Intelligent optimization method and system for airport service process

Abstract

The invention provides an airport service process intelligent optimization method and system, and the method comprises the steps: fusing positioning data, visual data and business system data through a multi-modal data collection module, and integrating the three types of data into multi-source real-time data to comprehensively sense passenger behaviors and equipment states; based on the multi-source real-time data, constructing a passenger whole-process behavior graph through an intelligent data processing module; detecting service abnormal nodes in real time by using a composite neural network model through a behavior analysis and bottleneck identification module based on the passenger whole-process behavior map; and aiming at the service abnormal node, generating a resource scheduling and flow recombination strategy through a dynamic optimization decision module in combination with simulation deduction and reinforcement learning algorithms, and outputting an optimization strategy. And accurate monitoring and dynamic optimization of the whole flow of airport service can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aviation services, in particular to an airport service process intelligent optimization method and system. BACKGROUND

[0002] The existing airport service process management mainly relies on manual patrol and isolated information systems, and it is difficult to capture the passenger full-process behavior trajectory and equipment state change in real time. The traditional data collection method has the problem of fragmentation of multi-source heterogeneous data, and the positioning, visual and business system data cannot be effectively fused, leading to one-sided behavior analysis. The data processing link lacks privacy protection mechanism, and sensitive information is easy to leak in the sharing process. Abnormal detection mostly uses static threshold rules, which cannot adapt to environmental changes, and the cross-airport data barrier hinders model collaborative optimization. The decision system is often based on fixed strategies, which is difficult to dynamically respond to sudden bottlenecks, and the resource scheduling is lagging. The visualization interface is limited to two-dimensional plane display, and the instruction transmission efficiency is low, affecting the execution timeliness. The above defects lead to long waiting time of passengers, frequent congestion of service nodes, and lack of personalized service, etc. Systematic bottlenecks. SUMMARY

[0003] The present application provides an airport service process intelligent optimization method, comprising: S1, integrating positioning data, visual data and business system data into multi-source real-time data by a multi-modal data acquisition module to comprehensively perceive passenger behavior and equipment state; S2, based on the multi-source real-time data, constructing a passenger full-process behavior graph by an intelligent data processing module; S3, based on the passenger full-process behavior graph, using a composite neural network model to detect abnormal service nodes in real time by a behavior analysis and bottleneck identification module; S4, for the abnormal service nodes, generating resource scheduling and process reorganization strategies by a dynamic optimization decision module combined with simulation deduction and reinforcement learning algorithm, and outputting the optimization strategies. The airport service full-process precise monitoring and dynamic optimization can be realized.

[0004] Among them, the multi-modal data acquisition module introduces an adaptive calibration mechanism, which automatically adjusts the sensor parameters of the positioning data and the visual data at regular intervals, and integrates a dynamic noise suppression technology to generate correction data to simulate sudden environmental interference to maintain high-precision perception. It can automatically adapt to environmental changes and maintain high-precision data perception.

[0005] Among them, the intelligent data processing module embeds a differential privacy mechanism, which adds noise protection to individual information in the processing stage of multi-source real-time data, which can effectively protect passenger privacy information at the data processing source.

[0006] The behavior analysis and bottleneck identification module, combined with a federated learning framework, shares a cross-airport model based on the passenger full-process behavior graph without leaking privacy data, thereby supporting cross-airport security collaborative modeling and improving anomaly detection capabilities.

[0007] The dynamic optimization decision-making module applies a multi-objective preference optimization algorithm to predict individual satisfaction indicators based on historical behavior in the passenger's full-process behavior graph and generate personalized service suggestions. It can predict passenger preferences and provide personalized services to improve satisfaction.

[0008] The behavior analysis and bottleneck identification module embeds a context-aware adaptive engine, which dynamically adjusts the model parameters according to the detection requirements of abnormal service nodes, thereby dynamically optimizing the model parameters and improving the detection accuracy of abnormal nodes.

[0009] The optimization strategy is provided in real-time through a visualization interaction module in the form of a dynamic topology diagram, which provides an intuitive and dynamic monitoring interface to facilitate strategy understanding and execution.

[0010] The visualization interaction module integrates an augmented reality interface, overlays resource scheduling strategy instructions onto the physical environment in real time, and deploys a distributed edge execution framework to achieve fast rendering and low-latency execution. It can combine augmented reality overlay instructions with edge computing to achieve fast execution.

[0011] The present invention also proposes a computer-readable storage medium storing a computer program thereon, characterized in that, when the program is executed, it implements the above-mentioned intelligent optimization method for airport service processes, so as to ensure that the intelligent optimization method can be implemented in different hardware environments.

[0012] This invention also proposes an intelligent optimization system for airport service processes, comprising: The multimodal data acquisition module is used to fuse positioning data, visual data, and business system data to generate multi-source real-time data; The intelligent data processing module is used to construct a full-process behavior map of passengers based on the multi-source real-time data; The behavior analysis and bottleneck identification module is used to detect abnormal service nodes based on the behavior graph. The dynamic optimization decision module is used to generate resource scheduling and process reorganization strategies for the abnormal nodes; The visualization and interaction module is used to transform strategies into augmented reality commands and output a real-time monitoring interface. The modular system can collaboratively achieve end-to-end airport service process optimization.

[0013] In summary, the methods, media, and systems described above enable precise perception, intelligent analysis, real-time optimization, and efficient execution throughout the entire airport service process, significantly improving operational efficiency and passenger satisfaction. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating an intelligent optimization method for airport service processes proposed in this invention. Detailed Implementation

[0015] refer to Figure 1 This invention provides an intelligent optimization method for airport service processes, comprising: S1. By fusing positioning data, visual data, and business system data through a multimodal data acquisition module, the three types of data are integrated into multi-source real-time data to comprehensively perceive passenger behavior and equipment status. The multimodal data acquisition module incorporates an adaptive calibration mechanism, periodically and automatically adjusting the sensor parameters of the positioning and visual data, and integrates dynamic noise suppression technology to simulate sudden environmental interference and generate correction data to maintain high-precision perception.

[0016] The operation of the multimodal data acquisition module relies on the technical characteristics of fusing positioning data, visual data, and business system data.

[0017] Specifically, the multimodal data acquisition module first collects raw input from multiple sensors, such as positioning data from Wi-Fi or Bluetooth beacons, visual data from surveillance cameras, and business system data from check-in or security systems. Then, the multimodal data acquisition module automatically adjusts the sensor parameter settings through an adaptive calibration mechanism. For example, in specific scenarios like airport security checkpoints, it periodically checks positioning accuracy and image clarity to prevent error accumulation due to equipment aging. Simultaneously, it integrates dynamic noise suppression technology to simulate sudden interference such as crowds or changes in lighting, generating correction data to compensate for distortion. This helps maintain high-precision sensing and prevents data bias from affecting subsequent analysis.

[0018] The multimodal data acquisition module is specifically a hardware and software integrated unit responsible for simultaneously capturing spatial location, image capture, and transaction log information. The adaptive calibration mechanism specifically includes an automatic sensor parameter adjustment protocol that triggers calibration cycles based on preset thresholds. Dynamic noise suppression technology involves algorithms that filter environmental fluctuations to generate stable calibration datasets. When deployed in security checkpoints, this multimodal data acquisition module captures passenger movement trajectories and queuing status in real time; in boarding gate areas, it is used to monitor equipment operation. Through rigorous derivation, the fragmentation problem of multi-source data can be solved, improving the comprehensiveness of perception with a unified data view and ensuring no omissions in passenger behavior and equipment status monitoring. It is understood that this working method allows those skilled in the art to implement it directly, for example, by configuring a sensor network through an embedded system and programming calibration logic to cope with changes in the airport environment.

[0019] S2. Based on the aforementioned multi-source real-time data, a full-process behavior map of passengers is constructed through an intelligent data processing module. The intelligent data processing module incorporates a differential privacy mechanism to add noise during the processing of the multi-source real-time data to protect individual information.

[0020] The intelligent data processing module operates based on the technical characteristics of constructing a full-process passenger behavior graph. This module extracts key information from multi-source real-time data and organizes passenger behavior sequences, such as check-in, security check, and boarding routes, through data cleaning and pattern recognition steps. During the data processing stage, a differential privacy mechanism is embedded to add random noise; for example, in check-in counter applications, individual identity markers are blurred to ensure anonymity. In the security check process, data is segmented into anonymized fragments, retaining only aggregated trends for graph construction.

[0021] The intelligent data processing module is specifically a computing engine that performs data integration and privacy protection functions. The differential privacy mechanism prevents the recovery of personal information by injecting controlled noise into the data stream. This intelligent data processing module analyzes group behavior patterns in flight delay scenarios and processes sudden traffic surges during peak hours. Through these steps, the risk of privacy breaches can be addressed, and the construction of a security graph facilitates compliance analysis of airport processes, supporting subsequent anomaly detection. In summary, those skilled in the art can design data processing pipelines based on the module architecture and integrate noise generation components to achieve bottleneck-free implementation.

[0022] S3. Based on the passenger full-process behavior graph, a composite neural network model is used in real time to detect service anomaly nodes through the behavior analysis and bottleneck identification module. The behavior analysis and bottleneck identification module combines a federated learning framework to share cross-airport models based on the passenger full-process behavior graph without leaking privacy data; and it embeds a context-aware adaptive engine to dynamically adjust model parameters according to the detection requirements of service anomaly nodes.

[0023] The behavior analysis and bottleneck identification module operates based on the technical characteristics of detecting abnormal service nodes using a composite neural network model. After receiving the behavior graph input, the module uses a federated learning framework during model training to share model parameters across multiple airports, such as synchronizing security check efficiency data between hub airports, without transmitting raw privacy information. A context-aware adaptive engine dynamically adjusts parameters according to real-time needs, such as optimizing model weights to adapt to sudden changes in boarding delay detection. Bottleneck nodes, such as queuing congestion locations, are identified in the check-in area, and real-time alerts are generated.

[0024] The behavior analysis and bottleneck identification module is specifically an analysis unit that performs real-time monitoring and diagnostic tasks. The composite neural network model specifically includes a combination of multilayer perceptrons and recurrent networks to process sequential behavior data. The federated learning framework specifically involves a distributed training protocol to ensure secure cross-airport collaboration. The context-aware adaptive engine is specifically a mechanism for dynamically adjusting model parameters in response to changes in environmental context. This behavior analysis and bottleneck identification module detects equipment malfunctions in baggage handling areas and optimizes passenger flow in security checkpoints. Through rigorous derivation, it addresses the privacy risks associated with model sharing, improves anomaly response speed through adaptive detection, and reduces service interruption time. Those skilled in the art can directly deploy this behavior analysis and bottleneck identification module, for example, by configuring federated learning nodes on an edge server and programming the engine logic.

[0025] S4. For the service anomaly nodes, the dynamic optimization decision module, combining simulation and reinforcement learning algorithms, generates resource scheduling and process reorganization strategies and outputs the optimization strategies. Specifically, the dynamic optimization decision module applies a multi-objective preference optimization algorithm to predict individual satisfaction indicators based on historical behavior in the passenger's full-process behavior graph and generate personalized service suggestions. The output optimization strategies are provided in a real-time monitoring interface in the form of a dynamic topology map through a visualization interaction module. The visualization interaction module integrates an augmented reality interface, overlaying resource scheduling strategy instructions onto the physical environment in real time, and deploys a distributed edge execution framework to achieve fast rendering and low-latency execution.

[0026] The dynamic optimization decision-making module's operation involves technical features such as generating resource scheduling and process reorganization strategies. This module applies simulation to different intervention schemes for abnormal nodes, such as security check process reorganization testing, and uses reinforcement learning algorithms to iteratively optimize strategies to maximize efficiency. A multi-objective preference optimization algorithm combines historical behavioral data to predict individual satisfaction and generate personalized suggestions, such as recommending lounge services during delays. Strategy outputs are transformed into an augmented reality interface through a visualization module, overlaying command views onto the physical environment, such as boarding gates. A distributed edge execution framework ensures fast rendering and reduces latency. During peak periods, additional personnel resources are scheduled, and real-time commands are pushed to staff via augmented reality.

[0027] The dynamic optimization decision-making module is specifically a strategy generation unit that integrates simulation and optimization functions. Simulation and deduction specifically include digital twin modeling technology to virtually reproduce process scenarios. The reinforcement learning algorithm is specifically a reward-based decision-making training process. The multi-objective preference optimization algorithm is specifically used to balance efficiency and satisfaction indicators. The visualization and interaction module is specifically an interactive interface system that outputs dynamic monitoring views. The augmented reality interface is specifically a head-mounted display layer that overlays strategies onto the real scene. The distributed edge execution framework is specifically a computing resource distribution architecture that supports low-latency rendering. This dynamic optimization decision-making module implements process reengineering in the baggage carousel area and provides personalized guidance at the check-in counter. Through the above processes, the problem of rigid resource allocation can be solved, and the adaptability of optimization strategies can be predicted through simulation to improve overall service smoothness. Those skilled in the art can implement deduction and reinforcement learning based on the algorithm library and deploy edge nodes to achieve bottleneck-free execution.

[0028] The present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed, implements the intelligent optimization method for airport service processes as described in the above-described method section.

[0029] In this specific embodiment, the operation of the storage medium strictly depends on the technical features of storing and executing computer programs. Specifically, a computer-readable storage medium, such as a solid-state drive or a cloud database, stores the program code. When the program is loaded onto the processor, it executes step by step the intelligent optimization method for airport service processes proposed in this invention. In specific scenarios, such as downloading to a local airport server, the program initialization phase calls module interfaces, for example, triggering the sensor calibration process of a multimodal data acquisition module via an API. The medium is deployed on a security inspection system server to run the optimization algorithm in real time; it is also deployed on mobile devices for remote monitoring. The program execution process includes compiling code to generate machine instructions, ensuring efficient processing of sensed data. The computer-readable storage medium is specifically a physical or virtual storage carrier, including hard drives, flash memory, or cloud storage space. The computer program is specifically an executable script or application program, encoding method logic. Through rigorous derivation, the bottleneck problem of offline program execution can be solved, enabling seamless implementation of airport service optimization in any hardware environment, supporting remote updates and maintenance. Those skilled in the art can directly copy the program to the medium and configure the execution environment for immediate deployment.

[0030] This invention also proposes an intelligent optimization system for airport service processes, comprising: The multimodal data acquisition module is used to fuse positioning data, visual data, and business system data to generate multi-source real-time data; The intelligent data processing module is used to construct a full-process behavior map of passengers based on the multi-source real-time data; The behavior analysis and bottleneck identification module is used to detect abnormal service nodes based on the behavior graph. The dynamic optimization decision module is used to generate resource scheduling and process reorganization strategies for the abnormal nodes; The visualization and interaction module is used to translate strategies into augmented reality commands and output a real-time monitoring interface.

[0031] In this specific embodiment, the operation of the airport service process intelligent optimization system relies on the technical characteristics of module collaborative operation. The multimodal data acquisition module first integrates positioning, visual, and operational data, such as deploying cameras and RFID sensors in the check-in hall to collect real-time passenger flow information. The intelligent data processing module processes this data to construct behavioral graphs, such as passenger path networks. The behavior analysis and bottleneck identification module uses models to detect anomalies, such as security checkpoint congestion nodes. The dynamic optimization decision-making module generates strategies, such as those for dispatching personnel, and the visualization interaction module converts these strategies into augmented reality instructions, which are then rendered in real-time on the boarding gate display screen. When deployed in large airports, the modules share data through bus communication; in small and medium-sized airports, the integration of edge computing units is simplified. The multimodal data acquisition module is specifically a sensor integration unit that captures multi-source real-time data streams. The intelligent data processing module is specifically a data processing engine that executes graph construction tasks. The behavior analysis and bottleneck identification module is specifically an analysis and diagnosis component that identifies service bottlenecks. The dynamic optimization decision-making module is specifically a strategy generator that optimizes resource allocation. The visualization interaction module is specifically an interface output unit that provides real-time monitoring functions. Through rigorous derivation, the problem of isolated system modules can be solved, improving airport operational efficiency through end-to-end collaboration and ensuring real-time policy execution. Those skilled in the art can connect the hardware interfaces according to the module specifications and deploy the system for direct implementation.

[0032] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent optimization of airport service processes, characterized in that, include: S1. By integrating positioning data, visual data, and business system data through a multimodal data acquisition module, the three types of data are integrated into multi-source real-time data to comprehensively perceive passenger behavior and equipment status. S2. Based on the multi-source real-time data, construct a full-process behavior map of passengers through the intelligent data processing module; S3. Based on the passenger full-process behavior map, use a composite neural network model to detect abnormal service nodes in real time through the behavior analysis and bottleneck identification module; S4. For the service anomaly node, the dynamic optimization decision module generates resource scheduling and process reorganization strategies by combining simulation and reinforcement learning algorithms, and outputs the optimization strategies.

2. The intelligent optimization method for airport service processes as described in claim 1, characterized in that, The multimodal data acquisition module introduces an adaptive calibration mechanism, which periodically and automatically adjusts the sensor parameters of positioning and visual data, and integrates dynamic noise suppression technology to simulate sudden environmental interference and generate correction data to maintain high-precision perception.

3. The intelligent optimization method for airport service processes as described in claim 1, characterized in that, The intelligent data processing module incorporates a differential privacy mechanism, adding noise to protect individual information during the processing of multi-source real-time data.

4. The intelligent optimization method for airport service processes as described in claim 1, characterized in that, The behavior analysis and bottleneck identification module, combined with a federated learning framework, shares a cross-airport model based on the passenger full-process behavior graph without leaking privacy data.

5. The intelligent optimization method for airport service processes as described in claim 1, characterized in that, The dynamic optimization decision-making module applies a multi-objective preference optimization algorithm to predict individual satisfaction indicators based on historical behavior in the passenger's full-process behavior graph and generate personalized service suggestions.

6. The intelligent optimization method for airport service processes as described in claim 1, characterized in that, The behavior analysis and bottleneck identification module embeds a context-aware adaptive engine, which dynamically adjusts the model parameters according to the detection requirements of abnormal service nodes.

7. The intelligent optimization method for airport service processes as described in claim 1, characterized in that, The optimization strategy is provided in a real-time monitoring interface in the form of a dynamic topology graph through a visualization interaction module.

8. The intelligent optimization method for airport service processes as described in claim 7, characterized in that, The visualization interaction module integrates an augmented reality interface, overlays resource scheduling strategy instructions onto the physical environment in real time, and deploys a distributed edge execution framework to achieve fast rendering and low-latency execution.

9. A computer-readable storage medium storing a computer program thereon, characterized in that, When the program is executed, it implements the airport service process intelligent optimization method as described in any one of claims 1-8.

10. An intelligent optimization system for airport service processes, characterized in that, include: The multimodal data acquisition module is used to fuse positioning data, visual data, and business system data to generate multi-source real-time data; The intelligent data processing module is used to construct a full-process behavior map of passengers based on the multi-source real-time data; The behavior analysis and bottleneck identification module is used to detect abnormal service nodes based on the behavior graph. The dynamic optimization decision module is used to generate resource scheduling and process reorganization strategies for the abnormal nodes; The visualization and interaction module is used to translate strategies into augmented reality commands and output a real-time monitoring interface.

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