CN116523212A - Multi-star collaborative semantic modeling and task planning method and system - Google Patents
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Abstract
The invention belongs to the field of satellite mission planning, and relates to a multi-satellite collaborative semantic modeling and mission planning method and system. The method comprises the following steps: carrying out formal modeling on satellite tasks and cooperative relations by adopting a knowledge graph; decomposing the multi-star cooperation requirement into a plurality of tasks by using a knowledge graph, wherein each task is a shooting task or a transmission task, and expressing a time sequence relationship between the tasks; and inquiring the knowledge graph to obtain a meta-task combination result meeting the task cooperative constraint requirement, and taking the meta-task combination result as a multi-star cooperative task planning scheme. The invention adopts the modeling method of the knowledge graph, can flexibly combine various demand scenes to construct a multi-star collaborative graph structure, comprises task entities and relations among the entities, and supports query reasoning under complex scenes. The invention realizes formalized representation of multi-star collaborative tasks and automatic task planning scheme generation.
Description
Technical Field
The invention relates to the field of satellite task planning, in particular to a multi-satellite collaborative semantic modeling and task planning method and system.
Background
The current satellite task planning system can provide an automatic planning method to a certain extent, overcomes the defect that satellite users manually configure various satellite resources, improves the accuracy and scientificity of planning, and greatly improves the utilization rate of space satellite resources and the success rate of task execution. However, under the circumstance that the number of satellites is gradually increased and the tasks borne by different satellites are different, how to effectively utilize various satellite resources and complete a specific space mission by multi-satellite cooperation becomes a difficulty in currently restricting multi-satellite mission planning.
At present, the complex cooperation of multiple satellites highly depends on the technology and experience of technicians in satellite planning and system use, and the task cooperation of the multiple satellites is scheduled in advance. However, for complex mission requirements, the constraint relationship of satellites to each other requires higher cost expert knowledge. How to formally express the cooperative relationship of satellites and complete logic reasoning on complex task execution conditions on the basis of the formalized expression, so that a task combination scheme meeting the multi-satellite cooperative conditions is formed, and is an important step for improving the multi-satellite cooperative capability. The knowledge graph stores knowledge in a net-shaped structure, can construct complex knowledge structural relations, has strong knowledge storage, expression and reasoning capabilities, and is widely applied to the fields of finance, medical treatment, industry and the like. The method uses the knowledge graph to formalize modeling of the satellite tasks and the cooperative relations, uses the relation reasoning to correlate the instantiated knowledge models, and obtains the task combination result meeting the task cooperative constraint requirement as a planning scheme by using a semantic query mechanism with flexible knowledge graph.
Disclosure of Invention
The invention provides a multi-star collaborative task planning method and a system, which aims to overcome the defects that the existing multi-star collaborative task planning requires high-cost expert knowledge, has low system automation degree and poor flexibility.
The technical scheme adopted by the invention is as follows:
a multi-star collaborative semantic modeling and task planning method comprises the following steps:
carrying out formal modeling on satellite tasks and cooperative relations by adopting a knowledge graph;
decomposing the multi-star cooperation requirement into a plurality of tasks by using a knowledge graph, wherein each task is a shooting task or a transmission task, and the time sequence relationship among the tasks is represented;
and inquiring the knowledge graph to obtain a meta-task combination result meeting the task cooperative constraint requirement, and taking the meta-task combination result as a multi-star cooperative task planning scheme.
Further, the knowledge graph is adopted to formally model the satellite tasks and the cooperative relations, wherein:
the entity elements of the knowledge graph include: requirements, shooting tasks, transmission tasks, scene types, service types, target types, sensor modes, regional target shapes, resolution, working modes and illumination conditions;
the relationship elements of the knowledge graph include: subtask relationships, parameter relationships, timing relationships.
Further, the timing relationship includes:
forward relationship: before, meaning i is Before j; meets, which means that the i end time is equal to the j start time; overlaps, meaning that i and j have time overlap; starts, which indicates that the start times of i and j are the same; during, meaning that i is within the time frame of j; finish, i is the same at the end time of j; equals, which means that i is exactly identical in time at j;
inverse relationship: after, metBy, overlappedBy, startedBy, contains, finishedBy is included, which is a relationship description after i, j interchange for the corresponding forward relationship.
Further, in order to improve the accuracy of the description of the multi-star time sequence relationship, the description of the time period relationship such as 'Before', 'over lap' and the like is accurately and quantitatively described through the time difference value, so that the requirement of accurate description of the relative time relationship of different tasks is met.
Further, the time sequence relation among the tasks is represented by a time constraint matrix C among the tasks, the dimension is n multiplied by n, n is the number of the tasks, and the element C of the matrix C i，j Is a time sequence relation function and related parameters of the task i and the task j.
Further, the decomposing the multi-star collaborative requirement into a plurality of tasks includes:
according to targets in the multi-satellite cooperative demand and related parameters of satellite resources, calculating a single shooting task or a single transmission task through an orbit calculation system to obtain a series of meta tasks for determining satellites in a determined time period;
the meta-task m generated by the shooting task is expressed as a triplet (task, s, t), wherein the task and the task represent the task to which the m belongs and the satellite respectively, and t represents a time entity of the execution time of the m, and the time entity comprises a start time st and an end time et; the meta-task m generated by the transmission task is expressed as a quadruple (task, sub, obj, t), wherein sub represents the sender of the information and obj represents the receiver of the information.
Further, the querying the knowledge graph to obtain the meta-task combination result meeting the requirement of task cooperative constraint includes:
storing the metadata as a vortex RDF format;
and searching RDF data formed by the meta-tasks by utilizing SPARQL query sentences according to the task cooperative constraint requirements to obtain meta-task combinations meeting the conditions.
Further, the RDF is a triplet composed of a subject, an object, and a predicate, the triplet including: (meta-task, belonging task, task), (meta-task, start time, time), (meta-task, end time, time), (meta-task, satellite used, satellite).
A multi-star collaborative semantic modeling and mission planning system, comprising:
the knowledge graph construction module is used for formally modeling satellite tasks and cooperative relations by adopting the knowledge graph;
the task decomposition module is used for decomposing the multi-star cooperation requirement into a plurality of tasks by utilizing a knowledge graph, wherein each task is a shooting task or a transmission task and represents the time sequence relation among the tasks;
and the query reasoning module is used for querying the knowledge graph to acquire a meta-task combination result meeting the task cooperative constraint requirement as a multi-star cooperative task planning scheme.
The beneficial effects of the invention are as follows:
the invention uses the knowledge graph to formalize modeling of satellite tasks and cooperative relations, and uses relation reasoning to correlate the instantiated knowledge model, and can obtain the task combination result meeting the task cooperative constraint requirement as a planning scheme by using a semantic query mechanism with flexible knowledge graph.
The invention adopts the modeling method of the knowledge graph, can flexibly combine various demand scenes to construct a multi-star collaborative graph structure, comprises task entities and relations among the entities, and supports query reasoning under complex scenes. The invention realizes formalized representation of multi-star collaborative tasks and automatic task planning scheme generation.
Drawings
Fig. 1 is a diagram of a satellite planning map.
FIG. 2 is a schematic diagram of task relative timing relationships.
Fig. 3 is a schematic diagram of an inter-satellite system shooting task.
Fig. 4 is an example of a relationship rule Before (i, j, Δt) query language.
Fig. 5 is a relational rule Before (i, j, nan) query language example.
Detailed Description
The present invention will be further described in detail with reference to the following examples and drawings, so that the above objects, features and advantages of the present invention can be more clearly understood.
The multi-star collaborative semantic modeling and task planning method realizes formalized representation of multi-star collaborative tasks and automatic task planning scheme generation, and mainly comprises the following steps.
(1) Design of atlas structure
In order to realize the support capability of formalized representation of various satellite mission planning scenes, a map structure containing multiple entities and multiple relation elements is designed.
The basic entity elements of satellite mission planning include: requirements, take a brief description of the requirements; shooting tasks, namely, specific shooting actions adopted for realizing requirements; a transmission task, which is to realize the specific data transmission action needed to be taken; scene type, scene category describing the demand from the perspective of application satellite (simple scene, different orbital height collaboration, same orbital height collaboration, etc.); the service type, the angle description requirement describing service, the shooting target scene type (forest, water area, mountain, etc.); the type of the object, the type of the shooting object (point object, area object, etc.) related to the description requirement; sensor mode, a mode of a satellite-mounted sensor (optical sensor, infrared sensor, etc.) that needs to be applied in order to complete a specific task. Besides, there are also entities such as area target shape, resolution, working mode, illumination condition, etc.
The satellite mission planning relationship elements include: subtask relationships, parameter relationships, timing relationships. The subtask relationship means that there is a decomposable relationship between the requirement, the shooting task and the transmission task, i.e. one requirement can be decomposed into a plurality of subtasks; parameter relation refers to the relation among requirements, tasks and satellite resource parameters; the time sequence relation is a time sequence relation of execution among shooting tasks, transmission tasks and transmission tasks.
The design map structure is shown in figure 1, wherein the dots are solid, and the connection lines with arrows are in a relation from the host to the object. Entities contained in the map structure: requirements, shooting tasks, transmission tasks, scene types, business types, object types, regional object shapes, resolutions, sensor modes, working modes, and illumination conditions. Relationships contained in the atlas structure: subtask relationships, parameter relationships, timing relationships (e.g., before), and the like.
(2) Multi-star collaborative task decomposition
In order to describe the time sequence relation of the cooperative satellites in a complex scene, the invention constructs a time entity with the attributes of 'start time' and 'end time'. The time entity can realize the complete time description of a single shooting task or a single data transmission task of a single satellite. In a cooperative scenario, satellites performing different subtasks have certain time correlation constraints. In order to improve the accuracy of the description of the multi-star time sequence relationship, the description of the time period relationship such as 'Before', 'over lap' and the like is accurately and quantitatively described through the time difference value, so that the requirement of accurate description of the relative time relationship of different tasks is met. The relationship between the two is specifically described below.
A time entity t (st, et), where st is the start time and et is the end time. Task i, execution time is defined by time entity t i ＝(st i ，et i ) Description is made. Similarly, task j, execution time is determined by entity t j ＝(st j ，et j ) Description is made.
Before: task i executes task j for an interval period deltat after execution.
Wherein Δt is ij For time interpolation, if the value nan is null, t is represented i 、t j Only qualitative sequential timing relationships exist.
Overlay (Overlap): task j execution begins and task i execution ends at intervals of time delta t.
The demand from multi-star collaboration can be broken down into multiple tasks, each of which is either a shooting task or a transmission task. Thus complex satellite requirements may involve time constraints of multiple mission co-occurrence during mission planning. Requirements= { task 1 ，task 2 ，…，task i ，…，task n Where n is the number of tasks for which the demand breaks down. The time constraint set TR consists of the time relation function shown in fig. 2. The time constraint matrix between tasks is C, the dimension is n multiplied by n, and the element C of the matrix C i，j Is a task i And task j And related parameters.
The timing relationship for the time periods i, j in fig. 2 specifically includes:
forward Relation (Relation):
before, meaning i is Before j;
meets, which means that the i end time is equal to the j start time;
overlaps, meaning that i and j have time overlap;
starts, which indicates that the start times of i and j are the same;
during, meaning that i is within the time frame of j;
finish, i is the same at the end time of j;
equals, indicates that i is exactly identical at j.
Reverse relationship (Reverse relationship): after, metBy, overlappedBy, startedBy, contains, finishedBy is a relationship description after i, j interchange for the corresponding forward relationship.
Demand for example, networking satellites with satellites 1 Satellite s 2 Satellite s 3 Shooting is realized, and the shooting sequence is s 1 、s 2 、s 3 ，s 2 Sum s 3 Shooting starts 30 minutes after the completion of receiving the information.
This requirement requires inter-satellite coordination to be completed, with three shooting tasks, two transmission tasks. The shooting tasks are respectively as follows: from satellites s 1 Shooting task for executing shooting 1 From satellites s 2 Shooting task for executing shooting 2 From satellites s 3 Shooting task for executing shooting 3 . The transmission tasks are as follows: the sender is satellite s 1 The receiver is satellite s 2 Is to be used for transmitting task 4 The sender is satellite s 2 The receiver is satellite s 3 Is to be used for transmitting task 5 . The time sequence relationship is as follows: shooting task 1 Is a shooting time t of (2) 1 ＝(st 1 ，et 1 ) And a transmission time t of the transmission task 4 4 ＝(st 4 ，et 4 ) There is a timing relationship Before (t) 1 ，t 4 Nan), wherein nan represents onlyThe relation between the front and the back is determined, and the specific time is not limited; task transmission 4 Is a transmission time t of (2) 4 ＝(st 4 ，et 4 ) And a shooting task 2 Is a shooting time t of (2) 2 ＝(st 2 ，et 2 ) There is a timing relationship Before (t) 4 ，t 2 30 min); shooting task 2 Is a shooting time t of (2) 2 ＝(st 2 ，et 2 ) And transmit any task s Is a transmission time t of (2) 5 ＝(st 5 ，et 5 ) There is a timing relationship Before (t) 2 ，t 5 Nan); task transmission 5 Is a transmission time t of (2) 5 ＝(st 5 ，et 5 ) And a shooting task 3 Is a shooting time t of (2) 3 ＝(st 3 ，et 3 ) There is a timing relationship Before (t) 5 ，t 3 30 min). A layout of this requirement is shown in fig. 3.
(3) Multi-star task generation
The meta-task is a series of shooting tasks or transmission tasks of the determined satellite in a determined time period, which are obtained by calculation of an orbit calculation system for a single shooting task or a single transmission task according to targets in requirements and relevant parameters of satellite resources. The target is a target to be shot by the satellite; the satellite resource related parameters comprise parameters such as satellite identification, working mode, sensor mode and the like; an orbit calculation system is a system that obtains a photographable time of a satellite to a target by calculating a target position and a satellite orbit.
A task may be computed by an orbital computing system to obtain a plurality of meta-tasks that may implement the task. The meta-task m generated by the shooting task can be expressed as a triplet (task, s, t), wherein task and s respectively represent a task to which m belongs and a satellite, and t represents a time entity of execution time of m. The meta-task m generated by the transmission task may be represented as a quadruple (task, sub, obj, t), where sub represents the sender of the information and obj represents the recipient of the information.
For the requirements in the above example, three shooting task tasks 1 、task 2 、task 3 Respectively specify the satellite s specifically adopted 1 、s 2 、s 3 Task for transmitting tasks 4 、task 5 S respectively for the sender and the receiver of (a) 1 Sum s 2 、s 2 Sum s 3 . The meta-tasks obtained by calculation of the track computing system can be expressed as:
(4) Collaborative relationship query reasoning
And inquiring the meta-task combination meeting the conditions according to the cooperative constraint requirement, and taking the meta-task combination as a cooperative planning method.
Because of the timing relationship constraints between tasks:
Before(t 1 ，t 4 ，nan)：et 1 ＜st 4
Before(t 4 ，t 2 ，30min)：st 2 -et 4 ＝30min
Before(t 2 ，t 5 ，nan)：et 2 ＜st 5
Before(t 5 ，t 3 ，30min)：st 3 -et 5 ＝30min
the meta-task combination meeting the corresponding task time constraint relation is screened out from a series of meta-tasks, and the meta-task data is firstly stored into a tunnel RDF (Resource Description Framework resource description framework) format. Turtle is a document format that can store RDF graphs in a compact form, RDF being a triplet of subjects, objects, predicates. The triples involved in this scenario include:
(meta-tasks, belonging tasks, tasks),
(meta-task, start time,
(meta-task, end time, time),
(meta-mission, satellite used, satellite).
The triplet may be semantically expressed as a "task" of "meta-task" where the "task" belongs to is a "task". Wherein, the task refers to a specific task identifier, the time refers to a specific time representation, and the satellite refers to a specific satellite identifier.
And describing the collaborative rule as an SPARQL query statement, and searching RDF data formed by meta-tasks. The query language of the relationship rule Before is shown in fig. 4 and 5, and is? Is a variable operator? mi,? mj, etc., represents a variable, filter is a filtering operation on a target set based on a certain condition, and BIND is to assign a specified operation result to the variable. And reasoning from the meta-tasks to obtain a combination of executable time windows which strictly meet the requirements of the cooperative rules, namely, a meta-task combination which meets the requirements of the cooperative relationship among multiple stars, and performing time sequence to obtain the generated multi-star task planning scheme.
Another embodiment of the present invention provides a multi-star collaborative semantic modeling and mission planning system, comprising:
the knowledge graph construction module is used for formally modeling satellite tasks and cooperative relations by adopting the knowledge graph;
the task decomposition module is used for decomposing the multi-star cooperation requirement into a plurality of tasks by utilizing a knowledge graph, wherein each task is a shooting task or a transmission task, and determining a time sequence relation among the tasks;
and the query reasoning module is used for querying the knowledge graph to acquire a task combination result meeting the task cooperative constraint requirement as a multi-star cooperative task planning scheme.
Wherein the specific implementation of each module is referred to the previous description of the method of the present invention.
Another embodiment of the invention provides a computer device (computer, server, smart phone, etc.) comprising a memory storing a computer program configured to be executed by the processor and a processor, the computer program comprising instructions for performing the steps of the method of the invention.
Another embodiment of the present invention provides a computer readable storage medium (e.g., ROM/RAM, magnetic disk, optical disk, etc.) storing a computer program which, when executed by a computer, performs the steps of the method of the present invention.
The above-disclosed embodiments of the present invention are intended to aid in understanding the contents of the present invention and to enable the same to be carried into practice, and it will be understood by those of ordinary skill in the art that various alternatives, variations and modifications are possible without departing from the spirit and scope of the invention. The invention should not be limited to what has been disclosed in the examples of the specification, but rather by the scope of the invention as defined in the claims.
Claims (10)
1. A multi-star collaborative semantic modeling and task planning method is characterized by comprising the following steps:
carrying out formal modeling on satellite tasks and cooperative relations by adopting a knowledge graph;
decomposing the multi-star cooperation requirement into a plurality of tasks by using a knowledge graph, wherein each task is a shooting task or a transmission task, and the time sequence relationship among the tasks is represented;
and inquiring the knowledge graph to obtain a meta-task combination result meeting the task cooperative constraint requirement, and taking the meta-task combination result as a multi-star cooperative task planning scheme.
2. The method of claim 1, wherein the knowledge-graph is used to formally model satellite tasks and collaborative relationships, wherein:
the entity elements of the knowledge graph include: requirements, shooting tasks, transmission tasks, scene types, service types, target types, sensor modes, regional target shapes, resolution, working modes and illumination conditions;
the relationship elements of the knowledge graph include: subtask relationships, parameter relationships, timing relationships.
3. The method of claim 2, wherein the timing relationship comprises:
forward relationship: before, meaning i is Before j; meets, which means that the i end time is equal to the j start time; overlaps, meaning that i and j have time overlap; starts, which indicates that the start times of i and j are the same; during, meaning that i is within the time frame of j; finish, i is the same at the end time of j; equals, which means that i is exactly identical in time at j;
inverse relationship: after, metBy, overlappedBy, startedBy, contains, finishedBy is included, which is a relationship description after i, j interchange for the corresponding forward relationship.
4. A method according to claim 3, wherein the description of the time period relationship is accurately and quantitatively described by a time difference value to improve the accuracy of the description of the multi-star time sequence relationship, and specifically comprising:
let the time entity be t (st, et), where st is the start time and et is the end time; the execution time of task i is defined by time entity t i ＝(st i ,et i ) Describing, the execution time of the same task j is determined by the entity t j ＝(st j ,et j ) Description is made;
before: executing task j at interval time deltat after executing task i;
wherein Δt is ij For time interpolation, if the value nan is null, t is represented i 、t j Only qualitative sequential time sequence relation exists;
overlay: the execution of task j is finished at interval time delta t after the execution of task i is started;
5. the method according to claim 1, wherein the timing relationship between the tasks is represented by a time constraint matrix C between the tasks, the dimension n x n, n being the number of tasks, element C of the matrix C i,j For task i and task jAnd related parameters.
6. The method of claim 1, wherein decomposing the multi-star collaboration requirement into a plurality of tasks comprises:
according to targets in the multi-satellite cooperative demand and related parameters of satellite resources, calculating a single shooting task or a single transmission task through an orbit calculation system to obtain a series of meta tasks for determining satellites in a determined time period;
the meta-task m generated by the shooting task is expressed as a triplet (task, s, t), wherein the task and the task represent the task to which the m belongs and the satellite respectively, and t represents a time entity of the execution time of the m, and the time entity comprises a start time st and an end time et; the meta-task m generated by the transmission task is expressed as a quadruple (task, sub, obj, t), wherein sub represents the sender of the information and obj represents the receiver of the information.
7. The method of claim 1, wherein the querying the knowledge graph to obtain the meta-task combination result meeting the task collaboration constraint requirement comprises:
storing the metadata as a vortex RDF format;
according to the task cooperative constraint requirement, searching RDF data formed by the meta-tasks by utilizing SPARQL query sentences to obtain meta-task combinations meeting the conditions;
the RDF is a triplet composed of a subject, an object, and a predicate, the triplet including: (meta-task, belonging task, task), (meta-task, start time, time), (meta-task, end time, time), (meta-task, satellite used, satellite).
8. A multi-star collaborative semantic modeling and mission planning system, comprising:
the knowledge graph construction module is used for formally modeling satellite tasks and cooperative relations by adopting the knowledge graph;
the task decomposition module is used for decomposing the multi-star cooperation requirement into a plurality of tasks by utilizing a knowledge graph, wherein each task is a shooting task or a transmission task and represents a time sequence relationship among the tasks;
and the query reasoning module is used for querying the knowledge graph to acquire a task combination result meeting the task cooperative constraint requirement as a multi-star cooperative task planning scheme.
9. A computer device comprising a memory and a processor, the memory storing a computer program configured to be executed by the processor, the computer program comprising instructions for performing the method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a computer, implements the method of any one of claims 1-7.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679352A (en) * | 2013-11-29 | 2014-03-26 | 中国资源卫星应用中心 | Satellite demand processing system based on negotiation countermeasure conflict resolution |
| CN103957268A (en) * | 2014-05-08 | 2014-07-30 | 中国人民解放军总参谋部气象水文空间天气总站 | Rule-driven data transmission method |
| CN108416500A (en) * | 2018-01-31 | 2018-08-17 | 中国地质大学（武汉） | One kind is based on the more stellar associations of PDDL with mission planning method, equipment and storage device |
| CN109933423A (en) * | 2018-12-31 | 2019-06-25 | 中国科学院软件研究所 | A kind of more satellite resource planing methods being performed in unison with complex task |
| CN112529437A (en) * | 2020-12-17 | 2021-03-19 | 中国科学院空天信息创新研究院 | Multi-target satellite imaging planning method |
| CN114139776A (en) * | 2021-11-15 | 2022-03-04 | 昆明理工大学 | Deep space exploration task planning method and system based on cognitive map |
| CN114169066A (en) * | 2021-09-18 | 2022-03-11 | 中国人民解放军63921部队 | Space target characteristic measuring and reconnaissance method based on micro-nano constellation approaching reconnaissance |
| CN114238652A (en) * | 2021-12-03 | 2022-03-25 | 南京航空航天大学 | Industrial fault knowledge map establishing method for end-to-end scene |
| CN114706672A (en) * | 2022-06-06 | 2022-07-05 | 安徽三禾一信息科技有限公司 | Satellite autonomous mission planning system and method based on event-driven dynamic assembly |
| CN114860884A (en) * | 2022-05-23 | 2022-08-05 | 中国科学院空天信息创新研究院 | Dynamic analysis-oriented spatio-temporal knowledge graph construction system and method |
| CN115422373A (en) * | 2022-09-05 | 2022-12-02 | 中国人民解放军国防科技大学 | Knowledge graph and user intention task decomposition method for satellite task planning |
| CN115510250A (en) * | 2022-11-10 | 2022-12-23 | 成都国恒空间技术工程股份有限公司 | Knowledge block chain-based aerospace information collaborative acquisition method and system |
-
2023
- 2023-04-12 CN CN202310387233.0A patent/CN116523212A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679352A (en) * | 2013-11-29 | 2014-03-26 | 中国资源卫星应用中心 | Satellite demand processing system based on negotiation countermeasure conflict resolution |
| CN103957268A (en) * | 2014-05-08 | 2014-07-30 | 中国人民解放军总参谋部气象水文空间天气总站 | Rule-driven data transmission method |
| CN108416500A (en) * | 2018-01-31 | 2018-08-17 | 中国地质大学（武汉） | One kind is based on the more stellar associations of PDDL with mission planning method, equipment and storage device |
| CN109933423A (en) * | 2018-12-31 | 2019-06-25 | 中国科学院软件研究所 | A kind of more satellite resource planing methods being performed in unison with complex task |
| CN112529437A (en) * | 2020-12-17 | 2021-03-19 | 中国科学院空天信息创新研究院 | Multi-target satellite imaging planning method |
| CN114169066A (en) * | 2021-09-18 | 2022-03-11 | 中国人民解放军63921部队 | Space target characteristic measuring and reconnaissance method based on micro-nano constellation approaching reconnaissance |
| CN114139776A (en) * | 2021-11-15 | 2022-03-04 | 昆明理工大学 | Deep space exploration task planning method and system based on cognitive map |
| CN114238652A (en) * | 2021-12-03 | 2022-03-25 | 南京航空航天大学 | Industrial fault knowledge map establishing method for end-to-end scene |
| CN114860884A (en) * | 2022-05-23 | 2022-08-05 | 中国科学院空天信息创新研究院 | Dynamic analysis-oriented spatio-temporal knowledge graph construction system and method |
| CN114706672A (en) * | 2022-06-06 | 2022-07-05 | 安徽三禾一信息科技有限公司 | Satellite autonomous mission planning system and method based on event-driven dynamic assembly |
| CN115422373A (en) * | 2022-09-05 | 2022-12-02 | 中国人民解放军国防科技大学 | Knowledge graph and user intention task decomposition method for satellite task planning |
| CN115510250A (en) * | 2022-11-10 | 2022-12-23 | 成都国恒空间技术工程股份有限公司 | Knowledge block chain-based aerospace information collaborative acquisition method and system |
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