Drone data protection method and system for power system collection terminal

Abstract

The application relates to the technical field of unmanned aerial vehicle communication information security, and specifically provides an unmanned aerial vehicle data protection method and system for a power system collection terminal, which comprises the following steps: in the case that an unmanned aerial vehicle and a collection terminal enter a near field communication range, collecting channel state information of communication signals in real time, extracting a multi-dimensional physical layer feature vector for device identity verification; the data collected by the unmanned aerial vehicle is divided into three levels of core control instructions, device operation parameters and environmental monitoring information, and different encryption algorithms with different security strengths are used for encryption processing of different levels before data transmission; in the case of performing a multi-hop relay task, the unmanned aerial vehicle encapsulates the collected data into a data packet containing an access strategy label, and then adopts a key policy hiding mechanism based on a bilinear pair to complete current data transmission, so that full-stack security protection from device access, data transmission, access control to system self-protection is realized.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) communication and information security technology, and in particular to a UAV data protection method and system for power system data acquisition terminals. Background Technology

[0002] With the deep integration of IoT and smart grid technologies, the demand for real-time data collection, transmission, and processing from terminals in power systems is growing rapidly. Drones, as a new type of mobile data acquisition platform, are widely used for power terminal inspection tasks in remote areas or complex terrains due to their high mobility and wide coverage. These applications require drones to efficiently acquire critical data such as meter readings, equipment status, and environmental parameters during flight, ensuring the integrity and security of the data during collection, storage, and transmission. Especially in unattended scenarios, data loss or tampering directly impacts the accuracy of power grid operation monitoring and the timeliness of fault response.

[0003] In multi-hop relay scenarios involving drones, data transmission requires passing through multiple drones hop-by-hop to achieve end-to-end transmission. Each drone acts as a temporary relay node, and intermittent connections are caused by drone movement, signal obstruction, and link interference, leading to frequent disconnections and reconnections. Furthermore, power system data acquisition terminals are typically located in open physical environments without effective physical protection. This exposes the data exchange link between drones and acquisition terminals during near-field communication, further amplifying the impact and destructive power of threats such as man-in-the-middle attacks, replay attacks, and signal jamming. Traditional encryption mechanisms based on centralized certificates, requiring frequent interaction with centralized nodes for certificate verification and key distribution, suffer from high communication overhead and response latency. They are completely unsuitable for the low-latency, secure access required for establishing temporary connections between drones and different terminals, as well as the core requirement for efficient data forwarding in multi-hop relay scenarios. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method and system for protecting UAV data acquisition terminals in power systems. It abandons the traditional public key infrastructure (PKI) system that relies on centralized certificate authorities. Instead, it extracts multi-dimensional physical layer feature vectors from real-time channel state information for bidirectional device authentication. Different levels of the acquired data are encrypted using different security strengths before data transmission. During multi-hop relay, the acquired data is encapsulated into data packets carrying access policy tags and encrypted using CP-ABE (Bilinear Algorithm-Based Encryption), embedding the access policy within the ciphertext structure as an implicit mathematical constraint. This provides fine-grained policy protection and anti-collusion characteristics.

[0005] To achieve the objectives of this application, the following technical solution is provided: In a first aspect, this application provides a method for protecting unmanned aerial vehicle (UAV) data in a power system data acquisition terminal, comprising: When the drone and the data acquisition terminal enter the near-field communication range, the channel status information of the communication signals between the two parties is collected in real time. The UAV extracts multi-dimensional physical layer feature vectors from the channel state information for device authentication. If the device authentication is successful, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vectors. The multi-dimensional physical layer feature vectors include signal strength indication, carrier phase, time difference of arrival, and Doppler shift. The drone acquires data from the acquisition terminal in real time and divides the data into three levels: core control commands, equipment operating parameters, and environmental monitoring information. Different encryption algorithms with different security strengths are used to encrypt the data before transmitting it. When the UAV performs a multi-hop relay mission, the UAV encapsulates the collected data into a data packet containing an access policy label, and then encrypts the data packet using the CP-ABE algorithm based on the mathematical tool of bilinear pairing. During encryption, the access policy is transformed into hidden mathematical parameters so that the legitimate receiver can perform the decryption operation to complete the current data transmission.

[0006] A further improvement in this application is that the step of extracting multi-dimensional physical layer feature vectors from the channel state information for device authentication includes: extracting parameters of each dimension from the channel state information, and concatenating the normalized values ​​corresponding to each dimension parameter to form the multi-dimensional physical layer feature vector; inputting the multi-dimensional physical layer feature vector into a pre-trained similarity discrimination model, calculating the feature distance between legitimate devices in real time, and determining whether the feature distance between legitimate devices is less than a preset threshold; and determining that device authentication is successful when both the first comparison result and the second comparison result satisfy the distance constraint; the first comparison result and the second comparison result together constitute the bidirectional comparison result between the UAV and the acquisition terminal; wherein, the similarity discrimination model is constructed based on a Siamese neural network architecture, the Siamese neural network architecture contains two structurally identical sub-network branches, each branch consisting of 3 fully connected layers; the similarity discrimination model is trained through contrastive learning, and the loss function is defined as a triplet loss, specifically: ; In the formula, For anchor point samples, Positive samples from the same device pair These are negative samples from different devices. Represents the network mapping function; This represents the boundary margin.

[0007] A further improvement of this application is that, in the case of device authentication, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vector, including: in the case of device authentication, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vector, and introduces an environmental noise disturbance factor in the generation process of the one-time dynamic key seed; the environmental noise disturbance factor includes the output signals from the accelerometer and gyroscope of the inertial measurement unit on the UAV; wherein, the environmental noise disturbance factor is used as a physically unclonable source in the key derivation function operation, and the physically unclonable source is used as an entropy enhancement module in the key derivation function; specifically: ; In the formula, This is the final one-time dynamic key seed. Derived function for key. The shared feature fingerprint is agreed upon by both the drone and the data acquisition terminal. The noise hash value of the inertial measurement unit. A one-time random number generated by the drone. A one-time random number generated by the data acquisition terminal. This refers to cryptographic hash operations.

[0008] A further improvement in this application is that the collected data is divided into three levels: core control commands, equipment operating parameters, and environmental monitoring information. Different encryption algorithms with varying security strengths are used for each level before data transmission. This includes: the UAV dividing the collected data into three levels according to a preset strategy; the first level consists of core control commands that directly affect the power system's operating status; the second level consists of equipment operating parameters reflecting the real-time operating status of the data acquisition terminal; and the third level consists of environmental monitoring information. For the core control commands, an elliptic curve-based digital signature algorithm is used for integrity protection; for the equipment operating parameters, the national cryptographic SM4 algorithm is used for symmetric encryption; and for the environmental monitoring information, a hash chain structure is used to implement tamper-proof verification of the data sequence.

[0009] A further improvement of this application is that the method further includes: the UAV detecting signal strength indication and bit error rate in real time, and determining the current communication link quality based on the signal strength indication and the bit error rate, so as to dynamically adjust the encryption parameter configuration according to the current communication link quality.

[0010] A further improvement of this application is that the step of determining the current communication link quality based on the signal strength indication and the bit error rate, and dynamically adjusting the encryption parameter configuration according to the current communication link quality, includes: when it is detected in real time that the signal strength indication is higher than a first preset strength threshold and the bit error rate is lower than a first preset bit error rate threshold, determining that the current link quality is a high-quality channel condition and enabling a high-throughput mode; in the high-throughput mode, reducing the number of hash chain iterations of the environmental monitoring information and disabling forward error correction coding; when it is detected in real time that the signal strength indication is lower than a second preset strength threshold, or the bit error rate is higher than a second preset bit error rate threshold, switching to a high-security mode; in the high-security mode, increasing the number of SM4 encryption rounds and enabling RS coding (Reed-Solomon Code) for forward error correction.

[0011] A further improvement in this application is that the access strategy is represented by a logic tree structure, with leaf nodes being atomic attributes and non-leaf nodes being AND or OR logic gates; the atomic attributes include: role permission attributes, time-limited window attributes, and geographical location range attributes; wherein, the role permission attributes are encoded through extended fields in the digital certificate; the time-limited window attributes are defined in the form of absolute time intervals, and combined with the UTC time synchronization of the UAV's built-in high-precision time synchronization module, the decryption operation is only valid if the current moment falls within the preset time period.

[0012] A further improvement of this application is that, during the encapsulation of the data packet, a key generation interface is invoked to perform encryption operations and output ciphertext; specifically: Let the plaintext message be M, and choose a random index. , Computation ciphertext component: ; ; ; In the formula, For the system master key component, To randomly select the index, For generators, For attributes in the strategy tree The corresponding share value; As the core encrypted component; To generate elements The result of the bilinear pairing operation on the input Exponentiation of subgroups; To assist in the ciphertext component; For attributes The bound attributed ciphertext component; during the decryption process, the data packet requires the user's private key attributes to satisfy at least one complete path condition from the root node to the leaf node of the logical tree structure.

[0013] A further improvement of this application is that the method further includes: the UAV monitors the spectrum occupancy in real time, and when narrowband jamming interference or broadband noise attack is detected, it automatically switches to the backup spread spectrum communication mode, adjusts the frequency hopping sequence and modulation order, and maintains the minimum critical control signaling path unobstructed.

[0014] Secondly, this application provides a UAV data protection system for a power system data acquisition terminal, used to implement the aforementioned UAV data protection method for a power system data acquisition terminal, including: The real-time acquisition module is used to acquire channel status information of the communication signals between the UAV and the acquisition terminal in real time when they enter the near-field communication range. An authentication module is used to extract multi-dimensional physical layer feature vectors from the channel state information for device authentication. If the device authentication is successful, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vectors. The multi-dimensional physical layer feature vectors include signal strength indication, carrier phase, time difference of arrival, and Doppler shift. The data encryption module is used by the UAV to acquire data from the acquisition terminal in real time, and divide the acquired data into three levels: core control commands, equipment operating parameters and environmental monitoring information. Different encryption algorithms with different security strengths are used to perform encryption processing for different levels before data transmission. The data transmission module is used to encapsulate the collected data into a data packet containing an access policy label when the UAV is performing a multi-hop relay mission. Then, based on the mathematical tool of bilinear pairing, the UAV uses the CP-ABE algorithm to encrypt the data packet, and converts the access policy into hidden mathematical parameters during encryption so that the legitimate receiver can perform the decryption operation to complete the current data transmission.

[0015] Compared with the prior art, this application has the following beneficial effects: The UAV data protection method and system for power system acquisition terminals provided in this application perform bidirectional device authentication by extracting multi-dimensional physical layer feature vectors from real-time acquired channel state information. Upon successful device authentication, the UAV divides the real-time acquired data into three levels: core control commands, device operating parameters, and environmental monitoring information. Different encryption algorithms with varying security strengths are used for each level before multi-hop relaying is performed to encapsulate the acquired data into data packets containing access policy tags. Then, based on bilinear pairing, the CP-ABE algorithm is used to encrypt the data packets, and the access policy is transformed into hidden mathematical parameters during encryption, enabling the legitimate receiver to perform decryption and complete the current data transmission. Therefore, the data security protection method for power system acquisition terminals in this application utilizes the inherent uniqueness of the wireless channel between the UAV and the acquisition terminal during the initial near-field handshake phase. It extracts multi-dimensional physical layer features based on signal strength indication, carrier phase, time difference of arrival, and Doppler frequency shift in real time to form a stable and difficult-to-copy device fingerprint. Then, a similarity discrimination model is used to complete two-way identity verification, fundamentally eliminating the risk of man-in-the-middle spoofing and device impersonation. Based on this trusted channel, the UAV performs encryption processing on different levels of the acquired data using encryption algorithms with different security strengths before transmitting the data. When performing multi-hop relay, the acquired data is encapsulated into data packets carrying access policy tags and encrypted using bilinear pairing mathematical tools (CP-ABE). This embeds the access policy into the ciphertext structure as an implicit mathematical constraint, possessing fine-grained policy and anti-collusion characteristics. Attached Figure Description

[0016] The accompanying drawings are provided to further understand this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. Figure 1 A schematic flowchart of a first method for protecting UAV data acquisition terminals in a power system according to an embodiment of this application; Figure 2 This is a second flowchart illustrating a method for protecting UAV data for a power system data acquisition terminal, as provided in an embodiment of this application. Figure 3 A schematic diagram of a third method for protecting UAV data acquisition terminals in power systems, provided in an embodiment of this application; Figure 4 This is a schematic diagram of the module structure of the lightweight layered encrypted transmission framework provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this application, unless otherwise stated, "multiple" means two or more.

[0019] With the deep integration of IoT and smart grid technologies, the demand for real-time data collection, transmission, and processing from terminals in power systems is growing rapidly. Drones, as a new type of mobile data acquisition platform, are widely used for power terminal inspection tasks in remote areas or complex terrains due to their high mobility and wide coverage. These applications require drones to efficiently acquire critical data such as meter readings, equipment status, and environmental parameters during flight, ensuring the integrity and security of the data during collection, storage, and transmission. Especially in unattended scenarios, data loss or tampering directly impacts the accuracy of power grid operation monitoring and the timeliness of fault response.

[0020] In multi-hop drone relay scenarios, data needs to be relayed by multiple drones hop by hop to complete end-to-end transmission. Each drone acts as a temporary relay node. However, intermittent connections are caused by factors such as drone movement, signal obstruction, and link interference, resulting in frequent disconnections and reconnections of the communication link. This dynamic, temporary, and multi-node collaborative communication mode itself poses a hidden danger to communication security. At the same time, the open physical environment in which the power system data acquisition terminal is located lacks effective physical protection. When drones communicate with the terminal in the near field, the data interaction link is completely exposed to the open space, further amplifying the scope and destructive power of threats such as man-in-the-middle eavesdropping, replay attacks, and signal jamming.

[0021] Specifically, man-in-the-middle attacks exploit the signal exposure of open environments to easily intercept data at each hop in a multi-hop transmission process, including authentication information, key negotiation content, and core power data collection data. The multi-hop forwarding link characteristic allows eavesdropping nodes to intercept data at any hop, making them difficult to detect quickly. Replay attacks take advantage of the instability of intermittent connections to intercept previously legitimate communication data packets and resend them during link reconnection or multi-hop forwarding intervals, impersonating legitimate devices to complete access or data interaction. Since drones need to quickly complete authentication and key negotiation when establishing temporary connections with different terminals, the time for verifying data packets is limited, making it easy for such attacks to bypass the verification mechanism. Signal jamming directly cuts off the communication links between drones and data collection terminals, and between drones themselves, causing data transmission interruptions and key negotiation failures. This not only affects the normal uploading of power data collection data but may also cause the authentication process to stall or be interrupted, giving illegal devices an opportunity to impersonate legitimate nodes and access the link. Traditional encryption mechanisms based on centralized certificates require frequent interactions with centralized nodes to complete certificate verification and key distribution, resulting in high communication overhead and response latency. They are completely unsuitable for the core requirements of low-latency secure access when drones establish temporary connections with different terminals in this scenario, as well as efficient data forwarding in multi-hop relay scenarios.

[0022] To address the aforementioned technical problems, this application proposes the following technical solutions and corresponding embodiments.

[0023] The following is combined with Figures 1 to 4 The embodiments shown illustrate the technical solutions of this application: Example 1 This application provides an embodiment of a method for protecting unmanned aerial vehicle (UAV) data from a power system data acquisition terminal, referring to... Figure 1 As shown, the process includes the following steps S101 to S104: Step S101: When the UAV and the data acquisition terminal enter the near-field communication range, collect the channel status information of the communication signals between the two parties in real time.

[0024] In this embodiment, when the UAV and the power system acquisition terminal enter the near-field communication range, both parties initiate a synchronization signal detection process, perform link discovery through a preset beacon frame, and collect the channel state information (CSI) of the communication signals between the two parties in real time. For example, the power system acquisition terminal can be one or more of the following: smart meter, FTU (Feeder Terminal Unit), DTU (Distribution Terminal Unit), TTU (Transformer Terminal Unit), smart sensor, and edge gateway.

[0025] Step S102: Extract multi-dimensional physical layer feature vectors from the channel state information for device authentication. If the device authentication is successful, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vectors. The multi-dimensional physical layer feature vectors include signal strength indication, carrier phase, time difference of arrival, and Doppler shift.

[0026] In this embodiment, Received Signal Strength Indication (RSSI), Carrier Phase, and Time Difference of Arrival (TDOA) are extracted based on real-time acquired channel state information, and Doppler Shift is estimated to generate a multi-dimensional physical layer feature vector containing RSSI, Carrier Phase, TDOA, and Doppler Shift. These parameters are directly output by the RF front-end hardware in the communication modules of both parties and form the raw data stream after analog-to-digital conversion. Here, both the UAV and the power acquisition terminal (power system acquisition terminal) are equipped with near-field communication antennas. Synchronous detection and link establishment can only be initiated when the wireless signal coverage areas of both parties overlap and the signal strength and channel quality simultaneously meet the demodulation threshold. Specifically, when the UAV and the power system data acquisition terminal enter each other's near-field communication coverage area, and their near-field communication modules detect each other's wireless signals, and the signal strength reaches the minimum threshold for link establishment, both parties simultaneously initiate a synchronization signal detection process. They interact using pre-agreed beacon frames, with each sending and receiving a pre-set beacon frame. Time synchronization and initial device identification are achieved through the synchronization identifier in the beacon frame, thus realizing communication synchronization and link discovery. Subsequently, the timing enters the channel state information acquisition phase, referring to... Figure 2 As shown, both parties synchronously and in real time collect each other's CSI data of communication signals, and directly extract three core parameters from the CSI: received signal strength indication, carrier phase, and time difference of arrival. At the same time, through the analysis of the time and frequency characteristics of the CSI signal, the Doppler frequency shift is estimated. Finally, these four parameters are integrated to generate a multi-dimensional physical layer feature vector containing RSSI, carrier phase, TDOA, and Doppler frequency shift, thus completing the feature-level processing and integration.

[0027] Specifically, the extraction process of the aforementioned multi-dimensional physical layer feature vectors includes: firstly, denoising the original CSI data by using a sliding window mean filtering algorithm (window length of 16 sampling points) to eliminate high-frequency noise interference and extracting parameters for each dimension; then, normalizing each dimension parameter to the [0,1] interval, the normalization formula being: In the formula, and These represent the minimum and maximum values ​​of each parameter obtained statistically from the historical training set; finally, the normalized values ​​of the four dimensions are concatenated to form a four-dimensional feature vector. , These correspond to RSSI, carrier phase, TDOA, and Doppler frequency shift, respectively. This four-dimensional feature vector has strong spatiotemporal uniqueness and exhibits dynamic characteristics that cannot be replicated due to environmental factors such as propagation path, obstacle reflection, and equipment micro-vibration.

[0028] As a feasible implementation method, the above acquisition process is carried out continuously with a period of 0.5 milliseconds to 2 milliseconds to ensure that continuous and stable multi-dimensional physical layer feature sequences can still be captured during the dynamic changes in the flight attitude of the UAV.

[0029] In this embodiment, the multi-dimensional physical layer feature vectors are normalized and then input into a pre-trained similarity discrimination model. This model is built on a Siamese neural network architecture and optimizes the feature space distribution through contrastive learning, ensuring that the feature distance between legitimate devices is less than a set threshold, while the feature distance generated by unauthorized access devices or replay attacks is significantly greater than the set threshold, thereby improving the robustness of identity verification. In other words, the multi-dimensional physical layer feature vectors are input into the pre-trained similarity discrimination model, which calculates the feature distance between legitimate devices in real time and determines whether the feature distance between legitimate devices is less than a preset threshold. If both the first comparison result and the second comparison result satisfy the distance constraint, the device identity verification is deemed successful. The first comparison result is based on the feature distance comparison between the UAV and the data acquisition terminal, and the second comparison result is based on the feature distance comparison between the data acquisition terminal (terminal local security processor) and the UAV. The first and second comparison results together constitute the bidirectional comparison result between the UAV and the data acquisition terminal.

[0030] Specifically, the Siamese neural network architecture in this embodiment includes two structurally identical sub-network branches, each consisting of three fully connected layers with 128, 64, and 32 hidden layer nodes respectively. The activation function is ReLU, and the output layer is an L2-normalized 128-dimensional embedding vector. In this embodiment, the similarity discrimination model is trained through contrastive learning, and the loss function is defined as the triplet loss, specifically: ; In the formula, For anchor point samples, Positive samples (from the same device pair) These are negative samples (from different devices or replay attack simulation data). Represents the network mapping function; The boundary margin is set to 0.5.

[0031] In this embodiment, after the similarity discrimination model is trained, it is deployed in the local security processor of the drone and the terminal to calculate in real time whether the feature distance between legitimate devices is less than a preset threshold δ, thereby completing the device legitimacy screening. If both bidirectional comparison results meet the distance constraint, the identity authentication is deemed successful; otherwise, access is rejected and the abnormal event is recorded. In this way, through bidirectional comparison by the similarity discrimination model, illegal access devices with obviously abnormal channel characteristics can be intercepted in advance, reducing the computation and communication overhead of subsequent authentication. For example, a typical value of the preset threshold δ is 0.35.

[0032] In this embodiment of the application, when the calculated feature distance between legitimate devices is less than a preset threshold, a one-time dynamic key seed is generated based on the multi-dimensional physical layer feature vector (the physical layer feature vector after the similarity discrimination model has verified its legitimacy), and the device identity is quickly authenticated through bidirectional comparison verification.

[0033] In this embodiment, an environmental noise disturbance factor is introduced during the generation of a one-time dynamic key seed. This environmental noise disturbance factor originates from the random fluctuations in acceleration output by the UAV's inertial measurement unit (IMU) and the slight offsets in the angular velocity of the gyroscope. This environmental noise disturbance factor is used as a physically unclonable function (PUF) in the key derivation function (KDF) operation. This physically unclonable function also participates in the KDF operation as an entropy enhancement module, making each negotiated session key unique and unpredictable.

[0034] Specifically, the one-time dynamic key seed in this embodiment The generating expression is: ; In the formula, This is the final one-time dynamic key seed. Derived function for key. The shared feature fingerprint is agreed upon by both the drone and the data collection terminal. The noise hash value of the inertial measurement unit (IMU) carried by the drone. A one-time random number generated by the drone. A one-time random number generated for the power system data acquisition terminal. This is a cryptographic hash operation. The key seed is only valid during the current session and is immediately deleted after the session ends, ensuring forward security. This way, even if an attacker obtains historical keys, they cannot predict future session keys, effectively resisting key prediction attacks based on statistical analysis.

[0035] This approach abandons the traditional public key infrastructure system that relies on centralized certificate authorities, and utilizes the spatiotemporal uniqueness of wireless channels to achieve decentralized device identification. The authentication process does not require certificate exchange or third-party verification, and it meets the low-latency requirements for drones to quickly access multiple heterogeneous terminals. The inherent anti-copying properties of the physical layer make it difficult for attackers to forge channel responses through recording and playback, effectively preventing man-in-the-middle attacks and cloned device intrusion.

[0036] Step S103: The UAV acquires data from the acquisition terminal in real time and divides the data into three levels: core control commands, equipment operating parameters, and environmental monitoring information. Different encryption algorithms with different security strengths are used to encrypt the data for each level before transmitting it.

[0037] In this embodiment, after identity authentication, the collected data from the power system acquisition terminal is divided into three levels—core control commands, equipment operating parameters, and environmental monitoring information—based on data sensitivity levels (preset security policies). Different encryption algorithms with varying security strengths are used for each level. Specifically, core control commands are protected for integrity using an elliptic curve-based digital signature algorithm (core control command processing unit); equipment operating parameters are symmetrically encrypted using the national cryptographic SM4 algorithm (equipment operating parameter processing unit); and environmental monitoring information is verified for tamper-proof data sequence through a hash chain structure (environmental monitoring information processing unit). Here, core control commands refer to critical commands that directly affect the system's operating status, such as remote start / stop commands, emergency disconnection signals, and firmware update packages; equipment operating parameters refer to telemetry data reflecting the terminal's real-time operating conditions, including voltage, current, power factor, temperature, and load rate; and environmental monitoring information refers to auxiliary, non-critical data such as meteorological data, ambient electromagnetic intensity, and visible light image thumbnails.

[0038] Specifically, for core control instructions, an elliptic curve digital signature algorithm (ECDSA) is used for integrity protection, employing the elliptic curve parameters (prime domain) of the national cryptographic standard SM2. The base point G has an order n of 256 bits. The signing process uses a private key to sign the SM3 hash value of the instruction content, and the receiving end verifies the signature validity using the public key. Each instruction is appended with a timestamp (accurate to milliseconds) and an incrementing sequence number (initial value randomly generated, ranging from 0 to...). 1) Prevent replay attacks; instructions with failed signatures or discontinuous serial numbers will be discarded and an alarm will be triggered.

[0039] For equipment operating parameters, the national standard SM4 symmetric encryption algorithm is used for confidentiality. The encryption mode employs Cipher Block Chaining (CBC) mode. The Initialization Vector (IV) is generated by XORing the current timestamp (Universal Time Coordinated (UTC) in milliseconds) with the sequence number of the previous message. This ensures that the same plaintext encrypted at different times produces different ciphertexts. The SM4 algorithm performs 32 rounds of iterative computation, with a key length of 128 bits, derived from the session key generated in step S101. Each encrypted data record is appended with a Message Authentication Code (MAC) generated based on the SM3 algorithm, calculated as follows: ,in, This represents a hash-based message authentication code algorithm implemented using the SM3 cryptographic hash algorithm, a national standard in China. For session key, This refers to the encrypted data record, which is the encrypted content of the original power acquisition data or interactive information to be transmitted after being converted by an encryption algorithm. In this embodiment, the receiving end independently calculates the MAC and compares it with the received MAC value. If they match, the data is considered complete; otherwise, it is considered tampered with and discarded.

[0040] For environmental monitoring information, a hash chain structure is used to implement tamper-proof verification of the data sequence. Let the... The environmental data is Its corresponding hash value Calculate as follows:

[0041] In the formula, Representing the The first environmental data obtained after calculation Rank hash value, This refers to the cryptographic hash algorithm in commercial cryptographic standards. Representing the Environmental data, Representing the The hash value corresponding to each piece of environmental data; The preset initial seed value (256-bit random number). This indicates a byte concatenation operation. In this embodiment, the drone sends... At the same time, it is also attached Compared with the previous one The receiving end recalculates and the received Comparison. If all data blocks pass the verification, a complete hash chain is formed, proving that the data sequence has not been inserted, deleted, or replaced.

[0042] As one possible implementation method, refer to Figure 4 As shown, the constructed lightweight layered encrypted transmission framework dynamically adjusts encryption parameter configurations based on communication link quality (dynamic encryption parameter configurator). Specifically, under high-quality channel conditions, a high-throughput mode is automatically enabled to reduce computational overhead. When link quality deteriorates, it switches to a high-security mode, increasing encryption strength and enabling forward error correction (FEC) to assist transmission, ensuring reliable delivery of critical data in complex electromagnetic environments. Specifically, the link quality assessment module of the lightweight layered encrypted transmission framework continuously monitors the Signal Strength Indicator (RSSI) and Bit Error Rate (BER). When the RSSI is detected to be higher than a first preset strength threshold and the BER is lower than a first preset BER threshold, the current link quality is determined to be a high-quality channel condition, and the high-throughput mode is automatically enabled. In this high-throughput mode, the number of hash chain iterations for environmental monitoring information is reduced from the default 3 times to 1 time (i.e., only one level of SM3 hash is calculated), reducing computational latency; simultaneously, forward error correction (FEC) is disabled to improve transmission efficiency. Conversely, when the link quality deteriorates to the point that the RSSI detected by the link quality assessment module in real time is lower than the second preset strength threshold, or the BER is higher than the second preset bit error rate threshold, the system switches to high security mode. In this high security mode, the number of SM4 encryption rounds is increased from 32 to 48 (achieved through an extended key scheduling table), and RS (255, 239) Reed-Solomon coding is enabled for forward error correction to improve redundancy and ensure reliable delivery of critical data in complex electromagnetic environments.

[0043] For example, the first preset strength threshold is -75dBm, the first preset bit error rate threshold is 1×10 to the power of negative 4; the second preset strength threshold is -85dBm, and the second preset bit error rate threshold is 5×10 to the power of negative 3.

[0044] Step S104: When the UAV performs a multi-hop relay mission, the UAV encapsulates the collected data into a data packet containing an access policy label, and then encrypts the data packet using the CP-ABE algorithm based on the mathematical tool of bilinear pairing. During encryption, the access policy is transformed into hidden mathematical parameters so that the legitimate receiver can perform the decryption operation to complete the current data transmission.

[0045] In this embodiment, when the UAV performs multi-hop relay or temporary caching tasks, the original data to be forwarded is encapsulated into a data packet containing an access policy label. This data packet employs a key policy hiding mechanism based on bilinear pairing to implement attribute-based encryption (ABE), specifically using the CP-ABE (Ciphertext-Policy ABE) scheme. This allows the data owner to preset the access policy and encode it in ciphertext. In other words, the access policy in this embodiment is preset by the data owner, defining a set of user attributes allowed for decryption, including role permissions, geographical location range, and time window. The receiver can only perform decryption operations if its attributes meet the access policy, thereby achieving fine-grained access control.

[0046] In this embodiment, the access policy is represented by a logic gate tree structure, where leaf nodes are atomic attributes and non-leaf nodes are AND or OR logic gates. Here, the set of atomic attributes includes role permission attributes (such as maintenance personnel, schedulers, and auditors), geographic location range attributes (such as a circular area with latitude and longitude coordinates ∈ [116.3°E, 39.9°N] ± 50 meters), and time window attributes (such as UTC time ∈ [2024-06-01T08:00:00Z, 2024-06-01T18:00:00Z]). The role-based access permission attribute is encoded through an extended field in the digital certificate and is uniformly managed and issued by the TAC (Trusted Authority Center). The time window attribute is defined as an absolute time interval, synchronized with the UAV's built-in high-precision time synchronization module to UTC time. The time synchronization error is less than 10 milliseconds, and decryption operations are only valid if the current time falls within the preset time period; requests outside this range are considered invalid. The geographic location range attribute is verified based on BeiDou Navigation Satellite System (BDS-B1I) positioning coordinates; if the location deviation exceeds a preset threshold, it is considered an unauthorized access request. For example, the geographic location range attribute is based on BDS-B1I signal positioning from the BDS-B1I system, with horizontal positioning accuracy better than 2 meters, and supports setting circular or rectangular area boundaries. Location matching verification is performed by calculating Euclidean distance. To achieve, in the formula, Euclidean distance. The x-coordinate of the plane where the device to be verified is currently located in real time. The horizontal coordinate of the preset authorized reference point is the plane coordinate. This represents the current real-time planar ordinate of the device to be verified. The plane ordinate of the preset authorized reference point; if If the distance is greater than 50 meters, decryption will be refused.

[0047] In this embodiment, the policy tree is transformed into an access structure matrix through post-order traversal, which serves as the encryption input parameter. As a feasible implementation, the public key parameter PK is embedded in the read-only storage area of ​​the UAV firmware and contains a bilinear group. Generator g, bilinear mapping e: The system master public key h; the private key is issued by the Trusted Authorization Center (TAC) based on user attributes, in the form of... ,in To access nodes in the tree The corresponding Lagrange interpolation polynomial output, Creates a private random number for the user.

[0048] In this embodiment, the key generation interface is called to output ciphertext during data packet encapsulation. The decryption process requires at least one complete path from the root node to the leaf node; any missing attribute will cause the bilinear pairing operation result to deviate from the original message recovery condition. Specifically, the key generation interface is called to perform encryption during data packet encapsulation. Let the plaintext message be M, and a random exponent is selected. , Computation ciphertext component: ; ; ; In the formula, For the system master key component, To randomly select the index, For generators, For attributes in the strategy tree The corresponding share value; As the core encrypted component; To generate elements The result of the bilinear pairing operation on the input Exponentiation of subgroups; To assist in the ciphertext component; For attributes The bound attributed ciphertext component. The decryption process requires the user's private key attributes to satisfy at least one complete path condition from the root node to a leaf node, reconstructed through bilinear pairing operations. to restore plaintext Any missing attribute will cause interpolation to fail, the bilinear pair output will deviate from the correct value, and decryption will be impossible.

[0049] In this embodiment, an anti-interference communication adaptive adjustment module is integrated into the UAV communication subsystem. This module monitors spectrum occupancy in real time via a Software Defined Radio (SDR) platform, scanning the 2.4 GHz ISM (Industrial, Scientific & Medical) band (2400–2483.5 MHz) and the 5.8 GHz band (5725–5850 MHz), with a resolution bandwidth of 100 kHz and a scan period of 10 ms. When narrowband blocking interference (bandwidth < 1 MHz, power > 0.5 MHz) is detected, the module will adjust its frequency accordingly. 60 dBm) or broadband noise attack (bandwidth > 5 MHz, power > When the signal-to-noise ratio (SNR) drops below 70 dBm, the interference avoidance protocol is automatically activated. The interference avoidance protocol execution process is as follows: First, the interference type is determined. If it is narrowband interference, frequency-hopping spread spectrum (FHSS) mode is activated. The frequency-hopping sequence is generated using a pseudo-random permutation algorithm with a period of 1 ms and 32 frequency-hopping points. If it is wideband interference, it switches to direct-sequence spread spectrum (DSSS) mode with a 64-bit Gold code and a processing gain of 18 dB. Simultaneously, the modulation order is adjusted: dynamically switching between QPSK (Quadrature Phase Shift Keying) and BPSK (Binary Phase Shift Keying). When the SNR is below 10 dB, it switches to BPSK to ensure reliability. Critical control signals (such as emergency disconnection commands) are transmitted independently using a fixed low-rate channel (128 kbps) to ensure a minimum command path remains open even under extreme interference.

[0050] As a feasible implementation, a secure boot and firmware integrity verification process is enforced on the UAV during each power-on initialization phase. Specifically, a trusted root certificate (a 2048-bit RSA (Ron Rivest, Adi Shamir, Leonard Adleman, RSA encryption algorithm) public key) is first loaded from the read-only fuse area to verify the SM2 digital signature of the bootloader. Upon successful verification, execution proceeds. The bootloader further verifies the integrity and authenticity of the operating system image using a segmented hash verification mechanism: the image is divided into 128 KB data blocks, the SM3 hash value of each block is calculated, and compared with a hash list pre-stored in the secure storage area. After all verifications pass, application software modules are verified level by level, including the communication module, encryption engine, flight control logic, etc., each carrying an independent SM2 signature. If any verification fails, the system immediately enters a secure lockout state: the wireless communication interface is shut down, the data acquisition sensors are disabled, the flight controller is only allowed to perform basic hovering or return-to-home actions, the screen displays the fault code "SEC_ERR_01," and an audible and visual warning is issued. The device must be connected to a dedicated debugging port and an administrator key must be entered to unlock it. This mechanism effectively prevents the spread of security vulnerabilities such as malicious code injection and firmware tampering, ensuring the trustworthiness of the entire data protection system's underlying layer.

[0051] As a feasible implementation, an immutable log chain is maintained in the drone's local storage module through data traceability and abnormal behavior auditing, with a Merkle tree structure used to organize log records. Each log record contains the following fields: operation type (enumerated values: 0=data acquisition, 1=data encryption, 2=data forwarding, 3=authentication, 4=firmware verification), execution entity identifier (64-bit universally unique identifier), UTC timestamp (millisecond level), pre-operation state hash (256-bit SM3 value), post-operation state hash (256-bit SM3 value), and random salt value (128 bits). Log records are written in batches, with each batch containing 128 records, forming a Merkle leaf node. The Merkle tree is a complete binary tree structure with a height of 7 levels, accommodating a maximum of 16,384 log records. The root hash value is stored in a dedicated secure register and periodically (hourly) uploaded to the edge computing node via an encrypted channel. The aggregation analysis module performs pattern recognition on the received log batches and uses a sliding window to count the number of failed decryption attempts per unit time. When the number of failures exceeds a threshold (set to 10) within 5 consecutive minutes, it is determined to be a brute-force attack. Simultaneously, it analyzes unauthorized access patterns, such as multiple decryption requests initiated from different geographical locations within a short period, triggering unauthorized access alarms. Alarm information includes attack signature vectors, source device fingerprints, and suggested response levels, and is pushed to the operations and maintenance management center.

[0052] The UAV data protection method for power system acquisition terminals provided in this embodiment extracts multi-dimensional physical layer feature vectors from real-time acquired channel state information for bidirectional device authentication. Upon successful device authentication, the UAV divides the real-time acquired data into three levels: core control commands, device operating parameters, and environmental monitoring information. Different encryption algorithms with varying security strengths are used for each level before multi-hop relaying is performed. The acquired data is encapsulated into data packets containing access policy tags. Then, based on bilinear pairing, the CP-ABE algorithm is used to encrypt the data packets. During encryption, the access policy is transformed into hidden mathematical parameters so that a legitimate receiver can perform decryption to complete the current data transmission. Therefore, the data security protection method for power system acquisition terminals in this application utilizes the inherent uniqueness of the wireless channel between the UAV and the acquisition terminal during the initial near-field handshake phase. It extracts multi-dimensional physical layer features based on signal strength indication, carrier phase, time difference of arrival, and Doppler frequency shift in real time to form a stable and difficult-to-copy device fingerprint. Then, a similarity discrimination model is used to complete two-way identity verification, fundamentally eliminating the risk of man-in-the-middle spoofing and device impersonation. Based on this trusted channel, the UAV performs encryption processing on different levels of the acquired data using encryption algorithms with different security strengths before transmitting the data. When performing multi-hop relay, the acquired data is encapsulated into data packets carrying access policy tags and encrypted using bilinear pairing mathematical tools (CP-ABE). This embeds the access policy into the ciphertext structure as an implicit mathematical constraint, possessing fine-grained policy and anti-collusion characteristics.

[0053] Example 2 Based on the above embodiments, to verify the effectiveness, efficiency, and security of the data protection method proposed in this invention in the scenario of unmanned aerial vehicle (UAV) inspection of power systems, a provincial power grid company deployed the method described in this invention in a UAV system for power transmission line inspection, and built a system as follows. Figure 3 The experimental platform shown consists of one DJI Motrice 300RTK UAV (equipped with a self-developed secure communication payload, an Intel Atomx 7-E3950 processor, and 8GB RAM), five Huawei Atlas 500 intelligent edge servers (serving as power acquisition terminals, with built-in SM2 / SM4 hardware acceleration modules), and a ground control station. The communication link operates in the 2.4GHz ISM band at a distance of 1.2 kilometers, and is subject to typical urban Wi-Fi interference (average RSSI = -65dBm). The comparison scheme uses a traditional PKI system, namely, TLS 1.3 protocol based on X.509 certificates (RSA-2048) for identity authentication and data encryption.

[0054] When the inspection mission was initiated, the drone flew to the vicinity of the substation (approximately 80 meters from the terminal) and automatically established a Wi-Fi Direct near-field link. Both parties collected CSI data at 1 ms intervals, continuously for 200 ms, obtaining a total of 200 sets of feature vectors. After normalization, these vectors were input into a Siamese neural network model to calculate the feature distance, resulting in successful authentication. Specifically, the IMU noise entropy source collected 1024 sample points, generating a 256-bit noise hash, which was used in KDF operations to generate the session key.

[0055] After authentication, the drone begins transmitting data: core control commands (such as initiating infrared thermometry) use SM2 signatures, and equipment operating parameters (such as phase A current = 142.3A) are encrypted using SM4-CBC; environmental monitoring information (such as visible light image thumbnails) is verified using a hash chain method. When the drone passes under a high-voltage tower, the RSSI drops from -68 dBm to -88 dBm, and the BER rises to 8 × 10⁻³, at which point the system automatically switches to a high-security mode: the number of SM4 encryption rounds increases to 48, and RS encoding is enabled to ensure that no critical telemetry data is lost.

[0056] When performing relay missions, the drone needs to forward the collected data to the next station. Data packets are encrypted using CP-ABE. The log module generates a batch of logs every hour, containing 4096 operation records, which are then constructed into a Merkle tree and uploaded to the edge node of the municipal company. During this period, the communication subsystem detected narrowband interference (power -58 dBm) at a center frequency of 2437 MHz in the 2.4 GHz band. It then switched to FHSS mode, rapidly hopping between 32 channels, and simultaneously reduced the modulation scheme from 64-QAM to QPSK to maintain control signaling connectivity. Every morning upon power-up, the firmware verification process runs automatically, sequentially verifying the digital signatures of the Bootloader, RTOS kernel, and application firmware. The entire process takes 1.2 seconds; if any module's hash does not match, it enters a locked state.

[0057] During the experiment, the drone collected one core command (<100 bytes), 10 operational parameters (approximately 500 bytes), and 100 environmental monitoring information entries (approximately 1KB) per second from each terminal, and uploaded them to the cloud via two relays. The following three key indicators were tested 100 times repeatedly, and the average value was taken: (1) Identity authentication delay: This scheme completes two-way authentication through physical layer feature comparison, with an average time of 87 milliseconds; while traditional PKI (Public Key Infrastructure) requires certificate request, OCSP (Online Certificate Status Protocol) verification, ECDH (Elliptic Curve Diffie-Hellman) key negotiation, and TLS (Transport Layer Security) record establishment, with an average time of 229 milliseconds. This scheme shortens the time by 229 milliseconds compared to traditional PKI. The authentication time is reduced by approximately 62% (87) / 229, significantly improving the efficiency of establishing temporary connections.

[0058] (2) System energy consumption and range: In this embodiment, the total energy consumption of a single complete operation (flight + communication) is recorded through the drone's Battery Management System (BMS). Due to the use of lightweight layered encryption (using only hash chain verification for 80% of environmental data, avoiding full SM4 encryption) and low-overhead authentication, the energy consumption for a single communication operation is 18.2 watt-hours (Wh); traditional PKI, due to continuous high-load encryption and decryption, consumes up to 33.1 Wh. Energy consumption is reduced (33.1 Wh). 18.2) / 33.1≈45%, which increases the single-operation endurance from 40 minutes to 58 minutes with the same battery capacity.

[0059] (3) Resistance to attack: Three typical attack types (replay attack, man-in-the-middle eavesdropping, and forged terminal access) were simulated, with 10,000 attempts for each type. Due to the integration of the time window (UTC synchronization ±10ms), geographical location constraints (BeiDou positioning ±50m), and high-entropy dynamic key seed (CSI+IMU joint entropy source ≥128bit), the probability of an unauthorized terminal successfully reading any valid data is only 0.02% (i.e., 2 successes / 10,000 attacks). Detailed performance comparison data is shown in Table 1 below.

[0060] Table 1

[0061] Through the aforementioned collaborative operation across the entire process, this invention achieves full-stack security protection, from device access, data transmission, and access control to system self-protection. Identity authentication takes an average of 87 milliseconds, a 62% reduction compared to traditional PKI systems; layered encryption reduces overall energy consumption by 45% and extends single-operation endurance to 58 minutes; attribute-based encryption reduces the success rate of unauthorized terminal data reading to 0.02%; log audit coverage reaches 100%, and anomaly detection response time is less than 3 seconds. All technical indicators meet design expectations and satisfy the stringent requirements of power systems for UAV data security.

[0062] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method in any of the embodiments of this application. Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the above embodiments is stored, and the computer (or CPU (Central Processing Unit) or MPU (Microprocessor Unit) of the system or apparatus may read and execute the program code stored in the storage medium.

[0063] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this application.

[0064] It should be noted that the computer-readable storage medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF (Radio Frequency), etc., or any suitable combination thereof.

[0065] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0066] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0067] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, modules, and methods can be implemented in other ways. For example, the module embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules or units, and may be electrical, mechanical, or other forms.

[0069] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. This application is not limited to the exact structures described above and illustrated in the accompanying drawings, and it should not be considered that the specific implementation of this application is limited to these descriptions. For those skilled in the art, various changes and modifications made without departing from the concept of this application should be considered to fall within the protection scope of this application.

Claims

1. A method for protecting UAV data in a power system data acquisition terminal, characterized in that, include: When the drone and the data acquisition terminal enter the near-field communication range, the channel status information of the communication signals between the two parties is collected in real time. The UAV extracts multi-dimensional physical layer feature vectors from the channel state information for device authentication. If the device authentication is successful, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vectors. The multi-dimensional physical layer feature vectors include signal strength indication, carrier phase, time difference of arrival, and Doppler shift. The drone acquires data from the acquisition terminal in real time and divides the data into three levels: core control commands, equipment operating parameters, and environmental monitoring information. Different encryption algorithms with different security strengths are used to encrypt the data before transmitting it. When the UAV performs a multi-hop relay mission, the UAV encapsulates the collected data into a data packet containing an access policy label, and then encrypts the data packet using the CP-ABE algorithm based on the mathematical tool of bilinear pairing. During encryption, the access policy is transformed into hidden mathematical parameters so that the legitimate receiver can perform the decryption operation to complete the current data transmission.

2. The method for protecting UAV data in a power system data acquisition terminal according to claim 1, characterized in that, The step of extracting multi-dimensional physical layer feature vectors from the channel state information for device authentication includes: Extract parameters of each dimension from the channel state information, and concatenate the normalized values ​​corresponding to each dimension parameter to form the multi-dimensional physical layer feature vector. The multi-dimensional physical layer feature vector is input into a pre-trained similarity discrimination model to calculate the feature distance between legitimate devices in real time and determine whether the feature distance between legitimate devices is less than a preset threshold; and if both the first comparison result and the second comparison result satisfy the distance constraint, the device authentication is determined to be successful; the first comparison result and the second comparison result together constitute the bidirectional comparison result between the UAV and the acquisition terminal; The similarity discrimination model is built on a Siamese neural network architecture, which includes two structurally identical sub-network branches, each consisting of three fully connected layers. The similarity discrimination model is trained using a contrastive learning approach, and the loss function is defined as a triplet loss, specifically: ； In the formula, For anchor point samples, Positive samples from the same device pair These are negative samples from different devices. Represents the network mapping function; This represents the boundary margin.

3. The method for protecting UAV data in a power system data acquisition terminal according to claim 2, characterized in that, In the case of device authentication, the drone generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vector, including: When device authentication is successful, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vector, and introduces an environmental noise disturbance factor during the generation of the one-time dynamic key seed; the environmental noise disturbance factor includes the output signals from the accelerometer and gyroscope of the inertial measurement unit carried by the UAV. Specifically, the environmental noise disturbance factor is used as a physically unclonable source in the key derivation function operation, and this physically unclonable source is used as an entropy enhancement module in the key derivation function; ； In the formula, This is the final one-time dynamic key seed. Derived function for key. The shared feature fingerprint is agreed upon by both the drone and the data acquisition terminal. The noise hash value of the inertial measurement unit. A one-time random number generated by the drone. A one-time random number generated by the data acquisition terminal. This refers to cryptographic hash operations.

4. The method for protecting UAV data in a power system data acquisition terminal according to claim 3, characterized in that, The collected data is divided into three levels: core control commands, equipment operating parameters, and environmental monitoring information. Different encryption algorithms with varying security strengths are used for each level before data transmission. The UAV divides the collected data into three levels according to a preset strategy; the first level is the core control commands that directly affect the operating status of the power system, the second level is the equipment operating parameters that reflect the real-time operating status of the data collection terminal, and the third level is environmental monitoring information. For the core control commands, an elliptic curve-based digital signature algorithm is used for integrity protection; for the device operating parameters, the national cryptographic SM4 algorithm is used for symmetric encryption; for the environmental monitoring information, a hash chain structure is used to implement tamper-proof verification of the data sequence.

5. The method for protecting UAV data in a power system data acquisition terminal according to claim 4, characterized in that, The method further includes: The UAV detects signal strength indication and bit error rate in real time, and determines the current communication link quality based on the signal strength indication and bit error rate, so as to dynamically adjust the encryption parameter configuration according to the current communication link quality.

6. The method for protecting UAV data in a power system data acquisition terminal according to claim 5, characterized in that, The step of determining the current communication link quality based on the signal strength indication and the bit error rate, and dynamically adjusting the encryption parameter configuration according to the current communication link quality, includes: When the signal strength indicator is detected to be higher than the first preset strength threshold and the bit error rate is lower than the first preset bit error rate threshold in real time, the current link quality is determined to be a high-quality channel condition, and a high-throughput mode is enabled; in the high-throughput mode, the number of hash chain iterations of the environmental monitoring information is reduced, and forward error correction coding is turned off. When the signal strength indicator is detected to be lower than the second preset strength threshold or the bit error rate is higher than the second preset bit error rate threshold in real time, the system switches to high security mode; in high security mode, the number of SM4 encryption rounds is increased and RS encoding is enabled for forward error correction.

7. The method for protecting UAV data in a power system data acquisition terminal according to claim 6, characterized in that, The access strategy is represented by a logic tree structure, with leaf nodes being atomic attributes and non-leaf nodes being AND or OR logic gates; the atomic attributes include: role permission attributes, time-limited window attributes, and geographical location range attributes. The role permission attribute is encoded through an extended field in the digital certificate; the time window attribute is defined in the form of an absolute time interval, and combined with the UTC time synchronization of the UAV's built-in high-precision time synchronization module, the decryption operation is only valid if the current moment falls within the preset time period.

8. The method for protecting UAV data in a power system data acquisition terminal according to claim 7, characterized in that, The data packet, during encapsulation, invokes the key generation interface to perform encryption operations and output ciphertext; specifically: Let the plaintext message be M, and choose a random index. , Computation ciphertext component: ； ； ； In the formula, For the system master key component, To randomly select the index, For generators, For attributes in the strategy tree The corresponding share value; As the core encrypted component; To generate elements The result of the bilinear pairing operation on the input Exponentiation of subgroups; To assist in the ciphertext component; For attributes Bound attributed ciphertext components; During the decryption process, the user's private key attributes are required to satisfy at least one complete path condition from the root node to the leaf node in the logical tree structure.

9. The method for protecting UAV data in a power system data acquisition terminal according to any one of claims 1-8, characterized in that, The method further includes: The UAV monitors spectrum occupancy in real time. When it detects narrowband jamming or broadband noise attack, it automatically switches to backup spread spectrum communication mode, adjusts the frequency hopping sequence and modulation order, and maintains the minimum critical control signaling path open.

10. A UAV data protection system for a power system data acquisition terminal, used to implement the UAV data protection method for a power system data acquisition terminal as described in any one of claims 1-9, characterized in that, include: The real-time acquisition module is used to acquire channel status information of the communication signals between the UAV and the acquisition terminal in real time when they enter the near-field communication range. An authentication module is used to extract multi-dimensional physical layer feature vectors from the channel state information for device authentication. If the device authentication is successful, the UAV generates a one-time dynamic key seed based on the multi-dimensional physical layer feature vectors. The multi-dimensional physical layer feature vectors include signal strength indication, carrier phase, time difference of arrival, and Doppler shift. The data encryption module is used by the UAV to acquire data from the acquisition terminal in real time, and divide the acquired data into three levels: core control commands, equipment operating parameters and environmental monitoring information. Different encryption algorithms with different security strengths are used to perform encryption processing for different levels before data transmission. The data transmission module is used to encapsulate the collected data into a data packet containing an access policy label when the UAV is performing a multi-hop relay mission. Then, based on the mathematical tool of bilinear pairing, the UAV uses the CP-ABE algorithm to encrypt the data packet, and converts the access policy into hidden mathematical parameters during encryption so that the legitimate receiver can perform the decryption operation to complete the current data transmission.

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