An intelligent airport operating system

Abstract

The present invention relates to the field of airport intelligent management technology, and in particular to an airport intelligent operating system, comprising a multimodal data acquisition layer, which acquires multi-source data such as flights, passengers, and baggage in real time through a heterogeneous sensor network; an intelligent analysis layer processes data using deep learning and unique algorithms to generate information such as flight delay predictions; a collaborative scheduling layer realizes cross-domain dynamic optimization of flight-boarding gate-baggage system-ground services based on digital twins and multi-agent reinforcement learning; a human-computer interaction layer is equipped with AR navigation and voice interaction equipment to provide personalized services; in addition, a blockchain evidence storage module is provided to ensure that key operation records are safe and traceable, and a federated learning framework realizes multi-airport knowledge sharing and privacy protection; the present invention improves airport operating efficiency, enhances safety, and optimizes passenger service experience through multi-module collaboration, effectively solves many problems faced by traditional airport operations, and has significant innovation and practicality.

Description

Technical Field

[0001] The present invention relates to the technical field of airport intelligent management, and in particular to an airport intelligent operating system. Background Art

[0002] With the rapid development of the global aviation industry, airports, as key nodes of air transportation, are facing increasing operational pressure and complex management challenges. The traditional airport operation and management model is gradually unable to meet the needs of modern air transportation in terms of efficiency, safety and service quality, making the intelligent upgrade of airports an inevitable trend.

[0003] The existing airport management has the following defects:

[0004] 1. Demand for improving airport operation efficiency

[0005] Complex flight scheduling and resource allocation: Modern airports have frequent flight takeoffs and landings, and complex route networks, which involve the coordinated deployment of many resources, such as boarding gates, runways, baggage handling systems, and ground service vehicles. Different flights have different aircraft types, passenger flows, and transfer requirements. Traditional static scheduling and resource allocation methods are difficult to adapt to real-time changes, often leading to flight delays, resource waste, and other problems. For example, during peak flight periods, unreasonable boarding gate allocation may require passengers to travel long distances to the boarding gate, increasing waiting time and fatigue. Inefficient routing of the baggage handling system may cause baggage miscarriage or delays, affecting passenger itineraries.

[0006] Insufficient information integration and decision support: Airport operations involve multiple independent subsystems, such as flight information systems, passenger management systems, and baggage tracking systems. These systems have diverse data formats and non-uniform interfaces, making it difficult to effectively integrate information. When making decisions, operators often lack comprehensive and real-time data support, making it difficult to accurately grasp the overall operation status of the airport, resulting in delayed or unreasonable decisions. For example, when faced with flight delays, it is difficult to quickly formulate the optimal resource adjustment plan due to the inability to obtain data from various related systems in a timely manner, further exacerbating the impact of delays.

[0007] 2. Increased airport security requirements

[0008] Diversified security threats: As a place with a high concentration of personnel and materials, airports face a variety of security threats, including illegal intrusions and abnormal behaviors. Traditional security monitoring methods mainly rely on manual patrols and simple video surveillance, which makes it difficult to conduct real-time and accurate monitoring and early warning of complex and changing security risks. For example, in a large terminal, manual patrols are difficult to cover all areas, and it is difficult to detect some hidden abnormal behaviors in time. Simple video surveillance lacks intelligent analysis capabilities and cannot automatically identify potential security threats.

[0009] Data security and privacy protection: A large amount of sensitive data is generated and stored during airport operations, such as passenger personal information and flight confidential information. With the development of information technology, the risk of data leakage is increasing. Once a data leakage incident occurs, it will cause serious losses to passengers and airports. At the same time, in the process of multi-airport collaborative operation or cooperation with external institutions, there is a contradiction between the demand for data sharing and the requirement for privacy protection, and an effective solution needs to be sought.

[0010] 3. Passenger service quality improvement expectations

[0011] Growing demand for personalized services: Modern passengers have higher and higher expectations for airport services and hope to enjoy personalized and convenient service experiences at the airport. For example, different passengers have different travel purposes, preferences and needs. Some passengers want to reach the boarding gate quickly, while others want a comfortable rest or shopping environment while waiting for their flight. The traditional airport service model lacks the ability to accurately identify and meet the personalized needs of passengers, making it difficult to provide customized services.

[0012] Insufficient convenience in information acquisition and interaction: Passengers need to obtain various information at the airport, such as flight status, boarding gate changes, and the location of airport facilities. Currently, the way to obtain information is relatively simple, mainly relying on traditional broadcasts and display screens, and the information display is not intuitive and personalized enough. In addition, the interaction between passengers and airport staff is not convenient and efficient enough, which affects the passenger experience.

[0013] Therefore, an airport intelligent operating system is proposed to address the above problems. Summary of the invention

[0014] The purpose of the present invention is to provide an intelligent airport operating system to solve the problems raised in the above background technology.

[0015] To achieve the above object, the present invention provides the following technical solutions:

[0016] An airport intelligent operating system, comprising:

[0017] Multimodal data collection layer: A heterogeneous sensor network deployed in the terminal, runway and apron to obtain flight status, passenger flow, baggage trajectory, vehicle location and environmental parameters in real time;

[0018] Intelligent analysis layer: includes a deep learning-driven dynamic prediction module that processes multi-source heterogeneous data through a spatiotemporal attention mechanism to generate flight delay predictions, passenger behavior patterns, and resource demand maps;

[0019] Collaborative scheduling layer: Based on the three-dimensional simulation environment built with digital twins, cross-domain dynamic optimization of flights, boarding gates, baggage systems, and ground services is implemented, and multi-agent reinforcement learning is used to achieve Nash equilibrium of resource allocation;

[0020] Human-computer interaction layer: Equipped with augmented reality navigation terminals and voice interaction devices to provide passengers and staff with personalized route planning and emergency command push.

[0021] As a preferred solution, the multimodal data acquisition layer includes:

[0022] Centimeter-level precision positioning network composed of UWB positioning base stations and RFID tags;

[0023] Anomaly detection array that integrates millimeter-wave radar and thermal imaging cameras;

[0024] Energy consumption monitoring sensor cluster based on LoRaWAN;

[0025] Edge computing nodes implement data cleaning and feature extraction preprocessing.

[0026] As a preferred solution, the intelligent analysis layer includes:

[0027] The core algorithm of the delay prediction model based on the improved spatiotemporal graph convolution is:

[0028] ,in, Indicates The hidden state matrix of the layer; is the graph convolution operation, For the The hidden state matrix of the layer, is a learnable weight matrix used to transform the features after the graph convolution operation. is a normalized adjacency matrix with self-loops, used to represent the connection relationship between airport-related elements including flights and boarding gates; The temporal features of the temporal convolutional network output, It is the input time series data, and the time dimension features are extracted through the time convolution network; It is the Hadamard product, which is used to multiply the features obtained by the graph convolution part by the temporal features output by the temporal convolution network element by element to achieve the fusion of spatiotemporal features; The order of graph convolution controls the propagation range of the graph convolution operation; Gated activation function;

[0029] Trajectory prediction algorithm integrating hidden Markov model:

[0030] ,in, represents the period from time step 1 to time step The entire observation sequence of express The hidden state at a certain moment, which indicates the current state of the passenger that is not directly observed in the passenger trajectory prediction scenario; is the observation data; is the state transition probability matrix, describing the transition from The hidden state at time is transferred to The probability of hiding the state at any moment;

[0031] Multimodal Contrastive Learning Anomaly Detection Model:

[0032] ;

[0033] in, represents the loss function of the entire multimodal contrastive learning anomaly detection model, is a negative sample selected from the negative sample set, is a visual feature encoder, used to encode the input visual data into a feature vector; An audio feature encoder, used for encoding input audio data into a feature vector; Input data for vision; Input data for audio; is a learnable balance coefficient used to balance the weights of the contrast loss of visual and audio features, and , dynamically adjusted through cross-validation; is a learnable balance coefficient used to balance the weight of the contrastive learning loss term, and , dynamically adjusted through cross-validation; is the cosine similarity function, which is used to calculate the similarity between two vectors; is a positive sample; is the current sample; is a negative sample; is the sample size; is the number of negative samples.

[0034] As a preferred solution, the collaborative scheduling layer implements:

[0035] Fuzzy logic driven gate assignment algorithm:

[0036] ,in, is the function used to calculate the compatibility of flights and gates, = (see matching degree for aircraft type, transfer time, and capacity density), which is a vector containing three dimensions of information, representing the matching degree between flight type and boarding gate, the time required for passenger transfer, and the passenger density near the boarding gate; It is the center point of the ideal value of each dimension, that is, the most ideal value of each dimension; is the fuzzy membership attenuation coefficient, which controls the attenuation rate of the membership function of each dimension, and , set according to actual scenario experience; Indicates the minimum value operation, which is used to integrate the membership of three dimensions; It means taking the non-negative part to ensure that the result is non-negative;

[0037] Improved Q-learning baggage routing algorithm:

[0038] ;

[0039] in, is the updated state-action value function, indicating that in state Take action Expected cumulative rewards; is the state-action value function before updating; is the learning rate, which controls the influence of new experience on the update of the old state-action value function. ; For immediate rewards, that is, taking action Rewards immediately received after is a discount factor used to weigh the importance of immediate rewards and future rewards, ; Output values ​​for the target network, which is used to provide more stable target values ​​and reduce fluctuations during the learning process; To take action The next state to transfer to; The action in the next state; Status Number of visits; state-action pair Number of visits; is the exploration reward coefficient, which is used to encourage the algorithm to explore state-action pairs that have not been fully visited. ;

[0040] Hybrid game vehicle scheduling model:

[0041] ;in, For the The comprehensive utility value of a task is used to evaluate the execution effect of the task; is the task completion time, that is, the execution The time spent on each task; For the task The maximum completion time allowed; is the energy consumption value, execute The energy consumed by each task; For the task Minimum energy consumption value; The service level agreement achievement rate measures whether the task execution meets the requirements of the service level agreement; is the weight coefficient of task completion time, ; is the weight coefficient of energy consumption, ; is the weight coefficient of the service level agreement achievement rate, ; The service level agreement achievement rate is used to measure whether the task execution meets the requirements of the service level agreement.

[0042] The human-computer interaction layer includes:

[0043] Indoor and outdoor seamless navigation system for AR glasses terminals, integrating visual SLAM and beacon positioning technology;

[0044] Multi-language natural language processing engine, supporting personalized service push based on voiceprint recognition;

[0045] The emergency response command platform generates hierarchical visual plans and automatically allocates disposal resources.

[0046] As a preferred solution, it also includes:

[0047] The blockchain evidence storage module implements timestamp encryption and storage for key operation records;

[0048] Federated learning framework to achieve knowledge sharing and privacy protection among multiple airports;

[0049] Quantum encrypted communication channel to ensure the security of transmission of core control instructions;

[0050] Federated learning parameter aggregation algorithm:

[0051] ,in, For the The global model parameters are the model parameters obtained after aggregation and used by all airports participating in federated learning. The number of airports participating in federated learning; For the The number of samples per airport; is the total number of samples of all airports; For the Airports in Local model parameters of the wheel; is the model similarity driving coefficient, which is used to control the influence of the parameter similarity gradient term on the global model parameter update. ; is the first and The cosine similarity of the local model parameters of the airports in the first round is the gradient of the parameters, which is used to measure the similarity between models; is the cosine similarity function in parameter space.

[0052] It can be seen from the technical solution provided by the present invention that the airport intelligent operating system provided by the present invention has the following beneficial effects:

[0053] 1. Improve airport operation efficiency

[0054] Precise collaborative scheduling:

[0055] By combining the three-dimensional simulation environment constructed by digital twins with multi-agent reinforcement learning, cross-domain dynamic optimization of flights, boarding gates, baggage systems and ground services can be achieved. For example, the fuzzy logic-driven boarding gate allocation algorithm comprehensively considers factors such as aircraft model matching, transfer time and passenger density to dynamically allocate the most suitable boarding gate for the flight, reducing passengers' walking distance and waiting time, and improving boarding efficiency.

[0056] The improved Q-learning baggage routing algorithm dynamically adjusts the baggage transportation route according to the real-time baggage location, flight information and route congestion, realizes intelligent routing, reduces the risk of baggage mishandling and delay, ensures accurate matching of baggage with flights, and improves overall operational efficiency;

[0057] The hybrid game vehicle scheduling model comprehensively considers task completion time, energy consumption and service level agreement achievement rate to allocate optimal resources for ground service vehicle tasks, improve vehicle utilization efficiency and ensure flight service quality;

[0058] Intelligent forecasting and planning:

[0059] The delay prediction model based on the improved spatiotemporal graph convolution introduces the temporal convolution network (TCN) to process time series features, effectively solving the information loss problem of the traditional graph convolution network (GCN) in the time dimension and accurately predicting flight delays. Airports can adjust resource allocation in advance according to the prediction results, such as arranging passengers' activities while waiting for flight and adjusting the work arrangements of ground staff, so as to reduce the impact of delays on operations.

[0060] The trajectory prediction algorithm integrated with the Hidden Markov Model uses multi-source data such as passengers' ticket purchase data, security check records, and commercial consumption behavior to accurately predict passenger behavior patterns. Airports can use this to plan service resources in advance, such as adding catering supplies and guides in areas where passengers may gather, to improve service efficiency and passenger experience.

[0061] 2. Enhance airport operation safety

[0062] Abnormal behavior detection and emergency response:

[0063] The multimodal contrastive learning anomaly detection model integrates multimodal data such as vision and audio, and accurately identifies abnormal behaviors in airport operations through contrastive learning. For example, the abnormal behavior detection array combining millimeter-wave radar and thermal imaging cameras can monitor the movement trajectory and abnormal behaviors of personnel in real time, such as long stays and sudden running, and issue alarms in time to ensure airport safety.

[0064] The airport emergency response method constructs a multi-level event impact propagation model to quantitatively evaluate the chain reaction of emergencies; initiates a dynamic resource preemption protocol, establishes a priority allocation channel for key resources, and generates adaptive evacuation paths, such as an ant colony algorithm that considers the dynamic changes in crowd density, to ensure that when an emergency occurs, resources can be deployed quickly and effectively to ensure personnel safety and reduce the damage to airport operations;

[0065] Data security:

[0066] The blockchain evidence storage module implements time-stamp encryption storage for key operation records, and uses blockchain's distributed ledger and encryption technology to ensure that data cannot be tampered with and is traceable. When dealing with issues such as flight delay disputes and safety accident investigations, it can provide accurate and reliable operation records and clarify responsibility.

[0067] The quantum encryption communication channel is based on the principles of quantum mechanics. It generates a secure and unique key through quantum key distribution to encrypt and transmit core control instructions. It has the characteristics of being unclonable and non-eavesdropping, providing extremely high security for the transmission of key information at the airport, effectively resisting various network attacks, and ensuring the stability of airport operations.

[0068] 3. Optimize passenger service experience

[0069] Personalized human-computer interaction:

[0070] The AR glasses terminal's indoor and outdoor seamless navigation system integrates visual SLAM and beacon positioning technology to provide passengers with accurate and personalized route planning. Whether looking for a boarding gate or restaurant in the terminal or going from the terminal to the apron for boarding, passengers can get the best route guidance, avoid congested areas, and improve travel convenience.

[0071] The multilingual natural language processing engine supports voiceprint recognition, can identify multiple languages, and provide personalized service push based on the passenger's voiceprint characteristics; for example, it can recommend specialty stores based on the passenger's historical preferences, push flight-related information, etc., making the passenger's experience at the airport more comfortable and convenient;

[0072] 4. Promote the coordinated development of multiple airports

[0073] Knowledge sharing and privacy protection: The federated learning framework enables knowledge sharing and privacy protection among multiple airports. All airports jointly train models through federated learning without directly sharing original data. For example, in the training of models such as flight delay prediction and passenger behavior analysis, each airport uploads model parameters, and the central coordination server uses aggregation algorithms to integrate the knowledge of each airport and improve the generalization ability of the model. At the same time, differential privacy, homomorphic encryption and other technologies are used to protect data privacy, which not only promotes collaboration among multiple airports, but also ensures the security and independence of each airport's data. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The figure is a schematic diagram of the overall structure of an intelligent airport operating system according to the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. 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.

[0076] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0077] like Figure 1 As shown, an embodiment of the present invention provides an airport intelligent operating system, including a multimodal data acquisition layer, an intelligent analysis layer, a collaborative scheduling layer and a human-computer interaction layer.

[0078] In this embodiment, the multimodal data collection layer is deployed in a heterogeneous sensor network in the terminal, runway, and apron to obtain flight status, passenger flow, baggage trajectory, vehicle location, and environmental parameters in real time;

[0079] Furthermore, as a basic component of the airport intelligent operating system, the multimodal data collection layer is responsible for collecting various key information of the airport in real time. It realizes the accurate collection of multi-source data such as flight status, passenger flow, baggage trajectory, vehicle location and environmental parameters through heterogeneous sensor networks deployed in key areas such as terminals, runways and aprons, providing comprehensive and accurate data support for subsequent intelligent analysis and coordinated scheduling. It includes:

[0080] Centimeter-level precision positioning network:

[0081] It is constructed by UWB (ultra-wideband) positioning base station and RFID (radio frequency identification) tag; UWB positioning base station has extremely high time resolution and can accurately measure the signal propagation time, thereby achieving high-precision positioning of the target object; while RFID tag is attached to the object to be tracked, such as luggage, vehicles and passengers in some special scenarios;

[0082] In terms of baggage management, each piece of luggage is affixed with an RFID tag when it is checked in. The UWB positioning base station can obtain its location information in real time, from the check-in counter to the baggage handling system, and finally loaded onto the flight, to achieve accurate positioning and tracking of the entire process, ensuring accurate matching of luggage with flights, greatly reducing the probability of luggage loss or mis-transportation;

[0083] For vehicle positioning, the service vehicles in the airport are equipped with RFID tags, and the UWB positioning base station network can monitor the driving tracks of vehicles on the runway, apron and around the terminal in real time, providing accurate location data for vehicle dispatch management, optimizing vehicle driving routes and improving ground service efficiency;

[0084] Abnormal behavior detection array:

[0085] The array combines two sensor technologies: millimeter-wave radar and thermal imaging camera. The millimeter-wave radar uses the echo generated by the interaction between the electromagnetic waves in the millimeter-wave frequency band and the target object to obtain information such as the distance, speed and angle of the target, and can monitor the movement state of the object in real time. The thermal imaging camera generates a thermal image by detecting the infrared heat radiation emitted by the object, and identifies the outline and position of the human body or other heat-generating objects.

[0086] In the complex flow of people at the airport, the millimeter-wave radar can monitor the movement trajectory and speed changes of people in real time, while the thermal imaging camera can assist in identifying abnormal behaviors of people, such as long-term stay, sudden running, abnormal gathering, etc. For example, when someone stays near the runway for a long time or runs abnormally, the data fusion analysis of the two sensors can quickly and accurately detect the abnormal situation and issue an alarm in time to ensure the safety of airport operations.

[0087] Energy consumption monitoring sensor cluster:

[0088] Built on LoRaWAN (Long Range Wide Area Network) technology, LoRaWAN has the characteristics of low power consumption and long-distance transmission, which is very suitable for deploying energy consumption monitoring sensors in large areas such as airports; the cluster is distributed in various key energy consumption nodes of the airport, such as the lighting system of the terminal, air conditioning equipment, runway lighting facilities and various power equipment;

[0089] Energy consumption monitoring sensors collect power consumption data of each device in real time, including parameters such as voltage, current, and power, and transmit the data to the data center through the LoRaWAN network. By analyzing these energy consumption data, the airport management department can grasp the energy usage in real time, conduct energy consumption analysis and optimization, formulate energy-saving strategies, reduce operating costs, and achieve sustainable development of the airport.

[0090] Edge computing nodes:

[0091] In the data collection process, edge computing nodes are deployed to reduce the pressure on the data transmission network and perform preliminary processing on massive raw data. These nodes are close to sensor devices and can receive data collected by sensors in real time, and perform data cleaning and feature extraction preprocessing locally.

[0092] The data cleaning stage mainly removes noise, outliers and duplicate data in the data to improve data quality; for example, the abnormal data points collected by the sensor due to electromagnetic interference and other reasons are corrected or eliminated; feature extraction is to extract feature information that is valuable for subsequent intelligent analysis from the original data, such as extracting key features such as flight take-off and landing times and delay duration from flight status data, and extracting personnel flow density features in different time periods and regions from passenger flow data; the pre-processed data is then transmitted to the intelligent analysis layer through the network, which effectively reduces the amount of data transmission and improves data processing efficiency;

[0093] Through the coordinated work of the various components of the above-mentioned multimodal data acquisition layer, the airport intelligent operating system can comprehensively, real-time and accurately obtain various key data in the airport operation process, providing a solid data foundation for subsequent intelligent analysis, collaborative scheduling, human-computer interaction and other functional modules, thereby realizing efficient and intelligent operation of the airport.

[0094] In this embodiment, the intelligent analysis layer includes a deep learning-driven dynamic prediction module, which processes multi-source heterogeneous data through a spatiotemporal attention mechanism to generate flight delay predictions, passenger behavior patterns, and resource demand maps;

[0095] Furthermore, the intelligent analysis layer is one of the core modules of the airport's intelligent operating system. It uses a deep learning-driven dynamic prediction module and a spatiotemporal attention mechanism to deeply process multi-source heterogeneous data, thereby generating flight delay predictions, passenger behavior patterns, and resource demand maps, providing key decision support for the efficient operation of the airport;

[0096] 1. Delay prediction model based on improved spatiotemporal graph convolution

[0097] Flight delays are an important factor affecting airport operational efficiency and passenger experience; this model aims to accurately predict flight delays. Its core algorithm is:

[0098] ;

[0099] Normalized adjacency matrix with self-loops :This matrix is ​​used to describe the connection relationship between various entities in the airport (such as flights, boarding gates, runways, etc.); through normalization processing, it ensures the stability of model calculation in airport networks of different scales and structures; adding self-loops allows each node to not only receive information from neighboring nodes, but also retain its own feature information, which helps to more comprehensively capture the interactive relationship between nodes and the surrounding environment;

[0100] Temporal features of the output of the temporal convolutional network : Represents input time-related sequence data, such as historical flight take-off and landing times, weather change data, etc. The temporal convolutional network (TCN) can effectively extract the time series features in these data and capture the dynamic change rules in the time dimension; for example, by analyzing the weather change trend over a period of time, its potential impact on flight delays can be predicted;

[0101] Hadamard : Used to fuse spatiotemporal features; in this model, the graph convolution part ( ) is responsible for extracting the spatial structure characteristics between airport entities, while TCN extracts time series characteristics; the two are multiplied element by element through the Hadamard product to achieve the organic integration of spatiotemporal characteristics, so that the model can simultaneously consider the comprehensive impact of spatial position relationship and time change factors on flight delays;

[0102] The order of graph convolution : Controls the propagation range of node information in graph convolution operations; larger A value of means that a node can aggregate the information of its more distant neighbor nodes, thereby capturing a wider range of spatial structural features, but it may also introduce too much noise; a smaller value of The value focuses on local information; by reasonably adjusting The model can accurately mine the spatial features related to flight delays in airport networks of different sizes and complexities.

[0103] Gated Activation Function : Introducing nonlinear transformations to enhance the model’s expressiveness; it controls the flow of information to determine which feature information can continue to be transmitted and play a role in the model, similar to a “switch” mechanism; this nonlinear transformation enables the model to learn more complex patterns and relationships, thereby improving the accuracy of flight delay prediction;

[0104] 2. Trajectory prediction algorithm integrating hidden Markov model

[0105] In order to better understand and predict the behavior trajectory of passengers in the airport, the algorithm combines the hidden Markov model, and its core formula is:

[0106] ;

[0107] Hidden State : In the passenger trajectory prediction scenario, Indicates a certain state that the passenger is in at the moment that is not directly observed; for example, the passenger may be in different states such as "going to the boarding gate", "dining in the restaurant", "shopping in the duty-free shop", etc. These hidden states cannot be obtained directly, but can be inferred through observation data;

[0108] Observational data : This includes directly accessible data such as security check records and commercial consumption behavior; for example, the time and location of passengers passing through security check, consumption records in duty-free shops, etc. These observational data provide clues for inferring passengers’ hidden status;

[0109] State transition probability matrix : Describes the traveler's The probability of the hidden state at time t being transferred to the hidden state at time t; for example, if the passenger is is always in the "dining in a restaurant" state, then the state transition probability matrix can be given in The probability of a passenger moving to another state such as "going to the boarding gate" or "continuing dining" at any given moment. By learning and analyzing a large amount of historical data, the model can continuously optimize this probability matrix, thereby more accurately predicting the passenger's behavior trajectory.

[0110] Through this algorithm, the model can predict the possible future paths of passengers based on their historical behavior data and current observation information, providing strong support for the airport's resource allocation and service guidance. For example, if it is predicted that a large number of passengers will gather in a certain area, the airport can arrange more service personnel in advance or adjust the operation mode of related facilities.

[0111] 3. Multimodal Contrastive Learning Anomaly Detection Model

[0112] In order to timely discover abnormal situations during airport operation and ensure the safety and normal operation of the airport, the model adopts a multimodal contrast learning method, and its loss function is:

[0113] ;

[0114] Visual feature encoder Audio Feature Encoder : They are used to encode the input visual data (such as surveillance video images) and audio data (such as airport broadcasts, environmental noise, etc.) into feature vectors; these feature vectors can represent the key information in the data for subsequent comparison and analysis; for example, the visual feature encoder can extract features such as the behavior of people and the location of objects in the video; the audio feature encoder can capture the frequency, intensity and other features of the sound;

[0115] Visual input data With audio input data : Various cameras and microphones deployed at the airport cover visual and audio information from all areas of the airport; these multimodal data can reflect the operating status of the airport from different angles and provide a richer source of information for anomaly detection;

[0116] Balance coefficient ( and : and is a learnable parameter used to balance the weights of the contrastive loss of visual and audio features and the weight of the contrastive learning loss term; its value range is and , dynamically adjusted through cross-validation; this can automatically optimize the model's dependence on different modal data according to different scenarios and data characteristics to achieve the best anomaly detection effect;

[0117] Cosine similarity function : Used to calculate the similarity between two vectors; in anomaly detection, by comparing the cosine similarity between the positive sample (normal data) and the current sample and the negative sample (abnormal data), it is determined whether the current sample is abnormal; for example, if the similarity between the current sample and the positive sample is much higher than the similarity with the negative sample, the sample is considered normal; otherwise, there may be anomalies;

[0118] Sample related parameters : Represents positive samples, i.e. known normal data samples; The sample that needs to be judged at present; Represents negative samples, i.e. known abnormal data samples; is the sample size, is the number of negative samples; through comparative learning of these samples, the model can learn the characteristic patterns of normal and abnormal data, so as to accurately identify abnormal events in airport operations, such as abnormal personnel behavior, equipment failure, etc., and issue alarms in time and take corresponding measures;

[0119] Through these three key models, the intelligent analysis layer conducts in-depth mining and analysis of various types of data collected by the multimodal data collection layer, providing airport operations with accurate predictions and insights on flight delays, passenger behavior, and abnormal situations, and providing a scientific basis for subsequent collaborative scheduling and decision-making.

[0120] In this embodiment, the collaborative scheduling layer implements cross-domain dynamic optimization of flight-boarding gate-baggage system-ground service based on the three-dimensional simulation environment constructed by digital twins, and adopts multi-agent reinforcement learning to achieve Nash equilibrium of resource allocation;

[0121] Furthermore, the collaborative scheduling layer is the key hub of the airport's intelligent operating system. Based on the three-dimensional simulation environment built by digital twins, it dynamically optimizes flights, boarding gates, baggage systems, and ground services across domains, and achieves Nash equilibrium in resource allocation through multi-agent reinforcement learning to ensure efficient and smooth airport operations; including:

[0122] 1. Fuzzy logic driven boarding gate assignment algorithm

[0123] The reasonable allocation of boarding gates is crucial to flight operation efficiency and passenger experience; the algorithm is based on fuzzy logic, and the core formula is:

[0124] ;in, is the function used to calculate the suitability of flights and gates;

[0125] Multidimensional input vector : = (see matching degree for aircraft type, transfer time, and capacity density), which comprehensively considers multiple key factors affecting boarding gate allocation; aircraft type matching degree reflects the degree of adaptation between flight type and boarding gate facilities. For example, large passenger aircraft need to be adapted to boarding gates with corresponding jet bridges and boarding facilities; transfer time reflects the time interval between the arrival of transfer passengers from one flight and the departure of the next flight, ensuring that transfer passengers have enough time to complete the transfer process; passenger density is related to the degree of gathering of passengers near the boarding gate to avoid congestion caused by excessive concentration of passengers;

[0126] Ideal value center point :The ideal value set for each dimension is the ideal target for boarding gate allocation; for example, the ideal value of aircraft type matching may be a perfect match, the ideal value of transfer time is the shortest time that satisfies the comfort of transfer passengers, and the ideal value of passenger density is a moderate density that can ensure service efficiency without causing congestion; these ideal values ​​are determined based on factors such as the actual operating experience of the airport, facility conditions, and passenger service standards;

[0127] Fuzzy membership attenuation coefficient : The value range is , set according to the actual scenario experience value; it controls the decay speed of the membership function of each dimension, that is, the degree of decrease of the membership when the actual value deviates from the ideal value; a smaller decay coefficient means a lower tolerance for deviations from the ideal value, and the algorithm has stricter matching requirements for this dimension; conversely, a larger decay coefficient means a relatively loose matching requirement for this dimension;

[0128] Operation symbol description: Indicates the minimum value operation. This is because the algorithm believes that the gate allocation scheme is optimal only when each dimension is as close to the ideal value as possible, so the minimum value of the membership degree of the three dimensions is taken as the comprehensive fitness; It means taking the non-negative part to ensure that the final comprehensive fitness is a non-negative value, which is in line with practical significance;

[0129] This algorithm can comprehensively consider multiple factors and dynamically allocate the most suitable boarding gate for each flight, thereby improving boarding efficiency, reducing passengers' waiting time and walking distance, and optimizing the utilization of airport resources.

[0130] 2. Improved Q-learning baggage routing algorithm

[0131] In the complex airport baggage transportation system, ensuring that baggage arrives at the corresponding flight accurately and quickly is the key; the improved Q-learning baggage routing algorithm is implemented through the following formula:

[0132] ;

[0133] State-action value function ( function): Indicates the current state Take action The expected cumulative reward of is the updated state-action value function; state It can include the current location of the baggage, target flight information, congestion status of each route, etc.; Action It represents the next transportation path selected by the luggage at the current location;

[0134] Learning Rate : The value range is , determined based on experience and experimental optimization; it controls the degree of influence of new experience on the update of the old state-action value function; a smaller learning rate makes the algorithm more dependent on past experience, and the learning process is more stable, but may converge more slowly; a larger learning rate can adapt to new situations faster, but may cause greater fluctuations in the learning process;

[0135] Instant Rewards : Take action The reward obtained immediately after the baggage is delivered; for example, when the baggage chooses a smooth path and reduces the transportation time, a positive immediate reward will be obtained; if the selected path causes congestion or delays, a negative immediate reward may be obtained; the immediate reward is used to guide the algorithm to learn in the direction of better path selection;

[0136] Discount Factor : The value range is , determined based on experience and experimental optimization; it is used to weigh the importance of immediate rewards and future rewards; a discount factor close to 1 means that the algorithm pays more attention to future long-term rewards, and encourages the selection of actions that can bring better subsequent benefits even though the current rewards are not high; a discount factor close to 0 makes the algorithm pay more attention to immediate rewards;

[0137] Target network output value :The target network is used to provide a more stable target value and reduce fluctuations in the learning process; function, the reference target network is in the new state Take different actions The output value when , take the maximum value , to guide the action selection in the current state towards the direction that may obtain the maximum long-term reward;

[0138] Explore Reward Factor : The value range is , dynamically adjusted through Monte Carlo cross-validation; it is similar to The items together constitute the exploration reward, which encourages the algorithm to explore state-action pairs that have not been fully visited; among them, Yes Status The number of visits, is a state-action pair When a state-action pair has a small number of visits, The value of will be larger, thus giving the action a larger exploration reward, prompting the algorithm to try new paths and avoid falling into the local optimal solution;

[0139] Through this algorithm, the baggage system can dynamically adjust the baggage transportation path according to the real-time operation status, realize intelligent routing, improve the efficiency and accuracy of baggage transportation, and reduce the risk of baggage mis-transportation and delay;

[0140] 3. Hybrid game vehicle scheduling model

[0141] The reasonable scheduling of airport ground service vehicles is crucial to ensure the normal operation and service quality of flights; the mixed game vehicle scheduling model evaluates the comprehensive utility of the task through the following formula:

[0142] ;

[0143] Comprehensive utility value : Used to measure the The execution effect of each task is taken into account, taking into account multiple factors such as task completion time, energy consumption and service level agreement achievement rate. A higher comprehensive utility value means that the execution plan of the task performs well in many aspects, which helps to optimize vehicle scheduling decisions.

[0144] Task completion time related items: is the task completion time, For the task The maximum completion time allowed; It reflects the degree of saving of task completion time relative to the maximum allowed time. The closer the value is to 1, the shorter the task completion time is and the greater the contribution to the comprehensive utility.

[0145] Energy consumption related items: To implement the The energy consumed by each task, For the task The minimum energy consumption value, It reflects the efficiency of energy consumption. The larger the value, the closer the energy consumption is to the theoretical minimum value, the higher the energy utilization efficiency, and the more significant the improvement in comprehensive utility.

[0146] Service level agreement achievement rate related items: Measure whether the task execution meets the requirements of the service level agreement, such as whether it arrives at the designated location on time, whether the service operation is completed accurately, etc. The service level agreement achievement rate is changed. As the achievement rate increases, its contribution to the overall utility gradually increases, but the growth rate gradually slows down, avoiding excessive reliance on a high achievement rate.

[0147] Weight coefficient , , : is the weight coefficient of task completion time, ; is the weight coefficient of energy consumption, ; is the weight coefficient of the service level agreement achievement rate, , these weight coefficients reflect the relative importance of different factors in vehicle scheduling decisions; for example, during peak flight hours, task completion time may be more critical, The weight of energy consumption is relatively large; in daily operation, in order to reduce operating costs, the weight of energy consumption is relatively large. It may be appropriately increased; The service level agreement achievement rate measures whether the task execution meets the requirements of the service level agreement;

[0148] Through this hybrid game vehicle scheduling model, multiple key factors are comprehensively considered to allocate the best vehicle resources for each ground service vehicle task, while ensuring the quality of flight services, optimizing energy consumption and operational efficiency;

[0149] The collaborative scheduling layer uses the above three algorithms to optimize boarding gate allocation, baggage routing and ground service vehicle scheduling, thereby achieving efficient collaborative scheduling of airport resources and improving the overall operational efficiency of the airport.

[0150] In this embodiment, the human-computer interaction layer is equipped with an augmented reality navigation terminal and a voice interaction device to provide personalized route planning and emergency instruction push for passengers and staff;

[0151] Furthermore, the human-machine interaction layer is a bridge between the airport's intelligent operating system and passengers and staff. It is equipped with advanced equipment and technology to provide personalized route planning and emergency command push, greatly improving the airport's service experience and operational efficiency; including:

[0152] 1. Seamless indoor and outdoor navigation system for AR glasses terminals

[0153] The system integrates visual SLAM (simultaneous localization and mapping) and beacon positioning technology to provide passengers and staff with accurate navigation services in the complex environment of the airport;

[0154] Visual SLAM technology: AR glasses collect image information of the surrounding environment through built-in cameras, use visual SLAM algorithms to analyze image features in real time, build environmental maps and determine their own positions in the map; for example, inside the terminal, the system can identify visual features such as logos on the wall and store signs, quickly and accurately locate the user's position, and plan the path to the destination based on the environmental map; this technology does not need to rely on pre-laid infrastructure, can adapt to various complex and changing indoor environments, and provide users with an autonomous and flexible navigation experience;

[0155] Beacon positioning technology: iBeacon devices are deployed in specific areas of the airport, such as the apron, runway and other open outdoor areas. AR glasses receive signals from beacons and use signal strength and triangulation positioning principles to accurately calculate their own positions. Combined with visual SLAM technology, seamless positioning switching from indoor to outdoor is achieved. For example, when passengers walk from the terminal to the apron to prepare for boarding, the navigation system can smoothly transition from indoor navigation based on visual SLAM to outdoor navigation based on beacon positioning, ensuring the continuity and accuracy of navigation.

[0156] Personalized route planning: The system plans the best personalized route based on the user's identity (passenger or staff), current location and destination information, combined with real-time airport passenger flow, flight status and other data; for passengers, it will give priority to the convenience and comfort of the route, avoid congested areas, and provide information about shops, restaurants and other facilities along the way; for staff, it will plan the fastest route to the work place according to the work task requirements, and prompt relevant work items along the route;

[0157] 2. Multilingual Natural Language Processing Engine

[0158] This engine supports voiceprint recognition and provides personalized service push for passengers and staff;

[0159] Multi-language processing capability: As an international transportation hub, the airport receives passengers from all over the world. The multi-language natural language processing engine can recognize and understand multiple languages, including common English, Chinese, Japanese, Korean, etc., as well as some minority languages. Whether passengers are asking for flight information, looking for service facilities, or staff are communicating at work, they can interact with the system in their own language. The engine uses deep learning technology to train a large amount of multi-language text data and can accurately understand the intentions expressed in various languages.

[0160] Voiceprint recognition technology: Through the analysis and recognition of the user's voice characteristics, accurate authentication of the user's identity is achieved; each user's voiceprint is unique like a fingerprint, and the system collects and stores the user's voiceprint information when it is used for the first time; when the user uses the voice interaction function again, the system quickly recognizes the user's identity and provides personalized service push based on the user's historical preferences and current scenarios; for example, for passengers who frequently take business class, the system will actively push relevant information such as the location of the business lounge and the fast security check channel; for staff, the system will push real-time work tasks and related instructions based on their job responsibilities and job requirements;

[0161] Personalized service push: Provide users with customized service information based on their historical behavior data, real-time needs, and current airport status; for example, push information about nearby rest areas, dining coupons, etc. based on the passenger's flight delays; for staff, push corresponding emergency handling procedures and personnel deployment instructions in a timely manner when special circumstances occur on the flight;

[0162] 3. Emergency response command platform

[0163] The platform can generate hierarchical visual emergency plans and automatically allocate disposal resources to ensure that the airport can respond quickly and effectively when encountering emergencies;

[0164] Hierarchical visual emergency plan generation: For emergencies of different types and severity, such as fires and major equipment failures, the platform has prepared detailed hierarchical emergency plans in advance. These plans are presented in a visual way, using graphics, charts, text descriptions, etc. to intuitively display key information such as the event response process, division of responsibilities, and resource allocation. For example, in the fire emergency plan, the location of fire-fighting equipment in each area, the direction of evacuation channels, and the rescue responsibilities of different departments will be clearly marked. Based on the real-time monitoring data of the event, the platform automatically determines the event level and quickly generates a visual emergency plan of the corresponding level, providing commanders with clear decision-making support.

[0165] Automatic allocation of disposal resources: Based on real-time resource status information, such as the number of rescue personnel, equipment location, material reserves, etc., as well as the needs of the event, the platform uses intelligent algorithms to automatically allocate disposal resources. For example, when a fire occurs, the system will automatically dispatch the nearest fire trucks, firefighters, and fire-fighting materials to the scene based on factors such as the location and size of the fire. At the same time, it coordinates with other relevant departments, such as medical emergency and security, to ensure that resources from all aspects can be invested in emergency disposal work in a timely and reasonable manner, thereby improving the efficiency and effectiveness of emergency response.

[0166] Real-time information exchange and feedback: The emergency response command platform is connected to the monitoring equipment, sensors and terminal devices of staff distributed in various areas of the airport in real time to obtain dynamic information on the incident site, such as casualties, fire spread trends, etc. At the same time, the command personnel can issue instructions to the on-site staff through the platform to achieve rapid information exchange and feedback. This real-time information flow mechanism helps the command personnel to adjust the emergency strategy in time to ensure the smooth progress of emergency response work;

[0167] Through the above three functions, the human-computer interaction layer provides convenient, efficient and personalized interactive experience for airport passengers and staff. While improving passenger service satisfaction, it also effectively ensures the efficiency and safety of airport operations.

[0168] The airport intelligent operating system also includes:

[0169] The blockchain evidence storage module implements timestamp encryption and storage for key operation records;

[0170] Federated learning framework to achieve knowledge sharing and privacy protection among multiple airports;

[0171] Quantum encrypted communication channel ensures the security of transmission of core control instructions.

[0172] In this embodiment, the blockchain evidence storage module, as an important component of the airport intelligent operating system, uses the characteristics of blockchain technology to implement time stamp encryption storage for key operation records in the airport operation process, providing highly reliable and tamper-proof information evidence services for airport operations, ensuring data integrity and traceability; including:

[0173] 1. Identification and collection of key operation records

[0174] Definition of key operations: In the complex operation system of the airport, various key operations are clearly defined, including flight dispatch instructions, passenger security information, baggage handling records, and changes in the operating status of important equipment. For example, the adjustment of flight take-off and landing time, the detailed time and results of passengers passing through security checks, records of every link of baggage from check-in to loading, and the start, stop, and fault maintenance records of important equipment such as runway lighting systems and baggage conveyor belts. These operation records are crucial for the traceability, responsibility identification, and data analysis of airport operations.

[0175] Data collection mechanism: Data collection interfaces are embedded in the information systems of each key business link of the airport; when key operations occur, the relevant system automatically triggers the data collection process and organizes the operation records in a specific data format, including detailed information such as operation time, operation subject, operation content, and objects involved; for example, when the flight dispatch system issues a flight delay instruction, it automatically packages the instruction content, issuance time, dispatcher information, and flight information involved into record data, and transmits it to the blockchain evidence storage module through the data collection interface;

[0176] 2. Timestamp encrypted storage

[0177] Application of encryption algorithms: Use advanced asymmetric encryption algorithms, such as RSA or elliptic curve encryption (ECC), to encrypt the collected key operation records. Taking the RSA algorithm as an example, first generate a pair of public and private keys. The public key is used to encrypt the operation record data, and the private key is safely kept by the blockchain evidence storage module. When the key operation record is received, the public key is used to encrypt the data to ensure the confidentiality of the data during transmission and storage, and prevent sensitive information from being stolen or tampered with.

[0178] Timestamp generation: add an accurate timestamp to each encrypted key operation record; the timestamp is generated based on the consensus mechanism in the blockchain network to ensure the accuracy and immutability of the time; the timestamp not only records the specific time when the operation occurs, accurate to the millisecond level, but also solidifies the time information in the entire blockchain ledger by combining it with the chain structure of the blockchain; for example, after a passenger's security check record is encrypted, a timestamp will be added immediately, and the timestamp information will be correlated with the previous and next records on the blockchain to form a time series;

[0179] Blockchain storage: Store encrypted key operation records with timestamps in the distributed ledger of the blockchain. The blockchain adopts a distributed storage structure, and multiple nodes jointly maintain the ledger data. Each node keeps a complete copy of the ledger. When a new record is added, a consensus algorithm (such as proof of work PoW, proof of stake PoS, etc.) is used to ensure that all nodes reach a consensus on the update of the ledger. For example, in a blockchain network that adopts the PoS consensus algorithm, nodes with certain rights (such as holding a certain amount of cryptocurrency) participate in the verification and recording of new key operation records. Only when the majority of nodes pass the verification will the record be officially added to the blockchain ledger, thereby ensuring the consistency and immutability of the data.

[0180] 3. Data traceability and verification

[0181] Traceability query function: When airport operations need to trace specific key operations, relevant personnel can enter query conditions such as operation time range, flight number, passenger identity information, etc. through the query interface provided by the blockchain evidence storage module to quickly locate relevant key operation records; due to the chain structure and timestamp characteristics of the blockchain, the query results can clearly present the sequence and development context of the operations; for example, when investigating a baggage delay incident, staff can enter the baggage order number to query the operation records of the baggage from the beginning of check-in to each link, including detailed information such as when it entered the baggage handling system and at which link the delay occurred;

[0182] Data verification mechanism: For key operation records stored in the blockchain, data integrity and authenticity can be verified through the blockchain's encryption verification mechanism; the verification process is based on public-private key pairs and digital signature technology; when a record needs to be verified, the digital signature of the record is decrypted using the corresponding private key, and then compared with the hash value recalculated using the same encryption algorithm for the current record content; if the two are consistent, it proves that the record has not been tampered with during the storage process, ensuring the credibility of the data; for example, when dealing with disputes involving flight scheduling instructions, the digital signature of the instruction record can be verified to determine whether the instruction is the original instruction, effectively solving the problem of responsibility identification;

[0183] The blockchain evidence storage module provides solid data security protection for airport operations through timestamp encrypted storage of key airport operation records and a reliable traceability verification mechanism, which helps to improve the transparency and fairness of airport management and its ability to deal with various issues.

[0184] In this embodiment, the federated learning framework plays a key role in realizing knowledge sharing and privacy protection among multiple airports. It breaks the limitations of traditional centralized learning in terms of data privacy and security, allowing multiple airports to jointly train models without directly sharing original data, thereby improving the system's generalization ability and service quality. This includes:

[0185] 1. Multi-airport data distribution and isolation

[0186] Data characteristics: Due to differences in geographical location, operating scale, passenger flow and other factors, the data accumulated by different airports are significantly different in scale, characteristics and distribution. For example, large hub airports have a huge amount of flight takeoff and landing data, with a wide range of passenger sources and rich international flight information; while small airports have relatively less data, with passengers mainly traveling domestically and relatively simple data characteristics. These data contain the unique operating models and rules of each airport, but also face the challenge of privacy protection;

[0187] Data isolation: The raw data of each airport is stored locally, and data access rights are strictly restricted to ensure that the data does not leave the local area. This data isolation mechanism not only meets the requirements of data privacy regulations, but also ensures the security and independence of each airport's data. For example, the flight delay data and passenger behavior data of Airport A are all stored and managed in the local data center of Airport A, and other airports cannot directly obtain these raw data.

[0188] 2. Federated Learning Process

[0189] Model initialization: A central coordination server generates an initial global model and distributes it to each airport participating in federated learning. This initial model can be a general model architecture based on past experience or pre-training, such as a neural network model for flight delay prediction or a machine learning model for passenger behavior analysis. After receiving the initial model, each airport uses it as a basis for training on local data.

[0190] Local training: Each airport uses the training data stored locally to train the global model. During the training process, each airport selects a suitable optimization algorithm (such as stochastic gradient descent SGD and its variants Adagrad, Adadelta, etc.) to update the model parameters according to its own data characteristics and business needs. For example, when training the flight delay prediction model, Airport B uses local historical flight data and combines the Adagrad algorithm to adjust the model parameters to minimize the error between the prediction results and the actual delay situation.

[0191] Parameter upload: After completing local training, each airport uploads the updated model parameters to the central coordination server; these parameters represent the knowledge gained by the airport through training on local data, but do not contain the original data itself; for example, the model parameters uploaded by Airport C reflect the adjustment direction and magnitude of Airport C in the flight resource allocation model training, but do not involve the specific data records of Airport C's flight resource allocation;

[0192] Parameter aggregation: After the central coordination server receives the model parameters uploaded by each airport, it uses the federated learning parameter aggregation algorithm to perform parameter aggregation; the core formula is:

[0193] ;

[0194] in, For the The global model parameters are the model parameters obtained after aggregation and used by all airports participating in federated learning. The number of airports participating in federated learning; For the The number of samples per airport; is the total number of samples of all airports, through The model parameters of each airport are weighted so that airports with a large sample size contribute more to the global model update; For the Airports in Local model parameters of the wheel; is the model similarity driving coefficient, which is used to control the influence of the parameter similarity gradient term on the global model parameter update. The value range is , which is determined through experimental optimization. It is used to control the influence of the parameter similarity gradient term on the global model parameter update; is the first and The cosine similarity of the local model parameters of the airports in the first round is the gradient of the parameters, which is used to measure the similarity between models; is the cosine similarity function in parameter space. This formula not only integrates the model parameters of each airport, but also considers the similarity between models to prevent model drift in multi-airport joint training.

[0195] Model distribution: The central coordination server redistributes the aggregated global model parameters to each airport, which then updates its local model copy and starts the next round of local training. Through multiple rounds of such iterative training, the global model is continuously optimized, gradually integrating the knowledge of each airport and improving the generalization ability of the model in different airport scenarios.

[0196] 3. Privacy protection mechanism

[0197] Differential privacy: During local training, airports can use differential privacy technology to perturb model parameters. Differential privacy adds carefully designed noise to model parameters, so that even if an attacker obtains part of the model parameters, it is impossible to infer the specific information of the original data. For example, when calculating gradient updates, noise that conforms to a specific distribution (such as Laplace distribution) is added, and the intensity of the noise is adjusted according to the privacy budget, which determines the balance between the degree of privacy protection of the data and the accuracy of the model.

[0198] Homomorphic encryption: Homomorphic encryption technology can be used to encrypt model parameters transmitted during federated learning. Homomorphic encryption allows specific calculations to be performed on encrypted data without first decrypting the data. In this way, when aggregating model parameters, the central coordination server can directly calculate the encrypted parameters and obtain the encrypted aggregation results. Only after each airport decrypts the data locally using its own private key can the correct model parameters be obtained. This method ensures data security during parameter transmission and aggregation. Even if the data is intercepted during transmission, attackers cannot obtain meaningful information.

[0199] Through the above mechanism, the federated learning framework realizes knowledge sharing and collaborative learning among multiple airports while ensuring the data privacy and security of each airport, thereby improving the overall performance and service level of the airport's intelligent operating system.

[0200] In this embodiment, the quantum encryption communication channel is responsible for ensuring the security of the transmission of core control instructions in the airport intelligent operating system. Relying on the basic principles of quantum mechanics, it builds an indestructible security line for the transmission of key airport information, effectively resisting all kinds of potential eavesdropping and attacks, and ensuring the stability and reliability of airport operations. It includes:

[0201] 1. Principles of quantum encryption

[0202] Quantum state characteristics: Quantum encryption is based on the superposition and entanglement characteristics of quantum. A quantum bit (qubit) is different from a traditional binary bit. It can be in a superposition state of 0 and 1 at the same time. This characteristic makes the way quantum information is carried and processed completely different from traditional information. For example, the polarization direction of a photon can be used to represent a quantum bit. It can be in a horizontal polarization (corresponding to the classical bit 0), a vertical polarization (corresponding to the classical bit 1), or a superposition state between the two. The entangled state refers to a special correlation between multiple quantum bits. Even if these quantum bits are far apart in space, the measurement of one quantum bit will instantly affect the state of other entangled quantum bits. This "ghostly action at a distance" is an important basis for quantum encryption communication.

[0203] Quantum Key Distribution (QKD): Quantum encryption communication channels mainly generate and distribute encryption keys through quantum key distribution protocols. Taking the most typical BB84 protocol as an example, the sender (Alice) randomly selects two different measurement bases (such as horizontal-vertical basis and diagonal basis) to prepare a series of single photons and sends these photons to the receiver (Bob). Bob also randomly selects a measurement basis to measure the received photons. Afterwards, Alice and Bob disclose the measurement bases they use through classical communication channels (such as traditional networks), and only retain the photon results measured using the same measurement basis. These results constitute the initial key. Due to the quantum no-cloning theorem, any eavesdropper (Eve) who attempts to measure or copy these single photons will inevitably interfere with the quantum state and be discovered by Alice and Bob. This ensures that the generated key is secure and unique, and only Alice and Bob know it.

[0204] 2. System architecture and components

[0205] Quantum signal generation and transmission: In the airport intelligent operating system, there is a special quantum signal generation device; this device can accurately prepare photons in a specific quantum state, such as generating single photons or entangled photon pairs through a laser source; these photons are modulated, carry the quantum information required for encryption, and are sent to the receiving end through optical fiber or free space channels; for example, a quantum signal transmission station is set up in the airport control center to send the generated quantum signals to various key receiving nodes, such as the runway control tower, the control center of the baggage handling system, etc.

[0206] Quantum signal reception and measurement: The receiving end is equipped with a highly sensitive quantum detector to accurately detect the received quantum signal; the detector can measure the photons according to the pre-set measurement basis and convert the measurement results into electrical signals or digital signals for subsequent processing; for example, a quantum signal receiving device is installed in the runway control tower, and the detector inside it can quickly and accurately measure the polarization state of the received photons and obtain key information;

[0207] Quantum key management: The generated quantum keys need to be effectively managed, including key storage, distribution and update. Quantum keys are stored in specially designed secure storage devices, and the keys themselves are encrypted and protected using quantum encryption technology to ensure the security of the keys. At the same time, the keys are distributed to the systems or devices that need to use them through a secure key distribution mechanism. In addition, in order to further improve security, quantum keys are updated regularly to reduce the risk of key cracking.

[0208] Classical communication assistance: Although quantum encryption communication channels use quantum properties to achieve secure key distribution, in the actual communication process, classical communication channels are still needed as an aid; for example, in the process of quantum key distribution, Alice and Bob need to disclose measurement basis information through classical communication; when using quantum keys for data encryption transmission, encryption and decryption control information also needs to be transmitted through classical communication channels; however, the information transmitted by the classical communication channel does not contain sensitive encryption keys, thus ensuring the security of communication;

[0209] 3. Core control instruction transmission guarantee

[0210] Encrypted transmission: The airport's core control instructions, such as flight takeoff and landing control instructions, key equipment operation instructions, etc., are encrypted using quantum keys before being sent; the encryption algorithm can use a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA), combined with the randomness and unpredictability of quantum keys, to encrypt the instructions with high intensity; for example, when the flight dispatch center sends a takeoff permission instruction to the runway control tower, it first uses a quantum key to encrypt the instruction content and convert the plaintext instruction into ciphertext;

[0211] Real-time monitoring and error correction: During the communication process, the transmission quality of quantum signals is continuously monitored in real time; by monitoring indicators such as the quantum bit error rate, possible interference or eavesdropping can be discovered in a timely manner; once an abnormality is detected, corresponding measures are taken immediately, such as re-distributing quantum keys, adjusting communication parameters, or interrupting communication and conducting security checks; at the same time, error correction coding technology is used to encode the transmitted data, and errors caused by channel noise and other reasons can be automatically detected and corrected at the receiving end to ensure the accurate transmission of core control instructions;

[0212] Security authentication: In order to prevent man-in-the-middle attacks, a strict security authentication mechanism is established between the communicating parties. The sender and the receiver verify the legitimacy of each other's identity by exchanging digital certificates or using an identity authentication protocol based on quantum characteristics. Only after the identity authentication of both parties is passed, the core control instructions are transmitted. For example, before each communication begins, the runway control tower and the flight dispatch center verify each other's digital certificates. The certificates contain identity information such as the public key generated based on quantum encryption technology to ensure that the communicating parties are authentic and reliable.

[0213] Through the above principles, architecture and security measures, the quantum encrypted communication channel provides a highly secure and reliable communication link for the transmission of core control instructions of the airport's intelligent operating system, which strongly supports the safe and efficient operation of the airport.

[0214] 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. An airport intelligent operating system, characterized by: include: Multimodal data collection layer: A heterogeneous sensor network deployed in the terminal, runway and apron to obtain flight status, passenger flow, baggage trajectory, vehicle location and environmental parameters in real time; Intelligent analysis layer: includes a deep learning-driven dynamic prediction module that processes multi-source heterogeneous data through a spatiotemporal attention mechanism to generate flight delay predictions, passenger behavior patterns, and resource demand maps; Collaborative scheduling layer: Based on the three-dimensional simulation environment built with digital twins, cross-domain dynamic optimization of flights, boarding gates, baggage systems, and ground services is implemented, and multi-agent reinforcement learning is used to achieve Nash equilibrium of resource allocation; Human-computer interaction layer: equipped with augmented reality navigation terminals and voice interaction devices to provide passengers and staff with personalized route planning and emergency instruction push; The intelligent analysis layer includes: The core algorithm of the delay prediction model based on the improved spatiotemporal graph convolution is: ,in, Indicates The hidden state matrix of the layer; is the graph convolution operation, For the The hidden state matrix of the layer, is a learnable weight matrix used to transform the features after the graph convolution operation. is the normalized adjacency matrix with self-loops; The temporal features of the temporal convolutional network output, It is the input time series data, and the time dimension features are extracted through the time convolution network; It is the Hadamard product, which is used to multiply the features obtained by the graph convolution part by the temporal features output by the temporal convolution network element by element to achieve the fusion of spatiotemporal features; The order of graph convolution controls the propagation range of the graph convolution operation; Gated activation function; Trajectory prediction algorithm integrating hidden Markov model: ,in, represents the time from time step 1 to time step The entire observation sequence of express The hidden state at a certain moment, which indicates the current state of the passenger that is not directly observed in the passenger trajectory prediction scenario; is the observation data; is the state transition probability matrix, describing the transition from The hidden state at time is transferred to The probability of hiding the state at all times; Multimodal Contrastive Learning Anomaly Detection Model: ； in, represents the loss function of the entire multimodal contrastive learning anomaly detection model, is a negative sample selected from the negative sample set, is a visual feature encoder, used to encode the input visual data into a feature vector; An audio feature encoder, used for encoding input audio data into a feature vector; Input data for vision; Input data for audio; is a learnable balance coefficient used to balance the weights of the contrast loss of visual and audio features, and , dynamically adjusted through cross-validation; is a learnable balance coefficient used to balance the weight of the contrastive learning loss term, and , dynamically adjusted through cross-validation; is the cosine similarity function, which is used to calculate the similarity between two vectors; is a positive sample; is the current sample; is a negative sample; is the sample size; is the number of negative samples.

2. The airport intelligent operating system according to claim 1, characterized in that: The multimodal data acquisition layer includes: Centimeter-level precision positioning network composed of UWB positioning base stations and RFID tags; Anomaly detection array that integrates millimeter-wave radar and thermal imaging cameras; Energy consumption monitoring sensor cluster based on LoRaWAN; Edge computing nodes implement data cleaning and feature extraction preprocessing.

3. The airport intelligent operating system according to claim 1, characterized in that: The collaborative scheduling layer implements: Fuzzy logic driven gate assignment algorithm: ,in, is the function used to calculate the compatibility of flights and gates, = (see matching degree for aircraft type, transfer time, and capacity density), which is a vector containing three dimensions of information, representing the matching degree between flight type and boarding gate, the time required for passenger transfer, and the passenger density near the boarding gate; It is the center point of the ideal value of each dimension, that is, the most ideal value of each dimension; is the fuzzy membership attenuation coefficient, which controls the attenuation rate of the membership function of each dimension, and , set according to actual scenario experience; Indicates the minimum value operation, which is used to integrate the membership of three dimensions; It means taking the non-negative part to ensure that the result is non-negative; Improved Q-learning baggage routing algorithm: ； in, is the updated state-action value function, indicating that in state Take action Expected cumulative rewards; is the state-action value function before updating; is the learning rate, which controls the influence of new experience on the update of the old state-action value function. ; For immediate rewards, that is, taking action Rewards immediately received after is a discount factor used to weigh the importance of immediate rewards and future rewards, ; Output values ​​for the target network, which is used to provide more stable target values ​​and reduce fluctuations during the learning process; To take action The next state to transfer to; The action in the next state; Status Number of visits; state-action pair Number of visits; is the exploration reward coefficient, which is used to encourage the algorithm to explore state-action pairs that have not been fully visited. ; Hybrid game vehicle scheduling model: ;in, For the The comprehensive utility value of a task is used to evaluate the execution effect of the task; is the task completion time, that is, the execution The time spent on each task; For the task The maximum completion time allowed; is the energy consumption value, execute The energy consumed by each task; For the task Minimum energy consumption value; The service level agreement achievement rate measures whether the task execution meets the requirements of the service level agreement; is the weight coefficient of task completion time, ; is the weight coefficient of energy consumption, ; is the weight coefficient of the service level agreement achievement rate, .

4. The airport intelligent operating system according to claim 1, characterized in that: The human-computer interaction layer includes: Indoor and outdoor seamless navigation system for AR glasses terminals, integrating visual SLAM and beacon positioning technology; Multi-language natural language processing engine, supporting personalized service push based on voiceprint recognition; The emergency response command platform generates hierarchical visual plans and automatically allocates disposal resources.

5. The airport intelligent operating system according to claim 1, characterized in that: Also includes: The blockchain evidence storage module implements timestamp encryption and storage for key operation records; Federated learning framework to achieve knowledge sharing and privacy protection among multiple airports; Quantum encrypted communication channel to ensure the security of transmission of core control instructions; Federated learning parameter aggregation algorithm: ,in, For the The global model parameters are the model parameters obtained after aggregation and used by all airports participating in federated learning. The number of airports participating in federated learning; For the The number of samples per airport; is the total number of samples of all airports; For the Airports in Local model parameters of the wheel; is the model similarity driving coefficient, which is used to control the influence of the parameter similarity gradient term on the global model parameter update. ; is the first and Airports in The cosine similarity of the local model parameters of the round is the gradient of the parameters, which is used to measure the similarity between models; is the cosine similarity function in parameter space.

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