Abstract: A driving record authentication method is provided. The method includes acquiring historical driving records of a driver and associated records of the driver. Once a non-fungible token image of the driver is minted based on the historical driving records and the associated records, the non-fungible token image is transmitted to a user terminal in response to a query request associated with the driver that sent the user terminal.

Abstract: An intelligent rescue method applied to a vehicle-mounted device and an airborne rescue device to enable semi-automatic warning and rescue of a broken-down, crashed, drowned, or stranded vehicle, enables communication between the vehicle-mounted device and the rescue device. The vehicle-mounted device determines by sensors a type of emergency of a vehicle, and performs a first assistance action and sends the rescue device a distress signal corresponding to the type of the emergency of the vehicle. The rescue device receives the distress signal and takes off from an initial position of the vehicle to a target position in response to the distress signal. Once the rescue device reaches the target position, the rescue device performs a second action for assistance.

Abstract: An employment recruitment method based on face recognition includes acquiring a candidate's data from a third-party website, analyzing the candidate's data by a semantic analysis method to identify human resources information of the candidate, and analyzing messages and postings in the human resources information of the candidate to determine candidate's personality. A terminal device acquires a second face image of the candidate by a second camera, analyzes the second face image of the candidate by a computer vision algorithm to determine a micro-expression of the candidate, and provides the candidate's human resources information, the candidate's personality, and the candidate's micro-expression to the recruiter to evaluate the candidate. The terminal device applying the method is also disclosed.

Abstract: A method for analyzing validity of responses to a questionnaire by students identifies each student in classroom images, and analyzes the defined classroom behaviors of each student over a period of time. The classroom behaviors can include learning behaviors and non-learning behaviors. The method determines whether the non-learning behaviors of each student meet (ie. are less than) or exceed (ie. are more than) one or more preset conditions and accordingly screens the questionnaire responses as being valid or not valid. An electronic device and a storage medium for performing the above-described method are also disclosed.

Abstract: A method for analyzing validity of responses to a questionnaire by students identifies each student in classroom images, and analyzes the defined classroom behaviors of each student over a period of time. The classroom behaviors can include learning behaviors and non-learning behaviors. The method determines whether the non-learning behaviors of each student meet (ie. are less than) or exceed (ie. are more than) one or more preset conditions and accordingly screens the questionnaire responses as being valid or not valid. An electronic device and a storage medium for performing the above-described method are also disclosed.

Abstract: An employment recruitment method based on face recognition includes acquiring a candidate's data from a third-party website, analyzing the candidate's data by a semantic analysis method to identify human resources information of the candidate, and analyzing messages and postings in the human resources information of the candidate to determine candidate's personality. A terminal device acquires a second face image of the candidate by a second camera, analyzes the second face image of the candidate by a computer vision algorithm to determine a micro-expression of the candidate, and provides the candidate's human resources information, the candidate's personality, and the candidate's micro-expression to the recruiter to evaluate the candidate. The terminal device applying the method is also disclosed.

Abstract: A camera calibration system includes a server, a camera communicatively coupled to the server, a drone communicatively coupled to the server, and an identification pattern coupled to the drone. The server controls the drone to fly in front of the camera at a calibration distance according to position and orientation information of the camera and position information and a distance setting rule of the drone, controls the camera to acquire at least one image of the identification pattern at each of the plurality of positions, and obtains parameters of the camera by performing calibration on the camera according to the acquired plurality of images. The drone flies in a number of positions at the calibration distance and flies in at least one plane.