CN116579582A - Regional target satellite task planning method for user diversified demands - Google Patents
Regional target satellite task planning method for user diversified demands Download PDFInfo
- Publication number
- CN116579582A CN116579582A CN202310798149.8A CN202310798149A CN116579582A CN 116579582 A CN116579582 A CN 116579582A CN 202310798149 A CN202310798149 A CN 202310798149A CN 116579582 A CN116579582 A CN 116579582A
- Authority
- CN
- China
- Prior art keywords
- observation
- scheme
- planning
- solution
- grid
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000002922 simulated annealing Methods 0.000 claims abstract description 9
- 238000003384 imaging method Methods 0.000 claims description 23
- 230000008901 benefit Effects 0.000 claims description 20
- 230000005611 electricity Effects 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000001816 cooling Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000011160 research Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000002068 genetic effect Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000000052 comparative effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000013138 pruning Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Software Systems (AREA)
- Primary Health Care (AREA)
- Image Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a regional target satellite task planning method for user diversity demands, and relates to applications such as satellite observation task demand planning, satellite observation task overall planning and the like in the field of space control. Firstly, dividing the earth into grids according to the longitude and latitude of the space, and calculating the visibility information of the grids in advance; then, the visibility information of the regional target coverage grid is taken out and restrained to be checked to form an observation scheme set; and finally, binary coding is carried out on the observation scheme set based on the global grid, and the optimal observation scheme is searched and optimized based on the weighted energy function of the diversified demands of the user by adopting a simulated annealing algorithm, so that an optimal regional target planning scheme is formed. The method has the characteristics of high time efficiency, flexible combination, accurate planning and the like, and is suitable for the fields of space observation task planning and the like.
Description
Technical Field
The invention relates to the field of space control, in particular to a regional target satellite task planning method for user diversified demands, which can be used for application scenes such as space observation task planning, regional target task planning, space observation task quick response and the like.
Background
With the increasing use of satellites such as high-score series and resource series, the earth observation satellite plays an important role in the fields of environment, weather, disaster monitoring and the like for a long time. To accomplish space tasks, a space system, i.e., a satellite constellation, is often composed of a plurality of satellites. In the earth observation task of the coverage area target, the coverage observation of the area target can be rapidly completed in a cooperative mode of a plurality of earth observation satellites.
At present, many researches on the task planning problem of the observation target satellite are carried out at home and abroad. The Jian et al study has the regional multi-star task scheduling model under the cloud meteorological constraint, converts the regional multi-star task scheduling model into an integer planning model, adopts a branch pruning algorithm to solve the regional multi-star task scheduling model, but the study does not involve comprehensively considering the effective coverage area, imaging timeliness and other actual complex constraints. In order to improve the observation benefits of regional targets, saeed and the like put forward 4 kinds of preemptive strategies, so that the observed part in the region is not observed repeatedly, and the genetic algorithm based on heuristic rules is utilized for optimization, so that the observation benefits and the optimization efficiency are effectively improved, but the comprehensive consideration of coverage effective area, observation resource utilization efficiency and the like is not carried out, and the algorithm calculation time is too long due to the mode of immediate calculation in the task planning stage. The research in the field of regional target satellite scheduling in China starts late, but the research is rapid, for example: related researchers of China electronic technology group company develop researches on a multi-star collaborative mission planning algorithm. The satellite mission planning problems are also researched by the university of Harbin industry, the university of Chinese academy of sciences and other institutions in related theoretical methods and key technologies. Yang Wenchen et al propose a method of non-tracking of regional targets for satellites that is difficult to accommodate in slightly larger or complex-shaped areas. The problem of target task planning of HXMT satellite area of Huang et al is solved by adopting a greedy algorithm and a genetic algorithm, and the target function designed by the method does not consider comprehensive observation benefits and cannot obtain an optimal task planning solution.
Disclosure of Invention
The invention aims to avoid the defects in the background art and provide the regional target satellite task planning method facing the diversified demands of users, which has the characteristics of high time efficiency, flexible combination, accurate planning and the like and is suitable for the fields of regional target task planning and the like.
The invention adopts the technical scheme that:
the regional target satellite task planning method for the user diversified demands comprises the following steps:
(1) Dividing the earth into grids according to the longitude and latitude of the space, and calculating the visibility of the satellite and each grid to obtain a global visibility grid; calculating a grid set covered by the area target, and obtaining visibility information of the covered grid to obtain an observation planning scheme set of the covered area target;
(2) Performing constraint inspection on the observation planning scheme, removing observation schemes which do not accord with the constraint, and obtaining an observation scheme set which accords with the observation constraint;
(3) Setting the initial temperature of simulated annealingCooling coefficient->Minimum temperature->Based on the observation scheme set conforming to the observation constraint, randomly generating an observation scheme solution set by adopting a binary coding mode，/>Representing the solution set of observation schemes->The%>Individual observation protocol->For the number of observation schemes meeting the observation constraint, +.>As an initial state of simulated annealing;
(4) Setting the duty ratio of user demand preference of three aspects of task consumption electricity quantity, task consumption storage and imaging timelinessWherein->The method comprises the steps of carrying out a first treatment on the surface of the Setting the ratio of user's demand preference in the two aspects of covering effective area and observing resource utilization benefit>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the Set at the solution set of the current observation scheme +.>Cover the effective area under the stateEnergy function with optimal weighting of observed resource usage benefit, task power consumption storage and imaging timelinessTo->As->Energy in state;
(5) According to the energy function of the step (4), calculatingEnergy in state->；
(6) At the position ofIs selected at random->A binary coding position, wherein->For each position, if it is 0, then 1 is set, if it is 1, then 0 is set, a new state is generated +.>And calculating +.>Energy in state->；
(7) Comparison ofAnd->The size of (1)>And->Let->，Returning to the step (5); if->And->And (2) andlet->，/>Returning to step (5), wherein ∈>Is Boltzmann constant 1.380649 ×10 -23 ，/>Representing taking a random number between 0 and 1; if it isAnd->And->Let->Returning to the step (5); if none of the above conditions is met, the iteration is ended in the state +.>As a final observation scheme solution set.
Wherein, the step (1) comprises the following steps:
(101) According toThe specification of (2) divides the world into grid sets +.>Wherein->；
(102) Introduction of available satellitesThe method comprises the steps of carrying out a first treatment on the surface of the Calculating each satellite by using a satellite and target visibility analysis algorithm>And->Is a visual information of the image;
(103) Importing area targets, computing grid sets covered by area targetsWherein->And obtaining the visibility information of the coverage grid, and obtaining an observation planning scheme set of the coverage area target.
Wherein, in the step (4), the solution set of the current observation scheme is setCovering effective area under state, observing resource usage benefit and task consumptionEnergy function with optimal weighting of charge, task consumption storage and imaging timeliness>To->As->The energy under the state specifically comprises the following steps:
(401) Solution of observation schemeThe electricity consumption is->Wherein->Representing the solution set of observation schemes->Medium observation protocol->The electricity consumption score of the observation scheme solution is:；
(402) Solution of observation schemeMemory consumption is +.>Wherein->Representing the solution set of observation schemes->Medium observation protocol->The storage consumption of the observation solution is scored as:；
(403) Let the observation start time required by the user beThe user-requested observation end time isObservation scheme solution->Medium observation protocol->Imaging start time +.>Observation scheme->The imaging timeliness of (2) is:
then the observation scheme solution setIs +.>Then observe the solution set +.>The imaging timeliness score of (2) is: />If->ThenOtherwise->；
(404) Calculating a score of the observed resource usage benefit:
(405) Let the area of the regional target beThe calculation formula of the ratio of the coverage effective area to the area target area is:
the score calculation formula for the coverage effective area is:
(406) Task electricity consumption, task electricity consumption storage, imaging timeliness, observation resource use benefit and weighted optimal energy covering effective area are as follows:
。
compared with the background technology, the invention has the following advantages:
1. the regional target satellite task planning method for the user diversity needs provided by the invention divides the earth based on the grids, calculates the visibility results of the satellites and the grids in advance and stores the visibility results, and can directly take out the visibility results when the regional planning is carried out, thereby overcoming the defect that the visibility of the satellites and the regional targets is required to be calculated on line in the existing method, greatly saving the time of regional target planning and greatly improving the timeliness of regional target task planning.
2. When the regional target is planned, the simulated annealing algorithm is improved, the factors of covering the effective area, observing the resource using benefit, the task electricity consumption storage, the imaging timeliness and the like are comprehensively considered, and the user can set the weight based on the own demand preference, so that the planning for the diversified demands of the user is realized, and the method has the characteristics of flexible combination and accurate planning.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of binary encoding of the present invention.
FIG. 3 is a schematic diagram of the random generation of new states according to the present invention.
Detailed Description
A regional target satellite mission planning method facing to user diversified demands, as shown in fig. 1, comprises the following steps:
(1) Dividing the earth into grids according to the longitude and latitude of the space, and calculating the visibility of the satellite and each grid to obtain a global visibility grid; calculating a grid set covered by the area target, and obtaining visibility information of the covered grid to obtain an observation planning scheme set of the covered area target;
(2) Performing constraint inspection on the observation planning scheme, removing observation schemes which do not accord with the constraint, and obtaining an observation scheme set which accords with the observation constraint; the method comprises the following steps of performing constraint checking on the switching-on time length constraint, the energy consumption constraint and the data storage constraint on an observation planning scheme, checking whether each observation scheme exceeds the maximum switching-on time, checking whether an executed satellite of each observation scheme has enough energy to execute the scheme, checking whether the executed satellite of each observation scheme has enough data storage capacity, removing the observation scheme which does not meet the constraint, and obtaining an observation scheme set which meets the observation constraint;
(3) Setting the initial temperature of simulated annealingCooling coefficient->Minimum temperatureAs shown in FIG. 2, based on the observation scheme set conforming to the observation constraint, the solution set of the observation scheme is randomly generated by adopting a binary coding mode>，/>Representing the solution set of observation schemes->The%>Individual observation protocol->For the number of observation schemes meeting the observation constraint, +.>As an initial state of simulated annealing;
(4) Setting the duty ratio of user demand preference of three aspects of task consumption electricity quantity, task consumption storage and imaging timelinessWherein->The method comprises the steps of carrying out a first treatment on the surface of the Setting the ratio of user's demand preference in the two aspects of covering effective area and observing resource utilization benefit>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the Set at the solution set of the current observation scheme +.>Energy function with optimal coverage effective area, observation resource use benefit, task electricity consumption, task consumption storage and imaging timeliness weighting under stateTo->As->Energy in state;
(5) According to the energy function of the step (4), calculatingEnergy in state->；
(6) As shown in FIG. 3, inIs selected at random->A binary coding position, wherein->For each position, if it is 0, then 1 is set, if it is 1, then 0 is set, a new state is generated +.>And calculating +.>Energy in state->；
(7) Comparison ofAnd->The size of (1)>And->Let->，Returning to the step (5); if->And->And (2) andlet->，/>Returning to step (5), wherein ∈>Is Boltzmann constant 1.380649 ×10 -23 ，/>Representing taking a random number between 0 and 1; if it isAnd->And->Let->Returning to the step (5); if none of the above conditions is met, the iteration is ended in the state +.>As a final observation scheme solution set.
Wherein, the step (1) comprises the following steps:
(101) According toThe specification of (2) divides the world into grid sets +.>Wherein->；
(102) Introduction of available satellitesThe method comprises the steps of carrying out a first treatment on the surface of the Calculating each satellite by using a satellite and target visibility analysis algorithm>And->Is a visual information of the image;
(103) Importing area targets, computing grid sets covered by area targetsWherein->And obtaining the visibility information of the coverage grid, and obtaining an observation planning scheme set of the coverage area target.
Wherein, in the step (4), the solution set of the current observation scheme is setEnergy function with optimal coverage effective area, observation resource use benefit, task power consumption storage and imaging timeliness weighting under state +.>To->As->The energy under the state specifically comprises the following steps:
(401) Solution of observation schemeThe electricity consumption is->Wherein->Representing the solution set of observation schemes->Medium observation protocol->The electricity consumption score of the observation scheme solution is:；
(402) Solution of observation schemeMemory consumption is +.>Wherein->Representing the solution set of observation schemes->Medium observation protocol->The storage consumption of the observation solution is scored as:；
(403) Let the observation start time required by the user beThe user-requested observation end time isObservation scheme solution->Medium observation protocol->Imaging start time +.>Observation scheme->The imaging timeliness of (2) is:
then the observation scheme solution setIs +.>Then observe the solution set +.>The imaging timeliness score of (2) is: />If->ThenOtherwise->；
(404) Calculating a score of the observed resource usage benefit:
(405) Let the area of the regional target beThe calculation formula of the ratio of the coverage effective area to the area target area is:
the score calculation formula for the coverage effective area is:
(406) Task electricity consumption, task electricity consumption storage, imaging timeliness, observation resource use benefit and weighted optimal energy covering effective area are as follows:
。
the advantages of the invention are verified from the two aspects of planning timeliness and planning effect respectively:
in the aspect of planning timeliness, compared with a traditional method based on online calculation of satellite and target visibility, the method provided by the invention has the following table of comparison results of visibility analysis of the area A and the area B under the condition of 1000 satellites:
it can be seen that the present invention is nearly 20 times faster in calculating visibility than conventional methods.
In the aspect of planning effectAnd->Comparative experiments were performed with different values and the results are shown in the following table:
from the points of task electricity consumption, task electricity consumption storage, imaging timeliness, coverage effective area and observation resource use efficiency, the planning result of the invention can effectively respond to diversified demands of users on planning strategies.
In a word, firstly, the earth is subjected to grid division according to the longitude and latitude of the space, and the grid visibility information is calculated in advance; then, the visibility information of the regional target coverage grid is taken out and restrained to be checked to form an observation scheme set; and finally, binary coding is carried out on the observation scheme set based on the global grid, and the optimal observation scheme is searched and optimized based on the weighted energy function of the diversified demands of the user by adopting a simulated annealing algorithm, so that an optimal regional target planning scheme is formed. The method has the characteristics of high time efficiency, flexible combination, accurate planning and the like, and is suitable for the fields of space observation task planning and the like.
Claims (3)
1. The regional target satellite mission planning method for the user diversified demands is characterized by comprising the following steps of:
(1) Dividing the earth into grids according to the longitude and latitude of the space, and calculating the visibility of the satellite and each grid to obtain a global visibility grid; calculating a grid set covered by the area target, and obtaining visibility information of the covered grid to obtain an observation planning scheme set of the covered area target;
(2) Performing constraint inspection on the observation planning scheme, removing observation schemes which do not accord with the constraint, and obtaining an observation scheme set which accords with the observation constraint;
(3) Setting the initial temperature of simulated annealingCooling coefficient->Minimum temperature->Based on the observation scheme set conforming to the observation constraint, randomly generating an observation scheme solution set by adopting a binary coding mode>，Representing the solution set of observation schemes->The%>Individual observation protocol->For the number of observation schemes meeting the observation constraint, +.>As an initial state of simulated annealing;
(4) Setting the duty ratio of user demand preference of three aspects of task consumption electricity quantity, task consumption storage and imaging timelinessWherein->The method comprises the steps of carrying out a first treatment on the surface of the Setting the ratio of user's demand preference in the two aspects of covering effective area and observing resource utilization benefit>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the Set at the solution set of the current observation scheme +.>Energy function with optimal coverage effective area, observation resource use benefit, task power consumption storage and imaging timeliness weighting under state +.>To->As->Energy in state;
(5) According to the energy function of the step (4), calculatingEnergy in state->；
(6) At the position ofIs selected at random->A binary coding position, wherein->For each position, if it is 0, then 1 is set, if it is 1, then 0 is set, a new state is generated +.>And calculating +.>Energy in state->；
(7) Comparison ofAnd->The size of (1)>And->Let->，Returning to the step (5); if->And->And (2) andlet->，/>Returning to step (5), wherein ∈>Is Boltzmann constant 1.380649 ×10 -23 ，/>Representing taking a random number between 0 and 1; if->And->And->Let->Returning to the step (5); if none of the above conditions is satisfied, the iteration is ended to a stateAs a final observation scheme solution set.
2. The regional target satellite mission planning method for user diversity requirements of claim 1, wherein the step (1) comprises:
(101) According toThe specification of (2) divides the world into grid sets +.>Wherein；
(102) Introduction of available satellitesWherein->The method comprises the steps of carrying out a first treatment on the surface of the Calculating each satellite by using a satellite and target visibility analysis algorithm>And->Is a visual information of the image;
(103) Importing area targets, computing grid sets covered by area targetsWherein->And obtaining the visibility information of the coverage grid, and obtaining an observation planning scheme set of the coverage area target.
3. The regional target satellite mission planning method for user diversity requirements of claim 2, wherein the step (4) comprises:
(401) Solution of observation schemeThe electricity consumption is->Wherein->Representing the solution set of observation schemes->Medium observation protocol->The electricity consumption score of the observation scheme solution is:；
(402) Solution of observation schemeMemory consumption is +.>Wherein->Representing the solution set of observation schemes->Medium observation protocol->The storage consumption of the observation solution is scored as:；
(403) Let the observation start time required by the user beThe user-requested observation end time isObservation scheme solution->Medium observation protocol->Imaging start time +.>Observation scheme->The imaging timeliness of (2) is:
solution set of observation schemesIs +.>Observation scheme solution->The imaging timeliness score of (2) is: />H is the number of elements of the grid set covered by the region object if +.>Then->Otherwise->；
(404) Calculating a score of the observed resource usage benefit:
(405) Let the area of the regional target beThe calculation formula of the ratio of the coverage effective area to the area target area is:
wherein SG (SG) j A j-th element in the grid set covered by the area target; the score calculation formula for the coverage effective area is:
(406) Task electricity consumption, task electricity consumption storage, imaging timeliness, observation resource use benefit and weighted optimal energy covering effective area are as follows:
。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310798149.8A CN116579582B (en) | 2023-07-03 | 2023-07-03 | Regional target satellite task planning method for user diversified demands |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310798149.8A CN116579582B (en) | 2023-07-03 | 2023-07-03 | Regional target satellite task planning method for user diversified demands |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116579582A true CN116579582A (en) | 2023-08-11 |
| CN116579582B CN116579582B (en) | 2023-09-26 |
Family
ID=87538068
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310798149.8A Active CN116579582B (en) | 2023-07-03 | 2023-07-03 | Regional target satellite task planning method for user diversified demands |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579582B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109636214A (en) * | 2018-12-19 | 2019-04-16 | 航天恒星科技有限公司 | A kind of fast worktodo planing method towards multi-source earth observation satellite |
| AU2020100051A4 (en) * | 2020-01-10 | 2020-02-13 | Jin, Yawen Miss | Method of the mission planning for the communication between the small satellite clusters |
| CN111832934A (en) * | 2020-07-10 | 2020-10-27 | 西安电子科技大学 | Simulated annealing multi-satellite emergency task planning method facing sudden demand |
| CN115659556A (en) * | 2022-09-20 | 2023-01-31 | 中国人民解放军国防科技大学 | Multi-satellite task planning method and system for large-area target |
| CN115795775A (en) * | 2022-06-16 | 2023-03-14 | 中国人民解放军国防科技大学 | Point group target-oriented satellite task planning method, system and device |
| CN116126493A (en) * | 2022-12-26 | 2023-05-16 | 北京航空航天大学 | SA-GA-based heterogeneous multi-star collaborative task planning method |
-
2023
- 2023-07-03 CN CN202310798149.8A patent/CN116579582B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109636214A (en) * | 2018-12-19 | 2019-04-16 | 航天恒星科技有限公司 | A kind of fast worktodo planing method towards multi-source earth observation satellite |
| AU2020100051A4 (en) * | 2020-01-10 | 2020-02-13 | Jin, Yawen Miss | Method of the mission planning for the communication between the small satellite clusters |
| CN111832934A (en) * | 2020-07-10 | 2020-10-27 | 西安电子科技大学 | Simulated annealing multi-satellite emergency task planning method facing sudden demand |
| CN115795775A (en) * | 2022-06-16 | 2023-03-14 | 中国人民解放军国防科技大学 | Point group target-oriented satellite task planning method, system and device |
| CN115659556A (en) * | 2022-09-20 | 2023-01-31 | 中国人民解放军国防科技大学 | Multi-satellite task planning method and system for large-area target |
| CN116126493A (en) * | 2022-12-26 | 2023-05-16 | 北京航空航天大学 | SA-GA-based heterogeneous multi-star collaborative task planning method |
Non-Patent Citations (2)
| Title |
|---|
| 宋宁博: "卫星网络突发任务规划方法研究", 《中国优秀硕士学位论文全文数据库》 * |
| 韩丽 等: "基于模拟退火的卫星地面站任务规划方法", 《专题技术与工程应用》, vol. 40, no. 9, pages 42 - 47 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116579582B (en) | 2023-09-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ahmadi et al. | Application of the Hybrid Big Bang–Big Crunch algorithm for optimal sizing of a stand-alone hybrid PV/wind/battery system | |
| Maleki et al. | Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms | |
| Erdinc et al. | Optimum design of hybrid renewable energy systems: Overview of different approaches | |
| Jahannoush et al. | Optimal designing and management of a stand-alone hybrid energy system using meta-heuristic improved sine–cosine algorithm for Recreational Center, case study for Iran country | |
| CN111144641B (en) | Improved particle swarm algorithm-based microgrid optimization scheduling method | |
| CN106849190A (en) | A kind of microgrid real-time scheduling method of providing multiple forms of energy to complement each other based on Rollout algorithms | |
| CN111445107A (en) | Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid | |
| CN113988714B (en) | Multi-uncertainty-based dynamic planning method, equipment and medium for park comprehensive energy system | |
| CN112257294A (en) | Energy hub subsection modeling method and system of comprehensive energy system | |
| CN110045440B (en) | Integrated prediction method and system for atmospheric haze numerical prediction | |
| CN113139675B (en) | Comprehensive electrical load interval prediction method for park comprehensive energy system | |
| CN116579582B (en) | Regional target satellite task planning method for user diversified demands | |
| CN110097235B (en) | Scheduling optimization method, device and medium for combined power generation | |
| Alfulayyih et al. | A Generic Algorithm for Planning the Year-Round Solar Energy Harvest/Storage to Supply Solar-Based Stable Power | |
| CN109449967B (en) | Wind power plant group delivery site selection and volume fixing combined optimization method considering load randomness | |
| CN109146297B (en) | Optimized scheduling method and system for haze reduction of power grid | |
| CN116415793A (en) | Comprehensive energy system for thermoelectric energy storage park and low-carbon optimal scheduling method thereof | |
| CN113285449B (en) | Intelligent economic dispatching method considering multi-microgrid joint dispatching optimization | |
| CN113054685B (en) | Solar micro-grid scheduling method based on crow algorithm and pattern search algorithm | |
| CN114204553A (en) | Power generation proportioning method, device and equipment | |
| CN114204551A (en) | Diversified optimization operation method and device for multi-energy complementary micro-grid comprising photovoltaic heat pump | |
| CN115222227A (en) | Database-based carbon emission determination method, system, medium and device | |
| Han et al. | Grid integration of solar power in northwest China | |
| CN111476424A (en) | Combined heat and power type multi-microgrid energy scheduling method comprising electric automobile | |
| CN112734451A (en) | Agricultural greenhouse multi-energy system based on non-cooperative game and optimization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |