http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112906936-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_29d92b70a850f7e23cbcf60d981ac8dd http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_47b17afb14f3a5c9ccc284373e64f1a4 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-06393 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-06 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04 |
| filingDate | 2021-01-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e0b771df9de1296ecfa5bb2528279f7c http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1b9ffa01c1b2a155f2973da760fb25e1 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4de62e839c5fc7a0189f00608a9d127a |
| publicationDate | 2021-06-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-112906936-A |
| titleOfInvention | Intelligent calculation and prediction method of river pollutant flux based on integrated neural network |
| abstract | The invention discloses an intelligent calculation and prediction method for river pollutant flux based on an integrated neural network, comprising the following steps: S1, inputting historical data of pollutant concentration and river flow, and judging the type of main pollutants in the river and the time-average change type of flow , using the support vector machine method of machine learning to realize the intelligent classification of the pollution source type and the time-average change degree of the main pollutants in the river; S2. According to the judgment of the main pollutants and the time-average change type in S1, combined with different needs and objective conditions, adjust the pollutant flux calculation formula, and calculate the pollutant flux; S3, apply the convolutional neural network method, combine the long short-term memory artificial neural network and the stack autoencoder to predict the pollutant concentration and river flow, and calculate the pollutant concentration and river flow. The predicted data of concentration and river flow are input into S1 for intelligent classification. According to the classification results of S1, combined with the predicted data, S2 is used to predict the pollutant flux. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113327056-B http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113327056-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114184760-A |
| priorityDate | 2021-01-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 31.