http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110705794-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_93da001efbf936da737b0ec4e89d74e4 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2019-10-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_24d1f0bd3d88b7cf50942f05252f97e0 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_684389e40dce53e3f949141018c54df3 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_017cd73f8f770714d3ac836df00b76fd http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8cb040ac5a30789693587700726d68eb http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_74c89116e3e834c81dd04dbe1d517657 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fcb7ec3d63e6a215255ec707b61b69d6 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4f081a38fdcf8fa1fa399f96dfdb8d69 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7614f76e211a56f6ff4e26e1feadd4ed |
| publicationDate | 2020-01-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-110705794-A |
| titleOfInvention | A Method for Predicting Window State Based on Support Vector Machine Algorithm |
| abstract | The invention discloses a method for predicting a window state based on a support vector machine algorithm. The method includes step 1: constructing a sample set according to the acquired environmental parameter data, wherein the environmental parameter data includes indoor air temperature and humidity, indoor carbon dioxide concentration, outdoor air temperature and humidity, wind speed, wind direction, PM 2.5 concentration, solar irradiance and The corresponding window state at the same time; Step 2: randomly divide the sample set into a training sample set and a prediction sample set; Step 3: train the support vector machine model according to the training sample set, and obtain the optimal support vector machine model Classify the hyperplane, and then obtain the trained SVM model; Step 4: Input the prediction sample set into the trained SVM model to obtain the final classification prediction result, Step 5: Compare the actual window state and check the prediction result . The method for predicting the window state based on the support vector machine algorithm provided by the present invention has better prediction effect and faster running speed. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113610007-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112926276-A |
| priorityDate | 2019-10-09-04:00^^<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/SID457698762 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID280 |
Total number of triples: 26.