http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108108836-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04 |
| filingDate | 2017-12-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2019-02-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2019-02-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-108108836-B |
| titleOfInvention | A kind of ozone concentration distribution forecasting method and system based on space-time deep learning |
| abstract | The present invention provides a kind of ozone concentration distribution forecasting method and system based on space-time deep learning, which comprises obtains current time ozone concentration distribution map, and obtains the meteorological data at moment to be predicted；By the ozone concentration prediction model based on meteorological data trained, current time ozone concentration distribution map and the meteorological data at moment to be predicted are handled, obtain the ozone concentration distribution map at moment to be predicted.Ozone concentration profile sequence and meteorology-time series are treated as by the methods of interpolation.Using the historical data of recurrent neural network processing a period of time, the trend feature of ozone concentration variation is extracted.Historical data before being handled one day and one week using convolutional neural networks utilizes the periodic feature of ozone to the greatest extent.Meanwhile the meteorological data and time data that prediction time is added further increase forecasting accuracy using meteorological and influence of the time for ozone as additional input. |
| priorityDate | 2017-12-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 18.