http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109840612-A

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assignee http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_09e5d518bd30e10ac75c6b0dde8805a6
classificationIPCInventive http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04
filingDate 2018-07-24-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_359dd34db00acd247041b1016b919ecd
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8eac95887e638340ef6379fd6c58ec09
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_2532895812707e2074ea6d0781aa5c61
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_098a27f0425829de24fbdde1f57c3d7e
publicationDate 2019-06-04-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber CN-109840612-A
titleOfInvention User driving behavior analysis method and system
abstract The invention discloses a user driving behavior analysis method and system. The method includes: collecting vehicle driving data of several users, and calculating several index items used to describe the user's driving behavior according to the vehicle driving data; obtaining traffic accident information of the user; using the index items as characteristic variables, analyzing the relationship between each characteristic variable and the The correlation of traffic accident information, screen out N characteristic variables with the highest correlation with the traffic accident information, and form an N-dimensional vector; use nonlinear dimensionality reduction algorithm to reduce the dimension to obtain a variable set; take the variable set as an independent variable, and use The traffic accident information is used as a dependent variable, and a user driving behavior evaluation model is trained; the user driving behavior evaluation model is used to evaluate the driving behavior of the user to be analyzed. The invention uses the traffic accident information as a quantitative index for evaluating the user's driving behavior, trains the user's driving behavior evaluation model, and improves the prediction and evaluation accuracy of the user's driving behavior.
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Total number of triples: 42.