Methods for determining an allocation scheme of accident rescue resource in a smart city and internet of things systems

A system of an Internet of Things for determining an allocation scheme of accident rescue resources in a smart city, the Internet of Things system comprising a user platform, a service platform, a management platform, a sensor network platform, and an object platform, wherein the Internet of Things system further comprises:

• a non-transitory computer-readable storage medium storing computer instructions;
• and at least one processor in communication with the non-transitory computer-readable storage medium, when executing the computer instructions, the at least one processor is directed to cause the Internet of Things system to perform operations including:
• obtaining accident information of an accident point through a terminal device based on the object platform;
• sending the accident information to the management platform based on the sensor network platform;
• determining resource demand of the accident point based on the accident information through the management platform, and storing the resource demand in a database;
• obtaining an available resource of at least one candidate rescue point based on the object platform;
• wherein to the available resource of the at least one candidate rescue point based on the object platform, the at least one processor is further directed to cause the Internet of Things system to perform operations including:
• determining the available resource of the at least one candidate rescue point based on a prediction model, wherein inputs of the prediction model at least include an idle resource and surrounding information of the at least one candidate rescue point, the prediction model is a machine learning model, the surrounding information is information related to a surrounding demand point corresponding to the at least one candidate rescue point, wherein
• the at least one candidate rescue point and the surrounding demand point corresponding to each candidate rescue point in the at least one candidate rescue point are determined based on a rescue map, wherein the rescue map includes at least two nodes and at least one edge, the at least two nodes include a regional node and a resource node, the regional node includes the accident point and the surrounding demand point, and the at least one edge is used to connect the regional node and the resource node where a transport path exists;
• the at least one candidate rescue point is a resource node including at least one level in the rescue map, the level is related to a transportation duration between the at least one candidate rescue point and the accident point, and the candidate rescue point with a high level is given priority to provide at least one rescue resource for the accident point;
• and
• the prediction model is obtained by training an initial prediction model based on multiple training samples with labels, the training comprising:
• inputting the multiple training samples and the labels into the initial prediction model, constructing a loss function from the labels and output results of the initial prediction model, updating parameters of the initial prediction model iteratively based on the loss function;
• completing model training and obtaining the prediction model when preset conditions are met;
• sending the available resource to the management platform based on the sensor network platform;
• determining the allocation scheme of rescue resources based on the resource demand and the available resource through the management platform;
• and
• sending the allocation scheme of rescue resources to the terminal device of the user platform through the service platform.