http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114048689-B
Outgoing Links
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|---|---|
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| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F9-44594 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F9-4881 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F9-5027 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-126 |
| classificationIPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F119-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F119-06 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F111-04 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F9-50 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F9-48 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F9-445 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-27 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate | 2022-01-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-04-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-04-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-114048689-B |
| titleOfInvention | Multi-unmanned aerial vehicle aerial charging and task scheduling method based on deep reinforcement learning |
| abstract | The invention discloses a multi-unmanned aerial vehicle aerial charging and task scheduling method based on deep reinforcement learning, which comprises the following steps: constructing a multi-unmanned aerial vehicle group auxiliary edge computing model; presetting computing resources of each unmanned aerial vehicle; constructing an optimization model of multi-unmanned aerial vehicle position deployment, user equipment unloading decision and computing resource allocation; the method comprises the steps that with the minimum energy consumption of the unmanned aerial vehicle group as an optimization target, a DDQN algorithm is adopted to solve unloading decisions of user equipment; solving a calculation resource allocation strategy of the unmanned aerial vehicle by adopting a differential evolution algorithm; optimizing the deployment strategy of the unmanned aerial vehicle by using a differential evolution algorithm; and iterating until a deployment strategy of the unmanned aerial vehicle, an optimal allocation strategy of the computing resources of the unmanned aerial vehicle and an optimal unloading decision of the user equipment are obtained. The invention considers the cooperation among multiple unmanned aerial vehicles and the balance of computing resources among the unmanned aerial vehicles, so that part of the unmanned aerial vehicles serve as relay stations and transmit tasks to other unmanned aerial vehicles for computation, thereby obtaining the optimal unloading decision with the minimum energy consumption of the unmanned aerial vehicle system. |
| priorityDate | 2022-01-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 33.