Cooperative blocking system for drone navigation signals based on quantum key distribution

Abstract

This invention provides a collaborative blocking system for drone navigation signals based on quantum key distribution, belonging to the field of communication security technology. This invention achieves high-precision threat identification through quantum key distribution and multi-layer convolutional neural networks, significantly reducing the risk of misjudgment. It combines standardized encryption parameters and an incremental update mechanism to ensure the accuracy of feature comparison, avoiding false interference caused by signal confusion. Simultaneously, an intelligent probability model evaluates flight data in real time, triggering directional interference or signal modulation only when the risk exceeds a threshold, ensuring the safety of legitimate drones. The monitoring module ensures controllable permissions through biometric authentication and operation logs, balancing security efficiency and flight safety.

Description

Technical Field

[0001] This invention relates to the field of communication security technology, specifically to a collaborative blocking system for UAV navigation signals based on quantum key distribution. Background Technology

[0002] A drone navigation signal cooperative blocking system is a technical system designed to disrupt drone navigation signals through cooperative methods, thereby preventing unauthorized flight or loss of control. This system typically integrates multiple techniques, such as signal jamming and spoofing, to interfere with drone navigation signals within a specific area.

[0003] In the prior art, publication number CN111385054A discloses a system and method for intelligently blocking satellite navigation signals from unmanned aerial vehicles (UAVs). This system includes a remote information acquisition module, a key target information database module, a remote information determination module, and a satellite navigation interference activation and deactivation module. These modules are sequentially connected. This invention can effectively counter low-altitude intruders (such as UAVs) while ensuring that key targets (such as civil aircraft) receive satellite navigation signals normally, preventing adverse environmental damage. It effectively protects government agencies, military facilities, energy storage depots / stations, large commercial venues, private locations, and sensitive areas.

[0004] In the aforementioned "System and Method for Intelligent Denial of Satellite Navigation Signals for Unmanned Aerial Vehicles," although its design aims to enhance security for large-scale events and control of unmanned aerial vehicle flight, this technology may have some technical drawbacks in practical applications. The following are two drawbacks with a cascading effect:

[0005] 1. Misjudgment leads to an expanded range of interference:

[0006] When this system compares and identifies target information in the critical target information database, it may mistakenly identify legitimate drones as threat targets if its judgment accuracy is insufficient. For example, if the signal frequencies of other drones or other devices in the vicinity are similar to those in the critical target information database, the system may issue interference commands. Such misjudgments can interfere with legitimate drones that should be flying normally, thereby affecting normal flight operations, such as medical rescue or emergency response missions.

[0007] 2. Potential safety risks of legal aircraft:

[0008] The expanded scope of interference due to misjudgment, resulting from the system's erroneous interference with legitimate drones, could lead to more serious consequences, such as causing legitimate drones to lose control or deviate from their paths, thereby increasing the risk of mid-air collisions. Furthermore, if these legitimate drones are involved in emergency situations, including medical transport, such interference could have severe consequences, impacting public safety and emergency response.

[0009] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0010] The purpose of this invention is to provide a collaborative blocking system for UAV navigation signals based on quantum key distribution, so as to solve the problems mentioned in the background art.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A quantum key distribution-based drone navigation signal cooperative blocking system is applied to the flight control of security drones for large-scale events, including:

[0013] Data processing module: used to collect navigation multimodal data of the target UAV when executing the planned path, and to generate dynamic encryption parameters for the navigation multimodal data using quantum key distribution technology, and to adjust the dynamic encryption parameters in real time through an adaptive communication protocol;

[0014] Intelligent identification module: Used to perform fusion analysis on the dynamic encryption parameters of multiple drones using deep learning algorithms, in order to extract and identify the navigation signal threat characteristics of potential threat drones;

[0015] Dynamic Database Module: Used to integrate information from public databases and private data sources, update the navigation signal threat characteristics of potential threat drones in real time, and establish an automated database update mechanism;

[0016] Decision-making module: It is used to match and judge the dynamic encrypted parameters of the target UAV adjusted in real time with the threat characteristics of the navigation signal, and to conduct risk assessment of the target UAV by constructing an intelligent probability model to obtain multi-dimensional risk assessment results;

[0017] Interference control module: Based on multi-dimensional risk assessment results, automatically selects targeted interference methods, including directional interference or signal strength adjustment; and monitors the interference effect in real time.

[0018] The regulatory authorization module restricts access to interfering functions through a dynamic user authentication mechanism; it also records all operation logs and generates transparent reports for post-event auditing and security analysis.

[0019] Furthermore, the data processing module specifically includes:

[0020] Through the sensor system equipped on the drone, real-time collection of the navigation multi-modal data of the drone during the execution of the predetermined path is carried out;

[0021] Then, preprocessing is performed on the navigation multi-modal data, including screening, filtering, and normalization processing, to remove noise and redundant data, and format the data into a structure suitable for encryption processing;

[0022] Generate dynamic encryption parameters for the navigation multi-modal data in real-time, including:

[0023] Generate a random single-photon quantum state, and then transmit the single-photon quantum state through an optical fiber;

[0024] Receive the transmitted single-photon quantum state and measure the quantum state according to a preset benchmark;

[0025] Perform error correction and privacy amplification operations on the quantum state sequences of the sending end and the receiving end through a non-quantized communication channel to generate a shared secret key;

[0026] Based on the shared secret key, generate a dynamic encryption secret key through a hash function combined with a timestamp, and use a symmetric encryption algorithm to encrypt the collected navigation multi-modal data.

[0027] Furthermore, the data processing module specifically further includes:

[0028] The communication real-time adjustment unit adjusts the dynamic encryption parameters in real-time according to the communication environment state of the drone. By real-time collecting the signal strength, bit error rate, and communication interference strength in the communication channel, an environment state vector V env =[Sa, Ea, Ia];

[0029] where Sa represents the signal strength, Ea represents the bit error rate, and Ia represents the communication interference strength;

[0030] Set an encryption parameter adjustment trigger mechanism module for defining a signal strength threshold Sb, a bit error rate threshold Eb, and an interference strength threshold Ib. When the environment state satisfies any one of Sa < Sb, or Ea > Eb, or Ia > Ib, trigger the adjustment of the dynamic encryption parameters;

[0031] Based on the environment state vector V env and the timestamp t, generate a dynamic encryption secret key Key = h(V env , t), where h is a preset encryption secret key generation algorithm;

[0032] Use the dynamic encryption secret key Key to perform frame-by-frame fragmentation encryption on the navigation data frames to generate encrypted data frames;

[0033] Based on the environment state vector V env Regenerate and refresh the evaluation function R(V) env ,Δt), where Δt represents the time interval since the last key refresh;

[0034] Based on the environmental state vector V env The refresh evaluation function dynamically adjusts the key refresh cycle to broadcast refresh notifications and synchronize key updates between the sender and receiver.

[0035] Furthermore, the intelligent recognition module specifically includes:

[0036] By collecting dynamic encryption parameters from multiple drones and standardizing the dynamic encryption parameters generated by different drones, a standardized encryption parameter set is formed.

[0037] A multi-layer convolutional neural network is constructed, comprising an input layer, a convolutional layer, a pooling layer, and a fully connected layer. The convolutional neural network is used to train dynamic encryption parameters to generate feature determination results.

[0038] Based on the convolutional neural network, features are extracted from the dynamic encryption parameters of the UAV, and a fused feature representation Ffu is generated through feature concatenation operation.

[0039] The classification results of potential threat drones are generated using fused feature representations, and the threat classification probability Yth is calculated using the Softmax activation function. k The specific calculation formula is as follows:

[0040]

[0041] Where j represents the category index to be traversed, used to enumerate all existing threat categories; m represents the total number of threat categories in the threat identification task, that is, the total number of categories classified by the model;

[0042] This represents the model output value corresponding to the k-th type of threat. Perform exponential calculation on the logits value of the k-th threat category, with the denominator calculated separately for each category;

[0043] This represents the predicted probability of the k-th class after calculation by the Softmax function, located in the interval [0,1], and the sum of the probabilities of all classes is 1;

[0044] Preset threat classification probability threshold Pth k , and the threat classification probability Yth k A comparative evaluation was conducted, the details of which are as follows:

[0045] If the threat classification probability Ythk If the threat classification probability threshold Pth is reached, the drone is marked as a threat, and the isolation module is notified to perform subsequent actions.

[0046] If the threat classification probability Yth k If the probability of the threat classification is less than or equal to the threat classification probability threshold Pth, then normal communication continues and no isolation operation is performed.

[0047] Based on the threat assessment results, a drone threat report is generated. The report includes the identification information and classification probability of potential threat drones, and an isolation command is sent to cut off the communication channels of the threat drones.

[0048] Furthermore, the dynamic database module specifically includes:

[0049] Import public databases and navigation signal feature data through an application programming interface protocol based on presentation layer state transition;

[0050] A connection is established with a private data source through an authentication mechanism. A token-based authorization protocol is used in the process, and a standardized conversion tool is used to convert the accessed information data into a unified JSON object representation format to obtain compatible standardized feature inputs.

[0051] Furthermore, the dynamic database module specifically includes:

[0052] Set a preset time interval and incremental update mechanism. When new data is detected, the incremental update mechanism is triggered, including updating only the characteristics of the new signal.

[0053] When no new signal features are added, batch updates are performed according to the set time intervals;

[0054] The specific details of the incremental update mechanism are as follows:

[0055] The correlation matching degree S is calculated and obtained based on the newly added navigation signal mode information;

[0056] At the same time, a preset association matching threshold Sth is set and compared with the association matching degree S. When the association matching degree S is lower than the preset association threshold Sth, it is determined to be existing data and no update is made.

[0057] When the correlation matching degree S is greater than or equal to the correlation threshold Sth, the information of the newly added signal feature is included in the database for updating and marked as a new feature.

[0058] Furthermore, the decision-making module specifically includes:

[0059] Based on the target UAV's real-time adjusted dynamic encryption parameters and navigation signal threat characteristics, and after dimensionless processing, the standardized values ​​of the dynamic encryption parameters and signal threat characteristics are obtained.

[0060] The standardized values ​​of the dynamic encryption parameters and the standardized values ​​of the signal threat characteristics are extracted, and the real-time matching index SSp is calculated. The specific calculation formula is as follows:

[0061]

[0062] In the formula, This represents the e-th dynamic encryption parameter. This is the corresponding e-th threat signal feature;

[0063] p represents the total number of parameters; e is used to iterate through all the dynamically encrypted parameters that need to be compared. and corresponding signal threat characteristics The index, whose value starts from 1 and goes up to p;

[0064] These are weighting coefficients used to reflect the importance of different features in the overall matching degree calculation;

[0065] Real-time flight-related data is collected, including flight distance, flight signal strength, flight path, and real-time environmental information. Statistical and logistic regression learning techniques are used to construct a risk prediction model based on flight-related data and the real-time matching index SSp.

[0066] The probability of occurrence of various threats is output using a risk prediction model, and a comparative evaluation is conducted by setting a preset security threshold.

[0067] The interference control module is activated when the assessed probability of any threat reaches or exceeds a preset security threshold.

[0068] When the assessed probabilities of all threats do not exceed the preset safety thresholds, the drone system maintains normal operation mode and continues to conduct routine risk monitoring.

[0069] Furthermore, the interference control module specifically includes:

[0070] When the assessed probability of any threat reaches or exceeds a preset security threshold, targeted jamming measures are employed, including directional jamming and signal strength modulation.

[0071] After implementing targeted jamming measures, the changes in target signals and feedback on the UAV status are monitored in real time, and the effectiveness of the jamming strategy is verified.

[0072] The effectiveness of the interference strategy was verified, and the details are as follows:

[0073] Collect real-time data on target signals and UAV status feedback, including monitoring changes in target signal strength, frequency fluctuations, as well as changes in UAV position and response speed;

[0074] The effectiveness of the currently implemented jamming strategy is evaluated based on real-time data of the target signal and the status feedback of the UAV.

[0075] If the actual monitored effect is less than the expected effect, the interference method and parameter settings will be automatically adjusted, and the verification will be repeated until the predetermined safety standard is reached.

[0076] Furthermore, the regulatory authorization module specifically includes:

[0077] Receive and verify the legitimacy of the user's identity based on the password and biometric data entered by the user; the specific process is as follows:

[0078] A secure input interface is set up for users to enter passwords and scan biometric data, including fingerprints or irises. Passwords are verified by encryption algorithms, and biometric data is compared and verified by a biometric scanner.

[0079] Access permissions for specific functions can be dynamically set based on authentication results, including role-based access control.

[0080] Furthermore, the regulatory authorization module also includes:

[0081] Simultaneously, all user actions and system responses based on permissions are recorded. The specific process is as follows:

[0082] Automatically capture and store each user's permission-based operation and the system's own response, including operation time, operator identity, operation type and result, and use timestamps and user identifiers to make the data complete and traceable;

[0083] The system automatically triggers audit report generation based on the needs of the audit department, extracts relevant log data from the database, and generates highly transparent audit reports containing information such as operation summaries, timestamps, and executor identities to support post-audit.

[0084] Compared with the prior art, the beneficial effects of the present invention are:

[0085] This invention effectively solves the problem of increased interference range caused by misjudgment; it generates dynamic encryption parameters through quantum key distribution technology and combines them with multi-layer convolutional neural networks for high-precision feature matching, significantly reducing the probability of legitimate drones being misidentified as threat targets; it uses a standardized set of encryption parameters and an incremental update mechanism to ensure the accuracy of database feature comparison and avoids false interference caused by signal frequency similarity.

[0086] This invention also significantly reduces the potential safety risks of legal aircraft; it evaluates flight-related data in real time through an intelligent probability model and performs multi-dimensional verification by setting safety thresholds; it adopts a dual dynamic response mechanism of directional interference and signal strength adjustment to ensure that interference only applies to drones identified as threats; and it combines the biometric authentication and operation log traceability functions of the regulatory authorization module to maximize the protection of drones' flight rights for emergency missions such as medical rescue from being wrongly deprived. Attached Figure Description

[0087] Figure 1 This is a schematic diagram of the overall system framework of the present invention. Detailed Implementation

[0088] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0089] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0090] Example 1:

[0091] Please see Figure 1 This invention provides a collaborative blocking system for drone navigation signals based on quantum key distribution, applicable to the flight control of security drones for large-scale events, including:

[0092] Data processing module: used to collect navigation multimodal data of the target UAV when executing the planned path, and to generate dynamic encryption parameters for the navigation multimodal data using quantum key distribution technology, and to adjust the dynamic encryption parameters in real time through an adaptive communication protocol;

[0093] Intelligent identification module: Used to perform fusion analysis on the dynamic encryption parameters of multiple drones using deep learning algorithms, in order to extract and identify the navigation signal threat characteristics of potential threat drones;

[0094] Dynamic Database Module: Used to integrate information from public databases and private data sources, update the navigation signal threat characteristics of potential threat drones in real time, and establish an automated database update mechanism;

[0095] Decision-making module: It is used to match and judge the dynamic encrypted parameters of the target UAV adjusted in real time with the threat characteristics of the navigation signal, and to conduct risk assessment of the target UAV by constructing an intelligent probability model to obtain multi-dimensional risk assessment results;

[0096] Interference control module: Based on multi-dimensional risk assessment results, automatically selects targeted interference methods, including directional interference or signal strength adjustment; and monitors the interference effect in real time.

[0097] The regulatory authorization module restricts access to interfering functions through a dynamic user authentication mechanism; it also records all operation logs and generates transparent reports for post-event auditing and security analysis.

[0098] In this embodiment, each module achieves collaborative and efficient functions, ensuring the intelligence and reliability of the UAV navigation signal blocking system throughout the entire chain from data acquisition to risk control;

[0099] The data processing module collects navigation multimodal data of the target UAV in real time while it is executing the planned path, including data acquired by sensors such as GPS, visual cameras, RFID readers, and thermal imagers;

[0100] Furthermore, it combines quantum key distribution technology to generate dynamic encryption parameters and uses an adaptive communication protocol to adjust the dynamic encryption parameters in real time, effectively avoiding the problem of encryption key failure due to changes in the communication environment;

[0101] The intelligent identification module uses deep learning algorithms to fuse and analyze the dynamic encryption parameters of multiple drones, enabling it to accurately extract the threat characteristics of navigation signals from potential threat drones. It then calculates the threat classification probability Yth using the Softmax activation function. k To improve the accuracy of threat identification;

[0102] The dynamic database module integrates public databases and private data sources accessed using the Open Access 2.0 protocol. By establishing an automated update mechanism, it can perform batch updates when the time interval ΔT is set to 5 minutes or trigger an incremental update mechanism based on the correlation matching degree S.

[0103] Further improve data update efficiency and maintain the real-time and reliability of signal characteristics; the decision-making module constructs an intelligent probability model based on the matching index SSp and flight-related data such as signal strength Sa, bit error rate Ea, and interference intensity Ia to conduct risk assessment of the target UAV and obtain multi-dimensional risk assessment results;

[0104] When the probability of any threat assessment exceeds a preset security threshold, the interference control module can be triggered. The interference control module automatically selects targeted interference methods such as directional interference or signal strength adjustment based on the assessment results and verifies in real time whether the interference effect meets the predetermined security standard.

[0105] The regulatory authorization module verifies the legality of the user's input password and biometric data through a dynamic user authentication mechanism to dynamically set access permissions for interfering functional modules and record all operation logs; combined with timestamps, it generates transparent reports for post-event auditing and security analysis to ensure the system's security and operational compliance.

[0106] Example 2

[0107] The data processing module specifically includes:

[0108] The sensor system equipped on the drone collects navigation multimodal data in real time as the drone executes the predetermined path;

[0109] Furthermore, the sensor system integrates multiple sensors, including GPS, visual cameras, RFID readers, and thermal imagers, to acquire data.

[0110] Next, the navigation multimodal data is preprocessed, including filtering, standardization, and screening, to remove noise and redundant data, and to format the data into a structure suitable for encryption.

[0111] Real-time generation of dynamically encrypted parameters from navigation multimodal data, including:

[0112] Random single-photon quantum states are generated and then transmitted via optical fiber.

[0113] Receive the transmitted single-photon quantum state and measure the quantum state according to a preset reference;

[0114] Error correction and privacy amplification operations are performed on the quantum state sequences of the sender and receiver through a non-quantum communication channel to generate a shared key;

[0115] Based on the shared key, a dynamic encryption key is generated by combining a hash function with a timestamp, and the collected navigation multimodal data is encrypted using a symmetric encryption algorithm.

[0116] The data processing module also includes:

[0117] The real-time communication adjustment unit adjusts the dynamic encryption parameters in real time according to the UAV's communication environment. It generates an environmental state vector V by real-time acquisition of signal strength, bit error rate, and communication interference intensity in the communication channel. env =[Sa,Ea,Ia];

[0118] Where Sa represents the signal strength, Ea represents the bit error rate, and Ia represents the communication interference strength;

[0119] Set an encryption parameter adjustment trigger mechanism module for defining a signal strength threshold Sb, a bit error rate threshold Eb, and an interference strength threshold Ib. When the environmental state satisfies any one of Sa < Sb, or Ea > Eb, or Ia > Ib, trigger dynamic encryption parameter adjustment;

[0120] Based on the environmental state vector V env and the timestamp t, generate a dynamic encryption key Key = h(V env , t), where h is a preset encryption key generation algorithm;

[0121] Use the dynamic encryption key Key to encrypt each navigation data frame piece by piece to generate encrypted data frames;

[0122] Based on the environmental state vector V env , generate a refresh evaluation function R(V env , Δt) again, where Δt represents the time interval since the last key refresh;

[0123] According to the environmental state vector V env and the refresh evaluation function, dynamically adjust the key refresh period to broadcast a refresh notice and synchronize the key update between the sender and the receiver.

[0124] Furthermore, the specific mathematical representation of the refresh evaluation function is:

[0125]

[0126] Where α1, α2, α3, and α4 are weight factors that satisfy the normalization condition of α1 + α2 + α3 + α4 = 1;

[0127] f1, f2, f3, and f4 are respectively normalized risk metric functions used to map various environmental parameter changes into a unified risk score;

[0128] Compare the function output value R with the set risk threshold Rth. When R > Rth is satisfied, trigger the key refresh operation, including broadcasting a refresh notice and synchronizing the key update between the sender and the receiver;

[0129] In addition, the role of the refresh evaluation function is a decision function for judging when to refresh the encryption key, and its core function is based on the environmental state vector V env and other operating state parameters;

[0130] Meanwhile, the quantification standard for refreshing the key mainly relies on the comparison result between the function output value R and the preset risk threshold Rth. Specifically, when the output value R exceeds the risk threshold Rth, it indicates that the current encryption key may face security threats or communication anomalies, thereby triggering the key refresh operation. This refresh operation includes broadcasting a refresh notification and synchronizing key updates between the sending and receiving ends to ensure that the security of encrypted communication is restored or strengthened.

[0131] It dynamically calculates whether the key refresh cycle needs to be adjusted to deal with potential security threats or communication anomalies; it is used to broadcast refresh notifications and synchronize the update process of encryption keys.

[0132] In this embodiment, the data processing module comprehensively adopts quantum key distribution technology and adaptive communication protocol to achieve efficient encryption and dynamic adjustment of UAV navigation signals, which is different from existing technologies.

[0133] By integrating data from sensors such as GPS, visual cameras, RFID readers, and thermal imagers equipped on drones, we can ensure that the navigation data collected in real time at large-scale events is accurate and comprehensive.

[0134] The data is then preprocessed to remove noise and redundant data, and formatted into a structure suitable for encryption to generate random single-photon quantum states and transmit the measurement quantum state to generate a shared key.

[0135] Finally, error correction and privacy amplification operations are performed within the classic communication channels. A dynamic encryption key is generated by combining a hash function with a timestamp, and the data is encrypted using a symmetric encryption algorithm to improve data security.

[0136] When the UAV's communication environment changes, the real-time communication adjustment unit generates an environment state vector by real-time acquisition of signal strength Sa, bit error rate Ea, and communication interference intensity Ia in the channel; it defines signal strength threshold Sb, bit error rate threshold Eb, and interference intensity threshold Ib to trigger the adjustment of encryption parameters, and also uses the generated dynamic encryption key to encrypt navigation data frame by frame.

[0137] The key refresh cycle is dynamically adjusted based on the refresh evaluation function to synchronize key updates between the sending and receiving ends in order to maintain communication stability. Therefore, this module significantly enhances the system's encryption processing capabilities and environmental adaptability. The accurate acquisition and dynamic adjustment of each parameter contribute to improving the security and operational efficiency of UAV navigation.

[0138] Example 3

[0139] The intelligent recognition module specifically includes:

[0140] By collecting dynamic encryption parameters from multiple drones and standardizing the dynamic encryption parameters generated by different drones, a standardized encryption parameter set is formed.

[0141] The standardization process is achieved through formulas. The implementation is shown in the figure, where μ(K) is the mean of the encryption parameters generated by all sampled drones, σ(K) is the standard deviation of the encryption parameters, and Kno represents the standardized encryption parameters. This represents the original dynamic encryption parameters generated by a specific drone.

[0142] A multi-layer convolutional neural network is constructed, comprising an input layer, convolutional layers, pooling layers, and fully connected layers. This convolutional neural network is used to train dynamic encryption parameters to generate feature determination results. The objective function of the convolutional neural network is defined by the following formula:

[0143]

[0144] In the formula, L represents the loss function, g represents the number of training samples, and 10 is the base of the logarithmic function; y i Indicates the target category label, Indicates the model's predicted value;

[0145] Based on the convolutional neural network, features are extracted from the dynamic encryption parameters of the UAV, and a fused feature representation Ffu is generated through feature concatenation operation.

[0146] The fusion feature is represented by the formula Define, where This represents the feature vector of the drone, where en represents the total number of drones.

[0147] The classification results of potential threat drones are generated using fused feature representations, and the threat classification probability Yth is calculated using the Softmax activation function. k The specific calculation formula is as follows:

[0148]

[0149] Where j represents the category index to be traversed, used to enumerate all existing threat categories; m represents the total number of threat categories in the threat identification task, that is, the total number of categories classified by the model;

[0150] This represents the model output value corresponding to the k-th type of threat. Perform exponential calculation on the logits value of the k-th threat category, with the denominator calculated separately for each category;

[0151] This represents the predicted probability of the k-th class after calculation by the Softmax function, located in the interval [0,1], and the sum of the probabilities of all classes is 1;

[0152] Furthermore, specifically, the Softmax activation function is used to transform the model's output logits into a probability distribution; in multi-class classification tasks, assuming the model's output is a vector z = (z1, z2, ..., zm), where each z... k This is the raw output value for the k-th type of threat;

[0153] The Softmax function calculates the probability of threat classification Yth for each category by exponentially calculating each logits value and then normalizing all categories. k ;

[0154] Preset threat classification probability threshold Pth k , and the threat classification probability Yth k A comparative evaluation was conducted, the details of which are as follows:

[0155] If the threat classification probability Yth k If the threat classification probability threshold Pth is reached, the drone is marked as a threat, and the isolation module is notified to perform subsequent actions.

[0156] If the threat classification probability Yth k If the probability of the threat classification is less than or equal to the threat classification probability threshold Pth, then normal communication continues and no isolation operation is performed.

[0157] Based on the threat assessment results, a drone threat report is generated. The report includes the identification information and classification probability of potential threat drones, and an isolation command is sent to cut off the communication channels of the threat drones.

[0158] The dynamic database module specifically includes:

[0159] Import public databases and navigation signal feature data through an application programming interface protocol based on presentation layer state transition;

[0160] A connection is established with a private data source through an authentication mechanism. A token-based authorization protocol is used in the process, and a standardized conversion tool is used to convert the accessed information data into a unified JSON object representation format to obtain compatible standardized feature inputs.

[0161] The dynamic database module specifically also includes:

[0162] Set a preset time interval and incremental update mechanism. When new data is detected, the incremental update mechanism is triggered, including updating only the characteristics of the new signal.

[0163] When no new signal features are added, batch updates are performed according to the set time interval; the time interval ΔT can be specifically set to 5 minutes.

[0164] The specific details of the incremental update mechanism are as follows:

[0165] The correlation matching degree S is calculated and obtained based on the newly added navigation signal mode information;

[0166] At the same time, a preset association matching threshold Sth is set and compared with the association matching degree S. When the association matching degree S is lower than the preset association threshold Sth, it is determined to be existing data and no update is made.

[0167] When the correlation matching degree S is greater than or equal to the correlation threshold Sth, the information of the newly added signal feature is included in the database for updating and marked as a new feature.

[0168] The formula for calculating the correlation matching degree S is:

[0169]

[0170] in: The u-component represents the characteristics of the newly acquired signal; The u-th component represents an existing feature in the database; n represents the dimension of the feature vector; u refers to the index of the current component in the feature vector, ranging from 1 to n.

[0171] In this embodiment, the intelligent identification module greatly improves the accuracy and real-time performance of drone threat identification through the fusion analysis of deep learning and encryption parameters. By standardizing the encryption parameters of multiple drones, using the formula μ(K) as the mean of the encryption parameters and σ(K) as the standard deviation of the encryption parameters, a standardized encryption parameter set is formed. Then, the data is trained through a multi-layer convolutional neural network (CNN) to effectively extract features and generate classification results for potential threat drones.

[0172] The threat classification probability Yth is calculated using the Softmax activation function. k In the formula, j represents the category index, m represents the total number of threat categories, and the probability calculated by the Softmax function ensures that the sum of the probabilities of all categories is 1, which can accurately assess the threat level of each drone; when the threat classification probability Yth k Exceeding the preset threshold Pth k If the drone is flagged as a threat and isolation measures are triggered, normal communication will continue without intervention.

[0173] In addition, the Open License 2.0 protocol dynamic database module connects to public and private data sources through the Open License 2.0 protocol and uses standardized conversion tools to unify the data format into JSON objects, making information input more compatible and standardized;

[0174] The database is updated regularly and in real time through an incremental update mechanism to ensure the timeliness of the feature library. The newly acquired signal feature u component in the correlation matching degree S formula is compared with the existing features. When the correlation matching degree S is greater than the set threshold Sth, the newly added feature information is included in the database, thus ensuring the real-time performance and reliability of the database.

[0175] Therefore, the entire module not only improves the accuracy of threat identification, but also enhances the efficiency of data updates and the adaptability of the system, effectively improving the system's threat response capabilities and security.

[0176] Furthermore, Table 1 shows the data acquisition and encryption parameter generation for navigation multimodal data;

[0177]

[0178] At this point, the system generates an environmental state vector based on the real-time signal environmental state:

[0179]

[0180] At this point, the encryption parameter adjustment thresholds are: Sb=0.70; Eb=0.03; Ib=0.05;

[0181] Judgment condition: Sa<Sb,Ea> If Eb and Ia > Ib, a dynamic encryption key update is triggered if either condition is met.

[0182] Furthermore, Table 2 shows the threat probability output by fusing features from the encryption parameters of each drone after CNN network training and using Softmax:

[0183]

[0184] Threshold setting: Pth=0.7;

[0185] Judgment result: Threat classification probability Yth for U002 k =0.81>Pth k It was identified as a threatening drone;

[0186] At this point, the newly added signal features in the database are matched with the existing features. Finally, the system triggers threat interference decisions based on the risk probability model, including threat interference decisions to initiate targeted interference against U002.

[0187] Example 4

[0188] The decision-making module specifically includes:

[0189] Based on the target UAV's real-time adjusted dynamic encryption parameters and navigation signal threat characteristics, and after dimensionless processing, the standardized values ​​of the dynamic encryption parameters and signal threat characteristics are obtained.

[0190] The standardized values ​​of the dynamic encryption parameters and the standardized values ​​of the signal threat characteristics are extracted, and the real-time matching index SSp is calculated. The specific calculation formula is as follows:

[0191]

[0192] In the formula, This represents the e-th dynamic encryption parameter. This corresponds to the e-th threat signal feature;

[0193] p represents the total number of parameters; e is used to iterate through all the dynamically encrypted parameters that need to be compared. and corresponding signal threat characteristics The index, whose value starts from 1 and goes up to p;

[0194] These are weighting coefficients used to reflect the importance of different features in the overall matching degree calculation;

[0195] Overall, this formula calculates the weighted difference between dynamic encryption parameters and external threat characteristics, and then transforms it into a matching score between 0 and 1 through appropriate standardization and transformation, where a score closer to 1 indicates a better match.

[0196] Real-time flight-related data is collected, including flight distance, flight signal strength, flight path, and real-time environmental information. Statistical and logistic regression learning techniques are used to construct a risk prediction model based on flight-related data and the real-time matching index SSp.

[0197] The probability of occurrence of various threats is output using a risk prediction model, and a comparative evaluation is conducted by setting a preset security threshold.

[0198] The interference control module is activated when the assessed probability of any threat reaches or exceeds a preset security threshold.

[0199] When the assessed probabilities of all threats do not exceed the preset safety thresholds, the drone system maintains normal operation mode and continues to conduct routine risk monitoring.

[0200] Furthermore, by collecting feature data including the target UAV's flight trajectory, environmental conditions, and communication signal quality, and using the Min-Max normalization method to unify the dimensions, the data is input into a pre-trained risk prediction model. The Softmax function is then used to output the probability of various threats, including signal interference, camouflage attacks, and navigation deception.

[0201] Then, the probability of each threat is compared with the preset security threshold one by one. If the probability of any threat type is greater than or equal to the corresponding threshold, the interference control module is directly activated and corresponding protective measures are implemented, namely, changing the flight path or activating encrypted communication.

[0202] If all threat probabilities are below their thresholds, the system will maintain normal operation and continuously monitor and update the model in real time to dynamically adapt to changes in the flight environment and ensure the safety of UAV navigation.

[0203] The interference control module specifically includes:

[0204] When the assessed probability of any threat reaches or exceeds a preset security threshold, targeted jamming measures are employed, including directional jamming and signal strength modulation.

[0205] After implementing targeted jamming measures, the changes in target signals and feedback on the UAV status are monitored in real time, and the effectiveness of the jamming strategy is verified.

[0206] The effectiveness of the interference strategy was verified, and the details are as follows:

[0207] Collect real-time data on target signals and UAV status feedback, including monitoring changes in target signal strength, frequency fluctuations, as well as changes in UAV position and response speed;

[0208] The effectiveness of the currently implemented jamming strategy is evaluated based on real-time data of the target signal and the status feedback of the UAV.

[0209] If the actual monitored effect is less than the expected effect, the interference method and parameter settings will be automatically adjusted, and the verification will be repeated until the predetermined safety standard is reached.

[0210] Furthermore, when the assessed probability of any threat from the target UAV reaches or exceeds a preset safety threshold, targeted interference or signal strength adjustment is implemented. Real-time data on changes in the target signal and the expected effect standard are collected, then the interference strategy adaptive optimization process begins. Specifically, this includes: real-time monitoring of human-machine operational feedback and quantitative evaluation of the current interference strategy's effectiveness. If the monitored result shows a low target signal strength change value (Sr) and UAV response speed (Vr), with preset expected values ​​Se and Ve respectively, the interference effectiveness function is used. Calculate the evaluation index, where α and β are empirical weighting coefficients;

[0211] When the interference effectiveness function E > the error threshold ε, it is determined that the current interference strategy has not achieved the expected effect.

[0212] After determining that the target is not met, the system automatically selects amplitude adjustment as the preferred optimization method. Specifically, the interference power is linearly increased based on the current signal strength change trend. The adjustment amplitude is set to not exceed 10% of the initial value each time. After adjustment, the interference strategy is reimplemented and the value of the interference effectiveness function E is evaluated again.

[0213] Repeat the above process until the interference effectiveness function E ≤ error threshold ε. After confirming that the interference effect meets the standard, maintain the current interference parameters and feed the adjustment results back to the control module for dynamic parameter learning and historical strategy updates.

[0214] The scheme chooses amplitude adjustment as the main optimization method because it has a fast response speed, high control precision, and is suitable for rapid response and correction in interference scenarios where navigation signals are prone to fluctuation. It effectively avoids strategy oscillation and excessive resource consumption, and has significant practicality.

[0215] In this embodiment, the accuracy of UAV security risk assessment is improved by real-time matching calculation of dynamic encryption parameters and navigation signal threat characteristics;

[0216] Specifically, by calculating the real-time matching index SSp, the dynamic encryption parameters are compared with the threat signal characteristics using a weighted difference formula, thereby obtaining a matching score between 0 and 1, which reflects the system's ability to identify and adapt to threat signals. The higher the matching score, the more accurate the threat identification.

[0217] Real-time flight-related data, such as flight distance, signal strength, flight path, and real-time environmental information, are collected and used to build a risk prediction model through statistical and logistic regression learning techniques. This model can output the probability of various threats and compare it with preset safety thresholds to determine whether to activate the interference control module.

[0218] When the assessed threat probability exceeds the safety threshold, targeted interference measures such as directional jamming or signal strength adjustment are automatically triggered. The system monitors changes in target signals and UAV status feedback in real time. The effectiveness of the interference strategy is evaluated by analyzing the monitoring results. If the effect is not satisfactory, the interference strategy and parameter settings are automatically adjusted until the predetermined safety standards are met.

[0219] Therefore, this module enhances the system's responsiveness and adaptability in the face of dynamic threats, ensuring the safety of the UAV's flight environment and effectively avoiding false or excessive interference.

[0220] Example 5

[0221] The regulatory authorization module specifically includes:

[0222] Receive and verify the legitimacy of the user's identity based on the password and biometric data entered by the user; the specific process is as follows:

[0223] A secure input interface is set up for users to enter passwords and scan biometric data, including fingerprints or irises. Passwords are verified by encryption algorithms, and biometric data is compared and verified by a biometric scanner.

[0224] Access permissions for specific functions can be dynamically set based on authentication results, including role-based access control.

[0225] The regulatory authorization module also includes:

[0226] Simultaneously, all user actions and system responses based on permissions are recorded. The specific process is as follows:

[0227] Automatically capture and store each user's permission-based operation and the system's own response, including operation time, operator identity, operation type and result, and use timestamps and user identifiers to make the data complete and traceable;

[0228] The system automatically triggers audit report generation based on the needs of the audit department, extracts relevant log data from the database, and generates highly transparent audit reports containing information such as operation summaries, timestamps, and executor identities to support post-audit.

[0229] In this embodiment, by combining password verification and biometric technology, a highly secure and accurate human-machine authentication is achieved, which is different from the traditional single authentication method and improves the security of the system and the reliability of user authentication.

[0230] The system allows users to enter a password and undergo fingerprint or iris scanning through a secure input interface. The password is verified using an encryption algorithm, and the biometric data is compared and verified by a biometric scanner to ensure that only authorized users can access the system.

[0231] Based on the verification results, the system automatically adjusts the access permissions to different functional modules dynamically according to the user's identity, role, and permission level through the permission management algorithm, thereby achieving fine-grained permission control. At the same time, by automatically recording all permission-related operations and system responses, the system captures and stores information such as operation time, operator identity, operation type, and result to ensure that the operation is traceable.

[0232] The audit department can automatically generate audit reports as needed, extract relevant log data from the database, generate highly transparent audit reports, support post-audit, and improve the auditability and compliance of the system.

[0233] Therefore, this module not only enhances the security of the system, but also ensures the legality of operations and the transparency of system responses, effectively preventing internal and external security threats.

[0234] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization.

[0235] The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random-access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0236] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0237] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0238] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A collaborative blocking system for drone navigation signals based on quantum key distribution, applied to the flight control of security drones for large-scale events, characterized in that, It includes: Data processing module: It is used to collect the navigation multimodal data of the target UAV when executing the planned path, and at the same time generate dynamic encryption parameters for the navigation multimodal data by using the quantum key distribution technology, and adjust the dynamic encryption parameters in real time through the adaptive communication protocol; Through the sensor system equipped on the UAV, the navigation multimodal data of the UAV when executing the predetermined path is collected in real time; Then, preprocess the navigation multimodal data, including screening, filtering and standardization processing, to remove noise and redundant data, and format the data into a structure suitable for encryption processing; Generate dynamic encryption parameters for the navigation multimodal data in real time, including: Generate a random single-photon quantum state, and then transmit the single-photon quantum state through an optical fiber; Receive the transmitted single-photon quantum state and measure the quantum state according to a preset benchmark; Perform error correction and privacy amplification operations on the quantum state sequences of the sending end and the receiving end through a non-quantized communication channel to generate a shared secret key; Generate a dynamic encryption secret key based on the shared secret key by combining a hash function with a timestamp, and encrypt the collected navigation multimodal data by using a symmetric encryption algorithm; The communication real-time adjustment unit adjusts the dynamic encryption parameters in real time according to the UAV communication environment state. By collecting the signal strength, bit error rate and communication interference strength in the communication channel in real time, an environment state vector Venv = [Sa, Ea, Ia] is generated; Where Sa represents the signal strength, Ea represents the bit error rate, and Ia represents the communication interference strength; Set an encryption parameter adjustment trigger mechanism module, which is used to define the signal strength threshold Sb, the bit error rate threshold Eb and the interference strength threshold Ib. When the environment state satisfies any one of Sa < Sb, or Ea > Eb, or Ia > Ib, trigger the dynamic encryption parameter adjustment; Generate a dynamic encryption secret key Key = h(Venv, t) based on the environment state vector Venv and the timestamp t, where h is a preset encryption secret key generation algorithm; Use the dynamic encryption secret key Key to encrypt each frame of the navigation data frame in fragments to generate an encrypted data frame; Based on the environment state vector Venv, generate a refresh evaluation function R(Venv, Δt) again, where Δt represents the time interval since the last key refresh; Dynamically adjust the key refresh period according to the environment state vector Venv and the refresh evaluation function to broadcast a refresh notification and synchronize the key update of the sending end and the receiving end; Intelligent recognition module: It is used to adopt a deep learning algorithm to perform fusion analysis on the dynamic encryption parameters of multiple UAVs to extract and identify the navigation signal threat characteristics of potential threat UAVs; Dynamic database module: It is used to integrate information from public databases and private data sources, update the navigation signal threat characteristics of potential threat UAVs in real time, and establish an automated update mechanism for the database; Decision-making judgment module: It is used to match and judge the dynamically adjusted encryption parameters of the target UAV with the navigation signal threat characteristics, perform risk assessment on the target UAV by constructing an intelligent probability model, and obtain multi-dimensional risk assessment results; Interference control module: Based on multi-dimensional risk assessment results, automatically selects targeted interference methods, including directional interference or signal strength adjustment; and monitors the interference effect in real time. The regulatory authorization module restricts access to interfering functions through a dynamic user authentication mechanism; it also records all operation logs and generates transparent reports for post-event auditing and security analysis.

2. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 1, characterized in that: The intelligent recognition module specifically includes: By collecting dynamic encryption parameters from multiple drones and standardizing the dynamic encryption parameters generated by different drones, a standardized encryption parameter set is formed. A multi-layer convolutional neural network is constructed, comprising an input layer, a convolutional layer, a pooling layer, and a fully connected layer. The convolutional neural network is used to train dynamic encryption parameters to generate feature determination results. Based on the convolutional neural network, features are extracted from the dynamic encryption parameters of the UAV, and a fused feature representation Ffu is generated through feature concatenation operation. A classification result of the potential threat UAV is generated by using the fusion feature representation, and a threat classification probability Yth is calculated by a Softmax activation function k The specific calculation formula is as follows: Where j represents the category index to be traversed, used to enumerate all existing threat categories; m represents the total number of threat categories in the threat identification task, that is, the total number of categories classified by the model; This represents the model output value corresponding to the k-th type of threat. Perform exponential calculation on the logits value of the k-th threat category, with the denominator calculated separately for each category; This represents the predicted probability of the k-th class after calculation by the Softmax function, located in the interval [0,1], and the sum of the probabilities of all classes is 1; Preset threat classification probability threshold Pth k , and the threat classification probability Yth k A comparative evaluation was conducted, the details of which are as follows: If the threat classification probability Yth k If the threat classification probability threshold Pth is reached, the drone is marked as a threat, and the isolation module is notified to perform subsequent actions. If the threat classification probability Yth k If the probability of the threat classification is less than or equal to the threat classification probability threshold Pth, then normal communication continues and no isolation operation is performed. Based on the threat assessment results, a drone threat report is generated. The report includes the identification information and classification probability of potential threat drones, and an isolation command is sent to cut off the communication channels of the threat drones.

3. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 2, characterized in that: The dynamic database module specifically includes: Import public databases and navigation signal feature data through an application programming interface protocol based on presentation layer state transition; A connection is established with a private data source through an authentication mechanism. A token-based authorization protocol is used in the process, and a standardized conversion tool is used to convert the accessed information data into a unified JSON object representation format to obtain compatible standardized feature inputs.

4. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 3, characterized in that: The dynamic database module specifically also includes: Set a preset time interval and incremental update mechanism. When new data is detected, the incremental update mechanism is triggered, including updating only the characteristics of the new signal. When no new signal features are added, batch updates are performed according to the set time intervals; The specific details of the incremental update mechanism are as follows: The correlation matching degree S is calculated and obtained based on the newly added navigation signal mode information; At the same time, a preset association matching threshold Sth is set and compared with the association matching degree S. When the association matching degree S is lower than the preset association threshold Sth, it is determined to be existing data and no update is made. When the correlation matching degree S is greater than or equal to the correlation threshold Sth, the information of the newly added signal feature is included in the database for updating and marked as a new feature.

5. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 4, characterized in that: The decision-making module specifically includes: Based on the target UAV's real-time adjusted dynamic encryption parameters and navigation signal threat characteristics, and after dimensionless processing, the standardized values ​​of the dynamic encryption parameters and signal threat characteristics are obtained. The standardized values ​​of the dynamic encryption parameters and the standardized values ​​of the signal threat characteristics are extracted, and the real-time matching index SSp is calculated. The specific calculation formula is as follows: In the formula, This represents the e-th dynamic encryption parameter. This is the corresponding e-th threat signal feature; p represents the total number of parameters; e is used to iterate through all the dynamically encrypted parameters that need to be compared. and corresponding signal threat characteristics The index, whose value starts from 1 and goes up to p; These are weighting coefficients used to reflect the importance of different features in the overall matching degree calculation; Real-time flight-related data is collected, including flight distance, flight signal strength, flight path, and real-time environmental information. Statistical and logistic regression learning techniques are used to construct a risk prediction model based on flight-related data and the real-time matching index SSp. The probability of occurrence of various threats is output using a risk prediction model, and a comparative evaluation is conducted by setting a preset security threshold. The interference control module is activated when the assessed probability of any threat reaches or exceeds a preset security threshold. When the assessed probabilities of all threats do not exceed the preset safety thresholds, the drone system maintains normal operation mode and continues to conduct routine risk monitoring.

6. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 5, characterized in that: The interference control module specifically includes: When the assessed probability of any threat reaches or exceeds a preset security threshold, targeted jamming measures are employed, including directional jamming and signal strength modulation. After implementing targeted jamming measures, the changes in target signals and feedback on the UAV status are monitored in real time, and the effectiveness of the jamming strategy is verified. The effectiveness of the interference strategy was verified, and the details are as follows: Collect real-time data on target signals and UAV status feedback, including monitoring changes in target signal strength, frequency fluctuations, as well as changes in UAV position and response speed; The effectiveness of the currently implemented jamming strategy is evaluated based on real-time data of the target signal and the status feedback of the UAV. If the actual monitored effect is less than the expected effect, the interference method and parameter settings will be automatically adjusted, and the verification will be repeated until the predetermined safety standard is reached.

7. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 6, characterized in that: The regulatory authorization module specifically includes: Receive and verify the legitimacy of the user's identity based on the password and biometric data entered by the user; the specific process is as follows: A secure input interface is set up for users to enter passwords and scan biometric data, including fingerprints or irises. Passwords are verified by encryption algorithms, and biometric data is compared and verified by a biometric scanner. Access permissions for specific functions can be dynamically set based on authentication results, including role-based access control.

8. The UAV navigation signal cooperative blocking system based on quantum key distribution according to claim 7, characterized in that: The regulatory authorization module also includes: Simultaneously, all user actions and system responses based on permissions are recorded. The specific process is as follows: Automatically capture and store each user's permission-based operation and the system's own response, including operation time, operator identity, operation type and result, and use timestamps and user identifiers to make the data complete and traceable; The system automatically triggers audit report generation based on the needs of the audit department, extracts relevant log data from the database, and generates highly transparent audit reports containing information such as operation summaries, timestamps, and executor identities to support post-audit.

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