CN-120875468-B - Airport public transportation preallocation processing system and method based on big data model

CN120875468BCN 120875468 BCN120875468 BCN 120875468BCN-120875468-B

Abstract

The invention belongs to the technical field of airport public transportation distribution and discloses an airport public transportation pre-distribution processing system and method based on a big data model. The method comprises the steps of obtaining historical data, utilizing a machine learning and deep learning model to infer passenger demands and public transportation service conditions in the historical data based on a big data processing technology, generating a data deduction model, inputting real-time weather, time and passenger data into the data deduction model, adopting an intelligent optimization algorithm to dynamically adjust public transportation resource allocation in combination with real-time traffic conditions, monitoring public transportation resource allocation results in real time, continuously optimizing dynamic adjustment correction results in combination with a feedback mechanism, and pushing the latest correction results to a downstream third-party system. The invention utilizes real-time data analysis and intelligent optimization algorithm to realize a more efficient traffic scheduling scheme, and has important practical significance and wide application prospect.

Inventors

CHEN XIAO
SHAO LIN
LIU XIAOJIANG
LIU BO
XUE LINGXIANG

Assignees

青岛民航凯亚系统集成有限公司

Dates

Publication Date: 20260505
Application Date: 20250925

Claims (4)

1. An airport public transportation pre-allocation processing method based on a big data model is characterized by comprising the following steps: s1, acquiring historical data of flight information, passenger flow, weather conditions, vehicle states, passenger separation information and passenger arrival intention multi-source data; S2, based on a big data processing technology, a machine learning and deep learning model is utilized to infer the passenger demand and the public transportation service condition in historical data, and a data deduction model is generated; S3, inputting real-time weather, time and passenger data into a data deduction model, and dynamically adjusting public transportation resource allocation by adopting an intelligent optimization algorithm and combining real-time traffic conditions; S4, monitoring public transportation resource allocation results in real time, continuously optimizing dynamic adjustment correction results by combining a feedback mechanism, and pushing the latest correction results to a downstream third-party system; in step S2, based on big data processing technology, the historical data is collected, cleaned and analyzed, the interference data of abnormal conditions is eliminated, a historical data set suitable for machine learning and deep learning is obtained, and then the machine learning and deep learning model is utilized to infer the passenger demand and the public transportation use condition in the historical data, so as to generate a data deduction model; Analyzing all historical data by utilizing machine learning and deep learning models to analyze data and analyze traffic speculation service conditions of airports in different times, different weather and different passenger numbers; Setting the time influencing factor as the current time , The influencing factors of (a) vary with time, and the expression is: (1) In the formula, As a factor of the influence of time, Half the difference between the highest and lowest population on the same day, Representing average people flow; from the historical data, the formula evolves: (2) In the formula, For the amount of use of the vehicle, For the weight of each influencing factor, As a matter of the type of weather, In order to be a degree of humidity, In order to be able to determine the temperature, As a value of the error it is, For the volume of the passenger flow, Indicating whether or not to save holidays; The model deduction and correction comprises substituting real-time weather, time and passenger data into formula (2) by analyzing the estimated use condition of public transportation in historical data and comparing with the actual condition at the time, and judging the result If the result is similar to the deduction result, if so, the adjustment is not performed, if not, the adjustment is performed A value that causes the deduction result to be similar to the final actual result; Repeating the steps, when the deduction result is always similar to the actual result, considering that the deduction of the deduction model is successful, and generating a data deduction model, wherein the data deduction model realizes the formula by writing codes and comprises realization logic of the formula and a historical data set for deduction; In step S4, monitoring public transportation resource allocation results in real time, continuously optimizing dynamic adjustment correction results by combining a feedback mechanism, and pushing the latest correction results to a downstream third-party system to acquire monitoring data, correct allocation results and feed back the data; The monitoring data acquisition comprises real-time monitoring of public transportation at an airport, distinguishing the condition that the real-time data output result is inconsistent with the presumed content in time in a manual mode, and adjusting the weight value of each part Error value ; The distribution result correction comprises that when deduction is carried out, special data is analyzed again, and the weight is readjusted Error value And correcting the data result; The data feedback comprises the steps of outputting the latest correction result and pushing the latest correction result to a downstream third-party system.
2. The airport public transportation preallocation processing method based on big data model of claim 1, wherein in step S1, the historical data of flight information, passenger flow, weather conditions, vehicle status, passenger separation information, passenger arrival intention multisource data is obtained, including data acquisition, data preprocessing, data clustering, data distribution; the data acquisition is to acquire flight information, passenger flow, passenger departure information, passenger arrival intention, weather conditions of urban areas where airports are located and traffic condition data of the urban areas where airports are located through a butt joint production operation, passenger service, airport environment comprehensive management and local city traffic management platform; the data preprocessing is to perform preliminary preprocessing on the data acquired by the data, and filter useless data and error data; the data clustering is to group the data according to the occurrence time period and the belonging passenger association information; the data distribution is to distribute the processed and clustered data to the downstream for processing.
3. The airport public transportation pre-allocation processing method based on the big data model according to claim 1, wherein in step S3, real-time weather, time and passenger data are input into the data deduction model, an intelligent optimization algorithm is adopted, and by combining with real-time traffic conditions, dynamic adjustment of public transportation resource allocation comprises real-time data analysis intelligent adjustment and resource allocation recommendation; The real-time data analysis intelligent adjustment comprises the steps of inputting real-time weather, time and passenger number key data into a data deduction model, dynamically adjusting and analyzing a current scene, and predicting public traffic resource allocation conditions used in future scenes; the resource allocation recommendation includes outputting the prediction results and pushing the prediction results to a downstream third party system.
4. An airport public transportation pre-allocation processing system based on a big data model, wherein the system implements the airport public transportation pre-allocation processing method based on the big data model according to any one of claims 1-3, the system comprises: the data acquisition module is used for acquiring historical data of flight information, passenger flow, weather conditions, vehicle states, passenger departure information and passenger arrival intention multi-source data; The data analysis and speculation module is used for speculating the passenger demands and the public transportation service conditions in the historical data by utilizing a machine learning and deep learning model based on a big data processing technology, and generating a data deduction model; the prediction scheduling module is used for inputting real-time weather, time and passenger data into the data deduction model, and dynamically adjusting public traffic resource allocation by adopting an intelligent optimization algorithm and combining real-time traffic conditions; The intelligent monitoring and feedback module is used for monitoring the public transportation resource allocation result in real time, continuously optimizing the dynamic adjustment correction result by combining a feedback mechanism, and pushing the latest correction result to a downstream third-party system.

Description

Airport public transportation preallocation processing system and method based on big data model Technical Field The invention belongs to the technical field of airport public transportation distribution, and particularly relates to an airport public transportation pre-distribution processing system and method based on a big data model. Background With the rapid development of the global air transportation industry, the passenger flow of airports is continuously increased, and the dispatching and allocation of public transportation systems face great challenges. The airport is used as a city comprehensive transportation hub, and reasonable distribution of public transportation systems has important significance for improving the travel experience of passengers, reducing traffic jams and improving the operation efficiency. However, existing airport public transportation distribution systems suffer from a number of deficiencies, mainly represented by the following: the information lag is that the public transportation scheduling of most airports at present depends on a fixed schedule and manual allocation, and the real-time response of flight dynamics, passenger flow changes and emergencies is difficult, so that the resource allocation is unreasonable. The existing allocation mode usually depends on historical experience or static rules, big data analysis and machine learning technology are not fully utilized, prediction accuracy is low, and a scheduling scheme is not flexible enough. The passengers experience poor, and due to unbalanced traffic resource allocation, the passengers can face the problems of long waiting time, overload of vehicles, resource waste and the like at the airport, and the travel satisfaction is affected. The resource utilization rate is low, public transportation means (such as taxis, buses, subways and the like) around the airport cannot be matched with actual demands dynamically, and the situation of excessive or shortage of resources exists, so that the overall operation efficiency is reduced. In recent years, the development of big data technology and artificial intelligence has provided new possibilities for airport public transportation scheduling optimization. Through the deep analysis of flight information, passenger flow, traffic tool states and historical data, intelligent prediction and dynamic allocation of public traffic resources can be realized, the operation efficiency of an airport traffic system is improved, the waiting time of passengers is reduced, and the overall travel experience is improved. Disclosure of Invention In order to overcome the problems in the related art, the disclosed embodiments of the invention provide an airport public transportation pre-allocation processing system and method based on a big data model. The invention aims to solve the problems of information lag, lack of intelligent optimization, poor passenger experience, low resource utilization rate and the like in the prior art. The technical scheme is that the airport public traffic pre-allocation processing method based on the big data model comprises the following steps: s1, acquiring historical data of flight information, passenger flow, weather conditions, vehicle states, passenger separation information and passenger arrival intention multi-source data; S2, based on a big data processing technology, a machine learning and deep learning model is utilized to infer the passenger demand and the public transportation service condition in historical data, and a data deduction model is generated; S3, inputting real-time weather, time and passenger data into a data deduction model, and dynamically adjusting public transportation resource allocation by adopting an intelligent optimization algorithm and combining real-time traffic conditions; And S4, monitoring the public transportation resource allocation result in real time, continuously optimizing the dynamic adjustment correction result by combining a feedback mechanism, and pushing the latest correction result to a downstream third-party system. In step S1, historical data of flight information, passenger flow, weather conditions, vehicle states, passenger departure information and passenger arrival intention multisource data are acquired, wherein the historical data comprise data acquisition, data preprocessing, data clustering and data distribution; The data acquisition comprises the steps of obtaining flight information, passenger flow, passenger departure information, passenger arrival intention, weather conditions of an urban area where an airport is located, traffic condition data of the urban area where the airport is located through platforms such as butt joint production operation, passenger service, comprehensive management of airport environments, and traffic management of the urban area where the airport is located; the data preprocessing is to perform preliminary preprocessing on the data acquired by the data, and filter useles