Airport check-in consignment counter configuration planning method and device

Abstract

The invention relates to the field of airport management and aviation services, and discloses an airport check-in consignment counter configuration planning method and device, and the method comprises the following steps: S1, collecting real-time or historical data, and obtaining a passenger arrival rate, a counter service rate, and system limitation condition parameters; s2, based on the collected data, constructing a queuing theory model, and performing preliminary evaluation on check-in counter configuration through an M / M / c queuing model; and S3, establishing a target function by using an optimal control theory, optimizing a configuration scheme, and minimizing a comprehensive target among queuing time, passenger waiting time and resource configuration cost. According to the dynamic counter configuration scheme based on the real-time data acquisition and feedback mechanism, the technical effect of dynamically adjusting the number of counters in the peak period is achieved, so that the problem that passengers queue for a long time in the peak period is effectively avoided, and resource waste in the valley period is also reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of airport management and aviation services, and specifically to a method and device for configuring and planning check-in and baggage check counters at airports. Background Art

[0002] Currently, during the process of configuring and planning check-in and baggage check counters at airports, an estimation method using empirical formulas is generally adopted. This method roughly calculates the number of counters based on the average ticket handling efficiency obtained from historical data analysis and the predicted number of passengers during peak hours. Although this method is simple and fast, in practical applications, it often leads to unreasonable allocation of resources in the check-in area. It may cause passengers to wait for a long time due to underestimated demand, or result in waste of investment and high equipment idle rate due to overestimated demand.

[0003] The industry has gradually started to use software simulation tools for more refined resource allocation analysis. By simulating the passenger flow, the operation efficiency and service quality under different configurations are evaluated. However, although this post hoc simulation verification improves the accuracy of the planning, due to the long modeling and simulation time and slow iteration speed, it is difficult to adapt to the rapid design change cycle, which limits the cost control and efficiency optimization in the planning stage.

[0004] Both the traditional empirical formula method and the later software simulation method have their respective limitations. The former lacks consideration of dynamic passenger flow and passenger experience, while the latter is limited by the design iteration efficiency and cannot respond to design changes in a timely manner, resulting in high initial investment and insufficient flexibility of airport facilities. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and device for configuring and planning check-in and baggage check counters at airports, which solves the problems of long modeling and simulation time, slow iteration speed, inability to respond to design changes in a timely manner, and lack of consideration of dynamic passenger flow and passenger experience in the prior art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for configuring and planning check-in and baggage check counters at airports, including the following steps: S1. Collect real-time or historical data to obtain the passenger arrival rate, counter service rate, and system constraint condition parameters; after the data is collected, it needs to be cleaned and standardized. The traffic data of different airports is uniformly converted into a standard format, abnormal data is removed, and missing values are filled to ensure the comparability and accuracy of the data. After standardization, the data can be better used for subsequent model construction and optimization calculation.

[0007] S2. Based on the collected data, construct a queuing theory model, and conduct a preliminary evaluation of the check-in counter configuration through the M / M / c queuing model; the role of the model is to simulate the situation of passenger arrival and service under a given number of counters. By calculating the queuing time Wq (t), the service efficiency and passenger waiting time under different time periods and counter configurations can be evaluated. By adjusting the number of counters c(t), the passenger waiting time and queue length under different configurations can be predicted, providing a reference for subsequent optimization.

[0008] S3. Using the optimal control theory, establish an objective function to optimize the configuration plan, minimizing the comprehensive objective among the queuing time, passenger waiting time, and resource allocation cost; by solving the optimal control problem, the number of counters c(t) can be dynamically adjusted to minimize this cost function. The goal of optimization is to adjust the counter configuration according to the changes in real-time data, thereby reducing the passenger waiting time and avoiding waste of resources.

[0009] S4. Combining the dynamic game theory, simulate the game between the airport management and airlines, and solve the optimal counter configuration and flight schedule through the Nash equilibrium; the introduction of game theory takes into account the interest game between the airport management and airlines. The airport management hopes to maximize the utilization rate of resources and reduce operating costs; airlines hope to improve customer satisfaction by reducing the passenger waiting time. In the game model, the decision of the airport management is to adjust the number of counters c(t), while airlines affect the passenger flow by adjusting flight schedules. The game balances the goals of both sides by solving the Nash equilibrium and generates the optimal counter configuration and flight scheduling strategy.

[0010] S5. Real-time collect operation data and feed it back to the optimization system. By adjusting the number of counters in real time, keep the system running in the optimal state; be able to respond to passenger flow fluctuations in real time. During the operation process, the system continuously collects operation data. Whenever the system state changes, the real-time data will be input into the optimization system, and the number of counters c(t) will be automatically adjusted through the feedback mechanism. When the number of arriving passengers increases, the system will automatically increase the number of counters c(t) to relieve the queuing pressure on passengers; during off-peak periods, the number of counters c(t) will be automatically adjusted to decrease to reduce unnecessary resource waste. The real-time feedback mechanism ensures that the counter configuration can match the actual operation needs, enhancing the system's adaptability and flexibility.

[0011] S6. Integrate the optimization algorithm into the airport management system, continuously optimize and ensure that the configuration and operation of the airport check-in and baggage check counters are always in the optimal state; through the automated control system, the optimization algorithm can be executed seamlessly to adjust the counter configuration in real time. The system continuously monitors the passenger flow and operation efficiency, automatically adjusts the strategy to ensure that the resource allocation is always in the optimal state during actual operation. During the system operation, airport management personnel check and fine-tune the optimization results according to real-time reports to further improve the system efficiency. The system can also update the model regularly based on long-term data to ensure the continuous effectiveness of the method.

[0012] Preferably, the queuing theory model is constructed through an M / M / c queuing model, and the queuing time W q (t) is calculated by the formula: where λ(t) is the passenger arrival rate, μ is the service rate of each counter, and c(t) is the number of counters configured at time t. The model assumes that the passenger arrival process follows a Poisson distribution, and the service process of the counter is an exponential distribution. On this basis, by analyzing the relationship between the passenger arrival rate λ(t) and the counter service rate μ, the model calculates the average waiting time W of passengers at each time t q (t).

[0013] By modeling the passenger flow in different time periods, the queuing theory model can provide predictions for different numbers of counters c(t). The model also allows for automatic adjustment of counter configuration according to changes in the passenger arrival rate λ(t) and the service rate μ to ensure the best service level and resource utilization.

[0014] Preferably, the objective function aims to minimize the queuing time, passenger waiting time, and resource allocation cost simultaneously. The optimization process of the optimal control theory is carried out through the following objective function: J = ∫ 0 T [Q(x(t)) + R(u(t))]dt where Q(x(t)) is a cost function related to the passenger waiting time and queue length, representing the cost of passenger waiting; R(u(t)) is the resource cost brought by the counter configuration, representing the equipment construction and operation cost.

[0015] The optimal control theory is applied to dynamically adjust the counter configuration to maximize resource utilization and reduce passenger waiting time. The objective function J weighs all the resource costs and social costs in the system to minimize the overall cost. Specifically, Q(x(t)) is a cost function related to the queue length and waiting time, which reflects the waiting cost of passengers, especially important during peak periods. R(u(t)) represents the cost of resource allocation, including equipment construction, personnel costs, and operating expenses.

[0016] By solving the optimal control, the number of counters c(t) can be adjusted in real time to achieve the best resource allocation in the shortest time. The model takes into account various external changes and dynamically adjusts the system to minimize the operating cost of the airport, minimize the passenger waiting time, and optimize the utilization of resources.

[0017] Preferably, the optimal control method dynamically adjusts the number of counters c(t) according to the system state x(t) by solving the minimization of the objective function J, so that the objective function in the optimization process reaches the optimal value, thereby realizing the optimal configuration scheme. The objective function J is the optimal scheme considering both the passenger waiting time and the resource allocation cost. The control variable is the number of counters c(t), and the system state x(t) includes the queue length and the service efficiency index. Each time, the objective function J is calculated and combined with real-time data to adjust the counter configuration, ensuring that the system always remains in the optimal state.

[0018] The solution process combines optimization algorithms. Through the dynamic monitoring of the system state, the optimal scheme for adjusting the number of counters is calculated in real time, so that the operating cost of the system and the passenger waiting time are at the lowest level.

[0019] Preferably, the dynamic game theory simulates the game between the airport management and the airline. The game model optimizes the decisions of the airport management and the airline in counter configuration and flight schedule by solving the Nash equilibrium, so that the goals of the two parties reach the optimal at the same time.

[0020] In airport management, the airport management and the airline are the two main stakeholders. The goal of the airport management is to maximize the resource utilization rate and reduce idle resources, while the airline hopes to improve passenger satisfaction and reduce the queuing time. Through the dynamic game model, the decision-making processes of both parties can be simulated to ensure that their goals are balanced in the overall system.

[0021] Using the Nash equilibrium solution, the airport management and the airline will adjust their strategies according to the strategies of the other party. Finally, the model finds an optimal balance point by optimizing the counter configuration and flight schedule, which not only meets the airline's requirements for service quality but also ensures the optimization of the airport management in resource allocation.

[0022] Preferably, the real-time data collection monitors and records the passenger arrival rate, queuing duration, and counter usage data through sensors and camera devices, and the data is further input into the optimization system for real-time analysis and adjustment.

[0023] Data collection is a key link in this method. Through sensors, cameras, and other monitoring devices installed at various key positions in the airport, the passenger arrival rate, queue length, and counter usage data are collected in real time. These data continuously flow into the optimization system for real-time analysis by the system.

[0024] The optimization system calculates in real time whether the current counter configuration meets the needs of passengers and adjusts it according to the current traffic flow. During peak hours, the system automatically increases the number of counters based on the queuing length and passenger arrival data collected in real time to relieve congestion. The real-time nature of data collection and feedback ensures that the counter configuration can respond to operational requirements at any time.

[0025] Preferably, the optimization system feeds back the adjustment result of the number of counters to the system through a real-time feedback mechanism, and adjusts the counter configuration according to the real-time needs of passengers to ensure that the system always operates in an optimal state.

[0026] The purpose of the real-time feedback mechanism is to automatically adjust the counter configuration according to the changing needs of passengers. Whenever the system state changes (the queuing length or arrival rate changes), the optimization system feeds back the adjustment result of the number of counters c(t) to the control execution module.

[0027] The real-time nature of this mechanism ensures that the counter configuration can quickly respond to changes. If the system detects an abnormal increase in the passenger arrival rate during a certain period, the system will immediately calculate and increase the number of counters according to the model to avoid long queues for passengers. At the same time, the system also monitors the idle rate of the counters to prevent waste of resources caused by over-configuration.

[0028] Preferably, the optimization system uses a real-time data analysis module to model historical operation data using machine learning or data mining algorithms, and adjusts the counter configuration plan based on the modeling results to achieve optimal resource allocation and reduce over-configuration or resource waste in dynamic adjustment.

[0029] To cope with future passenger flow fluctuations, the optimization system models historical operation data through a real-time data analysis module combined with machine learning or data mining algorithms. The system analyzes the passenger flow trends in the past few months or even years to predict future passenger arrival patterns and behaviors.

[0030] Based on these predicted data, the optimization system can make advance judgments on future peak or off-peak periods, dynamically adjust the counter configuration plan, and avoid over-configuration or resource waste. If it is predicted that the peak period of a certain holiday is coming, the system can configure more counters in advance according to past similar situations to ensure that passengers can be processed quickly during the peak period.

[0031] Preferably, the method further includes simulating and evaluating different counter configuration plans. By establishing multiple scenario simulation models, evaluating the performance of each configuration in different passenger flow environments, and comparing them in combination with operation costs and passenger satisfaction indicators, the optimal configuration plan is selected for implementation.

[0032] To ensure the feasibility and effectiveness of the optimal configuration plan, the system conducts simulation evaluations on different counter configuration plans by establishing various scenario simulation models. The system simulates multiple passenger flow environments, including peak holiday periods, regular daily peak hours, and off-peak hours.

[0033] Through the simulation of different configuration plans, the system can evaluate the performance of each configuration in a specific environment, and select the most suitable configuration plan for the current operating environment for implementation by considering factors such as operating costs and passenger satisfaction. This simulation evaluation provides data support for the configuration of airport check-in and baggage check counters, ensuring the scientificity and accuracy of decision-making.

[0034] An airport check-in and baggage check counter configuration planning device based on the above method, the device is used to execute the above method, and the device includes: A data collection module for collecting and transmitting data on passenger arrival rates, counter service rates, queuing durations, and system constraint conditions; a queuing theory model module for calculating passenger queuing times based on the M / M / c queuing model and conducting preliminary counter configuration evaluations; an optimal control module for optimizing the objective function according to the optimal control theory and dynamically adjusting the number of counters; A dynamic game module for simulating the game between the airport management and airlines, and obtaining the optimal configuration plan by solving the game model; A real-time feedback module for collecting and feedbacking operation status data in real time, and adjusting the number of counters according to the real-time data to maintain the optimal configuration of the system; A control execution module for outputting the optimal counter number configuration instruction to the airport management system and controlling the counter configuration in real time; through the coordinated work of different modules, an automated airport check-in and baggage check counter configuration planning is achieved. The data collection module, queuing theory model module, optimal control module, dynamic game module, and real-time feedback module each perform its specific tasks, and form a complete optimization control chain through data flow.

[0035] The control execution module of the system receives the optimization result and adjusts it through an automated control system without manual intervention. The coordinated work of all modules enables the airport to flexibly adjust the counter configuration in different time periods and under different passenger flow conditions, thereby improving efficiency and optimizing resource allocation.

[0036] The present invention provides an airport check-in and baggage check counter configuration planning method and device. It has the following beneficial effects: 1. The present invention adopts a dynamic counter configuration scheme based on real-time data collection and feedback mechanism, achieving the technical effect of dynamically adjusting the number of counters during peak hours. Compared with the existing solutions that simply rely on static models or empirical rules, the present invention can respond to passenger flow changes in real time, accurately adjust the counter configuration, thus effectively avoiding the problem of excessive queuing of passengers during peak periods and reducing resource waste during off-peak periods.

[0037] 2. By introducing the optimal control theory and game model, the present invention dynamically optimizes the configuration of airport check-in and baggage check counters, achieving the technical effect of balancing the waiting time of passengers and resource utilization. Compared with the traditional solution of setting the number of counters based on experience, the present invention can accurately adjust the number of counters according to the real-time passenger flow, thus significantly improving the satisfaction of passengers and avoiding the negative experience caused by excessive queuing in the past.

[0038] 3. By integrating a real-time feedback mechanism, the present invention can quickly adjust and optimize the solution according to the real-time changing environment, achieving the technical effect of coping with sudden passenger flow fluctuations. Different from the existing systems that mostly rely on fixed models or manual intervention, the present invention automatically adjusts the resource configuration under complex actual operating conditions, enabling the system to flexibly respond to changes in different scenarios such as holidays and special events and maintain stable operation.

[0039] 4. By optimizing the resource configuration between the airport management and airlines through a dynamic game model, the present invention achieves the cost control effect of win-win. Compared with the existing models that optimize unilaterally, the present invention can not only improve the resource utilization rate, reduce the waste of idle resources, but also reduce unnecessary investment while meeting the needs of passengers, effectively reducing the operating cost of the airport. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic flow chart of the method of the present invention; Figure 2 is a system architecture diagram of the device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to the attached Figure 1 - attached Figure 2 , the embodiment of the present invention provides a method for planning the configuration of airport check-in and baggage check counters, including: S1: Data collection and preprocessing: Data collection and acquisition: In this embodiment, data collection is carried out through various devices and sensors. Various devices within the airport, including passenger flow sensors, counter usage monitors, and queuing duration monitoring devices, are all incorporated into the data collection system. The data collection module can automatically collect the following data in various key areas of the airport: Passenger arrival rate λ(t): It represents the number of passengers arriving per unit time. These data are collected through sensors at access control systems, security check channels, and boarding gate locations; Counter service rate μ: The number of passengers that each counter can process per minute, usually obtained by monitoring the service duration and passenger processing time of each counter through the back-end system; Queuing duration L q (t): It represents the number of passengers waiting for service at a certain moment, usually counted in real time through sensors or monitoring devices in the queuing area.

[0043] Data cleaning and standardization: After data collection, the system needs to clean and standardize the original data. Generally, there may be missing values, outliers, or inconsistent formats in the data. If these data are not processed, they will directly affect the accuracy of the model and the optimization results. To ensure the quality of the data, the cleaning process includes the following steps: Removing invalid data: Such as duplicate records, error records (impossible negative values).

[0044] Filling in missing data: Using interpolation methods, mean filling methods, or other statistical methods to fill in the missing data to ensure the integrity of the input data for the model.

[0045] Removing outliers: When certain data values significantly exceed the reasonable range (sensor failures or human errors), these data are marked as outliers and removed from the dataset.

[0046] During the standardization process, all data units will be uniformly converted. All time units are unified to minutes, and the dimensions of different sensor data are unified to the same unit to ensure the consistency of subsequent model calculations.

[0047] Specifically, the counter service rate μ is adjusted according to the performance of the device, and the passenger arrival rate λ(t) fluctuates according to peak periods and holidays. Therefore, each piece of data needs to be standardized according to different data sources. Timestamp is used to perform time series normalization on all data to ensure the consistency of the data in the time dimension.

[0048] Estimation and prediction of passenger arrival rate: In some embodiments, the passenger arrival rate λ(t) is a key input parameter, which is affected by various factors such as time, season, and holidays. Therefore, in the data preprocessing of the present invention, the combination of historical data and real-time data is used to estimate the arrival rate for a future period of time.

[0049] First, the system will establish a passenger flow prediction model based on historical data, and fit a sine function through historical data to represent the periodic fluctuation of the passenger arrival rate within a day: Where: λ 0 Is the basic passenger arrival rate (daily average); λ(t) is the fluctuation amplitude of the arrival rate; T is the period (24 hours represents the passenger flow fluctuation within a day); t is the current time.

[0050] This estimation method calculates the passenger flow fluctuation for a future period based on historical data. Specifically, during holidays or large events, the system will adjust the estimation amplitude Δλ or period T of the arrival rate according to historical data to make the model closer to the actual situation.

[0051] Data storage and transmission: All cleaned and standardized data will be transmitted to a database or cloud platform for storage. These data are transmitted through a data transmission module, and an efficient data transmission protocol (MQTT, HTTPs) is used to ensure the real-time and security of the data. The data storage module provides stable support for the subsequent calculation of the model and ensures that the data can be read and written smoothly under high concurrency.

[0052] In some embodiments, the system adopts distributed storage technology to improve the fault tolerance and access efficiency of the data. Through a distributed database, the data can be allocated to multiple storage nodes as needed, thus avoiding single-point failures and being able to quickly recover the data when a failure occurs. The redundant storage mechanism of the data also enables the system to ensure the integrity and security of the data.

[0053] Data analysis and prediction preparation: Once the data is stored, the system will further analyze the data to prepare for the subsequent model optimization. The data analysis module will use statistical analysis and machine learning techniques to discover the laws behind the data. The system will identify that the passenger arrival rate has special fluctuations during certain specific time periods, or the queuing time in certain areas is too long. Based on these analysis results, the system can provide improvement suggestions and optimization solutions for the subsequent steps.

[0054] For example, during certain holidays, specific flights or airlines may bring significant passenger flow fluctuations. The system identifies these patterns through modeling and learning of historical data and adjusts the number of counters in advance based on the prediction results to cope with future changes.

[0055] S2: Establishment and adjustment of the queuing theory model: In this embodiment, based on the construction of the M / M / c queuing model, the arrival process of passengers and the service process of counters are first considered. In this model, the passenger arrival rate λ(t) is regarded as a function of time, representing the number of passengers arriving per unit time, while the counter service rate μ is the number of passengers processed per minute by each counter. For the multi-counter configuration, c(t) is used to represent the number of counters configured at time t. At this time, the queuing time W q (t) can be calculated by the following formula: Among them, W q (t): represents the average waiting time of passengers at time t (unit: minute), which reflects the time passengers need to wait during the queuing process.

[0056] λ(t): represents the passenger arrival rate (unit: person / minute), that is, the number of passengers arriving per unit time. This value is usually predicted through historical data and fluctuates according to different time periods or special events.

[0057] μ: represents the service rate of each counter (unit: person / minute), that is, the number of passengers that each counter can process per minute.

[0058] c(t): represents the number of counters configured at time t, which is the control variable in the model and needs to be dynamically adjusted according to the actual passenger flow.

[0059] The core objective of this queuing theory model is to determine whether to adjust the number of counters c(t) by calculating W q (t). If the calculated value of W q (t) is high, it indicates that the waiting time of passengers in the queue is too long and the number of counters needs to be increased; conversely, if the value of W q (t) is low, it means that there are too many counters configured, resulting in resource waste, and the system can reduce the number of counters.

[0060] Dynamic adjustment of the number of counters: To adapt to different passenger flow demands, the number of counters c(t) in the queuing theory model must be dynamically adjusted. Through the real-time collected passenger arrival rate λ(t) and counter service rate μ, combined with the calculated queuing time W q (t), the system can decide whether to increase or decrease the number of counters.

[0061] During peak hours or special events, the passenger arrival rate λ(t) increases sharply. At this time, the system needs to increase the number of counters to cope with the instantaneous passenger flow pressure. Correspondingly, when the passenger flow decreases (during off-peak hours or non-peak periods), the system can reduce the number of counters to avoid wasting resources. For example, during the peak hours of holidays, the system will increase the number of counters c(t) to handle more passengers, while during normal or late-night periods, unnecessary counter configurations will be reduced.

[0062] To achieve this dynamic adjustment, in this embodiment, an optimal control algorithm is used to automatically calculate the optimal number of counters. Through the optimal control theory, the system can dynamically adjust c(t) according to the changes of λ(t) and W q (t) to ensure the maximization of resource utilization and the minimization of passenger waiting time.

[0063] Special handling for peak and off-peak periods: The system's special handling strategy for peak and off-peak periods, through the combination of historical data analysis and real-time data, can predict and adjust the passenger flow in different time periods. During peak periods, the passenger arrival rate λ(t) increases, resulting in a significant increase in queuing time. At this time, the system increases the number of counters c(t) through the queuing theory model to shorten the waiting time and improve service quality. Specifically, the system will adjust the number of counters in advance according to historical holiday data or event predictions.

[0064] During off-peak periods, the passenger flow is relatively low, and the number of counters c(t) can be reduced accordingly. At this time, by reducing the number of counters, the airport can save operating costs and avoid over-investment. In actual operation, the system will predict the passenger flow in each time period according to the daily, weekly, and holiday regularities and flexibly adjust the number of counters.

[0065] For example, assume that from 9 am to 11 am on a holiday, the passenger arrival rate λ(t) is at a peak value. The system can increase more counters c(t) and reduce the number of counters during normal periods (2 pm to 5 pm), thereby effectively improving operational efficiency.

[0066] Dynamic adjustment of parameters of the queuing model To more accurately reflect the real situation, this embodiment also dynamically adjusts the parameters in the queuing model. In addition to the basic passenger arrival rate λ(t) and counter service rate μ, the model also considers other factors affecting service efficiency, such as equipment failures and the status of staff.

[0067] For example, assume that during a certain period, the service rate of a certain counter decreases due to equipment failure. The system will automatically adjust the service rate μ of this counter to more accurately simulate the actual service situation. To achieve this goal, the system will update the value of μ according to the actual service situation after each period to ensure that the calculation results of the queuing theory model are closer to the actual operation.

[0068] S3: Application of optimal control theory: Construction of the optimal control objective function: The optimal control theory requires us to first define an objective function JJJ, which comprehensively considers the impact of queuing time, resource allocation cost, and passenger experience. The form of the objective function is: J = ∫ 0 T [Q(x(t)) + R(u(t))]dt where J is the overall objective function, representing the total cost within the time interval [0, T]. By minimizing J, this embodiment can achieve the optimal counter configuration. Among them: Q(x(t)) is the cost function, representing the cost related to the waiting time of passengers and the queue length. Generally speaking, the longer the waiting time of passengers, the higher the social and economic costs, so this part of the cost needs to be minimized as much as possible.

[0069] R(u(t)) is the resource cost required to adjust the number of counters, representing the costs in multiple aspects such as equipment construction, operation, and maintenance. Each additional counter will generate additional operating costs, so this part of the cost function also needs to be minimized.

[0070] Among them: Q(x(t)): The cost function related to the queuing behavior of passengers. This part of the cost mainly considers factors such as queuing duration and queue length.

[0071] R(u(t)): The resource cost related to the counter quantity configuration. Specifically, increasing the number of counters will lead to an increase in facility and personnel costs, so this part of the cost needs to be optimized.

[0072] x(t): System state variables, including the passenger arrival rate λ(t), the counter service rate μ, and the queuing duration L q (t). The state variables represent the current state of the system and affect the decision-making process.

[0073] u(t): Control variable, that is, the number of counters c(t) configured at time t, and the optimal configuration is achieved by dynamically adjusting the number of counters.

[0074] Specifically, within the time interval [0, T], the optimal control system calculates the current passenger flow and queuing situation in real time, and dynamically adjusts the number of counters based on this data. Each adjustment will cause a change in the value of the objective function J, and the optimization algorithm will continuously adjust until the value of the objective function reaches the minimum, thereby achieving the optimal counter configuration.

[0075] Dynamic adjustment during the optimization process: To achieve the optimal control objective, a numerical optimization algorithm is used in the embodiments of the present invention. Specifically, the gradient descent method is used to solve the optimal value of the objective function J. The system can determine the best counter configuration scheme under the given passenger arrival rate, queuing situation, and resource constraints. The specific steps are as follows: 1. The gradient descent method calculates the gradient (i.e., derivative) of the objective function with respect to the control variable, and then updates the control variable along the negative gradient direction, thereby gradually approaching the optimal value of the objective function. In this embodiment, the control variable is the number of counters c(t), and c(t) is updated by the gradient descent method to minimize the total cost J; 2. In the gradient descent method, first, the partial derivative of the objective function J with respect to the control variable c(t) needs to be calculated. Let Δc(t) represent the adjustment amount of the number of counters in each iteration. The update formula is as follows: Where: c(t + 1): The updated number of counters at the next moment, c(t): The number of counters at the current moment, η: The learning rate, which controls the step size of the gradient descent. Usually, a smaller learning rate is selected to ensure the stable convergence of the algorithm. The partial derivative (gradient) of the objective function J with respect to the number of counters c(t) represents the sensitivity of the objective function to the change of the control variable.

[0076] 3. Calculation of the gradient: The objective function J is an integral of time. Therefore, in this embodiment, first, the partial derivative of the objective function J with respect to c(t) needs to be calculated. According to the chain rule, the partial derivative of the objective function can be decomposed into the following two parts: Where: Represents the sensitivity of the queuing-related cost to the change of the number of counters c(t), reflecting the impact of the change in the passenger waiting time on the system cost when the number of counters changes; Represents the sensitivity of the resource allocation cost to the number of counters c(t), reflecting the impact of resource consumption on the system cost when the number of counters changes.

[0077] Specifically, the calculation of the gradient involves modeling Q(x(t)) and R(u(t)) through queuing theory models and optimal control models. The following is the expression form: Gradient of the passenger waiting cost: where λ(t) is the passenger arrival rate and W q (t) is the average waiting time of passengers. Calculate the change in waiting time when the number of counters changes, so as to obtain the sensitivity of queuing time.

[0078] Gradient of resource cost: where α and β are resource cost coefficients related to counter configuration, usually used to reflect equipment construction and maintenance costs. By taking the derivative of the resource cost function, this embodiment can calculate the impact of the change in the number of counters on the resource cost.

[0079] Once the gradient is calculated, the system can adjust the number of counters through the update formula. This process will be repeated in each iteration until the objective function J converges to the minimum value. The specific iteration process is as follows: Initialization: Set the initial number of counters c(0) and select an appropriate learning rate η.

[0080] Iterative update: Calculate the gradient of the current objective function J with respect to c(t) and update the number of counters c(t): Convergence judgment: If the change in the objective function J is less than the set threshold, or the maximum number of iterations is reached, stop the update and consider that the gradient descent method has converged to obtain the optimal counter number configuration.

[0081] Dynamic adaptability during peak and off-peak periods: In this embodiment, the optimal control theory has a high degree of dynamic adaptability. Specifically, the system can flexibly adjust the number of counters c(t) according to the change in passenger flow. For example, during peak periods, the passenger arrival rate λ(t) will increase significantly, resulting in an increase in queuing time and queuing length. At this time, the optimal control theory will automatically calculate the best timing and number of increasing the number of counters to ensure that the waiting time of passengers will not be too long.

[0082] Specifically, assume that within a certain period, the passenger arrival rate λ(t) suddenly rises. The system predicts and analyzes the change in the arrival rate and adjusts the parameters in the objective function J, so that the number of counters c(t) can increase rapidly to cope with the sudden passenger flow pressure.

[0083] During the off-peak period, when the passenger flow decreases, the system saves resources by reducing the number of counters c(t). At this time, the optimal control system analyzes historical data and real-time information to accurately calculate the most resource-saving counter configuration plan while maintaining service quality.

[0084] Integration of real-time data and feedback mechanism: Another advantage of the present invention lies in the integration of the real-time data feedback mechanism. The system obtains real-time data on passenger arrival rates, counter service rates, and queue lengths, and transmits this information to the optimal control module. The optimal control system dynamically calculates and adjusts the number of counters c(t) based on this data.

[0085] For example, when the system detects a sudden increase in the passenger arrival rate during a certain period, the optimal control theory re-evaluates the objective function based on the new data and dynamically adjusts the number of counters. This process is adaptive and does not require manual intervention, thus greatly improving the system's response speed and decision-making efficiency.

[0086] S4: Application of dynamic game theory: Construction of the game model: The construction of the game model is based on the Nash equilibrium theory. In the game between the airport and the airline, both parties choose the optimal strategies to achieve the optimal counter configuration and flight schedule. Specifically, the airport management hopes to reduce the passenger queuing time by reasonably configuring the number of counters c(t) while ensuring the optimal utilization of resources; the airline hopes to optimize the passenger flow, reduce flight delays, and improve passenger satisfaction.

[0087] Objective function of the airport management: The objective function of the airport management is to maximize the resource utilization efficiency and minimize the operating costs. In this embodiment, the objective function of the airport management can be expressed as: J airport =∫ 0 T [R utilization (c(t))+C cost (c(t))]dt Where: R utilization (c(t)) represents the resource utilization efficiency, which is usually related to factors such as counter utilization rate and passenger flow. This cost function hopes to maximize the resource utilization efficiency by optimizing the counter configuration c(t).

[0088] C cost (c(t)) represents the resource cost associated with the counter configuration c(t). Generally, increasing the number of counters means more facility investment and operating costs. Therefore, this cost function hopes to minimize the resource configuration cost while ensuring service quality.

[0089] Objective function of the airline: The airline focuses on improving passenger satisfaction and flight punctuality. Especially during peak hours, the queuing time and waiting time of passengers directly affect flight punctuality and customer experience. Therefore, the objective function of the airline can be expressed as: J airline = ∫ 0 T [S satisfaction (L q (t)) + P punctuality (λ(t))]dt Where: S satisfaction (L q (t)) represents the passenger satisfaction, which is usually related to the queuing time L q (t), counter configuration, and service quality. The longer the passenger waiting time, the lower the satisfaction. The airline hopes to improve passenger satisfaction by reducing the queuing time.

[0090] P punctuality (λ(t)) represents the flight punctuality, which is usually related to the passenger arrival rate λ(t) and the counter service rate μ. The airline hopes to improve flight punctuality and reduce flight delays by optimizing the counter configuration c(t).

[0091] Strategy selection and optimal solution in the game model: The core of the game model is strategy selection. In the embodiments of the present invention, the airport management party and the airline each have a set of strategies to choose from. The strategy of the airport management party is to select an appropriate number of counters c(t), and the strategy of the airline is to select an appropriate flight schedule plan.

[0092] 1. Selection of control variables: The control variable of the airport management party is the number of counters c(t), and its goal is to optimize resource allocation; The control variable of the airline is the flight schedule plan, which affects the passenger arrival rate λ(t) and flight punctuality.

[0093] 2. The airport management party and the airline respectively update their control variables c(t) and flight schedule plan through the gradient descent method. The update formula can be expressed as: Where: c(t + 1) and λ(t + 1) are the updated number of counters and flight schedule plan respectively; η airport and η airline are the learning rates, which control the update step size; is the partial derivative of the objective function of the airport management side with respect to the number of counters c(t); is the partial derivative of the objective function of the airline with respect to the passenger arrival rate λ(t).

[0094] 3. Gradient calculation: To optimize these objective functions using the gradient descent method, this embodiment first calculates the gradient of the objective function with respect to the policy variables: Gradient of the airport management side: This gradient represents the relationship between resource utilization and cost for the airport management side under different counter configurations. If C cost (c(t)) = αc(t) 2 , then its derivative is: Gradient of the airline: If S satisfaction (L q (t)) = βL q (t) and L q (t) has a linear relationship with λ(t), then we can obtain: 4. Iterative process and convergence: The gradient descent method will perform iterations repeatedly, adjusting the policy variables according to the calculated gradient each time until the objective function converges to the minimum value. The system will update the number of counters c(t) and the flight schedule plan λ(t) at each iteration until the stopping criterion is met (the gradient is less than the preset threshold or the maximum number of iterations is reached).

[0095] Dynamic adjustment in the game: The game model in this embodiment is not only based on static analysis but also combines real-time data and a dynamic adjustment mechanism. At each moment, the system will perform real-time calculations and optimization decisions based on the latest passenger arrival rate λ(t), queuing duration L q (t), and counter configuration situation c(t) data.

[0096] When the airline requests an increase in flights, the system will increase the number of counters according to the new passenger flow prediction to ensure that passengers can check in in a timely manner and board the plane on time. When the airport management side adjusts the counter configuration, the airline will adjust the flight schedule according to the new configuration, thereby achieving the best resource utilization and passenger satisfaction.

[0097] In some embodiments, the system adopts distributed computing to accelerate the game process, quickly solving the optimal solutions under different strategies through parallel computing, so as to achieve real-time decision-making adjustment.

[0098] S5: Real-time data collection and feedback mechanism: Real-time data collection: Data collection is the basis for the system to respond to changes in the external environment. Through sensors, cameras and other monitoring devices deployed at different locations in the airport, the real-time data collection module can obtain information about passenger flow, counter service rate, and passenger queuing. Specifically, the key data collected includes but is not limited to: Passenger arrival rate λ(t): This is the data monitored by the system in real time, indicating the number of passengers arriving per unit time. Through sensors at security checkpoints and boarding gates, the passenger arrival situation can be obtained at any time.

[0099] Counter service rate μ: The number of passengers that each counter can process per unit time. This value is calculated by monitoring the passenger processing time of each counter in real time through the background system.

[0100] Queue length L q (t): Indicates the number of passengers queuing for service at a certain moment. The queue length and time are calculated by monitoring devices (infrared sensors) or video analysis systems in the queuing area.

[0101] Counter usage: The working status of each counter is monitored in real time through counter status sensors to determine whether it is idle or busy. If a certain counter fails, the system can also give feedback in time and take corresponding measures.

[0102] Generally, the data collection process is carried out by multiple sensors working together, and the collected data is uploaded to the central processing system using wireless communication technology. In this way, the system can understand the changes in key indicators in real time, providing real-time support for subsequent optimization decisions.

[0103] Data processing and real-time feedback: After the data collection is completed, the system enters the data processing stage. First, all the collected data will be processed by the data cleaning and filtering module. This process will delete invalid data or outliers to ensure that the data input into the system is accurate and effective. For example, when a certain sensor fails, the system can automatically mark and skip the abnormal data collected by this device.

[0104] After data processing, the system will standardize the key data of passenger arrival rate λ(t), queue length L q (t) and counter usage to ensure the consistency of information from different data sources. All processed data will be sent to the real-time optimization module for further analysis.

[0105] Through the real-time feedback mechanism, the system can automatically adjust the counter configuration c(t) according to the real-time data collected. q When (t) increases, the system will automatically increase the number of counters according to the optimal control algorithm; when the customer flow decreases, the number of counters will decrease to avoid wasting resources.

[0106] Adaptive and intelligent adjustment mechanism: Another important feature of this embodiment is the adaptive optimization mechanism. The system can not only automatically adjust the counter configuration according to real-time data, but also intelligently adjust the optimization strategy under different operating conditions. Specifically, when the system identifies atypical passenger flow patterns (holidays or special events), it will automatically adjust the relevant parameters in the objective function to optimize the counter configuration strategy.

[0107] System response speed and real-time decision-making: In order to ensure the efficiency of the real-time feedback mechanism, this embodiment uses an efficient data processing and computing platform, combined with multi-core processors and distributed computing technology, to ensure that the system can complete data processing and counter configuration adjustments within seconds. In this way, the system can quickly respond to passenger flow fluctuations, optimize counter configuration in real time, maximize airport resource utilization efficiency, and avoid long waiting times for passengers.

[0108] Data security and fault tolerance mechanism: Considering the high reliability requirements of airport operations, this embodiment also includes data security and fault tolerance mechanisms. All real-time data transmission and storage are encrypted to ensure the security of data during transmission. At the same time, the system has a built-in redundant backup mechanism. When a device fails or data is lost, the system can restore data from the backup node to ensure that the optimization decision is not interrupted.

[0109] S6: Optimize execution and integration: Execute optimization results and system integration: In this embodiment, the optimization execution module is combined with the aforementioned optimization algorithm to ensure that each optimized counter configuration c(t) can take effect quickly in the airport management system. The system will convert the optimization calculation results into specific operation instructions through the automation control module, notify the relevant departments in real time, and execute the adjustment plan. This process ensures that the resources in various aspects such as counter quantity adjustment, personnel scheduling, and equipment use can respond to changes immediately and effectively.

[0110] For example, when the system calculates through optimal control theory that the number of counters c(t) needs to be increased, the optimization execution module will not only generate adjustment instructions, but also ensure that additional staff are dispatched and the equipment is properly enabled or adjusted through the interface with the personnel scheduling system and equipment management system.

[0111] Real-time generation and feedback of execution instructions: In some embodiments, the optimization execution module is closely integrated with the optimization calculation through a real-time feedback mechanism to ensure that each adjustment can be executed promptly and accurately. When the system calculates the optimal number of counters c(t) based on real-time data, the execution module will immediately generate instructions and transmit them to the automated control system. The instructions include, but are not limited to, various aspects such as counter number adjustment, personnel scheduling, and equipment usage.

[0112] For example, assume that the system calculates that 4 additional counters c(t) need to be added during peak hours to handle the surging passenger flow. The execution module will transmit the instructions to the control system within a few seconds, automatically activate the corresponding counters, and ensure that the corresponding staff arrive at their posts. This automated execution method ensures that the adjustment plan can take effect immediately, avoiding delays in manual operations and improving the response efficiency.

[0113] Dynamic monitoring and adjustment of the execution module: The optimization execution module not only executes instructions but also monitors the effects of the adjusted counter configuration in real time. Specifically, the execution module will continuously monitor the following key indicators: queue length L q (t), passenger arrival rate λ(t), counter usage, and service rate μ. When the monitoring system detects that the queue time is too long or the resource configuration is inappropriate, the system will automatically make fine-tuning to ensure that the number of counters always remains within the optimal configuration range.

[0114] Collaborative work with other systems: In this embodiment, the optimization execution module not only focuses on the adjustment of the number of counters but also needs to coordinate the use of other operational resources to ensure the collaborative work of all systems. In addition to counter adjustment, the execution module will also exchange data and collaborate with the personnel scheduling, security inspection process, and equipment management systems to ensure that while optimizing the counter configuration, the resources in other links can also be adjusted synchronously.

[0115] For example, assume that the system decides to increase the number of counters c(t) during peak hours. The execution module will, through collaboration with the personnel scheduling system, automatically arrange more staff to the corresponding counter positions; at the same time, through the interface with the security inspection system, the system will request an adjustment of the number of security inspection channels to ensure the smooth operation of the entire airport. In this case, the execution module not only optimizes the counter configuration but also ensures the smooth operation of other operational links and avoids the occurrence of bottleneck problems.

[0116] The airport check-in and consignment counter configuration planning device described below can be correspondingly referred to the airport check-in and consignment counter configuration planning method described above.

[0117] The device includes: A data collection module for collecting and transmitting data on passenger arrival rates, counter service rates, queuing durations, and system limit conditions; a queuing theory model module for calculating passenger queuing times based on the M / M / c queuing model and performing preliminary counter configuration evaluations; an optimal control module for optimizing the objective function according to optimal control theory and dynamically adjusting the number of counters; A dynamic game module for simulating the game between the airport management and airlines and obtaining the optimal configuration plan by solving the game model; A real-time feedback module for collecting and feedbacking operation status data in real time and adjusting the number of counters according to the real-time data to maintain the optimal configuration of the system; A control execution module for outputting the optimal counter number configuration instruction to the airport management system and controlling the counter configuration in real time.

[0118] The device of this embodiment can be used to execute the above method embodiment, and its principle and technical effects are similar, so they will not be elaborated here.

[0119] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for planning the configuration of airport check-in and baggage consignment counters, characterized in that: The following steps are involved: S1. Collect real-time or historical data to obtain passenger arrival rate, counter service rate, and system restriction parameters; S2. Based on the collected data, a queuing theory model is constructed, and a preliminary evaluation of the check-in counter configuration is conducted through the M / M / c queuing model; S3. Use optimal control theory to establish an objective function and optimize the configuration plan to minimize the comprehensive goal between queuing time, passenger waiting time and resource allocation cost; S4. Combine dynamic game theory to simulate the game between airport management and airlines, and solve the optimal counter configuration and flight schedule through Nash equilibrium; S5. Collect operational data in real time and feed it back to the optimization system. By adjusting the number of counters in real time, the system operation is kept in the optimal state. S6. Integrate the optimization algorithm into the airport management system to continuously optimize and ensure that the configuration and operation of the airport check-in and baggage check-in counters are always in the best state.

2. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The queuing theory model is constructed by the M / M / c queuing model, and the queuing time W q (t) is calculated as: Among them, λ(t) is the passenger arrival rate, μ is the service rate of each counter, and c(t) is the number of counters configured at time t.

3. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The objective function aims to minimize the queuing time, passenger waiting time and resource allocation cost at the same time. The optimal control theory optimization process is carried out through the following objective function: J=∫0 T [Q(x(t))+R(u(t))]dt Among them, Q(x(t)) is a cost function related to the waiting time of passengers and the length of the queue, which represents the cost of passengers waiting; R(u(t)) is the resource cost brought by the counter configuration, which represents the equipment construction and operation cost.

4. The airport check-in and baggage consignment counter configuration planning method according to claim 3, characterized in that: The optimal control method solves the minimization objective function J and dynamically adjusts the number of counters c(t) according to the system state x(t) so that the objective function in the optimization process reaches the optimal value, thereby realizing the optimal configuration solution.

5. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The dynamic game theory simulates the game between the airport management and the airlines. The game model optimizes the decisions of the airport management and the airlines in counter configuration and flight arrangement by solving the Nash equilibrium, so that the goals of the two participants are simultaneously optimized.

6. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The real-time data collection monitors and records passenger arrival rate, queue length, and counter usage data through sensors and camera equipment, and the data is further input into the optimization system for real-time analysis and adjustment.

7. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The optimization system feeds back the adjustment result of the counter quantity to the system through a real-time feedback mechanism, and adjusts the counter configuration according to the real-time needs of passengers to ensure that the system operation always remains in the optimal state.

8. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The optimization system uses a real-time data analysis module to model historical operating data using machine learning or data mining algorithms, and predicts future passenger flow and passenger behavior patterns based on the modeling results, and adjusts the counter configuration plan based on this, thereby achieving optimal resource allocation and reducing over-configuration or resource waste in dynamic adjustment.

9. The airport check-in and baggage consignment counter configuration planning method according to claim 1, characterized in that: The method further includes simulation evaluation of different counter configuration schemes, by establishing multiple scenario simulation models, evaluating the performance of each configuration under different passenger flow environments, and comparing them in combination with operating costs and passenger satisfaction indicators, and selecting the optimal configuration scheme for implementation.

10. An airport check-in and baggage consignment counter configuration planning device based on the method of claim 1, characterized in that: The device is used to perform the method, and the device includes: The data acquisition module is used to collect and transmit data on passenger arrival rate, counter service rate, queue time, and system constraints; the queuing theory model module is used to calculate passenger queue time based on the M / M / c queuing model and conduct preliminary counter configuration evaluation; the optimal control module is used to optimize the objective function according to the optimal control theory and dynamically adjust the number of counters; Dynamic game module, used to simulate the game between airport management and airlines, and obtain the optimal configuration plan by solving the game model; Real-time feedback module, used to collect and feedback operation status data in real time, and adjust the number of counters according to real-time data to maintain the optimal configuration of the system; The control execution module is used to output the optimal counter quantity configuration instructions to the airport management system and control the counter configuration in real time.

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