Wireless fast self-organizing network node security access and key management method based on quantum random number

Abstract

The application discloses a wireless fast self-organizing network node security access and key management method based on quantum random numbers, and belongs to the technical field of wireless communication. The method is: constructing a dynamic architecture of a Mesh self-organizing network; updating the constructed dynamic architecture of the Mesh self-organizing network and finding an optimal communication path; combining the unpredictability of quantum physics with a real-time network state to construct a dynamic master key; fragmenting the master key to generate key fragments; storing the key fragments into the Mesh self-organizing network; and judging whether the key fragments are newly stored based on the state of the Mesh self-organizing network. The application avoids key loss caused by single node leakage and node failure by quickly establishing a Mesh self-organizing network architecture and combining a Mesh network distributed key fragmentation and storage mechanism, supports fast self-organizing access and dynamic key update of nodes, ensures secure communication under frequent topology changes, and constructs a security management system covering the whole life cycle of node access, topology change and fault recovery.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers. Background Technology

[0002] In recent years, with the rapid development of smart grid and digital substation technologies, the demand for data communication between equipment within substations has increased significantly. To achieve efficient communication between monitoring and control devices, smart terminals, mobile robots, and field operation terminals, wireless networks have become an important component of substation communication systems.

[0003] In the typical high electromagnetic interference, strong security constraints, and dynamic topology environment of substations, network nodes need to complete high-speed information exchange and secure access under conditions of decentralization, rapid recovery, and adaptive topology. However, unlike traditional wired or centralized wireless communication, WFAN nodes in substation environments face the following challenges in identity authentication, key management, and dynamic secure access: The presence of high-voltage equipment, switch operations, and power electronic devices in substation environments causes instantaneous fading and unstable connections in wireless channels; frequent entry and exit of mobile terminals of maintenance personnel, inspection robots, and wireless acquisition nodes make the topology difficult to predict; relying on a centralized server for key updates and renegotiation processes increases latency and affects the real-time performance of power protection and monitoring systems; simultaneously, control commands, measurement data, and protection information within substations are critical assets of the power system, and leakage or tampering will have serious consequences. Therefore, a comprehensive solution for rapid self-organizing wireless network communication is urgently needed to address the unique characteristics of the substation environment.

[0004] Existing wireless ad hoc network security access and key management technologies cannot simultaneously meet the following requirements: decentralized node authentication mechanism, dynamic key update and high randomness guarantee, and high robustness in strong electromagnetic interference environment; currently available technologies include node access authentication and encrypted communication through pre-allocated keys or distributed key management based on trust model or blockchain, which utilizes blockchain consensus to achieve distributed trust and traceable management.

[0005] However, the above methods have the following shortcomings: insufficient randomness in key generation, traditional pseudo-random algorithms are easily predicted or copied, making it difficult to meet the requirements of high-intensity anti-attack; key distribution relies on a trust center, and once the control node fails, the entire network key system fails; when a node is compromised, the process of restoring the key system is complex; and the lack of flexible key fragment redundancy and recovery mechanisms can easily lead to the risk of single-point loss of critical keys.

[0006] Therefore, how to solve the problems of key loss and control node failure under high-intensity attacks, improve key security, and ensure the continuity and reliability of substation wireless communication systems is the technical problem that this invention aims to solve. Summary of the Invention

[0007] The purpose of this invention is to provide a method for implementing a lightweight wireless encrypted communication module based on generative quantum random numbers, so as to solve the problems mentioned in the background art.

[0008] The objective of this invention is achieved as follows: a method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers, characterized by the following steps:

[0009] Step S1: Construct the dynamic architecture of the Mesh self-organizing network and determine the topological relationship between communication devices;

[0010] Step S2: Update the constructed Mesh self-organizing network dynamic architecture and find the optimal communication path;

[0011] Step S3: Construct a dynamic master key by combining the unpredictability of quantum physics with the real-time state of the network;

[0012] Step S4: Segment the master key to generate key fragments;

[0013] Step S5: Store the key fragment in the Mesh self-organizing network;

[0014] Step S6: Based on the Mesh self-organizing network status, determine whether the key fragment needs to be re-stored;

[0015] If the Mesh self-organizing network is in a normal state, the key fragment does not need to be stored again;

[0016] If the Mesh self-organizing network is in an abnormal state, migrate the key fragment to the optimal target node for storage.

[0017] Preferably, in step S1, constructing the dynamic architecture of the Mesh self-organizing network and determining the topological relationships between communication devices specifically involves:

[0018] Step S1-1: Determine the transmission channel of the communication device, specifically:

[0019] node In all available channels Send a Probe Request frame and listen for... The signal within a given time period enables the collection of wireless signal strength data in the environment.

[0020] in, Available channel set; This is the current probe channel; This refers to the single-channel scan duration.

[0021] Calculate channel interference strength: probe the channel All interference signal power After superposition, convert to dBm units and subtract noise floor. The net interference intensity is obtained, expressed as:

[0022] ;

[0023] in, For the first The power of the interference signal; The noise floor power detected locally at the node;

[0024] Calculating channel congestion: By weighting the collision rate and occupancy rate, the long-term load pressure of the channel is comprehensively assessed, expressed as:

[0025] ;

[0026] in, Indicates the packet collision rate. ; Indicates the probe channel The number of data packet retransmissions Indicates the probe channel Total number of data packets sent; Indicates channel occupancy rate. ; Indicates the probe channel The busy time duration within the scan period is used to measure the channel. The percentage of time occupied; Indicates the congestion weighting factor;

[0027] Transmission channel selection: in terms of interference strength and congestion level We weigh the options and select the channel with the lowest overall cost, expressed as:

[0028] ;

[0029] in, Congestion penalty factor; node The channel with the lowest combined interference and congestion value. It is set as the working channel for transmitting information and broadcast to the network via Beacon frames;

[0030] Step S1-2: Compare Mesh self-organizing network parameters to achieve Mesh self-organizing network node matching;

[0031] Steps S1-3: Construct an adjacency list to determine the Mesh self-organizing network nodes that can communicate.

[0032] Preferably, in step S1-2, comparing the Mesh self-organizing network parameters to achieve Mesh self-organizing network node matching specifically involves:

[0033] Calculate the differences in Mesh self-organizing network parameter configurations:

[0034] node Receive Mesh Profiles broadcast by surrounding nodes, compare them item by item with the local configuration, and average all parameter differences to obtain a comprehensive difference value.

[0035] ;

[0036] in, Represents a node The Parameter values, Represents a node The One parameter; Indicates the total number of configuration parameters. Represents the smoothing factor. ;

[0037] Compatibility decisions:

[0038]

[0039] in, This indicates that the configuration difference exceeds the tolerance threshold; compatible: add the neighboring node to the candidate list and continue the subsequent key negotiation process; incompatible: disconnect.

[0040] Preferably, in steps S1-3, constructing an adjacency list to determine communicable Mesh self-organizing network nodes specifically involves:

[0041] Step S1-3-1: Evaluate link quality using link reliability metrics:

[0042] Link reliability metrics calculation:

[0043] node arrive Throughput: ;

[0044] node arrive Packet loss rate: ;

[0045] in, Indicates the test packet size; RTT indicates round-trip time. Indicates the number of successfully received ACK frames; Indicates the total number of data frames sent;

[0046] Step S1-3-2: Detect and remove failed nodes to reduce unnecessary communication overhead;

[0047] Failed nodes are quickly identified based on the ACK timeout mechanism. If a node is identified as having lost packets three times consecutively, it is marked. It is invalid;

[0048] The ACK timeout mechanism is as follows: the sender sends data and then starts a timer. The receiver sends an ACK frame immediately after successfully receiving the data frame. If the sender does not receive the ACK within the timeout period, it is determined that the packet has been lost.

[0049] Step S1-3-3: Calculate link weights and dynamically adjust link priorities;

[0050] Link weight:

[0051] ;

[0052] in, Indicates the throughput weighting factor; This represents the theoretical maximum throughput of the network. Represents a node Initial weights, This represents the packet loss rate attenuation factor. Represents a node arrive throughput; Table Node arrive The packet loss rate;

[0053] Step S1-3-4: Determine the adjacency list classification principle and obtain the adjacency list, specifically as follows:

[0054] Nodes are categorized into three types to represent link status: candidate nodes, end-to-end nodes, and failed nodes. The candidate nodes are: This indicates that the communication link is normal; end-to-end node: the connection between the sender and receiver has been achieved;

[0055] If a candidate node appears, it is ranked according to link weight. Sort in descending order and select the first few. One node was selected as a candidate;

[0056] If a failed node is found, remove the failed node from the candidate list;

[0057] If an end-to-end node is encountered, sender-receiver authentication is initiated for candidate nodes. After authentication, nodes are migrated to the peer node list. Nodes that fail authentication are downgraded to candidate nodes or invalid nodes.

[0058] Obtain the adjacency list:

[0059] ;

[0060] in, Indicates a failed node. Represents a node Link weights.

[0061] Preferably, in step S2, updating the constructed Mesh self-organizing network dynamic architecture and finding the optimal communication path specifically involves:

[0062] The optimal path is selected using a path cost optimization algorithm, the expression of which is:

[0063] ;

[0064] in, This indicates the total number of hops in the path. Indicates the link weight coefficient. This represents the current jump count coefficient. This represents the congestion level coefficient of the next-hop channel; Represents a node Link weights; This indicates the current hop count. Indicates the level of congestion in the next-hop channel;

[0065] The end-to-end transmission path is found by combining link weight, hop count, and channel congestion level.

[0066] Preferably, in step S3, a dynamic master key is constructed by combining the unpredictability of quantum physics with the real-time state of the network, specifically as follows:

[0067] Quantum random numbers are generated using the quantum physics phenomenon of vacuum fluctuations, and the master key is constructed by combining it with the current network state parameters. The expression is as follows:

[0068] ;

[0069] in, Represents the master key; SHA-3() is a standardized hash function. This indicates a quantum random number generator; Encode() indicates binary encoding. This indicates a byte-level concatenation operation;

[0070] This represents the average link weight of the current network. ;

[0071] in, Represents the set of currently active nodes. Represents a node Link weights.

[0072] Preferably, in step S4, the master key is fragmented to generate key fragments, specifically as follows:

[0073] Convert the master key to A polynomial of degree n is used to generate a key fragment through the Lagrange interpolation theorem. It is broken down into multiple independent shards and stored on different nodes:

[0074] ;

[0075] in, This represents the set of currently active nodes; each node... Corresponding to a unique identifier The number of fragments generated by polynomial interpolation is ; This represents the minimum number of fragments required to recover the master key, which must satisfy the following: Ensure that the attacker needs to break through at least It takes a number of nodes to recover the master key, and the minimum number of shards required to recover the master key does not exceed the node size. Represents modulo operation; represents a large prime number, To ensure the safety of finite field operations; This represents a randomly generated coefficient.

[0076] Preferably, in step S5, storing the key fragment in the Mesh self-organizing network specifically involves:

[0077] Step S5-1: Encrypt the key fragment based on the dynamic seed and dynamic session key;

[0078] ;

[0079] in, For dynamic session keys, ; Indicates a dynamic seed. ; This indicates the XOR operation. Represents a hash function; Represents a key segment;

[0080] XOR budget and dynamic seeds Hash value fusion, obfuscation and key fragment cutting A direct mathematical association with the original key;

[0081] Step S5-2: Divide the encrypted fragment into 1MB data blocks, generate a directed acyclic graph using a Merkle tree, calculate the hash value of each 1MB data block as a leaf node, store the hash of the child node in the parent node, and finally the root hash is MerkleRoot;

[0082] Step S5-3: Generate a unique identifier for the content using the Merkle tree root hash value:

[0083] ;

[0084] in, , Indicates the data encoding type. Indicates the hash length. The root hash value of the Merkle tree; Indicates Base58 encoding;

[0085] Content unique identifiers enable content addressing; ultimately, the CID of all key fragments is... Upload the CID and key fragment to the Mesh self-organizing network storage.

[0086] Preferably, in step S6, determining whether the master key needs to be re-stored based on the Mesh self-organizing network status specifically involves:

[0087] Step S6-1: Determine the status of the Mesh self-organizing network, specifically:

[0088] Based on the current set of active nodes Node failure rate and confidence level of abnormal traffic Determine the status of the Mesh self-organizing network:

[0089] ;

[0090] When a node fails When this occurs, it indicates that the network topology is unstable and the confidence level of abnormal traffic is low. If the value is greater than 0.95, it indicates an abnormal traffic event, and the master key needs to be re-stored.

[0091] Step S6-2: Evaluate the failed key fragment nodes and determine the migration targets, specifically:

[0092] Prove the integrity of the key fragment using the Merkle path:

[0093] ;

[0094] Failed fragment set:

[0095] ;

[0096] Key segments assessed as invalid were identified as migration targets;

[0097] Step S6-3: Use the target node selection algorithm to determine the optimal migration node for the key segment;

[0098] The target node selection algorithm:

[0099] ;

[0100] in, , Represents a node Location, Represents a node Location: Prioritize physically nearby nodes to reduce cross-regional transmission latency; Indicates load balancing. , express The current load, This indicates the maximum capacity to prevent overload of high-load nodes. Indicates segmented affinity, , Represents a node With the original node Identify hash differences, Indicates the length of the hash value, if the node The identifier hash and the original node If the differences are small, migrate first and select the optimal node. ; The weighting coefficients for distance are... The weighting factor for the load. This is the weighting coefficient for affinity;

[0101] Step S6-4: Securely distribute key fragments through node key negotiation and transmission over multi-hop paths.

[0102] Preferably, in step S6-4, secure distribution of key fragments is achieved through node key negotiation and transmission over multi-hop paths, specifically as follows:

[0103] Step S6-4-1: Find the source node To the optimal target node Multi-hop paths: ;

[0104] in, This represents the total number of hops on the path.

[0105] Step S6-4-2: Form a temporary session key through key exchange and determine the transmission of encrypted key fragments;

[0106] The key exchange negotiation process is as follows:

[0107] node Generate private key Using private keys Generate public parameters ;

[0108] Next hop node Generate private key Calculate the temporary session key: ;

[0109] use The encrypted key fragment is transmitted. ;

[0110] Based on the Mesh network routing strategy, the optimal path is selected to send the encrypted key fragment to the optimal target node. ;

[0111] Step S6-4-3: Verify the migrated key fragment;

[0112] Recover the master key using Lagrange interpolation:

[0113] ;

[0114] in, Represent a Lagrange polynomial. ;pass Polynomial interpolation of each segment accurately recovers the master key;

[0115] Perform a consistency check on the key fragment:

[0116] ;

[0117] in, The master key to be recovered; if the verification fails, proceed to steps S6-1 to S6-4;

[0118] Step S6-4-4: Determine the key fragment update trigger condition;

[0119] The triggering condition is:

[0120] ;

[0121] in, The maximum number of hops allowed for a key segment. For key fragment lifespan, Master key update cycle;

[0122] When the number of key fragment transmission hops exceeds the limit or the master key's lifespan ends, a key fragment update is forcibly triggered.

[0123] Compared with the prior art, the present invention has the following improvements and advantages:

[0124] 1. By quickly building a Mesh self-organizing network architecture and combining it with the Mesh network's distributed key sharding and storage mechanism, it avoids key loss caused by single node leakage and node failure, supports rapid self-organizing node access and dynamic key updates, and ensures secure communication under frequent topology changes.

[0125] 2. By introducing a network state-aware key adaptive management mechanism, key fragments are automatically migrated when abnormal traffic or node failure is detected, thereby improving the security management of key fragments and further ensuring secure network communication.

[0126] 3. By utilizing quantum random number generation mechanisms to enhance the randomness and unpredictability of keys, the risk of pseudo-random leakage is prevented at the physical layer. At the same time, through a multi-hop path security negotiation mechanism, the secure distribution and recovery of encrypted fragments are achieved, ensuring the steady-state security of the entire self-organizing network. This ensures the safe and reliable operation of the substation wireless communication system in millisecond-level response scenarios. Attached Figure Description

[0127] Figure 1 This is a flowchart illustrating the method of the present invention.

[0128] Figure 2 This diagram illustrates the results of a comparative analysis of the key unpredictability of different schemes.

[0129] Figure 3 This diagram illustrates the performance comparison of different network schemes in responding to topology changes.

[0130] Figure 4 This is a diagram showing the comparison of reliability indicators for different schemes. Detailed Implementation

[0131] The invention will be further summarized below with reference to the accompanying drawings.

[0132] like Figure 1 As shown, a secure access and key management method for wireless fast self-organizing network nodes based on quantum random numbers is presented. This method includes the following steps:

[0133] Step S1: Construct the dynamic architecture of the Mesh self-organizing network and determine the topological relationships between communication devices, specifically:

[0134] Step S1-1: Determine the transmission channel of the communication device, specifically:

[0135] node In all available channels Send a Probe Request frame and listen for... The signal within a given time period enables the collection of wireless signal strength data in the environment.

[0136] in, Available channel set; This is the current probe channel; This refers to the single-channel scan duration.

[0137] Calculate channel interference strength: probe the channel All interference signal power After superposition, convert to dBm units and subtract noise floor. The net interference intensity is obtained, expressed as:

[0138] ;

[0139] in, For the first The power of the interference signal; The noise floor power detected locally at the node;

[0140] Calculating channel congestion: By weighting the collision rate and occupancy rate, the long-term load pressure of the channel is comprehensively assessed, expressed as:

[0141] ;

[0142] in, Indicates the packet collision rate. ; Indicates the probe channel The number of data packet retransmissions Indicates the probe channel Total number of data packets sent; Indicates channel occupancy rate. ; Indicates the probe channel The busy time duration within the scan period is used to measure the channel. The percentage of time occupied; Indicates the congestion weighting factor;

[0143] Transmission channel selection: in terms of interference strength and congestion level We weigh the options and select the channel with the lowest overall cost, expressed as:

[0144] ;

[0145] in, Congestion penalty factor; node The channel with the lowest combined interference and congestion value. It is set as the working channel for transmitting information and broadcast to the network via Beacon frames;

[0146] Step S1-2: Compare Mesh self-organizing network parameters to achieve Mesh self-organizing network node matching, specifically:

[0147] Calculate the differences in Mesh self-organizing network parameter configurations:

[0148] node Receive Mesh Profiles broadcast by surrounding nodes, compare them item by item with the local configuration, and average all parameter differences to obtain a comprehensive difference value.

[0149] ;

[0150] in, Represents a node The Parameter values, Represents a node The One parameter; Indicates the total number of configuration parameters. Represents the smoothing factor. Smoothing factor prevents division by zero errors;

[0151] Compatibility decisions:

[0152]

[0153] in, This indicates that the configuration difference exceeds the tolerance threshold;

[0154] Compatible: Add neighboring nodes to the candidate list and continue the subsequent key negotiation process; Incompatible: Disconnect;

[0155] Step S1-3: Construct an adjacency list to determine the communicable Mesh self-organizing network nodes, specifically:

[0156] Based on link quality assessment, failed node detection, and dynamic weight calculation, a real-time dynamic adjacency table is constructed to provide reliable one-hop neighbor information for key negotiation.

[0157] Step S1-3-1: Evaluate link quality using link reliability metrics:

[0158] Link reliability is quantified through interaction between the sender's RTS and the receiver's CTS to identify low-quality links and avoid data retransmissions; link reliability metrics are calculated as follows:

[0159] node arrive Throughput: ;

[0160] node arrive Packet loss rate: ;

[0161] in, Indicates the test packet size; RTT indicates round-trip time. Indicates the number of successfully received ACK frames; Indicates the total number of data frames sent;

[0162] The interaction between the sender RTS and the receiver CTS is as follows: The sender RTS sends an RTS frame to the receiver to declare that it will soon occupy the channel. If the receiver CTS can receive the frame, it replies with a CTS frame and notifies the surrounding nodes to enter a silent period.

[0163] Step S1-3-2: Detect and remove failed nodes to reduce unnecessary communication overhead;

[0164] The ACK timeout mechanism is used to quickly identify failed nodes. If a node is identified as having lost packets three times in a row, it is marked. It is invalid;

[0165] The ACK timeout mechanism works as follows: the sender sends data and then starts a timer. The receiver sends an ACK frame immediately after successfully receiving the data frame. If the sender does not receive the ACK within the timeout period, the packet is considered lost.

[0166] Step S1-3-3: Calculate link weights and dynamically adjust link priorities;

[0167] Link weight:

[0168] ;

[0169] in, Represents the throughput weighting factor. ; This represents the theoretical maximum throughput of the network. Represents a node Initial weights, This represents the packet loss rate attenuation factor. ; Represents a node arrive throughput; Table Node arrive The packet loss rate;

[0170] Step S1-3-4: Determine the adjacency list classification principle and obtain the adjacency list, specifically as follows:

[0171] Nodes are divided into three categories to represent link status: candidate nodes, end-to-end nodes, and failed nodes.

[0172] Candidate nodes: This indicates that the communication link is normal;

[0173] End-to-end node: The connection between the sender and receiver has been established;

[0174] Failed Nodes: Failed nodes are quickly identified based on the ACK timeout mechanism. If a node is identified as having lost packets three times consecutively, it is marked. It is invalid;

[0175] If a candidate node appears, it is ranked according to link weight. Sort in descending order and select the first few. One node was selected as a candidate;

[0176] If a failed node is found, remove the failed node from the candidate list;

[0177] If an end-to-end node is encountered, sender-receiver authentication is initiated for candidate nodes. After authentication, nodes are migrated to the peer node list. Nodes that fail authentication are downgraded to candidate nodes or invalid nodes.

[0178] Obtain the adjacency list:

[0179] ;

[0180] in, Indicates a failed node. Represents a node Link weights;

[0181] The adjacency list is used to classify and record the roles and topology information of nodes.

[0182] Step S2: Update the constructed Mesh self-organizing network dynamic architecture and find the optimal communication path, specifically:

[0183] Based on dynamic adjacency list information, the optimal path is selected using a path cost comprehensive algorithm. The expression for the path cost comprehensive algorithm is as follows:

[0184] ;

[0185] in, This indicates the total number of hops in the path. Indicates the link weight coefficient. This represents the current jump count coefficient. This represents the congestion level coefficient of the next-hop channel; Represents a node Link weights; This indicates the current hop count. Indicates the level of congestion in the next-hop channel;

[0186] The end-to-end transmission path is found by combining link weight, hop count, and channel congestion level.

[0187] Step S3: By combining the unpredictability of quantum physics with the real-time state of the network, a dynamic master key is constructed, specifically as follows:

[0188] Quantum random numbers are generated using the quantum physics phenomenon of vacuum fluctuations, and the master key is constructed by combining it with the current network state parameters. The expression is as follows:

[0189] ;

[0190] in, Represents the master key; SHA-3() is a standardized hash function. This indicates a quantum random number generator; Encode() indicates binary encoding. This indicates a byte-level concatenation operation;

[0191] This represents the average link weight of the current network. ;

[0192] in, Represents the set of currently active nodes. Represents a node Link weights;

[0193] Step S4: Fragment the master key to generate key fragments, specifically:

[0194] Convert the master key to A polynomial of degree n is used to generate a key fragment through the Lagrange interpolation theorem. It is broken down into multiple independent shards and stored on different nodes:

[0195] ;

[0196] in, This represents the set of currently active nodes; each node... Corresponding to a unique identifier The number of fragments generated by polynomial interpolation is , This represents the total number of nodes in the currently active node list set; This represents the minimum number of fragments required to recover the master key, which must satisfy the following: Ensure that the attacker needs to break through at least It takes a number of nodes to recover the master key, and the minimum number of shards required to recover the master key does not exceed the node size. Represents modulo operation; represents a large prime number, To ensure the safety of finite field operations; Represents randomly generated coefficients. .

[0197] In step S5, the key fragment is stored in the Mesh self-organizing network, specifically as follows:

[0198] Step S5-1: Encrypt the key fragment based on the dynamic seed and dynamic session key;

[0199] ;

[0200] in, For dynamic session keys, ; Indicates a dynamic seed. ; This indicates the XOR operation. Represents a hash function; Represents a key fragment; XOR budgeting is used to... and dynamic seeds Hash value fusion, obfuscation and key fragment cutting A direct mathematical association with the original key;

[0201] Step S5-2: Divide the encrypted fragment into 1MB data blocks, generate a directed acyclic graph using a Merkle tree, calculate the hash value of each 1MB data block as a leaf node, store the hash of the child node in the parent node, and finally the root hash is MerkleRoot;

[0202] Step S5-3: Generate a unique identifier for the content using the Merkle tree root hash value:

[0203] ;

[0204] in, , Indicates the data encoding type. Indicates the hash length. The root hash value of the Merkle tree; Indicates Base58 encoding;

[0205] Content unique identifiers enable content addressing; ultimately, the CID of all key fragments is... Upload the CID and key fragment to the Mesh self-organizing network storage.

[0206] Step S6: Based on the Mesh self-organizing network status, determine whether the master key needs to be re-stored, specifically:

[0207] Step S6-1: Determine the status of the Mesh self-organizing network, specifically:

[0208] Based on the current set of active nodes Node failure rate and confidence level of abnormal traffic Determine the status of the Mesh self-organizing network:

[0209] ;

[0210] When a node fails When this occurs, it indicates that the network topology is unstable and the confidence level of abnormal traffic is low. If the value is >0.95, an abnormal traffic event is detected, and the master key needs to be re-stored; if either of the two conditions is met, the master key re-store operation is triggered.

[0211] Step S6-2: Evaluate the failed key fragment nodes and determine the migration targets, specifically:

[0212] Prove the integrity of the key fragment using the Merkle path:

[0213] ;

[0214] Failed fragment set:

[0215] ;

[0216] Key segments assessed as invalid were identified as migration targets;

[0217] Step S6-3: Use the target node selection algorithm to determine the optimal migration node for the key segment;

[0218] Target node selection algorithm:

[0219] ;

[0220] in, , Represents a node Location, Represents a node Location: Prioritize physically nearby nodes to reduce cross-regional transmission latency; Indicates load balancing. , express The current load, This indicates the maximum capacity to prevent overload of high-load nodes. Indicates segmented affinity, , Represents a node With the original node Identify hash differences, Indicates the length of the hash value, if the node The identifier hash and the original node If the differences are small, migrate first and select the optimal node. ; The weighting coefficients for distance are... The weighting factor for the load. This is the weighting coefficient for affinity;

[0221] In step S6-4, secure distribution of key fragments is achieved through node key negotiation and transmission over multi-hop paths, specifically as follows:

[0222] Step S6-4-1: Find the source node To the optimal target node Multi-hop paths: ;

[0223] in, This represents the total number of hops on the path.

[0224] Step S6-4-2: Form a temporary session key through key exchange and determine the transmission of encrypted key fragments;

[0225] The key exchange negotiation process is as follows:

[0226] node Generate private key Using private keys Generate public parameters ;

[0227] Next hop node Generate private key Calculate the temporary session key: ;

[0228] use The encrypted key fragment is transmitted. ;

[0229] Based on the Mesh network routing strategy, the optimal path is selected to send the encrypted key fragment to the optimal target node. ;

[0230] Step S6-4-3: Verify the migrated key fragment;

[0231] Recover the master key using Lagrange interpolation:

[0232] ;

[0233] in, Represent a Lagrange polynomial. ;pass Polynomial interpolation of each segment accurately recovers the master key;

[0234] Perform a consistency check on the key fragment:

[0235] ;

[0236] in, The master key to be recovered; if the verification fails, proceed to steps S6-1 to S6-4;

[0237] Step S6-4-4: Determine the key fragment update trigger condition;

[0238] The triggering condition is:

[0239] ;

[0240] in, The maximum number of hops allowed for a key segment. For key fragment lifespan, Master key update cycle;

[0241] When the number of key fragment transmission hops exceeds the limit or the master key's lifespan ends, a key fragment update is forcibly triggered.

[0242] To verify the effectiveness and feasibility of this invention, the following experiments were conducted:

[0243] Experimental conditions: AES-256 master key generation algorithm and traditional PRNG based on OpenSSL;

[0244] QRNG-only: Pure quantum random number generation, simulating QRNG, based on quantum fluctuation algorithm;

[0245] Network-only: Based solely on network state parameters, such as average link weights and channel interference intensity;

[0246] The solution of this invention: QRNG combined with network parameters, key length: 256 bits (symmetric key).

[0247] Attack simulation tools:

[0248] Man-in-the-middle attack: Using the Scapy framework to forge routing information; predictive attack: training historical key sequences using a TensorFlow LSTM model; network parameters: 50 nodes, IEEE 802.11ax channel model; quantum random number simulation method based on QRNG hash function. Comparison of the performance of this invention with AES-256 (traditional), QRNG-only, and Network-only methods in terms of entropy and attack success rate.

[0249] like Figure 2 As shown, the entropy value comparison analysis shows that: the entropy value of the present invention is 127.9 bits, which is significantly higher than the 127.3 bits of the traditional AES-256 and the 127.2 bits of QRNG-only, and close to the theoretical maximum value of 128 bits; Network-only: entropy value 98.7 bits.

[0250] This invention provides true unpredictability through a quantum random number generator (QRNG), combined with real-time perturbations of network dynamic parameters such as link weights and interference intensity, breaking the statistical regularity of fixed entropy sources in traditional cryptography. It resists entropy source attacks: Traditional AES-256 relies on deterministic algorithms to generate keys, making it vulnerable to side-channel attacks such as timing attacks; while this invention, through a dynamic entropy source (quantum randomness + network noise), prevents attackers from modeling and predicting the key using a single entropy source.

[0251] Performance Analysis of Predicted Attack Success Rate: The proposed solution achieves an attack success rate of 8.5%, a 72.1% decrease compared to QRNG-only (34.1%) and an 87.5% decrease compared to traditional AES-256 (76.3%). Network-only: The attack success rate is 67.2%, indicating that relying solely on network parameters makes it vulnerable to attack. Technical Advantages of the Proposed Solution: Dynamic Key Generation Mechanism: The proposed solution introduces unpredictable key fragmentation through quantum randomness, combined with real-time changes in network parameters, making it impossible for attackers to statically capture the key generation pattern. Robustness Against Attacks: Traditional random number generation schemes have key sequences with periodicity or statistical bias, making them easily susceptible to temporal characteristics; however, the quantum-network hybrid entropy source in the proposed solution destroys temporal correlation, significantly reducing the generalization ability of the attack model.

[0252] The present invention achieves effective breakthroughs in both entropy value and anti-attack capability through a quantum-network hybrid entropy source and dynamic key management mechanism.

[0253] Experimental conditions:

[0254] Network topology model: Random Waypoint mobile model, simulating highly dynamic scenarios; node speed: 5-20 m / s, covering scenarios from low-speed mobile devices to high-speed vehicles; dwell time: 20 s; scale: 50 nodes.

[0255] Interference scenarios: Static interference: 5 fixed nodes, 2.4 GHz band, 20 dBm transmit power; Dynamic interference: 3 mobile nodes, 10 m / s speed, random path.

[0256] Comparison: Traditional AODV protocol, lacking dynamic topology awareness; TATP: a game theory-based topology control protocol;

[0257] Immune: an artificial immune response model; the proposed solution: a quantum-network fusion protocol. A comparison of the performance of these solutions under network topology changes.

[0258] like Figure 3 As shown, the response time of the present invention is 58.4ms and the success rate is 96.5%, which is significantly better than the response time of the ADDV scheme (175.2ms, success rate of 68.4%) and the response time of Immune (89.7ms, success rate of 89.1%). Compared with the response time of the TATP scheme (62.3ms, success rate of 92.7%), it also has a certain comparative advantage.

[0259] Performance bottlenecks of the compared solutions: The AODV solution's periodic flooding of routing requests leads to high control overhead. The TATP solution's game theory model requires real-time calculation of node trust values, increasing CPU utilization. The Immune solution's parallel path search results in excessive memory consumption. Our invention accelerates path selection using quantum randomness: dynamic weights are generated through quantum random numbers, avoiding traditional protocols from getting trapped in local optima. Dynamic adjacency list updates: link weights are calculated in real-time, combined with a rapid isolation mechanism for failed nodes, reducing invalid route calculations.

[0260] By enhancing the unpredictability of path selection through quantum randomness and optimizing routing computation efficiency through dynamic weights, the proposed scheme significantly outperforms traditional protocols in terms of topology change response time and success recovery rate.

[0261] Metric definition: Fragmented recovery success rate: ;

[0262] This indicates the number of fragments that were successfully recovered. This indicates the total number of fragments that need to be recovered;

[0263] Fault detection accuracy: ;

[0264] Wherein, TP (True Positive) represents the number of correctly detected faults, TN (True Negative) represents the number of correctly identified normal nodes, FP (False Positive) represents the number of falsely reported normal nodes, and FN (False Negative) represents the number of faults that were missed.

[0265] Experimental conditions:

[0266] Simulation tools: NS-3 supports dynamic topology and sharding management module expansion; node movement model adopts RandomDirection Model (speed 5-15m / s); sharding strategy: Shamir threshold-based sharding storage (t=3, n=5), shard size 1MB, redundancy 30%; fault injection: node failure: random node removal (single node / multi-node cascade failure); link interruption: based on link quality model (Nakagami fading model, signal-to-noise ratio 5-20dB).

[0267] Experimental comparison schemes: Static sharding, fixed sharding strategy, each shard stores 3 copies, fault scenario: cascading failure (first 10% of nodes fail, then triggering shard migration). Dynamic sharding + AI fault prediction: dynamic sharding based on node load, AI model (LSTM) predicts node failure, fault scenario: random failure + link fluctuation (signal-to-noise ratio <10dB). Hybrid sharding + blockchain: sharded data is stored in a consortium blockchain, node voting determines the recovery path, fault scenario: malicious node attack (30% of nodes forge faults). The performance of these three schemes and the scheme of this invention is compared in terms of shard recovery success rate, fault detection accuracy, and key update latency.

[0268] like Figure 4 As shown, the sharding recovery success rate of the present invention is as high as 98.7%, which is significantly better than the 82.4% of static sharding, the 91.3% of dynamic sharding + AI, and the 96.3% of hybrid sharding + blockchain.

[0269] The Shamir threshold scheme of this invention splits the master key into polynomial shards and achieves threshold recovery (t=3, n=5) using Lagrange interpolation. Even if 30% of the nodes fail, the key can still be reconstructed from the remaining shards. Blockchain shard management: Shard integrity is verified using a Merkle tree to prevent shard tampering and ensure data consistency after migration.

[0270] The fault detection accuracy of the quantum-safe solution reaches 99.8%, far exceeding the 89.2% accuracy of static sharding, 97.4% accuracy of dynamic sharding + AI, and 99.3% accuracy of hybrid sharding + blockchain. This invention employs a blockchain consensus mechanism, verifying shard status through multiple nodes to reduce single-point false positives. The dynamic sharding strategy is based on dynamic classification of node load and link quality, prioritizing the detection of high-risk nodes.

[0271] With minimal key update latency, the shard recovery success rate of this invention is as low as 0.3s, significantly better than the 1.2s of static sharding, 0.8s of dynamic sharding + AI, and 2.1s of hybrid sharding + blockchain. The superior performance of this invention is supported by the following technical features: QRNG generates dynamic keys in real time, eliminating the need for traditional key negotiation processes and reducing computational overhead. The improved Dijkstra algorithm's dynamic routing computation time is <100ms (at a scale of thousands of nodes), supporting rapid activation of backup paths. A lightweight key update mechanism updates only failed shards instead of the entire key set, reducing synchronization time.

[0272] This invention provides a secure management system covering the entire lifecycle of node access, topology changes, and fault recovery by enabling security at the quantum physics layer and dynamically optimizing the network protocol layer. It ensures key negotiation efficiency while providing a "high-security, high-reliability, and low-latency" end-to-end solution for wireless ad hoc networks, and is particularly suitable for protecting critical infrastructure under the threat of quantum computing.

[0273] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers, characterized in that: The method includes the following steps: Step S1: Construct the dynamic architecture of the Mesh self-organizing network and determine the topological relationship between communication devices; Step S2: Update the constructed Mesh self-organizing network dynamic architecture and find the optimal communication path; Step S3: By combining the unpredictability of quantum physics with the real-time state of the network, a dynamic master key is constructed, specifically as follows: Quantum random numbers are generated using the quantum physics phenomenon of vacuum fluctuations, and the master key is constructed by combining it with the current network state parameters. The expression is as follows: ； in, Represents the master key; SHA-3() is a standardized hash function. This indicates a quantum random number generator; Encode() indicates binary encoding. This indicates a byte-level concatenation operation; This represents the average link weight of the current network. ; in, Represents the set of currently active nodes. Represents a node Link weights; Step S4: Fragment the master key to generate key fragments, specifically: Convert the master key to A polynomial of degree n is used to generate key fragments using the Lagrange interpolation theorem; the master key is... The key is broken down into multiple independent fragments and stored on different nodes. The key fragments are as follows: ； in, This represents the set of currently active nodes; each node... Corresponding to a unique identifier The number of fragments generated by polynomial interpolation is , This represents the total number of nodes in the currently active node list set; This represents the minimum number of fragments required to recover the master key, which must satisfy the following: Ensure that the attacker needs to break through at least It takes a number of nodes to recover the master key, and the minimum number of shards required to recover the master key does not exceed the node size. Represents modulo operation; represents a large prime number, To ensure the safety of finite field operations; Indicates the coefficients generated randomly; Step S5: Store the key fragment in the Mesh self-organizing network, specifically: Step S5-1: Encrypt the key fragment based on the dynamic seed and dynamic session key; ； in, For dynamic session keys, ; Indicates a dynamic seed. ; This indicates the XOR operation. Represents a hash function; Represents a key segment; XOR budget and dynamic seeds Hash value fusion, obfuscation and key fragment cutting A direct mathematical association with the original key; Step S5-2: Divide the encrypted fragment into 1MB data blocks, generate a directed acyclic graph using a Merkle tree, calculate the hash value of each 1MB data block as a leaf node, store the hash of the child node in the parent node, and finally the root hash is MerkleRoot; Step S5-3: Generate a unique identifier for the content using the Merkle tree root hash value: ； in, , Indicates the data encoding type. Indicates the hash length. The root hash value of the Merkle tree; Indicates Base58 encoding; Content unique identifiers enable content addressing; ultimately, the CID of all key fragments is... Upload the CID and key fragment to the Mesh self-organizing network storage; Step S6: Based on the Mesh self-organizing network status, determine whether the key fragment needs to be re-stored; If the Mesh self-organizing network is in a normal state, the key fragment does not need to be stored again; If the Mesh self-organizing network is in an abnormal state, migrate the key fragment to the optimal target node for storage.

2. The method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers according to claim 1, characterized in that: In step S1, the dynamic architecture of the Mesh self-organizing network is constructed, and the topological relationship between communication devices is determined, specifically as follows: Step S1-1: Determine the transmission channel of the communication device, specifically: node In all available channels Send a Probe Request frame and listen for... The signal within a given time period enables the collection of wireless signal strength data in the environment. in, Available channel set; This is the current probe channel; This refers to the single-channel scan duration. Calculate channel interference strength: probe the channel All interference signal power After superposition, convert to dBm units and subtract noise floor. The net interference intensity is obtained, expressed as: ； in, For the first The power of the interference signal; The noise floor power detected locally at the node; Calculating channel congestion: By weighting the collision rate and occupancy rate, the long-term load pressure of the channel is comprehensively assessed, expressed as: ； in, Indicates the packet collision rate. ; Indicates the probe channel The number of data packet retransmissions Indicates the probe channel Total number of data packets sent; Indicates channel occupancy rate. ; Indicates the probe channel The busy time duration within the scan period is used to measure the channel. The percentage of time occupied; Indicates the congestion weighting factor; Transmission channel selection: in terms of interference strength and congestion level We weigh the options and select the channel with the lowest overall cost, expressed as: ； in, Congestion penalty factor; node The channel with the lowest combined interference and congestion value. It is set as the working channel for transmitting information and broadcast to the network via Beacon frames; Step S1-2: Compare Mesh self-organizing network parameters to achieve Mesh self-organizing network node matching; Steps S1-3: Construct an adjacency list to determine the Mesh self-organizing network nodes that can communicate.

3. The method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers according to claim 2, characterized in that: In step S1-2, comparing the Mesh self-organizing network parameters to achieve Mesh self-organizing network node matching specifically involves: Calculate the differences in Mesh self-organizing network parameter configurations: node Receive Mesh Profiles broadcast by surrounding nodes, compare them item by item with the local configuration, and average all parameter differences to obtain a comprehensive difference value. ； in, Represents a node The Parameter values, Represents a node The One parameter; Indicates the total number of configuration parameters. Represents the smoothing factor. ; Compatibility decisions: ； in, This indicates that the configuration difference exceeds the tolerance threshold; compatible: add the neighboring node to the candidate list and continue the subsequent key negotiation process; incompatible: disconnect.

4. The method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers according to claim 2, characterized in that: In steps S1-3, constructing an adjacency list and determining communicable Mesh self-organizing network nodes specifically involves: Step S1-3-1: Evaluate link quality using link reliability metrics: Link reliability metrics calculation: node arrive Throughput: ; node arrive Packet loss rate: ; in, Indicates the test packet size; RTT indicates round-trip time. Indicates the number of successfully received ACK frames; Indicates the total number of data frames sent; Step S1-3-2: Detect and remove failed nodes to reduce unnecessary communication overhead; The ACK timeout mechanism is used to quickly identify failed nodes. If a node is identified as having lost packets three times in a row, it is marked. It is invalid; The ACK timeout mechanism is as follows: the sender sends data and then starts a timer. The receiver sends an ACK frame immediately after successfully receiving the data frame. If the sender does not receive the ACK within the timeout period, it is determined that the packet has been lost. Step S1-3-3: Calculate link weights and dynamically adjust link priorities; Link weight: ； in, Indicates the throughput weighting factor; This represents the theoretical maximum throughput of the network. Represents a node Initial weights, This represents the packet loss rate attenuation factor. Represents a node arrive throughput; Table Node arrive The packet loss rate; Step S1-3-4: Determine the adjacency list classification principle and obtain the adjacency list, specifically as follows: Nodes are categorized into three types to represent link status: candidate nodes, end-to-end nodes, and failed nodes. The candidate nodes are: This indicates that the communication link is normal; end-to-end node: the connection between the sender and receiver has been achieved; If a candidate node appears, it is ranked according to link weight. Sort in descending order and select the first few. One node was selected as a candidate; If a failed node is found, remove the failed node from the candidate list; If an end-to-end node is present, initiate sender and receiver interactive authentication for the candidate node. After authentication, migrate to the peer node list. Nodes that fail authentication are downgraded to candidate nodes or invalid nodes. Obtain the adjacency list: ； in, Indicates a failed node. Represents a node Link weights.

5. The method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers according to claim 1, characterized in that: In step S2, the constructed Mesh self-organizing network dynamic architecture is updated, and the optimal communication path is found, specifically as follows: The optimal path is selected using a path cost optimization algorithm, the expression of which is: ； in, This indicates the total number of hops in the path. Indicates the link weight coefficient. This represents the current jump count coefficient. This represents the congestion level coefficient of the next-hop channel; Represents a node Link weights; This indicates the current hop count. Indicates the level of congestion in the next-hop channel; The end-to-end transmission path is found by combining link weight, hop count, and channel congestion level.

6. The method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers according to claim 1, characterized in that: In step S6, based on the Mesh self-organizing network status, it is determined whether the master key needs to be re-stored. Specifically: Step S6-1: Determine the status of the Mesh self-organizing network, specifically: Based on the current set of active nodes Node failure rate and confidence level of abnormal traffic Determine the status of the Mesh self-organizing network: ； When a node fails When this occurs, it indicates that the network topology is unstable and the confidence level of abnormal traffic is low. If the value is greater than 0.95, it indicates an abnormal traffic event, and the master key needs to be re-stored. Step S6-2: Evaluate the failed key fragment nodes and determine the migration targets, specifically: Prove the integrity of the key fragment using the Merkle path: ； Failed fragment set: ； Key segments assessed as invalid were identified as migration targets; Step S6-3: Use the target node selection algorithm to determine the optimal migration node for the key segment; The target node selection algorithm: ； in, , Represents a node Location, Represents a node Location, prioritize selection based on node The node with the closest physical distance is used as the target node to reduce cross-regional transmission latency; Indicates load balancing. , express The current load, This indicates the maximum capacity to prevent overload of high-load nodes. Indicates segmented affinity, , Represents a node With the original node Identify hash differences, Indicates the length of the hash value, if the node The identifier hash and the original node If the difference is less than a preset threshold, migration is prioritized, and the optimal node is selected. ; The weighting coefficients for distance are... The weighting factor for the load. This is the weighting coefficient for affinity; Step S6-4: Securely distribute key fragments through node key negotiation and transmission over multi-hop paths.

7. The method for secure access and key management of wireless fast self-organizing network nodes based on quantum random numbers according to claim 6, characterized in that: In step S6-4, secure distribution of key fragments is achieved through node key negotiation and transmission over multi-hop paths, specifically as follows: Step S6-4-1: Find the source node To the optimal target node Multi-hop paths: ; in, This represents the total number of hops on the path. Step S6-4-2: Form a temporary session key through key exchange and determine the transmission of encrypted key fragments; The key exchange negotiation process is as follows: node Generate private key Using private keys Generate public parameters ; Next hop node Generate private key Calculate the temporary session key: ; use The encrypted key fragment is transmitted. ; Based on the Mesh network routing strategy, the optimal path is selected to send the encrypted key fragment to the optimal target node. ; Step S6-4-3: Verify the migrated key fragment; Recover the master key using Lagrange interpolation: ； in, Represent a Lagrange polynomial. ;pass The master key is accurately recovered using polynomial interpolation of each segment; Perform a consistency check on the key fragment: ； in, The master key to be recovered; if the verification fails, proceed to steps S6-1 to S6-4; Step S6-4-4: Determine the key fragment update trigger condition; The triggering condition is: ； in, The maximum number of hops allowed for a key segment. For key fragment lifespan, Master key update cycle; When the number of key fragment transmission hops exceeds the limit or the master key's lifespan ends, a key fragment update is forcibly triggered.

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