A UAV flight status classification method, device and storage medium

Abstract

The present invention relates to the field of intelligent transportation technology, and more particularly to a method, apparatus, device, and computer storage medium for classifying the flight status of unmanned aerial vehicles (UAVs). This invention reduces interference from receiver hardware and the complex electromagnetic environment in which UAV communication radio frequency signals are received, as well as the impact of the black-box nature of neural network models inherent in RF-based UAV detection and identification methods on state recognition accuracy. This invention provides a reliable Bayesian causal reasoning-enhanced method for UAV target state recognition, providing a high-precision theoretical approach for low-altitude, three-dimensional traffic UAV detection and identification systems in complex urban environments. This method improves the accuracy of state classification and recognition.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method, device, equipment and computer storage medium for classifying the flight status of an unmanned aerial vehicle (UAV). Background Art

[0002] The development of IoT technology has made smart urban transportation possible. Drones, with their flexibility, adaptability, and safety, are currently generating enormous economic and social benefits. Many countries have already or plan to liberalize low-altitude airspace, providing policy support for drone applications in logistics, social governance, emergency rescue, and other scenarios. Low-altitude airspace is tightly coupled with ground transportation in both time and space, and elements are interwoven in the physical domain, forming a three-dimensional low-altitude transportation network. With the booming development of low-altitude transportation, accompanying threats and risks have also emerged. The emergence of malicious unauthorized drone flights has severely impacted the normal operation of low-altitude transportation and threatened public safety. Therefore, the development of a rapid and effective drone defense system is imperative. The first step in such a system is to detect drone targets and accurately identify their status.

[0003] In drone detection and identification systems, the following four methods are currently commonly used: acoustic, optical, radar, and radio frequency based methods.

[0004] Compared to acoustic and optical methods, RF detection is more stable and reliable. In urban environments, where the electromagnetic environment is complex, using active radar for detection can impact broadcasts and aircraft communications. RF detection, being a passive detection method, does not have these side effects. Therefore, in low-altitude, three-dimensional transportation networks, the drone detection and identification module of a drone defense system is more suitable for using RF-based methods to identify and classify drone patterns. The accuracy of this method for drone pattern recognition is primarily determined by the accuracy of machine learning or neural network classification. In low-altitude, three-dimensional transportation, RF-based drone identification and classification methods face the following three challenges. First, in complex electromagnetic environments, while this method does not affect the communication of other radio equipment, it is susceptible to electromagnetic interference from other devices and other uncertainties, affecting recognition accuracy. Second, drones can operate in two modes: hovering and flying without image transmission. These two modes of the same drone do not show significant differences in the frequency spectrum, which significantly affects recognition accuracy. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem of low accuracy in classification of UAV flight status in the prior art.

[0006] To solve the above technical problems, the present invention provides a method for classifying the flight status of a UAV, comprising:

[0007] Collect UAV radio frequency signal data in different scenarios, perform preprocessing and time-frequency conversion to obtain UAV spectrum data;

[0008] The uncertainty factors in the acquisition process of UAV RF signal data are modeled offline, and the distribution of uncertainty factors in the acquisition process of UAV RF signal data is estimated based on the uncertainty factor model;

[0009] Based on the impact of drones on communication quality under different flight states and combined with domain prior knowledge, a causal chain of drone radio frequency signals is constructed, and the causal chain of drone radio frequency signals is trained based on the drone spectrum data and the distribution of uncertain factors to obtain conditional probability distribution parameters;

[0010] Acquire drone spectrum data in real time and input it into the drone flight status classification neural network model to obtain the drone flight status classification results;

[0011] The drone flight state classification result is corrected based on the drone radio frequency signal causal chain and the conditional probability distribution parameters to obtain a target classification result.

[0012] Preferably, collecting drone radio frequency signal data in different scenarios includes:

[0013] Within the preset frequency band, radio signals larger than the preset bandwidth are collected from drones of different manufacturers and models in environments with no drone RF background, when the drone is turned on and connected, when the drone is hovering, when the drone is flying but not transmitting image data, and when the drone is flying and transmitting image data, to obtain drone RF signal data.

[0014] Preferably, the preprocessing and time-frequency conversion include

[0015] Performing denoising and decoupling processing on the UAV radio frequency signal data to obtain a desired UAV communication signal;

[0016] The desired UAV communication signal is transformed into a time-frequency signal using discrete Fourier transform, and its phase characteristics are ignored, while only its amplitude characteristics are retained to obtain the UAV spectrum data.

[0017] Preferably, the estimating the distribution of uncertainties in the acquisition process of the UAV radio frequency signal data based on the uncertainty factor model includes:

[0018] Based on the uncertainty factor model, the agent-based modeling and simulation method is used to obtain the initial distribution of uncertainty factors according to domain prior knowledge. Combined with the Monte Carlo simulation method, the uncertainty factor distribution of UAV RF signal data during the acquisition process is estimated through multiple iterations.

[0019] Preferably, the construction of a causal chain of UAV radio frequency signals based on the impact of UAVs on communication quality under different flight states and in combination with prior knowledge of the domain includes:

[0020] For the UAV RF signal data, the standard deviation and kurtosis are selected as causal nodes in the time domain, and the average value is selected as the causal node in the frequency domain;

[0021] Regarding communication quality, the communication signal-to-noise ratio, synchronization error, and co-frequency interference are selected as causal nodes;

[0022] Combined with domain prior knowledge, causal nodes are connected to build a causal chain of drone RF signals.

[0023] Preferably, the correcting the UAV flight state classification result based on the UAV radio frequency signal causal chain and the conditional probability distribution parameters to obtain the target classification result includes:

[0024] Based on the causal chain of the UAV radio frequency signal and the conditional probability distribution parameters, a Bayesian network model is used to perform causal reasoning analysis and correction on the UAV flight state classification result.

[0025] Preferably, when the UAV flight state classification neural network model is a multi-channel convolutional neural network model, the UAV spectrum data is evenly divided according to the bandwidth and then input into the channel convolutional neural network model in parallel;

[0026] When the UAV flight state classification neural network model is a long short-term memory neural network model, the UAV spectrum data is normalized and then input into the long short-term memory neural network model;

[0027] When the UAV flight status classification neural network model is a machine learning model, the UAV spectrum data is subjected to feature extraction and then input into the machine learning model.

[0028] The present invention also provides a UAV flight status classification device, comprising:

[0029] The data acquisition module is used to collect UAV radio frequency signal data in different scenarios, and perform preprocessing and time-frequency conversion to obtain UAV spectrum data;

[0030] The uncertainty factor acquisition module is used to perform offline modeling of the uncertainty factors in the acquisition process of the UAV RF signal data, and estimate the distribution of the uncertainty factors in the acquisition process of the UAV RF signal data based on the uncertainty factor model;

[0031] A causal chain construction module is used to construct a causal chain of drone radio frequency signals based on the impact of drones on communication quality under different flight states and in combination with domain prior knowledge, and to train the causal chain of drone radio frequency signals based on the drone spectrum data and the distribution of uncertain factors to obtain conditional probability distribution parameters;

[0032] The state classification module is used to obtain UAV spectrum data in real time and input it into the UAV flight state classification neural network model to obtain the UAV flight state classification results;

[0033] A result correction module is used to correct the UAV flight status classification result based on the UAV radio frequency signal causal chain and the conditional probability distribution parameters to obtain a target classification result.

[0034] The present invention also provides a UAV flight status classification device, comprising:

[0035] memory for storing computer programs;

[0036] The processor is used to implement the steps of the above-mentioned method for classifying the flight status of a drone when executing the computer program.

[0037] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for classifying the flight status of a drone are implemented.

[0038] The above technical solution of the present invention has the following advantages over the prior art:

[0039] The UAV flight state classification method described in the present invention first collects and preprocesses spectrum signals to construct a spectrum data set, then selects uncertainty factors and performs offline training to obtain parameter distribution, and constructs a causal chain for flight state classification, which is discretized and trained. Finally, based on the neural network classification, the category that needs further correction is selected and corrected according to the constructed causal chain and model parameters. The present invention reduces the interference of the receiver hardware and the complex electromagnetic environment in which the UAV communication radio frequency signal is received during the reception process, as well as the influence of the black box nature of the neural network model used in the radio frequency-based UAV detection and identification method on the state recognition accuracy. The method provides a reliable Bayesian causal reasoning-enhanced UAV target state recognition method for a high-precision recognition theoretical method for low-altitude three-dimensional traffic UAV detection and identification system in a complex urban environment, thereby improving the accuracy of state classification and recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0041] Figure 1 This is a flow chart of an implementation method of a UAV flight status classification method provided by the present invention;

[0042] Figure 2 The five radio frequency backgrounds without drones and the distribution diagram of drone spectrum data in different states provided by the embodiment of the present invention are as follows;

[0043] Figure 3 Schematic diagram of the cause-effect chain of the UAV radio frequency signal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The core of the present invention is to provide a method, device, equipment and computer storage medium for classifying the flight status of unmanned aerial vehicles, which effectively improves the accuracy of classification and identification.

[0045] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0046] Please refer to Figure 1 , Figure 1 This is a flowchart of the implementation of a UAV flight status classification method provided by the present invention; the specific operation steps are as follows:

[0047] S101: Collect UAV radio frequency signal data in different scenarios, perform preprocessing and time-frequency conversion, and obtain UAV spectrum data;

[0048] S102: Offline modeling of uncertainty factors in the acquisition process of the UAV radio frequency signal data, and estimating the distribution of uncertainty factors in the acquisition process of the UAV radio frequency signal data based on the uncertainty factor model;

[0049] S103: Based on the impact of the drone on communication quality under different flight states and in combination with domain prior knowledge, a causal chain of drone radio frequency signals is constructed, and the causal chain of drone radio frequency signals is trained based on the drone spectrum data and the distribution of uncertainty factors to obtain conditional probability distribution parameters;

[0050] S104: Acquire drone spectrum data in real time and input it into a drone flight state classification neural network model to obtain a drone flight state classification result;

[0051] S105: Correcting the drone flight state classification result based on the drone radio frequency signal causal chain and the conditional probability distribution parameters to obtain a target classification result.

[0052] Based on the above embodiment, this embodiment describes step S101 in detail:

[0053] The collection of drone radio frequency signal data in different scenarios includes:

[0054] Within the preset frequency band, radio signals larger than the preset bandwidth are collected from drones of different manufacturers and models in environments with no drone RF background, when the drone is turned on and connected, when the drone is hovering, when the drone is flying but not transmitting image data, and when the drone is flying and transmitting image data, to obtain drone RF signal data.

[0055] The present invention requires the use of an acquisition system and a data processing system to receive and preprocess drone RF data. In one embodiment, a radio receiving device, such as the USRP series software-defined radio peripheral, is used to collect radio signals with a bandwidth of at least 80 MHz in the 2.4 GHz or 5.8 GHz frequency bands for various scenarios, including those involving drones from different manufacturers and models without a RF background, when the drone is powered on and connected, when the drone is hovering, when the drone is flying but not transmitting image data, and when the drone is flying but transmitting image data. The 2.4 GHz and 5.8 GHz frequency bands are commonly used for drone communication, so when selecting a radio receiving device, it is important to consider the receivable frequency range to ensure that the device can collect data within these communication bands. Collecting UAV communication RF data requires collecting data with a bandwidth of at least 80MHz to be suitable for identification and analysis. Therefore, when selecting a radio receiving device, it is necessary to consider not only the receivable frequency range but also the maximum receiving bandwidth of the radio receiving device. If the maximum receiving bandwidth cannot meet the requirements, multiple radio receiving devices can be used for simultaneous data collection. However, a time synchronization module needs to be installed on each device to basically ensure that the collected data is collected at the same time. In this way, the collected data can be spliced in the frequency domain, and a radio frequency communication signal data set with a bandwidth of at least 80MHz can also be obtained.

[0056] The preprocessing and time-frequency conversion include:

[0057] The UAV radio frequency signal data is denoised and decoupled to obtain a desired UAV communication signal. In one embodiment, an underdetermined blind source separation method based on sparse variable analysis is used. The time domain signal is first converted to the time-frequency domain through a time-frequency transform such as a wavelet transform or a short-time Fourier transform, and a single source point is obtained using a data analysis method under a specific frequency window. The single source point is then clustered to obtain a mixing matrix. Finally, the desired UAV communication signal and other noise signals are reconstructed to achieve denoising and decoupling of the perception signal.

[0058] The desired drone communication signal is transformed into a time-frequency signal using a discrete Fourier transform, and its phase characteristics are ignored, retaining only its amplitude characteristics to obtain drone spectrum data. In one embodiment, assuming that the number of frequency windows is set to 2048, the frequency domain data after discrete Fourier transform has a total of 2048 frequency windows, that is, each drone communication power spectrum data is reduced from tens of thousands of data points in the time domain to 2048 data points in the frequency domain. The distribution diagrams of five types of RF backgrounds without drones and drone spectrum data in different states are shown below. Figure 2 shown.

[0059] Based on the above embodiment, this embodiment describes step S102 in detail:

[0060] In the offline state of the low-altitude UAV detection and identification system, a model of uncertainty factors that may be caused by the environment and the acquisition equipment during the acquisition process of the drone spectrum data is designed. When the maximum receiving bandwidth of the selected radio acquisition device is not enough to cover the bandwidth required for the analysis of the drone RF data, it is necessary to adopt a method of simultaneous acquisition of multiple devices. Although the synchronization module can be used to ensure time synchronization when performing multi-device acquisition, there is still a problem of synchronization error. And in the acquisition process, since the 2.4GHz frequency band is used by WIFI, Bluetooth, etc. in addition to drone communication signals, there will be a problem of co-channel interference. Therefore, when facing the collected drone RF data, it is essential to consider uncertainty factors. For example, considering uncertainty factors such as synchronization error and co-channel interference will affect the accuracy of RF-based drone target status recognition;

[0061] An agent-based modeling and simulation method is used to give the initial distribution of the proposed uncertainty factors according to the domain prior knowledge. Then, the Monte Carlo simulation method is used through multiple iterations to obtain the distribution of uncertainty factors that may be encountered in the collection of UAV communication RF data.

[0062] Based on the above embodiment, this embodiment describes step S103 in detail:

[0063] Causal nodes are selected based on the impact of drones on communication quality under different flight conditions. In one embodiment, for drone communication RF signals, the standard deviation and kurtosis are selected as causal nodes in the time domain, and the average value is selected as the causal node in the frequency domain. For communication quality, the communication signal-to-noise ratio is selected as the causal node, and the two uncertain factors of synchronization error and co-frequency interference are selected as causal nodes. Combined with domain prior knowledge, the causal nodes are connected to construct a causal chain of drone RF signals. The constructed drone RF signal causal chain is as follows: Figure 3 As shown in Figure 2, a causal chain is a directed acyclic graph consisting of nodes and directed edges connecting them. Each node represents a random variable, which can be an observed variable, a latent variable, or an unknown parameter, while the directed edges between nodes represent the causal relationship between the variables.

[0064] The causal chain constructed based on the acquired radio frequency signal and the sampling uncertainty factors is trained to obtain the conditional probability distribution parameters.

[0065] Based on the above embodiment, this embodiment describes step S104 in detail:

[0066] The drone's RF power spectrum data is processed based on the input requirements of different neural network models. Assuming the input neural network model is a multi-channel convolutional neural network, the 80MHz bandwidth drone communication power spectrum data needs to be evenly divided into eight independent 10MHz bandwidth segments. That is, each bandwidth segment corresponding to each drone power spectrum data piece contains 256 data points, and each bandwidth segment is input into the 8-channel convolutional neural network in parallel as a channel. Assuming the input neural network model is a long-short-term memory neural network, a simple normalization of the power spectrum is sufficient. Assuming the input is a machine learning model, the power spectrum data features need to be manually extracted and then input into the machine learning model. The processed power spectrum data is then fed into the neural network model for feature extraction, feature dimensionality reduction, and target classification to obtain the initial low-altitude drone flight status classification results.

[0067] Based on the above embodiment, this embodiment describes step S105 in detail:

[0068] Based on the initial low-altitude drone flight state classification results, categories requiring further correction are selected. For situations where different states of the same drone do not exhibit significant spectral differences, the neural network does not perform well in feature extraction and classification. As a black-box model, the opacity of the neural network model often leads to these false positives and false negatives. For these black-box model errors, a Bayesian network model is used for causal reasoning analysis and corrections, increasing the transparency and interpretability of the recognition model, thereby improving the accuracy of drone target state recognition. Combining the previously constructed causal chain and the trained probability distribution parameters, the neural network training model results are corrected to output the final urban low-altitude drone flight state classification results.

[0069] In summary, this invention, based on drone spectrum detection, can achieve high-precision identification of drone target states based on practical needs, reducing interference from receiver hardware and the complex electromagnetic environment in which drone communication RF signals are received, as well as the impact of the black-box nature of neural network models on state recognition accuracy. This method provides a reliable Bayesian causal reasoning-enhanced method for drone target state recognition, providing a theoretical approach for high-precision identification of low-altitude, three-dimensional traffic drones in complex urban environments. It demonstrates strong applicability.

[0070] The embodiment of the present invention further provides a device for classifying the flight status of a UAV; the specific device may include:

[0071] The data acquisition module is used to collect UAV radio frequency signal data in different scenarios, and perform preprocessing and time-frequency conversion to obtain UAV spectrum data;

[0072] The uncertainty factor acquisition module is used to perform offline modeling of the uncertainty factors in the acquisition process of the UAV RF signal data, and estimate the distribution of the uncertainty factors in the acquisition process of the UAV RF signal data based on the uncertainty factor model;

[0073] A causal chain construction module is used to construct a causal chain of drone radio frequency signals based on the impact of drones on communication quality under different flight states and in combination with domain prior knowledge, and to train the causal chain of drone radio frequency signals based on the drone spectrum data and the distribution of uncertain factors to obtain conditional probability distribution parameters;

[0074] The state classification module is used to obtain UAV spectrum data in real time and input it into the UAV flight state classification neural network model to obtain the UAV flight state classification results;

[0075] A result correction module is used to correct the UAV flight state classification result based on the UAV radio frequency signal causal chain and the conditional probability distribution parameters to obtain a target classification result.

[0076] The drone flight state classification device of this embodiment is used to implement the aforementioned drone flight state classification method. Therefore, the specific implementation methods of the drone flight state classification device can be seen in the embodiment part of the drone flight state classification method mentioned above. For example, the data acquisition module, the uncertainty factor acquisition module, the causal chain construction module, the state classification module, and the result correction module are respectively used to implement steps S101, S102, S103, S104 and S105 in the above-mentioned drone flight state classification method. Therefore, its specific implementation methods can refer to the descriptions of the corresponding embodiments of each part and will not be repeated here.

[0077] A specific embodiment of the present invention further provides a drone flight status classification device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned drone flight status classification method when executing the computer program.

[0078] A specific embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for classifying the flight status of a drone are implemented.

[0079] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0083] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for classifying the flight status of a UAV, characterized in that: include: Collect drone RF signal data in different scenarios, perform preprocessing and time-frequency conversion to obtain drone spectrum data. Within a preset frequency band, collect radio signals larger than the preset bandwidth from drones of different manufacturers and models in environments with no drone RF background, when the drone is turned on and connected, when the drone is hovering, when the drone is flying but not transmitting image data, and when the drone is flying and transmitting image data. Offline modeling of uncertainties in the acquisition process of drone radio frequency signal data is performed, and the distribution of uncertainties in the acquisition process of drone radio frequency signal data is estimated based on the uncertainty factor model. Based on the uncertainty factor model, an agent-based modeling and simulation method is used to obtain an initial distribution of uncertainties according to domain prior knowledge. The Monte Carlo simulation method is then used to estimate the distribution of uncertainties in the acquisition process of drone radio frequency signal data through multiple iterations. Based on the impact of drones on communication quality under different flight states and combined with domain prior knowledge, a causal chain of drone radio frequency signals is constructed. The causal chain of drone radio frequency signals is trained based on the drone spectrum data and the distribution of uncertain factors to obtain conditional probability distribution parameters. For the drone radio frequency signal data, the standard deviation and kurtosis are selected as causal nodes in the time domain, and the average value is selected as the causal node in the frequency domain. For communication quality, the communication signal-to-noise ratio, synchronization error, and co-frequency interference are selected as causal nodes. Combined with domain prior knowledge, the causal nodes are connected to construct a causal chain of drone radio frequency signals. Acquire drone spectrum data in real time and input it into the drone flight status classification neural network model to obtain the drone flight status classification results; The UAV flight state classification result is corrected based on the UAV radio frequency signal causal chain and the conditional probability distribution parameters to obtain a target classification result.

2. The method for classifying the flight status of a UAV according to claim 1, characterized in that: The preprocessing and time-frequency conversion include: Performing denoising and decoupling processing on the UAV radio frequency signal data to obtain a desired UAV communication signal; The desired UAV communication signal is transformed into a time-frequency signal using discrete Fourier transform, and its phase characteristics are ignored, while only its amplitude characteristics are retained to obtain the UAV spectrum data.

3. The method for classifying the flight status of a UAV according to claim 1, wherein: The correction of the UAV flight state classification result based on the UAV radio frequency signal causal chain and the conditional probability distribution parameters to obtain the target classification result includes: Based on the causal chain of the UAV radio frequency signal and the conditional probability distribution parameters, a Bayesian network model is used to perform causal reasoning analysis and correction on the UAV flight state classification result.

4. The method for classifying the flight status of a UAV according to claim 1, wherein: When the UAV flight state classification neural network model is a multi-channel convolutional neural network model, the UAV spectrum data is evenly divided according to the bandwidth and then input into the channel convolutional neural network model in parallel; When the UAV flight state classification neural network model is a long short-term memory neural network model, the UAV spectrum data is normalized and then input into the long short-term memory neural network model; When the UAV flight status classification neural network model is a machine learning model, the UAV spectrum data is subjected to feature extraction and then input into the machine learning model.

5. A drone flight status classification device, characterized in that: include: The data acquisition module is used to collect drone RF signal data in different scenarios and perform preprocessing and time-frequency conversion to obtain drone spectrum data. Specifically, within a preset frequency band, radio signals larger than a preset bandwidth are collected from drones of different manufacturers and models in environments with no drone RF background, when the drone is turned on and connected, when the drone is hovering, when the drone is flying but not transmitting image data, and when the drone is flying and transmitting image data. An uncertainty factor acquisition module is used to perform offline modeling of the uncertainty factors in the acquisition process of the UAV RF signal data, and estimate the distribution of the uncertainty factors in the acquisition process of the UAV RF signal data based on the uncertainty factor model. Based on the uncertainty factor model, an agent-based modeling and simulation method is used to obtain the initial distribution of the uncertainty factors according to the domain prior knowledge, and the Monte Carlo simulation method is combined to estimate the distribution of the uncertainty factors in the acquisition process of the UAV RF signal data through multiple iterations; A causal chain construction module is used to construct a causal chain for drone radio frequency signals based on the impact of drones on communication quality under different flight states and in combination with domain prior knowledge, and to train the drone radio frequency signal causal chain based on the drone spectrum data and the distribution of the uncertainty factors to obtain conditional probability distribution parameters. Specifically, for the drone radio frequency signal data, the standard deviation and kurtosis are selected as causal nodes in the time domain, and the average value is selected as the causal node in the frequency domain; for communication quality, the communication signal-to-noise ratio, synchronization error, and co-frequency interference are selected as causal nodes; combined with domain prior knowledge, the causal nodes are connected to construct a causal chain for drone radio frequency signals; The state classification module is used to obtain UAV spectrum data in real time and input it into the UAV flight state classification neural network model to obtain the UAV flight state classification results; A result correction module is used to correct the UAV flight state classification result based on the UAV radio frequency signal causal chain and the conditional probability distribution parameters to obtain a target classification result.

6. A drone flight status classification device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of a method for classifying the flight status of a drone as described in any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for classifying the flight status of a drone as claimed in any one of claims 1 to 4 are implemented.

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