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

Abstract

The invention relates to an airport core risk whole-process management method based on a dual-prevention working mechanism, and the method comprises the following main steps: a data collection step: collecting multi-source data related to the operation of an airport in real time through a plurality of sensors and systems; a data fusion step of replacing a multi-head attention mechanism in a traditional Transform model with a plurality of expert large models, including feature extraction and analysis, information aggregation and data synchronization and standardization; the method comprises the steps of generating adversarial data, generating diversified virtual risk scenes and adversarial samples by utilizing a plurality of expert large models, automatically constructing a virtual environment, automatically constructing a high-fidelity virtual simulation environment based on the large models, and performing risk assessment and decision making. Accurate risk assessment and decision support are carried out by using a self-adaptive risk scoring system and an intelligent decision support mechanism; the comprehensive, accurate and intelligent management of the airport core risk is realized, and the safety and the emergency response capability of airport operation are obviously improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of airport safety management, and specifically relates to a method for the whole-process management of core risks at airports based on a dual prevention working mechanism. Background Art

[0002] With the rapid development of the air transportation industry, the airport operation environment has become increasingly complex and changeable. Safety risks such as runway incursions and runway excursions pose a serious threat to flight safety. In the existing technology, although early warning systems and risk prevention and control methods for airport runway safety have been applied to some extent, there are still certain limitations and further optimization and improvement are urgently needed.

[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 acoustic-optic alarm unit. It detects and alarms runway incursions through the sensor network, and has the advantages of being economical, practical, safe, reliable, and flexible in configuration. However, this system mainly relies on the layout of physical sensors and simple data processing, making it difficult to accurately identify and dynamically respond to complex and changeable risk situations. Moreover, there are deficiencies in data fusion and risk assessment, and it cannot fully utilize the potential information of multi-source data.

[0004] Chinese invention patent CN112397071B discloses an approach and runway operation risk early warning method based on air traffic control voice recognition. This method uses voice recognition technology to convert air traffic control instructions into structured data, and combines surveillance and plan data for alarm logic calculation, providing timely, comprehensive, and intelligent risk prevention and control means to reduce the workload of air traffic controllers. However, this method mainly focuses on the recognition and processing of voice instructions, lacks the comprehensive utilization of other types of data (such as visual data, meteorological data, ground vehicle data, etc.), and the comprehensiveness and accuracy of risk assessment are limited, making it difficult to cope with the complexity brought by multi-source data fusion.

[0005] In summary, problems in the existing technology such as relying on a single data source, inaccurate risk assessment, and slow emergency response have become challenges that urgently need to be solved. Summary of the Invention

[0006] Aiming at the above-mentioned existing technology, the technical problem to be solved by the present invention is how to introduce a dual prevention working mechanism, combine multiple expert large models and generative adversarial data technology, comprehensively improve the recognition, assessment, and management capabilities of core risks at airports, and solve the problems of insufficient integration of multi-source data and untimely risk response in the existing technology.

[0007] To solve the above problems, the present invention provides a whole-process management method for airport core risks based on a dual prevention work mechanism, including the following steps:

[0008] S1. Data collection step: Real-time collect multi-source data related to airport operations through a variety of sensors and systems;

[0009] S2. Data fusion step: Use multiple expert large models to replace the multi-head attention mechanism in the traditional Transformer model while maintaining the advantages of multi-head attention, and perform fusion processing on multi-source data, specifically including:

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

[0011] S2b. Information aggregation: Integrate the outputs of each expert large model through the multi-head attention mechanism to achieve information aggregation from multiple perspectives;

[0012] S2c. Data synchronization and standardization: Perform time synchronization and format standardization processing on the fused data to ensure data consistency and accuracy;

[0013] S3. Generative adversarial data step: Use multiple expert large models to replace the multi-head attention mechanism in the traditional Transformer model while maintaining the advantages of multi-head attention, and generate adversarial samples to improve the robustness of the data fusion model in complex scenarios, specifically including:

[0014] S3a. Virtual risk scenario generation: Use multiple expert large models to generate virtual risk scenarios according to different scenario parameters, covering a variety of potential risk types;

[0015] S3b. Adversarial strategy implementation: Adopt diverse adversarial strategies such as random perturbation, malicious behavior simulation, and extreme condition simulation to generate adversarial samples;

[0016] S3c. Model training and optimization: Input the generated adversarial samples into the data fusion model for training and optimization to improve the model's recognition and processing capabilities in complex and extreme scenarios;

[0017] S4. Virtual environment automated construction step: Automatically construct a virtual simulation environment based on large models, and at the same time use multiple expert large models to replace the multi-head attention mechanism in the traditional Transformer model while maintaining the advantages of multi-head attention, specifically including:

[0018] S4a. Simulation engine selection and integration: Select a high-fidelity simulation engine and deeply integrate it with the large model through an open API to achieve data-driven simulation behavior;

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

[0020] S4c. Physical Phenomenon Simulation: Combine a physics engine to simulate the real physical phenomena of aircraft takeoff and landing and vehicle driving to ensure the authenticity and interactivity of the virtual environment;

[0021] S4d. Scene Optimization: Through optimization algorithms of procedural content generation and genetic algorithms, automatically generate diverse scene elements and optimize scene performance and visual effects;

[0022] S5. Risk Assessment and Decision-making Steps: Conduct risk assessment and decision-making based on the fused data and virtual environment, specifically including:

[0023] S5a. Risk Scoring: Use an adaptive risk scoring system to dynamically evaluate the weights of various risk factors according to the fused data and generate a comprehensive risk score;

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

[0025] S6. Risk Response and Disposal Steps: Implement corresponding risk response and disposal measures according to the decision-making support results, specifically including:

[0026] S6a. Automated Disposal: Automatically process low-level risks, such as notifying maintenance personnel for equipment maintenance;

[0027] S6b. Manual Intervention: Trigger manual intervention for high-level risks, initiate the emergency response process, and coordinate relevant departments for in-depth investigation and disposal.

[0028] By combining the dual prevention work mechanism, this invention uses multiple expert large models to replace the multi-head attention mechanism in the traditional Transformer model, improving the professionalism and efficiency of multi-source data fusion. At the same time, by generating adversarial data, the robustness of the model is enhanced, and the risk identification and processing capabilities of the system in complex scenarios are strengthened. Based on the automated construction of the virtual environment by large models, a highly realistic and interactive simulation platform is provided, 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, improves the safety management level and emergency response capabilities of the airport, and has significant economic benefits and market competitiveness.

[0029] The data collection step further includes:

[0030] Collect the position information and movement trajectories of aircraft through radar and ADS-B systems;

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

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

[0033] Collect meteorological data such as wind speed, humidity, and visibility through the meteorological monitoring system;

[0034] Collect the performance data of the aircraft through the aircraft health monitoring system.

[0035] The data fusion step further includes:

[0036] Each expert large model respectively extracts and analyzes the features of different types of data sources, where each expert large model adopts a targeted deep learning architecture;

[0037] Integrate the outputs of each expert large model through the multi-head attention mechanism, assign different weights to the outputs of different expert models, and achieve multi-perspective aggregation of information;

[0038] Perform time synchronization and format standardization processing on the fused data to ensure the consistency and accuracy of the data in subsequent analysis.

[0039] The generation of adversarial data step further includes:

[0040] Use multiple expert large models to generate virtual risk scenarios according to different scenario parameters, covering various potential risk types;

[0041] Adopt diverse adversarial strategies such as random perturbation, malicious behavior simulation, and extreme condition simulation to generate adversarial samples, ensuring the diversity and representativeness of the samples;

[0042] Input the generated adversarial samples into the data fusion model for training and optimization, improving the model's recognition and processing capabilities in complex and extreme scenarios.

[0043] The virtual environment automatic construction step further includes:

[0044] Select a high-fidelity simulation engine and deeply integrate it with the large model through an open API to achieve data-driven simulation behavior;

[0045] Use the large model for multi-modal generation, including converting text descriptions and image data into specific virtual scene parameters;

[0046] Combine the physics engine to simulate the real physical phenomena of aircraft takeoff and landing, and vehicle driving, ensuring the authenticity and interactivity of the virtual environment;

[0047] Through optimization algorithms such as procedural content generation and genetic algorithms, automatically generate diverse scene elements and optimize the scene performance and visual effects.

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

[0049] The adversarial strategies include, but are not limited to, scenarios such as sudden wind shear, runway slipperiness, illegal vehicle intrusion, and heavy rain and thick fog, and the ability of the model to respond to actual risks is improved by simulating these scenarios.

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

[0051] Multiple interaction methods such as natural language, gestures, and VR / AR are supported in the virtual environment, allowing users to interact with the virtual environment in real time through a multi-modal interface, improving the operation experience and decision-making efficiency.

[0052] The method uses a deployment method that combines cloud computing and edge computing. The powerful computing power of the cloud is used for large-scale data processing and model training, and the edge computing device is responsible for real-time data collection and preliminary processing, ensuring the real-time response of the virtual environment and the efficient operation of large-scale simulation training tasks.

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

[0054] 1. By using multiple dedicated expert large models to process different types of data sources such as visual data, radar data, and meteorological data respectively, the professionalism and accuracy of data processing are ensured. Each expert model uses a deep learning architecture most suitable for its data type, achieving efficient feature extraction and analysis.

[0055] Combined with the multi-head attention mechanism, information aggregation from multiple perspectives is performed on the outputs of each expert model, 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 using multiple expert large models to generate diverse virtual risk scenarios and adversarial samples, covering various complex scenarios such as sudden wind shear, runway slipperiness, illegal vehicle intrusion, and heavy rain and thick fog, the recognition and processing capabilities of the data fusion model in complex and extreme scenarios are effectively improved.

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

[0058] 3. By selecting high-fidelity simulation engines (such as Prepar3D, X-Plane, Microsoft Flight Simulator) and deeply integrating them with large models, a virtual environment that highly reproduces the real airport operation status is constructed, supporting simulation behaviors driven by dynamic data.

[0059] Combined with natural language processing and image recognition technologies, the automatic conversion of text descriptions and image data into virtual scene parameters is realized, enriching the details and authenticity of the virtual scene. At the same time, the introduction of a physics engine ensures the authenticity of object motion and interaction in the virtual environment.

[0060] Supporting multiple interaction methods such as natural language, gestures, VR / AR, etc., it provides an immersive operation experience and efficient decision-making support, enhancing the interactivity and operation efficiency of users in the virtual environment.

[0061] 4. Through dynamically adjusting the weights of risk factors and in-depth analysis of multi-expert models, the accurate assessment of various risks is achieved, generating a comprehensive risk score with high accuracy, ensuring the forward-looking and real-time nature of risk assessment.

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

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

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

[0065] 6. Through the comprehensive collection and efficient integration of multi-source data, the system can comprehensively cover various potential risks in airport operations, provide comprehensive security monitoring and management, and significantly enhance the overall safety of airport operations.

[0066] Based on a highly robust data fusion model and a highly realistic virtual environment, the system has an intelligent emergency response ability, can efficiently respond to various sudden risk events, and ensure the safety, smoothness, and efficiency of airport operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is the workflow of this application Figure 1 ;

[0068] Figure 2Workflow of the present application Figure 2 ;

[0069] Figure 3 Workflow of the present application Figure 3 。 Detailed implementation manners

[0070] In order 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 for the convenience of those skilled in the art to understand.

[0071] Embodiment 1:

[0072] As Figures 1 to 3 shown, this embodiment details the specific implementation manners of the "multi-source data collection and fusion" step in the whole-process management method of airport core risks based on the dual prevention work mechanism, covering data collection, data fusion and their effects.

[0073] Step S1: Data collection step

[0074] In the airport operation environment, real-time and multi-source data collection is the basis for realizing core risk management. The specific implementation manners are as follows:

[0075] Collection of aircraft position information and movement trajectories:

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

[0077] ADS-B system: Utilize the Automatic Dependent Surveillance-Broadcast (ADS-B) technology to obtain the accurate position information and dynamic movement data of aircraft, supplementing the refined monitoring of radar data.

[0078] Real-time image collection of the runway and surrounding areas:

[0079] Video surveillance system: Deploy high-definition cameras in the runway, taxiway and their surrounding areas to collect image data of key airport areas in real time all day long. Combining computer vision technology, real-time identification and tracking of the runway status, aircraft and ground vehicles are realized.

[0080] Collection of ground vehicle and personnel movement data:

[0081] Ground vehicle management system: Through RFID tags and GPS positioning technology, the driving route, speed and position of ground vehicles are monitored 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: Multiple meteorological sensors are deployed to collect meteorological parameters such as wind speed, humidity, visibility, and temperature in real time. Through the data acquisition terminal, the meteorological data is transmitted to the central processing system in real time to ensure timely response to meteorological changes.

[0084] Aircraft Performance Data Acquisition:

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

[0086] Step S2: Data Fusion Step

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

[0088] Feature Extraction and Analysis (Step S2a):

[0089] Visual Data Processing: The dedicated visual expert large model adopts the 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, aircraft position, etc.

[0090] Radar Data Processing: The radar expert large model adopts the Recurrent Neural Network (RNN) architecture to analyze the position information and movement trajectory of the aircraft and identify abnormal flight behaviors.

[0091] Meteorological Data Processing: The meteorological expert large model adopts the Long Short-Term Memory Network (LSTM) architecture to process real-time meteorological data and predict short-term meteorological change trends.

[0092] Ground Vehicle and Personnel Data Processing: The ground management expert large model adopts the 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 the image data collected by the airport video surveillance system, such as runway images, real-time videos of aircraft, etc.

[0095] Data Form: An image / video frame can usually 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 (usually 3 for RGB).

[0096] Model Structure and Feature Extraction Process

[0097] Convolutional Layer

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

[0099]

[0100] Where:

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

[0102] · and b (l,m) are the weight and bias 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] In some CNN structures, a pooling layer (such as max pooling MaxPooling or average pooling AveragePooling) is added to reduce the spatial resolution of the feature map, reduce the number of parameters and extract high-level features. Max pooling can be expressed as:

[0106]

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

[0108] Multi-layer stacking and fully connected layer

[0109] After multiple convolutions and poolings, the output feature map is usually flattened into a vector and input into a fully connected layer (FullyConnected Layer) for classification or regression output. The fully connected layer can

[0110] be expressed as:

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

[0112] Where x flat represents the vector after flattening the high-dimensional convolutional feature map, W fc and b fcThey 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, the output of the CNN is often the object position coordinates, class labels, or object detection boxes. During training, cross-entropy loss, regression loss (such as L1, L2), etc. can be used and selected according to the task requirements.

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

[0116] Radar data processing: RNN model

[0117] It is mainly used to analyze the position information and movement trajectory of the aircraft and identify abnormal flight behaviors (such as approaching too fast, abnormal ascending / descending, etc.).

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

[0119] RNN structure and feature extraction process

[0120] The calculation of the 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] Among them:

[0124] ·h t is the hidden state vector, and y t is the output vector;

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

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

[0127] Feature extraction

[0128] Input the radar time series into the RNN, and the last step h T or the output y at each time step t can be used as a high-level feature representation of the aircraft trajectory for identifying abnormal flight behaviors or predicting future trajectories.

[0129] Meteorological data processing: LSTM model

[0130] Predict short-term meteorological change trends, such as wind speed, temperature, humidity, visibility, etc., to provide data support for runway usage strategies and flight scheduling.

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

[0132] LSTM structure and feature extraction process

[0133] The calculation of the key formula of LSTM 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] where:

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

[0141] ·C t is the cell state, h t It is a hidden state (Hidden State);

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

[0143] W f ,W i ,W C ,W o etc. are trainable parameter matrices, b f , b i ,b C ,b o For bias.

[0144] Feature extraction and trend prediction

[0145] Through the LSTM structure, the temporal dependency in long-sequence meteorological data can be effectively captured, and the output hidden state h T and / or C T It contains a high-level expression of meteorological change trends for subsequent short-term meteorological 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 mistakenly entering the runway, conflicts between ground vehicles and aircraft, etc.).

[0148] Data format: The mobility of ground vehicles and personnel is abstracted into a graph structure with nodes and edges. Nodes can represent vehicles, personnel, or key locations, while edges can represent the distance or interaction 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. i Corresponding to a vector feature x i , edges can also contain features.

[0150] GNN structure and feature extraction process

[0151] Taking the graph convolutional network as an example, a layer of graph convolution operation can be expressed as:

[0152]

[0153] in:

[0154] · represents the embedding representation of the node in the graph at layer l, d l is the feature dimension of the lth layer;

[0155] · A is the adjacency matrix of the graph, I is the identity matrix (with self-connection to ensure that the node information itself is preserved);

[0156] · for degree matrix of diagonal elements;

[0157] W (l) is the trainable weight;

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

[0159] Neighbor aggregation and update

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

[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 decision-making module.

[0163] Feature extraction results

[0164] High-level graph representation: Node embeddings and graph-level features extracted by GNN can be used to identify potential risks such as abnormal vehicle scheduling and personnel crossing the boundary, or further used to 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 each expert model are integrated. The output of each expert model is used as a different "attention head" and assigned different weights. Information from various perspectives is integrated to achieve comprehensive information aggregation.

[0167] Weight allocation: Dynamically adjust the weights of each expert model’s output 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 models, each of which outputs a feature vector (or sequence) E i(i = 1, 2, …, M). These outputs can be regarded as the feature information carried by different "heads".

[0170] Map the outputs E of each expert model to Q, K, V i , define the trainable mapping weight matrix W Q , W K , W V (different expert models can use different or shared projection matrices), and map them to Query, Key, and Value:

[0171] Q i = E i W Q ,

[0172] K i = E i W K ,

[0173] V i = E i W V .

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

[0175] Attention calculation formula:

[0176]

[0177] Where:

[0178] ·d k is the dimension of the key vector K i , used to scale to avoid too large values;

[0179] ·softmax operation normalizes the attention weights.

[0180] Multi-head output concatenation and linear transformation:

[0181] After concatenating the attention outputs head1, head2,..., head M of M heads, and then through the linear transformation W O get the final aggregation result:

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

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

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

[0185] Time synchronization: Adopt high-precision clock synchronization technology to ensure the consistency of timestamps of different data sources and eliminate data inconsistencies caused by time differences.

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

[0187] System architecture:

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

[0189] Data fusion layer: It consists of multiple expert large models. Each model specializes in processing specific types of data, and integrates the outputs of each expert model through the multi-head attention mechanism to form a comprehensive data input.

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

[0191] Risk assessment and decision-making layer: Based on the fused data, conduct risk scoring and decision support, and provide real-time risk management strategies.

[0192] Technical implementation:

[0193] Design of expert large models: Each expert large model adopts the most suitable deep learning architecture according to the data type it processes, such as CNN, RNN, LSTM, GNN, etc., to ensure the efficiency and accuracy of feature extraction and analysis.

[0194] Integration of multi-head attention mechanism: In the data fusion layer, through a custom multi-head attention module, the outputs of each expert model are weighted and integrated according to the preset weights to achieve multi-perspective aggregation of information.

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

[0196] Through the collaborative work of multiple expert large models, specialized processing is carried out for different data types, significantly improving the accuracy and depth of data processing.

[0197] The application of the multi-head attention mechanism realizes the multi-perspective aggregation of information, 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 to enhance the robustness of the data fusion model, the stability and reliability of the system in complex and extreme scenarios are enhanced.

[0201] Efficient data synchronization and standardization processing ensure the stable operation of the system in a high-load and multi-source data environment.

[0202] The comprehensive collection and efficient fusion of multi-source data provide comprehensive monitoring of the airport operation status, timely detect and respond to potential risks, and significantly improve the safety and reliability of airport operations.

[0203] Specialized data processing and intelligent risk assessment help the airport achieve intelligent safety management and promote the operation towards a more efficient and safe direction.

[0204] By implementing the multi-source data collection and fusion method in this embodiment, through professional data processing based on multiple expert large models and the multi-head attention mechanism, 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] Embodiment 2:

[0206] As Figures 1 to 3 shown, this embodiment details the specific implementation methods of the "generating adversarial data step (S3)" and the "automated virtual environment construction step (S4)" in the whole-process management method of airport core risks based on the dual prevention work mechanism, covering the process and effect of generating adversarial data to enhance the model robustness and the construction and optimization of the virtual environment based on the large model.

[0207] Step S3: Generating adversarial data step

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

[0209] Step S3a: Generation of virtual risk scenarios

[0210] To comprehensively cover various risks that may be encountered in airport operations, this step generates diverse and representative virtual risk scenarios through the collaboration of multiple expert models. The specific implementation is as follows:

[0211] Collaboration of multiple expert models:

[0212] GPT series models:

[0213] Function: Using GPT series language models, generate detailed text descriptions of risk scenarios according to preset scenario parameters. These text descriptions cover various potential risk types, such as runway incursions, equipment failures, extreme weather, etc.

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

[0215] Stable Diffusion models:

[0216] Function: Using the Stable Diffusion image generation model, convert the text descriptions generated by GPT into high-quality visualized virtual risk scenario images.

[0217] Implementation method: Use the risk scenario descriptions generated by GPT as input, and the Stable Diffusion model generates corresponding images, such as visualized scenario images of "Unauthorized personnel activities appear at the runway edge under foggy weather conditions", for simulation and training.

[0218] Setting of scenario parameters:

[0219] Historical data and risk analysis:

[0220] Function: According to the airport's historical operation data and potential risk analysis, set multiple combinations of scenario parameters to ensure that the generated virtual risk scenarios are representative and diverse.

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

[0222] Diversified Scenario Generation:

[0223] Function: By combining different scenario parameters, virtual scenarios covering a wide range of risk types are generated to ensure that the system can handle various complex and changing risk situations.

[0224] Implementation Method: Using the set scenario parameters, input them into the GPT series models in sequence to generate diversified risk description texts, and then convert them into corresponding visualized scenario images through the Stable Diffusion model. For example, by combining the parameters of "heavy rain weather" and "equipment failure", a virtual scenario of "under heavy rain weather conditions, runway equipment fails, resulting in the blocking of the taxiway" is generated.

[0225] Specific Implementation Method

[0226] System Architecture:

[0227] Generation Module: Includes the GPT series models and the Stable Diffusion model, working together to generate text descriptions and corresponding visualized scenario images.

[0228] Scenario Parameter Library: Stores various combinations of scenario parameters obtained based on historical data and risk analysis, serving as the input for generating virtual risk scenarios.

[0229] Data-Driven Interface: Transfers scenario parameters to the GPT model through the API and transfers the generated text descriptions to the Stable Diffusion model to complete the automated generation process from text to image.

[0230] Workflow:

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

[0232] Text Description Generation: Input the scenario parameters into the GPT series models to generate detailed risk scenario description texts.

[0233] Image Generation: Input the generated text descriptions into the Stable Diffusion model to generate corresponding virtual risk scenario images.

[0234] Scenario Storage and Application: Store the generated text and image scenarios in the database for subsequent risk assessment, simulation training, and decision support.

[0235] Implementation of Adversarial Strategy (Step S3b):

[0236] Random Perturbation: Introduce random perturbations into the generated virtual risk scenarios, such as sudden wind shear, slippery runway, etc., to simulate unpredictable emergencies.

[0237] Malicious behavior simulation: Simulate malicious behaviors such as illegal vehicles breaking into the runway and unauthorized personnel entering key areas to test the emergency response capabilities of the system.

[0238] Extreme condition simulation: Simulate the operating conditions of the airport under extreme weather conditions such as heavy rain and thick fog to evaluate the risk identification and handling capabilities of the system in extreme environments.

[0239] Step S3b: Implementation of countermeasures

[0240] In this step, by adopting diverse countermeasures, including random perturbations, malicious behavior simulation, and extreme condition simulation, high-quality adversarial samples are generated to enhance the robustness and recognition capabilities of the data fusion model in complex scenarios. The specific implementation methods are as follows:

[0241] Random perturbations

[0242] Purpose: By introducing unpredictable emergencies in the virtual risk scenario, such as sudden wind shear and slippery runway, simulate the random risks that may be encountered in real operations to test the emergency response capabilities and stability of the system.

[0243] Specific implementation process:

[0244] Wind shear simulation:

[0245] Situation setting: Set the occurrence of sudden strong winds or sudden changes in wind direction around the airport at a certain moment.

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

[0247] Effect display: Through the simulation engine, real-time display the changes in the takeoff and landing trajectories of the aircraft under the sudden wind speed to test the system's recognition and response capabilities to abnormal flight behaviors.

[0248] Slippery runway simulation:

[0249] Situation setting: Simulate the scenario where the runway becomes slippery due to rain or snow accumulation.

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

[0251] Effect display: Display the takeoff and landing performance of the aircraft on the slippery runway to evaluate the system's monitoring and risk warning capabilities for changes in runway conditions.

[0252] Malicious behavior simulation

[0253] Purpose: By simulating malicious behaviors such as illegal vehicles breaking into the runway and unauthorized personnel entering key areas, test the system's security protection and emergency response mechanisms to ensure that effective measures can be quickly identified and taken in the event of real threats.

[0254] Specific implementation process:

[0255] Illegal vehicle breaking into the runway:

[0256] Scenario setting: In the virtual simulation environment, set up unauthorized vehicles attempting to enter the runway area.

[0257] Behavior simulation: Through script writing or behavior models, control the virtual vehicle to make abnormal movements near the runway, such as sudden acceleration or changing the driving route.

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

[0259] Unauthorized personnel entering key areas:

[0260] Scenario setting: Simulate unauthorized personnel attempting to enter key areas such as the aircraft apron or runway.

[0261] Behavior simulation: Through virtual character control, set abnormal activities of personnel within key areas, such as wandering and attempting to approach aircraft.

[0262] System response: Evaluate the accuracy and timeliness of the monitoring system's identification of abnormal personnel behaviors, as well as the effectiveness of emergency response measures (such as notifying security personnel and activating the alarm system).

[0263] Simulation of extreme conditions

[0264] Purpose: By simulating the operating conditions of the airport under extreme weather conditions such as heavy rain and thick fog, comprehensively evaluate the system's risk identification and handling capabilities in extreme environments to ensure efficient and safe operation under adverse weather conditions.

[0265] Specific implementation process:

[0266] Heavy rain simulation:

[0267] Scenario setting: Simulate a scenario where heavy rainfall deteriorates the airport 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 waterlogging, reduced visibility, and the operation of ground equipment.

[0269] Effect demonstration: Demonstrate the airport operation status under heavy rain conditions, such as runway waterlogging, taxiway slipperiness, reduced visibility, etc., and test the monitoring and risk warning capabilities of the system under adverse weather conditions.

[0270] Fog simulation:

[0271] Scenario setting: Simulate the impact of foggy weather on airport operations.

[0272] Parameter adjustment: In the virtual simulation environment, adjust the visibility parameter to simulate the impact of fog on aircraft takeoff and landing, navigation, and ground vehicle driving.

[0273] Effect demonstration: Demonstrate the airport operation status under fog conditions, such as blurred aircraft takeoff and landing trajectories, difficult ground vehicle driving, etc., and evaluate the system's risk identification and emergency response capabilities under low visibility conditions.

[0274] Specific implementation methods

[0275] System architecture:

[0276] Adversarial strategy module: Integrate three major adversarial strategies: random perturbation, malicious behavior simulation, and extreme condition simulation. Through modular design, it is convenient to flexibly call different adversarial strategies according to needs.

[0277] Scenario generation engine: Utilize simulation engines (such as Prepar3D, X-Plane, etc.) and generation models (such as GPT series, Stable Diffusion, etc.) to achieve the 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, and support quick call and dynamic adjustment.

[0279] Workflow:

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

[0281] Apply adversarial strategies:

[0282] According to the selected adversarial strategies (random perturbation, malicious behavior simulation, extreme condition simulation), introduce corresponding simulation events into the virtual risk scenario.

[0283] Use the simulation engine to adjust relevant parameters in the virtual environment (such as wind speed, rainfall, personnel and vehicle behavior, etc.) to generate specific risk scenarios.

[0284] Generate adversarial samples: Generate corresponding risk samples, including text descriptions and visualized images, from the adjusted virtual risk scenario through the generation model for use in training and optimizing the data fusion model.

[0285] Risk assessment and feedback: Input the generated adversarial samples into the data fusion model for risk identification and processing. The system records the response effect of the model as the feedback basis for subsequent model optimization.

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

[0287] Input of adversarial samples: Input the generated adversarial samples into the data fusion model for training and optimization. Through continuous iterative training, improve the recognition accuracy and processing efficiency of the model in complex and extreme scenarios.

[0288] Robustness assessment: Regularly evaluate the performance of the model under adversarial samples to ensure the robustness and stability of the model in the real operating environment.

[0289] Step S4: Steps for automated construction of the virtual environment

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

[0291] Selection and integration of the simulation engine (Step S4a):

[0292] Selection of high-fidelity simulation engines: Select high-fidelity simulation engines such as Prepar3D, X-Plane, Microsoft Flight Simulator, etc., which have open APIs and SDKs and support deep integration with large models.

[0293] Implementation of deep integration: Connect the data generated by the large model with the simulation engine through the open API to achieve data-driven simulation behaviors, such as real-time adjustment of aircraft takeoff and landing paths, ground vehicle movement routes, etc.

[0294] Multi-modal generation (Step S4b):

[0295] Text-to-scene conversion: Use a large language model (such as GPT-4) to convert text descriptions (such as "busy international airport runway, light fog in the evening, low visibility") into specific virtual scene parameters, including weather, lighting, runway status, etc.

[0296] Image-to-scene mapping: Combine an image generation model (such as Stable Diffusion) to extract elements from aerial photos or other image materials and automatically generate 3D runway and building models to enrich the details and authenticity of the virtual scene.

[0297] Step S4b: Multi-modal generation

[0298] The multi-modal generation process combines text-to-scene conversion and image-to-scene mapping, leveraging advanced large model technologies to achieve efficient conversion from various input forms to virtual scene parameters. The specific implementation is as follows:

[0299] Text-to-Scene Conversion

[0300] Purpose: Convert 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 operations.

[0301] Specific implementation process:

[0302] Text input:

[0303] The user or system inputs scene description text according to actual needs, such as: "Busy international airport runway, light fog in the evening, low visibility".

[0304] Large language model processing:

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

[0306] Semantic parsing: The model parses the input text description to identify key scene elements and parameters. For example, identify keywords such as "Busy international airport runway", "Evening", "Light fog", "Low visibility" from the above text.

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

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

[0309] Lighting parameters: Time period (sunset), light intensity, shadow distribution, etc.

[0310] Runway status: Runway usage (busy level), runway surface condition (slippery, waterlogged, 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 subsequent virtual environment construction.

[0314] Effect:

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

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

[0317] Image to Scene Mapping

[0318] Purpose: Use an image generation model to extract elements from aerial images or other image materials, and automatically generate 3D runways, building models, etc., to enrich the details and authenticity of the virtual scene.

[0319] Specific implementation process:

[0320] Image input:

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

[0322] Image generation model processing:

[0323] Model selection: Adopt an advanced image generation model, such as Stable Diffusion, for image processing and element extraction.

[0324] Image parsing:

[0325] Element recognition: The model recognizes key elements in the image, such as runways, buildings, vehicles, people, etc.

[0326] Feature extraction: Extract geometric features, colors, textures, etc. of each element to generate detailed 3D model data.

[0327] 3D model generation:

[0328] Model construction: Automatically generate 3D runways, building models and other necessary scene elements according to the extracted feature information.

[0329] Detail enhancement: Through detail optimization algorithms, improve the details and realism of the 3D model, such as runway markings, building exteriors, etc.

[0330] Scene integration:

[0331] Combine the generated 3D model 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, runway status, etc. to adjust the appearance and environmental settings of the 3D model to ensure the dynamics and authenticity of the virtual scene.

[0333] Effect:

[0334] High fidelity: Through the image generation model, various elements in the virtual scene highly restore the actual airport environment, enhancing the visual authenticity of the scene.

[0335] Rich in details: The automatically generated 3D model contains 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, text normalization, etc.

[0339] Multimodal data processing:

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

[0341] Scene construction and optimization:

[0342] Integrate the generated text scene parameters with the 3D model data, and use virtual environment construction tools (such as Unity, Unreal Engine, etc.) to construct a complete virtual airport operation scene.

[0343] Dynamic adjustment: According to real-time data and situational changes, dynamically adjust the parameters and models in the virtual scene to ensure that the virtual environment is always consistent with the actual operation environment.

[0344] Specific implementation methods

[0345] System architecture:

[0346] Multimodal generation module: Includes a text-to-scene conversion sub-module and an image-to-scene mapping sub-module, which work together to achieve the conversion of multimodal data.

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

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

[0349] Workflow:

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

[0351] Text processing: Convert the text description into structured scene parameters through the GPT-4 model.

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

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

[0354] Dynamic Optimization: Dynamically adjust the parameters and elements in the virtual scenario according to real-time operation data to maintain the consistency between the virtual environment and the actual environment.

[0355] Physical Phenomenon Simulation (Step S4c):

[0356] Real Physical Phenomenon Simulation: Integrate physical engines such as NVIDIA PhysX to simulate real physical phenomena such as aircraft takeoff and landing, vehicle driving, and personnel walking to ensure the operation authenticity and interactivity of the virtual environment.

[0357] Dynamic Environment Response: Dynamically adjust the physical phenomena in the virtual environment according to real-time data and changes in the risk scenario, such as adjusting the takeoff and landing paths of aircraft according to wind speed changes.

[0358] ">Scenario Optimization (Step S4d):

[0359] Procedural Content Generation: Set rule parameters to automatically generate diverse scenario elements, such as different types of aircraft, vehicles, personnel, etc., to maintain the richness and consistency of the environment.

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

[0361] System Architecture:

[0362] Virtual Environment Construction Module: Includes four sub-modules: simulation engine selection and integration, multi-modal generation, physical phenomenon simulation, and scenario optimization, which work together to achieve a high-fidelity, dynamically adjustable virtual simulation environment.

[0363] Data-Driven Interface: Interact with large models through open APIs to achieve real-time data-driven simulation behavior adjustment.

[0364] Optimization and Feedback Module: Utilize optimization techniques such as genetic algorithms, and according to the simulation result feedback, continuously optimize scenario generation and physical phenomenon simulation to improve the authenticity and interactivity of the virtual environment.

[0365] Technical Implementation:

[0366] Collaborative Generation of Multiple Expert Models: Utilize multiple expert large models to generate different types of virtual risk scenarios to ensure the diversity and professionalism of scenario generation.

[0367] Multimodal Data Processing: Through natural language processing and image recognition technologies, various input forms (text, image) are converted into virtual scene parameters, enhancing the automation and intelligence level of scene generation.

[0368] Physical Engine Integration: By deeply integrating the physical engine, the real movement and interaction of objects in the virtual environment are achieved, enhancing the operational authenticity of the simulation environment.

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

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

[0371] Enhanced Model Stability: Through continuous training and optimization of adversarial samples, the model can stably handle various sudden risks during actual operation, reducing false alarms and missed detections.

[0372] High-Fidelity Simulation: Through the deep integration of the high-fidelity simulation engine and the physical engine, the virtual environment highly reproduces the operating state of a real airport, providing a realistic operation experience.

[0373] Multimodal Interaction: Supports various interaction methods such as natural language, gestures, VR / AR, etc., enhancing the operation experience and decision-making efficiency of users in the virtual environment, and achieving immersive simulation training and risk response.

[0374] Real-Time Dynamic Assessment: Based on high-quality multi-source data fusion and generated virtual risk scenarios, the system can dynamically evaluate the weights of various risk factors in real time and generate accurate comprehensive risk scores.

[0375] Intelligent Decision Support: Combining the virtual simulation environment, it provides data-driven risk response strategies and decision support, ensuring the accuracy and timeliness of risk management, and effectively preventing and coping with various potential risks.

[0376] Comprehensive Risk Coverage: By generating diverse virtual risk scenarios and high-fidelity virtual environments, the system can comprehensively cover various potential risks in airport operations, enhancing the overall operational safety.

[0377] Intelligent Emergency Response: Based on the simulation and optimization in the virtual environment, the system can intelligently respond to and handle various risk events, ensuring the safety, smoothness, and efficiency of airport operations.

[0378] By implementing the method for generating adversarial data and automatically constructing a virtual environment in this embodiment, professional data processing and virtual environment construction based on multiple expert large models and the multi-head attention mechanism have achieved high robustness of the data fusion model and high authenticity and interactivity of the virtual environment. This method not only improves the accuracy and stability of risk identification and handling but also enhances the emergency response ability of the system in complex and extreme situations, significantly improving the overall safety and efficiency of airport operations.

[0379] Embodiment 3:

[0380] As Figures 1 to 3 shown, this embodiment details the specific implementation methods of "risk assessment and decision-making step (S5)" and "risk response and handling step (S6)" in the whole-process management method of airport core risks based on the dual prevention work mechanism, covering an adaptive risk scoring system, a decision support mechanism, and risk handling measures with automation and human intervention.

[0381] Step S5: Risk assessment and decision-making step

[0382] To achieve accurate risk assessment and effective decision support, in this step, through an adaptive risk scoring system and an intelligent decision support mechanism, comprehensive analysis and evaluation are carried out on the fused multi-source data and information in the virtual environment. The specific implementation methods are 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 adverse weather conditions, the weight of meteorological factors is significantly increased to enhance the impact of meteorological data on the overall risk score.

[0386] Support of multiple expert models: Use multiple expert large models (such as visual data experts, radar data experts, meteorological data experts, etc.) to evaluate different types of risk factors (such as runway incursions, equipment failures, extreme weather, etc.) respectively, and then through an adaptive mechanism, synthesize the scores of each expert model to generate a comprehensive risk score.

[0387] Time series analysis and trend prediction: Adopt time series models such as long short-term memory networks (LSTM) to analyze historical data, identify risk trends and potential risk patterns, and improve the forward-looking and accuracy of risk scores.

[0388] Step S5a: Risk scoring

[0389] Adaptive risk scoring system

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

[0391] Dynamic weight adjustment

[0392] Purpose: Dynamically adjust the weights of various risk factors according to real-time data and environmental changes to ensure that the influence 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, aircraft movement trajectories, etc.

[0396] Environmental change identification:

[0397] Through the data analysis module, identify the key change factors in the current operating environment, such as sudden weather changes (heavy rain, thick fog), runway condition changes (slippery, waterlogged), etc.

[0398] Weight adjustment mechanism:

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

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

[0401] When the runway equipment detects an abnormality, the weight of the equipment failure factor increases to enhance the attention to the equipment status.

[0402] Weight update:

[0403] The dynamically adjusted weight values 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] Purpose: Use multiple expert models to evaluate different types of risk factors respectively, and through an adaptive mechanism, integrate the scores of each expert model to generate a comprehensive risk score, and achieve a comprehensive assessment of multi-dimensional risks.

[0406] Specific implementation process:

[0407] Expert model configuration:

[0408] Visual data expert model:

[0409] Adopt a Convolutional Neural Network (CNN) architecture, which is responsible for evaluating visual-related risk factors such as runway incursions and abnormal aircraft positions.

[0410] Radar data expert model:

[0411] Adopt a Recurrent Neural Network (RNN) architecture to analyze the movement trajectories of aircraft and identify abnormal flight behaviors.

[0412] Meteorological data expert model:

[0413] Adopt a Long Short-Term Memory Network (LSTM) architecture to process real-time meteorological data and predict short-term meteorological change trends.

[0414] Ground vehicle and personnel data expert model:

[0415] Adopt a Graph Neural Network (GNN) architecture to analyze the movement patterns of ground vehicles and personnel and identify potential ground risks.

[0416] Independent risk assessment:

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

[0418] Scoring integration mechanism:

[0419] Through an adaptive mechanism, the independent scores of each expert model are integrated according to the dynamically adjusted weights to generate a comprehensive risk score. The specific process includes:

[0420] Unify the scoring criteria: Standardize the output risk scores of each expert model to ensure the comparability of the scores output by different models.

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

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

[0423] Time series analysis and trend prediction

[0424] Purpose: Through time series analysis and trend prediction, identify potential trends and patterns in the development of risks, improve the forward-looking and accuracy of risk scores, and support proactive risk management.

[0425] Specific implementation process:

[0426] Collection of historical data:

[0427] The system collects and stores past operation data and risk event records, including historical data such as meteorological changes, equipment failures, runway conditions, etc.

[0428] Application of time series model:

[0429] Model selection:

[0430] Adopt deep learning models suitable for processing time series data, such as long short-term memory network (LSTM), to analyze and predict the future trends of various risk factors.

[0431] Feature engineering:

[0432] Extract features from historical data, such as seasonal changes, trend fluctuations, periodic patterns, etc., as the input of the time series model.

[0433] Model training:

[0434] Use historical data to train the LSTM model, learn the time-dependent relationships and change patterns of various risk factors, and improve the model's prediction ability for future risk trends.

[0435] Trend prediction:

[0436] Utilize the trained LSTM model to predict the trends of various risk factors over a future period of time and generate predictive risk scores.

[0437] Risk trend integration:

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

[0439] Specific implementation methods

[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 sub-module: Adjust the weights of various risk factors according to real-time data and environmental changes.

[0445] Multi-expert score integration sub-module: Integrate the scores of multiple expert models to generate a comprehensive risk score.

[0446] Time series analysis sub-module: Apply models such as LSTM to conduct risk trend prediction and improve the forward-looking nature of the score.

[0447] Output layer:

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

[0449] Workflow:

[0450] Real-time data collection:

[0451] The system continuously receives real-time data from various data sources such as vision, radar, meteorology, and ground management, and conducts preliminary risk scoring through various expert models.

[0452] Dynamic weight adjustment:

[0453] Based on the current operating environment and real-time data, dynamically adjust the weights of various risk factors. For example, under adverse weather conditions, increase the weight of meteorological factors.

[0454] Multi-expert score integration:

[0455] Sum the independent risk scores of each expert model weighted by the dynamically adjusted weights to generate a comprehensive risk score.

[0456] Time series analysis and trend prediction:

[0457] Use the LSTM model to analyze historical data, predict the future trends of various risk factors, and integrate the prediction results into the current risk score to generate a forward-looking comprehensive risk score.

[0458] Risk score output:

[0459] Output the comprehensive risk score and trend prediction results 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: Establish a database containing various risk response strategies. According to different comprehensive risk scores, the system automatically matches and recommends corresponding response strategies. For example, for high-risk runway incursion events, the system recommends immediately initiating the emergency response process; for low-risk equipment anomalies, the system recommends automated maintenance notifications.

[0463] Decision-making algorithm: Adopt a method that combines rule-based decision-making algorithms and machine learning algorithms to ensure that the decision support system can quickly and accurately provide the optimal risk response plan in the 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 to ensure the continuous effectiveness of risk management.

[0465] Step S6: Risk Response and Disposal Steps

[0466] Based on the results of risk assessment and decision support, the system implements corresponding risk response and disposal measures to ensure the safety and smoothness of airport operations. The specific implementation methods are as follows:

[0467] Automated Disposal (Step S6a):

[0468] Low-level Risk Automated Processing:

[0469] Equipment Maintenance Notice: When the system detects low-level risks (such as minor equipment anomalies or runway status anomalies), it automatically sends maintenance notices to relevant maintenance personnel, instructing them to conduct equipment inspections and maintenance to ensure that problems are resolved in a timely manner.

[0470] Automatic Alarm System: When detecting low-level risks, the system issues warnings to relevant personnel through an automatic alarm device to remind them to pay attention and take necessary preventive measures.

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

[0472] Manual Intervention (Step S6b):

[0473] High-level Risk Triggers Manual Intervention:

[0474] Emergency Response Initiation: When the system assesses high-level risks (such as suspected runway incursions, major equipment failures, terrorist attack threats, etc.), it automatically triggers the emergency response process and notifies relevant departments and personnel to initiate the emergency plan.

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

[0476] In-depth Investigation and Disposal: A special emergency response team conducts in-depth investigations into high-level risk events, determines the source of the risk, and takes corresponding disposal 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 the process of manual intervention, the system continuously monitors the progress of risk disposal 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 large models and an adaptive risk scoring system, responsible for comprehensively evaluating various risk factors and generating a dynamic comprehensive risk score.

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

[0482] Risk Response and Disposal Module:

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

[0484] Manual Intervention Subsystem: Responsible for manual intervention and emergency response to high-level risks, coordinating relevant departments to ensure the effective implementation of emergency measures.

[0485] Technical Implementation:

[0486] Adaptive Risk Scoring System Design:

[0487] Dynamic Weight Algorithm: Adopts an adaptive weight adjustment algorithm to dynamically adjust the weights of various risk factors according to real-time data and environmental changes, ensuring the accuracy and timeliness of risk scores.

[0488] Integration of Multiple Expert Models: By integrating multiple dedicated expert large models, processes data on different types of risk factors respectively, enhancing the professionalism and depth of risk scoring.

[0489] Intelligent Decision Support System Design:

[0490] Combination of Decision Tree and Machine Learning: Combines rule-based decision trees and machine learning algorithms to ensure that the decision support system can quickly and accurately provide optimal risk response strategies in a complex and changing operating environment.

[0491] Strategy Library Management: Establishes and maintains a dynamically updated risk response strategy library to ensure that the system can recommend the most appropriate response strategies based on the latest operating data and risk situations.

[0492] Automation and Manual Intervention System Design:

[0493] Automated Processing Flow: Designs an automated processing flow for low-level risks, including functions such as automatic notifications, alarms, and data recording, to ensure that low-level risks can be quickly and effectively processed.

[0494] Emergency Response Process: Design an emergency response process for high-level risks, including links such as the initiation of emergency plans, multi-department coordination, in-depth investigation, and risk disposal, to ensure that high-level risks can be promptly and effectively addressed.

[0495] Accurate Risk Scoring: Through an adaptive risk scoring system and in-depth analysis combining multi-expert large models, accurate assessment of various risk factors is achieved, generating a comprehensive risk score with high accuracy.

[0496] Real-time Risk Monitoring: The system can monitor the airport operation status in real time and dynamically adjust the risk score to ensure the timeliness and forward-looking of risk assessment.

[0497] Intelligent Strategy Recommendation: Based on an intelligent decision support system, the system can quickly and accurately recommend the optimal risk response strategies, improving the intelligence level of decision support.

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

[0499] Rapid Disposal of Low-level Risks: Through an automated response subsystem, low-level risks can be quickly processed, reducing human intervention and improving the disposal efficiency.

[0500] Effective Response to High-level Risks: Through a manual intervention subsystem, high-level risks can be comprehensively and deeply addressed, 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 various potential risks in airport operations, enhancing the overall operation safety.

[0502] Intelligent Emergency Response Capability: The automated and manual intervention mechanisms of the system enable the airport to have intelligent emergency response capabilities, being able to efficiently respond to various sudden risk events and ensure the safety, smoothness, and efficiency of airport operations.

[0503] By implementing the risk assessment, decision support, risk response, and disposal methods in this embodiment, based on the 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 intervention risk disposal measures, the emergency response capability of the system and the overall operation safety are significantly improved. This method not only enhances the accuracy and real-time nature of airport risk management but also improves the stability and reliability of the system in 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 not to limit them. 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 in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A whole-process management method for core risks at airports based on the dual prevention work mechanism, characterized in that: It includes the following steps: S1. Data collection step: Multisource data related to airport operations is collected in real time through various sensors and systems; S2. Data fusion step: Multiple expert large models are used to replace the multi-head attention mechanism in the traditional Transformer model while maintaining the advantages of multi-head attention, and the multisource data is fused, specifically including: S2a. Feature extraction and analysis: Each expert large model extracts and analyzes features from different types of data sources respectively; S2b. Information aggregation: The outputs of each expert large model are integrated through the multi-head attention mechanism to achieve information aggregation from multiple perspectives; S2c. Data synchronization and standardization: The fused data is subjected to time synchronization and format standardization processing; S3. Generating adversarial data step: Multiple expert large models are used to replace the multi-head attention mechanism in the traditional Transformer model while maintaining the advantages of multi-head attention, and adversarial samples are generated to improve the robustness of the data fusion model in complex scenarios; S4. Virtual environment automated construction step: Based on large models, a virtual simulation environment is automatically constructed, and multiple expert large models are used to replace the multi-head attention mechanism in the traditional Transformer model while maintaining the advantages of multi-head attention, specifically including: S4a. Selection and integration of the simulation engine; S4b. Multimodal generation; S4c. Physical phenomenon simulation; S4d. Scene optimization; S5. Risk assessment and decision-making step: Risk assessment and decision-making are carried out based on the fused data and the virtual environment, specifically including: S5a. Risk scoring: Using an adaptive risk scoring system, the weights of various risk factors are dynamically evaluated according to the fused data to generate a comprehensive risk score; S5b. Decision support: Based on the risk scoring results, risk response strategies and decision support are provided to ensure the accuracy and timeliness of risk management; S6. Risk response and handling step: According to the decision support results, corresponding risk response and handling measures are implemented, specifically including: S6a. Automated handling: Low-level risks are automatically processed, such as notifying maintenance personnel to perform equipment maintenance; S6b. Manual intervention: High-level risks trigger manual intervention, starting an emergency response process to coordinate relevant departments for in-depth investigation and handling.

2. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The generating adversarial data step includes: S3a. Generation of virtual risk scenarios: Multiple expert large models are used to generate virtual risk scenarios according to different scenario parameters, covering various potential risk types; S3b. Implementation of adversarial strategies: Diversified adversarial strategies such as random perturbation, malicious behavior simulation, and extreme condition simulation are adopted to generate adversarial samples; S3c. Model training and optimization: The generated adversarial samples are input into the data fusion model for training and optimization to improve the model's recognition and processing capabilities in complex and extreme scenarios; The selection and integration of the simulation engine is to select a high-fidelity simulation engine and deeply integrate it with the large model through an open API to achieve data-driven simulation behavior; The multi-modal generation utilizes a large model for multi-modal generation, including converting text descriptions and image data into specific virtual scene parameters; The physical phenomenon simulation combines a physics engine to simulate the real physical phenomena of aircraft takeoff and landing and vehicle driving, ensuring the authenticity and interactivity of the virtual environment; The scene optimization uses optimization algorithms of procedural content generation and genetic algorithms to automatically generate diverse scene elements and optimize the scene performance and visual effects; The data collection step further includes: Collecting the position information and movement trajectories of aircraft through radar and ADS-B systems; Deploying a video surveillance system to collect real-time images of the runway and surrounding areas; Collecting the movement data of ground vehicles and personnel through a ground vehicle management system; Collecting meteorological data such as wind speed, humidity, and visibility through a meteorological monitoring system; Collecting the performance data of aircraft through an aircraft health monitoring system.

3. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The data fusion step further includes: Each expert large model respectively performs feature extraction and analysis on different types of data sources, where each expert large model adopts a targeted deep learning architecture; Integrating the outputs of each expert large model through a multi-head attention mechanism, assigning different weights to the outputs of different expert models, and achieving multi-perspective aggregation of information; Performing time synchronization and format standardization processing on the fused data to ensure the consistency and accuracy of the data in subsequent analysis.

4. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The generation of adversarial data step further includes: Using multiple expert large models to generate virtual risk scenarios according to different scenario parameters, covering various potential risk types; Adopting diverse adversarial strategies such as random perturbation, malicious behavior simulation, and extreme condition simulation to generate adversarial samples, ensuring the diversity and representativeness of the samples; Inputting the generated adversarial samples into the data fusion model for training and optimization to improve the model's recognition and processing capabilities in complex and extreme scenarios.

5. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The virtual environment automatic construction step further includes: Selecting a high-fidelity simulation engine and deeply integrating it with the large model through an open API to achieve data-driven simulation behavior; Using the large model for multi-modal generation, including converting text descriptions and image data into specific virtual scene parameters; Combining a physics engine to simulate the real physical phenomena of aircraft takeoff and landing and vehicle driving, ensuring the authenticity and interactivity of the virtual environment; Automatically generating diverse scene elements and optimizing the scene performance and visual effects through optimization algorithms of procedural content generation and genetic algorithms.

6. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The multiple expert large models include dedicated models for different data types such as visual data, radar data, and meteorological data, and each dedicated model adopts a targeted deep learning architecture for feature extraction.

7. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The adversarial strategies include, but are not limited to, situations such as sudden wind shear, runway slipperiness, illegal vehicle intrusion, and heavy rain and thick fog, and the model's ability to respond to actual risks is improved by simulating these situations.

8. The whole-process management method for core risks at the airport based on the dual prevention work mechanism according to claim 1, characterized in that: The multi-modal generation technology includes two parts: text-to-scene conversion and image-to-scene mapping. The former converts text descriptions into scene parameters through natural language processing, and the latter converts image data into three-dimensional scene elements through image recognition technology.

9. The whole-process management method for airport core risks based on the dual prevention work mechanism according to claim 1, characterized in that: The virtual environment supports various interaction methods for natural language, gestures, VR / AR, allowing users to interact with the virtual environment in real time through a multimodal interface, enhancing the operation experience and decision-making efficiency.

10. The whole-process management method for the core risks of an airport based on the dual prevention work mechanism according to claim 1, characterized in that: The method adopts a deployment method that combines cloud computing and edge computing. It uses the powerful computing power of the cloud for large-scale data processing and model training, and the edge computing device is responsible for real-time data collection and preliminary processing, ensuring the real-time response of the virtual environment and the efficient operation of large-scale simulation training tasks.

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