| CPC G06Q 10/0631 (2013.01) [G06Q 50/40 (2024.01)] | 18 Claims |
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1. A method for optimizing metro operation scheduling in a smart city realized by an Internet of Things (IoT) system for optimizing metro operation scheduling in a smart city, wherein the IoT system for optimizing metro operation scheduling in a smart city includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform, the IoT system being a part of or implemented by a processing device, the user platform being configured as a terminal device and used to obtain an input instruction of a user through the terminal device, query an operation scheduling scheme of a target station in a target period of time, issue a metro operation scheduling scheme query instruction to the service platform, receive the operation scheduling scheme uploaded by the service platform, and feed the operation scheduling scheme back to the user, the service platform being used to provide input and output services for the user, receive data uniformly, process the data uniformly, and send the data uniformly, the management platform being used to overall plans and coordinates connection and collaboration among the user platform, the service platform, the management platform, the sensor network platform, and the object platform, and bring together all information of the IoT system, the sensor network platform being configured as a communication network and a gateway device and used to realize functions of perceptual information sensor communication and control information sensor communication, the object platform being configured as various types of devices to obtain information including an image obtaining device, a metro entry gate device, a weight sensor and used to generate the perceptual information, the method being executed by the management platform, and the method comprises:
obtaining, based on the object platform, passenger flow data of at least one metro station related to the target station by the sensor network platform;
determining predicted passenger flow data of the target station in the target period of time based on the passenger flow data of the at least one metro station through a passenger flow prediction model, the passenger flow prediction model being a graph neural networks model, wherein
an input of the passenger flow prediction model further includes collection time information and target period of time information of the target station, an output of the passenger flow prediction model includes the predicted passenger flow data of the target station in the target period of time, wherein the collection time information and the target period of time information both include time information, weather information, and traffic environment information corresponding to a collection time and the target period of time;
the passenger flow prediction model is obtained through a first training based on a plurality of first training samples with first labels, wherein the first training samples include passenger flow data of a plurality of first sample metro stations during a first sample period of time, the first labels include actual passenger flow data of a second sample metro station during a second sample period of time, wherein the first sample metro station in the first training samples is a metro station related to the second sample metro station, the first labels are obtained by the management platform by querying or detecting the actual passenger flow data of the second sample metro station during the second sample period of time;
the first training includes: inputting the plurality of first training samples with first labels into an initial passenger flow prediction model, constructing a first loss function based on the first labels and output results of the initial passenger flow prediction model, updating parameters of the initial passenger flow prediction model based on the first loss function; and obtaining the passenger flow prediction model until the first loss function of the initial passenger flow prediction model meeting a first preset condition, wherein the first preset condition includes a convergence of the first loss function, a loss function value being smaller than a preset value, and a number of iterations reaching a first threshold;
determining a predicted passenger flow distribution of the target station in the target period of time at least based on the passenger flow data of the target station and the target period of time through a passenger flow distribution prediction model, wherein the passenger flow distribution includes a predicted passenger flow distribution interval and an interval probability, and the passenger flow distribution prediction model is a graph neural networks model;
determining the operation scheduling scheme of the target station in the target period of time based on the predicted passenger flow data, the operation scheduling scheme including at least a metro departure interval;
uploading the operation scheduling scheme to the service platform;
transmitting, based on the service platform, the operation scheduling scheme to the user platform; and
feeding, based on the user platform, the operation scheduling scheme back to the user.
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