Communication protocol security verification method based on quantum key distribution

Abstract

The invention relates to the technical field of quantum key distribution, and discloses a communication protocol security verification method based on quantum key distribution, which comprises the following steps of: establishing a quantum communication channel framework, loading protocol parameters to generate an initial security verification model, configuring a security state transition rule, and performing cross-layer data fusion in combination with quantum entity parameters; and obtaining a whole-process safety tracking model. And executing security path deduction through the model, generating a key exchange trajectory chain, constructing security trajectory simulation data, establishing a quantum conflict resolution mechanism, training and generating a security policy optimization model, and outputting security governance parameters. Security increment simulation information is generated based on security governance parameters and the like, a multi-target decision model is constructed in combination with protocol evolution constraint conditions, optimal security path parameters are adjusted and generated, a security verification strategy is verified and updated through a real-time quantum mapping network, and a whole-process security matching target is achieved. According to the method, the comprehensiveness, the accuracy and the dynamic adaptability of quantum communication protocol security verification are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quantum key distribution, and in particular to a communication protocol security verification method based on quantum key distribution. Background Art

[0002] With the rapid development of quantum communication technology, quantum key distribution (QKD), as a core technology for achieving absolutely secure communication, directly impacts the reliability of information transmission. However, existing communication protocol security verification methods have numerous shortcomings when applied to quantum communication scenarios. For one thing, traditional verification methods struggle to effectively address the unique quantum state characteristics and cross-layer data interaction issues inherent in quantum communication, hindering the establishment of comprehensive and accurate security verification models. This results in a lack of effective constraints and tracking of security state transitions within quantum communication channels, making it difficult to implement full-process security monitoring.

[0003] Existing technologies lack efficient fusion mechanisms and algorithms when processing cross-layer data fusion of quantum entity parameters, and are unable to accurately construct multi-layer quantum correlation equations and perform iterative solutions. This results in insufficient accuracy in key steps such as generating protocol evolution boundary sets, making it difficult to effectively identify areas with low conflict density, thereby affecting the accuracy and reliability of the entire security verification.

[0004] When dealing with dynamic changes in the quantum communication process, traditional methods lack effective conflict resolution mechanisms and security policy optimization models. They are unable to train and optimize based on security trajectory simulation data, and it is difficult to output accurate security governance parameters such as quantum correlation weights and security compliance thresholds. This leads to insufficient iterative adjustment capabilities for the security framework, inability to generate optimal security path parameters, and difficulty in achieving security matching goals throughout the entire process.

[0005] Existing technologies have defects in generating secure incremental simulation information and constructing multi-objective decision-making models. They are unable to perform effective incremental simulation and global optimization based on information such as security governance parameters, resulting in low protocol coverage efficiency and high quantum conflict density, which cannot meet the high security and high reliability requirements of quantum communication. Summary of the Invention

[0006] The purpose of the present invention is to provide a communication protocol security verification method based on quantum key distribution to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a communication protocol security verification method based on quantum key distribution, the method comprising: Establish a quantum communication channel framework and load the quantum key distribution protocol parameter set to generate an initial security verification model; Based on the initial security verification model, the security state transition rules are configured, and cross-layer data fusion is performed in combination with quantum entity parameters to obtain a full-process security tracking model. Execute security path deduction through the full-process security tracking model, generate key exchange trajectory chain, and construct security trajectory simulation data in combination with quantum state iteration frequency parameters; Establish a quantum conflict resolution mechanism and generate a security policy optimization model through training with security trajectory simulation data, thereby outputting security governance parameters. The security governance parameters include at least quantum correlation weight, security compliance threshold, and protocol optimization priority sequence. Generate secure incremental simulation information based on security governance parameters, full-process security tracking model, and quantum entity parameters; Combining security incremental simulation information with protocol evolution constraints, a multi-objective decision model is constructed and the security framework is iteratively adjusted to generate optimal security path parameters. Based on the real-time quantum mapping network, the current security path parameters are verified, the quantum consistency index is calculated, and the optimal security path parameters are combined with the historical trajectory chain to update the security verification strategy to achieve the full-process security matching goal.

[0008] Preferably, the security trajectory simulation data includes at least a quantum conflict set, quantum state constraint parameters, a protocol evolution boundary set, and a dynamic optimization priority sequence; The secure incremental simulation information includes at least quantum mapping deviation, protocol coverage completeness rate and quantum consistency evaluation index.

[0009] Preferably, establishing a quantum communication channel framework and loading a quantum key distribution protocol parameter set to generate an initial security verification model includes the following steps: Perform structured cleaning and classification on multi-source quantum communication data to generate a standardized set of quantum entities; Performing quantum attribute extraction on a set of quantum entities, wherein the extraction includes one or more of quantum clustering, attribute mapping, state classification, and quantum state identification; Based on the state classification results, an initial security verification model is constructed, wherein the model includes a quantum node set, an exchange edge set, and quantum constraint rules; The quantum correlation strength analysis is performed on the initial security verification model to generate a dynamic correlation weight matrix and assign a security status label to each quantum node.

[0010] Preferably, configuring the security state transfer rules based on the initial security verification model and combining the quantum entity parameters to perform cross-layer data fusion to obtain a full-process security tracking model includes the following steps: Loading security state transition rules into the initial security verification model to constrain state transition conditions and generate a first security framework; Loading cross-layer correlation parameters to the first security framework to generate a standardized security fusion framework; Cross-layer data fusion is performed based on a standardized security fusion framework. The specific process includes: Constructing a multi-layer quantum correlation equation, which includes at least a state similarity equation, an attribute matching equation, and an exchange consistency equation, and iteratively solving the multi-layer quantum correlation equation using a quantum evolution algorithm to obtain a quantum conflict set, quantum state constraint parameters, and a protocol evolution boundary set; The generation of the protocol evolution boundary set includes the following steps: Based on the quantum conflict set, the conflict density of each quantum node is calculated; Identify areas in the standardized security fusion framework where the conflict density is no greater than a preset threshold and generate a set of protocol evolution boundaries.

[0011] Preferably, the process of establishing a quantum conflict resolution mechanism and training with security trajectory simulation data to generate a security policy optimization model and then outputting security governance parameters includes the following steps: Construct a quantum conflict resolution mechanism based on the initial security verification model; The quantum conflict resolution mechanism is trained and verified through security trajectory simulation data to generate a security strategy optimization model; Input the real-time quantum entity parameters into the security policy optimization model to predict the set of protocol evolution boundaries; Based on the predicted set of protocol evolution boundaries, security governance parameters are output, where the security governance parameters include at least quantum correlation weight, security compliance threshold and protocol optimization priority sequence.

[0012] Preferably, the method further includes reconstructing features of the safety trajectory simulation data, specifically: Based on the quantum conflict set, quantum state constraint parameters and protocol evolution boundary set, an initial multi-layer input tensor is constructed; Normalize and enhance the features of the initial multi-layer input tensor to generate the final multi-layer input tensor; Construct a security optimization label tensor based on a dynamic optimization priority sequence; The final multi-layer input tensor is combined with the security optimized label tensor to form a training sample set.

[0013] Preferably, the outputting of security governance parameters based on the predicted protocol evolution boundary set comprises the following steps: Extract the quantum node with the lowest quantum conflict density from the predicted protocol evolution boundary set and generate the quantum correlation weight; Based on the predicted topological connection relationship of the protocol evolution boundary set, the distribution structure of the security compliance threshold is fitted to generate a protocol optimization priority sequence; The state similarity gradient direction of the predicted protocol evolution boundary set is calculated and normalized into a security reference vector, which is the security compliance adjustment direction.

[0014] Preferably, generating security incremental simulation information based on security governance parameters, a full-process security tracking model, and quantum entity parameters comprises the following steps: Map the quantum correlation weights to the full-process security tracking model, match the security compliance thresholds and protocol optimization priority sequence, adjust the quantum node density in the conflict area, update the full-process security tracking model, define incremental trigger conditions, protocol reconstruction rules and parameter update increments, and generate a security incremental simulation model; Based on the secure incremental simulation model, the quantum evolution algorithm is used to iteratively solve the multi-layer quantum correlation equation to generate secure incremental simulation information, including: When the incremental trigger condition is met, the quantum mapping deviation, protocol coverage completeness and quantum consistency evaluation index are updated, and the multi-layer quantum correlation equation is re-solved until the simulation termination condition is met; The incremental trigger condition includes triggering parameter update when the current protocol coverage completeness rate is no more than a preset completeness rate threshold; the protocol reconstruction rule includes adjusting the security compliance threshold based on the state similarity gradient direction; the parameter update increment is in a piecewise linear relationship with the current quantum consistency evaluation index.

[0015] Preferably, the construction of a multi-objective decision model and iterative adjustment of the security framework to generate optimal security path parameters includes the following steps: Construct a multi-objective decision model, where the decision variables include quantum node density, security compliance threshold, and quantum state iteration frequency. The decision objectives include maximizing protocol coverage efficiency and minimizing quantum conflict density. The constraints include protocol evolution boundary constraints and attribute matching accuracy thresholds. The multi-objective decision model is preliminarily solved by the Bayesian optimization algorithm to generate an initial decision path set; Based on the initial decision path set, the ant colony algorithm is used for global optimization to generate the optimal safe path parameters.

[0016] Preferably, the method of performing a preliminary solution to the multi-objective decision model by using a Bayesian optimization algorithm to generate an initial decision path set includes the following steps: Construct a parameter space structure based on quantum node density and security compliance thresholds to generate an initial sampling point set; Calculate the objective function based on the protocol coverage efficiency and quantum conflict density, perform Gaussian process regression and acquisition function optimization on the initial sampling point set, and generate a set of intermediate decision paths; Perform confidence interval screening and local exploration on the set of intermediate decision paths to generate an initial set of decision paths.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The communication protocol security verification method based on quantum key distribution, provided by this invention, establishes a quantum communication channel framework and loads relevant parameters to generate an initial security verification model, laying a comprehensive and accurate foundation for subsequent security verification. Based on this initial model, security state transition rules are configured and cross-layer data fusion is performed to obtain a full-process security tracking model. This effectively constrains state transition conditions, enables secure tracking of the entire quantum communication process, and improves the comprehensiveness and accuracy of security verification.

[0018] By implementing a full-process security tracking model to perform security path deduction, generate a key exchange trajectory chain, and construct security trajectory simulation data, this system provides rich data support for subsequent security policy optimization. A quantum conflict resolution mechanism is established, and a security policy optimization model is generated through training using security trajectory simulation data. This model accurately outputs security governance parameters, such as quantum correlation weights and security compliance thresholds. This improves the system's ability to resolve quantum conflicts and optimize security policies, enhancing system stability and reliability.

[0019] Generating incremental security simulation information based on security governance parameters enables dynamic adjustments to the security framework based on actual conditions, improving the system's adaptability and flexibility. Combining incremental security simulation information with protocol evolution constraints to build a multi-objective decision-making model and perform iterative adjustments to generate optimal security path parameters. This maximizes protocol coverage efficiency and minimizes quantum conflict density, optimizing resource allocation and improving the security and reliability of the communication protocol.

[0020] Finally, the real-time quantum mapping network verifies the current security path parameters and updates the security verification strategy, achieving a secure match throughout the entire process and further ensuring the security and reliability of quantum communication. This method, through the synergistic effect of multiple steps, comprehensively enhances the security verification capabilities of communication protocols based on quantum key distribution, resolving numerous issues existing in existing technologies and possessing significant technical advantages and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a diagram showing the working principle of the communication protocol security verification method based on quantum key distribution according to the present invention; Figure 2 Design diagrams generated for the initial safety validation model; Figure 3 Design diagrams generated for the full-process safety tracking model; Figure 4Design graph generated for the security policy optimization model. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] See also Figures 1-4 The present invention relates to a communication protocol security verification method based on quantum key distribution, and the specific implementation steps are as follows: Establish a quantum communication channel framework and load the quantum key distribution protocol parameter set to generate an initial security verification model.

[0024] Based on the initial security verification model, the security state transfer rules are configured, and cross-layer data fusion is performed in combination with quantum entity parameters to obtain a full-process security tracking model.

[0025] Security path deduction is performed through the full-process security tracking model to generate a key exchange trajectory chain, and security trajectory simulation data is constructed in combination with quantum state iteration frequency parameters.

[0026] A quantum conflict resolution mechanism is established and trained through security trajectory simulation data to generate a security policy optimization model, which then outputs security governance parameters. The security governance parameters include at least quantum correlation weight, security compliance threshold, and protocol optimization priority sequence.

[0027] Generate secure incremental simulation information based on security governance parameters, full-process security tracking model and quantum entity parameters.

[0028] Combining security incremental simulation information with protocol evolution constraints, a multi-objective decision-making model is constructed and the security framework is iteratively adjusted to generate the optimal security path parameters.

[0029] Based on the real-time quantum mapping network, the current security path parameters are verified, the quantum consistency index is calculated, and the optimal security path parameters are combined with the historical trajectory chain to update the security verification strategy to achieve the full-process security matching goal.

[0030] Example 1: Generating an initial security verification model is a key step in quantum key distribution-based communication protocol security verification methods. This process involves structured processing of quantum communication data, in-depth extraction of quantum properties, and the construction and optimization of a security verification model.

[0031] Quantum communication data typically originates from multiple heterogeneous network nodes, including quantum key distribution devices, quantum relay stations, and end-user nodes. This data can be subject to noise interference or protocol incompatibilities during transmission, necessitating structured cleaning and classification. This data cleaning process involves removing redundant information, repairing missing values, and standardizing data formats to ensure the integrity and consistency of quantum entity parameters. The classification process categorizes the data into standardized sets of quantum entities based on characteristics such as quantum state type, transmission protocol version, and key distribution mode.

[0032] Quantum attribute extraction is a key step in building an initial security verification model. Quantum clustering algorithms are used to identify similar features within a collection of quantum entities, grouping nodes with similar quantum states or key exchange patterns. The attribute mapping process converts abstract parameters in quantum communication data into quantifiable security metrics, such as quantum bit error rate, channel transmission efficiency, and key generation rate. State classification, based on the superposition and entanglement of quantum states, distinguishes different quantum communication phases, such as key negotiation, quantum state preparation, and quantum measurement. Quantum state identification assigns a unique security label to each quantum node, facilitating subsequent correlation analysis and state tracking.

[0033] Based on the state classification results, an initial security verification model is constructed. This model consists of a set of quantum nodes, a set of exchange edges, and quantum constraint rules. The quantum node set represents the quantum devices or terminals participating in the communication. Each node contains quantum state parameters, a security label, and an associated weight. The exchange edge set describes the quantum key distribution path between nodes, including channel type, transmission efficiency, and security level. Quantum constraint rules are used to constrain state transition conditions during quantum communication, such as the maximum number of key distribution attempts and the error tolerance threshold for quantum state measurement.

[0034] The initial security verification model undergoes a quantum correlation strength analysis to assess the security dependencies between nodes. A dynamic correlation weight matrix is generated by calculating the quantum state similarity, key exchange frequency, and collision probability between nodes. Each element of the weight matrix reflects the strength of the security correlation between two nodes, with higher values indicating a stronger security dependency. Based on the dynamic correlation weight matrix, each quantum node is assigned a security status label, with label types ranging from secure, potential risk, and high conflict.

[0035] The generation of security trajectory simulation data relies on the deductive capabilities of the full-process security tracking model. The quantum conflict set records abnormal events detected during communication, such as inconsistent quantum state measurements or key distribution failures. Quantum state constraint parameters are used to constrain the dynamic behavior of quantum communication, ensuring that the key exchange process meets preset security standards. The protocol evolution boundary set is generated by analyzing quantum conflict density and identifies regions in the network where the conflict density is below a preset threshold. These regions are considered feasible ranges for protocol optimization. The dynamic optimization priority sequence determines the order of protocol adjustments based on the security state labels and associated weights of quantum nodes.

[0036] Secure incremental simulation information is used to quantify the effectiveness of protocol optimization. The quantum mapping deviation reflects the difference between actual quantum communication data and the simulation model's predictions. A smaller deviation indicates a higher model accuracy. The protocol coverage completeness rate measures the extent to which the security policy covers the quantum communication network. A higher completeness rate indicates more comprehensive security governance. Quantum consistency assessment metrics are used to verify that the key distribution process complies with quantum mechanical principles, such as the quantum no-cloning theorem and the Heisenberg uncertainty principle.

[0037] Solving multi-layer quantum correlation equations is the core of cross-layer data fusion. The state similarity equation compares the security state differences between different quantum nodes; smaller differences indicate stronger inter-node collaboration. The attribute matching equation assesses the compatibility of quantum entity parameters with security policies, ensuring that protocol optimizations do not undermine the existing security framework. The exchange consistency equation detects logical conflicts in the key distribution process, such as the risk of key reuse or quantum state leakage.

[0038] The generation of the protocol evolution boundary set is based on the calculation of quantum conflict density. Conflict density is defined as the ratio of the number of quantum conflict events in a region to the total number of nodes in that region. By setting a conflict density threshold, low-conflict regions suitable for protocol optimization are identified. These regions typically have a stable topology and can support gradual adjustments to security policies.

[0039] The establishment of a quantum conflict resolution mechanism relies on training with security trajectory simulation data. The training process utilizes supervised learning, with input data consisting of a set of quantum conflicts, quantum state constraint parameters, and a set of protocol evolution boundaries. The security policy optimization model uses iterative learning to gradually reduce the probability of quantum conflicts and improve key distribution efficiency. The model's output is security governance parameters, including quantum correlation weights, security compliance thresholds, and a protocol optimization priority sequence.

[0040] The quantum correlation weight reflects the importance of the security dependency between nodes. A higher weight indicates a more urgent need for collaborative optimization between nodes. The security compliance threshold determines whether quantum communication behavior meets security standards. Operations exceeding the threshold are marked as high risk. The protocol optimization priority sequence guides the implementation of security policies, ensuring that key nodes are prioritized for optimization.

[0041] Example 2: Configuring security state transition rules and cross-layer data fusion are key steps in achieving full-process security tracking. This process involves expanding the initial security verification model, constructing and solving multi-layer quantum correlation equations, and establishing and training a quantum conflict resolution mechanism.

[0042] The process of loading the security state transition rules into the initial security verification model must fully consider the unique characteristics of quantum communication. These security state transition rules define the conditions and restrictions for quantum nodes to transition between different security states. These rules are based on the fundamental principles of quantum key distribution protocols and the characteristics of actual network environments. State transition conditions include verification of the validity of quantum state measurements, thresholds for the success probability of key distribution, and limits on channel noise levels. By loading these rules, the initial security verification model is expanded into a first security framework that more accurately describes the various security state changes that may occur during quantum communication.

[0043] The inclusion of cross-layer correlation parameters further enriches the information dimension of the security framework. These parameters include quantum channel characteristics at the physical layer, key management rules at the protocol layer, and security requirements at the application layer. During the generation of a standardized security fusion framework, parameters at different layers must be normalized to ensure that data from each layer can be compared and integrated under a unified metric. This normalization process takes into account the unique parameter characteristics of quantum communication, such as the non-replicability of qubits and the perturbability of quantum measurements. This ensures that cross-layer data fusion conforms to the principles of quantum mechanics while meeting the requirements of practical security verification.

[0044] The construction of multi-layer quantum correlation equations is a core technical approach for cross-layer data fusion. The state similarity equation is used to evaluate the consistency of the security states of different quantum nodes during the same communication phase. This assessment requires considering the special effects of superposition and entanglement of quantum states. The attribute matching equation focuses on the adaptability between quantum entity parameters and security policies, particularly maintaining the effectiveness of security policies in dynamically changing network environments. The exchange consistency equation focuses on analyzing the timing relationships and logical constraints in the key distribution process to ensure that each step of the key exchange meets the protocol requirements. These equations are solved using a quantum evolutionary algorithm, which can effectively handle the high dimensionality and nonlinearity of quantum communication data.

[0045] During the solution process, the quantum evolutionary algorithm automatically adjusts its search strategy based on the characteristics of the equations. For state similarity equations, the algorithm prioritizes the phase relationship between quantum states and the statistical properties of the measurement results. When solving property matching equations, the algorithm focuses on analyzing the functional relationships and constraints between parameters. Solving exchange consistency equations prioritizes sequential logic and the continuity of state transitions. Through multiple rounds of iteration, the algorithm ultimately outputs key data, including the set of quantum conflicts, quantum state constraint parameters, and the set of protocol evolution boundaries.

[0046] The generation process of the quantum conflict set utilizes a dynamic monitoring mechanism. This mechanism tracks abnormal events during quantum communication in real time, including quantum state measurement deviations exceeding allowable limits and key distribution success rates falling below a threshold. For each detected conflict event, the system records the time of occurrence, the quantum nodes involved, the conflict type, and the potential impact range. This information is structured and stored to form a complete quantum conflict set, providing a data foundation for subsequent conflict analysis and resolution.

[0047] The determination of quantum state constraint parameters is based on an analysis of the extreme performance of quantum communication systems. These parameters include the maximum permissible quantum bit error rate, the minimum acceptable channel transmission efficiency, and a safe lower bound on the key generation rate. In determining these parameters, not only are the physical limitations of quantum devices considered, but performance tradeoffs under different security requirements are also comprehensively evaluated. These constraint parameters serve as important boundary conditions for optimizing security policies, ensuring that the optimized protocol meets security requirements while remaining practically feasible.

[0048] The protocol evolution boundary set is generated using a conflict density analysis method. This method first calculates the conflict density of each quantum node—the ratio of the number of conflict events per unit time to the total traffic handled by that node. A spatial clustering algorithm is then used to identify characteristic regions of conflict density distribution within the network. Regions where the conflict density consistently falls below a preset threshold are marked as safe boundaries for protocol evolution. These boundary regions exhibit relatively stable communication performance and security, making them suitable test areas for protocol optimization.

[0049] The quantum conflict resolution mechanism utilizes a machine learning approach. Using a set of quantum conflicts and a set of protocol evolution boundaries as training data, the mechanism learns the characteristics and solutions of historical conflict events to establish a mapping from conflict detection to resolution strategies. The training process utilizes an incremental learning approach, allowing the resolution mechanism to continuously optimize its decision-making capabilities as new conflict data accumulates. The trained quantum conflict resolution mechanism is capable of rapidly classifying conflict events detected in real time and recommending appropriate resolution strategies based on conflict type and severity.

[0050] The generation of the security policy optimization model is an iterative feedback process. The model uses real-time quantum entity parameters as input and analyzes the differences between the current network state and historical data to predict potential protocol evolution requirements. This predictive capability, based on a deep understanding of the dynamic characteristics of quantum communication systems, accurately identifies trends in how network state changes impact protocol performance. The prediction results include the range of protocol parameters that require adjustment, possible optimization directions, and the expected performance improvement.

[0051] The output of security governance parameters utilizes a multi-dimensional assessment approach. The calculation of quantum correlation weights comprehensively considers factors such as inter-node communication frequency, security dependency, and historical conflict records. Security compliance thresholds are determined based on statistical analysis of the system's overall security status, ensuring that threshold settings effectively mitigate risks while not excessively restricting normal communication. The generation of protocol optimization priority sequences utilizes a risk-benefit-based ranking algorithm, prioritizing protocol parameters with high improvement potential and low implementation difficulty.

[0052] In practical applications, these security governance parameters form a dynamically adjusted closed-loop system. Quantum correlation weights guide resource allocation priorities, ensuring greater security for critical communication links. Security compliance thresholds serve as a benchmark for risk warnings, helping the system promptly identify potential security risks. The protocol optimization priority sequence provides a clear roadmap for system upgrades, maximizing security benefits from limited optimization resources.

[0053] Example 3: In the security verification system of quantum key distribution communication protocols, feature reconstruction of security trajectory simulation data is a key technical step in achieving efficient security policy optimization. This process, through multi-dimensional analysis and structured reconstruction of quantum conflict characteristics, constructs a high-quality training sample set for machine learning models, laying the data foundation for the accurate prediction of security governance parameters.

[0054] The construction of the initial multi-layer input tensor begins with a deep analysis of the quantum conflict set. Each conflict event recorded in the quantum conflict set is deconstructed into multiple characteristic dimensions, including a conflict type code, spatiotemporal distribution coordinates, feature vectors of the involved quantum nodes, and a snapshot of environmental parameters. The conflict type code utilizes a hierarchical classification system, classifying possible anomalies in quantum communication into several broad categories, such as measurement deviation, timing misalignment, and protocol mismatch. Each broad category is further subdivided into multiple specific subcategories. The spatiotemporal distribution coordinates not only record the physical location and time of the conflict but also include the hierarchical position of the event within the network topology. The quantum node feature vectors extract a complete description of the state of each node involved in the conflict at the time of the event, encompassing core attributes such as quantum state parameters, security labels, and association weights. The environmental parameter snapshot captures the instantaneous values of environmental factors such as channel noise level, device temperature, and synchronization accuracy at the time of the conflict.

[0055] The tensorization of quantum state constraint parameters utilizes feature embedding technology. This technology transforms originally discrete constraints into continuous vector representations, preserving the semantic information of the original constraints while adapting to the input requirements of deep learning models. During this process, different types of constraints are assigned to different embedding spaces. For example, the quantum bit error rate constraint and the channel transmission efficiency constraint are each transformed using separate embedding matrices. This approach ensures that the differences between the various constraints are fully preserved, avoiding feature aliasing.

[0056] Feature extraction of the protocol evolution boundary set focuses on the topological characteristics and dynamic changes of the boundary region. Each boundary region is represented as a data structure containing multiple attributes, including a region shape descriptor, node density distribution, and historical stability indicators. The region shape descriptor is generated using a graph theory-based approach, accurately characterizing the spatial distribution characteristics of the boundary region within the network topology. The node density distribution is calculated using a kernel density estimation algorithm, reflecting the concentration of quantum nodes within the boundary region. The historical stability indicator is derived by analyzing the fluctuations in the conflict density of the region over multiple time windows and is used to assess the reliability of the boundary region.

[0057] The characterization process for dynamically optimizing priority sequences incorporates a time series analysis approach. This approach not only considers the current priority ranking but also analyzes the patterns and trends of the priority sequence over time. Using a sliding window technique, the statistical, frequency, and time domain features of the sequence are extracted to construct a feature set that comprehensively reflects the dynamic patterns of priority changes. These features are strictly aligned with the quantum conflict features and constraint parameter features in the time dimension to ensure temporal consistency in subsequent feature fusion.

[0058] A hierarchical normalization strategy is employed for the initial normalization of multi-layer input tensors. The most appropriate normalization method is designed for each type of input feature. Numerical features employ an improved Robust Scaling method, which is more robust to outliers and particularly well-suited for handling extreme values that may exist in quantum communication data. Categorical features are converted to low-dimensional dense vectors via an embedding layer before normalization. Time series features employ sliding window normalization to ensure that the normalization process does not disrupt the continuity of the time series. This hierarchical processing approach maximizes the preservation of the effective information of the original data while eliminating the negative impact of differences in feature scale.

[0059] Feature enhancement is primarily achieved through two approaches: data transformation and feature interaction. Data transformation involves nonlinear transformations, polynomial expansion, and kernel mapping of the original features. These transformations can reveal hidden higher-order patterns in the data. Feature interaction uses an attention mechanism to calculate the correlation weights between different features and, based on these weights, generate more discriminative combined features. Specifically, to address the unique characteristics of quantum communication data, quantum state-aware transformation operations are designed during feature enhancement. These operations preserve the physical meaning of quantum features.

[0060] The construction of the security optimization label tensor utilizes a multi-task learning framework. This framework simultaneously considers multiple optimization objectives, including conflict resolution efficiency, protocol execution performance, and resource utilization. Each optimization objective is quantified as an independent label channel, with information exchange between channels achieved through a gating mechanism. Label values are determined not only based on the current system state but also informed by historical optimization case evaluations. This ensures that label information reflects both immediate needs and empirical insights. The temporal resolution of the label tensor is strictly synchronized with the input features, ensuring that each time step has a corresponding description of the optimization objective.

[0061] The training sample set is organized using a time-slice-based storage scheme. Each sample unit contains all feature and label data within a complete time window, with the window length dynamically adjusted based on the characteristics of the quantum communication protocol. Overlapping sampling is used to maintain temporal continuity between samples, while stratified sampling ensures a balanced distribution of samples with different conflict types. The sample set also maintains a complete metadata index, including data source, acquisition time, and preprocessing records, providing comprehensive context for subsequent model training and validation.

[0062] Quality control of training samples is implemented throughout the entire feature reconstruction process. The online data cleaning module detects and repairs outliers and missing values in feature data in real time, using a dynamically selected repair strategy based on feature type and data distribution. A consistency check mechanism regularly checks the logical relationship between features and labels to ensure the internal consistency of sample data. A sample weighting system automatically calculates the training weight of each sample based on its information content and representativeness, enabling the model to focus on data with higher learning value.

[0063] The feature reconstruction process also takes into account the dynamic evolution of quantum communication systems. An adaptive feature update mechanism continuously monitors changes in the system state and automatically triggers adjustments to the feature space when significant changes are detected. This adjustment is not a simple recalculation, but rather an incremental learning approach that gradually adapts to the new data distribution while preserving existing knowledge. An evolution-aware feature selection algorithm regularly evaluates the importance of each feature and dynamically adjusts the composition of the feature set to ensure that the input data always contains the most predictive information.

[0064] The feature reconstruction system forms a close collaboration with other modules of the security verification platform. Real-time interaction with the quantum conflict detection module ensures access to the latest conflict data; feedback loops with the protocol evolution prediction module help optimize feature selection strategies; and collaboration with the security policy execution module enables feature design to better align with actual application requirements. This comprehensive collaboration ensures that feature reconstruction work remains synchronized with actual security requirements.

[0065] Example 4: During the dynamic security maintenance of quantum key distribution communication systems, the generation of secure incremental simulation information forms the core mechanism for continuous protocol optimization. This process, through the establishment of a responsive parameter update system, enables real-time adaptation and adjustment to the state of the quantum communication network. This is implemented through a multi-layered, multi-stage collaborative operation model.

[0066] The mapping process of quantum correlation weights is implemented using a distributed computing architecture. When the system detects a fluctuation in the key exchange success rate on a quantum channel, the weight distribution module will immediately retrieve the corresponding node association record in the full-process security tracking model. For example, for the core quantum relay node connecting Beijing and Shanghai, the system analyzes its quantum state transmission stability indicators in the last 24 hours, compares these historical data with the real-time collected quantum bit error rate, and generates a new weight coefficient through predefined correlation calculation rules. This coefficient is synchronously updated to the security assessment matrix of all nodes in the network, triggering the state reassessment process of the relevant nodes. This dynamic weight adjustment ensures that the security policy can respond to changes in network conditions in a timely manner. In particular, in cross-border quantum communication scenarios, differences in regulatory requirements in different regions can be effectively balanced through the weight system.

[0067] The security compliance threshold matching mechanism operates using a case-based reasoning approach. The system maintains a case library containing various network anomaly scenarios. When the conflict characteristics of the current quantum node are found to be similar to historical cases, the security threshold configuration for the corresponding scenario is automatically retrieved. For example, if the quantum state retention time of a quantum memory node is monitored to show periodic decay, the system compares the records of quantum memory performance degradation in the case library and selects the closest case as a reference for threshold adjustment. The system also fine-tunes the threshold based on the actual operating environment parameters of the current node (such as temperature and electromagnetic interference level), ensuring that it meets safety standards while not being overly stringent and causing unnecessary communication interruptions.

[0068] The generation process of the protocol optimization priority sequence incorporates a multi-factor decision-making algorithm. In a regional quantum network upgrade project, the system needs to simultaneously address the optimization requirements of multiple protocol parameters, including key update frequency, quantum state encoding method, and error correction scheme. The decision-making algorithm first assesses the scope of impact of these parameters, analyzing, for example, how many quantum channels will be affected by adjusting the key update frequency. It then performs dependency detection to determine which parameter modifications must be implemented in a specific order. Finally, combined with resource availability analysis, it calculates the expected execution cost of each optimization operation. Through this comprehensive assessment, the system generates a dynamic priority queue, ensuring that limited optimization resources are allocated to the most critical protocol improvements.

[0069] Quantum node density adjustments in conflicting areas employ a gradual optimization strategy. When the system identifies persistent measurement basis mismatches between quantum nodes within a data center, it first analyzes the physical layout and communication patterns of these nodes and then gradually implements a density adjustment plan. Initially, adjustments may be made to the location distribution of only a few key nodes to observe trends in conflict metrics. Once proven effective, the optimization strategy is then extended to adjacent areas. This phased adjustment approach avoids network volatility caused by large-scale topology changes and is particularly suitable for smoothly upgrading security policies within an operating quantum communication network.

[0070] The setting of incremental trigger conditions reflects thorough consideration of system stability. Taking protocol coverage completeness monitoring as an example, the system not only sets a threshold for overall network coverage but also establishes differentiated trigger criteria for different types of quantum services. For backbone links responsible for core key distribution, the monitoring frequency and trigger sensitivity of their coverage completeness are significantly higher than those for ordinary user access links. When the protocol coverage completeness of a quantum exchange node falls below the threshold for three consecutive monitoring cycles, the system initiates a parameter update process for that node while maintaining the normal operation of other nodes. This refined triggering mechanism ensures the accuracy and minimal disruption of security maintenance operations.

[0071] The execution process of protocol reconstruction rules emphasizes compatibility with existing systems. When upgrading the communication protocol of a quantum satellite ground station, the reconstruction engine will first verify the degree of compatibility between the new rules and the existing hardware equipment, including physical layer parameters such as the response characteristics of the quantum detector and the control accuracy of the modulator. Only after confirming that the new rules can be supported by the existing equipment will they be deployed to the actual system. For rule improvements that must be implemented in conjunction with hardware upgrades, the system will automatically generate a phased implementation plan to ensure that service continuity is not affected. This cautious reconstruction method is particularly important during the upgrade process of quantum encryption communication systems in the financial industry, which can avoid business interruptions caused by protocol changes.

[0072] The calculation of parameter update increments utilizes an adaptive adjustment mechanism. When quantum consistency assessment indicators indicate a decrease in the state stability of a quantum channel, the system does not immediately apply the maximum parameter adjustment. Instead, it dynamically determines the increment size based on the speed and extent of the indicator deterioration. For example, for slowly developing performance degradation, the system will schedule multiple small parameter adjustments and observe the system response after each adjustment. For sudden, severe anomalies, however, the system may directly apply the preset maximum safe increment. This differentiated increment strategy ensures rapid response in emergency situations while avoiding system fluctuations caused by excessive adjustments.

[0073] The quantum evolutionary algorithm demonstrates unique adaptability when solving multi-layer quantum correlation equations. When addressing the effects of atmospheric turbulence in satellite-to-ground quantum communications, the algorithm prioritizes optimization of parameters most sensitive to channel perturbations. By analyzing key generation efficiency under different weather conditions in historical communication data, the algorithm automatically adjusts its parameter search direction, improving the system's environmental adaptability while ensuring security. This optimization approach, informed by real-world operational experience, enables security strategies to better address diverse and complex real-world challenges.

[0074] The simulation termination criteria are determined based on a comprehensive consideration of multiple factors. In addition to conventional iteration limits and convergence thresholds, the system also monitors actual operational indicators such as quantum node load changes and network energy consumption trends during the optimization process. If an optimization direction is found to potentially cause certain key nodes to exceed their operating capacity, the current simulation cycle is prematurely terminated and a warning message is output, even if the algorithm has not yet reached theoretical optimality. This pragmatic termination strategy mitigates the operational risks that can arise from purely mathematical optimization, embodying the principle of safety-first engineering implementation.

[0075] The output of the secure incremental simulation model provides a comprehensive reference for subsequent decision-making. The quantum mapping deviation not only provides numerical differences but also indicates the spatial distribution of the deviations. The protocol coverage completeness report is accompanied by a detailed gap analysis report, identifying specific protocol clauses that require strengthening. The quantum consistency assessment metric differentiates the impact of different quantum effects. This structured output enables network administrators to accurately understand system status and make targeted management decisions.

[0076] Example 5: In the optimization decision-making process of quantum key distribution networks, the construction and solution of multi-objective decision-making models constitute the core decision-making engine of protocol optimization. The implementation of this system embodies a hierarchical and progressive intelligent decision-making mechanism. By integrating the advantages of multiple optimization algorithms, it achieves secure path planning in complex quantum communication environments.

[0077] The construction of a multi-objective decision-making model begins with a comprehensive analysis of quantum network characteristics. Taking the quantum encryption communication network of a multinational financial institution as an example, the decision-making system first digitally models the infrastructure, abstracting quantum nodes distributed across 12 data centers around the world as vertices in a network topology diagram. Each vertex contains over 50 characteristic dimensions, including quantum memory coherence time at the physical layer, key refresh frequency at the protocol layer, and node connectivity at the network layer. Decision variables undergo special encoding processing, such as converting quantum node density into spatial distribution coefficients based on the Voronoi diagram, transforming the discrete node deployment problem into a continuous optimization problem. Security compliance thresholds are expressed as multi-dimensional constraints, including data compliance requirements for each jurisdiction and security standards for different business levels.

[0078] Adaptive grid technology is used to construct the parameter space structure. In a government quantum secure communication network upgrade project, the system identified the correlation of key parameters based on historical operation and maintenance data, reducing the originally high-dimensional parameter space to several key characteristic directions. For example, the quantum state iteration frequency and the channel bit error rate were identified as strongly correlated parameters, and the system merged them into a composite optimization dimension. This intelligent dimensionality reduction allows the subsequent sampling process to focus on the most influential parameter combinations, significantly improving optimization efficiency. The generation of the initial sampling point set not only considers uniform coverage of the parameter range, but also pays special attention to sensitive areas discovered during past operations and maintenance to ensure sufficient sampling density in these key areas.

[0079] The calculation of the objective function integrates multiple performance dimensions of quantum communication. When optimizing a submarine quantum relay system, the system simultaneously considers two core metrics: protocol coverage efficiency and quantum collision density. The calculation of protocol coverage efficiency not only counts the number of supported services but also assesses the quality level and priority weight of each service. The measurement of quantum collision density utilizes a multi-scale analysis approach, examining both the overall network collision rate at a macro level and the distribution of critical path collisions at a micro level. These metrics are integrated into a comprehensive objective function using a weighting system based on business needs to ensure that the optimization direction meets actual operational requirements.

[0080] The application of Gaussian process regression demonstrates its advantages in handling quantum uncertainty. When optimizing the ground station layout for a satellite quantum communication system, the system utilizes Gaussian processes to model the probabilistic relationship between parameters and performance. This approach is particularly well-suited to quantum communication environments because it naturally handles quantum noise and observation errors in measurements. The regression model not only predicts the expected value of the performance metric but also provides confidence intervals for the prediction, providing a basis for subsequent risk assessment. When dealing with periodic parameters such as quantum satellite overpass times, the system employs periodic kernel functions to better capture the temporal correlation of the parameters.

[0081] The acquisition function optimization phase achieves a balance between exploration and utilization. In the frequency planning of a city's quantum government network, the system dynamically adjusts its sampling strategy: during nighttime hours, when network load is low, exploratory sampling is favored, trying new parameter combinations; during peak hours, utilization sampling is favored, selecting parameter settings known to perform well. This adaptive strategy ensures that the optimization process can discover new optimization directions without significantly impacting ongoing services. The acquisition function calculation also considers the operational costs of different parameter adjustments, prioritizing optimization directions that are easy to implement but highly effective.

[0082] The confidence interval screening mechanism provides risk assessment for decision-making. When optimizing a quantum-protected communication network for a power system, the system focuses on parameter combinations with excellent predicted performance but wide confidence intervals. For these combinations, the system schedules additional validation sampling to reduce prediction uncertainty. Furthermore, the system maintains a knowledge base of risk parameters, documenting parameter characteristics that have historically led to performance degradation, and automatically avoids combinations with similar characteristics during the screening process. This conservative and rigorous screening strategy is particularly important in optimizing critical infrastructure.

[0083] The local exploration process employs gradient-aware intelligent search. When adjusting the fiber length parameters of a quantum data center, the system analyzes the local gradients of the performance surface to identify the most promising search directions. Unlike traditional optimization methods, the gradient calculation here accounts for the non-local characteristics unique to quantum communication, such as the cross-node effects of long-range quantum entanglement. The system also identifies flat regions in the parameter space, which often correspond to multiple near-optimal solutions, providing operators with flexible options.

[0084] The ant colony algorithm demonstrates unique advantages in the global optimization phase. When planning the upgrade path for a national quantum backbone network, the algorithm simulates "ants" exploring various possible optimization paths on the network topology. Each ant carries a specific optimization preference, with some prioritizing improved security and others focused on cost reduction. These exploration results are shared through a pheromone mechanism, ultimately integrating into a comprehensive optimization solution. The algorithm incorporates specially designed quantum-aware pheromone update rules that accurately handle the unique nonclassical correlations inherent in quantum networks.

[0085] The generation of optimal security path parameters emphasizes feasibility. The system outputs not only theoretically optimal parameter values but also a detailed implementation roadmap. For example, when optimizing the security protocols of a bank's quantum transaction system, the plan clearly distinguishes between critical modifications that must be implemented immediately and enhancements that can be implemented in phases. Each parameter adjustment is accompanied by an impact assessment report, detailing the potentially affected system components and the necessary supporting measures. This engineered output greatly facilitates practical operations for the operations and maintenance team.

[0086] The interaction between the decision-making system and the actual quantum network forms a closed loop of continuous improvement. After each parameter adjustment, the system monitors network performance in real time, comparing actual operating data with predicted results. Discovered discrepancies are used to calibrate the optimization model, continuously improving the accuracy of subsequent decisions. The system also maintains a case knowledge base, documenting the decision-making processes and implementation results of previous optimizations, providing empirical references for new optimization tasks. This self-improvement mechanism enables the system to adapt to the rapid development of quantum network technology.

[0087] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0088] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A communication protocol security verification method based on quantum key distribution, characterized in that: The following steps are involved: Establish a quantum communication channel framework and load the quantum key distribution protocol parameter set to generate an initial security verification model; Based on the initial security verification model, the security state transition rules are configured, and cross-layer data fusion is performed in combination with quantum entity parameters to obtain a full-process security tracking model. Execute security path deduction through the full-process security tracking model, generate key exchange trajectory chain, and construct security trajectory simulation data in combination with quantum state iteration frequency parameters; Establish a quantum conflict resolution mechanism and generate a security policy optimization model through training with security trajectory simulation data, thereby outputting security governance parameters. The security governance parameters include at least quantum correlation weight, security compliance threshold, and protocol optimization priority sequence. Generate secure incremental simulation information based on security governance parameters, full-process security tracking model, and quantum entity parameters; Combining security incremental simulation information with protocol evolution constraints, a multi-objective decision model is constructed and the security framework is iteratively adjusted to generate optimal security path parameters. Based on the real-time quantum mapping network, the current security path parameters are verified, the quantum consistency index is calculated, and the optimal security path parameters are combined with the historical trajectory chain to update the security verification strategy to achieve the full-process security matching goal.

2. The communication protocol security verification method based on quantum key distribution according to claim 1, characterized in that: The security trajectory simulation data includes at least a quantum conflict set, quantum state constraint parameters, a protocol evolution boundary set, and a dynamic optimization priority sequence; The secure incremental simulation information includes at least quantum mapping deviation, protocol coverage completeness rate and quantum consistency evaluation index.

3. The communication protocol security verification method based on quantum key distribution according to claim 1, characterized in that: The process of establishing a quantum communication channel framework and loading a quantum key distribution protocol parameter set to generate an initial security verification model includes the following steps: Perform structured cleaning and classification on multi-source quantum communication data to generate a standardized set of quantum entities; Performing quantum attribute extraction on a set of quantum entities, wherein the extraction includes one or more of quantum clustering, attribute mapping, state classification, and quantum state identification; Based on the state classification results, an initial security verification model is constructed, wherein the model includes a quantum node set, an exchange edge set, and quantum constraint rules; The quantum correlation strength analysis is performed on the initial security verification model to generate a dynamic correlation weight matrix and assign a security status label to each quantum node.

4. The communication protocol security verification method based on quantum key distribution according to claim 1, characterized in that: The configuration of the security state transfer rules based on the initial security verification model and the cross-layer data fusion combined with the quantum entity parameters to obtain the full-process security tracking model include the following steps: Loading security state transition rules into the initial security verification model to constrain state transition conditions and generate a first security framework; Loading cross-layer correlation parameters to the first security framework to generate a standardized security fusion framework; Cross-layer data fusion is performed based on a standardized security fusion framework. The specific process includes: Constructing a multi-layer quantum correlation equation, which includes at least a state similarity equation, an attribute matching equation, and an exchange consistency equation, and iteratively solving the multi-layer quantum correlation equation using a quantum evolution algorithm to obtain a quantum conflict set, quantum state constraint parameters, and a protocol evolution boundary set; The generation of the protocol evolution boundary set includes the following steps: Based on the quantum conflict set, the conflict density of each quantum node is calculated; Identify areas in the standardized security fusion framework where the conflict density is no greater than a preset threshold and generate a set of protocol evolution boundaries.

5. The communication protocol security verification method based on quantum key distribution according to claim 1, characterized in that: The establishment of a quantum conflict resolution mechanism and the generation of a security policy optimization model through training with security trajectory simulation data, and the output of security governance parameters, include the following steps: Construct a quantum conflict resolution mechanism based on the initial security verification model; The quantum conflict resolution mechanism is trained and verified through security trajectory simulation data to generate a security strategy optimization model; Input the real-time quantum entity parameters into the security policy optimization model to predict the set of protocol evolution boundaries; Based on the predicted set of protocol evolution boundaries, security governance parameters are output, where the security governance parameters include at least quantum correlation weight, security compliance threshold and protocol optimization priority sequence.

6. The communication protocol security verification method based on quantum key distribution according to claim 5, characterized in that: It also includes feature reconstruction of safety trajectory simulation data, specifically: Based on the quantum conflict set, quantum state constraint parameters and protocol evolution boundary set, an initial multi-layer input tensor is constructed; Normalize and enhance the features of the initial multi-layer input tensor to generate the final multi-layer input tensor; Construct a security optimization label tensor based on a dynamic optimization priority sequence; The final multi-layer input tensor is combined with the security optimized label tensor to form a training sample set.

7. The communication protocol security verification method based on quantum key distribution according to claim 6, characterized in that: Outputting security governance parameters based on the predicted protocol evolution boundary set includes the following steps: Extract the quantum node with the lowest quantum conflict density from the predicted protocol evolution boundary set and generate the quantum correlation weight; Based on the predicted topological connection relationship of the protocol evolution boundary set, the distribution structure of the security compliance threshold is fitted to generate a protocol optimization priority sequence; The state similarity gradient direction of the predicted protocol evolution boundary set is calculated and normalized into a security reference vector, which is the security compliance adjustment direction.

8. The communication protocol security verification method based on quantum key distribution according to claim 1, characterized in that: The method of generating secure incremental simulation information based on security governance parameters, a full-process security tracking model, and quantum entity parameters includes the following steps: Map the quantum correlation weights to the full-process security tracking model, match the security compliance thresholds and protocol optimization priority sequence, adjust the quantum node density in the conflict area, update the full-process security tracking model, define incremental trigger conditions, protocol reconstruction rules and parameter update increments, and generate a security incremental simulation model; Based on the secure incremental simulation model, the quantum evolution algorithm is used to iteratively solve the multi-layer quantum correlation equation to generate secure incremental simulation information, including: When the incremental trigger condition is met, the quantum mapping deviation, protocol coverage completeness and quantum consistency evaluation index are updated, and the multi-layer quantum correlation equation is re-solved until the simulation termination condition is met; The incremental trigger condition includes triggering parameter update when the current protocol coverage completeness rate is no more than a preset completeness rate threshold; the protocol reconstruction rule includes adjusting the security compliance threshold based on the state similarity gradient direction; the parameter update increment is in a piecewise linear relationship with the current quantum consistency evaluation index.

9. The communication protocol security verification method based on quantum key distribution according to claim 1, characterized in that: The multi-objective decision model is constructed and the security framework is iteratively adjusted to generate the optimal security path parameters, including the following steps: Construct a multi-objective decision model, where the decision variables include quantum node density, security compliance threshold, and quantum state iteration frequency. The decision objectives include maximizing protocol coverage efficiency and minimizing quantum conflict density. The constraints include protocol evolution boundary constraints and attribute matching accuracy thresholds. The multi-objective decision model is preliminarily solved by the Bayesian optimization algorithm to generate an initial decision path set; Based on the initial decision path set, the ant colony algorithm is used for global optimization to generate the optimal safe path parameters.

10. The communication protocol security verification method based on quantum key distribution according to claim 9, characterized in that: The method of performing a preliminary solution to the multi-objective decision model using a Bayesian optimization algorithm to generate an initial decision path set includes the following steps: Construct a parameter space structure based on quantum node density and security compliance thresholds to generate an initial sampling point set; Calculate the objective function based on the protocol coverage efficiency and quantum conflict density, perform Gaussian process regression and acquisition function optimization on the initial sampling point set, and generate a set of intermediate decision paths; Perform confidence interval screening and local exploration on the set of intermediate decision paths to generate an initial set of decision paths.

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