Airport ground traffic passenger flow prediction method and system

Abstract

The invention provides an airport ground traffic passenger flow prediction method and system, and is applied to the field of prediction data processing. The dynamic data pool is formed by integrating the flight dynamic data, the traffic flow data, the weather forecast data and the passenger movement data, it is ensured that the system has a comprehensive and timely information basis, the system can dynamically update and timely reflect the real-time conditions of the airport and the surroundings of the airport, the prediction accuracy and response speed are improved, and the prediction efficiency is improved. And meanwhile, by judging whether the dynamic data pool can execute the preset data updating strategy or not, it is ensured that subsequent prediction and scheduling operation can be triggered only when the data is complete enough and newest, inaccurate prediction caused by insufficient or outdated data is avoided, and the reliability of system decision making is improved.

Description

Technical Field

[0001] The invention relates to the field of forecast data processing, and in particular to a method and system for forecasting passenger flow in airport ground transportation. Background Art

[0002] Airport ground transportation refers to the traffic flow of passengers in the area surrounding the airport, covering various modes of transportation used by passengers to depart from or arrive at the airport, such as taxis, private cars, public transportation (subway, bus), special shuttle buses, shared travel services, etc. This part of the traffic flow is an important part of the overall operation of the airport and a key node in the urban transportation system.

[0003] At present, although passengers in the area surrounding the airport can choose different modes of transportation (such as taxis, public transportation, and private cars) to enter the airport, the behavioral patterns corresponding to different modes of transportation are complex. These diverse choices make it complicated to predict the usage of different modes of transportation. Traditional traffic flow forecasting methods usually assume that passenger behavior is homogeneous and predictable, but in actual situations, passenger behavior is often highly heterogeneous and uncertain, which cannot meet passenger needs and is difficult to improve the overall efficiency of the transportation system. Summary of the invention

[0004] The present invention aims to solve the problem that traditional traffic flow prediction methods cannot meet passenger needs and are difficult to improve the overall efficiency of the transportation system, and provides an airport ground transportation passenger flow prediction method and system.

[0005] The present invention adopts the following technical means to solve the technical problem: The present invention provides a method for predicting airport ground transportation passenger flow, comprising: Based on the real-time data pre-collected by the airport management terminal, the real-time data is integrated and summarized to form a dynamic data pool, wherein the real-time data specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data; Determining whether the dynamic data pool can execute the data update strategy preset by the airport management terminal; If yes, then the pre-trained prediction model is applied to perform rolling prediction on the latest data in the dynamic data pool to obtain prediction results, and the traffic management measures preset by the airport management terminal are adaptively triggered according to the prediction results, and the traffic resources in the area around the airport are dynamically dispatched, and feedback information uploaded by passengers in the area around the airport is received through the airport management terminal, wherein the traffic management measures specifically include adjusting the duration of traffic lights, optimizing vehicle dispatching, and issuing congestion warnings, and the traffic resources specifically include increasing traffic capacity, opening additional lanes, and activating temporary parking lots; Determining whether the feedback information matches a response event corresponding to the area surrounding the airport; If so, the flight information pre-entered by the passenger into the airport management terminal is collected, the traffic data corresponding to the response event and the flight information are input into the prediction model, a dynamic recommended route required by the passenger is generated, and the dynamic recommended route is synchronized from the airport management terminal to the passenger's preset terminal, wherein the dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport.

[0006] Furthermore, before the step of receiving, through the airport management terminal, feedback information uploaded by passengers in the surrounding area of ​​the airport, the method further includes: Based on the passenger information pre-entered into the airport management terminal by the passenger, the passenger group corresponding to the passenger is identified, and the traffic influencing factors of the passenger group for the feedback information are collected, wherein the passenger information specifically includes ticket purchase information, entry into the terminal waiting area and boarding records, the passenger groups specifically include business passengers, family passengers and leisure passengers, and the traffic influencing factors specifically include travel time, distance and weather conditions; Determining whether the traffic influencing factor is within an average value preset by the airport management terminal; If not, an exclusive channel window for the passenger group is established based on the original management strategy of the airport management terminal, and the required service content is configured for the passenger group according to the exclusive channel window. The exclusive channel window and the required service content are updated to the airport management terminal in real time, and the usage of the exclusive channel window and the required service content is displayed to the passengers in real time through the airport management terminal, wherein the exclusive channel window specifically includes an exclusive security check channel, an exclusive channel for family passengers and an exclusive boarding gate.

[0007] Furthermore, the step of applying the pre-trained prediction model to perform rolling prediction on the latest data in the dynamic data pool to obtain the prediction result also includes: Calculate the difference between the predicted result and the actual value based on a preset error metric, identify the change trend of the prediction error through the difference, and collect the prediction deviation in a specific period from the change trend; Determining whether the prediction deviation has a corresponding prediction weight, wherein the prediction weight specifically includes traffic weight, weather weight and flow weight; If so, the model parameters of the prediction model are adjusted according to the prediction deviation, corresponding weight conditions are added in real time to the prediction model, the interaction characteristics between the latest data and the weight conditions are obtained, the joint impact parameters of the prediction results are generated according to the interaction characteristics, the joint impact parameters are input into the prediction model, and the joint impact value of the joint impact parameters on the prediction results is revealed, wherein the weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information.

[0008] Furthermore, the step of generating the dynamic recommended route required by the passenger and synchronizing the dynamic recommended route from the airport management terminal to the passenger's preset terminal further includes: Based on the transportation preferences preset by the passenger at the airport management terminal, obtaining the passenger's feedback on the dynamically recommended route; Determining whether the feedback content agrees to apply the dynamic recommended route; If not, the route content without the traffic preference in the dynamically recommended route is shielded, a specific time difference between the route content and the best recommended route is generated, and the specific time difference is synchronized in real time to the mobile terminal preset by the passenger.

[0009] Furthermore, the step of determining whether the dynamic data pool can execute the data update strategy preset by the airport management terminal further includes: Based on the simulated load preset by the airport management terminal, obtaining the maximum number of concurrent requests that the dynamic data pool can accommodate; Determine whether the dynamic data pool can run stably for a preset period of time when it is at the maximum concurrent request amount; If so, the performance parameters of the dynamic data pool are monitored in real time, a preset sharp simulated traffic is applied to the dynamic data pool, and abnormal information of the dynamic data pool under different loads is recorded, wherein the performance parameters specifically include response time, throughput and resource usage, and the abnormal information specifically includes timeout, failed request and resource exhaustion.

[0010] Furthermore, the step of determining whether the feedback information matches the response event corresponding to the area surrounding the airport further includes: Based on the mobile terminal of the passenger pre-connected to the airport management terminal, obtaining the location information of the passenger in the surrounding area of ​​the airport; Determining whether the location information is within a preset range of the response event; If so, the response event is synchronously published to the online device pre-connected to the airport management terminal, and the corresponding processing time is adaptively generated according to the response event, wherein the online device specifically includes airport traffic equipment and airport visitor equipment.

[0011] Furthermore, the step of integrating and aggregating the real-time data pre-collected by the airport management terminal to form a dynamic data pool includes: Based on the traffic organization preset by the airport management terminal, the traffic organization is uniformly arranged on a pre-built cloud data platform, and real-time data uploaded by the traffic organization to the cloud data platform is collected, wherein the traffic organization specifically includes a traffic management department, an airport management department and a weather forecast agency; Determining whether the real-time data has a corresponding timestamp format; If not, the revoked data without the timestamp format will be transferred to the information island preset by the cloud data platform, and the public access to the revoked data in the information island will be restricted according to the access rights preset by the airport management terminal.

[0012] The present invention also provides an airport ground transportation passenger flow prediction system, comprising: An integration module is used to integrate and summarize the real-time data pre-collected by the airport management terminal to form a dynamic data pool, wherein the real-time data specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data; A judgment module, used to judge whether the dynamic data pool can execute the data update strategy preset by the airport management terminal; an execution module, for, if possible, applying a pre-trained prediction model to perform rolling prediction on the latest data in the dynamic data pool, obtaining prediction results, adaptively triggering the traffic management measures preset by the airport management terminal according to the prediction results, dynamically dispatching the traffic resources in the area surrounding the airport, and receiving feedback information uploaded by passengers in the area surrounding the airport through the airport management terminal, wherein the traffic management measures specifically include adjusting the duration of traffic lights, optimizing vehicle dispatching, and issuing congestion warnings, and the traffic resources specifically include increasing traffic capacity, opening additional lanes, and activating temporary parking lots; A second judgment module is used to judge whether the feedback information matches the response event corresponding to the area around the airport; The second execution module is used to collect the flight information pre-entered by the passenger into the airport management terminal, input the traffic data corresponding to the response event and the flight information into the prediction model, generate a dynamic recommended route required by the passenger, and synchronize the dynamic recommended route from the airport management terminal to the passenger's preset terminal, wherein the dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport.

[0013] Furthermore, it also includes: A collection module, for identifying the passenger group corresponding to the passenger based on the passenger information pre-entered into the airport management terminal, and collecting the traffic influencing factors of the passenger group for the feedback information, wherein the passenger information specifically includes ticket purchase information, entry into the terminal area and boarding records, the passenger groups specifically include business passengers, family passengers and leisure passengers, and the traffic influencing factors specifically include travel time, distance and weather conditions; A third judgment module is used to judge whether the traffic influencing factor is within the average value preset by the airport management terminal; a third execution module, which is used for, if not, establishing an exclusive channel window for the passenger group based on the original management strategy of the airport management terminal, configuring required service content for the passenger group according to the exclusive channel window, updating the exclusive channel window and the required service content to the airport management terminal in real time, and displaying the usage of the exclusive channel window and the required service content to the passengers in real time through the airport management terminal, wherein the exclusive channel window specifically includes an exclusive security check channel, an exclusive channel for family passengers and an exclusive boarding gate.

[0014] Furthermore, the execution module also includes: A collection unit, used to calculate the difference value between the prediction result and the actual value based on a preset error metric, identify the change trend of the prediction error through the difference value, and collect the prediction deviation in a specific period from the change trend; A judgment unit, used to judge whether the prediction deviation has a corresponding prediction weight, wherein the prediction weight specifically includes a traffic weight, a weather weight and a flow weight; An execution unit is used to adjust the model parameters of the prediction model according to the prediction deviation, add corresponding weight conditions in the prediction model in real time, obtain the interaction characteristics of the latest data and the weight conditions, generate joint impact parameters of the prediction results according to the interaction characteristics, input the joint impact parameters into the prediction model, and reveal the joint impact value of the joint impact parameters on the prediction results, wherein the weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information.

[0015] The present invention provides a method and system for predicting airport ground transportation passenger flow, which has the following beneficial effects: The present invention forms a dynamic data pool by integrating flight dynamic data, traffic flow data, weather forecast data and passenger movement data, thereby ensuring that the system has a comprehensive and timely information basis, enabling the system to dynamically update and timely reflect the real-time conditions of the airport and its surroundings, thereby improving the accuracy and response speed of the prediction. At the same time, by judging whether the dynamic data pool can execute the preset data update strategy, it is ensured that subsequent prediction and scheduling operations are triggered only when the data is sufficiently complete and up-to-date, thereby avoiding inaccurate predictions caused by insufficient or outdated data, thereby improving the reliability of system decision-making. Moreover, by applying a pre-trained prediction model, a rolling prediction is made for the latest data, thereby being able to dynamically and continuously predict traffic conditions, enabling the system to adjust the prediction results in real time, and adaptively trigger traffic management measures, thereby improving the flexibility and accuracy of traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for predicting passenger flow in airport ground transportation according to the present invention; Figure 2 The present invention is a structural block diagram of an embodiment of the airport ground transportation passenger flow prediction system. DETAILED DESCRIPTION

[0017] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The implementation of the objectives, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Reference Figure 1, is a method for predicting airport ground transportation passenger flow in one embodiment of the present invention, comprising: S1: Based on the real-time data pre-collected by the airport management terminal, the real-time data is integrated and summarized to form a dynamic data pool, wherein the real-time data specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data; S2: Determine whether the dynamic data pool can execute the data update strategy preset by the airport management terminal; S3: If yes, then apply the pre-trained prediction model to perform rolling prediction on the latest data in the dynamic data pool to obtain prediction results, adaptively trigger the traffic management measures preset by the airport management terminal according to the prediction results, dynamically dispatch the traffic resources in the area around the airport, and receive feedback information uploaded by passengers in the area around the airport through the airport management terminal, wherein the traffic management measures specifically include adjusting the duration of traffic lights, optimizing vehicle dispatching and issuing congestion warnings, and the traffic resources specifically include increasing traffic capacity, opening additional lanes and activating temporary parking lots; S4: Determine whether the feedback information matches a response event corresponding to the area surrounding the airport; S5: If so, the flight information pre-entered by the passenger into the airport management terminal is collected, the traffic data corresponding to the response event and the flight information are input into the prediction model, a dynamic recommended route required by the passenger is generated, and the dynamic recommended route is synchronized from the airport management terminal to the passenger's preset terminal, wherein the dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport.

[0020] In this embodiment, the system integrates and summarizes the real-time data pre-collected by the airport management terminal, which specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data, to form a dynamic data pool. The system then determines whether the dynamic data pool can execute the data update strategy pre-set by the airport management terminal to execute the corresponding steps. For example, when the system determines that the dynamic data pool cannot execute the data update strategy pre-set by the airport management terminal, the system will believe that the processing power or storage capacity of the dynamic data pool may have reached the upper limit, resulting in the system being unable to execute new data update operations. The system will recommend the airport management team to increase the processing power or storage capacity of the system based on the actual situation. , such as expanding server resources or optimizing database structure, compressing data, or cleaning out outdated or low-value data, freeing up storage space, improving system processing efficiency, and performing data update operations in batches to avoid processing a large amount of data at one time and causing the system to be overloaded; for example, when the system determines that the dynamic data pool can execute the data update strategy pre-set by the airport management terminal, the system will consider that the dynamic data pool can execute new data update operations, and the system will apply the pre-trained prediction model to perform rolling predictions on the latest data in the dynamic data pool. After obtaining the prediction results, the traffic management measures pre-set by the airport management terminal are adaptively triggered according to these prediction results. The traffic management measures specifically include adjusting the traffic lights. The system can optimize vehicle scheduling and issue congestion warnings, dynamically dispatch traffic resources in the area around the airport. Traffic resources specifically include increasing traffic capacity, opening additional lanes and enabling temporary parking lots, and receiving feedback information uploaded by passengers in the area around the airport through the airport management terminal; the system uses pre-trained prediction models to make rolling predictions on the latest data in the dynamic data pool. The system can capture information such as traffic flow, flight dynamics and weather changes in real time, so that the system can more accurately predict future traffic conditions and respond quickly, reduce prediction errors, and improve the reaction speed of the overall system. At the same time, it can adaptively trigger traffic management measures based on the prediction results, such as adjusting the duration of traffic lights, optimizing vehicle scheduling, issuing congestion warnings, etc. Dynamically adjust the traffic conditions in the area around the airport to reduce traffic congestion, improve traffic flow, and enhance passengers' travel experience. Guided by the prediction results, the system can dynamically dispatch traffic resources, such as increasing traffic capacity, opening additional lanes, and enabling temporary parking lots. It can effectively respond to passenger flow peaks or emergencies, maximize the use of available resources, optimize traffic operation efficiency, and ensure the orderly operation of traffic in the airport and its surrounding areas. The airport management terminal receives and processes feedback information uploaded by passengers, so that the actual experience of passengers can directly affect traffic management decisions. This feedback mechanism not only improves passenger participation and satisfaction, but also helps managers understand passenger needs more accurately, thereby optimizing subsequent traffic management strategies;The system then determines whether the feedback information matches the response events corresponding to the area around the airport to execute the corresponding steps; for example, when the system determines that the feedback information uploaded by the passenger in the area around the airport cannot match the response events corresponding to the area around the airport, the system will believe that the feedback information uploaded by the passenger may contain errors or misunderstandings, resulting in the system being unable to match it with the actual events. The system will verify the accuracy of the feedback information by cross-verifying other data sources (such as traffic monitoring videos, other passenger feedback). If a discrepancy is found, the system will prompt the passenger to resubmit the feedback or automatically adjust the feedback information. In the case of inaccurate feedback information, the system will send a request to the passenger for further confirmation or to provide more detailed information to improve the accuracy of the feedback. At the same time, based on the new feedback information, the system's response event library will be expanded to include these uncovered events in the system's processing scope so that similar feedback can be identified and matched in the future, and unmatched feedback information will be classified and recorded as a basis for subsequent improvement and expansion of the system. The system ensures that the system is gradually improved to cover more possible events, and performs data integrity checks when receiving feedback information to ensure that the data format is correct and the content is complete. If errors are found, the system requests passengers to resubmit feedback, and analyzes potential problems in the transmission process, such as network delays and packet loss, by checking system logs, and performing repairs and optimizations to ensure that feedback information can be accurately transmitted and interpreted; for example, when the system determines that the feedback information uploaded by passengers in the area surrounding the airport can match the response event corresponding to the area surrounding the airport, the system will believe that the response event corresponding to the feedback information has actually occurred in the area surrounding the airport. The system will collect the flight information pre-entered by the passenger into the airport management terminal, input the traffic data and flight information corresponding to the response event into the prediction model, and generate the dynamic recommended route currently required by the passenger. The dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport, and the dynamic recommended route is synchronized from the airport management terminal to the passenger's pre-set mobile terminal;By collecting passengers' flight information and combining it with actual response events, the system can generate a dynamic recommended route that best meets the needs of each passenger. This personalized service can provide the best travel plan based on real-time conditions, reduce passengers' waiting time and travel inconvenience in and around the airport, and improve passengers' smart flight display experience in the airport. At the same time, it generates the fastest traffic route based on the current traffic conditions and passengers' flight time to ensure that passengers can reach their destination in the shortest time. Through the adjustment of dynamic recommended routes, it can effectively avoid congested sections or delays and maximize the travel efficiency of passengers. In addition, it collects and analyzes feedback information in real time. After confirming the occurrence of a response event, it can quickly adjust the travel route of passengers. This dynamic response mechanism can quickly provide passengers with alternative solutions when emergencies (such as traffic accidents, temporary road closures, etc.) occur, avoiding unnecessary delays or troubles. By providing passengers with personalized dynamic recommended routes, the system can more reasonably dispatch available transportation tools in the airport. This precise dispatch can not only reduce unnecessary resource consumption, but also optimize the utilization efficiency of airport transportation resources and avoid traffic congestion or insufficient services caused by improper resource allocation. ;

[0021] It should be noted that the specific example of the training process of the prediction model is as follows: Taking the prediction of traffic congestion around the airport as an example, the whole process is explained in detail. First, the system obtains historical data from the airport management system, traffic monitoring system and weather forecast center for the past two years. These data include flight dynamic information (such as flight take-off and landing times, delays, etc.), traffic flow data (such as the number of vehicles on roads around the airport, average speed, etc.), weather data (such as temperature, precipitation, wind speed, etc.) and passenger movement data (such as the transportation methods used by passengers, travel time, etc.). These data provide the basis for model training. The system then cleans and standardizes the collected raw data to ensure data quality. The steps of data cleaning include removing noise data, filling missing values ​​and handling outliers. Standardization converts data of different scales into the same range so that they can be correctly understood by the model. The system then extracts more meaningful features from the raw data through feature engineering, such as creating a feature called "flight density" to indicate the density of flights at an airport in a certain period of time, and creating a "weather index" to comprehensively evaluate the impact of weather on traffic. In terms of model selection, the system uses the LSTM (Long Short-Term Memory Network) model because the LSTM model is very powerful in processing time series data and can effectively capture the long-term and short-term temporal dependencies in the data. The system uses the traffic flow, flight density and weather index of the past 6 hours as input to predict the traffic flow in the next hour; for example, if the system has historical data from 10 am to 3 pm, the system will use this data to predict the traffic situation from 3 pm to 4 pm; During the model training phase, the system divides the pre-processed data into a training set and a validation set. 80% of the data is used for training and 20% for validation. The training set is used to adjust the model parameters so that the model can predict future traffic flow as accurately as possible. In this process, the LSTM model minimizes the error between the predicted results and the actual flow through repeated iterations, and gradually optimizes the model's performance. After the model training is completed, the system evaluates the model on the validation set. The system selects the mean square error (MSE) as the evaluation indicator because it can effectively measure the difference between the model prediction value and the actual value. If the model performs well on the validation set with a small error, it means that the model has a high prediction accuracy and can accurately predict future traffic flow. However, if the model is found to perform poorly after evaluation, the system will optimize and tune the model. The optimization methods include adjusting the number of layers, number of neurons and learning rate of the LSTM network, or reselecting features, or even introducing new data sources to improve the performance of the model. The system will also combine other models (such as random forest or XGBoost) to enhance the generalization ability of the model through ensemble learning. After the model has been evaluated and optimized, the system deploys it to the airport management system, enabling it to receive new data and make predictions in real time. The model generates traffic forecasts for the next hour based on the latest flight dynamics, traffic flow and weather information, and provides passengers with dynamic travel route recommendations based on these forecasts. For example, if it is predicted that a certain road may be congested in the next hour, the system will recommend alternative routes for passengers to avoid congestion. Finally, continuous monitoring and updating of the model is equally important. After the model is deployed, the system needs to regularly monitor its performance in actual operation. If the model's prediction error begins to increase over time, it may be because new external factors have not been included in the model's training. At this time, the system needs to collect data again and update or retrain the model to ensure that the model can always maintain high-precision prediction capabilities. In summary, through the above steps, we can train and deploy an LSTM model that can predict traffic congestion around the airport. This not only improves the efficiency of airport traffic management, but also provides passengers with faster and more accurate travel services, significantly improving the overall travel experience.

[0022] In this embodiment, before step S3 of receiving, through the airport management terminal, feedback information uploaded by passengers in the surrounding area of ​​the airport, the method further includes: S301: Based on the passenger information pre-entered by the passenger into the airport management terminal, identify the passenger group corresponding to the passenger, and collect the traffic influencing factors of the passenger group for the feedback information, wherein the passenger information specifically includes ticket purchase information, entry status of the terminal waiting area and boarding record, the passenger group specifically includes business passengers, family passengers and leisure passengers, and the traffic influencing factors specifically include travel time, distance and weather conditions; S302: Determine whether the traffic influencing factor is within an average value preset by the airport management terminal; S303: If not, then an exclusive channel window for the passenger group is established based on the original management strategy of the airport management terminal, and the required service content is configured for the passenger group according to the exclusive channel window. The exclusive channel window and the required service content are updated to the airport management terminal in real time, and the usage of the exclusive channel window and the required service content is displayed to the passengers in real time through the airport management terminal, wherein the exclusive channel window specifically includes an exclusive security check channel, an exclusive channel for family passengers, and an exclusive boarding gate.

[0023] In this embodiment, the system identifies the passenger group to which the passenger belongs based on the passenger information pre-entered into the airport management terminal by the passenger, and the passenger groups specifically include business passengers, family passengers and leisure passengers, and collects the traffic influencing factors of these passenger groups for feedback information, and the traffic influencing factors specifically include travel time, distance and weather conditions. Then the system determines whether these traffic influencing factors are within the average values ​​pre-set by the airport management terminal to execute the corresponding steps; for example, when the system determines that the traffic influencing factors of the passenger group for feedback information are within the average values ​​pre-set by the airport management terminal, the system will consider that the current traffic conditions and passenger travel conditions are relatively stable, and the system does not need to make emergency adjustments or take additional measures. The system will continue to implement existing traffic management measures, including maintaining the current signal light duration, vehicle scheduling and traffic flow control strategies, and will not trigger additional traffic management measures or scheduling changes, such as continuing to operate according to the originally planned signal light timing, without the need to open additional lanes or activate temporary parking lots, and continuously monitoring the passenger's feedback information. information and traffic conditions in order to identify possible changes or potential problems in a timely manner, regularly update and analyze real-time data to ensure that all parameters remain within the expected range, and be prepared to deal with any emergencies, and record and analyze the data and feedback of the current status to form baseline data for future comparison and optimization, record the feedback information in the system, and update the data model so that it can respond faster when encountering similar situations in the future; for example, when the system determines that the traffic influencing factors of the passenger group for the feedback information are not within the average value pre-set by the airport management terminal, the system will consider that the current traffic conditions affect the travel of passengers, and the system will establish an exclusive channel window for the passenger group based on the original management strategy of the airport management terminal. The exclusive channel window specifically includes an exclusive security check channel, a family passenger exclusive channel and an exclusive boarding gate. According to different exclusive channel windows, the corresponding demand service content is configured for the passenger group, and the exclusive channel window and the demand service content are updated to the airport management terminal in real time, and the use of the exclusive channel window and the demand service content is displayed to the passengers in real time through the airport management terminal;The system sets up exclusive security check channels, exclusive channels for family passengers and exclusive boarding gates for different passenger groups, effectively diverting passengers, reducing congestion in the main security check areas and boarding gates, reducing passenger queuing time, and improving the efficiency of security check and boarding, thereby alleviating the overall airport traffic pressure. At the same time, it provides personalized and exclusive services to meet the special needs of different passengers, improve passenger satisfaction and travel comfort, and dynamically adjusts and optimizes the configuration of exclusive channel windows according to real-time data and forecast results, making resource allocation more flexible, effectively responding to changing passenger flow, and avoiding waste or shortage of resources. In addition, the use of exclusive channel windows and demand service content is updated in real time through the airport management terminal to ensure that passengers can obtain the latest information, that is, passengers can timely understand the opening status and use of each exclusive channel window, helping them to better plan their travel and avoid misunderstandings or inconveniences caused by information lags. ;

[0024] In this embodiment, the pre-trained prediction model is applied to perform rolling prediction on the latest data in the dynamic data pool, and the step S3 of obtaining the prediction result further includes: S31: calculating the difference value between the prediction result and the actual value based on a preset error metric, identifying the change trend of the prediction error through the difference value, and collecting the prediction deviation in a specific period from the change trend; S32: Determine whether the prediction deviation has a corresponding prediction weight, wherein the prediction weight specifically includes a traffic weight, a weather weight, and a flow weight; S33: If so, adjust the model parameters of the prediction model according to the prediction deviation, add corresponding weight conditions in the prediction model in real time, obtain the interaction characteristics between the latest data and the weight conditions, generate the joint impact parameters of the prediction results according to the interaction characteristics, input the joint impact parameters into the prediction model, and reveal the joint impact value of the joint impact parameters on the prediction results, wherein the weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information.

[0025] In this embodiment, the system calculates the difference value between the prediction result and the actual value based on a preset error metric, identifies the changing trend of the prediction error through the difference value, collects the prediction deviation in a specific time period from different changing trends, and then determines whether the prediction deviation has a corresponding prediction weight, and the prediction weight specifically includes traffic weight, weather weight and flow weight, so as to execute the corresponding steps; for example, when the system determines that there is no corresponding prediction weight for the prediction deviation, the system will consider that the traffic weight, weather weight or flow weight corresponding to the prediction deviation is not found, indicating that the influencing factors set by these weights fail to cover all factors that cause the prediction error. The system will analyze the deviation data and error indicators, confirm whether the cause of the prediction deviation is related to the traffic, weather or flow weight, check whether there are other influencing factors that have not been considered, compare the actual data with the predicted data, use statistical analysis methods to find potential factors affecting the deviation, and check whether there are new patterns or abnormal situations in the historical data. At the same time, re-evaluate and optimize the prediction model, check whether there are any problems with the model structure, redesign the model architecture or change the model parameters, increase the scope and depth of data collection, including collecting new data types or sources, ensuring that the model can capture more influencing factors, and expand the data source, including collecting more real-time data or historical data, especially those data that may affect the prediction results. Unconsidered data; for example, when the system determines that there is a corresponding prediction weight for the prediction deviation, the system will consider that the prediction deviation is related to the factors of the prediction weight. The system will adjust the model parameters of the prediction model according to the prediction deviation, and add corresponding weight conditions to the prediction model in real time. The weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information, obtain the interactive characteristics of the latest data and weight conditions, generate joint impact parameters of the prediction results based on the interactive characteristics, input the joint impact parameters into the prediction model, and reveal the joint impact value of the joint impact parameters on the prediction results;By adjusting the model parameters of the prediction model and adding new weight conditions in real time, the system can more accurately capture and consider various factors that affect the prediction results, so that the model can better adapt to the dynamically changing environment and improve the prediction accuracy of the actual situation. For example, adding sudden weather data can help the model better predict the traffic conditions caused by weather changes. At the same time, the system can quickly adjust the model according to emergencies (such as traffic accidents, flight delays, etc.) to ensure that the prediction results can reflect the actual situation in a timely manner, improve the ability to respond to emergencies, and reduce the prediction deviation caused by emergencies, thereby optimizing traffic management and passenger services. In addition, by obtaining the interactive characteristics of the latest data and weight conditions, the relationship between various factors can be revealed, the robustness of the model in complex environments can be improved, the adaptability of the model to complex and changing environments can be improved, and the stability and reliability of the prediction results can be enhanced. Finally, by generating the joint influence parameters of the prediction results, the system can understand in detail the impact of each weight condition on the prediction results, revealing the joint impact of different weight conditions on the prediction results, which is helpful to formulate more reasonable traffic management strategies and emergency plans. ;

[0026] It should be noted that the interaction characteristics of the latest data and the weight condition are obtained, and the joint impact parameter of the prediction result is generated according to the interaction characteristics. The specific examples are as follows: Assume that you need to predict the traffic flow in the area around the airport, temporarily including the following factors: Real-time data: traffic flow data, weather data, holiday information, traffic accident data; Weight conditions: traffic flow weight, weather weight, holiday weight; Traffic flow data: vehicle flow during the current period; Weather data: current weather conditions (e.g., rainfall, temperature); Holiday information: whether it is a holiday; Traffic accident data: recent traffic accident information; Weather and traffic flow: Analysis found that traffic flow decreased by 20% under rainy conditions; Holidays and traffic flow: Traffic flow increases by 15% during holidays; Traffic accidents and traffic flow: When a traffic accident occurs, the traffic flow decreases by 30%; Combined impact parameter: Considering the combined impact of rainfall, holidays and traffic accidents, the calculated combined impact parameter is: In the case of rainfall and holidays, the traffic volume is expected to decrease by 25%; Update model: Input the combined impact parameters (25% reduction) into the traffic flow prediction model; Verification results: After the model was updated, the forecast results showed that the traffic flow was indeed in line with the expected reduction, thus improving the accuracy of the forecast; In summary, by acquiring the interactive features of the latest data and weight conditions and generating joint influencing parameters, the system can more accurately reflect the actual situation, enable the prediction model to be dynamically adjusted, and consider the combined impact of multiple factors, thereby improving the accuracy of the prediction results and optimizing traffic management decisions.

[0027] In this embodiment, the step S5 of generating the dynamic recommended route required by the passenger and synchronizing the dynamic recommended route from the airport management terminal to the passenger's preset terminal further includes: S51: Based on the transportation preference preset by the passenger at the airport management terminal, obtaining the passenger's feedback on the dynamically recommended route; S52: Determine whether the feedback content agrees to apply the dynamic recommended route; S53: If not, shielding the route content without the traffic preference in the dynamically recommended route, generating a specific time difference between the route content and the best recommended route, and synchronizing the specific time difference in real time to the mobile terminal preset by the passenger.

[0028] In this embodiment, the system obtains the feedback content of the passengers on the dynamic recommended route based on the transportation preferences pre-set by the passengers in the airport management terminal, and then the system determines whether the feedback content agrees to apply the dynamic feedback route to execute the corresponding steps; for example, when the system determines that the passenger's feedback content on the dynamic recommended route is to agree to apply the dynamic recommended route, the system will consider that the passenger agrees to adaptively replace the currently applied navigation route with the dynamic recommended route, and the system will confirm the passenger's feedback content to ensure that he clearly agrees to apply the dynamic recommended route, receive and parse the feedback information provided by the passenger through the voice recognition application of the airport management terminal, verify the feedback content, ensure that the feedback indicates that he agrees to apply the recommended route, and at the same time, according to the confirmed dynamic recommended route, update the navigation system settings of the passenger in the airport management terminal, set the recommended route as the current navigation route, and if the passenger uses the navigation application of the mobile terminal, the system will synchronize the dynamic recommended route to the application, and generate the latest navigation route data, and push it to the passenger's terminal device, and update the detailed information of the dynamic recommended route (such as path, estimated time, traffic conditions, etc.) to the navigation system; for example When the system determines that the passenger's feedback on the dynamic recommended route is that he / she disagrees with the application of the dynamic recommended route, the system will consider that the passenger disagrees with the application of the dynamic recommended route because the dynamic recommended route does not contain the passenger's preferred transportation preferences. The system will block the route content without transportation preferences in the dynamic recommended route, generate the specific time difference between the route content and the best recommended route, and synchronize the specific time difference in real time to the mobile terminal pre-set by the passenger; the system ensures that the passenger's personal preferences and transportation preferences are respected and considered, and improves the passenger's satisfaction and comfort. By blocking routes that do not meet the passenger's preferences, the system can optimize the recommendation strategy and improve the accuracy and practicality of the recommendation. At the same time, it generates and synchronizes the specific time difference between the route content and the best recommended route, so that the passenger can clearly understand the time difference between choosing the dynamic recommended route and the best route, thereby helping them make more informed decisions and improving the passenger experience. In addition, providing time difference information enhances the transparency of the system, allowing passengers to understand the specific differences between different route options and improve the trust of the system. That is, passengers can quickly evaluate whether they are willing to accept the dynamic recommended route or choose other routes through the time difference information, thereby improving decision-making efficiency.

[0029] In this embodiment, the step S2 of judging whether the dynamic data pool can execute the data update strategy preset by the airport management terminal further includes: S21: Based on the simulated load preset by the airport management terminal, obtaining the maximum number of concurrent requests that the dynamic data pool can accommodate; S22: Determine whether the dynamic data pool can run stably for a preset period of time when it is at the maximum concurrent request amount; S23: If so, monitor the performance parameters of the dynamic data pool in real time, apply a preset sharp simulated traffic input to the dynamic data pool, and record the abnormal information of the dynamic data pool under different loads, wherein the performance parameters specifically include response time, throughput and resource usage, and the abnormal information specifically includes timeout, failed request and resource exhaustion.

[0030] In this embodiment, the system obtains the maximum concurrent request volume that the dynamic data pool can accommodate based on the simulated load pre-set by the airport management terminal, and then the system determines whether the dynamic data pool can stably operate for a pre-set period of time when it is at the maximum concurrent request volume, so as to execute the corresponding steps; for example, when the system determines that the dynamic data pool is at the maximum concurrent request volume and the stable operation cannot reach the pre-set period of time, the system will consider that the system's load capacity or performance is insufficient and cannot effectively handle high concurrent requests. The system will use performance monitoring tools to monitor the resource consumption and performance of various parts of the system, and identify the main bottlenecks that cause the system to be unable to operate stably, such as excessive CPU usage. High or slow database query, introduce a load balancer, distribute requests to multiple server nodes, reduce the load pressure of a single node, improve database performance by adding indexes, optimizing query statements, sharding or partitioning, and split the system into multiple independent service modules, improve the overall processing capacity through a distributed architecture, introduce a cache system to reduce the frequency of direct database queries, improve data response speed, and use an asynchronous processing mechanism for non-real-time requests to queue these requests for processing, reducing the real-time pressure of the system; for example, when the system determines that the dynamic data pool is at the maximum concurrent request volume and can run stably for a preset period of time, the system The system will consider that the load capacity of the system is sufficient to handle high concurrent requests. The system will monitor the performance parameters of the dynamic data pool in real time. The performance parameters include response time, throughput and resource usage. The application will input the pre-set sharp simulated traffic into the dynamic data pool and record the abnormal information of the dynamic data pool under different loads. The abnormal information includes timeout, failed request and resource exhaustion. By real-time monitoring of performance parameters such as response time, throughput and resource usage, the system can continuously detect its performance under high load to ensure stability during long-term operation. If the response time gradually increases or resource usage reaches a critical point, the system can promptly issue an alarm and take corresponding measures. By taking measures to prevent potential performance degradation and applying sharp simulated traffic tests, the system can identify problems that may occur in extreme situations in advance, such as timeouts, failed requests, and resource exhaustion, thereby preventing failures in actual operation. After simulating a large amount of burst traffic input, the system may find that the request failure rate increases under certain specific conditions, which can prompt developers to optimize related processing mechanisms. By recording abnormal information and analyzing its causes, the system can enhance its fault tolerance and optimize the adaptive adjustment strategy under extreme loads. The system can automatically trigger the resource expansion mechanism before resources are exhausted or adjust the timeout parameters when timeout events occur frequently to ensure service continuity.

[0031] In this embodiment, the step S4 of determining whether the feedback information matches the response event corresponding to the airport surrounding area further includes: S41: Acquiring location information of the passenger in the surrounding area of ​​the airport based on the mobile terminal of the passenger pre-connected to the airport management terminal; S42: Determine whether the location information is within a preset range of the response event; S43: If yes, the response event is synchronously published to the online device pre-connected to the airport management terminal, and a corresponding processing time is adaptively generated according to the response event, wherein the online device specifically includes airport traffic equipment and airport visitor equipment.

[0032] In this embodiment, the system obtains the location information of the passenger in the area around the airport based on the mobile terminal pre-connected by the passenger to the airport management terminal to realize regional search, and then the system determines whether the location information is within the preset range of the response event to execute the corresponding steps; for example, when the system determines that the location information of the passenger in the area around the airport is not within the preset range of the response event, the system will consider that the passenger's location is not within the range that needs to be paid attention to or processed, that is, the feedback information is untrue, so the current response event has no direct impact on the passenger, the system will check and adjust the preset range of the response event to ensure that all areas that may be affected are covered, expand the preset range of the response event according to actual conditions to ensure that the areas that may be affected are included, and if the passenger is indeed not within the scope of the event, the system will choose not to send relevant notifications to avoid unnecessary interference, record cases where the passenger's location information does not match the event range, generate possible reasons, and establish a passenger feedback mechanism, the passenger can manually confirm or adjust his position to improve the accuracy of positioning; for example, when the system determines that the passenger's location information in the area around the airport is within the preset range of the response event, the system will consider that the passenger's location will be affected by the response event, that is, the feedback information is true, The system will synchronously publish the response event to the online devices pre-connected to the airport management terminal. The online devices specifically include airport traffic equipment and airport visitor equipment, and the corresponding waiting time will be adaptively generated according to the response event. By synchronizing the response event to the online devices pre-connected to the airport management terminal in real time, the system can respond quickly and take appropriate countermeasures to shorten the event processing time. When traffic congestion or emergencies occur, airport traffic equipment such as traffic lights and traffic signs can be adjusted immediately to reduce the impact on passenger travel. At the same time, timely release of response event information to passengers can improve passengers' awareness of the current situation and help them make safer decisions. If a sudden severe weather event affects the area around the airport, the system can issue warnings to passengers through visitor equipment such as information screens or mobile applications, suggesting that they adjust their travel plans. According to the waiting time adaptively generated by the response event, the system can reasonably dispatch resources, optimize airport traffic management, and avoid waste of resources or untimely dispatch. During peak hours, if the system predicts that a certain area will be severely congested, the system can dispatch more vehicles in advance or open additional lanes to relieve pressure. Through real-time updates and responses, the system can reduce passenger waiting time and provide a smoother and more comfortable travel experience.

[0033] In this embodiment, based on the real-time data pre-collected by the airport management terminal, the step S1 of integrating and aggregating the real-time data to form a dynamic data pool includes: S11: Based on the traffic organization preset by the airport management terminal, the traffic organization is uniformly arranged on a pre-built cloud data platform, and real-time data uploaded by the traffic organization to the cloud data platform is collected, wherein the traffic organization specifically includes a traffic management department, an airport management department and a weather forecast agency; S12: Determine whether the real-time data has a corresponding timestamp format; S13: If not, the revoked data without the timestamp format is transferred to the information island preset by the cloud data platform, and the public access to the revoked data in the information island is restricted according to the access rights preset by the airport management terminal.

[0034] In this embodiment, the system is based on the traffic organization pre-set up by the airport management terminal. The traffic organization specifically includes the traffic management department, the airport management department and the weather forecast agency. These traffic organizations are uniformly arranged on a pre-built cloud data platform, and the real-time data uploaded to the cloud data platform by these traffic organizations are collected. Then the system determines whether these real-time data have the corresponding timestamp format to execute the corresponding steps; for example, when the system determines that the real-time data uploaded to the cloud data platform by the traffic organization has the corresponding timestamp format, the system will consider that these data have been correctly marked with time information and can accurately reflect the time point when the data was generated or collected. The system will check whether all uploaded data has not only time information but also time information. The format of the timestamp also includes all the required fields and content to ensure that the data is complete and correct. Run the data verification program to confirm that all data correctly contains the timestamp and other required information. At the same time, the real-time data with timestamps are synchronized and integrated in chronological order to ensure the temporal consistency between the data. All uploaded data are sorted according to the timestamp to ensure that the data processing order conforms to the time logic, and the data verified by the timestamp is stored in the cloud data platform, and regular backup is performed to ensure data security. A regular backup mechanism is set up to prevent data loss and ensure rapid recovery in the event of system failure or data loss; for example, when the system determines that the real-time data uploaded by the traffic organization to the cloud data platform does not have In this case, the system will consider that these data do not have time information and cannot accurately reflect the corresponding collection time point. The system will transfer the abolished data without timestamp format to the information island pre-set by the cloud data platform, and restrict the public access of the abolished data in the information island according to the access rights pre-set by the airport management terminal. By transferring the data without timestamp format to the information island, the system avoids the pollution of the overall data set by low-quality data, and ensures that the data used for analysis and decision-making in the system has sufficient accuracy and timeliness. If some traffic flow data cannot be correctly marked with timestamps due to format problems, the system will isolate these data to prevent them from affecting the accuracy of the traffic flow prediction model. At the same time, by limiting public access to abolished data, the system can prevent the misuse of these unverified data, thereby avoiding erroneous decisions or judgments caused by erroneous data. When the airport management department makes traffic scheduling decisions, the misuse of untimed data may lead to unreasonable resource allocation. Isolating such data can effectively avoid this situation, and isolating data without timestamps in information islands can provide a safe storage area for subsequent data cleaning and repair, making it possible to put the data back into use after repair. That is, authorized technicians can regularly check the data in the information islands, timestamp the parts that can be repaired, and put them back into use instead of completely discarding the data.

[0035] Reference Figure 2, is an airport ground transportation passenger flow prediction system in one embodiment of the present invention, comprising: The integration module 10 is used to integrate and summarize the real-time data pre-collected by the airport management terminal to form a dynamic data pool, wherein the real-time data specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data; A judgment module 20, used to judge whether the dynamic data pool can execute the data update strategy preset by the airport management terminal; An execution module 30 is used to apply the pre-trained prediction model to perform rolling prediction on the latest data in the dynamic data pool, if possible, to obtain prediction results, adaptively trigger the traffic management measures preset by the airport management terminal according to the prediction results, dynamically dispatch traffic resources in the area around the airport, and receive feedback information uploaded by passengers in the area around the airport through the airport management terminal, wherein the traffic management measures specifically include adjusting the duration of traffic lights, optimizing vehicle scheduling, and issuing congestion warnings, and the traffic resources specifically include increasing traffic capacity, opening additional lanes, and activating temporary parking lots; A second judgment module 40 is used to judge whether the feedback information matches the response event corresponding to the area surrounding the airport; The second execution module 50 is used to collect the flight information pre-entered by the passenger into the airport management terminal, input the traffic data corresponding to the response event and the flight information into the prediction model, generate a dynamic recommended route required by the passenger, and synchronize the dynamic recommended route from the airport management terminal to the passenger's preset terminal, wherein the dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport.

[0036] In this embodiment, the integration module 10 integrates and summarizes the real-time data pre-collected by the airport management terminal, which specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data, to form a dynamic data pool, and then the judgment module 20 judges whether the dynamic data pool can execute the data update strategy pre-set by the airport management terminal to execute the corresponding steps; for example, when the system determines that the dynamic data pool cannot execute the data update strategy pre-set by the airport management terminal, the system will believe that the processing power or storage capacity of the dynamic data pool may have reached the upper limit, resulting in the system being unable to execute new data update operations. The system will recommend the airport management team to increase the processing power of the system based on the actual situation. Or storage capacity, such as expanding server resources or optimizing database structure, compressing data at the same time, or cleaning out outdated or low-value data, freeing up storage space, improving system processing efficiency, and performing data update operations in batches to avoid processing a large amount of data at one time and causing the system to be overloaded; for example, when the system determines that the dynamic data pool can execute the data update strategy pre-set by the airport management terminal, the execution module 30 will think that the dynamic data pool can execute new data update operations, and the system will apply the pre-trained prediction model to perform rolling predictions on the latest data in the dynamic data pool. After obtaining the prediction results, the traffic management measures pre-set by the airport management terminal are adaptively triggered according to these prediction results. The traffic management measures specifically include Adjust the duration of traffic lights, optimize vehicle scheduling and issue congestion warnings, dynamically dispatch traffic resources in the area around the airport. Traffic resources specifically include increasing traffic capacity, opening additional lanes and enabling temporary parking lots, and receiving feedback information uploaded by passengers in the area around the airport through the airport management terminal; the system uses pre-trained prediction models to make rolling predictions on the latest data in the dynamic data pool. The system can capture information such as traffic flow, flight dynamics and weather changes in real time, so that the system can more accurately predict future traffic conditions and respond quickly, reduce prediction errors, and improve the reaction speed of the overall system. At the same time, it can adaptively trigger traffic management measures based on the prediction results, such as adjusting the duration of traffic lights, optimizing vehicle scheduling, and issuing congestion warnings. , can dynamically adjust the traffic conditions in the area around the airport, reduce traffic congestion, improve traffic flow, and enhance passengers' travel experience. Guided by the prediction results, the system can dynamically dispatch traffic resources, such as increasing traffic capacity, opening additional lanes, and enabling temporary parking lots. It can effectively respond to passenger flow peaks or emergencies, maximize the use of available resources, optimize traffic operation efficiency, and ensure the orderly operation of traffic in the airport and its surrounding areas. By receiving and processing feedback information uploaded by passengers through the airport management terminal, the actual experience of passengers can directly affect traffic management decisions. This feedback mechanism not only improves passenger participation and satisfaction, but also helps managers understand passenger needs more accurately, thereby optimizing subsequent traffic management strategies;Then the second judgment module 40 judges whether the feedback information matches the response event corresponding to the area around the airport to execute the corresponding steps; for example, when the system determines that the feedback information uploaded by the passenger in the area around the airport cannot match the response event corresponding to the area around the airport, the system will consider that the feedback information uploaded by the passenger may contain errors or misunderstandings, resulting in the system being unable to match it with the actual event. The system will verify the accuracy of the feedback information by cross-verifying other data sources (such as traffic monitoring videos, other passenger feedback). If a discrepancy is found, the system will prompt the passenger to resubmit the feedback or automatically adjust the feedback information. In the case of inaccurate feedback information, the system will send a request to the passenger for further confirmation or to provide more detailed information, thereby improving the accuracy of the feedback. At the same time, based on the new feedback information, the system's response event library is expanded to include these uncovered events in the system's processing scope so that similar feedback can be identified and matched in the future, and unmatched feedback information can be classified and recorded as a basis for subsequent improvement and expansion of the system. The system is based on the feedback information, ensuring that the system is gradually improved and covers more possible events. When receiving feedback information, a data integrity check is performed to ensure that the data format is correct and the content is complete. If an error is found, the system requests the passenger to resubmit the feedback, and by checking the system log, analyzes potential problems in the transmission process, such as network delay and data packet loss, and performs repairs and optimizations to ensure that the feedback information can be accurately transmitted and interpreted. For example, when the system determines that the feedback information uploaded by the passenger in the area surrounding the airport can match the response event corresponding to the area surrounding the airport, the second execution module 50 will believe that the response event corresponding to the feedback information has actually occurred in the area surrounding the airport. The system will collect the flight information pre-entered by the passenger into the airport management terminal, input the traffic data and flight information corresponding to the response event into the prediction model, and generate the dynamic recommended route currently required by the passenger. The dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport, and the dynamic recommended route is synchronized from the airport management terminal to the mobile terminal pre-set by the passenger.By collecting passengers' flight information and combining it with the actual response events, the system can generate a dynamic recommended route that best meets the needs of each passenger. This personalized service can provide the best travel plan based on real-time conditions, reduce passengers' waiting time and travel inconvenience in and around the airport, and improve the overall experience of passengers. At the same time, it generates the fastest traffic route based on the current traffic conditions and passengers' flight time to ensure that passengers can reach their destinations in the shortest time. Through the adjustment of the dynamic recommended route, it can effectively avoid congested sections or delays, and maximize the travel efficiency of passengers. In addition, the system collects and analyzes feedback information in real time, and can quickly adjust the travel route of passengers after confirming the occurrence of the response event. This dynamic response mechanism can quickly provide passengers with alternative solutions when emergencies (such as traffic accidents, temporary road closures, etc.) occur, avoiding unnecessary delays or troubles. By providing passengers with personalized dynamic recommended routes, the system can more reasonably dispatch available transportation tools in the airport. This precise dispatch can not only reduce unnecessary resource consumption, but also optimize the utilization efficiency of airport transportation resources, and avoid traffic congestion or insufficient services caused by improper resource allocation. ;

[0037] In this embodiment, it also includes: A collection module, for identifying the passenger group corresponding to the passenger based on the passenger information pre-entered into the airport management terminal, and collecting the traffic influencing factors of the passenger group for the feedback information, wherein the passenger information specifically includes ticket purchase information, entry into the terminal area and boarding records, the passenger groups specifically include business passengers, family passengers and leisure passengers, and the traffic influencing factors specifically include travel time, distance and weather conditions; A third judgment module is used to judge whether the traffic influencing factor is within the average value preset by the airport management terminal; a third execution module, which is used for, if not, establishing an exclusive channel window for the passenger group based on the original management strategy of the airport management terminal, configuring required service content for the passenger group according to the exclusive channel window, updating the exclusive channel window and the required service content to the airport management terminal in real time, and displaying the usage of the exclusive channel window and the required service content to the passengers in real time through the airport management terminal, wherein the exclusive channel window specifically includes an exclusive security check channel, an exclusive channel for family passengers and an exclusive boarding gate.

[0038] In this embodiment, the system identifies the passenger group to which the passenger belongs based on the passenger information pre-entered into the airport management terminal by the passenger, and the passenger groups specifically include business passengers, family passengers and leisure passengers, and collects the traffic influencing factors of these passenger groups for feedback information, and the traffic influencing factors specifically include travel time, distance and weather conditions. Then the system determines whether these traffic influencing factors are within the average values ​​pre-set by the airport management terminal to execute the corresponding steps; for example, when the system determines that the traffic influencing factors of the passenger group for feedback information are within the average values ​​pre-set by the airport management terminal, the system will consider that the current traffic conditions and passenger travel conditions are relatively stable, and the system does not need to make emergency adjustments or take additional measures. The system will continue to implement existing traffic management measures, including maintaining the current signal light duration, vehicle scheduling and traffic flow control strategies, and will not trigger additional traffic management measures or scheduling changes, such as continuing to operate according to the originally planned signal light timing, without the need to open additional lanes or activate temporary parking lots, and continuously monitoring the passenger's feedback information. information and traffic conditions in order to identify possible changes or potential problems in a timely manner, regularly update and analyze real-time data to ensure that all parameters remain within the expected range, and be prepared to deal with any emergencies, and record and analyze the data and feedback of the current status to form baseline data for future comparison and optimization, record the feedback information in the system, and update the data model so that it can respond faster when encountering similar situations in the future; for example, when the system determines that the traffic influencing factors of the passenger group for the feedback information are not within the average value pre-set by the airport management terminal, the system will consider that the current traffic conditions affect the travel of passengers, and the system will establish an exclusive channel window for the passenger group based on the original management strategy of the airport management terminal. The exclusive channel window specifically includes an exclusive security check channel, a family passenger exclusive channel and an exclusive boarding gate. According to different exclusive channel windows, the corresponding demand service content is configured for the passenger group, and the exclusive channel window and the demand service content are updated to the airport management terminal in real time, and the use of the exclusive channel window and the demand service content is displayed to the passengers in real time through the airport management terminal;The system sets up exclusive security check channels, exclusive channels for family passengers and exclusive boarding gates for different passenger groups, effectively diverting passengers, reducing congestion in the main security check areas and boarding gates, reducing passenger queuing time, and improving the efficiency of security check and boarding, thereby alleviating the overall airport traffic pressure. At the same time, it provides personalized and exclusive services to meet the special needs of different passengers, improve passenger satisfaction and travel comfort, and dynamically adjusts and optimizes the configuration of exclusive channel windows according to real-time data and forecast results, making resource allocation more flexible, effectively responding to changing passenger flow, and avoiding waste or shortage of resources. In addition, the use of exclusive channel windows and demand service content is updated in real time through the airport management terminal to ensure that passengers can obtain the latest information, that is, passengers can timely understand the opening status and use of each exclusive channel window, helping them to better plan their travel and avoid misunderstandings or inconveniences caused by information lags. ;

[0039] In this embodiment, the execution module further includes: A collection unit, used to calculate the difference value between the prediction result and the actual value based on a preset error metric, identify the change trend of the prediction error through the difference value, and collect the prediction deviation in a specific period from the change trend; A judgment unit, used to judge whether the prediction deviation has a corresponding prediction weight, wherein the prediction weight specifically includes a traffic weight, a weather weight and a flow weight; An execution unit is used to adjust the model parameters of the prediction model according to the prediction deviation, add corresponding weight conditions in the prediction model in real time, obtain the interaction characteristics of the latest data and the weight conditions, generate joint impact parameters of the prediction results according to the interaction characteristics, input the joint impact parameters into the prediction model, and reveal the joint impact value of the joint impact parameters on the prediction results, wherein the weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information.

[0040] In this embodiment, the system calculates the difference value between the prediction result and the actual value based on a preset error metric, identifies the changing trend of the prediction error through the difference value, collects the prediction deviation in a specific time period from different changing trends, and then determines whether the prediction deviation has a corresponding prediction weight, and the prediction weight specifically includes traffic weight, weather weight and flow weight, so as to execute the corresponding steps; for example, when the system determines that there is no corresponding prediction weight for the prediction deviation, the system will consider that the traffic weight, weather weight or flow weight corresponding to the prediction deviation is not found, indicating that the influencing factors set by these weights fail to cover all factors that cause the prediction error. The system will analyze the deviation data and error indicators, confirm whether the cause of the prediction deviation is related to the traffic, weather or flow weight, check whether there are other influencing factors that have not been considered, compare the actual data with the predicted data, use statistical analysis methods to find potential factors affecting the deviation, and check whether there are new patterns or abnormal situations in the historical data. At the same time, re-evaluate and optimize the prediction model, check whether there are any problems with the model structure, redesign the model architecture or change the model parameters, increase the scope and depth of data collection, including collecting new data types or sources, ensuring that the model can capture more influencing factors, and expand the data source, including collecting more real-time data or historical data, especially those data that may affect the prediction results. Unconsidered data; for example, when the system determines that there is a corresponding prediction weight for the prediction deviation, the system will consider that the prediction deviation is related to the factors of the prediction weight. The system will adjust the model parameters of the prediction model according to the prediction deviation, and add corresponding weight conditions to the prediction model in real time. The weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information, obtain the interactive characteristics of the latest data and weight conditions, generate joint impact parameters of the prediction results based on the interactive characteristics, input the joint impact parameters into the prediction model, and reveal the joint impact value of the joint impact parameters on the prediction results;By adjusting the model parameters of the prediction model and adding new weight conditions in real time, the system can more accurately capture and consider various factors that affect the prediction results, so that the model can better adapt to the dynamically changing environment and improve the prediction accuracy of the actual situation. For example, adding sudden weather data can help the model better predict the traffic conditions caused by weather changes. At the same time, the system can quickly adjust the model according to emergencies (such as traffic accidents, flight delays, etc.) to ensure that the prediction results can reflect the actual situation in a timely manner, improve the ability to respond to emergencies, and reduce the prediction deviation caused by emergencies, thereby optimizing traffic management and passenger services. In addition, by obtaining the interactive characteristics of the latest data and weight conditions, the relationship between various factors can be revealed, the robustness of the model in complex environments can be improved, the adaptability of the model to complex and changing environments can be improved, and the stability and reliability of the prediction results can be enhanced. Finally, by generating the joint influence parameters of the prediction results, the system can understand in detail the impact of each weight condition on the prediction results, revealing the joint impact of different weight conditions on the prediction results, which is helpful to formulate more reasonable traffic management strategies and emergency plans. ;

[0041] In this embodiment, the second execution module further includes: an acquisition unit, configured to acquire feedback from the passenger on the dynamically recommended route based on the transportation preference preset by the passenger at the airport management terminal; A second judgment unit is used to judge whether the feedback content agrees to apply the dynamic recommended route; The second execution unit is used for, if not, shielding the route content without the traffic preference in the dynamically recommended route, generating a specific time difference between the route content and the best recommended route, and synchronizing the specific time difference to the mobile terminal preset by the passenger in real time.

[0042] In this embodiment, the system obtains the feedback content of the passengers on the dynamic recommended route based on the transportation preferences pre-set by the passengers in the airport management terminal, and then the system determines whether the feedback content agrees to apply the dynamic feedback route to execute the corresponding steps; for example, when the system determines that the passenger's feedback content on the dynamic recommended route is to agree to apply the dynamic recommended route, the system will consider that the passenger agrees to adaptively replace the currently applied navigation route with the dynamic recommended route, the system will confirm the passenger's feedback content to ensure that he or she explicitly agrees to apply the dynamic recommended route, receive and parse the feedback information provided by the passenger through the airport management terminal or mobile terminal, verify the feedback content, ensure that the feedback indicates agreement to apply the recommended route, and at the same time, based on the confirmed dynamic recommended route, update the passenger's navigation system settings in the airport management terminal, set the recommended route as the current navigation route, and if the passenger uses the navigation application of the mobile terminal, the system will synchronize the dynamic recommended route to the application, generate the latest navigation route data, and push it to the passenger's terminal device, and update the detailed information of the dynamic recommended route (such as path, estimated time, traffic conditions, etc.) to the navigation system For example, when the system determines that the passenger's feedback on the dynamically recommended route is that he / she disagrees with the application of the dynamically recommended route, the system will consider that the passenger disagrees with the application of the dynamically recommended route because the dynamically recommended route does not contain the passenger's preferred transportation preferences. The system will block the route content without transportation preferences in the dynamically recommended route, generate the specific time difference between the route content and the best recommended route, and synchronize the specific time difference in real time to the passenger's pre-set mobile terminal; the system ensures that the passenger's personal preferences and transportation preferences are respected and taken into consideration, and improves the passenger's satisfaction and comfort. By blocking routes that do not meet the passenger's preferences, the system can optimize the recommendation strategy and improve the accuracy and practicality of the recommendation. At the same time, it generates and synchronizes the specific time difference between the route content and the best recommended route, so that the passenger can clearly understand the time difference between choosing the dynamically recommended route and the best route, thereby helping them make more informed decisions. Providing time difference information enhances the transparency of the system, allowing passengers to understand the specific differences between different route options and improve the trust in the system. That is, passengers can quickly evaluate whether they are willing to accept the dynamically recommended route or choose other routes through time difference information, thereby improving decision-making efficiency.

[0043] In this embodiment, the judgment module further includes: A second acquisition unit, configured to acquire a maximum number of concurrent requests that the dynamic data pool can accommodate based on a simulated load preset by the airport management terminal; A third judgment unit is used to judge whether the dynamic data pool can run stably for a preset period of time when it is at the maximum concurrent request amount; The third execution unit is used to monitor the performance parameters of the dynamic data pool in real time, apply a preset sharp simulated traffic input to the dynamic data pool, and record the abnormal information of the dynamic data pool under different loads, wherein the performance parameters specifically include response time, throughput and resource usage, and the abnormal information specifically includes timeout, failed request and resource exhaustion.

[0044] In this embodiment, the system obtains the maximum concurrent request volume that the dynamic data pool can accommodate based on the simulated load pre-set by the airport management terminal, and then the system determines whether the dynamic data pool can stably operate for a pre-set period of time when it is at the maximum concurrent request volume, so as to execute the corresponding steps; for example, when the system determines that the dynamic data pool is at the maximum concurrent request volume and the stable operation cannot reach the pre-set period of time, the system will consider that the system's load capacity or performance is insufficient and cannot effectively handle high concurrent requests. The system will use performance monitoring tools to monitor the resource consumption and performance of various parts of the system, and identify the main bottlenecks that cause the system to be unable to operate stably, such as excessive CPU usage. High or slow database query, introduce a load balancer, distribute requests to multiple server nodes, reduce the load pressure of a single node, improve database performance by adding indexes, optimizing query statements, sharding or partitioning, and split the system into multiple independent service modules, improve the overall processing capacity through a distributed architecture, introduce a cache system to reduce the frequency of direct database queries, improve data response speed, and use an asynchronous processing mechanism for non-real-time requests to queue these requests for processing, reducing the real-time pressure of the system; for example, when the system determines that the dynamic data pool is at the maximum concurrent request volume and can run stably for a preset period of time, the system The system will consider that the load capacity of the system is sufficient to handle high concurrent requests. The system will monitor the performance parameters of the dynamic data pool in real time. The performance parameters include response time, throughput and resource usage. The application will input the pre-set sharp simulated traffic into the dynamic data pool and record the abnormal information of the dynamic data pool under different loads. The abnormal information includes timeout, failed request and resource exhaustion. By real-time monitoring of performance parameters such as response time, throughput and resource usage, the system can continuously detect its performance under high load to ensure stability during long-term operation. If the response time gradually increases or resource usage reaches a critical point, the system can promptly issue an alarm and take corresponding measures. By taking measures to prevent potential performance degradation and applying sharp simulated traffic tests, the system can identify problems that may occur in extreme situations in advance, such as timeouts, failed requests, and resource exhaustion, thereby preventing failures in actual operation. After simulating a large amount of burst traffic input, the system may find that the request failure rate increases under certain specific conditions, which can prompt developers to optimize related processing mechanisms. By recording abnormal information and analyzing its causes, the system can enhance its fault tolerance and optimize the adaptive adjustment strategy under extreme loads. The system can automatically trigger the resource expansion mechanism before resources are exhausted or adjust the timeout parameters when timeout events occur frequently to ensure service continuity.

[0045] In this embodiment, the second determination module further includes: A third acquisition unit, configured to acquire location information of the passenger in the surrounding area of ​​the airport based on the mobile terminal of the passenger pre-connected to the airport management terminal; A fourth determination unit, configured to determine whether the location information is within a preset range of the response event; The fourth execution unit is used to synchronously publish the response event to the online device pre-connected to the airport management terminal, and adaptively generate a corresponding processing time according to the response event, wherein the online device specifically includes airport traffic equipment and airport visitor equipment.

[0046] In this embodiment, the system obtains the location information of the passenger in the area around the airport based on the mobile terminal that the passenger pre-connects to the airport management terminal, and then the system determines whether the location information is within the preset range of the response event to execute the corresponding steps; for example, when the system determines that the location information of the passenger in the area around the airport is not within the preset range of the response event, the system will consider that the passenger's location is not within the range that needs to be paid attention to or processed, that is, the feedback information is untrue, so the current response event has no direct impact on the passenger, the system will check and adjust the preset range of the response event to ensure that all areas that may be affected are covered, expand the preset range of the response event according to actual conditions to ensure that the areas that may be affected are included, and if the passenger is indeed not within the scope of the event, the system will choose not to send relevant notifications to avoid unnecessary interference, record cases where the passenger's location information does not match the event range, generate possible reasons, and establish a passenger feedback mechanism, whereby the passenger can manually confirm or adjust his or her location to improve the accuracy of positioning; for example, when the system determines that the passenger's location information in the area around the airport is within the preset range of the response event, the system will consider that the passenger's location will be affected by the response event, that is, the feedback information is authentic, and the system will The response event is synchronously published to the online devices pre-connected to the airport management terminal. The online devices specifically include airport traffic equipment and airport visitor equipment, and the corresponding waiting time is adaptively generated according to the response event. The system can respond quickly and take appropriate countermeasures to shorten the event processing time by synchronizing the response event to the online devices pre-connected to the airport management terminal in real time. When traffic congestion or emergencies occur, airport traffic equipment such as traffic lights and traffic signs can be adjusted immediately to reduce the impact on passenger travel. At the same time, timely release of response event information to passengers can improve passengers' awareness of the current situation and help them make safer decisions. If a sudden severe weather event affects the area around the airport, the system can issue warnings to passengers through visitor equipment such as information screens or mobile applications, suggesting that they adjust their travel plans. According to the waiting time adaptively generated by the response event, the system can reasonably dispatch resources, optimize airport traffic management, and avoid waste of resources or untimely dispatch. During peak hours, if the system predicts that a certain area will be severely congested, the system can dispatch more vehicles in advance or open additional lanes to relieve pressure. Through real-time updates and responses, the system can reduce passenger waiting time and provide a smoother and more comfortable travel experience.

[0047] In this embodiment, the integration module also includes: A second collection unit is used to uniformly arrange the traffic organization on a pre-built cloud data platform based on the traffic organization preset by the airport management terminal, and collect real-time data uploaded by the traffic organization to the cloud data platform, wherein the traffic organization specifically includes a traffic management department, an airport management department and a weather forecast agency; A fifth determination unit, used to determine whether the real-time data has a corresponding timestamp format; The fifth execution unit is used to transfer the abolished data that does not have the timestamp format to the information island preset by the cloud data platform if not, and limit the public access to the abolished data in the information island according to the access rights preset by the airport management terminal.

[0048] In this embodiment, the system is based on the traffic organization pre-set up by the airport management terminal. The traffic organization specifically includes the traffic management department, the airport management department and the weather forecast agency. These traffic organizations are uniformly arranged on a pre-built cloud data platform, and the real-time data uploaded to the cloud data platform by these traffic organizations are collected. Then the system determines whether these real-time data have the corresponding timestamp format to execute the corresponding steps; for example, when the system determines that the real-time data uploaded to the cloud data platform by the traffic organization has the corresponding timestamp format, the system will consider that these data have been correctly marked with time information and can accurately reflect the time point when the data was generated or collected. The system will check whether all uploaded data has not only time information but also time information. The format of the timestamp also includes all the required fields and content to ensure that the data is complete and correct. Run the data verification program to confirm that all data correctly contains the timestamp and other required information. At the same time, the real-time data with timestamps are synchronized and integrated in chronological order to ensure the temporal consistency between the data. All uploaded data are sorted according to the timestamp to ensure that the data processing order conforms to the time logic, and the data verified by the timestamp is stored in the cloud data platform, and regular backup is performed to ensure data security. A regular backup mechanism is set up to prevent data loss and ensure rapid recovery in the event of system failure or data loss; for example, when the system determines that the real-time data uploaded by the traffic organization to the cloud data platform does not have In this case, the system will consider that these data do not have time information and cannot accurately reflect the corresponding collection time point. The system will transfer the abolished data without timestamp format to the information island pre-set by the cloud data platform, and restrict the public access of the abolished data in the information island according to the access rights pre-set by the airport management terminal. By transferring the data without timestamp format to the information island, the system avoids the pollution of the overall data set by low-quality data, and ensures that the data used for analysis and decision-making in the system has sufficient accuracy and timeliness. If some traffic flow data cannot be correctly marked with timestamps due to format problems, the system will isolate these data to prevent them from affecting the accuracy of the traffic flow prediction model. At the same time, by limiting public access to abolished data, the system can prevent the misuse of these unverified data, thereby avoiding erroneous decisions or judgments caused by erroneous data. When the airport management department makes traffic scheduling decisions, the misuse of untimed data may lead to unreasonable resource allocation. Isolating such data can effectively avoid this situation, and isolating data without timestamps in information islands can provide a safe storage area for subsequent data cleaning and repair, making it possible to put the data back into use after repair. That is, authorized technicians can regularly check the data in the information islands, timestamp the parts that can be repaired, and put them back into use instead of completely discarding the data.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting passenger flow of airport ground transportation, characterized in that: The following steps are involved: Based on the real-time data pre-collected by the airport management terminal, the real-time data is integrated and summarized to form a dynamic data pool, wherein the real-time data specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data; Determining whether the dynamic data pool can execute the data update strategy preset by the airport management terminal; If yes, then the pre-trained prediction model is applied to perform rolling prediction on the latest data in the dynamic data pool to obtain prediction results, and the traffic management measures preset by the airport management terminal are adaptively triggered according to the prediction results, and the traffic resources in the area around the airport are dynamically dispatched, and feedback information uploaded by passengers in the area around the airport is received through the airport management terminal, wherein the traffic management measures specifically include adjusting the duration of traffic lights, optimizing vehicle dispatching, and issuing congestion warnings, and the traffic resources specifically include increasing traffic capacity, opening additional lanes, and activating temporary parking lots; Determining whether the feedback information matches a response event corresponding to the area surrounding the airport; If so, the flight information pre-entered by the passenger into the airport management terminal is collected, the traffic data corresponding to the response event and the flight information are input into the prediction model, a dynamic recommended route required by the passenger is generated, and the dynamic recommended route is synchronized from the airport management terminal to the passenger's preset terminal, wherein the dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport.

2. The method for predicting airport ground transportation passenger flow according to claim 1, characterized in that: Before the step of receiving, through the airport management terminal, feedback information uploaded by passengers in the surrounding area of ​​the airport, the step further includes: Based on the passenger information pre-entered into the airport management terminal by the passenger, the passenger group corresponding to the passenger is identified, and the traffic influencing factors of the passenger group for the feedback information are collected, wherein the passenger information specifically includes ticket purchase information, entry into the terminal waiting area and boarding records, the passenger groups specifically include business passengers, family passengers and leisure passengers, and the traffic influencing factors specifically include travel time, distance and weather conditions; Determining whether the traffic influencing factor is within an average value preset by the airport management terminal; If not, an exclusive channel window for the passenger group is established based on the original management strategy of the airport management terminal, and the required service content is configured for the passenger group according to the exclusive channel window. The exclusive channel window and the required service content are updated to the airport management terminal in real time, and the usage of the exclusive channel window and the required service content is displayed to the passengers in real time through the airport management terminal, wherein the exclusive channel window specifically includes an exclusive security check channel, an exclusive channel for family passengers and an exclusive boarding gate.

3. The method for predicting airport ground transportation passenger flow according to claim 1, characterized in that: The step of applying the pre-trained prediction model to perform rolling prediction on the latest data in the dynamic data pool to obtain the prediction result also includes: Calculate the difference between the predicted result and the actual value based on a preset error metric, identify the change trend of the prediction error through the difference, and collect the prediction deviation in a specific period from the change trend; Determining whether the prediction deviation has a corresponding prediction weight, wherein the prediction weight specifically includes traffic weight, weather weight and flow weight; If so, the model parameters of the prediction model are adjusted according to the prediction deviation, corresponding weight conditions are added in real time to the prediction model, the interaction characteristics between the latest data and the weight conditions are obtained, the joint impact parameters of the prediction results are generated according to the interaction characteristics, the joint impact parameters are input into the prediction model, and the joint impact value of the joint impact parameters on the prediction results is revealed, wherein the weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information.

4. The method for predicting airport ground transportation passenger flow according to claim 1, characterized in that: The step of generating the dynamic recommended route required by the passenger and synchronizing the dynamic recommended route from the airport management terminal to the passenger's preset terminal also includes: Based on the transportation preferences preset by the passenger at the airport management terminal, obtaining the passenger's feedback on the dynamically recommended route; Determining whether the feedback content agrees to apply the dynamic recommended route; If not, the route content without the traffic preference in the dynamically recommended route is shielded, a specific time difference between the route content and the best recommended route is generated, and the specific time difference is synchronized in real time to the mobile terminal preset by the passenger.

5. The method for predicting airport ground transportation passenger flow according to claim 1, characterized in that: The step of determining whether the dynamic data pool can execute the data update strategy preset by the airport management terminal also includes: Based on the simulated load preset by the airport management terminal, obtaining the maximum number of concurrent requests that the dynamic data pool can accommodate; Determine whether the dynamic data pool can run stably for a preset period of time when it is at the maximum concurrent request amount; If so, the performance parameters of the dynamic data pool are monitored in real time, a preset sharp simulated traffic is applied to the dynamic data pool, and abnormal information of the dynamic data pool under different loads is recorded, wherein the performance parameters specifically include response time, throughput and resource usage, and the abnormal information specifically includes timeout, failed request and resource exhaustion.

6. The method for predicting airport ground transportation passenger flow according to claim 1, characterized in that: The step of determining whether the feedback information matches the response event corresponding to the area surrounding the airport further includes: Based on the mobile terminal of the passenger pre-connected to the airport management terminal, obtaining the location information of the passenger in the surrounding area of ​​the airport; Determining whether the location information is within a preset range of the response event; If so, the response event is synchronously published to the online device pre-connected to the airport management terminal, and the corresponding processing time is adaptively generated according to the response event, wherein the online device specifically includes airport traffic equipment and airport visitor equipment.

7. The method for predicting airport ground transportation passenger flow according to claim 1, characterized in that: The step of integrating and aggregating the real-time data pre-collected by the airport management terminal to form a dynamic data pool includes: Based on the traffic organization preset by the airport management terminal, the traffic organization is uniformly arranged on a pre-built cloud data platform, and real-time data uploaded by the traffic organization to the cloud data platform is collected, wherein the traffic organization specifically includes a traffic management department, an airport management department and a weather forecast agency; Determining whether the real-time data has a corresponding timestamp format; If not, the revoked data without the timestamp format will be transferred to the information island preset by the cloud data platform, and the public access to the revoked data in the information island will be restricted according to the access rights preset by the airport management terminal.

8. An airport ground transportation passenger flow prediction system, characterized in that: include: An integration module is used to integrate and summarize the real-time data pre-collected by the airport management terminal to form a dynamic data pool, wherein the real-time data specifically includes flight dynamic data, traffic flow data, weather forecast data and passenger movement data; A judgment module, used to judge whether the dynamic data pool can execute the data update strategy preset by the airport management terminal; an execution module, for, if possible, applying a pre-trained prediction model to perform rolling prediction on the latest data in the dynamic data pool, obtaining prediction results, adaptively triggering the traffic management measures preset by the airport management terminal according to the prediction results, dynamically dispatching the traffic resources in the area surrounding the airport, and receiving feedback information uploaded by passengers in the area surrounding the airport through the airport management terminal, wherein the traffic management measures specifically include adjusting the duration of traffic lights, optimizing vehicle dispatching, and issuing congestion warnings, and the traffic resources specifically include increasing traffic capacity, opening additional lanes, and activating temporary parking lots; A second judgment module is used to judge whether the feedback information matches the response event corresponding to the area around the airport; The second execution module is used to collect the flight information pre-entered by the passenger into the airport management terminal, input the traffic data corresponding to the response event and the flight information into the prediction model, generate a dynamic recommended route required by the passenger, and synchronize the dynamic recommended route from the airport management terminal to the passenger's preset terminal, wherein the dynamic recommended route is specifically the fastest transportation route obtained by combining the available transportation tools in the airport.

9. The airport ground transportation passenger flow prediction system according to claim 8, characterized in that: Also includes: A collection module, for identifying the passenger group corresponding to the passenger based on the passenger information pre-entered into the airport management terminal, and collecting the traffic influencing factors of the passenger group for the feedback information, wherein the passenger information specifically includes ticket purchase information, entry into the terminal area and boarding records, the passenger groups specifically include business passengers, family passengers and leisure passengers, and the traffic influencing factors specifically include travel time, distance and weather conditions; A third judgment module is used to judge whether the traffic influencing factor is within the average value preset by the airport management terminal; a third execution module, which is used for, if not, establishing an exclusive channel window for the passenger group based on the original management strategy of the airport management terminal, configuring required service content for the passenger group according to the exclusive channel window, updating the exclusive channel window and the required service content to the airport management terminal in real time, and displaying the usage of the exclusive channel window and the required service content to the passengers in real time through the airport management terminal, wherein the exclusive channel window specifically includes an exclusive security check channel, an exclusive channel for family passengers and an exclusive boarding gate.

10. The airport ground transportation passenger flow prediction system according to claim 8, characterized in that: The execution module also includes: A collection unit, used to calculate the difference value between the prediction result and the actual value based on a preset error metric, identify the change trend of the prediction error through the difference value, and collect the prediction deviation in a specific period from the change trend; A judgment unit, used to judge whether the prediction deviation has a corresponding prediction weight, wherein the prediction weight specifically includes a traffic weight, a weather weight and a flow weight; An execution unit is used to adjust the model parameters of the prediction model according to the prediction deviation, add corresponding weight conditions in the prediction model in real time, obtain the interaction characteristics of the latest data and the weight conditions, generate joint impact parameters of the prediction results according to the interaction characteristics, input the joint impact parameters into the prediction model, and reveal the joint impact value of the joint impact parameters on the prediction results, wherein the weight conditions specifically include sudden weather data, holidays and special events, traffic conditions and traffic accidents and flight delay information.

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