple sensors, and performing satellite-ground integrated observation capability simulation analysis;
S4, aiming at a task target, adopting a method for dynamically updating weighting factors by parameters of on-orbit data to reflect the actual state and the actual observation capability of the on-orbit data; establishing a satellite task planning capacity adjustment model of on-orbit data, and realizing a self-adaptive dynamic adjustment strategy of observation capacity;
The method for dynamically updating the weighting factors of the parameters comprises the following steps: dynamically adjusting on-orbit multi-satellite observation capability, combining single-satellite on-orbit observation capability, distributing N tasks in an observation target task set T to N s satellites, maximizing the income of task completion in the set T, and balancing the satellite observation capability load;
assuming that the original parameter of the on-track data is a, and the actual value of the parameter calculated according to the on-track data is b, the updating mode based on the weighting factor lambda is as follows:
S5, selecting and adjusting track height and/or phase and/or intersection point reduction through an observation capacity dynamic regulator, and carrying out on-orbit networking efficiency optimization analysis to realize optimal observation capacity;
If the optimal observation capability is not achieved, carrying out multi-source satellite virtual networking deduction and observation capability simulation analysis in the step S3-S4 again, and dynamically adjusting the observation capability;
If the optimal observation capability is reached, entering a step S6;
S6, establishing a distributed multi-constellation observation resource overall operation control layered architecture, setting the upper layer in the layered architecture to correspond to various satellite resources, distributing observation capacity to different satellites or constellations, and constructing a virtual observation network at a target observation moment: the on-orbit observation capability model analyzes the observation constraint requirements of a plurality of observation tasks, dynamically adjusts the on-orbit observation capability model into optimal on-orbit observation and receiving parameters, and distributes the observation capability values to single observation resources according to the real-time on-orbit state of satellite resources; setting a ground-facing observation target at the lower layer in the layered architecture, dynamically adjusting an on-orbit observation capability model, and generating an observation scheme of each observation resource by an observation task execution algorithm through an observation element task directly identified by a single-star observation task scheduler;
S7, dynamically collecting single-star observation resource scheduling results through a networking coordinator, dynamically scheduling and distributing incomplete observation capacity gaps according to states, and optimizing an observation resource networking distribution scheme and an optimization target through distributed scheduling and distribution for a plurality of times The method comprises the following steps:
In equation (2), the decision variable x ik = {0,1} represents that the task job i is observed in the time window of the kth turn of satellite a, Optimizing an objective function, namely maximizing the income of the optimization allocation of the alpha satellite observation resources, wherein NT represents N tasks of an observation objective set T, and NT is the total number of tasks; Representing the range of the executable observation time window of the ith task in the alpha satellite observation resource, nl α represents the range of the executable roll angle in the alpha satellite observation resource, p i represents the profit or weight of the ith task, Decision variables indicating whether the alpha satellite observation resource is for the ith mission,And the method shows that the observation with the side swing angle v and the duration j is implemented in the kth circle by utilizing the alpha satellite observation resource, and the duty ratio of the observed duration of the task i to the total duration of the observation window of the circle is the task completion degree of the kth circle.
Further, the method for establishing the satellite mission planning capacity adjustment model of the on-orbit data in the step S4 comprises the following steps:
Establishing a model parameter prediction system framework (comprising data acquisition and processing) based on dynamic data driving, and providing input data for parameter estimation and data assimilation; aiming at the characteristics of time variation and uncertainty of the model, a Markov chain Monte Carlo method is adopted to estimate the parameters of the model, and the state space distribution of the model is established according to the random distribution characteristics of the parameter estimation result; and updating or optimizing the model prediction result according to the actual measurement result by using a Kalman filtering method.
Further, the adaptive dynamic adjustment strategy of the step S4 is adapted to save the bad block model, and if a bad block Δc i is detected, update the size Δc t+1 of the bad block:
△Ct+1＝△Ct+n·△C (3)
In the formula (3), Δc is the solid memory block (typically 128 KB) where the bad block is located, and n is the number of newly found bad blocks.
Further, the method for performing the on-orbit networking performance optimization analysis in the step S5 includes:
aiming at the use constraint and design characteristics of a satellite platform and a load, any one of a receiving relay mode, a first-record and second-transmission mode and a strip recording mode is adopted to develop optimization and improvement of the use and operation of the satellite observation load.
Further, the method for selectively adjusting the track height in the step S5 includes:
the actual networking of the on-orbit constellation is developed, and the orbit is adjusted in the energy allowable range by adjusting the orbit height, so that the constellation satellite tends to be single, and an equiphase constellation as few as possible is formed.
Further, the method for selectively adjusting the phase in the step S5 includes:
By orbit maintenance, each constellation satellite maintains uniform phase distribution.
Further, the method for selecting and adjusting the drop intersection point in the step S5 includes:
Adopting propulsion adjustment, wherein the speed increment is 1000 m/s when the intersection point is adjusted every half hour, and the fuel consumption is adjusted to be larger); or adopting slow drift adjustment, and using satellite orbit drift to drift for 10 minutes (longer period) every 1 year when the intersection point is lowered.
Further, in the upper layer of the distributed multi-constellation observation resource orchestration operation control hierarchical architecture of the step S6, the method for allocating the observation capability to different satellites or constellations includes:
the on-orbit observation capability model analyzes the observation constraint of multiple task requirements of complex land-sea observation, dynamically adjusts the on-orbit observation and receiving parameters to be optimal, and distributes the observation capability value to a single observation resource according to the real-time on-orbit state of the satellite resource.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of virtual networking optimization of a multi-source domestic satellite as described above.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, the processor executing the program to implement the steps of the method for optimizing virtual networking of a multi-source domestic satellite as described above.
Compared with the prior art, the invention has the beneficial effects that:
The method for optimizing the virtual networking of the multi-source domestic satellite provided by the invention is based on comprehensive analysis of various satellite resource characteristics, researches and establishes a distributed multi-constellation observation resource overall operation control layered architecture, and allocates an observation capacity model to different satellites or constellations by corresponding the upper layer of the layered architecture to a satellite resource model so as to construct a virtual observation network at a target observation moment; the lower layer of the layered architecture is directly identified by a single-star observation task scheduler to observe the meta-task, then an observation task execution algorithm generates an observation scheme of each observation resource, the real-time state of the satellite resources is subjected to distributed virtual networking of the observation resources and distributed to the single-star observation task to form a virtual networking calculation result, the imaging capability of the multi-source domestic satellite is improved to the lunar space observation level, and the requirement on the space observation level for improving the imaging capability of the domestic satellite is further met.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
In the drawings:
FIG. 1 is a schematic diagram of a basic flow of virtual networking optimization of a multi-source domestic satellite according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a distributed multi-constellation observation resource orchestration operation control layered architecture according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for optimizing virtual networking of a multi-source domestic satellite according to the present invention;
Fig. 4 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of systems and products consistent with some aspects of the present disclosure as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" depending on the context.
Embodiments of the present invention are described in further detail below.
The embodiment of the invention provides a method for optimizing virtual networking of a multi-source domestic satellite, which is shown in fig. 3 and comprises the following steps:
s1, collecting and arranging observation data of multi-source domestic satellite resources and operation parameters of a networked operation satellite constellation, and carrying out feature analysis on the observation data and the operation parameters;
the observation data of the multi-source domestic satellite resource comprises: track, sensor, parameter characteristics of acquisition capability;
The operation parameters of the networking operation satellite constellation comprise: on-orbit satellite load parameters, constellation regression periods, constellation revisit periods and emergency revisit periods;
S2, a standardized modeling and resource topological structure modeling method based on satellite observation and receiving resources is combined with dynamic management, so that unified resource management of various domestic satellite observation resources in three dimensions of a satellite platform, load and receiving is realized; the observation efficiency of the domestic satellite networking operation is improved;
The standardized modeling and resource topological structure modeling method for satellite observation and receiving resources comprises the following steps:
Aiming at on-orbit observation resources and ground receiving resources, a layering modeling and model aggregation mechanism is adopted at a model layer to form a model description specification, and a unified description model about resource characteristics and use constraints is established;
Analyzing the use constraint and restriction rules of various satellite platforms and loads thereof at a resource layer, abstracting the constraints of different types of satellite platforms and loads, and extracting common characteristics in the constraints of the platforms and loads;
Constructing a topological structure under a set space-time condition in real time at a topological layer, analyzing links aiming at information acquisition and transmission links, describing the connectivity relationship among nodes according to a description specification of the connectivity relationship among resource nodes, and describing the topological structure according to a description specification of a topological structure model;
Uniformly building a resource model and a topological structure based on the constraint and space-time relationship of the observation resource at a management layer, expressing the observation resource into a form which can be identified and processed by a computer, and generating various models for other subsystems to call interfaces; according to the characteristic of observing dynamic changes of resources, various resources are managed and maintained, dynamic modeling and real-time processing are supported, and the resource states are enabled to be globally consistent.
S3, observing influence factors facing ground targets, performing virtual networking calculation of multiple satellite constellations, multiple resolutions and multiple sensors, and performing satellite-ground integrated observation capability simulation analysis;
S4, aiming at a task target, adopting a method for dynamically updating weighting factors by parameters of on-orbit data to reflect the actual state and the actual observation capability of the on-orbit data; establishing a satellite task planning capacity adjustment model of on-orbit data, and realizing a self-adaptive dynamic adjustment strategy of observation capacity;
The method for dynamically updating the weighting factors of the parameters comprises the following steps: dynamically adjusting on-orbit multi-satellite observation capability, combining single-satellite on-orbit observation capability, distributing N tasks in an observation target task set T to N s satellites, maximizing the income of task completion in the set T, and balancing the satellite observation capability load;
assuming that the original parameter of the on-track data is a, and the actual value of the parameter calculated according to the on-track data is b, the updating mode based on the weighting factor lambda is as follows:
the method for establishing the satellite mission planning capacity adjustment model of the on-orbit data comprises the following steps:
Establishing a model parameter prediction system framework (comprising data acquisition and processing) based on dynamic data driving, and providing input data for parameter estimation and data assimilation; aiming at the characteristics of time variation and uncertainty of the model, a Markov chain Monte Carlo method is adopted to estimate the parameters of the model, and the state space distribution of the model is established according to the random distribution characteristics of the parameter estimation result; and updating or optimizing the model prediction result according to the actual measurement result by using a Kalman filtering method.
The adaptive dynamic adjustment strategy is suitable for fixing the bad block model, and if the bad block delta C t is detected, the size delta C t+1 of the bad block is updated:
△Ct+1＝△Ct+n·△C (3)
in the formula (3), Δc is the solid memory block (128 KB in this embodiment) where the bad blocks are located, and n is the number of newly found bad blocks.
S5, selecting and adjusting track height and/or phase and/or intersection point reduction through an observation capacity dynamic regulator, and carrying out on-orbit networking efficiency optimization analysis to realize optimal observation capacity;
If the optimal observation capability is not achieved, carrying out multi-source satellite virtual networking deduction and observation capability simulation analysis in the step S3-S4 again, and dynamically adjusting the observation capability;
If the optimal observation capability is reached, entering a step S6;
The method for selecting and adjusting the track height comprises the following steps: the actual networking of the on-orbit constellation is developed, and the orbit is adjusted in the energy allowable range by adjusting the orbit height, so that the constellation satellite tends to be single, and an equiphase constellation as few as possible is formed.
The method for selecting and adjusting the phase is as follows: by orbit maintenance, each constellation satellite maintains uniform phase distribution.
The method for selecting and adjusting the drop intersection point comprises the following steps: adopting propulsion adjustment, wherein the speed increment is 1000 m/s when the intersection point is adjusted every half hour; or adopting slow drift adjustment, and using satellite orbit drift to drift for 10 minutes every 1 year when the intersection point is lowered.
The method for carrying out the on-orbit networking efficiency optimization analysis comprises the following steps:
aiming at the use constraint and design characteristics of a satellite platform and a load, any one of a receiving relay mode, a first-record and second-transmission mode and a strip recording mode is adopted to develop optimization and improvement of the use and operation of the satellite observation load.
S6, a distributed multi-constellation observation resource overall operation control layered architecture (see the figure 2) is established, an upper layer in the layered architecture is set to correspond to various satellite resources, the observation capability is distributed to different satellites or constellations, and a virtual observation network at a target observation moment is established: the on-orbit observation capability model analyzes the observation constraint requirements of a plurality of observation tasks, dynamically adjusts the on-orbit observation capability model into optimal on-orbit observation and receiving parameters, and distributes the observation capability values to single observation resources according to the real-time on-orbit state of satellite resources; setting a ground-facing observation target at the lower layer in the layered architecture, dynamically adjusting an on-orbit observation capability model, and generating an observation scheme of each observation resource by an observation task execution algorithm through an observation element task directly identified by a single-star observation task scheduler;
in the upper layer of the distributed multi-constellation observation resource overall operation control layered architecture, the method for distributing the observation capacity to different satellites or constellations comprises the following steps:
the on-orbit observation capability model analyzes the observation constraint of multiple task requirements of complex land-sea observation, dynamically adjusts the on-orbit observation and receiving parameters to be optimal, and distributes the observation capability value to a single observation resource according to the real-time on-orbit state of the satellite resource.
S7, dynamically collecting single-star observation resource scheduling results through a networking coordinator, dynamically scheduling and distributing incomplete observation capacity gaps according to states, and optimizing an observation resource networking distribution scheme and an optimization target through distributed scheduling and distribution for a plurality of timesThe method comprises the following steps:
In equation (2), the decision variable x ik = {0,1} represents that the task job i is observed in the time window of the kth turn of satellite a, Optimizing an objective function, namely maximizing the income of the optimization allocation of the alpha satellite observation resources, wherein NT represents N tasks of an observation objective set T, and NT is the total number of tasks; Representing the range of the executable observation time window of the ith task in the alpha satellite observation resource, nl α represents the range of the executable roll angle in the alpha satellite observation resource, p i represents the profit or weight of the ith task, Decision variables indicating whether the alpha satellite observation resource is for the ith mission,And the method shows that the observation with the side swing angle v and the duration j is implemented in the kth circle by utilizing the alpha satellite observation resource, and the duty ratio of the observed duration of the task i to the total duration of the observation window of the circle is the task completion degree of the kth circle.
Fig. 1 shows a basic flow of virtual networking optimization of a multi-source domestic satellite in this embodiment.
Application example
The application case selects the land area range of China as an object, and the satellite coverage selects the domestic satellite.
Aiming at the land area of China, the target observation task is subjected to standardized description, and information such as constraint requirements in the land area of China is extracted; generating observation meta-tasks corresponding to the satellite single-star resources capable of participating in observation, and obtaining the observation capability of each meta-task;
And carrying out one-time numerical operation on the on-orbit observation capability based on a double-layer dynamic adjustment algorithm, carrying out distributed observation resource virtual networking on the real-time state of satellite resources every day, and distributing the distributed observation resource virtual networking to a single-star observation task to form a final virtual networking solution result every day.
By constructing a virtual observation network, the imaging capability of the multi-source domestic satellite is improved to the level of lunar space observation.
The embodiment of the invention also provides a computer device, and fig. 4 is a schematic structural diagram of the computer device provided by the embodiment of the invention; referring to fig. 4 of the drawings, the computer apparatus includes: an input system 23, an output system 24, a memory 22, and a processor 21; the memory 22 is configured to store one or more programs; the one or more programs, when executed by the one or more processors 21, cause the one or more processors 21 to implement the method of multi-source domestic satellite virtual networking optimization as provided by the above embodiments; wherein the input system 23, the output system 24, the memory 22 and the processor 21 may be connected by a bus or otherwise, for example in fig. 4.
The memory 22 is used as a readable storage medium of a computing device and can be used for storing software programs and computer executable programs, and the program instructions corresponding to the method for optimizing the virtual networking of the multi-source domestic satellite according to the embodiment of the invention; the memory 22 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the device, etc.; in addition, memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device; in some examples, memory 22 may further comprise memory located remotely from processor 21, which may be connected to the device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input system 23 is operable to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the device; output system 24 may include a display device such as a display screen.
The processor 21 executes various functional applications of the device and data processing, i.e. the method for optimizing virtual networking of the multi-source domestic satellite, by running software programs, instructions and modules stored in the memory 22.
The computer equipment provided by the embodiment can be used for executing the method for optimizing the virtual networking of the multi-source domestic satellite, and has corresponding functions and beneficial effects.
Embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing the method of multi-source domestic satellite virtual networking optimization as provided by the above embodiments, the storage medium being any of various types of memory devices or storage devices, the storage medium comprising: mounting media such as CD-ROM, floppy disk or tape systems; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, lanbas (Rambus) RAM, etc.; nonvolatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc.; the storage medium may also include other types of memory or combinations thereof; in addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a second, different computer system, the second computer system being connected to the first computer system through a network (such as the internet); the second computer system may provide program instructions to the first computer for execution. Storage media includes two or more storage media that may reside in different locations (e.g., in different computer systems connected by a network). The storage medium may store program instructions (e.g., embodied as a computer program) executable by one or more processors.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the method for optimizing virtual networking of the multi-source domestic satellite according to the above embodiments, and may also perform the related operations in the method for optimizing virtual networking of the multi-source domestic satellite provided in any embodiment of the present invention.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for optimizing the virtual networking of the multi-source domestic satellite is characterized by comprising the following steps of:
s1, collecting and arranging observation data of multi-source domestic satellite resources and operation parameters of a networked operation satellite constellation, and carrying out feature analysis on the observation data and the operation parameters;
the observation data of the multi-source domestic satellite resource comprises: track, sensor, parameter characteristics of acquisition capability;
The operation parameters of the networking operation satellite constellation comprise: on-orbit satellite load parameters, constellation regression periods, constellation revisit periods and emergency revisit periods;
s2, a standardized modeling and resource topological structure modeling method based on satellite observation and receiving resources is combined with dynamic management, so that unified resource management of various domestic satellite observation resources in three dimensions of a satellite platform, load and receiving is realized;
S3, observing influence factors facing ground targets, performing virtual networking calculation of multiple satellite constellations, multiple resolutions and multiple sensors, and performing satellite-ground integrated observation capability simulation analysis;
S4, aiming at a task target, adopting a method for dynamically updating weighting factors by parameters of on-orbit data to reflect the actual state and the actual observation capability of the on-orbit data; establishing a satellite task planning capacity adjustment model of on-orbit data, and realizing a self-adaptive dynamic adjustment strategy of observation capacity;
The method for dynamically updating the weighting factors of the parameters comprises the following steps: dynamically adjusting on-orbit multi-satellite observation capability, combining single-satellite on-orbit observation capability, distributing N tasks in an observation target task set T to N s satellites, maximizing the income of task completion in the set T, and balancing the satellite observation capability load;
assuming that the original parameter of the on-track data is a, and the actual value of the parameter calculated according to the on-track data is b, the updating mode based on the weighting factor lambda is as follows:
S5, selecting and adjusting track height and/or phase and/or intersection point reduction through an observation capacity dynamic regulator, and carrying out on-orbit networking efficiency optimization analysis to realize optimal observation capacity;
If the optimal observation capability is not achieved, carrying out multi-source satellite virtual networking deduction and observation capability simulation analysis in the step S3-S4 again, and dynamically adjusting the observation capability;
If the optimal observation capability is reached, entering a step S6;
S6, establishing a distributed multi-constellation observation resource overall operation control layered architecture, setting the upper layer in the layered architecture to correspond to various satellite resources, distributing observation capacity to different satellites or constellations, and constructing a virtual observation network at a target observation moment: the on-orbit observation capability model analyzes the observation constraint requirements of a plurality of observation tasks, dynamically adjusts the on-orbit observation capability model into optimal on-orbit observation and receiving parameters, and distributes the observation capability values to single observation resources according to the real-time on-orbit state of satellite resources; setting a ground-facing observation target at the lower layer in the layered architecture, dynamically adjusting an on-orbit observation capability model, and generating an observation scheme of each observation resource by an observation task execution algorithm through an observation element task directly identified by a single-star observation task scheduler;
S7, dynamically collecting single-star observation resource scheduling results through a networking coordinator, dynamically scheduling and distributing incomplete observation capacity gaps according to states, and optimizing an observation resource networking distribution scheme and an optimization target through distributed scheduling and distribution for a plurality of times The method comprises the following steps:
In equation (2), the decision variable x ik = {0,1} represents that the task job i is observed in the time window of the kth turn of satellite a, The optimization objective function represents the maximum profit of the optimization allocation of the alpha satellite observation resources, NT represents N tasks of the observation objective set T, and NT is the total number of tasks; Representing the range of the executable observation time window of the ith task in the alpha satellite observation resource, nl α represents the range of the executable roll angle in the alpha satellite observation resource, p i represents the profit or weight of the ith task, Decision variables indicating whether the alpha satellite observation resource is for the ith mission,And the method shows that the observation with the side swing angle v and the duration j is implemented in the kth circle by utilizing the alpha satellite observation resource, and the duty ratio of the observed duration of the task i to the total duration of the observation window of the circle is the task completion degree of the kth circle.
2. The method for optimizing virtual networking of multi-source domestic satellites according to claim 1, wherein the method for establishing the satellite mission planning capacity adjustment model of the on-orbit data in step S4 comprises the following steps:
Establishing a model parameter prediction system framework (comprising data acquisition and processing) based on dynamic data driving, and providing input data for parameter estimation and data assimilation; aiming at the characteristics of time variation and uncertainty of the model, a Markov chain Monte Carlo method is adopted to estimate the parameters of the model, and the state space distribution of the model is established according to the random distribution characteristics of the parameter estimation result; and updating or optimizing the model prediction result according to the actual measurement result by using a Kalman filtering method.
3. The method of optimizing virtual networking of a multi-source domestic satellite according to claim 1, wherein the method of modeling the standardized modeling and resource topology of the satellite observation and reception resources in step S2 comprises:
Aiming at on-orbit observation resources and ground receiving resources, a layering modeling and model aggregation mechanism is adopted at a model layer to form a model description specification, and a unified description model about resource characteristics and use constraints is established;
Analyzing the use constraint and restriction rules of various satellite platforms and loads thereof at a resource layer, abstracting the constraints of different types of satellite platforms and loads, and extracting common characteristics in the constraints of the platforms and loads;
Constructing a topological structure under a set space-time condition in real time at a topological layer, analyzing links aiming at information acquisition and transmission links, describing the connectivity relationship among nodes according to a description specification of the connectivity relationship among resource nodes, and describing the topological structure according to a description specification of a topological structure model;
Uniformly building a resource model and a topological structure based on the constraint and space-time relationship of the observation resource at a management layer, expressing the observation resource into a form which can be identified and processed by a computer, and generating various models for other subsystems to call interfaces; according to the characteristic of observing dynamic changes of resources, various resources are managed and maintained, dynamic modeling and real-time processing are supported, and the resource states are enabled to be globally consistent.
4. The method of optimizing virtual networking for multi-source domestic satellites according to claim 2 wherein the adaptive dynamic adjustment strategy of step S4 is adapted to persist bad block models, and if bad blocks Δc t are detected, update the size Δc t+1 of the bad blocks:
△Ct+1＝△Ct+n·△C (3)
in the formula (3), delta C is a solid-state storage block where the bad block is located, and n is the number of newly discovered bad blocks.
5. The method for optimizing virtual networking of multi-source domestic satellites according to claim 1, wherein the method for selectively adjusting the orbit height in step S5 comprises:
the actual networking of the on-orbit constellation is developed, and the orbit is adjusted in the energy allowable range by adjusting the orbit height, so that the constellation satellite tends to be single, and an equiphase constellation as few as possible is formed.
6. The method for optimizing virtual networking of multi-source domestic satellites according to claim 1, wherein the method for selectively adjusting the phase in step S5 comprises:
By orbit maintenance, each constellation satellite maintains uniform phase distribution.
7. The method for optimizing virtual networking of multi-source domestic satellites according to claim 1, wherein the method for selecting and adjusting the drop intersection point in step S5 comprises:
Adopting propulsion adjustment, wherein the speed increment is 1000 m/s when the intersection point is adjusted every half hour; or adopting slow drift adjustment, and using satellite orbit drift to drift for 10 minutes every 1 year when the intersection point is lowered.
8. The method for optimizing virtual networking of multi-source domestic satellites according to claim 1, wherein the method for allocating the observation capacity to different satellites or constellations in the upper layer of the distributed multi-constellation observation resource orchestration operation control layered architecture in the step S6 comprises:
the on-orbit observation capability model analyzes the observation constraint of multiple task requirements of complex land-sea observation, dynamically adjusts the on-orbit observation and receiving parameters to be optimal, and distributes the observation capability value to a single observation resource according to the real-time on-orbit state of the satellite resource.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method of virtual networking optimization of a multi-source domestic satellite according to any of claims 1-8.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of multisource domestic satellite virtual networking optimization according to any of claims 1-8 when the program is executed by the processor.
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