Systems and methods for traffic vehicle emergency management based on internet of things (iot) large models

A method for traffic vehicle emergency management based on an Internet of Things (IoT) large model, the method being executed by a government supervision management platform of a system for traffic vehicle emergency management based on the IoT large model, and the method comprising:

• determining a first anomaly confidence level of each of at least one vehicle based on a vehicle operational trajectory, vehicle thermal data, and acoustic data of each of the at least one vehicle in at least one monitoring region of a target road;
• in response to the first anomaly confidence level being greater than a confidence threshold, determining an anomalous vehicle, a second anomaly confidence level of the anomalous vehicle, and an anomaly type of the anomalous vehicle through an anomalous vehicle monitoring model based on vehicle sequence data of each of the at least one vehicle within a first preset time period, wherein the anomalous vehicle monitoring model is a machine learning model;
• determining a control type and a control parameter based on the anomalous vehicle, the second anomaly confidence level of the anomalous vehicle, and the anomaly type of the anomalous vehicle, wherein the control parameter includes a warning parameter of an early warning device;
• and
• in response to the control type being anomalous vehicle control, issuing an early warning on a travel route of the anomalous vehicle via the early warning device.

A non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions, and when a computer reads the computer instructions from the storage medium, the computer executes a method for traffic vehicle emergency management based on an Internet of Things (IoT) large model, the method comprising:

• determining a first anomaly confidence level of each of at least one vehicle based on a vehicle operational trajectory, vehicle thermal data, and acoustic data of each of the at least one vehicle in at least one monitoring region of a target road;
• in response to the first anomaly confidence level being greater than a confidence threshold, determining an anomalous vehicle, a second anomaly confidence level of the anomalous vehicle, and an anomaly type of the anomalous vehicle through an anomalous vehicle monitoring model based on vehicle sequence data of each of the at least one vehicle within a first preset time period, wherein the anomalous vehicle monitoring model is a machine learning model;
• determining a control type and a control parameter based on the anomalous vehicle, the second anomaly confidence level of the anomalous vehicle, and the anomaly type of the anomalous vehicle, wherein the control parameter includes a warning parameter of an early warning device;
• and
• in response to the control type being anomalous vehicle control, issuing an early warning on a travel route of the anomalous vehicle via the early warning device.