Airplane nationality number identification method, device and equipment

Abstract

The invention relates to the field of image recognition, and provides an aircraft nationality number recognition method, device and equipment, and the method comprises the steps: obtaining a video stream, and carrying out the preprocessing of each frame of image in the video stream; performing multi-model reasoning on the preprocessed video stream to obtain a nationality number recognition result; and determining whether an abnormal condition exists according to the nationality number identification result, and if the abnormal condition exists, triggering an alarm mechanism. According to the method, the problem of poor recognition performance in a complex scene in the prior art is solved, and efficient and accurate recognition of the nationality number of the airplane is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to a method, device and equipment for recognizing the nationality number of an aircraft. Background Art

[0002] In recent years, with the rapid development of computer vision and deep learning technologies, significant progress has been made in the field of image and video processing. These technologies are widely used in various recognition and detection tasks, greatly improving the level of automation and intelligence. Especially with the rapid development of embedded systems, small devices with high-performance computing capabilities such as the NVIDIA Jetson series have made it possible to run complex deep learning models in edge computing environments. This has opened up new paths for a variety of real-time processing applications including security monitoring, autonomous driving, drone navigation, etc.

[0003] Although computer vision and deep learning technologies have made great progress, single computer vision technology still has the defect of low accuracy in image recognition. Summary of the invention

[0004] The present invention provides a method, device and equipment for identifying the nationality number of an aircraft, which solves the problem of poor recognition performance in complex scenarios in the prior art and achieves efficient and accurate recognition of the nationality number of the aircraft.

[0005] The present invention provides a method for identifying an aircraft nationality number, comprising the following steps: Acquire a video stream, and preprocess each frame of the video stream; Perform multi-model inference on the preprocessed video stream to obtain the nationality number recognition result; Determine whether there is an abnormal situation based on the nationality number recognition result, and trigger an alarm mechanism if there is an abnormal situation.

[0006] According to a method for identifying an aircraft nationality number provided by the present invention, the preprocessed video stream is subjected to multi-model inference to obtain a nationality number recognition result, which specifically includes: inputting the preprocessed video stream into a trained target detection model to obtain a nationality number position recognition result; the trained target detection model is trained according to a training set, and the training set is image data marked with the aircraft and its nationality number position; based on the nationality number position recognition result, character recognition is performed on the detected nationality number position; and the character recognition result is filtered and corrected to obtain a final nationality number recognition result.

[0007] According to a method for identifying the nationality number of an aircraft provided by the present invention, a trained target detection model is trained according to a training set, specifically comprising: acquiring image data containing an aircraft, marking the image data, and obtaining image data with the position of the aircraft and its nationality number marked; dividing the marked image data into a training set and a verification set; acquiring a target detection model, and initializing the training parameters of the target detection model; loading the training set into a training framework, starting a training process, and allowing the target detection model to gradually optimize the training parameters by continuously learning the images and label information in the training set; during the training process, periodically using the verification set to perform performance evaluation on the target detection model, and continuing to iteratively optimize the target detection model according to the performance evaluation result; when the performance evaluation result reaches a preset condition, outputting the trained target detection model.

[0008] According to a method for identifying the nationality number of an aircraft provided by the present invention, the preprocessing of each frame image in the video stream specifically includes: denoising each frame image in the video stream; enhancing contrast of the denoised image; and resizing the contrast-enhanced image to obtain a preprocessed video stream.

[0009] According to a method for identifying an aircraft nationality number provided by the present invention, determining whether there is an abnormal condition based on the nationality number identification result specifically includes: comparing the nationality number identification result with a preset legal nationality number database to determine whether the identified nationality number is in the legal nationality number database; if the identified nationality number is not in the legal nationality number database, or the identification result is fuzzy and cannot be clearly identified, it is determined that an abnormal condition exists.

[0010] According to a method for identifying an aircraft nationality number provided by the present invention, after triggering an alarm mechanism, the method further includes: asynchronously saving scene images detected as violations; compressing the scene images by using image compression technology, and storing the compressed scene images.

[0011] The present invention also provides an aircraft nationality number identification device, comprising the following modules: A preprocessing module, used for acquiring a video stream and preprocessing each frame of the video stream; The nationality number recognition module is used to perform multi-model reasoning on the pre-processed video stream to obtain the nationality number recognition result; The abnormal alarm module is used to determine whether there is an abnormal situation according to the nationality number recognition result, and trigger an alarm mechanism if there is an abnormal situation.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for identifying the nationality number of an aircraft as described above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for identifying the nationality number of an aircraft as described in any one of the above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for identifying the nationality number of an aircraft as described above is implemented.

[0015] The invention provides an aircraft nationality number recognition method, device and equipment, which have the following beneficial effects: through multi-model reasoning, the aircraft nationality number in the video stream can be efficiently recognized, the processing time can be reduced, the real-time performance of the system can be improved, the aircraft nationality number can be accurately recognized, the false alarm rate can be reduced, and the detection accuracy can be improved. Abnormal situations in the recognition process can be detected in real time, such as failure to recognize the nationality number or detection of non-aircraft objects, and an alarm mechanism can be triggered to ensure timely response and handling of potential problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is one of the flow charts of the aircraft nationality number identification method provided by the present invention.

[0018] Figure 2 This is the second flow chart of the aircraft nationality number identification method provided by the present invention.

[0019] Figure 3 The present invention is a structural illustration of an aircraft nationality number recognition system.

[0020] Figure 4 The present invention is a schematic diagram of the structure of the aircraft nationality number identification device provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Some concepts involved in the present invention are described below.

[0024] NVIDIA Jetson Platform: NVIDIA Jetson is a series of high-performance embedded platforms designed for running deep learning and artificial intelligence applications. The Jetson series includes multiple models, such as Jetson Nano, Jetson TX2, and Jetson Xavier NX. These devices integrate GPU (graphics processing unit), CPU (central processing unit) and other necessary hardware resources to provide powerful computing capabilities in power-constrained environments.

[0025] DeepStream SDK: NVIDIA DeepStream SDK is a stream processing toolkit for video analytics, designed for the Jetson platform and NVIDIA GPUs. DeepStream supports a variety of input sources, such as cameras, video files, RTSP streams, etc., and can efficiently perform deep learning inference tasks. Its architecture is based on the GStreamer streaming framework, which can easily integrate and deploy various video analysis pipelines. DeepStream SDK provides rich functions, including object detection, classification, tracking, etc., and supports seamless integration with NVIDIA deep learning libraries such as TensorRT and cuDNN, significantly improving the speed and efficiency of model inference. With DeepStream, users can easily build, optimize and deploy complex video analysis applications.

[0026] The existing technology has shortcomings such as difficulty in ensuring real-time detection, poor recognition performance in complex scenarios, and slow regular performance evaluation and model updating.

[0027] In view of the difficulty in ensuring real-time detection, the present invention selects a high-performance Jetson terminal and optimizes its hardware configuration to perform hardware optimization. By using lightweight models (such as Tiny YOLO and MobileNetV2), real-time performance is improved. Through data cleaning and model optimization, the false alarm rate is reduced and the detection accuracy is improved.

[0028] To address the problem of poor recognition performance in complex scenarios, the present invention uses data enhancement techniques (such as rotation, scaling, and color adjustment) when training the model to enhance the robustness of the model and improve its performance under different weather and lighting conditions.

[0029] In view of the slow performance evaluation and model update, the present invention optimizes the model and algorithm by regularly updating the model and algorithm according to the latest data and scene changes. The continuous learning method is adopted to enable the model to continuously learn and adapt to new environments and challenges.

[0030] Combine the following Figure 1-Figure 5 Embodiments of the present invention are described in detail.

[0031] Figure 1 FIG. 1 is one of the flow charts of the method for identifying the nationality number of an aircraft provided by the present invention. Figure 1 As shown, the method comprises the following steps: S110: Obtain a video stream, and pre-process each frame of the video stream.

[0032] Specifically, in terms of hardware deployment, the present invention can use GPU acceleration, which is helpful to improve the speed of target detection, especially if the deep learning model is complex. Use high-performance cameras to ensure the acquisition of high-quality images, thereby improving detection accuracy. Use devices with edge computing capabilities to reduce the performance consumption of central servers, reduce the time consumption caused by network transmission, and improve the real-time performance of the system.

[0033] When placing cameras, ensure that the camera position and angle are optimal to cover the monitoring area and minimize blind spots. Multiple cameras can be used to increase the monitoring range and accuracy. Figure 2 The figure shows a flow chart of the method implemented by the present invention. The DeepStream framework is used to pull the stream from the camera to achieve real-time acquisition of the video stream.

[0034] According to a method for identifying the nationality number of an aircraft provided by the present invention, each frame image in a video stream is preprocessed, specifically including: denoising each frame image in the video stream; enhancing contrast of the denoised image; and resizing the contrast-enhanced image to obtain a preprocessed video stream.

[0035] Specifically, further control and enhancement of image quality are added to ensure the best image quality for the input model. Subsequent AI detection processing is performed for each frame of image. Preprocessing is performed for each acquired frame of image, including but not limited to denoising, contrast enhancement, etc., to improve the accuracy of subsequent detection and recognition.

[0036] S120, performing multi-model reasoning on the preprocessed video stream to obtain a nationality number recognition result.

[0037] Specifically, in practical applications, a single model often cannot meet all requirements, so multi-model reasoning becomes an effective solution. By combining multiple deep learning models, more sophisticated and comprehensive analysis can be achieved. For example, in an aircraft nationality number recognition system, one model can be used to detect the aircraft first, and then another model can be used to recognize the nationality number. The implementation of multi-model reasoning requires solving problems such as communication between models, data preprocessing and post-processing to ensure the efficient operation of the entire system.

[0038] According to a method for identifying the nationality number of an aircraft provided by the present invention, a preprocessed video stream is subjected to multi-model reasoning to obtain a nationality number recognition result, which specifically includes: inputting the preprocessed video stream into a trained target detection model to obtain a nationality number position recognition result; the trained target detection model is trained according to a training set, and the training set is image data marked with the aircraft and its nationality number position; based on the nationality number position recognition result, character recognition is performed on the detected nationality number position; the character recognition result is filtered and corrected to obtain a final nationality number recognition result.

[0039] Specifically, aircraft nationality number recognition is an application of great practical significance, which is widely used in airport management, air traffic control, border security and other fields. The nationality number is usually displayed on the aircraft fuselage in the form of text or numbers, so this type of recognition task involves two key links: target detection and character recognition.

[0040] By calling the camera interface, we can get real-time image information and consider using streaming media technology to more effectively transmit real-time video data in a distributed system. In the current image, we use YOLO target detection technology to identify the location of the nationality number; we use OCR (Optical Character Recognition) to perform secondary text recognition on the location of the nationality number; we filter the recognition results to get the final nationality number.

[0041] Specifically, first, locate the position of the aircraft in the image or video frame and the position of the nationality number through the target detection model. Apply the deep learning model to perform multi-task detection on each frame of the image, including aircraft recognition, nationality number detection, and extraction of other relevant features. Use lightweight models (such as MobileNet, EfficientNet) to improve real-time performance, especially in the resource-constrained environment of Jetson devices. Use more advanced deep learning models to improve detection accuracy. Advanced target detection models include YOLO (YouOnlyLookOnce), SSD (SingleShotMultiboxDetector), etc. Apply post-processing techniques such as non-maximum suppression (NMS) on the model output to filter redundant detection boxes.

[0042] Then, character recognition is performed on the detected aircraft area to extract the nationality number information. Specifically, the OCR (Optical Character Recognition) model is applied to the detected aircraft area to identify the nationality number. Using a specific optimized OCR model, the text output by the OCR model is processed and corrected to ensure that the nationality number can be accurately identified under different shooting angles and lighting conditions, and the final nationality number recognition result is obtained.

[0043] By running the DeepStream framework on the Jetson platform and combining multiple deep learning models, an efficient aircraft nationality number recognition system can be built. The system can process video streams in real time, accurately identify and record aircraft nationality number information, and provide technical support for related applications.

[0044] According to a method for identifying the nationality number of an aircraft provided by the present invention, a trained target detection model is trained according to a training set, specifically comprising: acquiring image data containing an aircraft, marking the image data, and obtaining image data with the position of the aircraft and its nationality number marked; dividing the marked image data into a training set and a validation set; acquiring a target detection model, and initializing the training parameters of the target detection model; loading the training set into a training framework, starting a training process, and allowing the target detection model to gradually optimize the training parameters by continuously learning the images and label information in the training set; during the training process, regularly using the validation set to perform performance evaluation on the target detection model, and continuing to iteratively optimize the target detection model according to the performance evaluation result; when the performance evaluation result reaches a preset condition, outputting the trained target detection model.

[0045] Specifically, first, it is necessary to obtain image data containing aircraft. This data can be obtained from various sources, such as public aviation image databases, airport surveillance cameras, etc. Then, the acquired image data is labeled. The labeling process includes identifying the aircraft in the image and further marking the specific location of the aircraft nationality number. This step requires professional labeling personnel or the use of special labeling tools to complete.

[0046] The labeled image data is divided into a training set and a validation set. The training set is used to train the object detection model, while the validation set is used to evaluate the performance of the model during the training process.

[0047] Get an object detection model. This model can be an existing model that already has some detection capabilities, or a model built from scratch. Initialize the training parameters of the object detection model. These parameters include learning rate, batch size, number of iterations, etc.

[0048] Load the training set into the training framework and start the training process. During the training process, the object detection model will continuously learn the image and label information in the training set, and gradually optimize its internal weights and bias parameters through the back propagation algorithm. Use the validation set to evaluate the performance of the object detection model regularly, and achieve performance evaluation by calculating the accuracy, recall rate, F1 score and other indicators of the model on the validation set. The results of the performance evaluation can reflect the effect of the model in the current training stage.

[0049] According to the performance evaluation results, the target detection model is iteratively optimized. If the evaluation results are not ideal, the performance of the model can be improved by adjusting the training parameters, improving the model structure, or increasing the training data. When the performance evaluation results meet the preset conditions (such as the accuracy reaches a certain threshold or the training time reaches the preset upper limit), the trained target detection model is output. This model already has the ability to identify aircraft and their nationality numbers, and can be applied to the actual aircraft nationality number recognition task.

[0050] S130. Determine whether there is an abnormal situation based on the nationality number recognition result, and trigger an alarm mechanism if there is an abnormal situation.

[0051] According to a method for identifying an aircraft nationality number provided by the present invention, whether an abnormal condition exists is determined based on the nationality number identification result, specifically including: comparing the nationality number identification result with a preset legal nationality number database to determine whether the identified nationality number is in the legal nationality number database; if the identified nationality number is not in the legal nationality number database, or the identification result is fuzzy and cannot be clearly identified, it is determined that an abnormal condition exists.

[0052] Specifically, timely alarms are issued for abnormal situations during the identification process (such as failure to identify the nationality number, detection of non-aircraft objects, etc.). Integrate real-time notification systems, such as SMS, email or instant messaging, and other possible communication channels to issue emergency alarms and notify relevant personnel for detected emergencies (such as possible fires, etc.).

[0053] According to an aircraft nationality number recognition method provided by the present invention, after the alarm mechanism is triggered, the scene image detected as a violation is asynchronously saved; the scene image is compressed by image compression technology, and the compressed scene image is stored.

[0054] Specifically, for scenes detected as violations (such as unauthorized aircraft, etc.), images are captured and saved. Asynchronous image saving is implemented to ensure that real-time detection performance is not affected. Image compression is used to save storage space. For saved images and detection data, effective management and storage are performed. Image compression technology is used to reduce storage space usage.

[0055] In terms of performance optimization, ensure that the system hardware (such as Jetson AGX Xavier or Jetson Nano) meets the computing requirements of real-time detection. Use data augmentation technology to enhance the model's adaptability to different weather and lighting conditions. Regularly update the model and optimize the algorithm to adapt to scene changes and improve performance.

[0056] like Figure 3 The system structure diagram of the present invention is shown. It includes: 1. Hardware equipment: jetson terminal, camera; 2. Software system: image processing module; AI processing module; aircraft different area detection; OCR recognition module; picture and log saving module.

[0057] The aircraft nationality number recognition device provided by the present invention is described below. The aircraft nationality number recognition device described below and the aircraft nationality number recognition method described above can be referenced to each other.

[0058] like Figure 4 The present invention provides an aircraft nationality number identification device, comprising: The preprocessing module 410 is used to obtain the video stream and preprocess each frame of the video stream; The nationality number recognition module 420 is used to perform multi-model reasoning on the pre-processed video stream to obtain a nationality number recognition result; The abnormal alarm module 430 is used to determine whether there is an abnormal situation according to the nationality number recognition result, and trigger an alarm mechanism if there is an abnormal situation.

[0059] Figure 5An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communications interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the aircraft nationality number recognition method, which includes: obtaining a video stream, preprocessing each frame of the video stream; performing multi-model reasoning on the preprocessed video stream to obtain a nationality number recognition result; determining whether there is an abnormal condition according to the nationality number recognition result, and triggering an alarm mechanism if there is an abnormal condition.

[0060] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0061] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the aircraft nationality number identification method provided by the above-mentioned methods, and the method includes: obtaining a video stream and preprocessing each frame image in the video stream; performing multi-model reasoning on the preprocessed video stream to obtain a nationality number identification result; determining whether there is an abnormal condition based on the nationality number identification result, and triggering an alarm mechanism if an abnormal condition exists.

[0062] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the aircraft nationality number identification method provided by the above-mentioned methods, the method comprising: acquiring a video stream, and preprocessing each frame image in the video stream; performing multi-model reasoning on the preprocessed video stream to obtain a nationality number identification result; determining whether there is an abnormal condition based on the nationality number identification result, and triggering an alarm mechanism if there is an abnormal condition.

[0063] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0064] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying an aircraft nationality number, characterized in that: include: Acquire a video stream, and preprocess each frame of the video stream; Perform multi-model inference on the preprocessed video stream to obtain the nationality number recognition result; Determine whether there is an abnormal situation based on the nationality number recognition result, and trigger an alarm mechanism if there is an abnormal situation.

2. The method for identifying the aircraft nationality number according to claim 1, characterized in that: The pre-processed video stream is subjected to multi-model reasoning to obtain the nationality number recognition result, which specifically includes: Inputting the preprocessed video stream into a trained target detection model to obtain a nationality number position recognition result; the trained target detection model is trained based on a training set, and the training set is image data marked with aircraft and nationality number positions; Based on the nationality number position recognition result, performing character recognition on the detected nationality number position; The character recognition results are filtered and corrected to obtain the final nationality number recognition results.

3. The method for identifying the aircraft nationality number according to claim 2, characterized in that: The trained target detection model is trained according to the training set, including: Acquire image data containing an aircraft, mark the image data, and obtain image data with the position of the aircraft and its nationality number marked; Divide the labeled image data into training set and validation set; Obtaining a target detection model and initializing training parameters of the target detection model; Loading the training set into a training framework, starting a training process, and allowing the object detection model to gradually optimize the training parameters by continuously learning the image and label information in the training set; During the training process, the validation set is used to periodically perform performance evaluation on the target detection model, and the target detection model is continuously iteratively optimized according to the performance evaluation result; When the performance evaluation result meets the preset conditions, the trained target detection model is output.

4. The method for identifying the aircraft nationality number according to claim 1, characterized in that: The preprocessing of each frame of the video stream specifically includes: Perform denoising on each frame of the video stream; Perform contrast enhancement on the denoised image; The contrast-enhanced image is resized to obtain a preprocessed video stream.

5. The method for identifying the aircraft nationality number according to claim 1, characterized in that: The determining whether there is an abnormality according to the nationality number recognition result specifically includes: Compare the nationality number recognition result with the preset legal nationality number database to determine whether the recognized nationality number is in the legal nationality number database; If the identified nationality number is not in the legal nationality number database, or the identification result is fuzzy and cannot be clearly identified, it is determined to be an abnormal situation.

6. The method for identifying the aircraft nationality number according to claim 1, characterized in that: After the alarm mechanism is triggered, the method further comprises: For scene images detected as violations, save them asynchronously; The scene image is compressed by using image compression technology, and the compressed scene image is stored.

7. An aircraft nationality number recognition device, characterized in that: include: A preprocessing module, used to obtain a video stream and preprocess each frame of the video stream; The nationality number recognition module is used to perform multi-model reasoning on the pre-processed video stream to obtain the nationality number recognition result; The abnormal alarm module is used to determine whether there is an abnormal situation according to the nationality number recognition result, and trigger an alarm mechanism if there is an abnormal situation.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for identifying the aircraft nationality number according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying the nationality number of an aircraft as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for identifying the nationality number of an aircraft as claimed in any one of claims 1 to 6 is implemented.

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