System recovery method and system based on time similarity

Abstract

The invention discloses a system recovery method and system based on time similarity, and the method comprises the steps: obtaining current system data in response to a system recovery request of a user; determining fault time information corresponding to the current system data based on a fault analysis algorithm; based on a time similarity algorithm, determining a target recovery point from a plurality of candidate recovery points according to the fault time information; and determining a system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point. Therefore, accurate recovery point selection based on fault time and data analysis can be realized, the system recovery efficiency and the data integrity are improved, and the recovery failure risk is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a system recovery method and system based on time similarity. Background Art

[0002] With the growing demand for system reliability and data protection, enterprises and institutions are placing increasing emphasis on implementing efficient recovery strategies to address system failures. Existing technologies typically collect current system data and determine recovery points based on user-selected instructions, rebuilding the system based on a common recovery process to ensure data availability. However, existing solutions lack precise analysis of failure times and dynamic screening of recovery point time similarities, making it difficult to accurately select the recovery point that best matches the failure state. These solutions are unable to adapt to complex system recovery scenarios, resulting in low recovery efficiency and the risk of incomplete data or recovery failures, limiting the accuracy and integrity of system recovery. Clearly, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a system recovery method and system based on time similarity, which can realize accurate recovery point selection based on failure time and data analysis, improve system recovery efficiency and data integrity, and reduce the risk of recovery failure.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a system recovery method based on time similarity, the method comprising: Responding to a user's system recovery request, obtaining current system data; Determining the fault time information corresponding to the current system data based on the fault analysis algorithm; Based on a time similarity algorithm, determining a target recovery point from multiple candidate recovery points according to the failure time information; A system recovery policy corresponding to the system recovery request is determined according to the current system data and the target recovery point.

[0005] As an optional implementation, in the first aspect of the present invention, the current system data includes at least one of system hardware information, system version information, system application data, system operation record data and system user information.

[0006] As an optional implementation manner, in the first aspect of the present invention, determining the fault time information corresponding to the current system data based on the fault analysis algorithm includes: Inputting each data portion of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data portion; the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation labels; Filter out all data parts whose fault correlation degree is greater than a preset degree threshold to obtain multiple fault data; According to the data time point corresponding to the fault data, the fault time information corresponding to the current system data is determined.

[0007] As an optional implementation manner, in the first aspect of the present invention, determining the fault time information corresponding to the current system data according to the data time point corresponding to the fault data includes: For each of the fault data, determining whether time information exists for the fault data based on the data content; If it exists, the corresponding time information is determined as the data time point corresponding to the fault data; If not, determine in the current system data relevant data that has data association or in-application communication with the fault data; Determine the time information corresponding to the relevant data as the data time point corresponding to the fault data; Based on a clustering algorithm, a centralized data set is screened out from all the fault data according to the data time point; The average value of the data time points corresponding to all the fault data in the centralized data set is calculated to obtain the fault time information corresponding to the current system data.

[0008] As an optional embodiment, in the first aspect of the present invention, the method of filtering out a centralized data set from all the fault data based on the data time point based on a clustering algorithm includes: Setting the objective function to minimize the total amount of all the fault data in the calculation data set; The setting restriction conditions include that the time difference between the data time points corresponding to any two of the fault data in the calculation data set is less than a preset time difference threshold and the fault type similarity between any two of the fault data in the calculation data set is greater than a preset similarity threshold; the fault type similarity is predicted by a trained similarity prediction model; the similarity prediction model is trained by a training data set including a plurality of training data pairs and corresponding fault type similarity annotations; According to the objective function and the constraint conditions, based on a clustering programming algorithm, an optimal calculation data set is iteratively calculated from all the fault data to obtain a centralized data set.

[0009] As an optional embodiment, in the first aspect of the present invention, the determining of the target recovery point from multiple candidate recovery points based on the failure time information based on the time similarity algorithm includes: For each candidate recovery point, obtain the historical recovery record corresponding to the candidate recovery point; Calculating the similarity of time types between each recorded time point in the historical recovery record and the failure time information; the time type includes at least one of the year, month, day, holiday type, preset time period, preset user operation time type, and preset work task time type to which the corresponding time point belongs; Calculating an average of the similarities of the time types corresponding to all the recorded time points to obtain a recovery point priority corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0010] As an optional implementation manner, in the first aspect of the present invention, determining the system recovery policy corresponding to the system recovery request based on the current system data and the target recovery point includes: Obtaining archive system data corresponding to the target recovery point; Calculating data similarity between the archived system data and the current system data; An execution time point corresponding to the system recovery request is determined according to the data similarity and the recovery point priority.

[0011] As an optional implementation, in the first aspect of the present invention, determining the execution time point corresponding to the system recovery request based on the data similarity and the recovery point priority includes: Calculating a predicted recovery speed proportional to the similarity of the data; Calculating a recovery urgency parameter proportional to the priority of the recovery point; Calculating the product of the predicted recovery speed and the recovery emergency parameter to obtain a recovery execution parameter; Determine a plurality of executable recovery time points corresponding to the user, and for each of the executable recovery time points, calculate a time difference between the executable recovery time point and a current time point; Calculating the ratio of the time difference to the recovery execution parameter to obtain the execution priority corresponding to the executable recovery time point; The executable recovery time point with the highest execution priority is determined as the execution time point corresponding to the system recovery request.

[0012] A second aspect of an embodiment of the present invention discloses a system recovery system based on time similarity, the system comprising: An acquisition module, configured to acquire current system data in response to a user's system recovery request; An analysis module, configured to determine the fault time information corresponding to the current system data based on a fault analysis algorithm; a determination module, configured to determine a target recovery point from a plurality of candidate recovery points according to the failure time information based on a time similarity algorithm; A recovery module is used to determine a system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point.

[0013] As an optional implementation, in the second aspect of the present invention, the current system data includes at least one of system hardware information, system version information, system application data, system operation record data and system user information.

[0014] As an optional implementation, in the second aspect of the present invention, the specific manner in which the analysis module determines the fault time information corresponding to the current system data based on the fault analysis algorithm includes: Inputting each data portion of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data portion; the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation labels; Filter out all data parts whose fault correlation degree is greater than a preset degree threshold to obtain multiple fault data; According to the data time point corresponding to the fault data, the fault time information corresponding to the current system data is determined.

[0015] As an optional implementation, in the second aspect of the present invention, the specific manner in which the analysis module determines the fault time information corresponding to the current system data based on the data time point corresponding to the fault data includes: For each of the fault data, determining whether time information exists for the fault data based on the data content; If it exists, the corresponding time information is determined as the data time point corresponding to the fault data; If not, determine in the current system data relevant data that has data association or in-application communication with the fault data; Determine the time information corresponding to the relevant data as the data time point corresponding to the fault data; Based on a clustering algorithm, a centralized data set is screened out from all the fault data according to the data time point; The average value of the data time points corresponding to all the fault data in the centralized data set is calculated to obtain the fault time information corresponding to the current system data.

[0016] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the analysis module selects a centralized data set from all the fault data according to the data time point based on a clustering algorithm includes: Setting the objective function to minimize the total amount of all the fault data in the calculation data set; The setting restriction conditions include that the time difference between the data time points corresponding to any two of the fault data in the calculation data set is less than a preset time difference threshold and the fault type similarity between any two of the fault data in the calculation data set is greater than a preset similarity threshold; the fault type similarity is predicted by a trained similarity prediction model; the similarity prediction model is trained by a training data set including a plurality of training data pairs and corresponding fault type similarity annotations; According to the objective function and the constraint conditions, based on a clustering programming algorithm, an optimal calculation data set is iteratively calculated from all the fault data to obtain a centralized data set.

[0017] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the target recovery point from multiple candidate recovery points based on the failure time information based on a time similarity algorithm includes: For each candidate recovery point, obtain the historical recovery record corresponding to the candidate recovery point; Calculating the similarity of time types between each recorded time point in the historical recovery record and the failure time information; the time type includes at least one of the year, month, day, holiday type, preset time period, preset user operation time type, and preset work task time type to which the corresponding time point belongs; Calculating an average of the similarities of the time types corresponding to all the recorded time points to obtain a recovery point priority corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0018] As an optional implementation, in the second aspect of the present invention, the recovery module determines, based on the current system data and the target recovery point, a specific manner in which the system recovery policy corresponding to the system recovery request comprises: Obtaining archive system data corresponding to the target recovery point; Calculating data similarity between the archived system data and the current system data; An execution time point corresponding to the system recovery request is determined according to the data similarity and the recovery point priority.

[0019] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the recovery module determines the execution time point corresponding to the system recovery request based on the data similarity and the recovery point priority includes: Calculating a predicted recovery speed proportional to the similarity of the data; Calculating a recovery urgency parameter proportional to the priority of the recovery point; Calculating the product of the predicted recovery speed and the recovery emergency parameter to obtain a recovery execution parameter; Determine a plurality of executable recovery time points corresponding to the user, and for each of the executable recovery time points, calculate a time difference between the executable recovery time point and a current time point; Calculating the ratio of the time difference to the recovery execution parameter to obtain the execution priority corresponding to the executable recovery time point; The executable recovery time point with the highest execution priority is determined as the execution time point corresponding to the system recovery request.

[0020] A third aspect of the present invention discloses another system recovery system based on time similarity, the system comprising: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the system recovery method based on time similarity disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all steps of the system recovery method based on time similarity disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention responds to system recovery requests to obtain current system data and uses a fault analysis algorithm to determine fault time information, screens target recovery points from candidate recovery points based on a time similarity algorithm, and formulates a system recovery strategy in combination with current system data and target recovery points, thereby enabling accurate recovery point selection based on fault time and data analysis, improving system recovery efficiency and data integrity, and reducing the risk of recovery failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a system recovery method based on time similarity disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of a system recovery system based on time similarity disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural diagram of another system recovery system based on time similarity disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0029] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] This invention discloses a system recovery method and system based on time similarity. This method obtains current system data in response to a system recovery request and uses a fault analysis algorithm to determine fault time information. A target recovery point is selected from candidate recovery points based on the time similarity algorithm. A system recovery strategy is formulated based on the current system data and target recovery point. This enables precise recovery point selection based on fault time and data analysis, improving system recovery efficiency and data integrity while reducing the risk of recovery failure. These are described in detail below.

[0031] Example 1 See also Figure 1 , Figure 1 This is a flow chart of a system recovery method based on time similarity disclosed in an embodiment of the present invention. Figure 1 The system recovery method based on time similarity described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the system recovery method based on time similarity may include the following operations: 101. In response to a system recovery request from a user, obtain current system data.

[0032] 102. Based on the fault analysis algorithm, determine the fault time information corresponding to the current system data. 103. Based on the time similarity algorithm, the target recovery point is determined from multiple candidate recovery points according to the failure time information. 104. Determine a system recovery strategy corresponding to the system recovery request based on current system data and the target recovery point.

[0033] It can be seen that the above-mentioned embodiment of the invention responds to the system recovery request to obtain the current system data and uses the fault analysis algorithm to determine the fault time information, screens the target recovery point from the candidate recovery points based on the time similarity algorithm, and formulates the system recovery strategy in combination with the current system data and the target recovery point, thereby realizing accurate recovery point selection based on fault time and data analysis, improving system recovery efficiency and data integrity, and reducing the risk of recovery failure.

[0034] As an optional embodiment, in the above steps, the current system data includes at least one of system hardware information, system version information, system application data, system operation record data and system user information.

[0035] It can be seen that through the above optional embodiments, the content of the current system data is limited to comprehensively characterize the relevant data characteristics of the system to be restored, so as to facilitate the subsequent determination of accurate recovery strategies, assist in the accurate selection of recovery points based on failure time and data analysis, improve system recovery efficiency and data integrity, and reduce the risk of recovery failure.

[0036] As an optional embodiment, in the above step, determining the fault time information corresponding to the current system data based on the fault analysis algorithm includes: Inputting each data portion of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data portion; optionally, the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation annotations; Filter out all data parts whose fault correlation degree is greater than a preset degree threshold to obtain multiple fault data; According to the data time point corresponding to the fault data, the fault time information corresponding to the current system data is determined.

[0037] It can be seen that through the above optional embodiments, by inputting each data part of the current system data into the fault-related prediction model to evaluate the degree of fault correlation, fault data exceeding the preset degree threshold is screened out, and the fault time information is determined according to its data time point, thereby achieving accurate fault time positioning based on data correlation analysis, improving the accuracy and efficiency of system recovery strategy formulation, and reducing the risk of data loss and recovery delay.

[0038] As an optional embodiment, in the above step, determining the fault time information corresponding to the current system data according to the data time point corresponding to the fault data includes: For each fault data, determine whether the fault data has time information based on the data content; If it exists, the corresponding time information is determined as the data time point corresponding to the fault data; If not, determine in the current system data the relevant data that is data-associated with the fault data or that has in-application communication; Determine the time information corresponding to the relevant data as the data time point corresponding to the fault data; Based on the clustering algorithm, a centralized data set is selected from all fault data according to the data time point; Calculate the average value of the data time points corresponding to all fault data in the centralized data set to obtain the fault time information corresponding to the current system data.

[0039] It can be seen that through the above optional embodiments, by analyzing the content of each fault data to determine whether it contains time information and directly determine the data time point, or when there is no time information, extract the time information of the relevant data as the data time point through data association or in-application communication, and based on the clustering algorithm, screen the centralized data set and calculate the average value of its data time point to determine the fault time information, thereby realizing accurate fault time positioning based on content analysis and clustering, improving the accuracy and efficiency of the system recovery strategy, and reducing the risk of data loss during the recovery process.

[0040] As an optional embodiment, in the above steps, based on a clustering algorithm, a centralized data set is screened from all fault data according to the data time point, including: The objective function is set to minimize the total amount of all fault data in the calculation data set; The set restriction conditions include that the time difference between data time points corresponding to any two fault data in the calculation data set is less than a preset time difference threshold and the fault type similarity between any two fault data in the calculation data set is greater than a preset similarity threshold; optionally, the fault type similarity is predicted by a trained similarity prediction model; the similarity prediction model is trained by a training data set including a plurality of training data pairs and corresponding fault type similarity annotations; According to the objective function and the constraints, based on the clustering programming algorithm, the optimal calculation data set is iteratively calculated from all the fault data to obtain the centralized data set.

[0041] It can be seen that through the above optional embodiments, by setting the objective function to minimize the total amount of fault data in the calculation data set, and using the constraint conditions to ensure that the time difference between any two fault data in the set is less than the preset threshold and the fault type similarity is greater than the similarity threshold, the optimal centralized data set is iteratively calculated based on the clustering planning algorithm, thereby realizing accurate fault data clustering based on time and fault type, improving the efficiency and accuracy of system fault time analysis, and reducing the data processing complexity and error risk in system recovery.

[0042] As an optional embodiment, in the above steps, determining the target recovery point from multiple candidate recovery points based on the failure time information based on a time similarity algorithm includes: For each candidate recovery point, obtain the historical recovery record corresponding to the candidate recovery point; Calculate the similarity of the time type between each recorded time point in the historical recovery record and the failure time information; optionally, the time type includes at least one of the year, month, day, holiday type, preset time period, preset user operation time type, and preset work task time type to which the corresponding time point belongs; Calculate the average similarity of the time types corresponding to all recorded time points to obtain the recovery point priority corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0043] It can be seen that through the above optional embodiments, by obtaining the historical recovery records of each candidate recovery point and calculating the average similarity between the record time point and the failure time information in time type as the recovery point priority, the candidate recovery point with the highest priority is selected as the target recovery point, thereby realizing accurate recovery point selection based on multi-dimensional time type similarity, improving the accuracy and efficiency of system recovery, and reducing the risk of data inconsistency.

[0044] As an optional embodiment, in the above step, determining the system recovery policy corresponding to the system recovery request based on the current system data and the target recovery point includes: Obtain the archive system data corresponding to the target recovery point; Calculate the data similarity between the archived system data and the current system data; The execution time point corresponding to the system recovery request is determined based on data similarity and recovery point priority.

[0045] It can be seen that through the above optional embodiments, by obtaining the archived system data of the target recovery point and calculating its data similarity with the current system data, the execution time point of the system recovery request is determined in combination with the recovery point priority, thereby achieving accurate recovery execution time determination based on data similarity and priority, improving the efficiency of system recovery and data consistency, and reducing the risk of data loss during the recovery process.

[0046] As an optional embodiment, in the above step, determining the execution time point corresponding to the system recovery request based on data similarity and recovery point priority includes: Calculate the predicted recovery speed proportional to the data similarity; Calculate a recovery urgency parameter proportional to the priority of the recovery point; Calculate the product of the predicted recovery speed and the recovery emergency parameter to obtain the recovery execution parameter; Determine multiple executable recovery time points corresponding to the user, and for each executable recovery time point, calculate the time difference between the executable recovery time point and the current time point; Calculate the ratio of the time difference and the recovery execution parameter to obtain the execution priority corresponding to the executable recovery time point; The executable recovery time point with the highest execution priority is determined as the execution time point corresponding to the system recovery request.

[0047] It can be seen that through the above optional embodiments, the recovery execution parameter is obtained by calculating the product of the predicted recovery speed proportional to the data similarity and the recovery urgency parameter proportional to the recovery point priority, and the time difference between the user's executable recovery time point and the current time point is combined to calculate the ratio of the time difference to the recovery execution parameter as the execution priority, and the executable recovery time point with the highest priority is selected as the execution time point of the system recovery request, thereby realizing accurate recovery execution time optimization based on recovery speed and urgency, improving system recovery efficiency and data consistency, and reducing the risk of recovery delay.

[0048] Example 2 See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a system recovery system based on time similarity disclosed in an embodiment of the present invention. Figure 2 The described system recovery system based on time similarity can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the system recovery system based on time similarity may include: The acquisition module 201 is configured to acquire current system data in response to a system recovery request from a user.

[0049] The analysis module 202 is configured to determine the fault time information corresponding to the current system data based on a fault analysis algorithm. The determination module 203 is configured to determine a target recovery point from multiple candidate recovery points based on a time similarity algorithm and according to the failure time information. The recovery module 204 is configured to determine a system recovery strategy corresponding to the system recovery request based on current system data and a target recovery point.

[0050] It can be seen that the above-mentioned embodiment of the invention responds to the system recovery request to obtain the current system data and uses the fault analysis algorithm to determine the fault time information, screens the target recovery point from the candidate recovery points based on the time similarity algorithm, and formulates the system recovery strategy in combination with the current system data and the target recovery point, thereby realizing accurate recovery point selection based on fault time and data analysis, improving system recovery efficiency and data integrity, and reducing the risk of recovery failure.

[0051] As an optional embodiment, the current system data includes at least one of system hardware information, system version information, system application data, system operation record data and system user information.

[0052] It can be seen that through the above optional embodiments, the content of the current system data is limited to comprehensively characterize the relevant data characteristics of the system to be restored, so as to facilitate the subsequent determination of accurate recovery strategies, assist in the accurate selection of recovery points based on failure time and data analysis, improve system recovery efficiency and data integrity, and reduce the risk of recovery failure.

[0053] As an optional embodiment, the analysis module determines the specific manner of the fault time information corresponding to the current system data based on the fault analysis algorithm, including: Inputting each data portion of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data portion; optionally, the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation annotations; Filter out all data parts whose fault correlation degree is greater than a preset degree threshold to obtain multiple fault data; According to the data time point corresponding to the fault data, the fault time information corresponding to the current system data is determined.

[0054] It can be seen that through the above optional embodiments, by inputting each data part of the current system data into the fault-related prediction model to evaluate the degree of fault correlation, fault data exceeding the preset degree threshold is screened out, and the fault time information is determined according to its data time point, thereby achieving accurate fault time positioning based on data correlation analysis, improving the accuracy and efficiency of system recovery strategy formulation, and reducing the risk of data loss and recovery delay.

[0055] As an optional embodiment, the specific method of determining the fault time information corresponding to the current system data according to the data time point corresponding to the fault data by the analysis module includes: For each fault data, determine whether the fault data has time information based on the data content; If it exists, the corresponding time information is determined as the data time point corresponding to the fault data; If not, determine in the current system data the relevant data that is data-associated with the fault data or that has in-application communication; Determine the time information corresponding to the relevant data as the data time point corresponding to the fault data; Based on the clustering algorithm, a centralized data set is selected from all fault data according to the data time point; Calculate the average value of the data time points corresponding to all fault data in the centralized data set to obtain the fault time information corresponding to the current system data.

[0056] It can be seen that through the above optional embodiments, by analyzing the content of each fault data to determine whether it contains time information and directly determine the data time point, or when there is no time information, extract the time information of the relevant data as the data time point through data association or in-application communication, and based on the clustering algorithm, screen the centralized data set and calculate the average value of its data time point to determine the fault time information, thereby realizing accurate fault time positioning based on content analysis and clustering, improving the accuracy and efficiency of the system recovery strategy, and reducing the risk of data loss during the recovery process.

[0057] As an optional embodiment, the specific method of the analysis module to filter out a centralized data set from all fault data based on the data time point based on the clustering algorithm includes: The objective function is set to minimize the total amount of all fault data in the calculation data set; The set restriction conditions include that the time difference between data time points corresponding to any two fault data in the calculation data set is less than a preset time difference threshold and the fault type similarity between any two fault data in the calculation data set is greater than a preset similarity threshold; optionally, the fault type similarity is predicted by a trained similarity prediction model; the similarity prediction model is trained by a training data set including a plurality of training data pairs and corresponding fault type similarity annotations; According to the objective function and the constraints, based on the clustering programming algorithm, the optimal calculation data set is iteratively calculated from all the fault data to obtain the centralized data set.

[0058] It can be seen that through the above optional embodiments, by setting the objective function to minimize the total amount of fault data in the calculation data set, and using the constraint conditions to ensure that the time difference between any two fault data in the set is less than the preset threshold and the fault type similarity is greater than the similarity threshold, the optimal centralized data set is iteratively calculated based on the clustering planning algorithm, thereby realizing accurate fault data clustering based on time and fault type, improving the efficiency and accuracy of system fault time analysis, and reducing the data processing complexity and error risk in system recovery.

[0059] As an optional embodiment, the specific method of determining the target recovery point from multiple candidate recovery points based on the failure time information by the determination module based on a time similarity algorithm includes: For each candidate recovery point, obtain the historical recovery record corresponding to the candidate recovery point; Calculate the similarity of the time type between each recorded time point in the historical recovery record and the failure time information; optionally, the time type includes at least one of the year, month, day, holiday type, preset time period, preset user operation time type, and preset work task time type to which the corresponding time point belongs; Calculate the average similarity of the time types corresponding to all recorded time points to obtain the recovery point priority corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0060] It can be seen that through the above optional embodiments, by obtaining the historical recovery records of each candidate recovery point and calculating the average similarity between the record time point and the failure time information in time type as the recovery point priority, the candidate recovery point with the highest priority is selected as the target recovery point, thereby realizing accurate recovery point selection based on multi-dimensional time type similarity, improving the accuracy and efficiency of system recovery, and reducing the risk of data inconsistency.

[0061] As an optional embodiment, the recovery module determines the specific method of the system recovery policy corresponding to the system recovery request based on the current system data and the target recovery point, including: Obtain the archive system data corresponding to the target recovery point; Calculate the data similarity between the archived system data and the current system data; The execution time point corresponding to the system recovery request is determined based on data similarity and recovery point priority.

[0062] It can be seen that through the above optional embodiments, by obtaining the archived system data of the target recovery point and calculating its data similarity with the current system data, the execution time point of the system recovery request is determined in combination with the recovery point priority, thereby achieving accurate recovery execution time determination based on data similarity and priority, improving the efficiency of system recovery and data consistency, and reducing the risk of data loss during the recovery process.

[0063] As an optional embodiment, the recovery module determines the specific execution time point corresponding to the system recovery request based on data similarity and recovery point priority, including: Calculate the predicted recovery speed proportional to the data similarity; Calculate a recovery urgency parameter proportional to the priority of the recovery point; Calculate the product of the predicted recovery speed and the recovery emergency parameter to obtain the recovery execution parameter; Determine multiple executable recovery time points corresponding to the user, and for each executable recovery time point, calculate the time difference between the executable recovery time point and the current time point; Calculate the ratio of the time difference and the recovery execution parameter to obtain the execution priority corresponding to the executable recovery time point; The executable recovery time point with the highest execution priority is determined as the execution time point corresponding to the system recovery request.

[0064] It can be seen that through the above optional embodiments, the recovery execution parameter is obtained by calculating the product of the predicted recovery speed proportional to the data similarity and the recovery urgency parameter proportional to the recovery point priority, and the time difference between the user's executable recovery time point and the current time point is combined to calculate the ratio of the time difference to the recovery execution parameter as the execution priority, and the executable recovery time point with the highest priority is selected as the execution time point of the system recovery request, thereby realizing accurate recovery execution time optimization based on recovery speed and urgency, improving system recovery efficiency and data consistency, and reducing the risk of recovery delay.

[0065] Example 3 See also Figure 3 , Figure 3 This is another system recovery system based on time similarity disclosed in an embodiment of the present invention. Figure 3 The system recovery system based on time similarity is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the system recovery system based on time similarity may include: A memory 301 storing executable program code; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the system recovery method based on time similarity described in the first embodiment.

[0066] Example 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the system recovery method based on time similarity described in the first embodiment.

[0067] Example 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the system recovery method based on time similarity described in the first embodiment.

[0068] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0077] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0079] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0080] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0081] Finally, it should be noted that the system recovery method and system based on time similarity disclosed in the embodiment of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A system recovery method based on time similarity, characterized in that: The method comprises: Responding to a user's system recovery request, obtaining current system data; Determining the fault time information corresponding to the current system data based on the fault analysis algorithm; Based on a time similarity algorithm, determining a target recovery point from multiple candidate recovery points according to the failure time information; A system recovery policy corresponding to the system recovery request is determined according to the current system data and the target recovery point.

2. The system recovery method based on time similarity according to claim 1, characterized in that: The current system data includes at least one of system hardware information, system version information, system application data, system operation record data and system user information.

3. The system recovery method based on time similarity according to claim 1, characterized in that: The determining, based on a fault analysis algorithm, the fault time information corresponding to the current system data includes: Inputting each data portion of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data portion; the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation labels; Filter out all data parts whose fault correlation degree is greater than a preset degree threshold to obtain multiple fault data; According to the data time point corresponding to the fault data, the fault time information corresponding to the current system data is determined.

4. The system recovery method based on time similarity according to claim 3, characterized in that: The determining, based on the data time point corresponding to the fault data, the fault time information corresponding to the current system data includes: For each of the fault data, determining whether time information exists for the fault data based on the data content; If it exists, the corresponding time information is determined as the data time point corresponding to the fault data; If not, determining relevant data that is data-associated with the fault data or has in-application communication with the fault data in the current system data; Determine the time information corresponding to the relevant data as the data time point corresponding to the fault data; Based on a clustering algorithm, a centralized data set is screened out from all the fault data according to the data time points; The average value of the data time points corresponding to all the fault data in the centralized data set is calculated to obtain the fault time information corresponding to the current system data.

5. The system recovery method based on time similarity according to claim 4, characterized in that: The method of filtering out a centralized data set from all the fault data based on the data time point based on the clustering algorithm includes: Setting the objective function to minimize the total amount of all the fault data in the calculation data set; The setting restriction conditions include that the time difference between the data time points corresponding to any two of the fault data in the calculation data set is less than a preset time difference threshold and the fault type similarity between any two of the fault data in the calculation data set is greater than a preset similarity threshold; the fault type similarity is predicted by a trained similarity prediction model; the similarity prediction model is trained by a training data set including a plurality of training data pairs and corresponding fault type similarity annotations; According to the objective function and the constraint conditions, based on a clustering programming algorithm, an optimal calculation data set is iteratively calculated from all the fault data to obtain a centralized data set.

6. The system recovery method based on time similarity according to claim 1, characterized in that: The method of determining a target recovery point from a plurality of candidate recovery points based on the time similarity algorithm according to the failure time information includes: For each candidate recovery point, obtain the historical recovery record corresponding to the candidate recovery point; Calculating the similarity of time types between each recorded time point in the historical recovery record and the failure time information; the time type includes at least one of the year, month, day, holiday type, preset time period, preset user operation time type, and preset work task time type to which the corresponding time point belongs; Calculating an average of the similarities of the time types corresponding to all the recorded time points to obtain a recovery point priority corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

7. The system recovery method based on time similarity according to claim 6, characterized in that: The determining, based on the current system data and the target recovery point, a system recovery policy corresponding to the system recovery request includes: Obtaining archive system data corresponding to the target recovery point; Calculating data similarity between the archived system data and the current system data; An execution time point corresponding to the system recovery request is determined according to the data similarity and the recovery point priority.

8. The system recovery method based on time similarity according to claim 7, characterized in that: The determining, based on the data similarity and the recovery point priority, the execution time point corresponding to the system recovery request includes: Calculating a predicted recovery speed proportional to the similarity of the data; Calculating a recovery urgency parameter proportional to the priority of the recovery point; Calculating the product of the predicted recovery speed and the recovery emergency parameter to obtain a recovery execution parameter; Determine a plurality of executable recovery time points corresponding to the user, and for each of the executable recovery time points, calculate a time difference between the executable recovery time point and a current time point; Calculating the ratio of the time difference to the recovery execution parameter to obtain the execution priority corresponding to the executable recovery time point; The executable recovery time point with the highest execution priority is determined as the execution time point corresponding to the system recovery request.

9. A system recovery system based on time similarity, characterized in that: The system comprises: An acquisition module, configured to acquire current system data in response to a user's system recovery request; An analysis module, configured to determine the fault time information corresponding to the current system data based on a fault analysis algorithm; a determination module, configured to determine a target recovery point from a plurality of candidate recovery points according to the failure time information based on a time similarity algorithm; A recovery module is used to determine a system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point.

10. A system recovery system based on time similarity, characterized in that: The system comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the system recovery method based on time similarity according to any one of claims 1 to 8.

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