Airport core risk whole-process management method based on double prevention working mechanism

Abstract

This invention relates to a comprehensive airport core risk management method based on a dual-prevention mechanism. The method includes the following main steps: a data acquisition step, which involves real-time acquisition of multi-source data related to airport operations through various sensors and systems; a data fusion step, which replaces the multi-head attention mechanism in the traditional Transformer model with multiple expert large-scale models, including feature extraction and analysis, information aggregation, and data synchronization and standardization; an adversarial data generation step, which uses multiple expert large-scale models to generate diverse virtual risk scenarios and adversarial samples; an automated virtual environment construction step, which automatically constructs a high-fidelity virtual simulation environment based on the large-scale models; and a risk assessment and decision-making step, which utilizes an adaptive risk scoring system and intelligent decision support mechanisms for precise risk assessment and decision support. This method achieves comprehensive, accurate, and intelligent management of airport core risks, significantly improving the safety and emergency response capabilities of airport operations.

Description

Technical Field

[0001] This invention belongs to the field of airport safety management technology, specifically involving a method for full-process management of core airport risks based on a dual prevention mechanism. Background Technology

[0002] With the rapid development of the air transport industry, the airport operating environment has become increasingly complex and volatile. Safety risks such as runway incursions and runway deviations pose a serious threat to flight safety. Although existing technologies have applied some early warning systems and risk control methods for airport runway safety, they still have certain limitations and urgently need further optimization and improvement.

[0003] Chinese invention patent CN105575021B discloses an airport runway safety early warning system and method. The system includes a sensor network, a data conversion unit, a data interface unit, a switch, a data processing server, a human-computer interaction unit, and an audible and visual alarm unit. It detects and alarms runway intrusions through the sensor network and has the advantages of being economical, practical, safe, reliable, and flexible in configuration. However, the system mainly relies on the deployment of physical sensors and simple data processing, making it difficult to accurately identify and dynamically respond to complex and ever-changing risk situations. Furthermore, it has shortcomings in data fusion and risk assessment, and cannot fully utilize the potential information from multiple data sources.

[0004] Chinese invention patent CN112397071B discloses a method for early warning of approach and runway operation risks based on control voice recognition. This method uses voice recognition technology to convert tower control instructions into structured data and combines it with monitoring and planning data to perform alarm logic calculations, providing timely, comprehensive, and intelligent risk prevention and control measures and reducing the workload of controllers. However, this method mainly focuses on the recognition and processing of voice instructions and lacks the comprehensive utilization of other types of data (such as visual data, meteorological data, and ground vehicle data). The comprehensiveness and accuracy of risk assessment are limited, and it is difficult to cope with the complexity brought about by the fusion of multi-source data.

[0005] In summary, the problems of relying on a single data source, insufficiently accurate risk assessment, and slow emergency response in existing technologies have become urgent challenges that need to be addressed. Summary of the Invention

[0006] In view of the above-mentioned prior art, the technical problem to be solved by the present invention is how to introduce a dual prevention mechanism, combining multiple expert large models and generative adversarial data technology, to comprehensively improve the identification, assessment and management capabilities of airport core risks, and solve the problems of insufficient integration of multi-source data and untimely risk response in the prior art.

[0007] To address the aforementioned problems, this invention provides a method for full-process management of core airport risks based on a dual-prevention mechanism, comprising the following steps:

[0008] S1. Data Acquisition Steps: Real-time acquisition of multi-source data related to airport operations through various sensors and systems;

[0009] S2. Data Fusion Steps: This involves replacing the multi-head attention mechanism in the traditional Transformer model with multiple expert large models, while maintaining the advantages of multi-head attention, to fuse multi-source data. Specifically, this includes:

[0010] S2a. Feature Extraction and Analysis: Each expert model performs feature extraction and analysis on different types of data sources;

[0011] S2b. Information Aggregation: Integrates the outputs of various expert models through a multi-head attention mechanism to achieve multi-perspective information aggregation;

[0012] S2c. Data Synchronization and Standardization: The merged data is synchronized in time and standardized in format to ensure data consistency and accuracy;

[0013] S3. Steps for generating adversarial data: Replace the multi-head attention mechanism in the traditional Transformer model with multiple expert large models, while maintaining the advantages of multi-head attention, to generate adversarial examples to improve the robustness of the data fusion model in complex scenarios. Specifically, this includes:

[0014] S3a. Virtual Risk Scenario Generation: Utilizes multiple expert models to generate virtual risk scenarios based on different contextual parameters, covering a variety of potential risk types;

[0015] S3b. Implementation of adversarial strategies: Adversarial samples are generated using diverse adversarial strategies, including random perturbation, malicious behavior simulation, and extreme condition simulation.

[0016] S3c. Model Training and Optimization: The generated adversarial examples are input into the data fusion model for training and optimization, improving the model's ability to identify and process complex and extreme situations;

[0017] S4. Automated Virtual Environment Construction Steps: A virtual simulation environment is automatically constructed based on a large model. Simultaneously, multiple expert large models are used to replace the multi-head attention mechanism in the traditional Transformer model, maintaining the advantages of multi-head attention. Specifically, this includes:

[0018] S4a. Simulation Engine Selection and Integration: Select a high-fidelity simulation engine and deeply integrate it with the large model through open APIs to achieve data-driven simulation behavior;

[0019] S4b. Multimodal Generation: Multimodal generation using large models, including converting text descriptions and image data into specific virtual scene parameters;

[0020] S4c. Physics Simulation: Combines a physics engine to simulate real physical phenomena such as aircraft takeoff and landing and vehicle movement, ensuring the realism and interactivity of the virtual environment;

[0021] S4d Scene Optimization: Through procedural content generation and genetic algorithm optimization, it automatically generates diverse scene elements and optimizes scene performance and visual effects;

[0022] S5. Risk Assessment and Decision-Making Steps: Risk assessment and decision-making are based on integrated data and a virtual environment, specifically including:

[0023] S5a. Risk Scoring: Using an adaptive risk scoring system, the weights of various risk factors are dynamically assessed based on the fused data to generate a comprehensive risk score;

[0024] S5b. Decision Support: Based on risk scoring results, provide risk response strategies and decision support to ensure the accuracy and timeliness of risk management;

[0025] S6. Risk Response and Handling Procedures: Based on the decision support results, implement corresponding risk response and handling measures, specifically including:

[0026] S6a. Automated handling: Automated handling of low-risk situations, such as notifying maintenance personnel to perform equipment repairs;

[0027] S6b. Manual intervention: When a high-level risk triggers manual intervention, the emergency response process is initiated, and relevant departments are coordinated to conduct in-depth investigations and handle the situation.

[0028] This invention combines a dual-prevention mechanism, replacing the multi-head attention mechanism in the traditional Transformer model with multiple expert large models, thereby improving the professionalism and efficiency of multi-source data fusion. Simultaneously, it enhances the robustness of the model by generating adversarial data, strengthening the system's risk identification and handling capabilities in complex scenarios. The automated construction of a virtual environment based on the large model provides a highly realistic and interactive simulation platform, supporting the accuracy and timeliness of risk assessment and decision-making. Economically, this method reduces operating costs by optimizing data processing and risk management processes, improving airport safety management and emergency response capabilities, demonstrating significant economic benefits and market competitiveness.

[0029] The data acquisition steps further include:

[0030] The aircraft's position information and trajectory are collected using radar and ADS-B systems;

[0031] Deploy a video surveillance system to collect real-time images of the runway and surrounding areas;

[0032] The movement data of ground vehicles and personnel are collected through the ground vehicle management system;

[0033] Meteorological data such as wind speed, humidity, and visibility are collected through a meteorological monitoring system.

[0034] Aircraft performance data is collected through an aircraft health monitoring system.

[0035] The data fusion step further includes:

[0036] Each expert big model performs feature extraction and analysis on different types of data sources, with each expert big model employing a targeted deep learning architecture;

[0037] By integrating the outputs of various expert models through a multi-head attention mechanism, different weights are assigned to the outputs of different expert models, thereby achieving multi-perspective aggregation of information.

[0038] The merged data is synchronized in time and standardized in format to ensure consistency and accuracy in subsequent analysis.

[0039] The steps for generating adversarial data further include:

[0040] Using multiple expert models, virtual risk scenarios are generated based on different contextual parameters, covering a variety of potential risk types;

[0041] Diverse adversarial strategies, including random perturbation, malicious behavior simulation, and extreme condition simulation, are employed to generate adversarial samples, ensuring the diversity and representativeness of the samples.

[0042] The generated adversarial examples are input into the data fusion model for training and optimization, thereby improving the model's ability to identify and process complex and extreme situations.

[0043] The automated virtual environment build process further includes:

[0044] Choose a high-fidelity simulation engine and deeply integrate it with large models through open APIs to achieve data-driven simulation behavior;

[0045] Multimodal generation is achieved using large models, including converting text descriptions and image data into specific virtual scene parameters;

[0046] By combining a physics engine to simulate real physical phenomena such as aircraft takeoff and landing and vehicle movement, the realism and interactivity of the virtual environment are ensured.

[0047] Through programmatic content generation and optimization algorithms using genetic algorithms, diverse scene elements are automatically generated, and scene performance and visual effects are optimized.

[0048] Multiple expert models include specialized models for different data types, such as visual data, radar data, and meteorological data, and each specialized model uses a targeted deep learning architecture for feature extraction.

[0049] Countermeasures include, but are not limited to, scenarios such as sudden wind shear, slippery runways, unauthorized vehicle intrusion, and heavy rain and dense fog. These scenarios are simulated to improve the model's ability to respond to real-world risks.

[0050] Multimodal generation technology includes two parts: text-to-scene conversion and image-to-scene mapping. The former uses natural language processing to convert text descriptions into scene parameters, while the latter uses image recognition technology to convert image data into three-dimensional scene elements.

[0051] The virtual environment supports multiple interaction methods such as natural language, gestures, and VR / AR, allowing users to interact with the virtual environment in real time through a multimodal interface, thereby improving the user experience and decision-making efficiency.

[0052] The method combines cloud and edge computing deployment. The cloud leverages its powerful computing capabilities for large-scale data processing and model training, while edge computing devices are responsible for real-time data acquisition and preliminary processing, ensuring real-time response of the virtual environment and efficient operation of large-scale simulation training tasks.

[0053] In summary, this application has the following beneficial effects:

[0054] 1. By utilizing multiple dedicated expert models to process different types of data sources such as visual data, radar data, and meteorological data, the professionalism and accuracy of data processing are ensured. Each expert model adopts a deep learning architecture best suited to its data type, achieving efficient feature extraction and analysis.

[0055] By combining a multi-head attention mechanism, the outputs of various expert models are aggregated from multiple perspectives, enhancing the depth and breadth of data fusion. The dynamic weight adjustment mechanism flexibly assigns different importance to different data sources according to real-time environmental changes, ensuring the comprehensiveness and accuracy of information integration.

[0056] 2. By generating diverse virtual risk scenarios and adversarial examples through multiple expert models, covering a variety of complex situations such as sudden wind shear, runway slippery conditions, illegal vehicle intrusion, and heavy rain and dense fog, the data fusion model's ability to identify and process data in complex and extreme situations has been effectively improved.

[0057] The continuous generation of adversarial examples and iterative training of the model enable the data fusion model to have stronger robustness and stability, which can more accurately identify potential risks in actual operation, reduce false alarms and false negatives, and improve the reliability of the system.

[0058] 3. By selecting a high-fidelity simulation engine (such as Prepar3D, X-Plane, Microsoft FlightSimulator) and deeply integrating it with a large model, a virtual environment that highly replicates the actual airport operation status was constructed, supporting dynamic data-driven simulation behavior.

[0059] By combining natural language processing and image recognition technologies, the system automatically converts text descriptions and image data into virtual scene parameters, enriching the details and realism of the virtual scene. At the same time, the introduction of a physics engine ensures the realism of object movement and interaction in the virtual environment.

[0060] It supports multiple interaction methods such as natural language, gestures, and VR / AR, providing an immersive operating experience and efficient decision support, thereby improving user interactivity and operational efficiency in the virtual environment.

[0061] 4. By dynamically adjusting the weights of risk factors and conducting in-depth analysis using multi-expert models, we have achieved accurate assessment of various risks and generated highly accurate comprehensive risk scores, ensuring the forward-looking and real-time nature of risk assessment.

[0062] By combining rule-based decision trees and machine learning algorithms, the system can quickly and accurately recommend optimal risk response strategies, shortening decision-making time and improving the intelligence and efficiency of decision-making.

[0063] 5. For different levels of risk, the system can automatically handle low-level risks and quickly notify relevant personnel to carry out equipment maintenance; for high-level risks, the system can trigger manual intervention, initiate emergency response procedures, coordinate multiple departments to conduct in-depth investigations and handling, and ensure that risk events are effectively controlled and eliminated.

[0064] The system continuously monitors the progress of risk management and adjusts strategies based on real-time feedback to ensure the timeliness and effectiveness of emergency response.

[0065] 6. Through comprehensive collection and efficient integration of multi-source data, the system can fully cover all kinds of potential risks in airport operations, provide all-round security monitoring and management, and significantly improve the overall security of airport operations.

[0066] Based on a highly robust data fusion model and a highly realistic virtual environment, the system possesses intelligent emergency response capabilities, enabling it to efficiently handle various sudden risk events and ensure the safe, smooth, and efficient operation of the airport. Attached Figure Description

[0067] Figure 1 Workflow for this application Figure 1 ;

[0068] Figure 2Workflow for this application Figure 2 ;

[0069] Figure 3 Workflow for this application Figure 3 . Detailed Implementation

[0070] To make the objectives, technical solutions, and beneficial effects of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, so as to facilitate understanding by those skilled in the art.

[0071] Example 1:

[0072] like Figures 1 to 3 As shown in the figure, this embodiment describes in detail the specific implementation of the "multi-source data collection and fusion" step in the airport core risk whole-process management method based on the dual prevention mechanism, covering data collection, data fusion and its effects.

[0073] Step S1: Data Acquisition Step

[0074] In the airport operating environment, real-time, multi-source data acquisition is the foundation for achieving core risk management. The specific implementation methods are as follows:

[0075] Aircraft position information and motion trajectory collection:

[0076] Radar System: Deploy a high-precision airport radar system to monitor the flight paths, speeds, and altitudes of aircraft in real time, ensuring comprehensive coverage of all aircraft entering and leaving the airport.

[0077] ADS-B system: Utilizes Automatic Dependent Surveillance-Broadcast (ADS-B) technology to acquire precise position information and dynamic motion data of aircraft, supplementing radar data for more refined monitoring.

[0078] Real-time image capture of the runway and surrounding area:

[0079] Video surveillance system: High-definition cameras are deployed on the runway, taxiway and surrounding areas to collect real-time image data of key areas of the airport around the clock. Combined with computer vision technology, it enables real-time identification and tracking of runway status, aircraft and ground vehicles.

[0080] Ground vehicle and personnel movement data collection:

[0081] Ground vehicle management system: Through RFID tags and GPS positioning technology, the system monitors the driving routes, speeds and locations of ground vehicles in real time. Personnel movement is collected through wearable devices or mobile terminals to ensure comprehensive tracking of all moving entities within the airport.

[0082] Meteorological data collection:

[0083] Meteorological monitoring system: Deploys multiple meteorological sensors to collect meteorological parameters such as wind speed, humidity, visibility, and temperature in real time. Through data acquisition terminals, the meteorological data is transmitted to the central processing system in real time to ensure timely response to meteorological changes.

[0084] Aircraft performance data collection:

[0085] Aircraft Health Monitoring System: Through the Flight Data Recorder (FDR) and Engine Health Monitoring System (EHM), it acquires real-time performance data such as aircraft speed, altitude, acceleration, and engine status, providing key performance indicators for risk assessment.

[0086] Step S2: Data Fusion Step

[0087] To achieve efficient and professional data integration, this embodiment replaces the multi-head attention mechanism in the traditional Transformer model with multiple expert large models, while maintaining the advantages of multi-head attention. The specific implementation method is as follows:

[0088] Feature extraction and analysis (step S2a):

[0089] Visual data processing: The dedicated visual expert large model adopts a convolutional neural network (CNN) architecture and is responsible for extracting key features from the image data collected by the video surveillance system, such as runway status and aircraft position.

[0090] Radar data processing: The radar expert big model uses a recurrent neural network (RNN) architecture to analyze the aircraft's position information and trajectory, and identify abnormal flight behavior.

[0091] Meteorological data processing: The meteorological expert big model uses a long short-term memory network (LSTM) architecture to process real-time meteorological data and predict short-term weather change trends.

[0092] Ground vehicle and personnel data processing: The ground management expert big model uses a graph neural network (GNN) architecture to analyze the movement patterns of ground vehicles and personnel and identify potential ground risks.

[0093] Visual data processing: CNN model

[0094] It is mainly used to process image data collected by airport video surveillance systems, such as runway images and real-time aircraft videos.

[0095] Data format: Image / video frames can typically be represented as X∈R H×W×C Where H is the image height, W is the image width, and C is the number of channels (RGB is usually 3).

[0096] Model structure and feature extraction process

[0097] Convolutional Layer

[0098] The input image X is convolved to obtain the output Y. Let the kernel size of the l-th layer be k×k, and the number of input channels be C. in The number of output channels is C out Then the convolution operation can be expressed as:

[0099]

[0100] in:

[0101] · This represents the output of the m-th convolutional kernel in the l-th layer at position (i,j);

[0102] · and b (l,m) These are the weights and biases corresponding to the m-th convolutional kernel in the l-th layer, respectively;

[0103] ·σ(·) is generally a non-linear activation function (such as ReLU, ReLU(x)=max(0,x)).

[0104] Pooling Layer

[0105] Pooling layers (such as max pooling or average pooling) are added to some CNN structures to reduce the spatial resolution of feature maps, decrease the number of parameters, and extract high-level features. Max pooling can be expressed as:

[0106]

[0107] in This represents the pooling window, and s is the pooling stride.

[0108] Multilayer stacking and fully connected layers

[0109] After multiple convolutions and pooling operations, the output feature map is typically unfolded into a vector and fed into a fully connected layer for classification or regression output. The fully connected layer can...

[0110] Represented as:

[0111] z = W fc x flat +b fc

[0112] Where x flat W represents the vector obtained by flattening the high-dimensional convolutional feature map. fc and b fcThese are the weight matrix and the bias, respectively.

[0113] Training process and output

[0114] Object detection or classification: In applications such as runway status detection and aircraft position recognition, CNN outputs are often the target's position coordinates, category labels, or target detection boxes. During training, cross-entropy loss, regression loss (such as L1 or L2), etc., can be used, selected according to task requirements.

[0115] Key feature extraction: The extracted high-level visual features will be input into the subsequent multi-source data fusion module of this invention for comprehensive analysis of runway conditions, aircraft attitude, etc.

[0116] Radar data processing: RNN model

[0117] It is mainly used to analyze the position information and trajectory of aircraft and identify abnormal flight behaviors (such as excessively fast approach, abnormal ascent / descent, etc.).

[0118] Data format: Radar data can be represented as a time series {X1, X2, ... X}. T}, where X T ∈R d It may contain information such as location coordinates, velocity, and angle, where T is the length of the time series.

[0119] RNN Structure and Feature Extraction Process

[0120] The computation of a standard RNN at time step t can be expressed as:

[0121] h t =σ(W xh x t +W hh h t-1 +b h ),

[0122] y t =W hy h t +b y ,

[0123] in:

[0124] ·h t Let y be the hidden state vector. t This is the output vector;

[0125] ·W xh W hh W hy and b h b y These are the trainable weights and biases of the RNN, respectively.

[0126] ·σ(·) is usually a tanh or ReLU activation function.

[0127] Feature extraction

[0128] The radar time series is input into the RNN, and the final step is h. T Or the output y at each time step t It can serve as a high-level feature representation of an aircraft's trajectory, used to identify abnormal flight behavior or predict future trajectories.

[0129] Meteorological data processing: LSTM model

[0130] Predicting short-term weather trends, such as wind speed, temperature, humidity, and visibility, provides data support for runway usage strategies and flight scheduling.

[0131] Data format: Meteorological data can also be viewed as a time series {X1, X2, ... X} T} contains historical records of multiple meteorological parameters.

[0132] LSTM Structure and Feature Extraction Process

[0133] The key formula for LSTM calculation at time step t can be expressed as:

[0134] f t =σ(W f [h t-1 ,x t ]+b f ),

[0135] i t =σ(W i [h t-1 ,x t ]+b i ),

[0136]

[0137] o t =σ(W o [h t-1 ,x t ]+b o ),

[0138] h t =o t ☉tanh(C t ),

[0139] in:

[0140] ·f t i t ,o t These are the forget gate, input gate, and output gate, respectively.

[0141] ·C t For cell state, h t It is in a hidden state;

[0142] σ is the Sigmoid activation function, tanh is the hyperbolic tangent activation function, and ☉ indicates element-wise multiplication;

[0143] ·W f W i W C W o Let b be a trainable parameter matrix. f b i ,b C ,b o For bias.

[0144] Feature extraction and trend prediction

[0145] The LSTM structure can effectively capture the temporal dependencies in long-sequence meteorological data, and the output hidden state h T and / or C T It contains a high-level expression of meteorological change trends, which is used for subsequent short-term weather forecasts or risk assessments.

[0146] Ground vehicle and personnel data processing: GNN model

[0147] Analyze the movement patterns of ground vehicles and personnel to identify potential ground risks (such as personnel accidentally entering the runway, conflicts between ground vehicles and aircraft, etc.).

[0148] Data format: The movement of ground vehicles and people is abstracted as a graph structure with nodes and edges. Nodes can represent vehicles, people, or key locations, and edges can represent the distance or interaction relationship between two nodes.

[0149] The graph can be represented as G = (V, E), where V is the set of nodes, |V| = N, and E is the set of edges. Each node v i Corresponding to a vector feature x i Features can also be included on the edges.

[0150] GNN Structure and Feature Extraction Process

[0151] Taking graph convolutional networks as an example, a single layer of graph convolution operation can be represented as:

[0152]

[0153] in:

[0154] · d represents the embedding of a node in the graph at layer l. l Let l be the feature dimension of the l-th layer;

[0155] · A is the adjacency matrix of the graph, and I is the identity matrix (with self-connections to ensure that the information of the nodes themselves is preserved).

[0156] · for A degree matrix of diagonal elements;

[0157] ·W (l) These are trainable weights;

[0158] σ is the activation function (such as ReLU).

[0159] Neighbor aggregation and updates

[0160] Through the graph convolution operation described above, node v i The new representation is related not only to its own characteristics, but also to the characteristics of its neighboring nodes, thereby capturing the interaction relationships and spatial distribution characteristics between ground vehicles and people.

[0161] Multi-layer stacking

[0162] Multi-layer GNN stacking can gradually aggregate a wider range of graph structure information, and the final output node representation can be used for risk identification or the next step decision module.

[0163] Feature extraction results

[0164] High-level graph representation: The node embeddings and graph-level features extracted by GNN can be used to identify potential risks such as abnormal vehicle scheduling and personnel crossing boundaries, or to further predict ground traffic flow and personnel distribution.

[0165] Information aggregation (step S2b):

[0166] Multi-head attention mechanism integration: Through the multi-head attention mechanism, the outputs of various expert models are integrated. The output of each expert model serves as a different "attention head" and is assigned different weights. By integrating information from various perspectives, comprehensive information aggregation is achieved.

[0167] Weighting: The weights of each expert model output are dynamically adjusted based on real-time data and environmental changes. For example, under severe weather conditions, the weight of the meteorological expert model will be significantly increased to enhance the impact of meteorological data on risk assessment.

[0168] Expert model output preparation

[0169] Suppose there are M expert large models, each model outputs a feature vector (or sequence) E i(i = 1, 2, ..., M). These outputs can be viewed as feature information carried by different "heads".

[0170] Mapping to Q, K, V for the output E of each expert model i Define a trainable mapping weight matrix W Q W K W V (Different expert models may use different or shared projection matrices), mapped to queries, keys, and values:

[0171] Q i =E i W Q ,

[0172] K i =E i W K ,

[0173] V i =E i W V .

[0174] Q i ,K i V i The three representations of the output of the i-th expert model in the attention space are given.

[0175] Attention calculation formula:

[0176]

[0177] in:

[0178] ·d k For the key vector K i The dimension is used for scaling to avoid excessively large values;

[0179] The softmax operation normalizes the attention weights.

[0180] Multi-output splicing and linear transformation:

[0181] Attention output for M heads: head1, head2, ..., head M After splicing, it undergoes a linear transformation W O The final aggregation result is obtained:

[0182] H=Concat(head1,head2,...,head M W O .

[0183] Here, H represents the comprehensive information output by all expert models from the multi-head attention layer, which is used for subsequent risk assessment or other processing steps.

[0184] Data synchronization and standardization (step S2c):

[0185] Time synchronization: High-precision clock synchronization technology is used to ensure that the timestamps of different data sources are consistent, eliminating data inconsistencies caused by time differences.

[0186] Format standardization: Through data cleaning and preprocessing techniques, data in different formats are uniformly converted into a standardized format to eliminate data redundancy and outliers and ensure data quality.

[0187] System Architecture:

[0188] Data acquisition layer: This layer includes hardware equipment for various sensors and systems (radar, ADS-B, video surveillance, ground management system, meteorological monitoring system, aircraft health monitoring system), and is responsible for the real-time data acquisition and preliminary processing.

[0189] Data fusion layer: Composed of multiple expert models, each model specializes in processing a specific type of data. The outputs of each expert model are integrated through a multi-head attention mechanism to form a comprehensive data input.

[0190] Data processing and storage layer: Employs a high-performance computing platform and distributed storage system to ensure efficient processing and storage of large-scale data.

[0191] Risk assessment and decision-making level: Based on the fused data, risk scoring and decision support are conducted, and real-time risk management strategies are provided.

[0192] Technical Implementation:

[0193] Expert Big Model Design: Each expert big model adopts the most suitable deep learning architecture, such as CNN, RNN, LSTM, GNN, etc., based on the data type it is processing, to ensure the efficiency and accuracy of feature extraction and analysis.

[0194] Multi-head attention mechanism integration: In the data fusion layer, a custom multi-head attention module is used to integrate the outputs of each expert model according to preset weights, thereby realizing the aggregation of information from multiple perspectives.

[0195] Real-time data synchronization and standardization: Utilize distributed data processing frameworks (such as Apache Kafka and Apache Spark) to achieve real-time data synchronization and standardization, ensuring the accuracy and consistency of subsequent analysis.

[0196] By collaborating on multiple expert models, specialized processing is performed on different data types, significantly improving the accuracy and depth of data processing.

[0197] The application of multi-head attention mechanism enables the aggregation of information from multiple perspectives, ensuring the comprehensiveness and efficiency of data integration.

[0198] High-quality multi-source data fusion provides a solid data foundation for subsequent risk scoring and decision support, improving the accuracy and real-time nature of risk assessment.

[0199] The dynamic weight adjustment mechanism enables the system to flexibly adjust the weights of various risk factors according to real-time environmental changes, ensuring the reliability of the assessment results.

[0200] By generating adversarial data, the robustness of the data fusion model is improved, enhancing the stability and reliability of the system in complex and extreme situations.

[0201] Efficient data synchronization and standardized processing ensure the stable operation of the system under high load and multi-source data environments.

[0202] The comprehensive collection and efficient integration of multi-source data provides comprehensive monitoring of airport operations, timely detection and response to potential risks, and significantly improves the safety and reliability of airport operations.

[0203] Professional data processing and intelligent risk assessment help airports achieve intelligent safety management and drive operations towards greater efficiency and safety.

[0204] By implementing the multi-source data acquisition and fusion method in this embodiment, and through professional data processing based on multiple expert models and multi-head attention mechanisms, efficient and accurate risk management is achieved. This method not only improves the professionalism and efficiency of data integration, but also enhances the robustness and adaptability of the system, providing solid technical support for airport operation safety.

[0205] Example 2:

[0206] like Figures 1 to 3 As shown, this embodiment details the specific implementation of the "Generate Adversarial Data Step (S3)" and the "Automatic Virtual Environment Construction Step (S4)" in the airport core risk whole-process management method based on the dual prevention mechanism. It covers the process and effect of generating adversarial data to improve model robustness and the construction and optimization of virtual environment based on large model.

[0207] Step S3: Generating Adversarial Data

[0208] To improve the robustness of the data fusion model in complex scenarios, this step enhances the model's recognition and processing capabilities by generating adversarial examples. The specific implementation method is as follows:

[0209] Step S3a: Virtual Risk Scenario Generation

[0210] To comprehensively cover all types of risks that may be encountered in airport operations, this step utilizes multi-expert model collaboration to generate diverse and representative virtual risk scenarios. The specific implementation method is as follows:

[0211] Multi-expert model collaboration:

[0212] GPT series models:

[0213] Function: Utilizing the GPT series of language models, this feature generates detailed risk scenario descriptions based on preset contextual parameters. These descriptions cover various potential risk types, such as runway incursions, equipment failures, and extreme weather.

[0214] Implementation: Input specific scenario parameters (e.g., "Unauthorized personnel activity occurs at the runway edge under foggy weather conditions"), and the GPT model generates a corresponding detailed risk scenario description, providing a textual basis for subsequent visualization generation.

[0215] Stable Diffusion Model:

[0216] Function: Utilizes the Stable Diffusion image generation model to transform text descriptions generated by GPT into high-quality visual virtual risk scene images.

[0217] Implementation: The risk scenario description generated by GPT is used as input, and the Stable Diffusion model generates corresponding images, such as a visual scene image of "unauthorized personnel activity at the edge of the runway under foggy weather conditions", which is used for simulation and training.

[0218] Context parameter settings:

[0219] Historical data and risk analysis:

[0220] Function: Based on historical airport operation data and potential risk analysis, various combinations of scenario parameters can be set to ensure that the generated virtual risk scenarios are representative and diverse.

[0221] Implementation method: Analyze past safety incidents and potential risks to determine key scenario parameters (such as different weather conditions, time periods, flight densities, etc.), and combine these parameters to generate various risk scenarios. For example, set up multiple scenario combinations such as "runway incursion under high flight density" and "nighttime equipment failure".

[0222] Diverse scene generation:

[0223] Function: By combining different scenario parameters, virtual scenarios covering a wide range of risk types can be generated, ensuring that the system can cope with various complex and ever-changing risk situations.

[0224] Implementation: Using predefined scenario parameters, the data is sequentially input into the GPT series models to generate diverse risk description texts, which are then transformed into corresponding visual scene images using the Stable Diffusion model. For example, by combining the parameters of "heavy rain weather" and "equipment failure," a virtual scenario is generated: "Under heavy rain weather conditions, runway equipment malfunctions, leading to taxiway blockage."

[0225] Specific implementation method

[0226] System Architecture:

[0227] Generation module: Includes GPT series models and Stable Diffusion models, which work together to generate text descriptions and corresponding visual scene images.

[0228] Context parameter library: Stores various context parameter combinations derived from historical data and risk analysis, which serve as inputs for generating virtual risk scenarios.

[0229] Data-driven interface: Pass contextual parameters to the GPT model via API and pass the generated text description to the Stable Diffusion model to complete the automated generation process from text to image.

[0230] Workflow:

[0231] Input context parameters: Select or dynamically generate a set of context parameters from the context parameter library.

[0232] Text description generation: Input the scenario parameters into the GPT series model to generate detailed risk scenario description text.

[0233] Image generation: Input the generated text description into the Stable Diffusion model to generate a corresponding virtual risk scene image.

[0234] Scene storage and application: The generated text and image scenes are stored in the database for subsequent risk assessment, simulation training and decision support.

[0235] Implementation of countermeasures (step S3b):

[0236] Random disturbances: Random disturbances, such as sudden wind shear and runway slipperiness, are introduced into the generated virtual risk scenario to simulate unpredictable emergencies.

[0237] Malicious Behavior Simulation: Simulate malicious behaviors such as illegal vehicles entering the runway and unauthorized personnel entering key areas to test the system's emergency response capabilities.

[0238] Extreme Condition Simulation: Simulates airport operations under extreme weather conditions such as heavy rain and dense fog, and evaluates the system's risk identification and handling capabilities in extreme environments.

[0239] Step S3b: Implementation of the countermeasure strategy

[0240] In this step, high-quality adversarial examples are generated by employing diverse adversarial strategies, including random perturbation, malicious behavior simulation, and extreme condition simulation, to improve the robustness and recognition capability of the data fusion model in complex scenarios. The specific implementation is as follows:

[0241] random perturbation

[0242] Objective: To simulate random risks that may be encountered in real operations by introducing unpredictable emergencies, such as sudden wind shear and runway slipperiness, into virtual risk scenarios, and to test the system's emergency response capabilities and stability.

[0243] Specific implementation process:

[0244] Wind shear simulation:

[0245] Scenario setting: Imagine a sudden strong wind or a change in wind direction around the airport at a certain moment.

[0246] Parameter adjustment: In the virtual simulation environment, wind speed and wind direction parameters are dynamically adjusted to simulate the impact of sudden wind shear on aircraft takeoff and landing.

[0247] Demonstration: The simulation engine displays the changes in the aircraft's takeoff and landing trajectories under sudden changes in wind speed in real time, testing the system's ability to identify and respond to abnormal flight behavior.

[0248] Runway slippery simulation:

[0249] Scenario setting: Simulate a scenario where the track becomes slippery due to rain or snow.

[0250] Parameter adjustment: In the virtual simulation environment, adjust the friction coefficient and humidity parameters of the runway surface to simulate the impact of runway slipperiness on aircraft taxiing and takeoff and landing.

[0251] Demonstration: Showcases aircraft takeoff and landing performance on wet runways, and assesses the system's ability to monitor and warn of changes in runway conditions.

[0252] Malicious Behavior Simulation

[0253] Objective: To test the system's security protection and emergency response mechanisms by simulating malicious behaviors such as unauthorized vehicles entering the runway and unauthorized personnel entering critical areas, so as to ensure that effective measures can be taken quickly when real threats occur.

[0254] Specific implementation process:

[0255] Illegal vehicles entered the runway:

[0256] Scenario setting: In a virtual simulation environment, an unauthorized vehicle attempts to enter the runway area.

[0257] Behavioral simulation: Through scripting or behavioral models, control virtual vehicles to make abnormal movements near the track, such as sudden acceleration or changing the driving route.

[0258] System Response: Test the effectiveness of the monitoring system in real-time detection, alarm triggering, and emergency response measures (such as activating alarms and automatically preventing vehicles from entering) for unauthorized vehicle intrusion.

[0259] Unauthorized personnel entering critical areas:

[0260] Scenario setting: Simulate unauthorized personnel attempting to enter critical areas of the aircraft apron or runway.

[0261] Behavioral simulation: By controlling virtual characters, abnormal activities of personnel in key areas can be set up, such as loitering or attempting to approach aircraft.

[0262] System Response: Evaluate the accuracy and timeliness of the monitoring system in identifying abnormal personnel behavior, as well as the effectiveness of emergency response measures (such as notifying security personnel and activating the alarm system).

[0263] Extreme condition simulation

[0264] Objective: By simulating airport operations under extreme weather conditions such as heavy rain and dense fog, this study aims to comprehensively evaluate the system's risk identification and handling capabilities in extreme environments, ensuring efficient and safe operation even under adverse weather conditions.

[0265] Specific implementation process:

[0266] Heavy rain simulation:

[0267] Scenario setting: Simulate a scenario where heavy rainfall deteriorates the airport's operating environment.

[0268] Parameter adjustment: In the virtual simulation environment, adjust the rainfall intensity, duration and rainfall distribution to simulate the impact of heavy rain on runway water accumulation, reduced visibility and ground equipment operation.

[0269] Demonstration: Showcasing the airport's operational status under heavy rain conditions, such as runway flooding, slippery taxiways, and reduced visibility, to test the system's ability to monitor and provide early warnings of risks under severe weather conditions.

[0270] dense fog simulation:

[0271] Scenario setting: Simulate the impact of dense fog on airport operations.

[0272] Parameter adjustment: In the virtual simulation environment, adjust the visibility parameters to simulate the impact of dense fog on aircraft take-off and landing, navigation, and ground vehicle traffic.

[0273] Demonstration of Results: This section showcases airport operations under dense fog conditions, such as blurred aircraft takeoff and landing trajectories and difficulties for ground vehicles, assessing the system's risk identification and emergency response capabilities in low visibility conditions.

[0274] Specific implementation method

[0275] System Architecture:

[0276] The adversarial strategy module integrates three major adversarial strategies: random perturbation, malicious behavior simulation, and extreme condition simulation. Through modular design, it is easy to flexibly call different adversarial strategies as needed.

[0277] Scene generation engine: Utilizes simulation engines (such as Prepar3D, X-Plane, etc.) and generative models (such as GPT series, Stable Diffusion, etc.) to achieve automated generation and adjustment of virtual risk scenarios.

[0278] Parameter Management System: Establish a scenario parameter library to store different types of scenario parameter combinations, supporting quick access and dynamic adjustment.

[0279] Workflow:

[0280] Input context parameters: Select or dynamically generate a specific set of context parameters from the context parameter library as the basis for implementing the adversarial strategy.

[0281] Application of countermeasures:

[0282] Based on the selected adversarial strategy (random perturbation, malicious behavior simulation, extreme condition simulation), corresponding simulated events are introduced into the virtual risk scenario.

[0283] By using a simulation engine to adjust relevant parameters in a virtual environment (such as wind speed, rainfall, and the behavior of people and vehicles), specific risk scenarios can be generated.

[0284] Generate adversarial examples: Generate corresponding risk examples, including text descriptions and visual images, from the adjusted virtual risk scenarios using a generative model for use in training and optimizing the data fusion model.

[0285] Risk assessment and feedback: The generated adversarial examples are input into the data fusion model for risk identification and processing. The system records the model's response effect as a feedback basis for subsequent model optimization.

[0286] Model training and optimization (step S3c):

[0287] Adversarial example input: The generated adversarial examples are input into the data fusion model for training and optimization. Through continuous iterative training, the model's recognition accuracy and processing efficiency in complex and extreme situations are improved.

[0288] Robustness assessment: Regularly evaluate the model's performance under adversarial examples to ensure the model's robustness and stability in real-world operating environments.

[0289] Step S4: Automated Virtual Environment Construction Steps

[0290] The virtual simulation environment is automatically built based on a large model to support the simulation and optimization of risk management. The specific implementation method is as follows:

[0291] Simulation engine selection and integration (step S4a):

[0292] High-fidelity simulation engine selection: Choose high-fidelity simulation engines such as Prepar3D, X-Plane, and Microsoft Flight Simulator, which have open APIs and SDKs and support deep integration with large models.

[0293] Deep integration: By connecting the data generated by the large model with the simulation engine through open APIs, data-driven simulation behavior can be realized, such as real-time adjustment of aircraft take-off and landing paths and ground vehicle movement routes.

[0294] Multimodal generation (step S4b):

[0295] Text-to-scene conversion: Using large language models (such as GPT-4), text descriptions (such as "busy international airport runway, light fog at dusk, low visibility") are converted into specific virtual scene parameters, including weather, lighting, runway status, etc.

[0296] Image-to-scene mapping: Combining image generation models (such as Stable Diffusion), elements are extracted from aerial photographs or other image materials to automatically generate 3D runways and building models, enriching the details and realism of virtual scenes.

[0297] Step S4b: Multimodal generation

[0298] The multimodal generation step combines text-to-scene transformation and image-to-scene mapping, utilizing advanced large-model technology to achieve efficient conversion from multiple input formats to virtual scene parameters. The specific implementation is as follows:

[0299] Text to Scene Conversion

[0300] Objective: To transform natural language-based scene descriptions into specific virtual scene parameters, including weather, lighting, runway status, etc., to ensure that the virtual environment can accurately reflect various situations in actual operation.

[0301] Specific implementation process:

[0302] Text input:

[0303] Users or the system input scene description text according to actual needs, such as: "busy international airport runway, light fog at dusk, low visibility".

[0304] Large-scale language model processing:

[0305] Model selection: Advanced language models, such as GPT-4, are used for natural language understanding and processing.

[0306] Semantic parsing: The model parses the input text description and identifies key scene elements and parameters. For example, it identifies keywords such as "busy international airport runway," "evening," "light fog," and "low visibility" from the text above.

[0307] Parameter generation: Based on the parsing results, specific virtual scene parameters are generated. These parameters may include:

[0308] Weather parameters: wind speed, rainfall, humidity, visibility, etc.

[0309] Lighting parameters: time of day (sunset), light intensity, shadow distribution, etc.

[0310] Runway condition: Runway usage (busyness), runway surface condition (slippery, water accumulation, etc.).

[0311] Traffic conditions: aircraft takeoff and landing frequency, ground vehicle density, etc.

[0312] Scene parameter output:

[0313] The generated scene parameters are output in a structured data format for use in subsequent virtual environment construction.

[0314] Effect:

[0315] Accuracy: Through natural language understanding, ensure that the parameters of the virtual scene accurately reflect the various situations in the text description.

[0316] Flexibility: Supports the conversion of various complex scenario descriptions to adapt to different risk situation requirements.

[0317] Image to scene mapping

[0318] Objective: To use image generation models to extract elements from aerial photographs or other image materials and automatically generate 3D runways, building models, etc., to enrich the details and realism of virtual scenes.

[0319] Specific implementation process:

[0320] Image input:

[0321] Provide aerial photographs or other relevant image materials as the basis for virtual scene generation.

[0322] Image generation model processing:

[0323] Model selection: Advanced image generation models, such as Stable Diffusion, are used for image processing and element extraction.

[0324] Image analysis:

[0325] Element recognition: The model identifies key elements in an image, such as runways, buildings, vehicles, and people.

[0326] Feature extraction: Extract the geometric features, color, texture and other information of each element to generate detailed 3D model data.

[0327] 3D model generation:

[0328] Model building: Based on the extracted feature information, automatically generate 3D runway, building models and other necessary scene elements.

[0329] Detail Enhancement: Through detail optimization algorithms, the details and realism of 3D models are improved, such as track markings and building appearances.

[0330] Scene integration:

[0331] The generated 3D model is combined with the parameters generated in the text-to-scene conversion step to construct a complete virtual scene.

[0332] Parameter application: Apply parameters such as weather, lighting, and runway status to adjust the appearance and environmental settings of the 3D model to ensure the dynamism and realism of the virtual scene.

[0333] Effect:

[0334] High fidelity: Through image generation models, various elements in the virtual scene are highly reproduced to match the actual airport environment, enhancing the visual realism of the scene.

[0335] Rich in detail: The automatically generated 3D models contain rich details, enhancing the interactivity and immersion of the virtual environment.

[0336] Comprehensive Implementation Process

[0337] Data input and preprocessing:

[0338] Collect and organize text descriptions and image materials, and perform necessary preprocessing, such as improving image clarity and standardizing text.

[0339] Multimodal data processing:

[0340] Simultaneously, two sub-processes are initiated: text-to-scene conversion and image-to-scene mapping, which generate text scene parameters and 3D model data, respectively.

[0341] Scene building and optimization:

[0342] The generated text scene parameters are integrated with the 3D model data, and a complete virtual machine field running scene is constructed using virtual environment building tools (such as Unity, Unreal Engine, etc.).

[0343] Dynamic adjustment: Based on real-time data and changes in the context, the parameters and models in the virtual scene are dynamically adjusted to ensure that the virtual environment is always consistent with the actual operating environment.

[0344] Specific implementation method

[0345] System Architecture:

[0346] Multimodal generation module: includes text-to-scene conversion submodule and image-to-scene mapping submodule, which work together to realize the conversion of multimodal data.

[0347] Scene parameter library: Stores various scene parameters generated by language models and image generation models, supporting quick access and updates.

[0348] Virtual environment construction platform: integrates a high-fidelity simulation engine and 3D modeling tools to achieve automated construction and optimization of virtual scenes.

[0349] Workflow:

[0350] Input collection: Collect user-input scene description text and related image materials.

[0351] Text processing: The text description is converted into structured scene parameters using the GPT-4 model.

[0352] Image processing: Transforming image materials into 3D scene elements using the Stable Diffusion model.

[0353] Scene integration: Integrate text parameters and 3D models on a virtual environment construction platform to generate a complete virtual risk scenario.

[0354] Dynamic optimization: Based on real-time operational data, dynamically adjust the parameters and elements in the virtual scene to maintain consistency between the virtual environment and the real environment.

[0355] Simulation of physical phenomena (step S4c):

[0356] Realistic physics simulation: Integrating physics engines such as NVIDIA PhysX, it simulates real physical phenomena such as aircraft take-off and landing, vehicle movement, and people walking, ensuring the realism and interactivity of the virtual environment.

[0357] Dynamic environment response: Based on real-time data and changes in risk scenarios, dynamically adjust the physical phenomena in the virtual environment, such as adjusting the take-off and landing paths of aircraft according to changes in wind speed.

[0358] Scene optimization (step S4d):

[0359] Programmatic content generation: Set rule parameters to automatically generate diverse scene elements, such as different types of airplanes, vehicles, and people, maintaining the richness and consistency of the environment.

[0360] Genetic Algorithm Optimization: Optimization algorithms such as genetic algorithms are used to balance the performance and visual effects of virtual scenes, optimize the distribution and quantity of objects, reduce computational load, and improve simulation efficiency and visual effects.

[0361] System Architecture:

[0362] The virtual environment construction module includes four sub-modules: simulation engine selection and integration, multimodal generation, physical phenomenon simulation, and scene optimization. These modules work together to achieve a high-fidelity, dynamically adjustable virtual simulation environment.

[0363] Data-driven interface: Interacts with large models via open APIs to enable real-time data-driven adjustments to simulation behavior.

[0364] Optimization and Feedback Module: Utilizing optimization techniques such as genetic algorithms, the module continuously optimizes scene generation and physical phenomenon simulation based on simulation results, thereby enhancing the realism and interactivity of the virtual environment.

[0365] Technical Implementation:

[0366] Multi-expert model collaborative generation: Utilizes multiple expert models to generate different types of virtual risk scenarios, ensuring the diversity and professionalism of scenario generation.

[0367] Multimodal data processing: Through natural language processing and image recognition technologies, various input formats (text, images) are transformed into virtual scene parameters, improving the automation and intelligence of scene generation.

[0368] Physics Engine Integration: By deeply integrating the physics engine, realistic movement and interaction of objects in the virtual environment are achieved, enhancing the operational realism of the simulation environment.

[0369] Algorithm Optimization: Genetic algorithms are used to optimize the performance and visual effects of virtual scenes, ensuring that the simulation environment can still run efficiently under high load.

[0370] Diverse adversarial examples: By generating a variety of virtual risk scenarios and diverse adversarial strategies, the training data is greatly enriched, and the robustness and recognition ability of the data fusion model in complex and extreme situations are significantly improved.

[0371] Enhance model stability: Through continuous training and optimization using adversarial examples, ensure that the model can stably cope with various unexpected risks in actual operation, and reduce false positives and false negatives.

[0372] High-fidelity simulation: Through the deep integration of a high-fidelity simulation engine and a physics engine, the virtual environment highly replicates the operational status of a real airport, providing a realistic operating experience.

[0373] Multimodal interaction: Supports multiple interaction methods such as natural language, gestures, VR / AR, etc., to improve the user's operating experience and decision-making efficiency in the virtual environment, and realize immersive simulation training and risk response.

[0374] Real-time dynamic assessment: Based on high-quality multi-source data fusion and the generated virtual risk scenarios, the system can dynamically assess the weight of various risk factors in real time and generate an accurate comprehensive risk score.

[0375] Intelligent decision support: Combining a virtual simulation environment, it provides data-driven risk response strategies and decision support to ensure the accuracy and timeliness of risk management and effectively prevent and respond to various potential risks.

[0376] Comprehensive risk coverage: By generating diverse virtual risk scenarios and high-fidelity virtual environments, the system can comprehensively cover all kinds of potential risks in airport operations, thereby improving overall operational safety.

[0377] Intelligent emergency response: Based on simulation and optimization in a virtual environment, the system can intelligently respond to and handle various risk events, ensuring the safe, smooth and efficient operation of the airport.

[0378] By implementing the generative adversarial data and automated virtual environment construction method in this embodiment, based on professional data processing and virtual environment construction using multiple expert large models and multi-head attention mechanisms, the high robustness of the data fusion model and the high realism and interactivity of the virtual environment are achieved. This method not only improves the accuracy and stability of risk identification and handling, but also enhances the system's emergency response capability in complex and extreme situations, significantly improving the overall safety and efficiency of airport operations.

[0379] Example 3:

[0380] like Figures 1 to 3 As shown, this embodiment details the specific implementation of the "risk assessment and decision-making step (S5)" and "risk response and handling step (S6)" in the airport core risk full-process management method based on the dual prevention mechanism, covering adaptive risk scoring system, decision support mechanism and automated and manual risk handling measures.

[0381] Step S5: Risk Assessment and Decision-Making Steps

[0382] To achieve accurate risk assessment and effective decision support, this step utilizes an adaptive risk scoring system and intelligent decision support mechanism to comprehensively analyze and evaluate the fused multi-source data and information from the virtual environment. The specific implementation method is as follows:

[0383] Risk scoring (step S5a):

[0384] Adaptive risk scoring system:

[0385] Dynamic weight adjustment: The system dynamically adjusts the weights of various risk factors based on real-time data and environmental changes. For example, under severe weather conditions, the weight of meteorological factors is significantly increased to enhance the impact of meteorological data on the overall risk score.

[0386] Multi-expert model support: Multiple expert models (such as visual data experts, radar data experts, meteorological data experts, etc.) are used to evaluate different types of risk factors (such as runway incursion, equipment failure, extreme weather, etc.). Then, the scores of each expert model are combined through an adaptive mechanism to generate a comprehensive risk score.

[0387] Time series analysis and trend prediction: Using time series models such as Long Short-Term Memory Network (LSTM), historical data is analyzed to identify risk trends and potential risk patterns, thereby improving the foresight and accuracy of risk scoring.

[0388] Step S5a: Risk Scoring

[0389] Adaptive risk scoring system

[0390] The adaptive risk scoring system aims to dynamically assess and adjust the weights of various risk factors based on real-time data and environmental changes, generating a comprehensive risk score to ensure the accuracy and timeliness of risk assessment.

[0391] Dynamic weight adjustment

[0392] Objective: To dynamically adjust the weights of various risk factors based on real-time data and environmental changes, ensuring that the impact of key risk factors on the overall risk score is reasonably reflected in different scenarios.

[0393] Specific implementation process:

[0394] Real-time data monitoring:

[0395] The system continuously monitors real-time data from various data sources, such as meteorological sensor data, runway status information, and aircraft movement trajectories.

[0396] Environmental change identification:

[0397] The data analysis module identifies key changing factors in the current operating environment, such as sudden weather changes (heavy rain, dense fog) and changes in runway conditions (slippery, water accumulation).

[0398] Weighting adjustment mechanism:

[0399] Based on preset rules or learned patterns, the system automatically adjusts the weights of various risk factors. For example:

[0400] Under severe weather conditions, the weight of meteorological factors is significantly increased to enhance the impact of meteorological data on the overall risk score.

[0401] When an anomaly is detected in the runway equipment, the weight of equipment failure factors is increased, raising awareness of the equipment status.

[0402] Weight update:

[0403] The dynamically adjusted weights are applied to the current risk assessment through the risk scoring model to ensure that the risk score reflects the latest operating environment and potential risks.

[0404] Multi-expert model support

[0405] Objective: To evaluate different types of risk factors using multiple expert models, and to generate a comprehensive risk score by integrating the scores from each expert model through an adaptive mechanism, thereby achieving a comprehensive assessment of multi-dimensional risks.

[0406] Specific implementation process:

[0407] Expert model configuration:

[0408] Visual data expert model:

[0409] Employing a convolutional neural network (CNN) architecture, it is responsible for assessing visually relevant risk factors such as runway incursions and aircraft positional anomalies.

[0410] Radar data expert model:

[0411] The Recurrent Neural Network (RNN) architecture is used to analyze the aircraft's trajectory and identify abnormal flight behavior.

[0412] Meteorological data expert model:

[0413] A Long Short-Term Memory (LSTM) network architecture is used to process real-time meteorological data and predict short-term weather trends.

[0414] Ground vehicle and personnel data expert model:

[0415] A graph neural network (GNN) architecture is used to analyze the movement patterns of ground vehicles and people to identify potential ground risks.

[0416] Independent risk assessment:

[0417] Each expert model independently processes the data source it is responsible for and generates a corresponding risk score. For example, a visual expert model outputs the probability of runway incursion, while a meteorological expert model outputs a risk index for extreme weather.

[0418] Overall scoring mechanism:

[0419] An adaptive mechanism is used to synthesize the independent scores from each expert model according to dynamically adjusted weights, generating a comprehensive risk score. The specific process includes:

[0420] Standardized scoring criteria: The risk scores output by each expert model are standardized to ensure that the scores output by different models are comparable.

[0421] Weighted summation: Based on the dynamically adjusted weights, the scores of each expert model are weighted and summed to obtain the final comprehensive risk score.

[0422] Comprehensive score output: The comprehensive risk score serves as the core indicator for overall risk management and is used for subsequent decision support and risk response.

[0423] Time series analysis and trend forecasting

[0424] Objective: To identify potential trends and patterns in risk development through time series analysis and trend forecasting, improve the foresight and accuracy of risk scoring, and support proactive risk management.

[0425] Specific implementation process:

[0426] Historical data collection:

[0427] The system collects and stores past operational data and risk event records, including historical data such as weather changes, equipment failures, and runway status.

[0428] Applications of time series models:

[0429] Model selection:

[0430] We employ deep learning models such as Long Short-Term Memory (LSTM) networks, which are well-suited for processing time series data, to analyze and predict the future trends of various risk factors.

[0431] Feature engineering:

[0432] Features such as seasonal variations, trend fluctuations, and periodic patterns are extracted from historical data and used as input for time series models.

[0433] Model training:

[0434] The LSTM model is trained using historical data to learn the time dependence and change patterns of various risk factors, thereby improving the model's ability to predict future risk trends.

[0435] Trend Forecast:

[0436] Using a trained LSTM model, trends of various risk factors over a future period are predicted, generating predictive risk scores.

[0437] Risk Trend Integration:

[0438] By combining the prediction results of the time series model with the current comprehensive risk score, the current risk score is adjusted and optimized to ensure that the score not only reflects the current risk level, but also takes into account potential future risk changes.

[0439] Specific implementation method

[0440] System Architecture:

[0441] Data input layer:

[0442] Receive independent risk scores and real-time data from various expert models.

[0443] Risk scoring module:

[0444] Dynamic weight adjustment submodule: Adjusts the weights of each risk factor based on real-time data and environmental changes.

[0445] Multi-expert scoring integration submodule: Integrates scores from multiple expert models to generate a comprehensive risk score.

[0446] Time series analysis submodule: Utilizes models such as LSTM to predict risk trends and improve the foresight of the scoring.

[0447] Output layer:

[0448] It provides comprehensive risk scores and trend prediction results for use by the risk assessment and decision support module.

[0449] Workflow:

[0450] Real-time data acquisition:

[0451] The system continuously receives real-time data from various data sources, including vision, radar, meteorology, and ground management, and performs preliminary risk assessments using expert models.

[0452] Dynamic weight adjustment:

[0453] Based on the current operating environment and real-time data, the weights of each risk factor are dynamically adjusted. For example, under severe weather conditions, the weight of meteorological factors is increased.

[0454] Comprehensive evaluation by multiple experts:

[0455] The independent risk scores of each expert model are weighted and summed according to dynamically adjusted weights to generate a comprehensive risk score.

[0456] Time series analysis and trend forecasting:

[0457] The LSTM model is used to analyze historical data, predict the future trends of each risk factor, and integrate the prediction results into the current risk score to generate a forward-looking comprehensive risk score.

[0458] Risk score output:

[0459] The combined risk score and trend prediction results are output to the risk assessment and decision support module for further risk management and decision-making.

[0460] Decision support (step S5b):

[0461] Intelligent Decision Support System:

[0462] Risk Response Strategy Library: A database containing various risk response strategies is established. Based on different comprehensive risk scores, the system automatically matches and recommends corresponding response strategies. For example, for high-risk runway intrusion events, the system recommends immediately initiating emergency response procedures; for low-risk equipment anomalies, the system recommends automated maintenance notifications.

[0463] Decision-making algorithms: A combination of rule-based decision-making algorithms and machine learning algorithms is used to ensure that the decision support system can quickly and accurately provide optimal risk response solutions in a changing airport operating environment.

[0464] Real-time decision feedback: The system can receive feedback information after the implementation of decisions in real time, dynamically adjust and optimize decision-making strategies, and ensure the continuous effectiveness of risk management.

[0465] Step S6: Risk Response and Handling Steps

[0466] Based on the results of risk assessment and decision support, the system implements corresponding risk response and handling measures to ensure the safety and smooth operation of the airport. The specific implementation method is as follows:

[0467] Automated processing (step S6a):

[0468] Automated handling of low-level risks:

[0469] Equipment maintenance notification: When the system detects a low-level risk (such as a minor equipment malfunction or runway condition abnormality), it automatically sends a maintenance notification to the relevant maintenance personnel, instructing them to perform equipment inspection and maintenance to ensure that the problem is resolved in a timely manner.

[0470] Automatic alarm system: When a low-level risk is detected, the system will issue a warning to relevant personnel through an automatic alarm device, reminding them to pay attention and take necessary preventive measures.

[0471] Data recording and analysis: The system automatically records detailed information on low-level risk events for subsequent analysis and optimization, thereby improving the overall risk management level.

[0472] Manual intervention (step S6b):

[0473] Advanced risks trigger manual intervention:

[0474] Emergency Response Activation: When the system assesses a high-risk situation (such as suspected runway intrusion, major equipment failure, terrorist attack threat, etc.), it automatically triggers the emergency response process and notifies relevant departments and personnel to activate the emergency plan.

[0475] Multi-department coordination: The system coordinates multiple departments such as air traffic control, ground support, and security to ensure information sharing and collaborative operations among them, thereby improving the efficiency and effectiveness of emergency response.

[0476] In-depth investigation and response: A dedicated emergency response team will conduct an in-depth investigation of high-risk events, identify the source of the risk, and take corresponding measures, such as closing the affected area, evacuating personnel, and activating backup systems, to ensure that the risk is effectively controlled and eliminated.

[0477] Real-time monitoring and feedback: During manual intervention, the system continuously monitors the progress of risk management and provides real-time feedback of the latest information to support the emergency response team in making timely adjustments and decisions.

[0478] System Architecture:

[0479] Risk assessment and decision-making module:

[0480] Adaptive scoring engine: Integrates multiple expert models and an adaptive risk scoring system, responsible for comprehensively assessing various risk factors and generating a dynamic comprehensive risk score.

[0481] Decision Support Engine: Based on the risk scoring results, it calls upon the intelligent decision support system to recommend and formulate corresponding risk response strategies.

[0482] Risk Response and Handling Module:

[0483] Automated response subsystem: Responsible for automated handling of low-level risks, including functions such as equipment maintenance notifications and automatic alarms.

[0484] Human intervention subsystem: responsible for human intervention and emergency response for high-risk situations, coordinating relevant departments to ensure the effective implementation of emergency measures.

[0485] Technical Implementation:

[0486] Adaptive Risk Scoring System Design:

[0487] Dynamic weighting algorithm: Adaptive weighting algorithm is adopted to dynamically adjust the weights of various risk factors based on real-time data and environmental changes, so as to ensure the accuracy and real-time performance of risk scoring.

[0488] Multi-expert model integration: By integrating multiple specialized expert models, different types of risk factor data are processed separately, thereby improving the professionalism and depth of risk scoring.

[0489] Design of Intelligent Decision Support System:

[0490] Combining decision trees with machine learning: By combining rule-based decision trees with machine learning algorithms, the decision support system can quickly and accurately provide optimal risk response strategies in complex and ever-changing operating environments.

[0491] Strategy library management: Establish and maintain a dynamically updated risk response strategy library to ensure that the system can recommend the most appropriate response strategy based on the latest operational data and risk situation.

[0492] Design of automated and human-interventional systems:

[0493] Automated processing flow: Design automated processing flow for low-level risks, including functions such as automatic notification, alarms, and data logging, to ensure that low-level risks can be handled quickly and effectively.

[0494] Emergency Response Process: Design an emergency response process for high-risk situations, including emergency plan activation, multi-department coordination, in-depth investigation, and risk management, to ensure that high-risk situations can be addressed in a timely and effective manner.

[0495] Accurate risk scoring: Through an adaptive risk scoring system, combined with in-depth analysis of a multi-expert large model, it achieves accurate assessment of various risk factors and generates a highly accurate comprehensive risk score.

[0496] Real-time risk monitoring: The system can monitor the airport's operational status in real time, dynamically adjust risk scores, and ensure the timeliness and foresight of risk assessment.

[0497] Intelligent strategy recommendation: Based on the intelligent decision support system, the system can quickly and accurately recommend the optimal risk response strategy, thereby improving the level of intelligence in decision support.

[0498] Efficient decision-making process: Through automated risk scoring and intelligent decision support, decision-making time is significantly shortened, ensuring the efficiency and timeliness of risk management.

[0499] Rapid handling of low-level risks: Through an automated response subsystem, low-level risks can be handled quickly, reducing human intervention and improving handling efficiency.

[0500] Effective Response to Advanced Risks: Through the human intervention subsystem, advanced risks can be addressed comprehensively and thoroughly, ensuring the effective control and elimination of risk events.

[0501] Comprehensive risk coverage: Through accurate risk assessment and intelligent decision support, the system can comprehensively cover all kinds of potential risks in airport operations, thereby improving overall operational safety.

[0502] Intelligent emergency response capabilities: The system's automation and human intervention mechanisms enable the airport to possess intelligent emergency response capabilities, efficiently responding to various sudden risk events and ensuring the safe, smooth, and efficient operation of the airport.

[0503] By implementing the risk assessment and decision support, and risk response and handling methods in this embodiment, based on an adaptive risk scoring system and intelligent decision support mechanism, accurate risk assessment and efficient decision support are achieved. At the same time, through automated and manual risk handling measures, the system's emergency response capability and overall operational safety are significantly improved. This method not only enhances the accuracy and real-time performance of airport risk management, but also improves the stability and reliability of the system under complex and extreme situations, providing a solid technical guarantee for the safety and efficiency of airport operations.

[0504] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for full-process management and control of core airport risks based on a dual prevention mechanism, characterized by: Includes the following steps: S1. Data Acquisition Steps: Real-time acquisition of multi-source data related to airport operations through various sensors and systems; S2. Data fusion step: The multi-source data is fused, specifically including: S2a. Feature Extraction and Analysis: Each expert model performs feature extraction and analysis on different types of data sources; S2b. Information Aggregation: Integrate the outputs of various expert models through a multi-head attention mechanism to achieve multi-perspective information aggregation; S2c. Data Synchronization and Standardization: Perform time synchronization and format standardization on the merged data to ensure data consistency and accuracy; S3. Step to generate adversarial data: Generate adversarial examples to improve the robustness of the data fusion model in complex scenarios, specifically including: S3a. Virtual Risk Scenario Generation: Utilizes multiple expert models to generate virtual risk scenarios based on different contextual parameters, covering a variety of potential risk types; S3b. Implementation of adversarial strategies: Adversarial samples are generated by employing diverse adversarial strategies, including random perturbation, malicious behavior simulation, and extreme condition simulation. S3c. Model Training and Optimization: The generated adversarial examples are input into the data fusion model for training and optimization to improve the model's ability to identify and process complex and extreme situations. S4. Automated Virtual Environment Construction Steps: Automated construction of a virtual simulation environment based on a large model, specifically including: S4a. Simulation Engine Selection and Integration: Select a high-fidelity simulation engine and deeply integrate it with the large model through open APIs to achieve data-driven simulation behavior; S4b. Multimodal generation: Multimodal generation using large models, including converting text descriptions and image data into specific virtual scene parameters; S4c. Physics simulation: Combines a physics engine to simulate real physical phenomena such as aircraft takeoff and landing and vehicle movement, ensuring the realism and interactivity of the virtual environment; S4d Scene Optimization: Through procedural content generation and genetic algorithms, it automatically generates diverse scene elements and optimizes scene performance and visual effects; S5. Risk Assessment and Decision-Making Steps: Risk assessment and decision-making are conducted based on integrated data and a virtual environment, specifically including: S5a. Risk Scoring: Using an adaptive risk scoring system, the weights of various risk factors are dynamically assessed based on the fused data to generate a comprehensive risk score; S5b. Decision Support: Based on risk scoring results, provide risk response strategies and decision support to ensure the accuracy and timeliness of risk management; S6. Risk Response and Handling Procedures: Based on the decision support results, implement corresponding risk response and handling measures, specifically including: S6a. Automated handling: Automated handling of low-risk situations, including notifying maintenance personnel to perform equipment maintenance; S6b. Manual intervention: When a high-level risk triggers manual intervention, the emergency response process is initiated, and relevant departments are coordinated to conduct in-depth investigations and handle the situation. The data acquisition step further includes: The aircraft's position information and trajectory are collected using radar and ADS-B systems; Deploy a video surveillance system to collect real-time images of the runway and surrounding areas; The movement data of ground vehicles and personnel are collected through the ground vehicle management system; Meteorological data such as wind speed, humidity, and visibility are collected through a meteorological monitoring system. Aircraft performance data is collected through an aircraft health monitoring system.

2. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The data fusion step further includes: Each expert big model performs feature extraction and analysis on different types of data sources, with each expert big model employing a targeted deep learning architecture; By integrating the outputs of various expert models through a multi-head attention mechanism, different weights are assigned to the outputs of different expert models, thereby achieving multi-perspective aggregation of information. The merged data is synchronized in time and standardized in format to ensure consistency and accuracy in subsequent analysis.

3. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The step of generating adversarial data further includes: Using multiple expert models, virtual risk scenarios are generated based on different contextual parameters, covering a variety of potential risk types; Diverse adversarial strategies, including random perturbation, malicious behavior simulation, and extreme condition simulation, are employed to generate adversarial samples, ensuring the diversity and representativeness of the samples. The generated adversarial examples are input into the data fusion model for training and optimization, thereby improving the model's ability to identify and process complex and extreme situations.

4. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The multiple expert models include specialized models for processing different data types such as visual data, radar data, and meteorological data, and each specialized model uses a targeted deep learning architecture for feature extraction.

5. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The countermeasures include, but are not limited to, scenarios such as sudden wind shear, slippery runways, unauthorized vehicle intrusion, and heavy rain and dense fog. By simulating these scenarios, the model's ability to respond to real-world risks is improved.

6. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The multimodal generation technology includes two parts: text-to-scene conversion and image-to-scene mapping. The former uses natural language processing to convert text descriptions into scene parameters, while the latter uses image recognition technology to convert image data into three-dimensional scene elements.

7. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The virtual environment supports multiple interaction methods, including natural language, gestures, and VR / AR, allowing users to interact with the virtual environment in real time through a multimodal interface, thereby improving the user experience and decision-making efficiency.

8. The airport core risk whole-process management method based on a dual prevention mechanism according to claim 1, characterized in that: The method employs a deployment approach that combines cloud and edge computing. It leverages the powerful computing capabilities of the cloud for large-scale data processing and model training, while edge computing devices are responsible for real-time data acquisition and preliminary processing, ensuring real-time response of the virtual environment and efficient operation of large-scale simulation training tasks.

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