US 12,148,294 B2
Methods and systems for accident rescue in a smart city based on the internet of things
Zehua Shao, Chengdu (CN); Haitang Xiang, Chengdu (CN); Yaqiang Quan, Chengdu (CN); Yong Li, Chengdu (CN); and Xiaojun Wei, Chengdu (CN)
Assigned to CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Chengdu (CN)
Filed by CHENGDU QINCHUAN IOT TECHNOLOGY CO., LTD., Sichuan (CN)
Filed on Jul. 18, 2022, as Appl. No. 17/813,330.
Claims priority of application No. 202210528755.3 (CN), filed on May 16, 2022.
Prior Publication US 2023/0368657 A1, Nov. 16, 2023
Int. Cl. G08G 1/01 (2006.01); G08G 1/00 (2006.01); G16Y 10/40 (2020.01); G16Y 40/10 (2020.01)
CPC G08G 1/0116 (2013.01) [G08G 1/0145 (2013.01); G08G 1/205 (2013.01); G16Y 10/40 (2020.01); G16Y 40/10 (2020.01)] 5 Claims
OG exemplary drawing
 
1. A method for accident rescue in a smart city based on the Internet of Things, wherein the Internet of Things includes a rescue management platform, a sensor network platform, and an object monitoring platform, and the method is implemented by the rescue management platform, the method comprising:
accessing the object monitoring platform by the sensor network platform and obtaining monitoring information of a target area photographed by a monitoring device located in the target area from the object monitoring platform;
judging whether an abnormal accident occurs in the target area based on the monitoring information;
determining an accident type of the abnormal accident when the abnormal accident occurs in the target area;
generating rescue reminder information based on the accident type, wherein the rescue reminder information includes a rescue mode of the abnormal accident; and
sending the rescue reminder information to a rescuer;
wherein the method further comprises:
obtaining road monitoring information of each road in a preset road network area corresponding to the target area within a preset time period;
determining a degree of road congestion of the each road caused by the abnormal accident in a target time period through a prediction model based on the road monitoring information, the prediction model being a machine learning model; wherein the prediction model is obtained by a training process including:
obtaining a plurality of training samples with labels, wherein the training samples include historical intersection features of intersections and historical first road features of roads between the intersections represented by a graph in the sense of graph theory in a historical period, the labels of the training samples are a historical degree of road congestion of each road in the graph;
inputting the plurality of training samples with labels into an initial prediction model;
constructing a loss function based on the labels and output results of the initial prediction model;
updating parameters of the initial prediction model based on the loss function; and
obtaining the prediction model until the loss function meeting a preset condition;
determining a degree of area congestion of the preset road network area caused by the abnormal accident in the target time period based on the degree of road congestion; and
starting traffic emergency treatment when the degree of area congestion is greater than a preset degree threshold.