| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_3f1130e23cff1328c0ae14101d12103f |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y02A20-152 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-26
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-067
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-06395
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-26 |
| filingDate |
2022-01-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_28b5bdff86cd0a0ef63eb49705088ea0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bc793ab74561b4d2787650510abe1f3c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fa34e3da0928fb46ececad7394bebfc0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9bb2ba3c9b56f7383a0400d63c713dce |
| publicationDate |
2022-04-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-114399103-A |
| titleOfInvention |
A spatiotemporal continuous prediction method of river water quality based on CNN |
| abstract |
The invention discloses a CNN-based land-water integrated river water quality spatiotemporal continuous prediction method. The runoff results of each sub-basin are input as the flow boundary of the EFDC model, and the pollutant flux is input as the water quality boundary of the EFDC model to simulate the continuous distribution image of water quality in time and space. The images are input as training samples to train the CNN model; the trained CNN model is used to predict the water quality of the river continuously in time and space. The invention realizes the all-round spatiotemporal continuous prediction of the water quality of the watershed by artificial intelligence technology, and can provide strong technical support for water environment supervision. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115688491-A |
| priorityDate |
2022-01-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |