CN-115946702-B - Vehicle-mounted driver health detection method and system

CN115946702BCN 115946702 BCN115946702 BCN 115946702BCN-115946702-B

Abstract

The invention discloses a vehicle-mounted driver health detection method and system, wherein the method comprises the following steps of collecting driver health index information to form perception data; and carrying out fusion analysis on the perception data, combining a thousand-person and thousand-face database to determine an execution level, and carrying out corresponding reminding or intervention on the vehicle according to the execution level. The method can systematically judge whether the driving safety is influenced or not, timely influence or intervene the driving state, ensure the personal and property safety of the client, provide better monitoring environment and conditions for people needing long-time health monitoring, save monitoring cost of the client and hospital, provide greater convenience, provide health report and warm care experience for most people, and take a step more in improving the vehicle quality.

Inventors

WANG WENCHONG
YIN SHANHUI
HE ZHIYU
ZHANG QIANG
LI JING
TAO PENGPENG

Assignees

奇瑞汽车股份有限公司

Dates

Publication Date: 20260505
Application Date: 20220930

Claims (4)

1. A vehicle-mounted driver health detection method is characterized by comprising the following steps of, An ID account number of each user is established on the cloud platform and used for storing personal health index data to form a thousand-person and thousand-face database; The method comprises the steps of collecting health index information of a driver to form perception data, comparing the perception data with a thousand-person thousand-face database, performing scene analysis when the perception data is abnormal, and performing corresponding reminding or intervention according to analysis results, wherein the thousand-person thousand-face database is used for counting average data collected after each vehicle is on, correcting cloud platform personal data by using each hundred-time average data, and performing first correction by combining an initial thousand-person ten-face database to achieve a first version of thousand-person thousand-face, wherein the thousand-person ten-face database is 10 grades for classifying health indexes by combining user attribute identification and referring to a medical statistics database; collecting health index information of a driver to form perception data; performing fusion analysis on the perception data, and determining an execution level by combining a thousand-person thousand-surface database; Corresponding reminding or intervention is carried out on the vehicle according to the execution level; the perception data comprises attribute perception, emotion perception, behavior perception and customer data perception; The execution level specifically includes the level of execution, When the abnormal range of the health index is below 15%, only warm reminding is carried out under the condition that all other detection dimensions are normal; When the abnormal range of the health index is below 15%, the emotion is abnormal, and under other normal conditions, a mild intervention action and a warm reminding are executed; When the abnormal range of the health index is below 15%, the emotion is normal, and the driving is abnormal, a mild intervention action and a warm reminding are executed; when the abnormal range of the health index is below 15%, under the condition that all other detection dimensions are abnormal, carrying out heavy intervention and light intervention after confirmation by a user; When the abnormal range of the health index is 15% -30%, the monitoring dimension is all normal, only warm reminding is carried out; When the abnormal range of the health index is 15% -30%, the emotion is abnormal, and under other normal conditions, a mild intervention action and a warm reminding are executed; When the abnormal range of the health index is 15% -30%, the emotion is normal, and under other abnormal conditions, the user performs severe intervention and slight intervention after confirmation; when the abnormal range of the health index is 15% -30%, under the condition that all monitoring dimensions are abnormal, taking over driving and slightly intervening after the user confirms; When the abnormal range of the health index exceeds 30%, under the condition that the monitoring dimension is all positive, performing mild intervention action and warm reminding; When the abnormal range of the health index exceeds 30%, the emotion is abnormal, and under other normal conditions, the user confirms that the serious intervention and the slight intervention are carried out; When the abnormal range of the health index exceeds 30%, under the abnormal condition of the emotion abnormal behavior, taking over driving and slightly intervening after the user confirms; when the abnormal range of the health index exceeds 30%, under the condition that all monitoring dimensions are abnormal, taking over driving and slightly intervening after the user confirms; The corresponding reminding or intervention of the vehicle comprises the following four steps: Generating a health file report, and displaying the owner health file report in a man-machine interaction interface of the vehicle machine end; a warm reminding, wherein when the systematic health index is slightly abnormal, the warm reminding is carried out, and care greetings are carried out through the voice in the vehicle and a warm interface; light intervention, wherein when the systemic health index reaches a light intervention threshold, light intervention is carried out, and actions of the light intervention comprise atmosphere lamp adjustment, air conditioning cold air starting and refreshing or music relaxation adjustment; And (3) carrying out severe intervention when the systematic index exceeds a threshold value, wherein the severe intervention comprises the steps of connecting telemedicine, automatically connecting an ECALL function and stopping the vehicle by the side or taking over the automatic driving under the automatic driving capability to directly visit a nearby hospital.
2. A vehicle-mounted driver health detection system based on the vehicle-mounted driver health detection method as claimed in claim 1, which is characterized by comprising a perception module, a decision module and an execution module; The sensing module is used for detecting health indexes of a driver to form sensing data; The decision module is used for carrying out fusion analysis on the perceived data, combining thousands of people and thousands of faces of data, analyzing and deciding by depending on an continuously corrected algorithm model to form a decision instruction; The execution module is used for receiving the decision instruction to execute the action.
3. A vehicle-mounted driver health detection system based on the vehicle-mounted driver health detection method of claim 1, comprising a vehicle-end system; the vehicle end system is used for collecting health indexes of the driver to form sensing data and uploading the sensing data to the cloud system; The vehicle-end system receives a decision instruction formed by analysis and decision formation of the cloud system, and executes corresponding reminding or intervention according to the decision instruction.
4. A vehicle-mounted driver health detection system based on the vehicle-mounted driver health detection method of claim 1, comprising a cloud system; the cloud system is used for receiving perception data formed by the fact that the vehicle-end system collects health indexes of a driver, storing the received data, carrying out decision processing on the received data according to the thousands of people and thousands of sides database, and sending decision instructions to the vehicle-end system.

Description

Vehicle-mounted driver health detection method and system Technical Field The invention belongs to the field of intelligent automobiles, and particularly relates to a vehicle-mounted driver health detection method and system. Background Along with the higher living water side, people have higher and higher attention to health, not only are various popular whole body physical examination carried out, but also more intelligent health monitoring methods and scenes are continuously flushed out, such as various household small monitoring instruments and equipment, on-line remote diagnosis and remote medical treatment; At present, a certain scheme exists in cabin health monitoring, but a series of problems exist in single health monitoring, and the following aspects are mainly highlighted. Firstly, health index monitoring is incomplete, for example, a monitoring scheme based on a steering wheel conductive material exists at present, based on sensor monitoring installed on a gear shifting handle, based on Doppler monitoring of millimeter wave radar in a cabin, the scheme can only monitor heart rate indexes, and has limitation of monitoring conditions, for example, a driver is required to put both hands on the steering wheel at any time or put the driver handle on the gear shifting handle, obviously, driving habit is not adapted, meanwhile, the problem of cost and arrangement position still exists for the monitoring scheme, particularly in the background of continuously thinning the cost of the whole vehicle, the cost of increasing more than hundred yuan by a single function definitely brings greater pressure, and meanwhile, millimeter wave radar has stricter requirements on the installation position, and inherent space and fixed position in the cabin face great challenges. Secondly, even though health monitoring is relatively comprehensive, thousands of people cannot be achieved, for example, although the scheme based on the cabin DMS camera can monitor heart rate, respiratory heart rate variability and even blood pressure, how to judge what indexes are normal and what indexes are abnormal cannot be combined with the abnormal indexes of the body of each person to give accurate judgment, so that a brand new experience is originally intended for a user, and the result frequency is wrong, so that the user can generate an untrustworthy sense and even complain. Thirdly, even if the monitored indexes are relatively comprehensive, thousands of people can be achieved, but the situation that the system gives wrong judgment caused by the occurrence of various car use scenes is not excluded, for example, when a driver just moves back to drive, all indexes of the driver are obviously far higher than the normal range, for example, when the driver is abnormal due to moods or other reasons, all the scenes are required to be comprehensively considered, and otherwise, the customer experience is greatly discounted. In summary, the vehicle-mounted health monitoring method in the prior art has the problems of inaccurate monitoring and limited application scenarios. Disclosure of Invention In order to solve the problems in the prior art, the invention provides a vehicle-mounted driver health detection method and system for solving the problems. In order to achieve the above purpose, the present invention provides the following technical solutions: A vehicle-mounted driver health detection method comprises the following steps, Collecting health index information of a driver to form perception data; performing fusion analysis on the perception data, and determining an execution level by combining a thousand-person thousand-surface database; and correspondingly reminding or intervening the vehicle according to the execution level. Preferably, the method specifically comprises the following steps, An ID account number of each user is established on the cloud platform and used for storing personal health index data to form a thousand-person and thousand-face database; And acquiring health index information of the driver to form perception data, comparing the perception data with a thousand-person thousand-surface database, performing scene analysis when the perception data is abnormal, and performing corresponding reminding or intervention on the vehicle according to an analysis result. Preferably, the thousand face database is used for counting the average data acquired after each vehicle is on, correcting the personal data of the cloud platform by using the average data of each hundred times, carrying out first correction by combining the initial thousand face database to achieve the thousand face of the thousand persons of the first version, and dividing the health index into 10 grades by combining user attribute identification and referencing the medical statistics database. Preferably, the sensory data includes attribute awareness, emotion awareness, behavioral awareness, and customer data awareness. Preferably, the execution level co