A secure encrypted communication method and system based on quantum key management

Abstract

The present invention provides a secure encryption communication method and system based on quantum key management. The method includes: obtaining system status data, generating node status data, network connection diagram data and system status matrix; receiving communication request data packets, generating request feature vectors, service weight coefficients and service demand matrices; collecting real-time network status data, building an enhanced Bayesian network model, and generating a network status scoring matrix; collecting key pool status data, building a key consumption prediction model, and generating a key resource distribution matrix; building a multi-objective optimization model, generating a scheduling strategy matrix; executing the scheduling strategy and performing performance evaluation. The present invention takes into account the quantum state decoherence effect and the influence of channel noise, and by establishing a complete evaluation and prediction system, realizes efficient management and secure distribution of quantum keys, and improves the performance and reliability of the communication system.

Description

Technical Field

[0001] The present invention relates to security encryption technology, and in particular to a security encryption communication method and system based on quantum key management. Background Art

[0002] Under the current network security situation, studying secure encryption communication methods based on quantum key management has important theoretical significance and practical value.

[0003] By conducting secondary development based on the existing quantum communication platform, we can make full use of the infrastructure such as the deployed quantum key distribution equipment, quantum state measurement modules and quantum channel characterization tools to achieve efficient quantum key management and secure communication. At present, the standard QKD system has realized the basic quantum key distribution functions, including core functions such as quantum bit error rate measurement and key generation rate monitoring. The existing quantum state tomography (QST) module can realize photon loss rate detection and quantum state retention time measurement. Standard quantum channel characterization tools can analyze channel characteristics and evaluate channel performance. In terms of key management, existing research has mainly focused on static key pool management, simple key distribution strategies and basic routing selection algorithms. These technologies provide basic support for the deployment and operation of quantum communication networks, but they are still insufficient in the face of complex network environments and dynamic business needs, resulting in obstacles to promotion.

[0004] In actual use, the following technical problems were found: First, the existing Bayesian network model does not consider the impact of quantum state decoherence effect on network state prediction, resulting in insufficient prediction accuracy; second, the key consumption prediction model only uses a simple exponential smoothing method, which fails to fully consider the random fluctuation characteristics of quantum channel noise, affecting the accuracy of prediction; third, in the multi-objective optimization process, the Shannon limit of quantum channel capacity is not used as a constraint, which may lead to unreasonable resource allocation; fourth, the network state evaluation has not established a clear update cycle mechanism, making it difficult to reflect network state changes in a timely manner; fifth, in the process of collecting and analyzing performance indicator data, there is a lack of comprehensive evaluation of quantum characteristic parameters, which affects the effect of system performance optimization; sixth, in the key pool health status evaluation process, the impact of quantum channel noise on key quality is not fully considered, reducing the accuracy of health evaluation. The existence of these technical problems restricts the performance and reliability of quantum key management systems, and they urgently need to be solved through innovative technical solutions. Summary of the invention

[0005] The purpose of the invention is to provide a secure encryption communication method and system based on quantum key management, in order to solve the above-mentioned problems existing in the prior art.

[0006] Technical solution: A secure encryption communication method based on quantum key management, comprising the following steps:

[0007] Step S1, obtain the system status data of the communication network and construct the system status matrix; obtain the communication request data packet and generate the business demand matrix; read the real-time collected network status data and generate the network status scoring matrix; read the key pool status data of each node and generate the key resource distribution matrix;

[0008] Step S2: Based on the system state matrix, the business demand matrix, the network state scoring matrix and the key resource distribution matrix, a multi-objective optimization model is constructed and solved to generate a scheduling strategy matrix, and a feasibility verification report is generated for the scheduling strategy matrix;

[0009] Step S3: construct and issue a scheduling instruction set according to the scheduling strategy matrix, collect performance indicator data for monitoring and evaluation, update system historical data to obtain data update logs, and generate a comprehensive performance report.

[0010] A secure encryption communication system based on quantum key management, comprising:

[0011] at least one processor; and,

[0012] a memory communicatively connected to at least one of the processors; wherein,

[0013] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the secure encryption communication method based on quantum key management described in any of the above technical solutions.

[0014] Beneficial effects: Considering the quantum state decoherence effect and channel noise, by establishing a complete evaluation and prediction system, efficient management and secure distribution of quantum keys are achieved, and the performance and reliability of the communication system are improved. A new routing management algorithm is provided for the popularization of quantum encryption communication. The relevant technical effects are described in detail in conjunction with the specific embodiments below. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the present invention.

[0016] Figure 2 It is a flow chart of step S1 of the present invention.

[0017] Figure 3 It is a flow chart of step S2 of the present invention.

[0018] Figure 4 It is a flow chart of step S3 of the present invention. DETAILED DESCRIPTION

[0019] like Figure 1 As shown, a secure encryption communication method based on quantum key management comprises the following steps:

[0020] Step S1, obtain the system status data of the communication network and construct a system status matrix; obtain the communication request data packet and generate a business demand matrix; read the real-time collected network status data and generate a network status scoring matrix; read the key pool status data of each node and generate a key resource distribution matrix.

[0021] Step S2: Based on the system state matrix, business demand matrix, network state scoring matrix and key resource distribution matrix, a multi-objective optimization model is constructed and solved to generate a scheduling strategy matrix, and a feasibility verification report is generated for the scheduling strategy matrix.

[0022] Step S3: construct and issue a scheduling instruction set according to the scheduling strategy matrix, collect performance indicator data for monitoring and evaluation, update system historical data to obtain data update logs, and generate a comprehensive performance report.

[0023] By integrating core functional modules such as system status analysis, request feature extraction, network status prediction, key resource management, and multi-objective optimization, a complete quantum key management and secure encryption communication system is constructed. An evaluation model based on quantum channel theory is adopted, combined with an enhanced Bayesian network prediction mechanism and a quantum genetic algorithm optimization method, to achieve intelligent management of quantum communication networks. The system can not only accurately predict network status and key consumption trends, but also dynamically adjust resource allocation strategies according to real-time status, while ensuring communication security through multi-level security mechanisms. This comprehensive solution significantly improves the operating efficiency, resource utilization, and service quality of quantum communication systems, and provides reliable technical support for the deployment and operation of large-scale quantum communication networks.

[0024] like Figure 2 As shown, according to one aspect of the present application, step S1 is specifically:

[0025] Step S11, obtaining system status data including network topology configuration data, node configuration data and key pool configuration data, processing the system status data to generate node status data, constructing network connection diagram data, and generating a system status matrix.

[0026] Step S12, receiving a communication request data packet, extracting basic request information including a source node identifier, a target node identifier, a data volume, and an expected completion time to generate a request feature vector, calculating a service weight coefficient based on the request feature vector and preset quantum communication evaluation parameters, and generating a service demand matrix based on the service weight coefficient and the request feature vectors of multiple active requests.

[0027] Step S13: collect real-time network status data from the network, build an enhanced Bayesian network model based on quantum channel theory, combine the quantum state evolution model to predict and generate predicted status data, calculate the link reliability index based on the predicted status data, and calculate and generate a network status scoring matrix.

[0028] Step S14: collect the key pool status data of each node, build a key consumption prediction model considering quantum channel noise, calculate the key pool health index based on the key consumption prediction model, and integrate the key resource information of all nodes to generate a key resource distribution matrix.

[0029] like Figure 3 As shown, according to one aspect of the present application, step S2 is specifically:

[0030] Step S21, extracting quantum communication service demand parameters from the service demand matrix to construct an objective function set; setting system constraints according to quantum channel capacity theory; constructing an optimization variable matrix in combination with a network state scoring matrix and a key resource distribution matrix; combining the objective function set, system constraints and the optimization variable matrix to generate a multi-objective optimization model that considers quantum characteristics.

[0031] Optimize the objective function Z(x) = [f1(x), f2(x), f i (x)..., f n (x)]; each sub-goal: f i (x) = ∑ j (w j ·y j (x))·exp(-θ i C(x));y j (x) is the performance index function, y j (x) = (1 / δ j )·∑ k (a k ·exp(-b k x)); C(x) is the quantum channel constraint, C(x) = B·log2(1 + SNR)·(1 - ε) k ;w j is the indicator weight; θ i is the target adjustment coefficient; δ j is the normalization factor; a k 、b k is the fitting coefficient; ε is the bit error rate; k is the security parameter. This model incorporates the constraints of quantum communication characteristics.

[0032] Step S22: calculate all reachable paths based on the network connection graph data to obtain an initial path set; use the health index data in the key resource distribution matrix to update the path weight to obtain an updated path set; score the updated path set according to the performance parameters in the network status scoring matrix to generate path scoring data.

[0033] Step S23, designing a genetic algorithm coding scheme in a quantum communication scenario based on a multi-objective optimization model; using path scoring data to generate an initial population to obtain an initial solution set; performing crossover operations and mutation operations considering quantum constraints to obtain an evolutionary solution set; calculating fitness according to the multi-objective optimization model to obtain an optimal solution set; converting the optimal solution set into a specific path selection scheme and resource allocation scheme to generate a scheduling strategy matrix.

[0034] Fitness function F(s) = ∑ i (λ i Q i (s))·exp(-μ·E(s)); where: Q i (s) is the quantum performance index, Q i (s) = |<ψ s |O i |ψ s >| 2 ,|ψ s > is the quantum state, O i is the observation operator; E(s) is the entanglement cost, E(s) = -Tr(ρ s log2ρ s ), ρ s is a simplified density matrix; the crossover operator C(p1,p2) = U(θ)|p1> + V(φ)|p2>, U(θ) and V(φ) are unitary operators; the mutation operator M(p) = exp(-iH·γ)|p>, H is the mutation Hamiltonian; λ i is the indicator weight; μ is the cost coefficient. The algorithm introduces quantum gate operation to realize population evolution.

[0035] Step S24, verify the resource constraints of the scheduling strategy matrix according to the key resource distribution matrix to obtain resource verification data; use the network status scoring matrix to verify the performance constraints to obtain performance verification data; check the conflict with the existing scheduling tasks to obtain conflict verification data; integrate various verification results to generate a verification report.

[0036] Among them, the path optimization process considering the quantum channel capacity constraint includes the following data processing flow: obtaining quantum channel parameter data, and calculating the channel capacity data according to the quantum Shannon theory; establishing a capacity constraint model based on the channel capacity data; obtaining the current network status data and business demand data; inputting the network status data and business demand data into the preset quantum genetic algorithm to obtain the initial path plan; using the capacity constraint model to verify the initial path plan to obtain the verification result data; optimizing the initial path plan according to the verification result data to obtain the optimal path plan.

[0037] Among them, the quantum key scheduling strategy generation process is specifically as follows: obtain the optimal path plan, extract the path information therein to obtain path data; calculate the required key amount based on the path data to obtain key demand data; obtain key pool status data from the system; generate a key distribution plan based on the key demand data and key pool status data; calculate the key supplement strategy of each node based on the key distribution plan to obtain supplement strategy data; integrate path data, key distribution plan and supplement strategy data to generate scheduling strategy data.

[0038] The multi-objective optimization model constructed based on quantum Shannon information theory realizes the intelligent optimization of quantum communication routing and resource allocation. This step constructs a complete optimization variable matrix by setting system constraints that take into account quantum characteristics, combining the network state scoring matrix and the key resource distribution matrix. The quantum genetic algorithm is used for path optimization, which not only considers traditional network performance indicators, but also incorporates quantum characteristics such as quantum channel capacity and entanglement state retention time into the optimization objectives, and generates an optimal solution set through crossover and mutation operations. This optimization method can achieve optimal resource allocation while ensuring the quality of quantum communication, significantly improving the system's operating efficiency and service quality.

[0039] like Figure 4 As shown, according to one aspect of the present application, step S3 is specifically:

[0040] Step S31, extract the path selection scheme from the scheduling strategy matrix to generate routing configuration instructions; extract the key distribution scheme to generate key distribution instructions; extract the bandwidth distribution scheme to generate bandwidth reservation instructions; combine various instructions to generate a scheduling instruction set; use a two-stage submission method to issue instructions to relevant nodes and record instruction issuance logs.

[0041] Step S32: According to the preset performance monitoring cycle, the actual completion time of the communication task is collected to obtain time indicator data; the actual key consumption is counted to obtain key consumption data; the actual bandwidth usage rate is measured to obtain bandwidth usage data; the actual transmission delay is measured to obtain delay indicator data; and various indicator data are combined to generate performance indicator data.

[0042] Comprehensive performance index P(s) = ∑i (α i ·M i (s))·exp(-β·L(s)); where: M i (s) is the performance measurement function, M i (s) = (1 - |v i -v o | / v max )·exp(-γ i t), v i is the measured value, v o is the standard value; L(s) is the performance loss function, L(s) = ∑ j (w j ·(1 - exp(-λ j ·Δt j ))), Δt j is the delay deviation; α i is the indicator weight; β is the loss impact factor; γ i is the time attenuation coefficient; w j is the delay weight; j is the attenuation coefficient. This method combines the delay characteristic evaluation.

[0043] Step S33, calculate various deviation indicators according to the performance indicator data and the expected value in the scheduling strategy matrix to obtain performance deviation data; write the performance indicator data and the performance deviation data into the historical database to obtain data update records; perform data cleaning and compression to obtain data cleaning records; generate a data update log.

[0044] Step S34, calculate the scheduling success rate, resource utilization and service quality satisfaction based on the performance indicator data to obtain key performance indicators; analyze the performance deviation data to obtain trend analysis data; generate system optimization suggestions based on the analysis results to obtain optimization suggestion data; integrate various analysis results to generate a comprehensive performance report.

[0045] Optimization benefit evaluation E(o) = ∑ i (π i ·B i (o))·exp(-σ·C(o)); where: B i (o) is the optimization benefit function, B i (o) = ΔP i / P io ·exp(-μ i t), ΔP i is the performance improvement; C(o) is the implementation cost function, C(o) = ∑ j (c j ·(1 - exp(-ν j ·τj ))), c j is the resource cost, τ j is the implementation time; π i is the benefit weight; σ is the cost sensitivity; μ i is the time attenuation coefficient; ν j is the cost decay coefficient. The algorithm introduces the time decay characteristic to evaluate the optimization benefit.

[0046] Through the construction and issuance of the scheduling instruction set, a two-stage submission method is adopted to ensure the consistency and reliability of instruction execution. In terms of performance monitoring, a multi-dimensional indicator collection mechanism is introduced, including the real-time collection of time indicator data, key consumption data, bandwidth usage data and delay indicator data, combined with the monitoring of quantum bit error rate and entanglement state fidelity, to achieve comprehensive monitoring of the system operation status. Especially in the performance evaluation link, the system calculates key performance indicators such as scheduling success rate, resource utilization and service quality satisfaction, combined with performance fluctuation law analysis and development trend prediction, to timely identify system performance bottlenecks and optimization space.

[0047] According to one aspect of the present application, step S11 is specifically:

[0048] Step S111, read the network topology configuration data including the node position coordinate information and the physical connection information between the nodes from the preset configuration database; obtain the node configuration data including the node type information, QKD device parameter information and quantum channel parameter information; obtain the key pool configuration data including the maximum capacity information, the warning threshold information and the key supplement strategy information; perform validity verification based on the obtained network topology configuration data, node configuration data and key pool configuration data, and generate configuration status report data.

[0049] Step S112: According to the node configuration data, CPU utilization data, memory utilization data and storage space utilization data are obtained from each network node; quantum bit error rate data, photon detection efficiency data and quantum state coherence time data of each node are collected; quantum channel capacity data is calculated according to a preset quantum channel model; node computing power data is calculated using various utilization data and preset weight coefficients; quantum key generation rate data is calculated using quantum channel capacity data and environmental parameters; node status data is generated based on the node computing power data and quantum key generation rate data.

[0050] Quantum channel capacity C(t) = B·log2(1 + SNR(t)) - α·L(t)·(1 - F(t)) - β·D(t); where: B is the channel bandwidth; SNR(t) is the signal-to-noise ratio at time t; L(t) is the photon loss rate, L(t) = L oexp(-γd), L o is the initial loss rate, γ is the attenuation coefficient, d is the transmission distance; F(t) is the quantum state fidelity, F(t) = exp(-t / T2), T2 is the decoherence time; D(t) is the dark count rate; α and β are weight coefficients used to balance the influence of various factors; the unit of quantum channel capacity C(t) is bit / second, which represents the number of quantum bits reliably transmitted per unit time. This method incorporates quantum state fidelity and dark count rate into capacity calculation, providing a more accurate channel assessment.

[0051] Step S113, extracting physical connection information from the system state data, constructing adjacency matrix data representing the connection relationship between nodes; calculating edge weight data based on the bandwidth information, delay information and quantum channel quality information in the node state data; inputting the adjacency matrix data and edge weight data into a preset graph optimization algorithm to generate network connection graph data; performing topology analysis based on the network connection graph data to generate topology optimization data including connectivity analysis results; generating a system state matrix based on the topology optimization data.

[0052] The network topology optimization process is as follows: topology optimization metric E(G) = λ1·C(G) + λ2·R(G) +λ3·S(G); where: C(G) is the network connectivity index, C(G) = ∑(i,j)∈E w(i,j)·f(i,j), w(i,j) is the edge weight, f(i,j) is the connection state; R(G) is the quantum entanglement resource distribution index, R(G) = ∑ i -n (E i / E max )·exp(-d i / d o ), E i is the entanglement resource of node i, d i is the distance to the central node; S(G) is the network security index, S(G) = ∑ v ∈V min{P(v→t)}, P(v→t) is the set safety of the path from node v to target t; λ1, λ2, λ3 are weight coefficients used to balance the importance of each indicator. The algorithm introduces quantum entanglement resource distribution as a key factor in topology optimization.

[0053] In another embodiment of the present application, it also includes: step S114, using node status data and network connection diagram data to construct system status matrix data including processing capability information, storage status information, bandwidth status information, quantum channel status information and key pool status information of each node; calculating the status matrix update cycle data according to the network scale and business volume; establishing a timed update mechanism based on the status matrix update cycle data to regularly update the system status matrix data; generating status update log data including an update timestamp and update content after each update; and distributing the updated system status matrix data to relevant network nodes.

[0054] By integrating the collection and processing mechanism of network topology configuration data, node configuration data, and key pool configuration data, full-dimensional monitoring of the state of the quantum communication system is achieved. This step obtains physical layer information in real time based on the preset configuration database, and combines node computing power evaluation and quantum key generation rate calculation to build a complete system status data system. By introducing weight coefficients and quantum channel models, the system can accurately evaluate the actual processing power and quantum key generation capabilities of the nodes, and ensure the timeliness of system status data through a regular update mechanism. This multi-dimensional data collection and processing method enables the system to grasp the network topology status, node performance, and key resource distribution in real time, providing a reliable data foundation for subsequent quantum key management and communication scheduling, and significantly improving the reliability and security of the system.

[0055] According to one aspect of the present application, step S112 is specifically:

[0056] Step S1121, extracting quantum device parameters from node configuration data to generate device parameter data; collecting beam jitter data of the quantum device to obtain beam stability data; measuring the dark count rate of the photon detector to obtain dark count data; generating device status data according to the device parameter data, beam stability data and dark count data.

[0057] Step S1122, collecting the photon polarization state measurement results of each node to obtain polarization measurement data; obtaining the attenuation coefficient of the quantum channel to obtain channel attenuation data; measuring the ambient temperature and vibration parameters to obtain environmental interference data; using the polarization measurement data, channel attenuation data and environmental interference data to calculate and obtain quantum channel quality data.

[0058] Step S1123, calculate the single-photon detection efficiency according to the device status data to obtain detection efficiency data; calculate the channel transmission loss based on the quantum channel quality data to obtain transmission loss data; measure and correct the phase drift to obtain phase compensation data; input the detection efficiency data, transmission loss data and phase compensation data into the preset Shannon information theory model to calculate and obtain quantum channel capacity data.

[0059] Step S1124, collecting the CPU usage data and memory usage data of the node; calculating the quantum key cache utilization rate according to the device status data to obtain the cache status data; estimating the key extraction efficiency based on the quantum channel quality data to obtain the key efficiency data; and weighting the CPU usage data, memory usage data, cache status data and key efficiency data according to the preset weight coefficient to obtain the node computing power data.

[0060] Step S1125, using the quantum channel quality data to calculate the number of effective photons per unit time to obtain effective photon rate data; calculating the phase stability based on the phase compensation data to obtain phase stability data; calculating the number of photon pairs that are finally successfully paired in combination with the detection efficiency data to obtain pairing efficiency data; inputting the effective photon rate data, phase stability data and pairing efficiency data into a preset key generation rate model to calculate and obtain quantum key generation rate data.

[0061] Step S1126: establish a node performance evaluation model based on the node computing power data and the quantum key generation rate data; calculate the quantum communication processing capability of the node based on the evaluation model to obtain processing power data; analyze the key generation and management efficiency of the node to obtain management efficiency data; integrate the processing power data and management efficiency data to generate node status data.

[0062] By integrating the comprehensive evaluation mechanism of quantum device parameter monitoring, quantum channel quality evaluation and node performance calculation, the accurate quantification of quantum communication node status is achieved. This step first constructs a complete quantum channel quality evaluation system by collecting key parameters such as beam stability and dark count rate of quantum devices, combining the measurement results of photon polarization state and environmental interference data. In terms of performance calculation, by introducing the phase compensation mechanism and Shannon information theory model, the system can accurately calculate the quantum channel capacity and key generation rate. In particular, in the process of node computing power evaluation, a multi-dimensional weight calculation method is adopted, which comprehensively considers indicators such as CPU utilization, memory utilization, quantum key cache utilization and key extraction efficiency, and realizes an accurate evaluation of node processing power. This multi-level state evaluation method not only improves the accuracy of system performance evaluation, but also provides a reliable decision-making basis for resource scheduling.

[0063] According to one aspect of the present application, step S113 is specifically:

[0064] Step S1131, extracting physical topology information from system status data to obtain physical connection data; constructing node connection relationships based on the physical connection data to obtain initial adjacency matrix data; analyzing the physical characteristics of each connection to obtain link type data; and correcting the adjacency matrix data according to the link type data to obtain corrected adjacency matrix data.

[0065] Step S1132, extract the bandwidth information of each node according to the node status data to obtain bandwidth data; calculate the fidelity of the quantum entangled state between nodes to obtain entanglement fidelity data; measure the classical channel delay between nodes to obtain channel delay data; combine the bandwidth data, entanglement fidelity data and channel delay data to generate connection status data.

[0066] Step S1133, using various parameters in the connection status data to calculate the link quantum bit error rate to obtain bit error rate data; analyzing the quantum channel attenuation characteristics to obtain attenuation characteristic data; measuring the entangled state decoherence time to obtain decoherence time data; and calculating the link quality data based on the bit error rate data, attenuation characteristic data and decoherence time data.

[0067] Step S1134, calculating the quantum communication capacity of the link according to the link quality data to obtain communication capacity data; evaluating the link stability based on the connection status data to obtain stability data; analyzing the anti-interference ability of the link to obtain anti-interference data; calculating the communication capacity data, stability data and anti-interference data according to preset weights to obtain edge weight data.

[0068] Step S1135, input the modified adjacency matrix data and edge weight data into the quantum routing optimization algorithm to obtain initial routing data; analyze the topological stability based on the quantum network evolution model to obtain topological stability data; evaluate the distribution of quantum entanglement resources to obtain entanglement distribution data; integrate the initial routing data, topological stability data and entanglement distribution data to generate network connection graph data.

[0069] Step S1136: Analyze network connectivity based on network connection diagram data to obtain connectivity data; calculate redundancy of key links to obtain redundancy data; evaluate network anti-attack capability to obtain security data; generate topology optimization data using connectivity data, redundancy data and security data; and update system state matrix data based on topology optimization data.

[0070] Based on the quantum network evolution model and graph optimization algorithm, the intelligent optimization and dynamic adjustment of the topological structure of the quantum communication network are realized. This step first constructs an adjacency matrix that reflects the actual state of the network by analyzing the physical connection characteristics and the fidelity of the quantum entangled state. In the edge weight calculation process, evaluation indicators such as quantum communication capacity, link stability and anti-interference ability are introduced, and the comprehensive edge weight is calculated by the preset weight system. In particular, in the topology optimization process, by analyzing the distribution of quantum entangled resources and network connectivity, the system can adaptively adjust the network structure and improve the redundancy and anti-attack capability of key links. The reliability and security of the quantum communication network are significantly improved.

[0071] According to one aspect of the present application, step S12 is specifically:

[0072] Step S121, parse the received communication request data packet, extract the source node identifier and the target node identifier to generate node identifier data; extract the quantum key requirement, data transmission amount and expected completion time information from the data packet header of the communication request data packet to generate business parameter data; retrieve the authentication information corresponding to the node identifier data from the system authentication database to generate authentication status data; query the system status matrix according to the node identifier data to obtain the quantum channel status between nodes to generate channel status data; combine the node identifier data, business parameter data, authentication status data and channel status data to generate request information data and store it in the request queue.

[0073] Step S122, read the data packet type information in the communication request data packet, and generate security level data by comparing it with the preset quantum communication security level mapping table; calculate the quantum key consumption requirement according to the data amount in the service parameter data to obtain the key requirement data; calculate the available quantum channel capacity based on the channel state data to obtain the channel capacity data; obtain the service quality parameters from the system service configuration library to generate the service quality data; combine the security level data, the key requirement data, the channel capacity data and the service quality data to generate the request feature vector.

[0074] The quantum security level assessment is as follows: security level assessment value Q(r) = θ1·K(r) + θ2·N(r) + θ3·T(r); where: K(r) is the key consumption characteristic, K(r) = K r / K max exp(-μt), K r is the key requirement for request r, μ is the time decay coefficient; N(r) is the quantum noise impact, N(r) = 1 - (1-ε) n ·exp(-ηL), ε is the bit error rate, η is the channel loss coefficient, L is the transmission distance; T(r) is the eavesdropping threat level, T(r) = ∑ ik (p i ·log2(1 / p i )), p i is the detection probability of the i-th attack mode; θ1, θ2, θ3 are dynamic weight coefficients. The algorithm combines the key consumption characteristics and quantum noise influence.

[0075] Step S123, constructing a judgment matrix according to preset quantum communication service evaluation indicators; calculating eigenvalue data by considering quantum channel characteristics and key consumption characteristics; using the eigenvalue data to calculate the consistency ratio to generate consistency verification data; when the consistency verification data is less than a preset threshold, using the eigenvector corresponding to the eigenvalue data as the service weight coefficient; when the consistency verification data is greater than or equal to the preset threshold, adjusting the quantum communication parameters in the judgment matrix and recalculating; comparing the finally determined service weight coefficient with the preset quantum service benchmark to generate weight verification data.

[0076] Business weight W(b) = ∑ ik (α i ·V i (b))·exp(-βE(b)); where: V i (b) is the i-th evaluation index value of business b, V i (b) = (v i -v min ) / (v max -v min ); E(b) is the quantum entanglement consumption, E(b) = -∑ i n (q i log2q i ), q i is the probability distribution of quantum state i; α i is the characteristic weight coefficient, obtained through quantum linear discriminant analysis; β is the entanglement influence factor. This method introduces quantum entanglement consumption as a weight adjustment factor.

[0077] Step S124: Perform weighted calculation on the request feature vector and service weight coefficient of each active request to obtain quantum service priority data; prioritize all active requests based on the quantum service priority data to generate request sorting data; calculate resource allocation priority according to the request sorting data to obtain resource allocation data; combine the identification information, request feature vector, service weight coefficient, quantum service priority data and resource allocation data of each request to generate a service demand matrix. Analyze the service mode characteristics based on the service demand matrix to obtain service mode data, which is used to guide subsequent quantum key scheduling.

[0078] The request feature vector generation method based on quantum communication evaluation parameters is adopted, combined with the business weight coefficient calculation mechanism, to achieve accurate quantification and classification of communication requests. This step constructs a multi-dimensional request feature vector by parsing the communication request data packet, extracting key information such as source node identification, target node identification, data volume and expected completion time. In particular, in the calculation process of the business weight coefficient, the evaluation indicators unique to the quantum communication scenario are introduced, the quantum channel characteristics and key consumption characteristics are taken into account, and the rationality of the weight distribution is ensured through eigenvalue calculation and consistency verification. This method can not only accurately reflect the characteristics and priorities of different business requests, but also dynamically adjust the business demand matrix according to the real-time network status, thereby improving the resource allocation efficiency and service quality of the system.

[0079] According to one aspect of the present application, step S122 is specifically:

[0080] Step S1221, parse the encryption type identifier in the communication request data packet to obtain encryption type data; analyze the quantum key requirement of the service to obtain key requirement characteristic data; evaluate the delay sensitivity of the service to obtain delay sensitivity data; query the preset security level mapping table according to the encryption type data, key requirement characteristic data and delay sensitivity data to generate initial security level data.

[0081] Step S1222: Analyze the quantum key consumption pattern based on the initial security level data to obtain key consumption pattern data; evaluate the quantum channel eavesdropping risk to obtain eavesdropping risk data; calculate the impact of channel noise on security to obtain noise impact data; and use the key consumption pattern data, eavesdropping risk data and noise impact data to correct and obtain security level data.

[0082] Step S1223, extracting data volume information from the service parameter data to obtain data scale data; analyzing the time requirements for data transmission to obtain time constraint data; evaluating the burst characteristics of the service to obtain burst characteristic data; and calculating key requirement data based on the data scale data, time constraint data and burst characteristic data.

[0083] Step S1224, calculating the maximum capacity of the quantum channel based on the channel state data to obtain channel capacity data; analyzing the stability parameters of the channel to obtain channel stability data; evaluating the noise characteristics of the channel to obtain noise characteristic data; combining the channel capacity data, channel stability data and noise characteristic data to generate channel evaluation data.

[0084] Step S1225, read the service quality configuration from the system configuration library to obtain service configuration data; determine the service quality requirements based on the security level data to obtain service requirement data; analyze the service characteristics to obtain service feature data; generate service quality data based on the service configuration data, service requirement data and service feature data.

[0085] Step S1226, integrate security level data, key requirement data, channel assessment data and service quality data to construct a feature vector generation algorithm; use the algorithm to calculate the comprehensive characteristics of the service to obtain feature calculation data; evaluate the importance weight of the feature to obtain feature weight data; combine the feature calculation data and feature weight data to generate a request feature vector.

[0086] The multi-dimensional quantum security level assessment and feature vector generation method are used to achieve accurate classification and priority division of quantum communication service requests. This step builds a complete security level assessment system by analyzing the encryption type, key consumption mode and quantum channel eavesdropping risk. In the feature vector generation process, the time constraint analysis and burst feature evaluation mechanism are introduced, combined with channel capacity calculation and service quality requirements, to achieve a comprehensive quantification of service request characteristics. In particular, in the feature weight calculation process, the system can dynamically adjust the importance weights of various indicators according to the service type and network status, ensuring the representativeness and accuracy of the feature vector. This request classification method based on quantum security characteristics not only improves the accuracy of service processing, but also provides a scientific decision-making basis for subsequent resource allocation.

[0087] According to one aspect of the present application, step S123 is specifically:

[0088] Step S1231, read the quantum communication service evaluation index from the system configuration library to obtain the evaluation index data; analyze the quantum dependency between the indicators to obtain the dependency data; calculate the relative importance between the indicators to obtain the importance data; construct an initial judgment matrix according to the evaluation index data, the dependency data and the importance data.

[0089] Step S1232: Analyze the rationality of the initial judgment matrix based on the characteristics of the quantum communication scenario to obtain rationality evaluation data; calculate the quantum correlation between matrix elements to obtain correlation data; evaluate the quantum entanglement influence between the elements to obtain entanglement influence data; optimize the judgment matrix based on the rationality evaluation data, correlation data and entanglement influence data.

[0090] Step S1233, using a numerical calculation method to process the judgment matrix to obtain initial eigenvalue data; analyzing the quantum state stability of the eigenvalue to obtain stability data; evaluating the physical feasibility of the eigenvalue to obtain feasibility data; integrating the initial eigenvalue data, stability data and feasibility data to generate eigenvalue data.

[0091] Step S1234, based on the eigenvalue data, calculate the corresponding eigenvector to obtain initial eigenvector data; analyze the quantum coherence of the eigenvector to obtain coherence data; evaluate the physical meaning of the eigenvector to obtain physical meaning data; combine the initial eigenvector data, coherence data and physical meaning data to generate eigenvector data.

[0092] Step S1235, use the eigenvalue data to calculate the consistency index to obtain the consistency index data; analyze the quantum characteristic influence of the matrix to obtain the quantum influence data; evaluate the reliability of the consistency test to obtain the reliability data; calculate the consistency test data based on the consistency index data, quantum influence data and reliability data.

[0093] Step S1236: When the consistency check data is less than a preset threshold, standardize the feature vector data to obtain weight vector data; analyze the physical rationality of the weight to obtain rationality data; evaluate the actual application effect of the weight to obtain application effect data; integrate the weight vector data, rationality data and application effect data to generate a business weight coefficient.

[0094] Step S1237, when the consistency check data is greater than or equal to the preset threshold, analyze the sensitivity of the quantum communication parameters in the judgment matrix to obtain parameter sensitivity data; identify the matrix elements that need to be adjusted based on the parameter sensitivity data to obtain adjustment item data; use the quantum communication empirical model to calculate the parameter adjustment amount to obtain parameter adjustment data; update the judgment matrix according to the adjustment item data and the parameter adjustment data to obtain an optimized judgment matrix; re-input the optimized judgment matrix into step S1233 for calculation.

[0095] Step S1238, compare and analyze the finally determined business weight coefficient with the preset quantum business benchmark data in the system configuration library to obtain benchmark comparison data; evaluate the physical rationality of the weight deviation to obtain deviation analysis data; calculate the fitness of the weight to obtain fitness data; generate weight verification data based on the benchmark comparison data, deviation analysis data and fitness data.

[0096] By constructing a judgment matrix and an adaptive weight calculation mechanism that take into account quantum dependencies, accurate calculation and dynamic adjustment of quantum communication service weights are achieved. This step first constructs an initial judgment matrix based on the characteristics of the quantum communication scenario, and optimizes the relationship between matrix elements by analyzing the effects of quantum correlation and entanglement. In the process of eigenvalue calculation, quantum state stability analysis and physical feasibility evaluation are introduced to ensure the practical significance of the eigenvalue vector. Especially in the consistency test link, the system can automatically adjust the judgment matrix parameters according to the test results until the service weight coefficient that meets the requirements is obtained. This weight calculation method based on quantum characteristics not only improves the accuracy of service priority division, but also can dynamically adjust the weight allocation strategy according to the network status, significantly improving the system's adaptability and service quality.

[0097] According to one aspect of the present application, step S13 is specifically:

[0098] Step S131, collect the photon loss rate data, quantum bit error rate data and signal-to-noise ratio data of each network node, and calculate the link quality parameters according to the preset weights; monitor the request queue length data, processing delay time data and resource occupancy data of each node to generate node load parameters; measure the quantum state transmission delay data, classical channel delay data and processing delay data and calculate the network delay parameters; refresh the above parameters according to the preset update cycle to generate real-time network status data containing a complete timestamp.

[0099] Step S132: construct a quantum state evolution model that takes decoherence effects into account based on quantum channel theory; calculate quantum state retention time data from the quantum state evolution model; construct an enhanced Bayesian network model by combining the quantum state retention time data and historical operation data; use the enhanced Bayesian network model to predict the network state and generate network prediction state data; calculate the confidence of the prediction result to obtain prediction reliability data; and correct the prediction result based on the prediction reliability data to obtain corrected prediction data.

[0100] Among them, the state prediction process of the Bayesian network is as follows: obtain quantum state measurement data, and calculate the coherence time data based on the quantum state decoherence model; construct a quantum state evolution model based on the coherence time data; use the quantum state evolution model to process the quantum state measurement data to obtain quantum state estimation data; calculate the state probability distribution data based on the quantum state estimation data; generate network update data based on the state probability distribution data and the preset Bayesian rule; input the network update data into the Bayesian network to obtain the predicted state data.

[0101] The quantum state evolution prediction model is: state prediction function P(ρ,t) = exp(-iHt / h-)ρ o exp(iHt / h-) -γ∑ i (L i ρL i + - ½{L i +L i ,ρ}); where: ρ is the density matrix, H is the system Hamiltonian, H = ∑ i (ω i σ i z +g i σ i x );L i is the Lindblad operator, describing the environmental effect; γ is the decoherence rate; ω i is the energy level difference; g i is the coupling strength; {A, B} represents the anti-commutation relationship. This model introduces multi-body interaction terms to improve the prediction accuracy.

[0102] Step S133, read quantum channel fault records, performance fluctuation records and recovery time records from the system history database; calculate the mean time between failures and the mean repair time to generate time statistics; calculate the link reliability index based on the quantum state retention time data; calculate the quantum channel stability based on the historical performance fluctuation data to obtain the link stability data; combine the link reliability index and the link stability data to generate a link evaluation report.

[0103] Link reliability index R(l) = ω1·M(l) + ω2·F(l) + ω3·Q(l); where: M(l) is the mean time between failures, M(l) = MTTF / (MTTF + MTTR)·exp(-λt), MTTF is the mean time between failures, MTTR is the mean time to repair, and λ is the failure rate; F(l) is the quantum fidelity, F(l) = |<ψ o |U(t)|ψ o >| 2 exp(-t / T2), U(t) is the evolution operator, T2 is the decoherence time; Q(l) is the quantum bit error rate correction term, Q(l) = (1-QBER) n ·exp(-αL), QBER is the quantum bit error rate, α is the attenuation coefficient, L is the transmission distance; ω1, ω2, ω3 are adaptive weight coefficients. This algorithm combines classical reliability theory and quantum property evaluation.

[0104] Step S134: according to the preset scoring rules, the quality scoring data is calculated using the link quality parameters in the real-time network status data; the load scoring data is calculated based on the node load parameters; the delay scoring data is calculated based on the network delay parameters; the reliability scoring data is calculated using the link reliability index; the predicted data is integrated and corrected to generate the predicted scoring data; and each scoring data is combined and calculated according to the preset weights to generate a network status scoring matrix.

[0105] Based on the prediction method of quantum state evolution model and enhanced Bayesian network, accurate prediction and evaluation of quantum communication network status is achieved. This step collects real-time network status data, including key parameters such as photon loss rate, quantum bit error rate and signal-to-noise ratio, and combines quantum channel theory to construct a quantum state evolution model that takes into account decoherence effects. By introducing enhanced Bayesian network, the system can learn the laws of quantum state changes in historical operation data and predict the evolution trend of network status. This prediction mechanism not only takes into account the special properties of quantum communication systems, but also can dynamically correct the prediction results according to the prediction reliability data, significantly improving the accuracy and reliability of network status evaluation.

[0106] According to one aspect of the present application, step S132 is specifically:

[0107] Step S1321, collecting phase information of quantum bits from the quantum communication system to obtain phase measurement data; obtaining population information of the quantum state to obtain population data; measuring coherence items of the quantum state to obtain coherence item data; constructing a quantum state density matrix based on the phase measurement data, population data and coherence item data.

[0108] Step S1322, analyzing the influence of environmental noise on quantum state to obtain environmental noise data; measuring system temperature fluctuation to obtain temperature fluctuation data; evaluating the influence of mechanical vibration to obtain vibration interference data; establishing a decoherence model according to the environmental noise data, temperature fluctuation data and vibration interference data.

[0109] Step S1323, input the quantum state density matrix into the decoherence model for evolution calculation to obtain state evolution data; analyze the decay characteristics of the quantum state to obtain decay characteristic data; calculate the coherence retention time to obtain coherence time data; integrate the state evolution data, decay characteristic data and coherence time data to generate a quantum state evolution model.

[0110] Step S1324, read the network operation record from the historical database to obtain historical operation data; analyze the change of quantum channel characteristics to obtain channel change data; evaluate the key generation efficiency fluctuation to obtain efficiency fluctuation data; generate pattern recognition data based on the historical operation data, channel change data and efficiency fluctuation data.

[0111] Step S1325, combining the quantum state evolution model and pattern recognition data to construct an initial Bayesian network to obtain initial network data; calculating the quantum correlation between nodes to obtain correlation data; evaluating the state transition probability to obtain transition probability data; integrating the initial network data, correlation data and transition probability data to construct an enhanced Bayesian network model.

[0112] Step S1326: Use the enhanced Bayesian network model to predict the network state and obtain initial prediction data; evaluate the accuracy of the predicted quantum characteristics to obtain accuracy data; analyze the physical feasibility of the prediction results to obtain feasibility data; generate network prediction state data based on the initial prediction data, accuracy data and feasibility data.

[0113] Step S1327, calculate the quantum state fidelity of the prediction result to obtain fidelity data; analyze the stability of the prediction to obtain stability data; evaluate the credibility of the prediction to obtain credibility data; generate prediction reliability data based on the fidelity data, stability data and credibility data.

[0114] Step S1328: Use the prediction reliability data to correct the network prediction status data to obtain corrected prediction data; evaluate the physical rationality of the correction result to obtain rationality data; analyze the corrected prediction accuracy to obtain accuracy evaluation data; integrate the corrected prediction data, rationality data and accuracy evaluation data to generate the final corrected prediction status data.

[0115] By constructing a quantum state evolution model and an enhanced Bayesian network prediction mechanism, high-precision prediction and dynamic evaluation of the state of the quantum communication network are achieved. This step first collects the phase information, population information and coherence term data of the quantum bits, and constructs a complete quantum state density matrix. In the process of building the decoherence model, environmental noise analysis, temperature fluctuation evaluation and mechanical vibration impact evaluation mechanisms are introduced to achieve an accurate description of the quantum state evolution process. In particular, in the Bayesian network construction link, the system analyzes the changes in quantum channel characteristics and key generation efficiency fluctuations in historical operation data, establishes a prediction model that takes into account quantum correlation and state transition probability, and can dynamically adjust the prediction results based on the prediction reliability data. This state prediction method based on quantum physics characteristics not only improves the accuracy of network state evaluation, but also provides reliable data support for preventive maintenance and optimization of the system.

[0116] According to one aspect of the present application, step S133 is specifically:

[0117] Step S1331, read quantum communication fault records from the system history database to obtain fault record data; analyze the type distribution of faults to obtain fault type data; evaluate the severity of the fault to obtain fault level data; generate fault feature data based on the fault record data, fault type data and fault level data.

[0118] Step S1332, analyzing the quantum channel performance fluctuation record to obtain fluctuation record data; calculating the change trend of the performance parameter to obtain trend data; evaluating the periodic characteristics of the fluctuation to obtain periodic characteristic data; generating performance fluctuation data according to the fluctuation record data, trend data and periodic characteristic data.

[0119] Step S1333, obtain the system fault repair record to obtain the repair record data; analyze the time distribution of the repair process to obtain the repair time data; evaluate the stability of the repair effect to obtain the repair effect data; integrate the repair record data, repair time data and repair effect data to generate the repair feature data.

[0120] Step S1334, calculate the average fault interval time based on the fault characteristic data to obtain fault interval data; use the repair characteristic data to calculate the average repair time to obtain average repair data; evaluate the quantum state loss in the repair process to obtain quantum loss data; generate time statistical data based on the fault interval data, average repair data and quantum loss data.

[0121] Step S1335, calculate the reliability index of the quantum communication link according to the time statistical data to obtain the initial reliability data; evaluate the quantum entanglement maintenance ability to obtain the entanglement maintenance data; analyze the key distribution success rate to obtain the distribution success rate data; integrate the initial reliability data, entanglement maintenance data and distribution success rate data to generate the link reliability index.

[0122] Step S1336: Calculate the standard deviation of each performance parameter based on the performance fluctuation data to obtain standard deviation data; analyze the correlation of parameter fluctuations to obtain correlation data; evaluate the impact of fluctuations on quantum communication quality to obtain influence data; combine the standard deviation data, correlation data and influence data to generate link stability data.

[0123] Step S1337: construct a link evaluation model based on the link reliability index and the link stability data to obtain evaluation model data; calculate the comprehensive performance of the link to obtain comprehensive performance data; analyze the long-term availability of the link to obtain availability data; integrate the evaluation model data, comprehensive performance data and availability data to generate a link evaluation report.

[0124] The multi-dimensional quantum link reliability assessment and stability analysis method is used to achieve accurate quantification and dynamic monitoring of quantum communication link performance. This step builds a complete link evaluation system by analyzing fault records, performance fluctuations and repair characteristics. In the process of generating time statistical data, the quantum state loss evaluation mechanism is introduced, and the average failure interval and repair time calculation are combined to achieve an accurate assessment of link reliability. Especially in the link stability analysis link, the system generates a comprehensive link evaluation report by calculating the standard deviation and fluctuation correlation of performance parameters, combined with the quantum communication quality impact assessment. This link evaluation method based on historical data can not only accurately reflect the actual performance status of the link, but also provide effective decision support for link optimization and fault prevention.

[0125] According to one aspect of the present application, step S14 is specifically:

[0126] Step S141, collect the current available key quantity data from each network node; count the key usage data within the preset time window to obtain the key consumption rate data; record the key generation rate data of the quantum key distribution module; calculate the effective key generation rate in combination with the quantum state retention time data to obtain the effective key replenishment rate data; calculate the remaining capacity ratio of the key pool of each node to obtain the key remainder data; combine the above data to generate the key pool status data.

[0127] Step S142: construct a key consumption prediction model based on quantum channel noise characteristics, including photon loss, bit error rate and environmental disturbance; train the key consumption prediction model using key pool status data and historical consumption data; optimize the prediction model based on the business demand information in the business demand matrix; generate key consumption prediction data within a preset time period.

[0128] Among them, the key consumption prediction process based on quantum channel characteristics is as follows: Collect quantum channel noise data, including photon loss rate, bit error rate and environmental disturbance parameters; establish a channel characteristic model based on the quantum channel noise data; obtain key usage records from a preset historical database; use the channel characteristic model to process the key usage records to obtain noise impact data; train the key consumption prediction model based on the noise impact data; input the real-time quantum channel noise data into the key consumption prediction model to obtain key consumption prediction data.

[0129] Key consumption prediction value K(t) = ∑ i (π i ·k i (t))·exp(-γN(t)) + β·ΔS(t); where: k i (t) is the basic consumption function of the i-th type of business, k i (t) = a i t + b i ·sin(ω i t); N(t) is the quantum noise influence function, N(t) = ∑ j (ε j ·exp(-μ j t), ε j is the j-th noise intensity, μ j is the attenuation coefficient; ΔS(t) is the entropy change correction term, ΔS(t) = -kᵦ∑ k (p k ·ln(p k )), p k is the probability of quantum state distribution; π i is the business weight coefficient; γ is the noise sensitivity; β is the entropy correction coefficient. This model introduces quantum entropy change as a correction factor.

[0130] Step S143, calculate the capacity ratio data according to the amount of available keys in the key pool status data and the key pool capacity; calculate the compensation ratio data based on the effective key replenishment rate data and the key consumption rate data; calculate the volatility data by extracting the key usage fluctuation characteristics from the key consumption prediction model; calculate the initial health index according to the capacity ratio data, the compensation ratio data and the volatility data according to the preset weights; calculate the correction coefficient using the initial health index; and apply the correction coefficient to the predicted data to obtain the final health index.

[0131] Health index H(p) = ∑ i (μ i I i (p))·exp(-ν·D(p)); where: I i (p) is the i-th health assessment index, I i (p) = (1 - |x i -x o | / x max )·exp(-λ i t), x i is the current value, x o is the ideal value; D(p) is the key distribution entropy, D(p) = -∑ j (q j ·log2(q j )),q j is the key distribution probability; μ i is the index weight coefficient; ν is the distribution impact factor; λ i is the time decay coefficient. This method applies information entropy theory to health status assessment.

[0132] Step S144, map the key pool status data of each node to the network topology structure to generate key distribution data; calculate the predicted distribution data based on the key consumption prediction data; integrate the final health index to generate healthy distribution data; combine the key distribution data, predicted distribution data and healthy distribution data to generate a key resource distribution matrix.

[0133] By establishing a key consumption prediction model that takes into account quantum channel noise, accurate management and optimized allocation of quantum key resources are achieved. This step collects the key pool status data of each node, and combines the characteristics of quantum channel noise to build a prediction model that includes factors such as photon loss, bit error rate, and environmental disturbance. By analyzing the fluctuation characteristics of key usage and calculating the compensation ratio and capacity ratio, the system can accurately evaluate the health status of the key pool. This key resource management method based on multi-dimensional indicators can not only predict the trend of key consumption, but also dynamically adjust the key distribution strategy according to the health index, effectively improving the utilization efficiency of quantum key resources and the security of the system.

[0134] According to one aspect of the present application, step S142 is specifically:

[0135] Step S1421: collecting photon loss data of the quantum channel to obtain photon loss data; measuring the bit error rate of the channel to obtain bit error rate data; analyzing environmental disturbance parameters to obtain environmental disturbance data; and constructing a quantum channel noise model based on the photon loss data, bit error rate data and environmental disturbance data.

[0136] Step S1422: Analyze the decoherence characteristics of the quantum channel to obtain decoherence characteristic data; evaluate the thermal noise effect to obtain thermal noise data; measure the mechanical vibration effect to obtain vibration effect data; generate noise feature data based on the decoherence characteristic data, thermal noise data and vibration effect data.

[0137] Step S1423: Obtain key usage records from the historical database to obtain historical consumption data; analyze consumption pattern characteristics to obtain pattern characteristic data; evaluate periodic changes to obtain periodic change data; and construct consumption pattern data using historical consumption data, pattern characteristic data, and periodic change data.

[0138] Step S1424: input the noise characteristic data into a preset quantum channel capacity model to obtain channel capacity data; calculate the expected key demand based on the consumption pattern data to obtain demand forecast data; evaluate the system processing capacity to obtain processing capacity data; integrate the channel capacity data, demand forecast data and processing capacity data to construct an initial prediction model.

[0139] Step S1425: Process the initial prediction model using a machine learning method to obtain learning model data; optimize model parameters based on quantum channel characteristics to obtain parameter optimization data; evaluate the prediction accuracy of the model to obtain accuracy evaluation data; generate a key consumption prediction model based on the learning model data, parameter optimization data and accuracy evaluation data.

[0140] Step S1426: input the key pool state data into the key consumption prediction model to obtain initial prediction data; consider the influence of quantum channel fluctuation to obtain fluctuation influence data; analyze the reliability of the prediction result to obtain reliability data; integrate the initial prediction data, fluctuation influence data and reliability data to generate key consumption prediction data.

[0141] Step S1427: extract business characteristics from the business demand matrix to obtain business characteristic data; evaluate the impact of the business on key consumption to obtain consumption impact data; calculate the correction coefficient to obtain correction coefficient data; correct the prediction result according to the business characteristic data, consumption impact data and correction coefficient data to generate corrected prediction data of key consumption.

[0142] Step S1428: Perform physical feasibility verification on the revised prediction data to obtain feasibility data; evaluate the accuracy of the prediction to obtain accuracy data; analyze the stability of the prediction results to obtain stability data; integrate the feasibility data, accuracy data and stability data to generate the final optimized prediction data.

[0143] By constructing a key consumption prediction model that takes into account quantum channel noise, accurate prediction and optimized management of quantum key resource usage are achieved. This step first collects the photon loss, bit error rate and environmental disturbance parameters of the quantum channel to build a complete quantum channel noise model. In the process of building the prediction model, decoherence characteristics analysis, thermal noise impact assessment and mechanical vibration effect analysis are introduced, and combined with the pattern characteristics of historical consumption data, accurate prediction of key consumption trends is achieved. Especially in the model optimization link, the system can dynamically adjust the prediction parameters according to the real-time quantum channel noise data to ensure the accuracy and reliability of the prediction results. This prediction method based on quantum physics characteristics not only improves the efficiency of key resource management, but also provides a scientific basis for the formulation of key replenishment strategies.

[0144] According to one aspect of the present application, step S143 is specifically:

[0145] Step S1431, extract the available key quantity from the key pool status data to obtain available quantity data; obtain the total capacity of the key pool to obtain capacity data; evaluate the key update frequency to obtain update frequency data; calculate the capacity ratio data based on the available quantity data, capacity data and update frequency data.

[0146] Step S1432, analyze the time distribution of the effective key replenishment rate data to obtain the replenishment distribution data; evaluate the changing trend of the key consumption rate data to obtain the consumption trend data; calculate the dynamic balance between replenishment and consumption to obtain the equilibrium state data; calculate the replenishment and consumption ratio data based on the replenishment distribution data, the consumption trend data and the equilibrium state data.

[0147] Step S1433, extract the fluctuation characteristics of key usage from the key consumption prediction model to obtain usage fluctuation data; analyze the periodicity of the fluctuation to obtain periodic characteristic data; evaluate the amplitude distribution of the fluctuation to obtain amplitude distribution data; comprehensively analyze the usage fluctuation data, periodic characteristic data and amplitude distribution data to obtain volatility data.

[0148] Step S1434, standardize the capacity ratio data, the compensation ratio data and the volatility data to obtain standardized data; assign weights based on the quantum key management strategy to obtain weight distribution data; perform weighted calculation to obtain weighted calculation data; integrate the standardized data, the weight distribution data and the weighted calculation data to generate an initial health index.

[0149] Step S1435, analyze the reliability of the key consumption prediction data to obtain prediction reliability data; evaluate the current network load status to obtain load status data; calculate the resource scheduling efficiency to obtain scheduling efficiency data; calculate the correction coefficient based on the prediction reliability data, load status data and scheduling efficiency data.

[0150] Step S1436, applying the correction coefficient to the initial health index to obtain the adjusted index data; evaluating the rationality of the adjustment result to obtain the rationality data; analyzing the stability of the index to obtain the stability data; generating a corrected health index based on the adjusted index data, the rationality data and the stability data.

[0151] Step S1437, perform a threshold test on the modified health index to obtain threshold test data; analyze the changing trend of the index to obtain trend analysis data; evaluate the warning level of the index to obtain warning level data; integrate the threshold test data, trend analysis data and warning level data to generate a health assessment report.

[0152] Step S1438, formulate a key pool adjustment strategy according to the health assessment report to obtain adjustment strategy data; calculate the adjustment priority to obtain priority data; evaluate the impact range of the adjustment to obtain impact range data; integrate the adjustment strategy data, priority data and impact range data to generate a final health index.

[0153] The multi-indicator key pool health assessment and dynamic correction mechanism are adopted to achieve accurate monitoring and optimized management of the state of quantum key resources. This step builds a complete health assessment system by analyzing the amount of available keys, key pool capacity and update frequency. In the process of health index calculation, the replenishment and consumption ratio analysis, the use of fluctuation characteristics assessment and the prediction reliability assessment mechanism are introduced to achieve a comprehensive quantification of the key pool state. Especially in the correction coefficient calculation link, the system can dynamically adjust the health index according to the predicted reliability data and network load conditions, and generate a complete health assessment report through threshold testing and trend analysis. This state assessment method based on multi-dimensional indicators not only improves the accuracy of key resource management, but also provides reliable decision support for the optimization of key scheduling strategies.

[0154] According to one aspect of the present application, step S21 is specifically:

[0155] Step S211, extracting service performance requirements from the service requirement matrix to obtain performance requirement data; analyzing quantum communication service characteristics to obtain service characteristic data; evaluating service quality requirements to obtain service quality data; and constructing a target performance function based on the performance requirement data, service characteristic data, and service quality data.

[0156] Step S212, based on quantum channel theory, the upper limit of channel capacity is calculated to obtain the upper limit of capacity data; the influence of channel noise is analyzed to obtain the noise influence data; the channel loss characteristics are evaluated to obtain the loss characteristic data; the upper limit of capacity data, the noise influence data and the loss characteristic data are used to construct a quantum constraint function.

[0157] Step S213, extracting node processing capacity data from the system status matrix to obtain processing capacity data; analyzing the storage status to obtain storage status data; evaluating computing resource distribution to obtain resource distribution data; and constructing a resource constraint function based on the processing capacity data, storage status data and resource distribution data.

[0158] Step S214, combining the target performance function, the quantum constraint function and the resource constraint function to obtain a constraint condition set; evaluating the correlation of the constraint conditions to obtain correlation data; analyzing the conflict of the constraint conditions to obtain conflict data; optimizing the constraint condition set based on the correlation data and the conflict data.

[0159] Step S215, construct decision variables reflecting the network topology state to obtain topology variable data; establish decision variables representing resource allocation to obtain resource variable data; design decision variables representing route selection to obtain routing variable data; combine the topology variable data, resource variable data and routing variable data to generate an optimization variable matrix.

[0160] Step S216, designing an optimization objective function based on the characteristics of the quantum communication scenario to obtain objective function data; analyzing the weight relationship between the objective functions to obtain weight relationship data; evaluating the reachability of the optimization target to obtain reachability data; integrating the objective function data, weight relationship data and reachability data to generate an objective function set.

[0161] Step S217, combining the constraint set, the optimization variable matrix and the objective function set to construct an initial optimization model to obtain initial model data; evaluating the computational complexity of the model to obtain complexity data; analyzing the feasibility of the solution of the model to obtain feasibility data; generating a multi-objective optimization model based on the initial model data, complexity data and feasibility data.

[0162] In this embodiment, it also includes: step S218, theoretically verifying the multi-objective optimization model to obtain verification result data; evaluating the optimization effect of the model to obtain optimization effect data; analyzing the adaptability of the model to obtain adaptability data; and generating a model evaluation report based on the verification result data, optimization effect data and adaptability data.

[0163] By constructing a multi-objective optimization model based on quantum Shannon information theory, precise control and intelligent optimization of resource allocation in quantum communication systems are achieved. This step first extracts performance requirements, business characteristics, and service quality requirements from the business demand matrix, and constructs a complete objective function system. In the process of setting constraints, the quantum channel capacity theory is introduced, and a set of constraint functions reflecting the characteristics of quantum communication is established in combination with the analysis of channel noise influence and loss characteristics. In particular, in the optimization variable construction link, the system achieves global optimization of system resources by designing decision variables that reflect network topology status, resource allocation, and routing selection, and designs optimization objective functions based on the characteristics of quantum communication scenarios. This optimization model based on quantum theory not only takes into account the unique properties of quantum communication systems, but also ensures the feasibility of optimization results through theoretical verification and adaptability analysis, significantly improving the resource utilization efficiency and service quality of the system.

[0164] According to one aspect of the present application, step S23 is specifically:

[0165] Step S231, designing a quantum chromosome coding scheme based on a multi-objective optimization model to obtain coding scheme data; analyzing the physical feasibility of the coding to obtain feasibility data; evaluating the coding efficiency to obtain coding efficiency data; and generating quantum coding rules according to the coding scheme data, feasibility data and coding efficiency data.

[0166] Step S232, constructing a fitness function according to the path scoring data to obtain fitness function data; analyzing the fitness evaluation index to obtain evaluation index data; designing a fitness calculation method to obtain calculation method data; integrating the fitness function data, evaluation index data and calculation method data to generate fitness evaluation rules.

[0167] Step S233, using quantum coding rules to generate an initial population to obtain initial population data; evaluating the diversity of the population to obtain diversity data; analyzing the distribution characteristics of the population to obtain distribution characteristic data; generating an initial solution set based on the initial population data, diversity data and distribution characteristic data.

[0168] Step S234, designing a quantum crossover operator to obtain crossover operator data; analyzing the quantum characteristics of the crossover operation to obtain quantum characteristic data; evaluating the crossover efficiency to obtain crossover efficiency data; performing a crossover operation according to the crossover operator data, the quantum characteristic data and the crossover efficiency data to obtain crossover result data.

[0169] Step S235, construct a quantum mutation operator to obtain mutation operator data; design an adaptive mutation strategy to obtain mutation strategy data; evaluate the mutation effect to obtain mutation effect data; perform a mutation operation based on the mutation operator data, mutation strategy data and mutation effect data to obtain mutation result data.

[0170] Step S236, integrating the crossover result data and the mutation result data to obtain an evolutionary solution set; evaluating the quality of the evolutionary solution to obtain solution quality data; analyzing the convergence of the evolution to obtain convergence data; optimizing the evolutionary solution set according to the solution quality data and the convergence data.

[0171] Step S237, calculating the fitness of the evolutionary solution set based on the fitness evaluation rule to obtain fitness data; analyzing the Pareto advantage of the solution to obtain advantage analysis data; evaluating the diversity of the solution to obtain diversity evaluation data; integrating the fitness data, advantage analysis data and diversity evaluation data to obtain the optimal solution set.

[0172] Step S238, decode the optimal solution set into a specific path plan to obtain path plan data; convert it into a resource allocation plan to obtain resource plan data; generate a scheduling execution plan to obtain scheduling plan data; integrate the path plan data, resource plan data and scheduling plan data to generate a scheduling strategy matrix.

[0173] The genetic algorithm optimization mechanism of quantum coding is adopted to realize the intelligent generation and dynamic optimization of quantum communication scheduling strategies. This step first designs the quantum chromosome coding scheme based on the multi-objective optimization model, and constructs a complete quantum coding rule by analyzing the physical feasibility and coding efficiency of the coding. In the evolutionary process, the quantum crossover operator and adaptive mutation strategy are designed, combined with the fitness evaluation rule, to achieve effective exploration of the solution space. Especially in the optimal solution generation link, the system ensures the global optimality of the final scheduling strategy by evaluating the Pareto advantage and diversity of the solution. This optimization method based on quantum coding not only improves the efficiency of strategy generation, but also ensures the practicality and reliability of the scheduling strategy through multi-objective trade-offs.

[0174] According to one aspect of the present application, step S32 is specifically:

[0175] Step S321, collect the start time and completion time of the communication task to obtain time recording data; analyze the delay distribution of task execution to obtain delay distribution data; evaluate the real-time performance of the task to obtain real-time performance data; generate time indicator data based on the time recording data, delay distribution data and real-time performance data.

[0176] Step S322, monitor the key consumption process to obtain key usage data; analyze the key distribution efficiency to obtain distribution efficiency data; evaluate the key utilization to obtain utilization data; integrate the key usage data, distribution efficiency data and utilization data to generate key consumption data.

[0177] Step S323, collect the real-time bandwidth occupancy of the link to obtain bandwidth occupancy data; analyze the dynamic change of bandwidth to obtain bandwidth change data; evaluate the rationality of bandwidth allocation to obtain allocation rationality data; generate bandwidth usage data according to the bandwidth occupancy data, bandwidth change data and allocation rationality data.

[0178] Step S324, measuring the end-to-end delay of quantum communication to obtain end-to-end delay data; analyzing the channel transmission delay to obtain transmission delay data; evaluating the processing node delay to obtain node delay data; integrating the end-to-end delay data, transmission delay data and node delay data to generate delay index data.

[0179] Step S325, monitor the quantum bit error rate to obtain error rate data; analyze the entangled state fidelity to obtain fidelity data; evaluate the key extraction efficiency to obtain extraction efficiency data; generate quantum performance data based on the error rate data, fidelity data and extraction efficiency data.

[0180] Step S326, integrating time indicator data, key consumption data, bandwidth usage data, delay indicator data and quantum performance data to obtain comprehensive indicator data; analyzing the correlation between indicators to obtain correlation data; evaluating the importance of indicators to obtain importance data; generating performance indicator data based on the comprehensive indicator data, correlation data and importance data.

[0181] By constructing a multi-dimensional performance indicator monitoring and evaluation mechanism, real-time monitoring and accurate evaluation of the operating status of the quantum communication system are achieved. This step establishes a complete performance monitoring system by monitoring the time indicators, key consumption, bandwidth usage and delay parameters of the communication task. In the process of indicator data collection, quantum bit error rate monitoring and entanglement state fidelity analysis are introduced, combined with key extraction efficiency evaluation, to achieve a comprehensive quantification of quantum communication performance. Especially in the comprehensive indicator generation link, the system can dynamically adjust the evaluation weight by analyzing the correlation and importance between indicators, ensuring the accuracy and representativeness of the performance evaluation. This multi-level performance monitoring method not only improves the observability of the system operation status, but also provides reliable data support for performance optimization.

[0182] According to one aspect of the present application, step S34 is specifically:

[0183] Step S341, calculate the scheduling success rate based on the performance indicator data to obtain the success rate data; analyze the task completion quality to obtain the completion quality data; evaluate the service reliability to obtain the reliability data; integrate the success rate data, completion quality data and reliability data to generate key performance indicators.

[0184] Step S342, analyze the performance indicator change trend to obtain trend analysis data; evaluate the performance fluctuation law to obtain fluctuation law data; predict the performance development trend to obtain development trend data; generate trend prediction data based on the trend analysis data, fluctuation law data and development trend data.

[0185] Step S343, identify the system performance bottleneck to obtain bottleneck identification data; analyze resource utilization efficiency to obtain efficiency analysis data; evaluate the system optimization space to obtain optimization space data; generate problem analysis data based on the bottleneck identification data, efficiency analysis data and optimization space data.

[0186] Step S344, generate resource configuration optimization suggestions based on the problem analysis data to obtain resource optimization data; formulate an algorithm parameter adjustment plan to obtain parameter adjustment data; design a system configuration update strategy to obtain configuration update data; integrate the resource optimization data, parameter adjustment data and configuration update data to generate optimization suggestion data.

[0187] Step S345, evaluate the feasibility of the optimization suggestion to obtain feasibility data; analyze the implementation cost to obtain cost analysis data; predict the optimization effect to obtain effect prediction data; generate suggestion evaluation data based on the feasibility data, cost analysis data and effect prediction data.

[0188] Step S346: Integrate key performance indicators, trend forecast data, problem analysis data, optimization suggestion data and suggestion evaluation data to obtain initial report data; prepare a performance analysis report to obtain analysis report data; formulate an optimization implementation plan to obtain implementation plan data; integrate the initial report data, analysis report data and implementation plan data to generate a comprehensive performance report.

[0189] By adopting a comprehensive system performance evaluation and optimization suggestion generation mechanism, the intelligent operation and maintenance and continuous optimization of the quantum communication system are realized. This step first calculates the key performance indicators based on the performance indicator data, and builds a complete performance evaluation system by analyzing the task completion quality and service reliability. In the trend analysis process, the performance fluctuation law analysis and development trend prediction are introduced, and targeted optimization suggestions are generated by combining system bottleneck identification and efficiency analysis. Especially in the suggestion evaluation link, the system ensures the feasibility and economy of the optimization plan by analyzing the implementation cost and expected effect. This data-driven optimization mechanism not only improves the efficiency of system maintenance, but also improves the overall performance and service quality of the system through continuous optimization.

[0190] According to another aspect of the present application, a secure encryption communication system based on quantum key management is also provided, comprising:

[0191] at least one processor; and,

[0192] a memory communicatively connected to at least one of the processors; wherein,

[0193] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the secure encryption communication method based on quantum key management described in any of the above technical solutions.

[0194] It should be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A secure encryption communication method based on quantum key management, characterized in that: The steps include: Step S1, obtain the system status data of the communication network and construct the system status matrix; obtain the communication request data packet and generate the business demand matrix; read the real-time collected network status data and generate the network status scoring matrix; read the key pool status data of each node and generate the key resource distribution matrix; Step S2: Based on the system state matrix, the business demand matrix, the network state scoring matrix and the key resource distribution matrix, a multi-objective optimization model is constructed and solved to generate a scheduling strategy matrix, and a feasibility verification report is generated for the scheduling strategy matrix; Step S3: construct and issue a scheduling instruction set according to the scheduling strategy matrix, collect performance indicator data for monitoring and evaluation, update system historical data to obtain data update logs, and generate a comprehensive performance report; The step S1 is specifically as follows: Step S11, obtaining system status data including network topology configuration data, node configuration data and key pool configuration data, processing the system status data to generate node status data, constructing network connection diagram data, and generating a system status matrix; Step S12, receiving a communication request data packet, extracting basic request information including a source node identifier, a target node identifier, a data volume, and an expected completion time to generate a request feature vector, calculating a service weight coefficient based on the request feature vector and a preset quantum communication evaluation parameter, and generating a service demand matrix based on the service weight coefficient and the request feature vectors of multiple active requests; Step S13: collect real-time network status data from the network, build an enhanced Bayesian network model based on quantum channel theory, generate predicted status data in combination with the quantum state evolution model, calculate the link reliability index based on the predicted status data, and calculate and generate a network status scoring matrix; Step S14: collect the key pool status data of each node, build a key consumption prediction model considering quantum channel noise, calculate the key pool health index based on the key consumption prediction model, and integrate the key resource information of all nodes to generate a key resource distribution matrix; The step S2 is specifically as follows: Step S21, extracting quantum communication service demand parameters from the service demand matrix to construct an objective function set; setting system constraints according to quantum channel capacity theory; constructing an optimization variable matrix in combination with the network state scoring matrix and the key resource distribution matrix; combining the objective function set, system constraints and the optimization variable matrix to generate a multi-objective optimization model considering quantum characteristics; Step S22: Calculate all reachable paths based on the network connection graph data to obtain an initial path set; use the health index data in the key resource distribution matrix to update the path weight to obtain an updated path set; score the updated path set according to the performance parameters in the network status scoring matrix to generate path scoring data; Step S23, designing a genetic algorithm encoding scheme in a quantum communication scenario based on a multi-objective optimization model; generating an initial population using path scoring data to obtain an initial solution set; performing crossover operations and mutation operations considering quantum constraints to obtain an evolutionary solution set; calculating fitness according to the multi-objective optimization model to obtain an optimal solution set; converting the optimal solution set into a specific path selection scheme and resource allocation scheme to generate a scheduling strategy matrix; Step S24: Verify the resource constraints of the scheduling strategy matrix according to the key resource distribution matrix to obtain resource verification data; verify the performance constraints using the network status scoring matrix to obtain performance verification data; check the conflict with the existing scheduling tasks to obtain conflict verification data; integrate various verification results to generate a verification report; The step S3 is specifically as follows: Step S31, extracting the path selection scheme from the scheduling strategy matrix to generate routing configuration instructions; extracting the key distribution scheme to generate key distribution instructions; extracting the bandwidth distribution scheme to generate bandwidth reservation instructions; combining various instructions to generate a scheduling instruction set; using a two-stage submission method to issue instructions to relevant nodes and record instruction issuance logs; Step S32: According to a preset performance monitoring cycle, the actual completion time of the communication task is collected to obtain time indicator data; Count the actual key consumption to obtain key consumption data; measure the actual bandwidth usage rate to obtain bandwidth usage data; measure the actual transmission delay to obtain delay index data; combine various index data to generate performance index data; Step S33, calculating various deviation indicators according to the performance indicator data and the expected value in the scheduling strategy matrix to obtain performance deviation data; writing the performance indicator data and the performance deviation data into the historical database to obtain data update records; performing data cleaning and compression to obtain data cleaning records; generating a data update log; Step S34, calculating the scheduling success rate, resource utilization rate and service quality satisfaction based on the performance indicator data to obtain key performance indicators; Analyze performance deviation data to obtain trend analysis data; generate system optimization suggestions based on analysis results to obtain optimization suggestion data; integrate various analysis results to generate a comprehensive performance report.

2. A secure encryption communication method based on quantum key management according to claim 1, characterized in that: The step S11 is specifically as follows: Step S111, reading network topology configuration data including node location coordinate information and physical connection information between nodes from a preset configuration database; obtaining node configuration data including node type information, QKD device parameter information and quantum channel parameter information; obtaining key pool configuration data including maximum capacity information, warning threshold information and key supplement strategy information; performing validity verification based on the obtained network topology configuration data, node configuration data and key pool configuration data, and generating configuration status report data; Step S112: According to the node configuration data, obtain CPU usage data, memory usage data and storage space usage data from each network node; collect quantum bit error rate data, photon detection efficiency data and quantum state coherence time data of each node; The quantum channel capacity data is calculated based on the preset quantum channel model; the node computing capacity data is calculated using the various utilization rate data and the preset weight coefficient; The quantum key generation rate data is calculated using quantum channel capacity data and environmental parameters; Generate node status data based on node computing power data and quantum key generation rate data; Step S113, extracting physical connection information from the system state data, constructing adjacency matrix data representing the connection relationship between nodes; calculating edge weight data based on bandwidth information, delay information and quantum channel quality information in the node state data; inputting the adjacency matrix data and edge weight data into a preset graph optimization algorithm to generate network connection graph data; Perform topology analysis based on network connection graph data to generate topology optimization data including connectivity analysis results; Generate a system state matrix from topology optimization data.

3. A secure encryption communication method based on quantum key management according to claim 1, characterized in that: The step S12 is specifically as follows: Step S121, parsing the received communication request data packet, extracting the source node identifier and the target node identifier to generate node identifier data; extracting the quantum key requirement, data transmission amount and expected completion time information from the data packet header of the communication request data packet to generate service parameter data; Retrieving authentication information corresponding to the node identification data from the system authentication database to generate authentication status data; querying the system state matrix according to the node identification data to obtain the quantum channel state between the nodes to generate channel state data; Combine the node identification data, the service parameter data, the authentication status data and the channel status data to generate request information data and store it in the request queue; Step S122, reading the data packet type information in the communication request data packet, and generating security level data by comparing it with a preset quantum communication security level mapping table; calculating the quantum key consumption requirement according to the data volume in the service parameter data to obtain key requirement data; calculating the available quantum channel capacity based on the channel state data to obtain channel capacity data; Obtaining service quality parameters from the system business configuration library to generate service quality data; combining security level data, key requirement data, channel capacity data and service quality data to generate a request feature vector; Step S123, constructing a judgment matrix according to preset quantum communication service evaluation indicators; The eigenvalue data is calculated by considering the quantum channel characteristics and key consumption characteristics; the consistency ratio is calculated using the eigenvalue data to generate consistency verification data; When the consistency check data is less than the preset threshold, the eigenvector corresponding to the eigenvalue data is used as the business weight coefficient; When the consistency check data is greater than or equal to the preset threshold, the quantum communication parameters in the judgment matrix are adjusted and recalculated; the final determined service weight coefficient is compared with the preset quantum service benchmark to generate weight verification data; Step S124: performing weighted calculation on the request feature vector and service weight coefficient of each activity request to obtain quantum service priority data; Based on the quantum business priority data, all active requests are prioritized to generate request sorting data; the resource allocation priority is calculated according to the request sorting data to obtain resource allocation data; the identification information, request feature vector, business weight coefficient, quantum business priority data and resource allocation data of each request are combined to generate a business demand matrix.

4. A secure encryption communication method based on quantum key management according to claim 1, characterized in that: The step S13 is specifically as follows: Step S131, collecting photon loss rate data, quantum bit error rate data and signal-to-noise ratio data of each network node, and calculating link quality parameters according to preset weights; monitoring request queue length data, processing delay time data and resource occupancy rate data of each node to generate node load parameters; measuring quantum state transmission delay data, classical channel delay data and processing delay data and calculating network delay parameters; regularly refreshing the above parameters according to a preset update cycle to generate real-time network status data containing a complete timestamp; Step S132: construct a quantum state evolution model taking decoherence effect into consideration based on quantum channel theory; calculate quantum state retention time data from the quantum state evolution model; construct an enhanced Bayesian network model by combining the quantum state retention time data and historical operation data; predict the network state using the enhanced Bayesian network model to generate network prediction state data; calculate the confidence of the prediction result to obtain prediction reliability data; and correct the prediction result based on the prediction reliability data to obtain corrected prediction data; Step S133, reading quantum channel failure records, performance fluctuation records and recovery time records from the system history database; Calculate mean time between failures and mean time to repair to generate time statistics; Combined with the quantum state retention time data, the link reliability index is calculated; based on the historical performance fluctuation data, the quantum channel stability is calculated to obtain the link stability data; Combine link reliability indicators and link stability data to generate a link assessment report; Step S134: according to the preset scoring rules, the quality scoring data is calculated using the link quality parameters in the real-time network status data; the load scoring data is calculated based on the node load parameters; the delay scoring data is calculated based on the network delay parameters; the reliability scoring data is calculated using the link reliability index; the predicted data is integrated and corrected to generate the predicted scoring data; and each scoring data is combined and calculated according to the preset weights to generate a network status scoring matrix.

5. A secure encryption communication method based on quantum key management according to claim 1, characterized in that: The step S14 is specifically as follows: Step S141, collecting currently available key quantity data from each network node; obtaining key consumption rate data by counting key usage data within a preset time window; recording key generation rate data of the quantum key distribution module; calculating effective key generation rate in combination with quantum state retention time data to obtain effective key replenishment rate data; calculating the remaining capacity ratio of the key pool of each node to obtain key surplus data; and combining the above data to generate key pool status data; Step S142: construct a key consumption prediction model based on quantum channel noise characteristics, including photon loss, bit error rate and environmental disturbance; train the key consumption prediction model using key pool state data and historical consumption data; optimize the prediction model in combination with the business demand information in the business demand matrix; generate key consumption prediction data within a preset time period; Step S143, calculating capacity ratio data according to the available key quantity in the key pool status data and the key pool capacity; calculating replenishment and elimination ratio data based on the effective key replenishment rate data and the key consumption rate data; Extract key usage fluctuation characteristics from the key consumption prediction model to calculate volatility data; The capacity ratio data, the compensation ratio data and the volatility data are calculated according to the preset weights to obtain the initial health index; Calculate the correction factor using the initial health index; Applying the correction factor to the predicted data yields the final health index; Step S144, map the key pool status data of each node to the network topology structure to generate key distribution data; calculate the predicted distribution data based on the key consumption prediction data; integrate the final health index to generate healthy distribution data; combine the key distribution data, predicted distribution data and healthy distribution data to generate a key resource distribution matrix.

6. A secure encryption communication method based on quantum key management according to claim 4, characterized in that: The step S21 is specifically as follows: Step S211, extracting service performance requirements from the service requirement matrix to obtain performance requirement data; analyzing quantum communication service characteristics to obtain service characteristic data; and evaluating service quality requirements to obtain service quality data; Construct a target performance function based on performance requirement data, business feature data, and service quality data; Step S212, calculating the upper limit of the channel capacity based on quantum channel theory to obtain the upper limit capacity data; analyzing the influence of channel noise to obtain the noise influence data; evaluating the channel loss characteristics to obtain the loss characteristic data; constructing the quantum constraint function with the upper limit capacity data, the noise influence data and the loss characteristic data; Step S213, extracting node processing capacity data from the system state matrix to obtain processing capacity data; Analyze the storage status to obtain storage status data; evaluate the computing resource distribution to obtain resource distribution data; construct a resource constraint function based on the processing capacity data, storage status data and resource distribution data; Step S214, combining the target performance function, the quantum constraint function and the resource constraint function to obtain a constraint condition set; evaluating the correlation of the constraint conditions to obtain correlation data; analyzing the conflict of the constraint conditions to obtain conflict data; optimizing the constraint condition set based on the correlation data and the conflict data; Step S215, constructing a decision variable reflecting the network topology state to obtain topology variable data; establishing a decision variable representing resource allocation to obtain resource variable data; designing a decision variable representing route selection to obtain route variable data; Combining topology variable data, resource variable data and routing variable data to generate an optimization variable matrix; Step S216, designing and optimizing the objective function based on the characteristics of the quantum communication scenario to obtain objective function data; Analyze the weight relationship between objective functions to obtain weight relationship data; evaluate the reachability of the optimization target to obtain reachability data; Integrate the objective function data, weight relationship data and reachability data to generate an objective function set; Step S217, combining the constraint condition set, the optimization variable matrix and the objective function set to construct an initial optimization model to obtain initial model data; evaluating the computational complexity of the model to obtain complexity data; analyzing the solution feasibility of the model to obtain feasibility data; Generate a multi-objective optimization model based on initial model data, complexity data and feasibility data.

7. A secure encryption communication system based on quantum key management, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the secure encryption communication method based on quantum key management as described in any one of claims 1 to 6.

Images