http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113849479-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d6a6f422b091ba12ea61d4adbf1b0e8e |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01M3-32 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-242 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-2465 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-212 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-215 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-242 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-2458 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-21 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-215 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01M3-32 |
| filingDate | 2021-09-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d50c72959ba04d3b091f2a47a49a59c7 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_83d1838cc8b9830b1e97320ed08d96b6 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ffb411ba11fd4da093a348f0ce86ce66 |
| publicationDate | 2021-12-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113849479-A |
| titleOfInvention | Comprehensive energy supply station oil tank leakage detection method based on instant learning and self-adaptive threshold |
| abstract | The invention discloses a comprehensive energy supply station oil tank leakage detection method based on instant learning and self-adaptive threshold. The invention adopts an instant learning non-parametric modeling method, when leakage judgment is needed to be carried out on an online real-time data stream, a local data set which is most matched with an online data mode is searched and constructed from a historical database to establish a leakage detection model based on oil level soft measurement, and a detection threshold value is adaptively updated according to a detection result so as to continuously adapt to small amplitude fluctuation of data characteristics. After the operation is carried out for a period of time, the steps are repeated to update the model so as to adapt to the problem of data characteristic change caused by large change of factors such as oil quantity, temperature and the like. When a local model is established, a plurality of prediction results of the query sample are subjected to post-processing fusion by adopting an exponential weighted average method, so that the prediction performance of the oil height is effectively improved. Finally, experiments based on real oil tank data prove that the method can effectively improve the accuracy, the applicability and the timeliness of the leakage detection. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114459691-A |
| priorityDate | 2021-09-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419548780 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID6288 |
Total number of triples: 24.