Whole-network distributed domain name resolution method based on block chain technology

Abstract

The invention discloses a whole network distributed domain name resolution method based on a block chain technology. The method comprises the following steps: S1, deploying a domain name registration smart contract developed based on solidity in a block chain network; s2, performing dynamic structure transformation on the domain name information by applying a data metamorphic privacy protection technology; s3, when a user requests domain name resolution, each node queries and returns the processed domain name information through the smart contract; s4, dynamically adjusting node weight distribution by adopting a fixed period weight rotation mechanism; s5, dynamically adjusting key parameters of the domain name resolution algorithm through a topological resonance optimization algorithm; s6, generating a snapshot backup of the domain name information by using a multi-version management mechanism; s7, a domain name access rate limit is set in the smart contract, and access is automatically limited when the set access frequency is exceeded; and S8, supporting decentralized domain name auction and lease, and performing public bidding and short-term lease through an intelligent contract. According to the invention, the security, stability and expansibility of domain name resolution are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a full-network distributed domain name resolution method based on blockchain technology. Background Art

[0002] With the rapid development of blockchain technology, distributed ledger systems have gradually become an important support for the digital economy. However, with the continuous expansion of blockchain application scenarios, especially in the key Internet infrastructure of domain name resolution, the traditional centralized domain name resolution system has gradually exposed a variety of technical bottlenecks and security risks. As an important part of the Internet, the core of domain name resolution is to convert the domain name entered by the user into the corresponding network address, so as to achieve access to network resources. However, traditional domain name resolution systems mainly rely on centralized domain name servers, such as the domain name system, which have the following key problems:

[0003] 1. Single point failure risk caused by centralization: The traditional domain name resolution system is highly dependent on centralized servers. Any failure or attack on any server may cause a large area of ​​network paralysis. Once the domain name system server encounters a DDoS attack, a large number of domain names may not be resolved, affecting the normal operation of the Internet.

[0004] 2. Insufficient privacy protection of domain name information: In the existing domain name resolution system, domain name registration information and data in the resolution process are vulnerable to third-party snooping and attacks, and users' privacy information cannot be effectively protected. Especially in cross-regional and cross-national domain name resolution, data is vulnerable to interception and tampering during transmission.

[0005] 3. Limitations of resolution efficiency: The traditional domain name system adopts a hierarchical structure, and the resolution process relies on communication between multiple nodes. This structure is prone to response delays and low efficiency when faced with a large number of resolution requests, especially in a large-scale network environment, where performance bottlenecks are more obvious.

[0006] 4. Poor system scalability: The centralized domain name resolution system has limited scalability when facing the growing scale of the Internet. As the number of domain names and resolution requests increase, the system needs to continuously increase server and bandwidth resources, which not only increases operating costs, but also makes it difficult to cope with the sudden increase in resolution needs.

[0007] 5. Insufficient version management and data recovery capabilities: Most existing domain name resolution systems lack an effective version management mechanism, and the update and rollback of domain name information are relatively complicated. Once data is damaged or updated incorrectly, restoring to the previous version requires a lot of time and resources, which increases the difficulty of maintenance.

[0008] 6. Lack of domain name access frequency management: The current system mainly relies on basic traffic restriction measures to deal with malicious attacks and excessive access, and it is difficult to monitor and adjust domain name access frequency in real time. This passive defense method cannot effectively deal with emerging network attack methods and is prone to cause system overload and service interruption.

[0009] Therefore, how to provide a network-wide distributed domain name resolution method based on blockchain technology is an urgent problem that technicians in this field need to solve. Summary of the invention

[0010] One purpose of the present invention is to propose a full-network distributed domain name resolution method based on blockchain technology. The present invention adopts blockchain smart contracts, data transformation privacy protection technology, topological resonance optimization algorithm and multi-version management mechanism, and describes in detail the steps of realizing safe and efficient domain name resolution in a distributed environment. It has the advantages of decentralization, strong security, perfect privacy protection, high resolution efficiency and strong system scalability.

[0011] According to an embodiment of the present invention, a distributed domain name resolution method based on blockchain technology includes the following steps:

[0012] S1. Deploy a domain name registration smart contract developed based on solidity in the blockchain network. Users submit domain name registration requests through smart contracts. After verification, the domain name name, network address and public key information of the domain name owner are recorded in the blockchain ledger, and support porting and deployment on other blockchains.

[0013] S2. After the registration information is entered, the data transformation privacy protection technology is applied to dynamically transform the domain name information recorded in the blockchain account book, so that the data structure presents different forms at different time points to avoid external analysis and data leakage;

[0014] S3. When a user requests domain name resolution, the resolution request is broadcast to all nodes in the blockchain network through the gateway contract and the routing contract. Each node queries the domain name information after data metamorphosis from the blockchain ledger through the smart contract and returns the corresponding network address. The gateway contract associates multiple functional contracts, and the routing contract connects different functional contracts in series to obtain the resolution relationship from the storage contract.

[0015] S4. In the process of parsing request processing, a fixed-period weight rotation mechanism is adopted to dynamically adjust the weight distribution of nodes so that requests are evenly distributed among different nodes, supporting the horizontal expansion and online upgrade of bound content on the same chain;

[0016] S5. Use the topology resonance optimization algorithm to introduce the resonance effect into the topology of the blockchain network, dynamically adjust the key parameters of the domain name resolution algorithm, and enable the system to maintain resolution capabilities under complex network structures;

[0017] S6. Through the multi-version management mechanism, snapshot backups of domain name information are generated regularly, allowing the domain name status to be restored to a specific point in time when a problem occurs. It supports switching between different versions to maintain data consistency and recoverability.

[0018] S7. Set a blockchain-based domain name access rate limit in the smart contract, and set the upper limit of the access frequency of each domain name. Once the set access frequency is exceeded, the system automatically limits access to the domain name;

[0019] S8. Support decentralized domain name auction and leasing system, conduct open bidding auction and short-term leasing of domain names through smart contracts. All transaction processes are transparent and cannot be tampered with. It also supports flexible use and secure management of domain name resources, as well as subsequent cross-chain interoperability.

[0020] Optionally, S2 includes the following steps:

[0021] S21. After the registration information is entered into the blockchain ledger, the data metamorphosis privacy protection technology is applied to perform a preliminary conversion of the domain name information recorded in the blockchain ledger. The conversion is based on a dynamic mapping of the data fields by a random number generator, and a hash value associated with a timestamp is generated by a hash function, so that the data structure at each time point presents a different form;

[0022] S22. In the data transformation process, the original domain name information data is first nonlinearly mapped with a time-related random matrix, and rearranged and combined through a multidimensional transformation matrix. Finally, the data is processed by an adaptive transformation function to generate complex transformation data.

[0023]

[0024] Among them, D orig is the original domain name information data vector, is the time point t of the i-th stage i The associated random matrix, is the multidimensional data transformation matrix, is the adaptive transformation function, G(D orig ) is the nonlinear perturbation function, represents the Hadamard product, ⊙ represents the matrix dot multiplication operation;

[0025] S23. During the multi-stage random noise injection process, the system generates multiple independent random noise matrices at each transformation stage, and generates an optimized noise matrix through weighted integration processing. The optimized noise matrix is ​​combined with the complex transformation data Combined to form noise-enhanced data Then, the data is passed through a global nonlinear adjustment function G nonlinear (·) Perform global optimization and finally generate encrypted data D enc :

[0026]

[0027] in, represents a bitwise XOR operation, used to combine the degenerate data with the noise matrix. is a nonlinear noise adjustment function used to perform complex processing on the noise matrix. nonlinear (·) is a global nonlinear adjustment function, which is used to globally optimize the combined data. E(·) is an encryption function, which is used to encrypt the globally optimized data.

[0028] S24, encrypt the data D enc Re-recording to the blockchain ledger makes the data structure present different forms at different time points.

[0029] Optionally, S4 includes the following steps:

[0030] S41. During the resolution request processing, a fixed-period weight rotation mechanism is adopted to periodically allocate weights to each node in the blockchain network through a preset fixed period, and set the initial weight of each node;

[0031] S42. At the beginning of each fixed period, the quantum walk algorithm is used to simulate the walking path of quantum particles in the blockchain network, calculate the walking probability of each node, and dynamically adjust the weight of each node according to the following formula:

[0032]

[0033] in, is the weight of the i-th node in the t+1th cycle, is the weight of the i-th node in the t-th cycle, P i is the walking probability of node i calculated by the quantum walking algorithm, is the load value of the node with the largest load in the tth period, is the load value of the i-th node in the t-th period, α is the load balancing coefficient, β is the data complexity and noise adjustment coefficient, and n is the total number of nodes in the network;

[0034] S43, through the weight distribution optimized by quantum walk algorithm and data complexity processing, the resolution request is distributed in the network along the optimal path, balancing the load of each node;

[0035] S44. After the weight adjustment in each fixed period, the system horizontally expands the content bound to the same chain, sets expansion parameters, and dynamically updates the node load

[0036]

[0037] in, is the updated load value of node i, is the node weight generated by the quantum walk algorithm in the t+1th period, γ is the expansion parameter used to adjust the load distribution after expansion, R i is the resource matrix of node i, is the load value of the maximum load node in the t+1th cycle;

[0038] S45. After completing load adjustment and horizontal expansion, the system performs online upgrade operations on the nodes, dynamically updates the software version and hardware configuration of the nodes through a preset upgrade strategy, and applies the updated configuration to the processing of the resolution request in real time.

[0039] Optionally, S5 includes the following steps:

[0040] S51, after completing the preliminary processing of the parsing request, enter the node online upgrade phase, first evaluate the current status of each node, including computing power Load conditions and network connection quality Indicators, use the topology resonance optimization algorithm to resonate the network topology and generate adjusted evaluation indicators;

[0041] S52. Calculate the upgrade priority of the node based on the adjusted evaluation index

[0042]

[0043] Among them, α and β are weight factors used to balance the impact of complex transformation data and load conditions. It is the combination of complex transformation data and optimization data for all nodes j. is the load value of the node with the largest load in the tth period, is the load value of the i-th node in the t-th cycle;

[0044] S53. After determining the upgrade priority, the system will upgrade the node according to the priority of each node. Determine the order in which nodes should be upgraded

[0045]

[0046] Among them, γ is the adjustment parameter, Used to standardize the upgrade order of each node;

[0047] S54, according to the calculated upgrade order The system upgrades the software version and hardware configuration of each node in parallel, giving priority to nodes with low load and good network quality.

[0048] S55. During the upgrade process, the system dynamically monitors the upgrade status of each node and adjusts the upgrade strategy according to the actual situation to adapt to changes in network load and fluctuations in node performance.

[0049] Optionally, the S6 comprises the following steps:

[0050] S61. After completing the processing of the parsing request, the system enters the multi-version management mechanism. First, all available versions in the system are evaluated, and the version performance is comprehensively considered. stability User feedback and node load index:

[0051]

[0052] in, is the weight of the i-th node in the t-th cycle, and are the generated complex metamorphosis data and the optimized noise matrix respectively;

[0053] S62. Calculate the comprehensive priority of each version based on the evaluation results

[0054]

[0055] in, is the computing resource of the i-th node in the t-th cycle, is the load value of the i-th node in the t-th cycle, is the network quality of the jth node in the tth period, α, β, and γ are adjustment parameters used to balance the impact of stability, user feedback, and load conditions on priority. and is the load value and maximum load value of the node, and m is the total number of versions;

[0056] S63, based on the calculated comprehensive priority The version with the highest priority is selected as the current best version. The system updates the configuration according to this version and applies the updated content to all nodes in the network.

[0057] S64. During the version switching process, the system uses an adaptive version switching algorithm to dynamically adjust the switching strategy based on real-time load conditions, user feedback, and system stability;

[0058] S65. After the version switch is completed, the system continues to monitor the operating status of the new version, collects relevant operating data, evaluates the switching results based on these data, and triggers the preset rollback mechanism through the multi-version management mechanism when necessary. At the same time, it regularly generates snapshot backups of domain name information, allowing the domain name status to be restored to a specific point in time when a problem occurs, and supports switching between different versions to maintain data consistency and recoverability.

[0059] Optionally, the S7 includes the following steps:

[0060] S71. Deploy parsing nodes in multiple heterogeneous environments, and use the heterogeneous environment meta-reinforcement learning algorithm to perform real-time monitoring and data collection on these nodes to form a multi-environment data set;

[0061] S72. Based on multi-environment datasets, a meta-reinforcement learning model for heterogeneous environments is constructed, and the key features of each environment are extracted through enhanced cross-domain knowledge distillation technology. And adopt an adaptive weight update mechanism to dynamically weight the characteristics of each environment and generate a weight matrix that adapts to the characteristics of multiple environments.

[0062] S73, extract the cross-domain knowledge features Perform quantum feature fusion to generate a unified feature representation with quantum state enhancement

[0063]

[0064] in, is the cross-domain knowledge feature of the i-th environment, is the quantum state characteristic matrix, is a dynamic weight matrix based on time t, which adjusts the contribution of different environmental features. represents the tensor product operation, which is used to simulate the feature fusion of quantum states;

[0065] S74. Based on the generated quantum state enhanced feature representation, a blockchain-based adaptive access rate limiting mechanism is set through a smart contract. This mechanism calculates the real-time access frequency of each domain name. And with the set access frequency cap Flimit For comparison:

[0066]

[0067] in, represents the number of requests for the tth domain name in the ith time period, is a unified characteristic representation of quantum state enhancement, Represents the key features extracted The logarithmic transformation of , λ is the adjustment parameter used to control the sensitivity of load balancing, is the load value of the i-th node, is the maximum load value of all nodes in the current time period, T (t) is the current time period, represents an exponential decay function based on the load value;

[0068] S75. Once the real-time access frequency of a domain name Exceeding the set access frequency limit The system automatically triggers the access restriction mechanism through smart contracts, and combines asynchronous update strategies to optimize the system's access restriction strategy in real time to adapt to dynamically changing access requirements and prevent system overload caused by excessive access or malicious attacks.

[0069] The beneficial effects of the present invention are:

[0070] (1) The present invention significantly improves the security of domain name information and the resolution efficiency of the system by combining blockchain technology, data transformation privacy protection technology and fixed-period weight rotation mechanism. In particular, in terms of data privacy protection and dynamic load balancing of resolution requests, the present invention overcomes the shortcomings of the traditional centralized domain name resolution system and can dynamically transform the data structure at different time points to ensure the security and stability of the resolution process.

[0071] (2) The present invention optimizes the system's parsing capability under complex network topologies and enhances the system's recovery capability by introducing a topology resonance optimization algorithm and a multi-version management mechanism. The method regularly generates snapshot backups of domain name information, supports recovery to a specific time point when a problem occurs, and enables flexible switching between multiple versions, ensuring efficient operation of the system and data consistency.

[0072] (3) The present invention provides an efficient, secure, and scalable distributed domain name resolution solution for the entire network by comprehensively utilizing the access rate limiting mechanism of smart contracts and the decentralized domain name auction and leasing system. The system can monitor the access frequency of domain names in real time, prevent malicious attacks and excessive access, and ensure transparent management and safe use of domain name resources through decentralized transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0074] Figure 1 This is a flow chart of a network-wide distributed domain name resolution method based on blockchain technology proposed by the present invention;

[0075] Figure 2 A schematic diagram of the fixed-period weight rotation mechanism proposed in the present invention for dynamically adjusting node weight distribution;

[0076] Figure 3 A schematic diagram of generating snapshot backup and recovery operations for the multi-version management mechanism proposed in the present invention. DETAILED DESCRIPTION

[0077] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0078] refer to Figure 1-3 , a distributed domain name resolution method for the entire network based on blockchain technology, comprising the following steps:

[0079] S1. Deploy a domain name registration smart contract developed based on solidity in the blockchain network. Users submit domain name registration requests through smart contracts. After verification, the domain name name, network address and public key information of the domain name owner are recorded in the blockchain ledger, and support porting and deployment on other blockchains.

[0080] S2. After the registration information is entered, the data transformation privacy protection technology is applied to dynamically transform the domain name information recorded in the blockchain account book, so that the data structure presents different forms at different time points to avoid external analysis and data leakage;

[0081] S3. When a user requests domain name resolution, the resolution request is broadcast to all nodes in the blockchain network through the gateway contract and the routing contract. Each node queries the domain name information after data metamorphosis from the blockchain ledger through the smart contract and returns the corresponding network address. The gateway contract associates multiple functional contracts, and the routing contract connects different functional contracts in series to obtain the resolution relationship from the storage contract.

[0082] S4. In the process of parsing request processing, a fixed-period weight rotation mechanism is adopted to dynamically adjust the weight distribution of nodes so that requests are evenly distributed among different nodes, supporting the horizontal expansion and online upgrade of bound content on the same chain;

[0083] S5. Use the topology resonance optimization algorithm to introduce the resonance effect into the topology of the blockchain network, dynamically adjust the key parameters of the domain name resolution algorithm, and enable the system to maintain resolution capabilities under complex network structures;

[0084] S6. Through the multi-version management mechanism, snapshot backups of domain name information are generated regularly, allowing the domain name status to be restored to a specific point in time when a problem occurs. It supports switching between different versions to maintain data consistency and recoverability.

[0085] S7. Set a blockchain-based domain name access rate limit in the smart contract, and set the upper limit of the access frequency of each domain name. Once the set access frequency is exceeded, the system automatically limits access to the domain name;

[0086] S8. Support decentralized domain name auction and leasing system, conduct open bidding auction and short-term leasing of domain names through smart contracts. All transaction processes are transparent and cannot be tampered with. It also supports flexible use and secure management of domain name resources, as well as subsequent cross-chain interoperability.

[0087] In this implementation, S2 includes the following steps:

[0088] S21. After the registration information is entered into the blockchain ledger, the data metamorphosis privacy protection technology is applied to perform a preliminary conversion of the domain name information recorded in the blockchain ledger. The conversion is based on a dynamic mapping of the data fields by a random number generator, and a hash value associated with a timestamp is generated by a hash function, so that the data structure at each time point presents a different form;

[0089] S22. In the data transformation process, the original domain name information data is first nonlinearly mapped with a time-related random matrix, and rearranged and combined through a multidimensional transformation matrix. Finally, the data is processed by an adaptive transformation function to generate complex transformation data.

[0090]

[0091] Among them, D orig is the original domain name information data vector, R ti is the time point t of the i-th stage i The associated random matrix, is the multidimensional data transformation matrix, is the adaptive transformation function, G(D orig ) is the nonlinear perturbation function, represents the Hadamard product, ⊙ represents the matrix dot multiplication operation;

[0092] S23. During the multi-stage random noise injection process, the system generates multiple independent random noise matrices at each transformation stage, and generates an optimized noise matrix through weighted integration processing. The optimized noise matrix is ​​combined with the complex transformation data Combined to form noise-enhanced data Then, the data is passed through a global nonlinear adjustment function G nonlinear (·) Perform global optimization and finally generate encrypted data D enc :

[0093]

[0094] in, represents a bitwise XOR operation, used to combine the degenerate data with the noise matrix. is a nonlinear noise adjustment function used to perform complex processing on the noise matrix. nonlinear (·) is a global nonlinear adjustment function, which is used to globally optimize the combined data. E(·) is an encryption function, which is used to encrypt the globally optimized data.

[0095] S24, encrypt the data D enc Re-recording to the blockchain ledger makes the data structure present different forms at different time points.

[0096] In this implementation manner, S4 includes the following steps:

[0097] S41. During the resolution request processing, a fixed-period weight rotation mechanism is adopted to periodically allocate weights to each node in the blockchain network through a preset fixed period, and set the initial weight of each node;

[0098] S42. At the beginning of each fixed period, the quantum walk algorithm is used to simulate the walking path of quantum particles in the blockchain network, calculate the walking probability of each node, and dynamically adjust the weight of each node according to the following formula:

[0099]

[0100] in, is the weight of the i-th node in the t+1th cycle, is the weight of the i-th node in the t-th cycle, P i is the walking probability of node i calculated by the quantum walking algorithm, is the load value of the node with the largest load in the tth period, is the load value of the i-th node in the t-th period, α is the load balancing coefficient, β is the data complexity and noise adjustment coefficient, and n is the total number of nodes in the network;

[0101] S43, through the weight distribution optimized by quantum walk algorithm and data complexity processing, the resolution request is distributed in the network along the optimal path, balancing the load of each node;

[0102] S44. After the weight adjustment in each fixed period, the system horizontally expands the content bound to the same chain, sets expansion parameters, and dynamically updates the node load

[0103]

[0104] in, is the updated load value of node i, is the node weight generated by the quantum walk algorithm in the t+1th period, γ is the expansion parameter used to adjust the load distribution after expansion, R i is the resource matrix of node i, is the load value of the maximum load node in the t+1th cycle;

[0105] S45. After completing load adjustment and horizontal expansion, the system performs online upgrade operations on the nodes, dynamically updates the software version and hardware configuration of the nodes through a preset upgrade strategy, and applies the updated configuration to the processing of the resolution request in real time.

[0106] In this implementation, S5 includes the following steps:

[0107] S51, after completing the preliminary processing of the parsing request, enter the node online upgrade phase, first evaluate the current status of each node, including computing power Load conditions and network connection quality Indicators, use the topology resonance optimization algorithm to resonate the network topology and generate adjusted evaluation indicators;

[0108] S52. Calculate the upgrade priority of the node based on the adjusted evaluation index

[0109]

[0110] Among them, α and β are weight factors used to balance the impact of complex transformation data and load conditions. It is the combination of complex transformation data and optimization data for all nodes j. is the load value of the node with the largest load in the tth period, is the load value of the i-th node in the t-th cycle;

[0111] S53. After determining the upgrade priority, the system will upgrade the node according to the priority of each node. Determine the order in which nodes should be upgraded

[0112]

[0113] Among them, γ is the adjustment parameter, Used to standardize the upgrade order of each node;

[0114] S54, according to the calculated upgrade order The system upgrades the software version and hardware configuration of each node in parallel, giving priority to nodes with low load and good network quality.

[0115] S55. During the upgrade process, the system dynamically monitors the upgrade status of each node and adjusts the upgrade strategy according to the actual situation to adapt to changes in network load and fluctuations in node performance.

[0116] In this implementation manner, S6 includes the following steps:

[0117] S61. After completing the processing of the parsing request, the system enters the multi-version management mechanism. First, all available versions in the system are evaluated, and the version performance is comprehensively considered. stability User feedback and node load index:

[0118]

[0119] in, is the weight of the i-th node in the t-th cycle, and are the generated complex metamorphosis data and the optimized noise matrix respectively;

[0120] S62. Calculate the comprehensive priority of each version based on the evaluation results

[0121]

[0122] in, is the computing resource of the i-th node in the t-th cycle, is the load value of the i-th node in the t-th cycle, is the network quality of the jth node in the tth period, α, β, and γ are adjustment parameters used to balance the impact of stability, user feedback, and load conditions on priority. and is the load value and maximum load value of the node, and m is the total number of versions;

[0123] S63, based on the calculated comprehensive priority The version with the highest priority is selected as the current best version. The system updates the configuration according to this version and applies the updated content to all nodes in the network.

[0124] S64. During the version switching process, the system uses an adaptive version switching algorithm to dynamically adjust the switching strategy based on real-time load conditions, user feedback, and system stability;

[0125] S65. After the version switch is completed, the system continues to monitor the operating status of the new version, collects relevant operating data, evaluates the switching results based on these data, and triggers the preset rollback mechanism through the multi-version management mechanism when necessary. At the same time, it regularly generates snapshot backups of domain name information, allowing the domain name status to be restored to a specific point in time when a problem occurs, and supports switching between different versions to maintain data consistency and recoverability.

[0126] In this implementation manner, the S7 includes the following steps:

[0127] S71. Deploy parsing nodes in multiple heterogeneous environments, and use the heterogeneous environment meta-reinforcement learning algorithm to perform real-time monitoring and data collection on these nodes to form a multi-environment data set;

[0128] S72. Based on multi-environment datasets, a meta-reinforcement learning model for heterogeneous environments is constructed, and the key features of each environment are extracted through enhanced cross-domain knowledge distillation technology. And adopt an adaptive weight update mechanism to dynamically weight the characteristics of each environment and generate a weight matrix that adapts to the characteristics of multiple environments.

[0129] S73, extract the cross-domain knowledge features Perform quantum feature fusion to generate a unified feature representation with quantum state enhancement

[0130]

[0131] in, is the cross-domain knowledge feature of the i-th environment, is the quantum state characteristic matrix, is a dynamic weight matrix based on time t, which adjusts the contribution of different environmental features. represents the tensor product operation, which is used to simulate the feature fusion of quantum states;

[0132] S74. Based on the generated quantum state enhanced feature representation, a blockchain-based adaptive access rate limiting mechanism is set through a smart contract. This mechanism calculates the real-time access frequency of each domain name. And with the set access frequency cap F limit For comparison:

[0133]

[0134] in, represents the number of requests for the tth domain name in the ith time period, is a unified characteristic representation of quantum state enhancement, Represents the key features extracted The logarithmic transformation of , λ is the adjustment parameter used to control the sensitivity of load balancing, is the load value of the i-th node, is the maximum load value of all nodes in the current time period, T (t) is the current time period, represents an exponential decay function based on the load value;

[0135] S75. Once the real-time access frequency of a domain name Exceeding the set access frequency limit The system automatically triggers the access restriction mechanism through smart contracts, and combines asynchronous update strategies to optimize the system's access restriction strategy in real time to adapt to dynamically changing access requirements and prevent system overload caused by excessive access or malicious attacks.

[0136] Embodiment 1:

[0137] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a global online retail platform, which faces extremely high access pressure and network security threats during the annual promotional activities. During the promotional activities, the number of user visits surged, the resolution requests were frequent, and the system had to handle a large number of concurrent requests from different regions around the world. During this period, the network security risks increased significantly. Traditional domain name resolution and protection mechanisms were difficult to cope with such high loads and potential DDoS attacks, and the stable operation of the system faced major challenges. In this embodiment, we selected a full-network distributed domain name resolution method based on blockchain technology of the present invention.

[0138] In the preparation stage of the promotion, a domain name registration smart contract developed based on solidity was first deployed in the blockchain network. Users submit domain name registration requests through this smart contract, and after multiple layers of verification, the domain name, network address and related information are recorded in the blockchain ledger. After that, the data metamorphosis privacy protection technology is applied to dynamically transform the domain name information recorded in the blockchain ledger, so that the data structure presents different forms at different time points, ensuring the security and anti-analysis capabilities of the data.

[0139] When a user initiates a domain name resolution request, the system will broadcast the resolution request to all nodes in the blockchain network through the gateway contract and the routing contract. After receiving the request, each node queries the domain name information after data metamorphosis from the blockchain ledger through the smart contract and returns the corresponding network address. In this process, the system adopts a fixed-period weight rotation mechanism to dynamically adjust the weight distribution of each node, so that the resolution request is evenly distributed among the nodes, avoiding the situation where a single point is overloaded. At the same time, this mechanism supports horizontal expansion and online upgrade of bound content on the same chain, ensuring the continuous and stable operation of the system under high load.

[0140] In response to frequent DDoS attacks during promotional activities, we introduced a topology resonance optimization algorithm. This algorithm analyzes the resonance effect in the topology of the blockchain network and dynamically adjusts the key parameters of the domain name resolution algorithm, enabling the system to maintain efficient resolution capabilities in complex network environments. The algorithm adjusts parameters in real time according to network load conditions, improving the system's defense capabilities against malicious traffic and ensuring the access experience of normal users.

[0141] In addition, the multi-version management mechanism of the present invention is particularly prominent during promotional activities. The system regularly generates snapshot backups of domain name information, and when a system abnormality or failure is detected, it can quickly restore to the state at a specific point in time. This process is automatically executed through smart contracts, greatly reducing the time for manual intervention and ensuring the high availability of the system. Under the multi-version management mechanism, the system can quickly switch between different versions and select the optimal version to run according to actual needs, thereby avoiding the performance degradation of the system due to version problems under high load.

[0142] To further improve the security of the system, we set a blockchain-based domain name access rate limit in the smart contract. This mechanism dynamically calculates the real-time access frequency of each domain name through the smart contract and compares it with the set access frequency limit. Once the access frequency of a domain name exceeds the set limit, the system automatically triggers the access restriction mechanism to avoid system overload caused by malicious access or excessive access. This function significantly reduced the proportion of malicious attack traffic during the promotion and ensured the stable operation of the system.

[0143] Table 1: System performance improvements

[0144] Time period Average parsing time (ms) Parsing success rate Recovery time (seconds) Recovery success rate June 2023 200 93％ 180 85％ July 2023 150 96％ 120 90％ August 2023 80 99.8％ 30 98％

[0145] Table 2: Network attack defense effect

[0146] Time period Malicious attack traffic ratio (%) Normal traffic ratio (%) June 2023 50 50 July 2023 30 70 August 2023 5 95

[0147] During the promotion, we recorded and analyzed the system's operating data. From June to August 2023, the system's average resolution time was shortened from 200 milliseconds in 2022 to 80 milliseconds, and the resolution success rate increased from 93% to 99.8%. During the peak period of malicious attacks, the proportion of attack traffic dropped from 50% to 5%, and the proportion of normal traffic increased to 95%. In addition, in terms of system fault recovery, the recovery time was shortened from 180 seconds in 2022 to 30 seconds, and the recovery success rate increased from 85% to 98%. These data show that the present invention has significant advantages in dealing with high-concurrency requests, network attacks and system failures, and provides a strong technical guarantee for the stable operation of online retail platforms.

[0148] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A distributed domain name resolution method for the entire network based on blockchain technology, characterized in that: The steps include: S1. Deploy a domain name registration smart contract developed based on Solidity in the blockchain network. Users submit domain name registration requests through smart contracts. After verification, the information is recorded in the blockchain ledger and supports porting and deployment on other blockchains. S2. After the registration information is entered, the data transformation privacy protection technology is applied to dynamically transform the domain name information recorded in the blockchain account book; S3. When a user requests domain name resolution, the resolution request is broadcast to all nodes in the blockchain network through the gateway contract and the routing contract. Each node queries the domain name information after data metamorphosis from the blockchain ledger through the smart contract and returns the corresponding network address. The gateway contract associates multiple functional contracts, and the routing contract connects different functional contracts in series to obtain the resolution relationship from the storage contract. S4. During the resolution request processing, a fixed-period weight rotation mechanism is used to dynamically adjust the weight distribution of nodes; S5. Use the topological resonance optimization algorithm to dynamically adjust the key parameters of the domain name resolution algorithm by introducing the resonance effect in the topological structure of the blockchain network; S6. Through the multi-version management mechanism, snapshot backups of domain name information are generated regularly, allowing the domain name status to be restored to a specific point in time when a problem occurs; S7. Set a blockchain-based domain name access rate limit in the smart contract, and set the upper limit of the access frequency of each domain name. Once the set access frequency is exceeded, the system automatically limits access to the domain name; S8. Support decentralized domain name auction and leasing system, conduct open bidding auction and short-term leasing of domain names through smart contracts, and all transaction processes are transparent and cannot be tampered with.

2. According to claim 1, a distributed domain name resolution method based on blockchain technology is characterized in that: The S2 specifically includes: S21. After the registration information is entered into the blockchain ledger, the data metamorphosis privacy protection technology is applied to perform a preliminary conversion of the domain name information recorded in the blockchain ledger. The conversion is based on a dynamic mapping of the data fields by a random number generator, and a hash value associated with a timestamp is generated by a hash function, so that the data structure at each time point presents a different form; S22. In the data transformation process, the original domain name information data is first nonlinearly mapped with a time-related random matrix, and rearranged and combined through a multidimensional transformation matrix. Finally, the data is processed by an adaptive transformation function to generate complex transformation data. Among them, D orig is the original domain name information data vector, is the time point t of the i-th stage i The associated random matrix, is the multidimensional data transformation matrix, is the adaptive transformation function, G(D orig ) is the nonlinear perturbation function, represents the Hadamard product, ⊙ represents the matrix dot multiplication operation; S23. During the multi-stage random noise injection process, the system generates multiple independent random noise matrices at each transformation stage, and generates an optimized noise matrix through weighted integration processing. The optimized noise matrix is ​​combined with the complex transformation data Combined to form noise-enhanced data Then, the data is passed through a global nonlinear adjustment function G nonlinear (·) Perform global optimization and finally generate encrypted data D enc : in, Represents a bitwise exclusive OR operation, is the nonlinear noise adjustment function, G nonlinear (·) is the global nonlinear adjustment function, E(·) is the encryption function; S24, encrypt the data D enc Re-recording to the blockchain ledger makes the data structure present different forms at different time points.

3. According to claim 1, a method for distributed domain name resolution based on blockchain technology is characterized in that: The S4 specifically includes: S41. During the resolution request processing, a fixed-period weight rotation mechanism is adopted to periodically allocate weights to each node in the blockchain network through a preset fixed period, and set the initial weight of each node; S42. At the beginning of each fixed period, the quantum walk algorithm is used to simulate the walking path of quantum particles in the blockchain network, calculate the walking probability of each node, and dynamically adjust the weight of each node according to the following formula: in, is the weight of the i-th node in the t+1th cycle, is the weight of the i-th node in the t-th cycle, P i is the walking probability of node i calculated by the quantum walking algorithm, is the load value of the node with the largest load in the tth period, is the load value of the i-th node in the t-th period, α is the load balancing coefficient, β is the data complexity and noise adjustment coefficient, and n is the total number of nodes in the network; S43, through the weight distribution optimized by quantum walk algorithm and data complexity processing, the resolution request is distributed in the network along the optimal path, balancing the load of each node; S44. After the weight adjustment in each fixed period, the system horizontally expands the content bound to the same chain, sets expansion parameters, and dynamically updates the node load in, is the updated load value of node i, is the node weight generated by the quantum walk algorithm in the t+1th period, γ is the expansion parameter, R i is the resource matrix of node i, is the load value of the maximum load node in the t+1th cycle; S45. After completing load adjustment and horizontal expansion, the system performs online upgrade operations on the nodes, dynamically updates the software version and hardware configuration of the nodes through a preset upgrade strategy, and applies the updated configuration to the processing of the resolution request in real time.

4. According to claim 1, a distributed domain name resolution method based on blockchain technology is characterized in that: The S5 specifically includes: S51, after completing the preliminary processing of the parsing request, enter the node online upgrade phase, first evaluate the current status of each node, including computing power Load conditions and network connection quality Indicators, use the topology resonance optimization algorithm to resonate the network topology and generate adjusted evaluation indicators; S52. Calculate the upgrade priority of the node based on the adjusted evaluation index Among them, α and β are weight factors, It is the combination of complex transformation data and optimization data for all nodes j. is the load value of the node with the largest load in the tth period, is the load value of the i-th node in the t-th cycle; S53. After determining the upgrade priority, the system will upgrade the node according to the priority of each node. Determine the order in which nodes should be upgraded Among them, γ is the adjustment parameter, Used to standardize the upgrade order of each node; S54, according to the calculated upgrade order The system upgrades the software version and hardware configuration of each node in parallel, giving priority to nodes with low load and good network quality. S55. During the upgrade process, the system dynamically monitors the upgrade status of each node and adjusts the upgrade strategy according to the actual situation to adapt to changes in network load and fluctuations in node performance.

5. According to claim 1, a distributed domain name resolution method based on blockchain technology is characterized in that: The S6 specifically includes: S61. After completing the processing of the parsing request, the system enters the multi-version management mechanism. First, all available versions in the system are evaluated, and the version performance is comprehensively considered. stability User feedback and node load index: in, is the weight of the i-th node in the t-th cycle, and are the generated complex metamorphosis data and the optimized noise matrix respectively; S62. Calculate the comprehensive priority of each version based on the evaluation results in, is the computing resource of the i-th node in the t-th cycle, is the load value of the i-th node in the t-th cycle, is the network quality of the jth node in the tth period, α, β and γ are adjustment parameters, and is the load value and maximum load value of the node, and m is the total number of versions; S63, based on the calculated comprehensive priority The version with the highest priority is selected as the current best version. The system updates the configuration according to this version and applies the updated content to all nodes in the network. S64. During the version switching process, the system uses an adaptive version switching algorithm to dynamically adjust the switching strategy based on real-time load conditions, user feedback, and system stability; S65. After the version switch is completed, the system continues to monitor the operating status of the new version, collects relevant operating data, evaluates the switching results based on these data, and triggers the preset rollback mechanism through the multi-version management mechanism when necessary. At the same time, it regularly generates snapshot backups of domain name information, allowing the domain name status to be restored to a specific point in time when a problem occurs, and supports switching between different versions to maintain data consistency and recoverability.

6. According to claim 1, a method for distributed domain name resolution based on blockchain technology is characterized in that: The S7 specifically includes: S71. Deploy parsing nodes in multiple heterogeneous environments, and use the heterogeneous environment meta-reinforcement learning algorithm to perform real-time monitoring and data collection on these nodes to form a multi-environment data set; S72. Based on multi-environment datasets, a meta-reinforcement learning model for heterogeneous environments is constructed, and the key features of each environment are extracted through enhanced cross-domain knowledge distillation technology. And adopt an adaptive weight update mechanism to dynamically weight the characteristics of each environment and generate a weight matrix that adapts to the characteristics of multiple environments. S73, extract the cross-domain knowledge features Perform quantum feature fusion to generate a unified feature representation with quantum state enhancement in, is the cross-domain knowledge feature of the i-th environment, is the quantum state characteristic matrix, is a dynamic weight matrix based on time t, which adjusts the contribution of different environmental features. represents the tensor product operation, which is used to simulate the feature fusion of quantum states; S74. Based on the generated quantum state enhanced feature representation, a blockchain-based adaptive access rate limiting mechanism is set through a smart contract. This mechanism calculates the real-time access frequency of each domain name. And with the set access frequency cap F limit For comparison: in, represents the number of requests for the tth domain name in the ith time period, is a unified characteristic representation of quantum state enhancement, Represents the key features extracted The logarithmic transformation of , λ is the adjustment parameter, is the load value of the i-th node, is the maximum load value of all nodes in the current time period, T (t) is the current time period, represents an exponential decay function based on the load value; S75. Once the real-time access frequency of a domain name Exceeding the set access frequency limit The system automatically triggers the access restriction mechanism through smart contracts, and combines it with asynchronous update strategies to optimize the system's access restriction strategy in real time to adapt to dynamically changing access requirements.

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