A flexible system fault elimination method based on blockchain

Abstract

This invention provides a method for flexible fault elimination in blockchain-based systems, belonging to the field of fault recovery technology. The method includes: acquiring historical fault data of the blockchain-based system; constructing a fault diagnosis model based on the historical fault data; collecting real-time operational data of the blockchain-based system in real time to determine real-time fault data; determining a strategy effect mapping table for each type and level of fault data based on the historical fault data, and determining a fault elimination strategy library; determining a real-time elimination strategy for the real-time fault data based on the fault elimination strategy library; executing the real-time elimination strategy to achieve flexible fault elimination in the blockchain-based system and generating a fault elimination report. This method can improve the accuracy of fault diagnosis and elimination, ensure the transparency, security, and efficiency of the system processing, automate and enhance the flexibility of blockchain system fault handling, improve the self-healing capability and reliability of the blockchain system, and meet the needs of efficient fault management in complex environments.

Description

Technical Field

[0001] This invention relates to the field of fault recovery technology, and in particular to a flexible method for eliminating system faults based on blockchain. Background Technology

[0002] With the widespread application of blockchain technology in finance, logistics, supply chain, and the Internet of Things, its decentralized, data immutable, and highly reliable characteristics have significantly improved the security and transparency of the system. However, the complex distributed architecture of blockchain systems also brings new challenges. For example, node failures, communication delays, and smart contract execution failures can seriously affect the system's operating efficiency and stability.

[0003] Traditional fault handling methods often rely on centralized monitoring systems, using manual or semi-automated methods to analyze historical faults and formulate handling strategies. This approach is not only inefficient and unable to respond promptly to high-frequency faults, but also lacks the ability to accurately handle complex faults.

[0004] Therefore, the present invention provides a flexible method for eliminating system faults based on blockchain. Summary of the Invention

[0005] This invention provides a flexible fault elimination method for blockchain-based systems. It constructs a fault diagnosis model and determines fault elimination strategies based on historical fault data. Real-time fault data is identified based on real-time collected operational data and the fault diagnosis model. Real-time elimination strategies are determined based on the real-time fault data and a fault elimination strategy library. The real-time elimination strategies are executed, and a fault elimination report is generated. This method improves the accuracy of fault diagnosis and elimination, ensures the transparency, security, and efficiency of the system processing, automates and enhances the flexibility of blockchain system fault handling, improves the self-healing capability and reliability of the blockchain system, and meets the needs of efficient fault management in complex environments.

[0006] This invention provides a flexible system fault elimination method based on blockchain, comprising:

[0007] 101: Obtain historical fault data of blockchain-based systems, build fault diagnosis models based on historical fault data, evaluate the diagnostic accuracy of fault diagnosis models, and optimize fault diagnosis models;

[0008] 102: Real-time acquisition of real-time operational data of blockchain-based systems, and identification of real-time fault data based on fault diagnosis models;

[0009] 103: Based on historical fault data, determine the set of elimination strategies and the strategy effect mapping table for each type-level fault data, and determine the fault elimination strategy library;

[0010] 104: Based on the fault elimination strategy library, determine the real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data, and determine the real-time elimination strategy for the real-time fault data;

[0011] 105: Implement real-time elimination strategies to achieve flexible fault elimination in blockchain-based systems and generate fault elimination reports.

[0012] According to the present invention, a flexible system fault elimination method based on blockchain is provided. The historical fault data includes historical sub-fault data of multiple sub-faults. The historical sub-fault data includes the sub-fault occurrence time, sub-fault impact range, sub-fault triggering conditions, sub-fault type, sub-fault level, sub-fault elimination strategy, and sub-fault handling effect.

[0013] According to the present invention, a flexible system fault elimination method based on blockchain technology is provided, which involves acquiring historical fault data of a blockchain-based system, constructing a fault diagnosis model based on the historical fault data, evaluating the diagnostic accuracy of the fault diagnosis model, and optimizing the fault diagnosis model. The method includes:

[0014] Historical fault data is divided into training fault data and test fault data. The training fault data includes historical sub-fault data of multiple sub-faults, and the test fault data includes historical sub-fault data of multiple sub-faults.

[0015] The fault diagnosis model is trained by taking the occurrence time, impact range, and triggering conditions of all sub-faults in the historical sub-fault data of all sub-faults in the training fault data as inputs and taking the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the training fault data as outputs.

[0016] The occurrence time, impact range, and triggering conditions of all sub-faults in the test fault data are input into the fault diagnosis model. Based on the output of the fault diagnosis model, the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test fault data are determined.

[0017] The diagnostic accuracy of the fault diagnosis model is evaluated based on the sub-fault type, sub-fault level, historical diagnosis sub-type, and historical diagnosis sub-level of all sub-faults in the test fault data, and the fault diagnosis model is optimized based on the diagnostic accuracy.

[0018] According to the present invention, a flexible system fault elimination method based on blockchain technology is provided, which involves real-time acquisition of real-time operational data of the blockchain-based system, and identification and determination of real-time fault data based on a fault diagnosis model, including:

[0019] Based on all the detection nodes deployed in the blockchain network, real-time operational data of the blockchain-based system is collected in real time.

[0020] The real-time operating data is preprocessed, and the preprocessed real-time operating data is input into the optimized fault diagnosis model;

[0021] Real-time fault data is determined based on the output of the fault diagnosis model. The real-time fault data includes real-time sub-fault data of multiple real-time sub-faults, and the real-time sub-fault data includes the sub-fault prediction time, real-time diagnosis sub-type, and real-time diagnosis sub-level.

[0022] According to the present invention, a flexible system fault elimination method based on blockchain is provided, which determines a set of elimination strategies and a strategy effect mapping table for each type-level fault data based on historical fault data, and determines a fault elimination strategy library, including:

[0023] Based on the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the historical fault data, the historical fault data is classified to determine multiple type-level fault data, where the type-level fault data includes the historical sub-fault data of multiple sub-faults.

[0024] Extract sub-fault elimination strategies from the historical sub-fault data of all sub-faults in each type-level fault data to determine the elimination strategy set for each type-level fault data, wherein the elimination strategy set includes multiple sub-fault elimination strategies;

[0025] Based on the sub-fault processing effect of each sub-fault corresponding to each sub-fault elimination strategy in the elimination strategy set for each type-level fault data, determine the fitting processing effect of each sub-fault elimination strategy in the elimination strategy set for each type-level fault data.

[0026] Based on all sub-fault elimination strategies in the elimination strategy set for each type-level fault data and the fitting processing effect of each sub-fault elimination strategy, determine the strategy effect mapping table for each type-level fault data.

[0027] The fault elimination strategy library is determined based on the strategy effect mapping table of all types and levels of fault data.

[0028] According to the present invention, a flexible system fault elimination method based on blockchain is provided, which determines a real-time elimination sub-strategy for each real-time sub-fault in real-time fault data based on a fault elimination strategy library, and determines a real-time elimination strategy for real-time fault data, including:

[0029] Extract the real-time diagnostic subtype and real-time diagnostic sublevel from the real-time sub-fault data of each real-time sub-fault in the real-time fault data, and determine the fault type-level of each real-time sub-fault in the real-time fault data;

[0030] Match the fault type-level of each real-time sub-fault in the real-time fault data with all type-level fault data in the fault elimination strategy library to determine the strategy effect mapping table corresponding to each real-time sub-fault in the real-time fault data.

[0031] Based on the real-time sub-fault data of each real-time sub-fault in the real-time fault data and the corresponding strategy effect mapping table, determine the matching value of each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table;

[0032] The sub-fault elimination strategy in the strategy effect mapping table with the highest matching value with the real-time sub-fault is selected as the real-time elimination sub-strategy for the real-time sub-fault.

[0033] Based on the real-time elimination sub-strategies of all real-time sub-faults in the real-time fault data, the real-time elimination strategy for the real-time fault data is determined.

[0034] According to the present invention, a flexible system fault elimination method based on blockchain is provided, which determines the matching value of each real-time sub-fault and the corresponding strategy effect mapping table for each sub-fault elimination strategy, including:

[0035] ;

[0036] ;

[0037] ;

[0038] in, Indicates real-time sub-fault And the corresponding strategy effect mapping table The matching value of the k-th sub-fault elimination strategy. This represents a strategy effect mapping table for type b to level c. Indicates real-time sub-fault The real-time diagnostic sub-level j's rank factor, These represent real-time sub-faults. The type factors of real-time diagnostic subtype i and real-time diagnostic subtype b, These represent the strategy effect mapping tables respectively. Strategy effect mapping table The fitting effect value of the k-th sub-fault elimination strategy. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The stability value of the k-th sub-fault elimination strategy. These represent the strategy effect mapping tables respectively. Strategy Effect Mapping Table The resource consumption value of the k-th sub-fault elimination strategy. Indicates real-time sub-fault Sub-fault prediction time, Indicates the current time. These represent the strategy effect mapping tables respectively. Strategy Effect Mapping Table The estimated processing time of the k-th sub-fault elimination strategy. Represents the strategy effect mapping table The sensitivity factor of the k-th sub-fault elimination strategy is given by Q, where Q represents the diagnostic accuracy of the optimized fault diagnosis model. Represents the strategy effect mapping table The multidimensional performance factor of the k-th sub-fault elimination strategy. Represents the strategy effect mapping table The interaction factor of the k-th sub-fault elimination strategy. Indicates the first indicator function, The number of sub-fault types, where N2 represents the number of sub-fault type levels. This represents a strategy effect mapping table for type b to level c. Represents the strategy effect mapping table Includes strategy effect mapping table The k-th sub-fault elimination strategy in the process, Represents the strategy effect mapping table Excluding strategy effect mapping table The kth sub-fault elimination strategy in the process.

[0039] According to the present invention, a flexible fault elimination method for blockchain-based systems is provided, which executes a real-time elimination strategy to achieve flexible fault elimination of blockchain-based systems and generates a fault elimination report, including:

[0040] The real-time elimination sub-strategy for each real-time sub-fault in the real-time elimination strategy is sent to the execution node of the blockchain-based system. The execution node performs the fault elimination operation based on the real-time elimination sub-strategy and determines the fault elimination result.

[0041] A fault elimination report is generated based on the real-time elimination sub-strategy for all real-time sub-faults and the fault elimination results.

[0042] Compared with the prior art, the beneficial effects of this application are as follows:

[0043] By constructing a fault diagnosis model and determining fault elimination strategies based on historical fault data, identifying real-time fault data based on real-time collected operational data and the fault diagnosis model, determining real-time elimination strategies based on real-time fault data and a fault elimination strategy library, executing real-time elimination strategies, and generating fault elimination reports, the accuracy of fault diagnosis and elimination can be improved. This ensures the transparency, security, and efficiency of the system processing, automates and enhances the flexibility of blockchain system fault handling, improves the self-healing capability and reliability of the blockchain system, and meets the needs of efficient fault management in complex environments. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a method for flexibly eliminating system faults based on blockchain, as provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0047] Example 1:

[0048] This invention provides a method for flexible elimination of system faults based on blockchain, such as... Figure 1 As shown, it includes:

[0049] 101: Obtain historical fault data of blockchain-based systems, build fault diagnosis models based on historical fault data, evaluate the diagnostic accuracy of fault diagnosis models, and optimize fault diagnosis models;

[0050] 102: Real-time acquisition of real-time operational data of blockchain-based systems, and identification of real-time fault data based on fault diagnosis models;

[0051] 103: Based on historical fault data, determine the set of elimination strategies and the strategy effect mapping table for each type-level fault data, and determine the fault elimination strategy library;

[0052] 104: Based on the fault elimination strategy library, determine the real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data, and determine the real-time elimination strategy for the real-time fault data;

[0053] 105: Implement real-time elimination strategies to achieve flexible fault elimination in blockchain-based systems and generate fault elimination reports.

[0054] In this embodiment, historical fault data is extracted from a blockchain-based system, and a fault diagnosis model is built based on this data.

[0055] In this embodiment, the operating data of the blockchain system is collected in real time by sensors or monitoring tools, and this real-time data is input into the fault diagnosis model to identify possible faults.

[0056] In this embodiment, a fault elimination strategy library is established based on historical fault data, which stores all processing solutions and their effect evaluations for different types and levels of faults.

[0057] In this embodiment, real-time fault data is matched with a fault elimination strategy library to select the optimal real-time elimination strategy, ensuring the accuracy and timeliness of fault handling.

[0058] In this embodiment, the generated real-time elimination strategy is sent to the system's execution node to perform specific fault elimination operations.

[0059] The beneficial effects of the above technical solution are as follows: by constructing a fault diagnosis model and determining fault elimination strategies based on historical fault data, identifying real-time fault data based on real-time collected operational data and the fault diagnosis model, determining real-time elimination strategies based on real-time fault data and the fault elimination strategy library, executing real-time elimination strategies and generating fault elimination reports, the accuracy of fault diagnosis and elimination can be improved, the transparency, security and efficiency of the system processing process can be ensured, the automation and flexibility of blockchain system fault handling can be realized, the self-healing ability and reliability of the blockchain system can be improved, and the needs of efficient fault management in complex environments can be met.

[0060] Example 2:

[0061] This invention provides a method for flexible elimination of system faults based on blockchain. The historical fault data includes historical sub-fault data of multiple sub-faults, wherein the historical sub-fault data includes the sub-fault occurrence time, sub-fault impact range, sub-fault triggering conditions, sub-fault type, sub-fault level, sub-fault elimination strategy, and sub-fault handling effect.

[0062] In this embodiment, the historical fault data includes historical data of multiple sub-faults, each sub-fault representing an abnormal situation that occurred in a specific component or module of the system.

[0063] In this embodiment, the sub-fault occurrence time is recorded as the specific moment when the sub-fault occurs.

[0064] In this embodiment, the sub-fault impact range describes the degree of impact of the sub-fault on the system.

[0065] In this embodiment, the sub-fault triggering condition specifies the conditions under which the sub-fault is triggered, helping the subsequent system to eliminate similar faults.

[0066] In this embodiment, classifying fault types into sub-fault types (such as hardware faults, network faults, software faults, etc.) helps in subsequent problem localization.

[0067] In this embodiment, the sub-fault levels are classified according to the severity of the fault to determine its impact on system stability.

[0068] In this embodiment, the sub-fault elimination strategy records the elimination measures (such as restarting nodes, rolling back transactions, etc.) that have been taken for different types and levels of sub-faults.

[0069] In this embodiment, the sub-fault handling effect evaluation assesses whether the elimination strategy adopted is successful, ensuring the quality of problem resolution.

[0070] The beneficial effects of the above technical solution are: determining historical fault data can provide data basis for building fault diagnosis models and improve the accuracy of fault diagnosis.

[0071] Example 3:

[0072] This invention provides a method for flexible fault elimination in blockchain-based systems. The method involves acquiring historical fault data from the blockchain-based system, constructing a fault diagnosis model based on the historical fault data, evaluating the diagnostic accuracy of the fault diagnosis model, and optimizing the fault diagnosis model. The method includes:

[0073] Historical fault data is divided into training fault data and test fault data. The training fault data includes historical sub-fault data of multiple sub-faults, and the test fault data includes historical sub-fault data of multiple sub-faults.

[0074] The fault diagnosis model is trained by taking the occurrence time, impact range, and triggering conditions of all sub-faults in the historical sub-fault data of all sub-faults in the training fault data as inputs and taking the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the training fault data as outputs.

[0075] The occurrence time, impact range, and triggering conditions of all sub-faults in the test fault data are input into the fault diagnosis model. Based on the output of the fault diagnosis model, the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test fault data are determined.

[0076] The diagnostic accuracy of the fault diagnosis model is evaluated based on the sub-fault type, sub-fault level, historical diagnosis sub-type, and historical diagnosis sub-level of all sub-faults in the test fault data, and the fault diagnosis model is optimized based on the diagnostic accuracy.

[0077] In this embodiment, historical fault data is divided into two parts: training fault data for training the fault diagnosis model and test fault data for verifying the model's performance. Each part of the data contains detailed historical information on multiple sub-faults.

[0078] In this embodiment, the occurrence time, impact range, and triggering conditions of sub-faults in the training fault data are used as input features. This information describes the background and triggering pattern of the fault, providing a diagnostic basis for the model. The sub-fault type and sub-fault level in the training data are used as the output targets of the model. The sub-fault type is used to classify fault types, and the level is used to measure the severity of the fault (e.g., low, medium, high level).

[0079] In this embodiment, the model learns the mapping relationship between fault features and fault types and levels by training the input features and output targets.

[0080] In this embodiment, the occurrence time, impact range, and triggering conditions of the sub-faults in the test data are input into the fault diagnosis model. Based on this information, the model predicts the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test data.

[0081] In this embodiment, the actual sub-fault types and sub-fault levels in the test data are compared with the historical diagnostic sub-types and historical diagnostic sub-levels diagnosed by the model to calculate the diagnostic accuracy of the model. By analyzing the differences, the shortcomings of the model (such as the false diagnosis rate or false negative rate) can be found, thereby optimizing the model's parameters or algorithms and improving its performance.

[0082] The beneficial effects of the above technical solution are as follows: acquiring historical fault data of blockchain-based systems, constructing fault diagnosis models based on historical fault data, evaluating the diagnostic accuracy of fault diagnosis models and optimizing fault diagnosis models can build high-precision fault diagnosis models, improve the accuracy and efficiency of fault diagnosis, and reduce the need for manual intervention.

[0083] Example 4:

[0084] This invention provides a method for flexible fault elimination in blockchain-based systems. The method involves real-time collection of operational data from the blockchain-based system, and identification of real-time fault data based on a fault diagnosis model. The method includes:

[0085] Based on all the detection nodes deployed in the blockchain network, real-time operational data of the blockchain-based system is collected in real time.

[0086] The real-time operating data is preprocessed, and the preprocessed real-time operating data is input into the optimized fault diagnosis model;

[0087] Real-time fault data is determined based on the output of the fault diagnosis model. The real-time fault data includes real-time sub-fault data of multiple real-time sub-faults, and the real-time sub-fault data includes the sub-fault prediction time, real-time diagnosis sub-type, and real-time diagnosis sub-level.

[0088] In this embodiment, the detection nodes deployed in the blockchain network are distributed in different locations and are responsible for monitoring the real-time operating status of the system (such as network traffic, node performance, transaction status, etc.). These nodes collect operating data in real time to ensure comprehensive coverage of the entire blockchain system's operation.

[0089] In this embodiment, the collected raw data may contain noise, incomplete or redundant information, so preprocessing is required. Preprocessing includes data cleaning (removing abnormal data or noise), data formatting (unifying the data format), and feature extraction (extracting key data that is meaningful for fault diagnosis) to ensure the quality and effectiveness of the input data of the fault diagnosis model.

[0090] In this embodiment, preprocessed real-time operating data is used as input and fed into a previously optimized fault diagnosis model. The model identifies the types and levels of faults that may occur in the system based on the input data and makes predictions by combining historical learning experience.

[0091] In this embodiment, the model outputs real-time fault information and generates real-time fault data. The real-time fault data contains detailed information on multiple real-time sub-faults: sub-fault prediction time: the time when the system expects the fault to occur; real-time diagnostic sub-type: the type of sub-fault diagnosed by the model; real-time diagnostic sub-level: the severity level of the diagnosed sub-fault, used to distinguish priorities and guide processing strategies.

[0092] The beneficial effects of the above technical solution are: real-time acquisition of real-time operating data of the blockchain-based system, identification and determination of real-time fault data based on the fault diagnosis model, which can realize the automation and real-time prediction of blockchain system faults, and improve the system's fault response speed and fault tolerance.

[0093] Example 5:

[0094] This invention provides a blockchain-based method for flexible system fault elimination. It determines a set of elimination strategies and a strategy effect mapping table for each type and level of fault data based on historical fault data, and establishes a fault elimination strategy library, including:

[0095] Based on the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the historical fault data, the historical fault data is classified to determine multiple type-level fault data, where the type-level fault data includes the historical sub-fault data of multiple sub-faults.

[0096] Extract sub-fault elimination strategies from the historical sub-fault data of all sub-faults in each type-level fault data to determine the elimination strategy set for each type-level fault data, wherein the elimination strategy set includes multiple sub-fault elimination strategies;

[0097] Based on the sub-fault processing effect of each sub-fault corresponding to each sub-fault elimination strategy in the elimination strategy set for each type-level fault data, determine the fitting processing effect of each sub-fault elimination strategy in the elimination strategy set for each type-level fault data.

[0098] Based on all sub-fault elimination strategies in the elimination strategy set for each type-level fault data and the fitting processing effect of each sub-fault elimination strategy, determine the strategy effect mapping table for each type-level fault data.

[0099] The fault elimination strategy library is determined based on the strategy effect mapping table of all types and levels of fault data.

[0100] In this embodiment, each sub-fault in the historical fault data is classified according to its sub-fault type (such as hardware fault, communication fault) and sub-fault level (such as minor, moderate, severe), generating multiple type-level fault data. Each type-level fault data contains historical data of all sub-faults with the same type and level.

[0101] In this embodiment, the elimination strategy for each type-level fault data consists of multiple different sub-fault elimination strategies. These strategies record the solutions or processing procedures for the corresponding sub-faults (such as restarting nodes, reallocating resources, etc.).

[0102] In this embodiment, for each sub-fault elimination strategy, its actual processing effect in historical data is statistically analyzed. The processing effect is evaluated by the data performance after the sub-fault is resolved, such as processing time and fault recurrence rate. By analyzing historical data, the performance of each sub-fault elimination strategy is fitted.

[0103] In this embodiment, a strategy effect mapping table is generated based on the elimination strategy for each type-level fault data and its corresponding fitting processing effect. The mapping table lists the actual processing effect of each strategy based on the sub-fault elimination strategy, which is used to intuitively present the advantages and disadvantages of different strategies.

[0104] In this embodiment, the strategy effect mapping tables of all types and levels of fault data are combined to form a fault elimination strategy library, which contains all elimination strategies and actual processing effects for each type and level of fault data.

[0105] The beneficial effects of the above technical solution are as follows: Based on historical fault data, a set of elimination strategies and a strategy effect mapping table for each type and level of fault data are determined, and a fault elimination strategy library is established. This enables data-driven strategy optimization and reuse, achieves accurate and efficient fault handling, and improves the self-healing capability and operation and maintenance efficiency of the blockchain system.

[0106] Example 6:

[0107] This invention provides a blockchain-based method for flexible system fault elimination. It determines a real-time elimination sub-strategy for each real-time sub-fault in real-time fault data based on a fault elimination strategy library, and determines a real-time elimination strategy for the real-time fault data, including:

[0108] Extract the real-time diagnostic subtype and real-time diagnostic sublevel from the real-time sub-fault data of each real-time sub-fault in the real-time fault data, and determine the fault type-level of each real-time sub-fault in the real-time fault data;

[0109] Match the fault type-level of each real-time sub-fault in the real-time fault data with all type-level fault data in the fault elimination strategy library to determine the strategy effect mapping table corresponding to each real-time sub-fault in the real-time fault data.

[0110] Based on the real-time sub-fault data of each real-time sub-fault in the real-time fault data and the corresponding strategy effect mapping table, determine the matching value of each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table;

[0111] The sub-fault elimination strategy in the strategy effect mapping table with the highest matching value with the real-time sub-fault is selected as the real-time elimination sub-strategy for the real-time sub-fault.

[0112] Based on the real-time elimination sub-strategies of all real-time sub-faults in the real-time fault data, the real-time elimination strategy for the real-time fault data is determined.

[0113] In this embodiment, diagnostic information for each sub-fault is extracted from real-time fault data, including real-time diagnostic sub-types (e.g., network latency, node failure) and real-time diagnostic sub-levels (e.g., minor, moderate, severe). By analyzing this diagnostic information, the specific fault type and level of each sub-fault are determined.

[0114] In this embodiment, the diagnostic sub-types and diagnostic sub-levels of real-time sub-faults are matched with all type-level fault data in the fault elimination strategy library to find the corresponding strategy effect mapping table.

[0115] In this embodiment, the sub-fault elimination strategy with the highest matching value is selected from the strategy effect mapping table as the best real-time elimination sub-strategy for the current real-time sub-fault. The selection of this sub-strategy ensures that the optimal solution is adopted for the corresponding sub-fault.

[0116] In this embodiment, all real-time elimination sub-strategies for real-time sub-faults are integrated to form a comprehensive real-time elimination strategy covering the entire real-time fault data.

[0117] The beneficial effects of the above technical solution are as follows: Based on the fault elimination strategy library, the real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data is determined, and the real-time elimination strategy for the real-time fault data is determined. The optimal elimination strategy for real-time faults can be dynamically generated, realizing the precision, intelligence and automation of real-time fault diagnosis and processing, improving fault processing efficiency, avoiding the delay and uncertainty of manual decision-making, and ensuring the stable operation and efficient recovery capability of the blockchain system.

[0118] Example 7:

[0119] This invention provides a blockchain-based method for flexible system fault elimination, which determines the matching value of each sub-fault elimination strategy in a mapping table between each real-time sub-fault and its corresponding strategy effect, including:

[0120] ;

[0121] ;

[0122] ;

[0123] in, Indicates real-time sub-fault And the corresponding strategy effect mapping table The matching value of the k-th sub-fault elimination strategy. This represents a strategy effect mapping table for type b to level c. Indicates real-time sub-fault The real-time diagnostic sub-level j's rank factor, These represent real-time sub-faults. The type factors of real-time diagnostic subtype i and real-time diagnostic subtype b, These represent the strategy effect mapping tables respectively. Strategy effect mapping table The fitting effect value of the k-th sub-fault elimination strategy. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The stability value of the k-th sub-fault elimination strategy. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The resource consumption value of the k-th sub-fault elimination strategy. Indicates real-time sub-fault Sub-fault prediction time, Indicates the current time. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The estimated processing time of the k-th sub-fault elimination strategy. Represents the strategy effect mapping table The sensitivity factor of the k-th sub-fault elimination strategy is given by Q, where Q represents the diagnostic accuracy of the optimized fault diagnosis model. Represents the strategy effect mapping table The multidimensional performance factor of the k-th sub-fault elimination strategy. Represents the strategy effect mapping table The interaction factor of the k-th sub-fault elimination strategy. Indicates the first indicator function, The number of sub-fault types, where N2 represents the number of sub-fault type levels. This represents a strategy effect mapping table for type b to level c. Represents the strategy effect mapping table Includes strategy effect mapping table The k-th sub-fault elimination strategy in the process, Represents the strategy effect mapping table Excluding strategy effect mapping table The kth sub-fault elimination strategy in the process.

[0124] In this embodiment, Amplifying real-time sub-faults using an exponential function The impact of the real-time diagnostic sub-level j on the level factor shows that the contribution of high-risk faults to the matching value increases exponentially.

[0125] In this embodiment, Using logarithmic functions to analyze real-time sub-faults The type factor of the real-time diagnostic subtype i is smoothed to avoid its linear effect on the matching value.

[0126] In this embodiment, By using the square root of the product, the excessive influence of extreme values ​​of a single variable on the matched value is avoided, thus reflecting the real-time sub-fault. And the corresponding strategy effect mapping table The balance between the historical performance and current fit of the k-th sub-fault elimination strategy.

[0127] In this embodiment, Using a logarithmic function ensures that strategies with high stability receive bonuses while avoiding excessive dominance of stability scores over matching values.

[0128] In this embodiment, The sine function is used to periodically amplify and reduce it, reflecting the complex nonlinear interaction characteristics.

[0129] The beneficial effects of the above technical solution are: determining the matching value of each real-time sub-fault and the corresponding strategy effect mapping table for each sub-fault elimination strategy can provide data basis for determining the real-time elimination sub-strategy for each real-time sub-fault and dynamically generate the optimal elimination strategy for real-time faults.

[0130] Example 8:

[0131] This invention provides a blockchain-based method for flexible fault elimination in systems. It executes a real-time elimination strategy to achieve flexible fault elimination in blockchain-based systems and generates a fault elimination report, including:

[0132] The real-time elimination sub-strategy for each real-time sub-fault in the real-time elimination strategy is sent to the execution node of the blockchain-based system. The execution node performs the fault elimination operation based on the real-time elimination sub-strategy and determines the fault elimination result.

[0133] A fault elimination report is generated based on the real-time elimination sub-strategy for all real-time sub-faults and the fault elimination results.

[0134] In this embodiment, the real-time elimination sub-strategy corresponding to each sub-fault in the real-time fault data is sent to the execution node of the blockchain-based system. The execution node is a specific functional module in the distributed system, which is responsible for receiving, parsing and executing the corresponding fault handling instructions.

[0135] In this embodiment, each execution node initiates corresponding fault handling operations based on the received real-time elimination sub-policy, such as restarting the node, switching to backup hardware, and reallocating system resources.

[0136] In this embodiment, since the execution node is based on blockchain technology, each operation can be recorded on the chain, ensuring the transparency, traceability and immutability of the operation.

[0137] In this embodiment, after the execution node completes the fault elimination operation, it monitors the status of the sub-fault and generates processing results, such as whether the fault is completely eliminated, the remaining scope of influence, or the possibility of fault recurrence.

[0138] In this embodiment, the real-time elimination sub-strategies and corresponding elimination results of all real-time sub-faults are summarized to generate a comprehensive elimination report. The report includes the handling plan for each sub-fault, the actual execution process, the elimination results, and the evaluation of the strategy's handling effect.

[0139] The beneficial effects of the above technical solution are: enabling flexible fault elimination in blockchain-based systems and generating fault elimination reports, which can improve the transparency, accuracy and efficiency of the fault handling process, and enhance the intelligence and self-healing capabilities of the blockchain system.

[0140] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for flexible elimination of system faults based on blockchain, characterized in that, include: 101: Obtain historical fault data of blockchain-based systems, build fault diagnosis models based on historical fault data, evaluate the diagnostic accuracy of fault diagnosis models and optimize fault diagnosis models. Historical fault data includes historical sub-fault data of multiple sub-faults, and historical sub-fault data includes sub-fault elimination strategies for sub-faults. 102: Real-time collection of real-time operation data of blockchain-based systems, identification and determination of real-time fault data based on fault diagnosis models, wherein real-time fault data includes real-time sub-fault data of multiple real-time sub-faults, and real-time sub-fault data includes sub-fault prediction time, real-time diagnosis sub-type and real-time diagnosis sub-level; 103: Based on historical fault data, determine the set of elimination strategies and the strategy effect mapping table for each type-level fault data, and determine the fault elimination strategy library. Specifically, based on all sub-fault elimination strategies in the elimination strategy set for each type-level fault data and the fitting processing effect of each sub-fault elimination strategy, determine the strategy effect mapping table for each type-level fault data. 104: Extract the real-time diagnostic sub-type and real-time diagnostic sub-level from the real-time sub-fault data of each real-time sub-fault in the real-time fault data to determine the fault type-level of each real-time sub-fault in the real-time fault data; match the fault type-level of each real-time sub-fault in the real-time fault data with all type-level fault data in the fault elimination strategy library to determine the strategy effect mapping table corresponding to each real-time sub-fault in the real-time fault data; based on the real-time sub-fault data of each real-time sub-fault in the real-time fault data and the corresponding strategy effect mapping table, determine the matching value between each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table, including: ; ； ； in, Indicates real-time sub-fault And the corresponding strategy effect mapping table The matching value of the k-th sub-fault elimination strategy. This represents a strategy effect mapping table for type b to level c. Indicates real-time sub-fault The real-time diagnostic sub-level j's rank factor, These represent real-time sub-faults. The type factors of real-time diagnostic subtype i and real-time diagnostic subtype b, These represent the strategy effect mapping tables respectively. Strategy effect mapping table The fitting effect value of the k-th sub-fault elimination strategy. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The stability value of the k-th sub-fault elimination strategy. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The resource consumption value of the k-th sub-fault elimination strategy. Indicates real-time sub-fault Sub-fault prediction time, Indicates the current time. These represent the strategy effect mapping tables respectively. Strategy effect mapping table The estimated processing time of the k-th sub-fault elimination strategy. Represents the strategy effect mapping table The sensitivity factor of the k-th sub-fault elimination strategy is given by Q, where Q represents the diagnostic accuracy of the optimized fault diagnosis model. Represents the strategy effect mapping table The multidimensional performance factor of the k-th sub-fault elimination strategy. Represents the strategy effect mapping table The interaction factor of the k-th sub-fault elimination strategy. Indicates the first indicator function, N1 represents the number of sub-fault types, and N2 represents the number of levels of the sub-fault types. Represents the strategy effect mapping table Includes strategy effect mapping table The k-th sub-fault elimination strategy in the process, Represents the strategy effect mapping table Excluding strategy effect mapping table The k-th sub-fault elimination strategy in; The sub-fault elimination strategy with the highest matching value with the real-time sub-fault in the strategy effect mapping table is selected as the real-time elimination sub-strategy for the real-time sub-fault; based on the real-time elimination sub-strategies of all real-time sub-faults in the real-time fault data, the real-time elimination strategy for the real-time fault data is determined. 105: Implement real-time elimination strategies to achieve flexible fault elimination in blockchain-based systems and generate fault elimination reports.

2. The method for flexible elimination of system faults based on blockchain according to claim 1, characterized in that, Historical fault data includes historical sub-fault data for multiple sub-faults. The historical sub-fault data includes the sub-fault occurrence time, sub-fault impact range, sub-fault triggering conditions, sub-fault type, sub-fault level, sub-fault elimination strategy, and sub-fault handling effect.

3. The method for flexible elimination of system faults based on blockchain according to claim 2, characterized in that, Acquire historical fault data from blockchain-based systems, construct fault diagnosis models based on this data, evaluate the diagnostic accuracy of the models, and optimize them, including: Historical fault data is divided into training fault data and test fault data. The training fault data includes historical sub-fault data of multiple sub-faults, and the test fault data includes historical sub-fault data of multiple sub-faults. The fault diagnosis model is trained by taking the occurrence time, impact range, and triggering conditions of all sub-faults in the historical sub-fault data of all sub-faults in the training fault data as inputs and taking the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the training fault data as outputs. The occurrence time, impact range, and triggering conditions of all sub-faults in the test fault data are input into the fault diagnosis model. Based on the output of the fault diagnosis model, the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test fault data are determined. The diagnostic accuracy of the fault diagnosis model is evaluated based on the sub-fault type, sub-fault level, historical diagnosis sub-type, and historical diagnosis sub-level of all sub-faults in the test fault data, and the fault diagnosis model is optimized based on the diagnostic accuracy.

4. The method for flexible elimination of system faults based on blockchain according to claim 3, characterized in that, Real-time acquisition of operational data from the blockchain-based system; identification and determination of real-time fault data based on a fault diagnosis model, including: Based on all the detection nodes deployed in the blockchain network, real-time operational data of the blockchain-based system is collected in real time. The real-time operating data is preprocessed, and the preprocessed real-time operating data is input into the optimized fault diagnosis model; Real-time fault data is determined based on the output of the fault diagnosis model. The real-time fault data includes real-time sub-fault data of multiple real-time sub-faults, and the real-time sub-fault data includes the sub-fault prediction time, real-time diagnosis sub-type, and real-time diagnosis sub-level.

5. A method for flexible elimination of system faults based on blockchain according to claim 4, characterized in that, Based on historical fault data, a set of elimination strategies and a strategy effect mapping table are determined for each type and level of fault data, and a fault elimination strategy library is established, including: Based on the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the historical fault data, the historical fault data is classified to determine multiple type-level fault data, where the type-level fault data includes the historical sub-fault data of multiple sub-faults. Extract sub-fault elimination strategies from the historical sub-fault data of all sub-faults in each type-level fault data to determine the elimination strategy set for each type-level fault data, wherein the elimination strategy set includes multiple sub-fault elimination strategies; Based on the sub-fault processing effect of each sub-fault corresponding to each sub-fault elimination strategy in the elimination strategy set for each type-level fault data, determine the fitting processing effect of each sub-fault elimination strategy in the elimination strategy set for each type-level fault data. Based on all sub-fault elimination strategies in the elimination strategy set for each type-level fault data and the fitting processing effect of each sub-fault elimination strategy, determine the strategy effect mapping table for each type-level fault data. The fault elimination strategy library is determined based on the strategy effect mapping table of all types and levels of fault data.

6. The method for flexible elimination of system faults based on blockchain according to claim 1, characterized in that, The system effectively implements real-time fault elimination strategies, enabling flexible fault elimination in blockchain-based systems and generating fault elimination reports, including: The real-time elimination sub-strategy for each real-time sub-fault in the real-time elimination strategy is sent to the execution node of the blockchain-based system. The execution node performs the fault elimination operation based on the real-time elimination sub-strategy and determines the fault elimination result. A fault elimination report is generated based on the real-time elimination sub-strategy for all real-time sub-faults and the fault elimination results.

Images