Abstract: A method and an IoT large model system for smart city falling object emergency supervision are provided. The method includes: obtaining a surveillance video stream including a building facade; identifying potential falling object(s) and corresponding risk feature label(s) according to the surveillance video stream; determining falling object risk level(s) of the potential falling object(s) according to the potential falling object(s) and the risk feature label(s); determining a regional risk level of each region in the building facade according to the falling object risk level(s); in response to determining that there is a risk area where the regional risk level is greater than a preset risk level, controlling a lighting device corresponding to the risk area to operate with a warning color; and determining early warning information according to the risk feature label(s), and controlling an early warning device corresponding to the risk area to issue the early warning information.

Abstract: Disclosed is an IoT large model system for emergency supervision of low-lying road sections, the system includes an emergency supervision management platform; the emergency supervision management platform is configured to: obtain vehicle operation data of a low-lying area through an emergency supervision object platform; determine waterlogging data based on the vehicle operation data; determine a traffic risk based on the waterlogging data; in response to determining that the traffic risk is greater than a first threshold, determine drainage parameters; determine dispatch parameters based on the drainage parameters; and send, based on the dispatch parameters, a dispatch packet including the drainage parameters to an unmanned vehicle fleet through a heterogeneous communication network, control an unmanned vehicle equipped with a drainage device to travel to the low-lying area, and control a pumping power of the drainage device to be set to a pumping power in the drainage parameters.

Abstract: A method and a system for traffic vehicle emergency management based on an Internet of Things (IoT) large model are provided. The method includes: determining a first anomaly confidence level of each of at least one vehicle; 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; 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; 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.

Abstract: The present disclosure relates to a method and system for city lifeline pipeline burst prevention based on an IoT large model. The method includes: obtaining fluid pressure data; determining a pipe burst probability of a pipeline network node based on the fluid pressure data; and determining a target valve and a valve opening degree of the target valve based on the pipe burst probability, and controlling the target valve to the valve opening degree.

Abstract: Provided is a system and a method for suburban gas pipeline emergency supervision used in an urban lifeline project, the system includes an emergency supervision and management platform and an emergency supervision object platform, and the emergency supervision and management platform is configured to: in response to completion of a remote regulation operation of a target gas pipeline, obtain a first information sequence; determine a first regulation feature based on the first information sequence and a remote regulation parameter of the remote regulation operation; determine failure probability of the remote regulation operation based on the first regulation feature; in response to the failure probability satisfying a predetermined failure condition, determine a failure cause distribution; determine a failure cause and send a control signal to the emergency supervision object platform for executing a regulation strategy based on the failure cause distribution.

Abstract: The present disclosure relates to a method, a system, and a storage medium for smart city emergency supervision based on an IoT large model, the method including: in response to receiving an emergency management request from a sub-platform, determining a data retrieval prioritization for the emergency management request based on a first emergency level of the emergency management request; retrieving emergency management data corresponding to the emergency management request from a database based on the data retrieval prioritization.