Unmanned aerial vehicle airport data transmission method and system

Abstract

The invention belongs to the technical field of data transmission, and discloses an unmanned aerial vehicle airport data transmission method and system. The method comprises the following steps: constructing a network quality analysis model, a traffic anomaly detection model and a data anomaly analysis model; performing network quality analysis and channel switching on the network channel by using the network quality analysis model to obtain a final network channel; based on the final network channel, encrypting and transmitting real-time unmanned aerial vehicle airport data to a cloud data center by using an OPC UA transmission scheme and an asymmetric encryption algorithm; according to the collected real-time access traffic data, traffic anomaly detection and access interception are carried out by using a traffic anomaly detection model; and according to the real-time unmanned aerial vehicle airport data, performing data anomaly analysis and alarm generation by using the data anomaly analysis model. According to the invention, the problems of unstable network quality, insufficient data security and weak data analysis capability in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data transmission, and in particular relates to a method and system for transmitting data at an unmanned aerial vehicle airport. Background Art

[0002] Drone airports, also known as drone hangars or drone nests, are an important development in the drone industry. The main functions of drone airports include providing a protective place for drones, charging or replacing batteries, and data transmission. They play an important role in improving the intelligence and efficiency of drone systems, solving the limitations of drones in industry applications, and promoting the maturity of drone technology and the industrial chain. Data transmission technology at drone airports is an important part of the drone industry.

[0003] The existing data transmission technology of drone airports has the following defects: 1) Unstable network quality: Existing technologies often lack an effective network quality monitoring mechanism, which may lead to network delays, packet loss and other problems during data transmission, affecting the real-time and integrity of data. In addition, when the network quality deteriorates, there is no mechanism to automatically switch to a better network channel, resulting in data transmission interruption or inefficiency. 2) Insufficient data security: The existing data transmission methods lack encryption measures or the encryption strength is not strong enough, which makes the data easy to be intercepted or tampered with during the transmission process. There is no mechanism to detect and intercept abnormal traffic during the data transmission process, which makes it vulnerable to network attacks, such as DDoS attacks and data injection attacks; 3) Weak data analysis capabilities: Existing technologies often lack the ability to detect real-time anomalies in transmitted data, and are unable to promptly discover and process errors or anomalies in the data, which may lead to incorrect decisions or operations. In addition, the anomaly detection algorithm may not be advanced enough to adapt to complex data environments and changes, and the detection accuracy is not high. Summary of the invention

[0004] In order to solve the problems of unstable network quality, insufficient data security and weak data analysis capability in the prior art, the present invention aims to provide a method and system for transmitting data at an unmanned aerial vehicle airport.

[0005] The technical solution adopted by the present invention is: A method for transmitting data from an unmanned aerial vehicle to an airport, comprising the following steps: In the cloud data center, a network quality analysis model, a traffic anomaly detection model, and a data anomaly analysis model are constructed, and the network quality analysis model is deployed on the drone airport server; In the drone airport server, the network quality analysis model is used to perform network quality analysis and channel switching on the network channel to obtain the final network channel; Based on the final network channel, the real-time drone airport data is encrypted and transmitted to the cloud data center using the OPC UA transmission solution and asymmetric encryption algorithm; In the cloud data center, based on the collected real-time access traffic data, the traffic anomaly detection model is used to perform traffic anomaly detection and access interception; In the cloud data center, data anomaly analysis and alarm generation are performed based on real-time drone airport data using a data anomaly analysis model.

[0006] Furthermore, in the cloud data center, a network quality analysis model, a traffic anomaly detection model, and a data anomaly analysis model are constructed, and the network quality analysis model is deployed on the drone airport server, including the following steps: In the cloud data center, a network quality analysis model is constructed based on a number of historical network channel data using a deep learning algorithm, and model metadata of the network quality analysis model is extracted; Based on some historical access traffic data, a deep learning algorithm is used to build a traffic anomaly detection model, and based on some historical drone airport data, a deep learning algorithm is used to build a data anomaly analysis model; The model metadata is sent to all drone airport servers connected to the cloud data center, where a reconstructed network quality analysis model is generated based on the model metadata.

[0007] Furthermore, the network quality analysis model is constructed based on the NSDA-LSTM algorithm, and the network quality analysis model includes a network signal dynamic module constructed based on the NSDA algorithm and a network quality analysis module constructed based on the LSTM algorithm, which are connected in sequence.

[0008] Furthermore, the traffic anomaly detection model is constructed based on the RF-Attention-Elman algorithm, and the traffic anomaly detection model includes a key feature extraction module constructed based on the RF algorithm, an attention weight module constructed based on the Attention mechanism, and a traffic anomaly detection module constructed based on the Elman algorithm, which are connected in sequence.

[0009] Furthermore, the data anomaly analysis model is constructed based on the GCN-Elman algorithm, and the data anomaly analysis model includes a graph feature extraction module constructed based on the GCN algorithm and a data anomaly analysis module constructed based on the Elman algorithm, which are connected in sequence.

[0010] Furthermore, in the drone airport server, a network quality analysis model is used to perform network quality analysis and channel switching on the network channel to obtain the final network channel, including the following steps: In the drone airport server, real-time network channel data of the network channel for data transmission between the drone airport server and the cloud data center is collected; Input the real-time network channel data into the network quality analysis model, and use the network signal dynamic module to generate the corresponding real-time network signal dynamic data; Input the real-time network signal dynamic data into the network quality analysis module, and use the network quality analysis module to extract the corresponding real-time network signal dynamic data features; According to the dynamic data characteristics of real-time network signals, network quality analysis is performed to obtain real-time network quality analysis results; If the real-time network quality analysis result is that the network quality is poor, the current network channel is switched to obtain the final network channel; otherwise, the current network channel is used as the final network channel.

[0011] Furthermore, based on the final network channel, the real-time drone airport data is encrypted and transmitted to the cloud data center using the OPC UA transmission scheme and asymmetric encryption algorithm, including the following steps: Deploy the OPC UA server in the cloud data center and build the corresponding OPC UA information model based on the basic information of the drone airport; According to the OPC UA information model, a corresponding OPC UA information model instance is created in the address space of the OPC UA server; According to the OPC UA information model, collect real-time drone airport data of drone airports and store it in the drone airport server; Using an asymmetric encryption algorithm, encrypt the real-time drone airport data stored in the drone airport server to obtain the encrypted real-time drone airport data; Based on the final network channel, the encrypted real-time drone airport data is transmitted to the cloud data center using the OPC UA protocol.

[0012] Furthermore, in the cloud data center, based on the collected real-time access traffic data, a traffic anomaly detection model is used to perform traffic anomaly detection and access interception, including the following steps: In the cloud data center, traffic probes are used to collect real-time access traffic data from drone airport servers to the cloud data center; The real-time access traffic data is input into the traffic anomaly detection model, and a key feature extraction module is used to extract several real-time key data features of the real-time access traffic data; According to the attention weight value preset by the attention weight module, a plurality of real-time key data features are spliced ​​to obtain a real-time spliced ​​feature; According to the real-time splicing features, the traffic anomaly detection module is used to perform traffic anomaly detection and obtain real-time traffic anomaly detection results; If the real-time traffic anomaly detection result shows that there is abnormal traffic, the access to the drone airport server is intercepted and the data transmission is terminated. Otherwise, the data analysis step is entered.

[0013] Furthermore, in the cloud data center, based on the real-time drone airport data, a data anomaly analysis model is used to perform data anomaly analysis and alarm generation, including the following steps: In the cloud data center, an asymmetric decryption algorithm is used to decrypt the encrypted real-time drone airport data to obtain the decrypted real-time drone airport data; Write the decrypted real-time drone airport data into the corresponding OPC UA information model instance in the OPC UA server, and extract the real-time OPC UA information data of the OPC UA information model instance; Input the real-time OPC UA information data into the data anomaly analysis model, use the graph feature extraction module to extract the corresponding real-time graph features, and input the real-time graph features into the data anomaly analysis module; According to the real-time graph features, use the data anomaly analysis module to perform data anomaly analysis and obtain real-time data anomaly analysis results; If the result of the real-time data anomaly analysis is that abnormal data exists, a corresponding real-time abnormal data alarm is generated, and the real-time abnormal data alarm is returned to the corresponding drone airport server.

[0014] A drone airport data transmission system is used to implement a drone airport data transmission method, the system includes a cloud data center and a plurality of drone airport servers, and the plurality of drone airport servers are all connected to the cloud data center in communication; The drone airport server includes a network quality analysis unit and an encryption transmission unit connected in sequence; The cloud data center includes a model building unit, a traffic anomaly detection unit, and a data anomaly analysis unit which are connected in sequence.

[0015] The beneficial effects of the present invention are: The present invention discloses a data transmission method and system for a drone airport. The constructed network quality analysis model can monitor the quality of a network channel in real time, and automatically switch to an optimal network channel according to the analysis result, thereby ensuring the continuity and stability of data transmission, avoiding data transmission interruption, and improving data transmission efficiency. An asymmetric encryption algorithm is adopted to encrypt and transmit the real-time data of the drone airport, effectively preventing the leakage and tampering of data during transmission, and a traffic anomaly detection model is deployed in a cloud data center, which can collect access traffic data in real time, detect abnormal traffic, and intercept access to prevent network attacks, thereby providing additional security protection and improving data security. The OPC UA transmission scheme simplifies the data processing flow, improves data transmission and processing efficiency, and can quickly respond to the writing and query of real-time data to meet real-time requirements. The constructed data anomaly analysis model performs real-time anomaly analysis on the transmission data, and can generate a real-time alarm when abnormal data is detected, so as to timely discover data anomalies and provide timely feedback to the operation and maintenance personnel of the drone airport, thereby quickly responding to potential problems.

[0016] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the drone airport data transmission method of the present invention.

[0018] Figure 2 It is a structural block diagram of the drone airport data transmission system in the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0020] Embodiment 1: like Figure 1 As shown, this embodiment provides a method for transmitting data from a drone to an airport, comprising the following steps: S1: In the cloud data center, a network quality analysis model, a traffic anomaly detection model, and a data anomaly analysis model are constructed, and the network quality analysis model is deployed on the drone airport server, including the following steps: S1-1: In the cloud data center, a network quality analysis model is constructed based on a number of historical network channel data using a deep learning algorithm, and model metadata of the network quality analysis model is extracted; The network quality analysis model is constructed based on the Network Signal - Dynamic Analysis (NSDA) - Long Short-Term Memory (LSTM) algorithm, and the network quality analysis model includes a network signal dynamic module constructed based on the NSDA algorithm and a network quality analysis module constructed based on the LSTM algorithm, which are connected in sequence; The network signal dynamic module is provided with a signal quality dynamic function, a data transmission rate dynamic function, a network delay dynamic function and a network stability dynamic function. The network signal dynamic module is used to collect dynamic data related to network signal stability through dynamic functions to express the quality of network signals; the network quality analysis module predicts the characteristics of network signal dynamic data to realize network signal quality analysis; The formula is:

[0021] In the formula, is a dynamic function of signal quality; Calculate functions for signal quality indicators; is the received signal strength value;

[0022] In the formula, is a dynamic function of data transmission rate; The amount of data transferred; is the transmission time;

[0023] In the formula, is a dynamic function of network delay; is the sending time; is the signal propagation time; is the signal processing time;

[0024] In the formula, is the dynamic function of network stability; is the number of network interruptions; The total number of connection attempts; The network communication analysis dynamic sub-model includes a number of analysis weight setting modules and a network communication channel scoring module. The number of analysis weight setting modules are connected to the number of indicator dynamic sub-models one by one, and the number of analysis weight setting modules are connected to the network communication channel scoring module. The formula of the network communication channel scoring module is:

[0025] In the formula, Score network communication channels; All are scoring weights; S1-2: Based on some historical access traffic data, use deep learning algorithms to build a traffic anomaly detection model, and based on some historical drone airport data, use deep learning algorithms to build a data anomaly analysis model; The traffic anomaly detection model is built based on the Random Forest (RF)-Attention-Elman algorithm, and the traffic anomaly detection model includes a key feature extraction module built based on the RF algorithm, an attention weight module built based on the Attention mechanism, and a traffic anomaly detection module built based on the Elman algorithm, which are connected in sequence; The anomaly detection module uses the internal Classification and Regression Tree (CART) to filter the features of the input traffic data, which can process a large number of features and select the most stable and discriminative key features. The attention weight module uses the preset attention weight value to strengthen the influence of important components in the key features, so that the model can pay more attention to the features related to abnormal traffic and realize feature splicing. The traffic anomaly detection module uses a large amount of label data for pre-training, realizes the accurate prediction of the splicing features of the new input, and uses feedback connections to remember the previous state, enhancing the model's understanding of the dynamics of the time series. The traffic anomaly detection model combines the feature selection capability of random forest, the weight allocation capability of the Attention mechanism, and the processing capability of the Elman neural network for time series data, which can more effectively identify complex network attacks and abnormal behaviors. The data anomaly analysis model is constructed based on the Graph Convolutional Network (GCN)-Elman algorithm, and the data anomaly analysis model includes a graph feature extraction module constructed based on the GCN algorithm and a data anomaly analysis module constructed based on the Elman algorithm, which are connected in sequence; The graph feature extraction module can extract node features, edge features and graph features from graph structure data; the data anomaly analysis module makes predictions based on graph features; S1-3: sending the model metadata to all drone airport servers connected to the cloud data center, where a reconstructed network quality analysis model is generated based on the model metadata; S2: In the drone airport server, use the network quality analysis model to perform network quality analysis and channel switching on the network channel to obtain the final network channel, including the following steps: S2-1: In the drone airport server, real-time network channel data of the network channel for data transmission between the drone airport server and the cloud data center is collected; S2-2: input the real-time network channel data into the network quality analysis model, and use the network signal dynamic module to generate corresponding real-time network signal dynamic data; S2-3: input the real-time network signal dynamic data into the network quality analysis module, and use the network quality analysis module to extract the corresponding real-time network signal dynamic data features; S2-4: Perform network quality analysis based on the dynamic data characteristics of real-time network signals to obtain real-time network quality analysis results; S2-5: If the real-time network quality analysis result is that the network quality is poor, the current network channel is switched to obtain the final network channel; otherwise, the current network channel is used as the final network channel; S3: Based on the final network channel, the real-time drone airport data is encrypted and transmitted to the cloud data center using the Object Linking and Embedding for Process Control Unified Architecture (OPC UA) transmission scheme and asymmetric encryption algorithm, including the following steps: S3-1: Deploy the OPC UA server in the cloud data center and build the corresponding OPC UA information model based on the basic information of the drone airport, including the following steps: S3-1-1: Deploy the OPC UA server in the cloud data center and determine the requirements of the OPC UA information model based on the basic information of the drone airport, including the status of the drones in the drone airport (such as battery power, flight mode, location, etc.), health status (such as fault code, maintenance status, etc.), mission status (such as mission number, mission progress, mission results, etc.) and the environmental status of the airport (such as wind speed, temperature, weather conditions, etc.); S3-1-2: Defines the OPC UA object nodes of the OPC UA information model, including the Drone Airport object (as a top-level object, containing all drones and environmental information), the Drone object (representing a single drone, containing its status and properties), and the Environmental Monitoring object (containing environmental monitoring of the airport); S3-1-3: Create corresponding OPC UA variable nodes according to OPC UA object nodes, including drone status variables (such as BatteryLevel, FlightMode, GPSLocation, etc.), drone health status variables (such as ErrorCode, MaintenanceStatus, etc.), environment status variables (such as WindSpeed, Temperature, WeatherCondition, etc.) and mission status variables (such as MissionID, MissionProgress, MissionOutcome, etc.); S3-1-4: Add OPC UA attribute nodes to OPC UA object nodes, including drone attributes (such as Model, SerialNumber, MaxAltitude, etc.) and airport attributes (such as AirportID, Capacity, OperatingHours, etc.); S3-1-5: Implement OPC UA method nodes, including drone control methods (such as TakeOff, Land, ReturnToBase, etc.) and task assignment methods (such as AssignMission, AbortMission, etc.); S3-1-6: Establish an OPC UA reference relationship, including taking the drone object as a sub-object of the airport object, taking the drone state variable, health state variable and mission state variable as sub-variables of the drone object, taking the environmental monitoring object as a sub-object of the airport object, and obtaining the OPC UA information model; S3-2: According to the OPC UA information model, a corresponding OPC UA information model instance is created in the address space of the OPC UA server, including the following steps: S3-2-1: extracting information model metadata of the OPC UA information model, and sending the information model metadata to the OPC UA server; S3-2-2: Access the address space of the OPC UA server and build the architecture of the OPC UA information model instance according to the graph structure of the OPC UA information model; S3-2-3: write the node information of the OPC UA information model into the node corresponding to the architecture of the OPC UA information model instance, and create the corresponding OPC UA information model instance; S3-3: Collect real-time drone airport data of drone airports according to the OPC UA information model and store it in the drone airport server; S3-4: using an asymmetric encryption algorithm, encrypting the real-time drone airport data stored in the drone airport server to obtain encrypted real-time drone airport data; The formula is:

[0026] In the formula, It is encrypted real-time drone airport data; is an asymmetric encryption function; for real-time drone airport data; Airport Server for Drones asymmetric encryption keys; Indicates the amount of drone airport server; S3-5: Based on the final network channel, the encrypted real-time drone airport data is transmitted to the cloud data center using the OPC UA protocol; S4: In the cloud data center, based on the collected real-time access traffic data, a traffic anomaly detection model is used to perform traffic anomaly detection and access interception, including the following steps: S4-1: In the cloud data center, use the traffic probe to collect the real-time access traffic data of the drone airport server accessing the cloud data center; S4-2: input the real-time access traffic data into the traffic anomaly detection model, and use the key feature extraction module to extract several real-time key data features of the real-time access traffic data; S4-3: splicing a number of real-time key data features according to the attention weight value preset by the attention weight module to obtain a real-time splicing feature; S4-4: Based on the real-time splicing features, a traffic anomaly detection module is used to perform traffic anomaly detection to obtain a real-time traffic anomaly detection result; S4-5: If the real-time traffic anomaly detection result shows that there is abnormal traffic, the access to the drone airport server is intercepted and the data transmission is terminated. Otherwise, the data analysis step is entered; S5: In the cloud data center, based on the real-time drone airport data, the data anomaly analysis model is used to perform data anomaly analysis and alarm generation, including the following steps: S5-1: In the cloud data center, an asymmetric decryption algorithm is used to decrypt the encrypted real-time drone airport data to obtain the decrypted real-time drone airport data; The formula is:

[0027] In the formula, To provide decrypted real-time drone airport data; is an asymmetric decryption function; It is encrypted real-time drone airport data; Airport Server for Drones asymmetric decryption key; S5-2: writing the decrypted real-time drone airport data into the corresponding OPC UA information model instance in the OPC UA server, and extracting the real-time OPC UA information data in XML format from the OPC UA information model instance; XML is a widely used format, and there are many mature tools and libraries to process XML data. OPC UA clients can parse XML files and convert them back to OPC UA information models for further processing or display. At the same time, real-time OPC UA information data in XML format can represent the graph structure information of the corresponding OPC UA information model instance. The data anomaly analysis model built based on the GCN algorithm can accurately identify and process XML files. S5-3: input the real-time OPC UA information data into the data anomaly analysis model, use the graph feature extraction module to extract the corresponding real-time graph features, and input the real-time graph features into the data anomaly analysis module; S5-4: According to the real-time graph features, use the data anomaly analysis module to perform data anomaly analysis and obtain real-time data anomaly analysis results; S5-5: If the result of the real-time data anomaly analysis is that abnormal data exists, a corresponding real-time abnormal data alarm is generated, and the real-time abnormal data alarm is returned to the corresponding drone airport server.

[0028] Embodiment 2: like Figure 2 As shown, this embodiment provides a drone airport data transmission system for implementing a drone airport data transmission method, the system includes a cloud data center and a plurality of drone airport servers, and the plurality of drone airport servers are all communicatively connected with the cloud data center; The drone airport server includes a network quality analysis unit and an encryption transmission unit connected in sequence; A network quality analysis unit, used to use a network quality analysis model to perform network quality analysis and channel switching on a network channel to obtain a final network channel; An encryption transmission unit is used to encrypt and transmit the real-time drone airport data to the cloud data center based on the final network channel using the OPC UA transmission solution and asymmetric encryption algorithm; The cloud data center includes a model building unit, a traffic anomaly detection unit, and a data anomaly analysis unit connected in sequence; A model building unit, used to build a network quality analysis model, a traffic anomaly detection model, and a data anomaly analysis model in a cloud data center, and deploy the network quality analysis model on a drone airport server; A traffic anomaly detection unit is used to perform traffic anomaly detection and access interception in a cloud data center based on collected real-time access traffic data and using a traffic anomaly detection model; The data anomaly analysis unit is used to perform data anomaly analysis and alarm generation in a cloud data center based on real-time drone airport data using a data anomaly analysis model.

[0029] The present invention discloses a data transmission method and system for a drone airport. The constructed network quality analysis model can monitor the quality of a network channel in real time, and automatically switch to an optimal network channel according to the analysis result, thereby ensuring the continuity and stability of data transmission, avoiding data transmission interruption, and improving data transmission efficiency. An asymmetric encryption algorithm is adopted to encrypt and transmit the real-time data of the drone airport, effectively preventing the leakage and tampering of data during transmission, and a traffic anomaly detection model is deployed in a cloud data center, which can collect access traffic data in real time, detect abnormal traffic, and intercept access to prevent network attacks, thereby providing additional security protection and improving data security. The OPC UA transmission scheme realizes standardized conversion between different data sources, simplifies the data processing flow, improves processing efficiency, and can quickly respond to the writing and query of real-time data to meet real-time requirements. The constructed data anomaly analysis model performs real-time anomaly analysis on the transmission data, and can generate a real-time alarm when abnormal data is detected, so as to timely discover data anomalies and provide timely feedback to the operation and maintenance personnel of the drone airport, thereby quickly responding to potential problems.

[0030] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for transmitting data from an unmanned aerial vehicle airport, characterized in that: The steps include: In the cloud data center, a network quality analysis model, a traffic anomaly detection model, and a data anomaly analysis model are constructed, and the network quality analysis model is deployed on the drone airport server; In the drone airport server, the network quality analysis model is used to perform network quality analysis and channel switching on the network channel to obtain the final network channel; Based on the final network channel, the real-time drone airport data is encrypted and transmitted to the cloud data center using the OPC UA transmission solution and asymmetric encryption algorithm; In the cloud data center, based on the collected real-time access traffic data, the traffic anomaly detection model is used to perform traffic anomaly detection and access interception; In the cloud data center, data anomaly analysis and alarm generation are performed based on real-time drone airport data using a data anomaly analysis model.

2. A method for transmitting data at an unmanned aerial vehicle airport according to claim 1, characterized in that: In the cloud data center, a network quality analysis model, a traffic anomaly detection model, and a data anomaly analysis model are constructed, and the network quality analysis model is deployed on the drone airport server, including the following steps: In the cloud data center, a network quality analysis model is constructed based on a number of historical network channel data using a deep learning algorithm, and model metadata of the network quality analysis model is extracted; Based on some historical access traffic data, a deep learning algorithm is used to build a traffic anomaly detection model, and based on some historical drone airport data, a deep learning algorithm is used to build a data anomaly analysis model; The model metadata is sent to all drone airport servers connected to the cloud data center, where a reconstructed network quality analysis model is generated based on the model metadata.

3. A method for transmitting data at an unmanned aerial vehicle airport according to claim 2, characterized in that: The network quality analysis model is constructed based on the NSDA-LSTM algorithm, and the network quality analysis model includes a network signal dynamic module constructed based on the NSDA algorithm and a network quality analysis module constructed based on the LSTM algorithm, which are connected in sequence.

4. The method for transmitting data from an unmanned aerial vehicle airport according to claim 2, characterized in that: The traffic anomaly detection model is constructed based on the RF-Attention-Elman algorithm, and the traffic anomaly detection model includes a key feature extraction module constructed based on the RF algorithm, an attention weight module constructed based on the Attention mechanism, and a traffic anomaly detection module constructed based on the Elman algorithm, which are connected in sequence.

5. The method for transmitting data from an unmanned aerial vehicle airport according to claim 2, characterized in that: The data anomaly analysis model is constructed based on the GCN-Elman algorithm, and the data anomaly analysis model includes a graph feature extraction module constructed based on the GCN algorithm and a data anomaly analysis module constructed based on the Elman algorithm, which are connected in sequence.

6. A method for transmitting data at an unmanned aerial vehicle airport according to claim 4, characterized in that: In the drone airport server, the network quality analysis model is used to perform network quality analysis and channel switching on the network channel to obtain the final network channel, including the following steps: In the drone airport server, real-time network channel data of the network channel for data transmission between the drone airport server and the cloud data center is collected; Input the real-time network channel data into the network quality analysis model, and use the network signal dynamic module to generate the corresponding real-time network signal dynamic data; Input the real-time network signal dynamic data into the network quality analysis module, and use the network quality analysis module to extract the corresponding real-time network signal dynamic data features; According to the dynamic data characteristics of real-time network signals, network quality analysis is performed to obtain real-time network quality analysis results; If the real-time network quality analysis result is that the network quality is poor, the current network channel is switched to obtain the final network channel; otherwise, the current network channel is used as the final network channel.

7. A method for transmitting data at an unmanned aerial vehicle airport according to claim 6, characterized in that: Based on the final network channel, the real-time drone airport data is encrypted and transmitted to the cloud data center using the OPC UA transmission solution and asymmetric encryption algorithm, including the following steps: Deploy the OPC UA server in the cloud data center and build the corresponding OPC UA information model based on the basic information of the drone airport; According to the OPC UA information model, a corresponding OPC UA information model instance is created in the address space of the OPC UA server; According to the OPC UA information model, collect real-time drone airport data of drone airports and store it in the drone airport server; Using an asymmetric encryption algorithm, encrypt the real-time drone airport data stored in the drone airport server to obtain the encrypted real-time drone airport data; Based on the final network channel, the encrypted real-time drone airport data is transmitted to the cloud data center using the OPC UA protocol.

8. The method for transmitting data at an unmanned aerial vehicle airport according to claim 4, characterized in that: In the cloud data center, based on the collected real-time access traffic data, the traffic anomaly detection model is used to perform traffic anomaly detection and access interception, including the following steps: In the cloud data center, traffic probes are used to collect real-time access traffic data from drone airport servers to the cloud data center; The real-time access traffic data is input into the traffic anomaly detection model, and a key feature extraction module is used to extract several real-time key data features of the real-time access traffic data; According to the attention weight value preset by the attention weight module, a plurality of real-time key data features are spliced ​​to obtain a real-time spliced ​​feature; According to the real-time splicing features, the traffic anomaly detection module is used to perform traffic anomaly detection and obtain real-time traffic anomaly detection results; If the real-time traffic anomaly detection result shows that there is abnormal traffic, the access to the drone airport server is intercepted and the data transmission is terminated. Otherwise, the data analysis step is entered.

9. The method for transmitting data from an unmanned aerial vehicle airport according to claim 7, characterized in that: In the cloud data center, based on the real-time drone airport data, the data anomaly analysis model is used to perform data anomaly analysis and alarm generation, including the following steps: In the cloud data center, an asymmetric decryption algorithm is used to decrypt the encrypted real-time drone airport data to obtain the decrypted real-time drone airport data; Write the decrypted real-time drone airport data into the corresponding OPC UA information model instance in the OPC UA server, and extract the real-time OPC UA information data of the OPC UA information model instance; Input the real-time OPC UA information data into the data anomaly analysis model, use the graph feature extraction module to extract the corresponding real-time graph features, and input the real-time graph features into the data anomaly analysis module; According to the real-time graph features, use the data anomaly analysis module to perform data anomaly analysis and obtain real-time data anomaly analysis results; If the result of the real-time data anomaly analysis is that abnormal data exists, a corresponding real-time abnormal data alarm is generated, and the real-time abnormal data alarm is returned to the corresponding drone airport server.

10. A drone airport data transmission system, used to implement the drone airport data transmission method according to any one of claims 1 to 9, characterized in that: The system includes a cloud data center and a plurality of drone airport servers, wherein the plurality of drone airport servers are all communicatively connected to the cloud data center; The drone airport server includes a network quality analysis unit and an encryption transmission unit connected in sequence; The cloud data center includes a model building unit, a traffic anomaly detection unit and a data anomaly analysis unit which are connected in sequence.

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