Flexible system fault elimination method based on block chain

Abstract

The invention provides a system fault flexible elimination method based on a block chain, and belongs to the technical field of fault recovery, and the method comprises the steps: obtaining historical fault data of a system based on the block chain, and constructing a fault diagnosis model based on the historical fault data; collecting real-time operation data of the system based on the block chain in real time, and determining real-time fault data; determining a strategy effect mapping table of each type-level fault data based on the historical fault data, determining a fault elimination strategy library, and determining a real-time elimination strategy of the real-time fault data based on the fault elimination strategy library; and executing a real-time elimination strategy, realizing flexible elimination of faults of the system based on the block chain, and generating a fault elimination report. According to the invention, the method can improve the accuracy of fault diagnosis and elimination, guarantees the transparency, safety and high efficiency of a system processing process, achieves the automation and flexibility of fault processing of the block chain system, improves the self-healing capability and reliability of the block chain system, and meets the requirements of high-efficiency fault management in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of fault recovery technology, and in particular to a method for flexibly eliminating system faults based on blockchain. Background Art

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

[0003] Traditional fault handling methods rely on centralized monitoring systems, manual or semi-automatic means to analyze historical faults and formulate handling strategies. This approach is not only inefficient and difficult to respond to high-frequency faults in a timely manner, but also lacks the ability to accurately handle complex faults.

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

[0005] The present invention provides a method for flexibly eliminating system faults based on blockchain, which constructs a fault diagnosis model and determines a fault elimination strategy through historical fault data, identifies real-time fault data based on real-time operation data collected in real time and the fault diagnosis model, determines a real-time elimination strategy based on the real-time fault data and a fault elimination strategy library, executes the real-time elimination strategy and generates a fault elimination report, which can improve the accuracy of fault diagnosis and elimination, ensure the transparency, security and efficiency of the system processing process, realize the automation and flexibility of blockchain system fault processing, improve the self-healing ability and reliability of the blockchain system, and meet the needs of efficient fault management in complex environments.

[0006] The present invention provides a method for flexibly eliminating system failures based on blockchain, comprising: 101: Obtain historical fault data of the blockchain-based system, build a fault diagnosis model based on the historical fault data, evaluate the diagnostic accuracy of the fault diagnosis model and optimize the fault diagnosis model; 102: Real-time collection of real-time operation data of the blockchain-based system, and identification of the real-time operation data based on the fault diagnosis model to determine the real-time fault data; 103: Determine a set of elimination strategies for each type-level fault data and a strategy effect mapping table based on historical fault data, and determine a fault elimination strategy library; 104: Determine a real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data based on the fault elimination strategy library, and determine a real-time elimination strategy for the real-time fault data; 105: Execute real-time elimination strategies to achieve flexible fault elimination of blockchain-based systems and generate fault elimination reports.

[0007] According to a blockchain-based flexible system fault elimination method provided by the present invention, 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 scope, sub-fault triggering condition, sub-fault type, sub-fault level, sub-fault elimination strategy and sub-fault processing effect of the sub-fault.

[0008] According to a blockchain-based system fault flexible elimination method provided by the present invention, historical fault data of the blockchain-based system is obtained, a fault diagnosis model is constructed based on the historical fault data, the diagnostic accuracy of the fault diagnosis model is evaluated and the fault diagnosis model is optimized, including: Dividing the historical fault data into training fault data and testing fault data, wherein the training fault data includes historical sub-fault data of multiple sub-faults, and the testing fault data includes historical sub-fault data of multiple sub-faults; The sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition in the historical sub-fault data of all sub-faults in the training fault data are used as the input of the fault diagnosis model, and the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the training fault data are used as the output of the fault diagnosis model to train the fault diagnosis model; Input the sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition in the historical sub-fault data of all sub-faults in the test fault data into the fault diagnosis model, and determine the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test fault data based on the output result of the fault diagnosis model; The diagnostic accuracy of the fault diagnosis model is evaluated based on the sub-fault types, sub-fault levels, historical diagnosis sub-types, and historical diagnosis sub-levels of all sub-faults in the test fault data, and the fault diagnosis model is optimized based on the diagnostic accuracy.

[0009] According to a blockchain-based system fault flexible elimination method provided by the present invention, real-time operation data of a blockchain-based system is collected in real time, and real-time operation data is identified and determined based on a fault diagnosis model to determine real-time fault data, including: Based on all detection nodes deployed in the blockchain network, real-time operation data of the blockchain-based system is collected in real time; Preprocessing the real-time operation data, and inputting the preprocessed real-time operation data into the optimized fault diagnosis model; Real-time fault data is determined based on the output result of the fault diagnosis model, wherein 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 sub-fault prediction time, real-time diagnosis sub-type and real-time diagnosis sub-level.

[0010] According to a blockchain-based system fault flexible elimination method provided by the present invention, a set of elimination strategies for each type-level fault data and a strategy effect mapping table are determined based on historical fault data, and a fault elimination strategy library is determined, including: Classifying the historical fault data based on the sub-fault types and sub-fault levels in the historical sub-fault data of all sub-faults in the historical fault data, and determining a plurality of type-level fault data, wherein the type-level fault data includes the historical sub-fault data of a plurality of sub-faults; Extracting sub-fault elimination strategies from historical sub-fault data of all sub-faults in each type-level fault data, and determining an elimination strategy set for each type-level fault data, wherein the elimination strategy set includes multiple sub-fault elimination strategies; Determine the fitting processing effect of each sub-fault elimination strategy in the elimination strategy set for each type-level fault data based on the sub-fault processing effects in the sub-fault data of all sub-faults corresponding to each sub-fault elimination strategy in the elimination strategy set for each type-level fault data; Determine a strategy effect mapping table 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; A fault elimination strategy library is determined based on a strategy effect mapping table of all type-level fault data.

[0011] According to a blockchain-based system fault flexible elimination method provided by the present invention, a real-time elimination sub-strategy for each real-time sub-fault in real-time fault data is determined based on a fault elimination strategy library, and a real-time elimination strategy for real-time fault data is determined, including: Extracting the real-time diagnosis subtype and the real-time diagnosis sublevel in the real-time sub-fault data of each real-time sub-fault in the real-time fault data, and determining 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 a 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, determining a matching value between each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table; Select the sub-fault elimination strategy in the strategy effect mapping table with the highest matching value with the real-time sub-fault 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, a real-time elimination strategy for the real-time fault data is determined.

[0012] According to a blockchain-based system fault flexible elimination method provided by the present invention, determining each real-time sub-fault and the matching value of each sub-fault elimination strategy in the corresponding strategy effect mapping table includes: ; ; ; in, Indicates real-time sub-fault And the corresponding strategy effect mapping table The matching value of the kth sub-fault elimination strategy in, The strategy effect mapping table representing type b-level c, Indicates real-time sub-fault The level factor of the real-time diagnosis sublevel j, Represents real-time sub-faults The type factors of real-time diagnosis subtype i and real-time diagnosis subtype b, Respectively represent the strategy effect mapping table , Strategy effect mapping table The fitting treatment effect value of the kth sub-fault elimination strategy in, Respectively represent the strategy effect mapping table , Strategy effect mapping table The stability value of the k-th sub-fault elimination strategy in, Respectively represent the strategy effect mapping table , Strategy effect mapping table The resource consumption value of the kth sub-fault elimination strategy in, Indicates real-time sub-fault The sub-fault prediction time, Indicates the current time. Respectively represent the strategy effect mapping table , Strategy effect mapping table The estimated processing time length of the k-th sub-fault elimination strategy in, Indicates the policy effect mapping table The sensitivity factor of the kth sub-fault elimination strategy in , Q represents the diagnostic accuracy of the optimized fault diagnosis model, Indicates the policy effect mapping table The multidimensional performance factor of the k-th sub-fault elimination strategy in, Indicates the policy effect mapping table The interaction factor of the k-th sub-fault elimination strategy in, represents the first indicator function, The number of sub-fault types, N2 represents the number of levels of sub-fault types, The strategy effect mapping table representing type b-level c, Indicates the policy effect mapping table Includes strategy effect mapping table The k-th sub-fault elimination strategy in Indicates the policy effect mapping table Does not include policy effect mapping table The kth sub-fault elimination strategy in .

[0013] According to a method for flexible elimination of system faults based on blockchain provided by the present invention, a real-time elimination strategy is executed to realize flexible elimination of system faults based on blockchain, and a fault elimination report is generated, including: Sending a real-time elimination sub-strategy for each real-time sub-fault in the real-time elimination strategy to an execution node of the blockchain-based system, the execution node performing a fault elimination operation based on the real-time elimination sub-strategy and determining a fault elimination result; A fault elimination report is generated based on the real-time elimination sub-strategies of all real-time sub-faults and the fault elimination results.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By building a fault diagnosis model and determining the fault elimination strategy through historical fault data, identifying real-time fault data based on the real-time operation data collected in real time and the fault diagnosis model, determining the real-time elimination strategy based on the real-time fault data and the fault elimination strategy library, executing the real-time elimination strategy and generating a fault elimination report, 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 processing can be achieved, the self-healing ability and reliability of the blockchain system can be improved, and the efficient fault management needs in complex environments can be met. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flowchart of a method for flexibly eliminating system failures based on blockchain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a method for flexibly eliminating system failures based on blockchain, such as Figure 1 As shown, including: 101: Obtain historical fault data of the blockchain-based system, build a fault diagnosis model based on the historical fault data, evaluate the diagnostic accuracy of the fault diagnosis model and optimize the fault diagnosis model; 102: Real-time collection of real-time operation data of the blockchain-based system, and identification of the real-time operation data based on the fault diagnosis model to determine the real-time fault data; 103: Determine a set of elimination strategies for each type-level fault data and a strategy effect mapping table based on historical fault data, and determine a fault elimination strategy library; 104: Determine a real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data based on the fault elimination strategy library, and determine a real-time elimination strategy for the real-time fault data; 105: Execute real-time elimination strategies to achieve flexible fault elimination of blockchain-based systems and generate fault elimination reports.

[0019] In this embodiment, historical fault data is extracted from a blockchain-based system, and a fault diagnosis model is constructed based on this data.

[0020] In this embodiment, the operation data of the blockchain system is collected in real time through sensors or monitoring tools, and these real-time data are input into the fault diagnosis model to identify possible faults.

[0021] In this embodiment, a fault elimination strategy library is established based on historical fault data, in which all processing solutions for faults of different types and levels and their effect evaluations are stored.

[0022] In this embodiment, real-time fault data is matched with a fault elimination strategy library to select the optimal real-time elimination strategy to ensure the accuracy and timeliness of fault handling.

[0023] In this embodiment, the generated real-time elimination strategy is sent to the execution node of the system to perform specific fault elimination operations.

[0024] The beneficial effects of the above technical solution are as follows: building a fault diagnosis model and determining a fault elimination strategy through historical fault data, identifying real-time fault data based on real-time operation data and the fault diagnosis model collected in real time, determining a real-time elimination strategy based on real-time fault data and a fault elimination strategy library, executing the real-time elimination strategy and generating a fault elimination report, which can improve the accuracy of fault diagnosis and elimination, ensure the transparency, security and efficiency of the system processing process, realize the automation and flexibility of blockchain system fault processing, improve the self-healing ability and reliability of the blockchain system, and meet the needs of efficient fault management in complex environments.

[0025] Embodiment 2: An embodiment of the present invention provides a flexible method for eliminating system faults based on blockchain, wherein 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 scope, sub-fault triggering condition, sub-fault type, sub-fault level, sub-fault elimination strategy and sub-fault processing effect of the sub-fault.

[0026] In this embodiment, the historical fault data includes historical data of multiple sub-faults, and each sub-fault represents an abnormal situation occurring in a specific component or module in the system.

[0027] In this embodiment, the sub-fault occurrence time records the specific time when the sub-fault occurs.

[0028] In this embodiment, the sub-fault impact range describes the impact of the sub-fault on the system.

[0029] In this embodiment, the sub-fault triggering condition indicates the conditions under which the sub-fault is triggered, so as to help the subsequent system eliminate similar faults.

[0030] In this embodiment, the sub-fault type classifies the fault type (such as hardware fault, network fault, software fault, etc.), which is helpful for subsequent problem location.

[0031] In this embodiment, the sub-fault levels are graded according to the severity of the fault to determine its impact on system stability.

[0032] In this embodiment, the sub-fault elimination strategy records the elimination measures that have been taken (such as restarting nodes, rolling back transactions, etc.) for sub-faults of different types and levels.

[0033] In this embodiment, the sub-fault processing effect evaluates whether the elimination strategy adopted is successful, thereby ensuring the quality of problem solving.

[0034] The beneficial effects of the above technical solution are as follows: determining historical fault data can provide a data basis for building a fault diagnosis model, thereby improving the accuracy of fault diagnosis.

[0035] Embodiment 3: The embodiment of the present invention provides a method for flexibly eliminating system faults based on blockchain, obtaining historical fault data of a system based on blockchain, building 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, including: Dividing the historical fault data into training fault data and testing fault data, wherein the training fault data includes historical sub-fault data of multiple sub-faults, and the testing fault data includes historical sub-fault data of multiple sub-faults; The sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition in the historical sub-fault data of all sub-faults in the training fault data are used as the input of the fault diagnosis model, and the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the training fault data are used as the output of the fault diagnosis model to train the fault diagnosis model; Input the sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition in the historical sub-fault data of all sub-faults in the test fault data into the fault diagnosis model, and determine the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test fault data based on the output result of the fault diagnosis model; The diagnostic accuracy of the fault diagnosis model is evaluated based on the sub-fault types, sub-fault levels, historical diagnosis sub-types, and historical diagnosis sub-levels of all sub-faults in the test fault data, and the fault diagnosis model is optimized based on the diagnostic accuracy.

[0036] In this embodiment, the historical fault data is divided into two parts: the training fault data is used to train the fault diagnosis model, and the test fault data is used to verify the performance of the model. Each part of the data contains detailed historical information of multiple sub-faults.

[0037] In this embodiment, the sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition of the sub-fault in the training fault data are used as input features. This information can describe the background and triggering mode of the fault and provide a diagnosis basis for the model; the sub-fault type and sub-fault level of the sub-fault in the training data are used as the output target of the model. The sub-fault type is used to classify the fault type, and the level is used to measure the severity of the fault (such as low, medium, and high levels).

[0038] In this embodiment, through data training of input features and output targets, the model learns the mapping relationship between fault features and fault types and levels.

[0039] In this embodiment, the sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition of the sub-fault in the test data are input into the fault diagnosis model, and the model predicts the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test data based on this information.

[0040] In this embodiment, the actual sub-fault types and sub-fault levels in the test data are compared with the historical diagnosis sub-types and historical diagnosis sub-levels diagnosed by the model, and the diagnostic accuracy of the model is calculated. By analyzing the differences, the deficiencies of the model (such as misdiagnosis rate or missed diagnosis rate) can be discovered, thereby optimizing the parameters or algorithms of the model and improving its performance.

[0041] The beneficial effects of the above technical solution are: obtaining historical fault data of the blockchain-based system, building 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, so as to build a high-precision fault diagnosis model, improve the accuracy and efficiency of fault diagnosis, and reduce the need for manual intervention.

[0042] Embodiment 4: The embodiment of the present invention provides a method for flexibly eliminating system faults based on blockchain, which collects real-time operation data of a system based on blockchain in real time, identifies the real-time operation data based on a fault diagnosis model and determines the real-time fault data, including: Based on all detection nodes deployed in the blockchain network, real-time operation data of the blockchain-based system is collected in real time; Preprocessing the real-time operation data, and inputting the preprocessed real-time operation data into the optimized fault diagnosis model; Real-time fault data is determined based on the output result of the fault diagnosis model, wherein 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 sub-fault prediction time, real-time diagnosis sub-type and real-time diagnosis sub-level.

[0043] 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 operation of the entire blockchain system.

[0044] In this embodiment, the collected raw data may contain noise, incomplete or redundant information, and therefore needs to be preprocessed. The preprocessing includes data cleaning (removing abnormal data or noise), data formatting (unifying 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.

[0045] In this embodiment, the preprocessed real-time operation data is used as input and sent to a previously optimized fault diagnosis model. The model identifies the possible fault types and levels in the system based on the input data and makes predictions based on historical learning experience.

[0046] In this embodiment, the model outputs real-time fault information and generates real-time fault data, which includes detailed information of multiple real-time sub-faults: sub-fault prediction time: the time when the system expects the fault to occur; real-time diagnosis sub-type: the sub-fault type diagnosed by the model; real-time diagnosis sub-level: the severity level of the diagnosed sub-fault, which is used to distinguish priorities and guide processing strategies.

[0047] The beneficial effects of the above technical solution are: real-time collection of real-time operation data of the blockchain-based system, identification of real-time operation data based on the fault diagnosis model to determine real-time fault data, which can realize the automation and real-time prediction of blockchain system faults and improve the system's fault response speed and fault tolerance.

[0048] Embodiment 5: The embodiment of the present invention provides a method for flexibly eliminating system faults based on blockchain, which determines an elimination strategy set 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: Classifying the historical fault data based on the sub-fault types and sub-fault levels in the historical sub-fault data of all sub-faults in the historical fault data, and determining a plurality of type-level fault data, wherein the type-level fault data includes the historical sub-fault data of a plurality of sub-faults; Extracting sub-fault elimination strategies from historical sub-fault data of all sub-faults in each type-level fault data, and determining an elimination strategy set for each type-level fault data, wherein the elimination strategy set includes multiple sub-fault elimination strategies; Determine the fitting processing effect of each sub-fault elimination strategy in the elimination strategy set for each type-level fault data based on the sub-fault processing effects in the sub-fault data of all sub-faults corresponding to each sub-fault elimination strategy in the elimination strategy set for each type-level fault data; Determine a strategy effect mapping table 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; A fault elimination strategy library is determined based on a strategy effect mapping table of all type-level fault data.

[0049] In this embodiment, each sub-fault in the historical fault data is classified according to its sub-fault type (such as hardware failure, communication failure) 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.

[0050] In this embodiment, the elimination strategy for each type-level fault data is composed of a plurality of different sub-fault elimination strategies, which record the solutions or processing procedures (such as restarting nodes, reallocating resources, etc.) for the corresponding sub-faults.

[0051] In this embodiment, for each sub-fault elimination strategy, its actual processing effect in historical data is counted, and the processing effect is evaluated by the data performance after the sub-fault is resolved, such as processing time, fault recurrence rate, etc. By analyzing the historical data, the effect performance of each sub-fault elimination strategy is fitted.

[0052] In this embodiment, a strategy effect mapping table is generated according to the elimination strategy for each type-level fault data and its corresponding fitting processing effect. The mapping table is based on the sub-fault elimination strategy and lists the actual processing effect of each strategy, which is used to intuitively present the advantages and disadvantages of different strategies.

[0053] In this embodiment, the strategy effect mapping tables of all types-level fault data are integrated to form a fault elimination strategy library, which contains all elimination strategies and actual processing effects for each type-level fault data.

[0054] The beneficial effects of the above technical solution are as follows: based on historical fault data, a set of elimination strategies for each type-level of fault data and a strategy effect mapping table are determined, and a fault elimination strategy library is determined, which can realize data-driven strategy optimization and reuse, achieve accurate and efficient fault handling, and improve the self-healing ability and operation and maintenance efficiency of the blockchain system.

[0055] Embodiment 6: The embodiment of the present invention provides a method for flexibly eliminating system faults based on blockchain, 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 the real-time fault data, including: Extracting the real-time diagnosis subtype and the real-time diagnosis sublevel in the real-time sub-fault data of each real-time sub-fault in the real-time fault data, and determining 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 a 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, determining a matching value between each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table; Select the sub-fault elimination strategy in the strategy effect mapping table with the highest matching value with the real-time sub-fault 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, a real-time elimination strategy for the real-time fault data is determined.

[0056] In this embodiment, diagnostic information of each sub-fault is extracted from the real-time fault data, including real-time diagnostic sub-types (such as network delays, node downtime) and real-time diagnostic sub-levels (such as minor, moderate, and severe). By analyzing these diagnostic information, the specific fault type and level of each sub-fault are determined.

[0057] In this embodiment, the diagnosis subtype and diagnosis sublevel of the real-time sub-fault are matched with all type-level fault data in the fault elimination strategy library to find the corresponding strategy effect mapping table.

[0058] 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 best solution is adopted for the corresponding sub-fault.

[0059] In this embodiment, the real-time elimination sub-strategies of all real-time sub-faults are integrated to form a comprehensive real-time elimination strategy covering the entire real-time fault data.

[0060] 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, which can dynamically generate the optimal elimination strategy for the real-time fault, realize the precision, intelligence and automation of real-time fault diagnosis and processing, improve the fault handling efficiency, avoid the delay and uncertainty of manual decision-making, and ensure the stable operation and efficient recovery capability of the blockchain system.

[0061] Embodiment 7: The embodiment of the present invention provides a method for flexibly eliminating system faults based on blockchain, which determines the matching value of each sub-fault elimination strategy in each real-time sub-fault and 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 kth sub-fault elimination strategy in, The strategy effect mapping table representing type b-level c, Indicates real-time sub-fault The level factor of the real-time diagnosis sublevel j, Represents real-time sub-faults The type factors of real-time diagnosis subtype i and real-time diagnosis subtype b, Respectively represent the strategy effect mapping table , Strategy effect mapping table The fitting treatment effect value of the kth sub-fault elimination strategy in, Respectively represent the strategy effect mapping table , Strategy effect mapping table The stability value of the k-th sub-fault elimination strategy in, Respectively represent the strategy effect mapping table , Strategy effect mapping table The resource consumption value of the kth sub-fault elimination strategy in, Indicates real-time sub-fault The sub-fault prediction time, Indicates the current time. Respectively represent the strategy effect mapping table , Strategy effect mapping table The estimated processing time length of the k-th sub-fault elimination strategy in, Indicates the policy effect mapping table The sensitivity factor of the kth sub-fault elimination strategy in , Q represents the diagnostic accuracy of the optimized fault diagnosis model, Indicates the policy effect mapping table The multidimensional performance factor of the k-th sub-fault elimination strategy in, Indicates the policy effect mapping table The interaction factor of the k-th sub-fault elimination strategy in, represents the first indicator function, The number of sub-fault types, N2 represents the number of levels of sub-fault types, The strategy effect mapping table representing type b-level c, Indicates the policy effect mapping table Includes strategy effect mapping table The k-th sub-fault elimination strategy in Indicates the policy effect mapping table Does not include policy effect mapping table The kth sub-fault elimination strategy in .

[0062] In this embodiment, Amplify real-time sub-faults by exponential function Due to the influence of the level factor of the real-time diagnosis sub-level j, the contribution of high-risk faults to the matching value increases exponentially.

[0063] In this embodiment, Use logarithmic function to calculate real-time sub-faults The type factor of the real-time diagnosis subtype i is smoothed to avoid its linear influence on the matching value.

[0064] In this embodiment, Through product square root processing, the extreme value of a single variable is prevented from excessively affecting the matching value, reflecting the real-time sub-fault And the corresponding strategy effect mapping table The balance between the historical performance and the current fitting effect of the k-th sub-fault elimination strategy.

[0065] In this embodiment, Using a logarithmic function ensures that strategies with high stability are given extra points, while preventing the stability score from dominating the matching value excessively.

[0066] In this embodiment, A sine function is used to periodically amplify and reduce it to reflect the complex nonlinear interaction characteristics.

[0067] The beneficial effect of the above technical solution is: determining the matching value of each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table can provide data basis for determining the real-time elimination sub-strategy of each real-time sub-fault, and dynamically generate the optimal elimination strategy for real-time faults.

[0068] Embodiment 8: The embodiment of the present invention provides a method for flexibly eliminating system faults based on blockchain, executes a real-time elimination strategy, realizes flexible elimination of system faults based on blockchain, and generates a fault elimination report, including: Sending a real-time elimination sub-strategy for each real-time sub-fault in the real-time elimination strategy to an execution node of the blockchain-based system, the execution node performing a fault elimination operation based on the real-time elimination sub-strategy and determining a fault elimination result; A fault elimination report is generated based on the real-time elimination sub-strategies of all real-time sub-faults and the fault elimination results.

[0069] 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.

[0070] In this embodiment, each execution node starts corresponding fault handling operations according to the received real-time elimination sub-strategy, such as restarting the node, switching backup hardware, reallocating system resources, etc.

[0071] 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 non-tamperability of the elimination operation.

[0072] In this embodiment, after the execution node completes the fault elimination operation, it monitors the status of the sub-faults and generates a processing result, such as whether the fault is completely eliminated, the remaining impact range, or the possibility of fault recurrence.

[0073] In this embodiment, the real-time elimination sub-strategies of all real-time sub-faults and the corresponding fault elimination results are summarized to generate a comprehensive fault elimination report, which includes the processing plan for each sub-fault, the actual execution process, the fault elimination result, and the processing effect evaluation of the strategy.

[0074] The beneficial effects of the above technical solution are: flexible fault elimination of blockchain-based systems and generation of fault elimination reports can improve the transparency, accuracy and efficiency of the fault handling process, and enhance the intelligence and self-healing of the blockchain system.

[0075] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0076] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flexible method for eliminating system failures based on blockchain, characterized in that: include: 101: Obtain historical fault data of the blockchain-based system, build a fault diagnosis model based on the historical fault data, evaluate the diagnostic accuracy of the fault diagnosis model and optimize the fault diagnosis model; 102: Real-time collection of real-time operation data of the blockchain-based system, and identification of the real-time operation data based on the fault diagnosis model to determine the real-time fault data; 103: Determine a set of elimination strategies for each type-level fault data and a strategy effect mapping table based on historical fault data, and determine a fault elimination strategy library; 104: Determine a real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data based on the fault elimination strategy library, and determine a real-time elimination strategy for the real-time fault data; 105: Execute real-time elimination strategies to achieve flexible fault elimination of blockchain-based systems and generate fault elimination reports.

2. According to claim 1, a method for flexibly eliminating system failures based on blockchain is characterized in that: 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 condition, sub-fault type, sub-fault level, sub-fault elimination strategy and sub-fault processing effect of the sub-fault.

3. According to claim 2, a method for flexibly eliminating system failures based on blockchain is characterized in that: Obtain historical fault data of the blockchain-based system, build a fault diagnosis model based on the historical fault data, evaluate the diagnostic accuracy of the fault diagnosis model and optimize the fault diagnosis model, including: Dividing the historical fault data into training fault data and testing fault data, wherein the training fault data includes historical sub-fault data of multiple sub-faults, and the testing fault data includes historical sub-fault data of multiple sub-faults; The sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition in the historical sub-fault data of all sub-faults in the training fault data are used as the input of the fault diagnosis model, and the sub-fault type and sub-fault level in the historical sub-fault data of all sub-faults in the training fault data are used as the output of the fault diagnosis model to train the fault diagnosis model; Input the sub-fault occurrence time, sub-fault impact range and sub-fault triggering condition in the historical sub-fault data of all sub-faults in the test fault data into the fault diagnosis model, and determine the historical diagnosis sub-type and historical diagnosis sub-level of each sub-fault in the test fault data based on the output result of the fault diagnosis model; The diagnostic accuracy of the fault diagnosis model is evaluated based on the sub-fault types, sub-fault levels, historical diagnosis sub-types, and historical diagnosis sub-levels of all sub-faults in the test fault data, and the fault diagnosis model is optimized based on the diagnostic accuracy.

4. According to claim 3, a method for flexibly eliminating system failures based on blockchain is characterized in that: Collect real-time operation data of the blockchain-based system in real time, identify the real-time operation data based on the fault diagnosis model to determine the real-time fault data, including: Based on all detection nodes deployed in the blockchain network, real-time operation data of the blockchain-based system is collected in real time; Preprocessing the real-time operation data, and inputting the preprocessed real-time operation data into the optimized fault diagnosis model; Real-time fault data is determined based on the output result of the fault diagnosis model, wherein 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 sub-fault prediction time, real-time diagnosis sub-type and real-time diagnosis sub-level.

5. According to claim 4, a method for flexibly eliminating system failures based on blockchain is characterized in that: Based on the historical fault data, a set of elimination strategies for each type-level fault data and a strategy effect mapping table are determined, and a fault elimination strategy library is determined, including: Classifying the historical fault data based on the sub-fault types and sub-fault levels in the historical sub-fault data of all sub-faults in the historical fault data, and determining a plurality of type-level fault data, wherein the type-level fault data includes the historical sub-fault data of a plurality of sub-faults; Extracting sub-fault elimination strategies from historical sub-fault data of all sub-faults in each type-level fault data, and determining an elimination strategy set for each type-level fault data, wherein the elimination strategy set includes multiple sub-fault elimination strategies; Determine the fitting processing effect of each sub-fault elimination strategy in the elimination strategy set for each type-level fault data based on the sub-fault processing effects in the sub-fault data of all sub-faults corresponding to each sub-fault elimination strategy in the elimination strategy set for each type-level fault data; Determine a strategy effect mapping table 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; A fault elimination strategy library is determined based on a strategy effect mapping table of all type-level fault data.

6. According to claim 5, a method for flexibly eliminating system failures based on blockchain is characterized in that: A real-time elimination sub-strategy for each real-time sub-fault in the real-time fault data is determined based on the fault elimination strategy library, and a real-time elimination strategy for the real-time fault data is determined, including: Extracting the real-time diagnosis subtype and the real-time diagnosis sublevel in the real-time sub-fault data of each real-time sub-fault in the real-time fault data, and determining 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 a 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, determining a matching value between each real-time sub-fault and each sub-fault elimination strategy in the corresponding strategy effect mapping table; Select the sub-fault elimination strategy in the strategy effect mapping table with the highest matching value with the real-time sub-fault 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, a real-time elimination strategy for the real-time fault data is determined.

7. A method for flexibly eliminating system failures based on blockchain according to claim 6, characterized in that: Determine the matching value of each sub-fault elimination strategy in each real-time sub-fault and 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 kth sub-fault elimination strategy in, The strategy effect mapping table representing type b-level c, Indicates real-time sub-fault The level factor of the real-time diagnosis sublevel j, Represents real-time sub-faults The type factors of real-time diagnosis subtype i and real-time diagnosis subtype b, Respectively represent the strategy effect mapping table , Strategy effect mapping table The fitting treatment effect value of the kth sub-fault elimination strategy in, Respectively represent the strategy effect mapping table , Strategy effect mapping table The stability value of the k-th sub-fault elimination strategy in, Respectively represent the strategy effect mapping table , Strategy effect mapping table The resource consumption value of the kth sub-fault elimination strategy in, Indicates real-time sub-fault The sub-fault prediction time, Indicates the current time. Respectively represent the strategy effect mapping table , Strategy effect mapping table The estimated processing time length of the k-th sub-fault elimination strategy in, Indicates the policy effect mapping table The sensitivity factor of the kth sub-fault elimination strategy in , Q represents the diagnostic accuracy of the optimized fault diagnosis model, Indicates the policy effect mapping table The multidimensional performance factor of the k-th sub-fault elimination strategy in, Indicates the policy effect mapping table The interaction factor of the k-th sub-fault elimination strategy in, represents the first indicator function, The number of sub-fault types, N2 represents the number of levels of sub-fault types, The strategy effect mapping table representing type b-level c, Indicates the policy effect mapping table Includes strategy effect mapping table The k-th sub-fault elimination strategy in Indicates the policy effect mapping table Does not include policy effect mapping table The kth sub-fault elimination strategy in .

8. According to the blockchain-based flexible system failure elimination method of claim 1, it is characterized in that: Ensure the execution of real-time elimination strategies, realize flexible fault elimination of blockchain-based systems, and generate fault elimination reports, including: Sending a real-time elimination sub-strategy for each real-time sub-fault in the real-time elimination strategy to an execution node of the blockchain-based system, the execution node performing a fault elimination operation based on the real-time elimination sub-strategy and determining a fault elimination result; A fault elimination report is generated based on the real-time elimination sub-strategies of all real-time sub-faults and the fault elimination results.

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