公开(公告)号：US11841839B1

公开(公告)日：2023-12-12

申请号：US18143059

申请日：2023-05-03

Applicant: ZHEJIANG LAB

Inventor： Jiaxi Yang ,  Chongning Na ,  Ye Yang ,  Kai Ding ,  Yao Yang ,  Yihan Wang

IPC: G06F16/215 ,  G06N3/0475 ,  G06N3/0455

CPC classification number: G06F16/215 ,  G06N3/0455 ,  G06N3/0475

Abstract: The present invention discloses a preprocessing and imputing method for structural data, comprising: step 1, querying the missing information of an original data, counting missing values, and obtaining a missing rate for the original data; step 2, based on the missing rate, performing listwise deletion on the original data, and then traversing the rows to generate corresponding dichotomous arrays, converting the arrays to the form of histogram, calculating the maximum rectangular area formed by the corresponding histogram, and then sorting all rectangular areas to obtain the maximum complete information matrix; step 3, using multiple imputation by chained equations, auto-encoders, or generative adversarial imputation networks to impute missing values on the original data. The present invention can carry out missing information statistics on the original data, automatically search the maximum complete information that meets the conditions, impute the structural data, greatly improve the quality of the original dataset and convenience for subsequent prediction tasks.

公开(公告)号：US11887292B1

公开(公告)日：2024-01-30

申请号：US18197069

申请日：2023-05-13

Applicant: ZHEJIANG LAB

Inventor： Jinni Dong ,  Jiaxi Yang ,  Kai Ding ,  Chongning Na

IPC: G06K9/00 ,  G06T7/00 ,  G06T7/70 ,  G06Q40/08 ,  G06V20/60

CPC classification number: G06T7/0002 ,  G06Q40/08 ,  G06T7/70 ,  G06V20/60 ,  G06T2207/30168 ,  G06V2201/08

Abstract: The present invention discloses a two-step anti-fraud vehicle insurance image collecting and quality testing method, system and device, the method comprises: step 1, collecting vehicle insurance scene images and marking vehicle orientation; step 2, performing object detection on the collected vehicle insurance scene images and screening to obtain object coordinates; step 3, according to the vehicle orientation and the object coordinates, obtaining the specific position of the object coordinates located in the whole vehicle; step 4, according to the object coordinates screened in step 2, performing vehicle component detection on the vehicle insurance scene images, obtaining the component coordinates of the vehicle components, and screening to obtain the vehicle component closest to the object coordinates; step 5, according to the specific position of the object coordinates located in the whole vehicle and the vehicle components closest to the object coordinates, obtaining the position of the vehicle components closest to the object coordinates that are located in the whole vehicle, and abstracting them into the tabular data. The present invention avoids the existence of low-quality images in the traditional insurance industry and the large amount of time spent on manual identification.