System recovery method and system based on time similarity

Abstract

The application discloses a system recovery method and system based on time similarity, and the method comprises the following steps: in response to a system recovery request of a user, current system data is acquired; based on a fault analysis algorithm, fault time information corresponding to the current system data is determined; based on a time similarity algorithm, a target recovery point is determined from a plurality of candidate recovery points according to the fault time information; and based on the current system data and the target recovery point, a system recovery strategy corresponding to the system recovery request is determined. It can be seen that the application can realize accurate recovery point selection based on fault time and data analysis, improve system recovery efficiency and data integrity, and reduce the risk of recovery failure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a system recovery method and system based on time similarity. BACKGROUND

[0002] With the increasing demand for system reliability and data protection, enterprises and institutions pay more and more attention to coping with system failure through efficient recovery strategy. The existing technology usually collects current system data and determines the recovery point based on the selection instruction of the user to reconstruct the system based on the general recovery process to ensure data availability. The existing solution is difficult to accurately select the recovery point that best matches the fault state due to the lack of precise analysis of fault time and dynamic screening of recovery point time similarity, which cannot adapt to complex system recovery scenarios, resulting in low recovery efficiency, incomplete data or recovery failure, and limiting the accuracy of system recovery and data integrity. It can be seen that the existing technology has defects and needs to be solved. SUMMARY

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

[0004] To solve the above technical problems, the first aspect of the present application discloses a system recovery method based on time similarity, the method comprising:

[0005] In response to a user's system recovery request, obtaining current system data;

[0006] Based on the fault analysis algorithm, determining the fault time information corresponding to the current system data;

[0007] Based on the time similarity algorithm, determining the target recovery point from a plurality of candidate recovery points according to the fault time information;

[0008] According to the current system data and the target recovery point, determining the system recovery strategy corresponding to the system recovery request.

[0009] As an optional implementation, in the first aspect of the present application, the current system data includes at least one of system hardware information, system version information, system internal application data, system operation record data and system user information.

[0010] As an optional implementation, in the first aspect of the present application, the fault analysis algorithm is used to determine the fault time information corresponding to the current system data, comprising:

[0011] inputting each data part of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data part, wherein the fault correlation prediction model is obtained by training a training data set including a plurality of training data and corresponding fault correlation labels;

[0012] screening data parts with a fault correlation degree greater than a preset degree threshold to obtain a plurality of fault data;

[0013] determining fault time information corresponding to the current system data according to data time points corresponding to the fault data.

[0014] As an optional implementation, in the first aspect of the present application, the determination of the fault time information corresponding to the current system data according to the data time points corresponding to the fault data comprises:

[0015] for each fault data, determining whether there is time information in the fault data based on data content;

[0016] if there is, determining the corresponding time information as the data time point corresponding to the fault data;

[0017] if there is not, determining relevant data having data association or application internal communication with the fault data in the current system data;

[0018] determining time information corresponding to the relevant data as the data time point corresponding to the fault data;

[0019] screening a centralized data set from all the fault data according to the data time points based on a clustering algorithm;

[0020] calculating an average value of the data time points corresponding to all the fault data in the centralized data set to obtain the fault time information corresponding to the current system data.

[0021] As an optional implementation, in the first aspect of the present application, the screening of the centralized data set from all the fault data according to the data time points based on the clustering algorithm comprises:

[0022] setting a target function as the total data amount of all the fault data in the data set reaching a minimum;

[0023] The setting restriction condition includes that a 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 a 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 labels;

[0024] According to the target function and the restriction condition, an optimal calculation data set is iteratively calculated from all the fault data based on a clustering planning algorithm, to obtain a centralized data set.

[0025] As an optional implementation form, in the first aspect, the time similarity algorithm includes:

[0026] For each candidate recovery point, a historical recovery record corresponding to the candidate recovery point is obtained;

[0027] A time type similarity between each record time point in the historical recovery record and the fault time information is calculated; the time type includes at least one of an year, a month, a date, a holiday type, a preset time period, a preset user operation time type, and a preset work task time type to which a corresponding time point belongs;

[0028] An average value of the time type similarities of all the record time points is calculated, to obtain a recovery point priority corresponding to the candidate recovery point;

[0029] The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0030] As an optional implementation form, in the first aspect, the determining of the system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point includes:

[0031] Archive system data corresponding to the target recovery point is obtained;

[0032] A data similarity between the archive system data and the current system data is calculated;

[0033] An execution time point corresponding to the system recovery request is determined according to the data similarity and the recovery point priority.

[0034] As an optional implementation form, in the first aspect, the determining of the execution time point corresponding to the system recovery request according to the data similarity and the recovery point priority includes:

[0035] calculating a predicted recovery speed in proportion to the similarity degree of the data;

[0036] calculating a recovery urgency parameter in proportion to the priority of the recovery point;

[0037] calculating a product of the predicted recovery speed and the recovery urgency parameter to obtain a recovery execution parameter;

[0038] determining a plurality of executable recovery time points corresponding to the user, and for each executable recovery time point, calculating a time difference value between the executable recovery time point and a current time point;

[0039] calculating a ratio of the time difference value and the recovery execution parameter to obtain an execution priority corresponding to the executable recovery time point;

[0040] determining the executable recovery time point with the highest execution priority as an execution time point corresponding to the system recovery request.

[0041] The second aspect of the embodiment of the present application discloses a system recovery system based on time similarity, and the system comprises:

[0042] an acquisition module configured to acquire current system data in response to a system recovery request of a user;

[0043] an analysis module configured to determine fault time information corresponding to the current system data based on a fault analysis algorithm;

[0044] a determination module configured to determine a target recovery point from a plurality of candidate recovery points based on a time similarity algorithm according to the fault time information;

[0045] a recovery module configured to determine a system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point.

[0046] As an optional implementation, in the second aspect of the present application, the current system data comprises at least one of system hardware information, system version information, system internal application data, system operation record data and system user information.

[0047] As an optional implementation, in the second aspect of the present application, the analysis module determines the fault time information corresponding to the current system data based on the fault analysis algorithm in the following specific manner:

[0048] inputting each data part of the current system data into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data part, wherein the fault correlation prediction model is obtained by training a training data set including a plurality of training data and corresponding fault correlation labels;

[0049] screening data parts with a fault correlation degree greater than a preset degree threshold to obtain a plurality of fault data;

[0050] determining fault time information corresponding to the current system data according to a data time point corresponding to the fault data.

[0051] As an optional implementation, in the second aspect of the present application, the specific manner in which the analysis module determines the fault time information corresponding to the current system data according to the data time point corresponding to the fault data includes:

[0052] for each fault data, determining whether the fault data has time information based on data content;

[0053] if yes, determining the corresponding time information as the data time point corresponding to the fault data;

[0054] if no, determining relevant data having data association or application internal communication with the fault data in the current system data;

[0055] determining time information corresponding to the relevant data as the data time point corresponding to the fault data;

[0056] screening a centralized data set from all the fault data according to the data time point based on a clustering algorithm;

[0057] calculating an average value of the data time points corresponding to all the fault data in the centralized data set to obtain the fault time information corresponding to the current system data.

[0058] As an optional implementation, in the second aspect of the present application, the specific manner in which the analysis module screens the centralized data set from all the fault data according to the data time point based on a clustering algorithm includes:

[0059] setting a target function as minimizing the total data amount of all the fault data in the data set;

[0060] The setting restriction condition comprises that a 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 a 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 comprising a plurality of training data pairs and corresponding fault type similarity labels;

[0061] According to the target function and the restriction condition, an optimal calculation data set is iteratively calculated from all the fault data based on a clustering planning algorithm, so as to obtain a centralized data set.

[0062] As an optional implementation, in the second aspect, the determining module determines the target recovery point from the plurality of candidate recovery points based on a time similarity algorithm according to the fault time information, and the specific manner comprises:

[0063] For each candidate recovery point, a historical recovery record corresponding to the candidate recovery point is obtained;

[0064] A time type similarity between each record time point in the historical recovery record and the fault time information is calculated; the time type comprises at least one of a year, a month, a date, a holiday type, a preset time period, a preset user operation time type, and a preset work task time type corresponding to a time point;

[0065] An average value of the time type similarities corresponding to all the record time points is calculated, so as to obtain a recovery point priority corresponding to the candidate recovery point;

[0066] The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0067] As an optional implementation, in the second aspect, the recovery module determines a system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point, and the specific manner comprises:

[0068] Archive system data corresponding to the target recovery point is obtained;

[0069] A data similarity between the archive system data and the current system data is calculated;

[0070] An execution time point corresponding to the system recovery request is determined according to the data similarity and the recovery point priority.

[0071] As an optional implementation, in the second aspect of the present application, the specific manner of determining the execution time point corresponding to the system recovery request according to the data similarity and the recovery point priority comprises:

[0072] calculating a predicted recovery speed proportional to the data similarity;

[0073] calculating a recovery urgency parameter proportional to the recovery point priority;

[0074] calculating a product of the predicted recovery speed and the recovery urgency parameter to obtain a recovery execution parameter;

[0075] determining a plurality of executable recovery time points corresponding to the user, and for each executable recovery time point, calculating a time difference value between the executable recovery time point and a current time point;

[0076] calculating a ratio of the time difference value and the recovery execution parameter to obtain an execution priority corresponding to the executable recovery time point;

[0077] determining the executable recovery time point with the highest execution priority as the execution time point corresponding to the system recovery request.

[0078] The third aspect of the present application discloses another system for system recovery based on time similarity, which comprises:

[0079] a memory storing executable program codes;

[0080] a processor coupled with the memory;

[0081] the processor invokes the executable program codes stored in the memory to execute part or all of the steps of the system recovery method based on time similarity disclosed in the first aspect of the present application.

[0082] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, which when invoked, are used to execute part or all of the steps of the system recovery method based on time similarity disclosed in the first aspect of the present application.

[0083] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0084] The present application responds to a system recovery request to obtain current system data and determines fault time information by using a fault analysis algorithm, screens a target recovery point from candidate recovery points based on a time similarity algorithm, and formulates a system recovery strategy in combination with the current system data and the target recovery point, thereby being capable of realizing precise recovery point selection based on fault time and data analysis, improving system recovery efficiency and data integrity, and reducing the risk of recovery failure. Attached Figure Description

[0085] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0086] Figure 1 This is a flowchart illustrating a system recovery method based on time similarity disclosed in an embodiment of the present invention.

[0087] 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.

[0088] Figure 3 This is a schematic diagram of another system recovery system based on time similarity disclosed in an embodiment of the present invention. Detailed Implementation

[0089] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0091] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0092] The application discloses a system recovery method and system based on time similarity, responds to a system recovery request to acquire current system data and determines fault time information by using a fault analysis algorithm, screens a target recovery point from candidate recovery points based on a time similarity algorithm, and formulates a system recovery strategy in combination with the current system data and the target recovery point, so that accurate recovery point selection based on fault time and data analysis can be realized, system recovery efficiency and data integrity are improved, and the risk of recovery failure is reduced. The following will be described in detail.

[0093] Embodiment one

[0094] Please refer to Figure 1 , Figure 1 is a flowchart of a system recovery method based on time similarity disclosed by the embodiment of the application. Wherein, 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). As Figure 1 indicated, the system recovery method based on time similarity can include the following operations:

[0095] 101, in response to a system recovery request of a user, acquiring current system data.

[0096] 102, based on a fault analysis algorithm, determining fault time information corresponding to the current system data.

[0097] 103, based on a time similarity algorithm, determining a target recovery point from a plurality of candidate recovery points according to the fault time information.

[0098] 104, according to the current system data and the target recovery point, determining a system recovery strategy corresponding to the system recovery request.

[0099] As can be seen, the above-mentioned embodiment of the application responds to a system recovery request to acquire current system data and determines fault time information by using a fault analysis algorithm, screens a target recovery point from candidate recovery points based on a time similarity algorithm, and formulates a system recovery strategy in combination with the current system data and the target recovery point, so that accurate recovery point selection based on fault time and data analysis can be realized, system recovery efficiency and data integrity are improved, and the risk of recovery failure is reduced.

[0100] As an optional embodiment, in the above-mentioned steps, the current system data includes at least one of system hardware information, system version information, system internal application data, system operation record data and system user information.

[0101] It can be seen that through the above optional embodiments, the content of the current system data is limited to comprehensively represent the relevant data characteristics of the system to be recovered, so as to facilitate the determination of subsequent accurate recovery strategies, assist in realizing accurate recovery point selection based on fault time and data analysis, improve system recovery efficiency and data integrity, and reduce the risk of recovery failure.

[0102] As an optional embodiment, in the above step, the fault time information corresponding to the current system data is determined based on a fault analysis algorithm, including:

[0103] Each data part of the current system data is input into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data part; optionally, the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation labels;

[0104] Data parts with a fault correlation degree greater than a preset degree threshold are screened out to obtain a plurality of fault data;

[0105] The fault time information corresponding to the current system data is determined according to the data time point corresponding to the fault data.

[0106] It can be seen that through the above optional embodiments, by inputting each data part of the current system data into the fault correlation prediction model to evaluate the fault correlation degree, the fault data exceeding the preset degree threshold is screened out, and the fault time information is determined according to the data time point, thereby realizing 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.

[0107] As an optional embodiment, in the above step, the fault time information corresponding to the current system data is determined according to the data time point corresponding to the fault data, including:

[0108] For each fault data, based on the data content, it is determined whether the fault data has time information;

[0109] If so, the corresponding time information is determined as the data time point corresponding to the fault data;

[0110] If not, relevant data that has data association or intra-application communication with the fault data is determined in the current system data;

[0111] The time information corresponding to the relevant data is determined as the data time point corresponding to the fault data;

[0112] Based on a clustering algorithm, a centralized data set is screened out from all fault data according to the data time point;

[0113] 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.

[0114] As can be seen, through the above optional embodiments, by analyzing the content of each fault data to determine whether it contains time information and directly determining the data time point, or when there is no time information, extracting the time information of the related data as the data time point through data association or application internal communication, and based on the clustering algorithm to filter the centralized data set and calculate the average value of the data time points to determine the fault time information, the precise fault time positioning based on content analysis and clustering is realized, the accuracy and efficiency of the system recovery strategy are improved, and the risk of data loss in the recovery process is reduced.

[0115] As an optional embodiment, in the above step, based on the clustering algorithm, the centralized data set is filtered from all fault data according to the data time points, including:

[0116] The objective function is set to minimize the total data amount of all fault data in the calculation data set;

[0117] The constraint condition includes that the time difference between the 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 labels;

[0118] According to the objective function and the constraint condition, the optimal calculation data set is iteratively calculated from all fault data based on a clustering programming algorithm to obtain the centralized data set.

[0119] As can be seen, through the above optional embodiments, by setting the objective function to minimize the total data amount of the fault data in the calculation data set, and ensuring 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 by the constraint condition, the optimal centralized data set is iteratively calculated based on the clustering programming algorithm, thereby realizing the precise fault data clustering based on time and fault type, improving the efficiency and accuracy of the system fault time analysis, and reducing the data processing complexity and error risk in system recovery.

[0120] As an optional embodiment, in the above step, based on the time similarity algorithm, the target recovery point is determined from the plurality of candidate recovery points according to the fault time information, including:

[0121] For each candidate recovery point, the historical recovery record corresponding to the candidate recovery point is obtained;

[0122] calculate the similarity of time type between each record time point in the history recovery record and the fault time information; optionally, the time type comprises at least one of year, month, date, holiday type, preset time period, preset user operation time type and preset work task time type to which the corresponding time point belongs;

[0123] calculate the average value of the similarity of time type corresponding to all record time points to obtain the recovery point priority corresponding to the candidate recovery point;

[0124] determine the candidate recovery point with the highest recovery point priority as the target recovery point.

[0125] It can be seen that, through the above optional embodiments, by obtaining the history recovery record of each candidate recovery point and calculating the average value of the similarity of time type between the record time point and the fault time information 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.

[0126] As an optional embodiment, in the above steps, determining the system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point comprises:

[0127] obtaining the archived system data corresponding to the target recovery point;

[0128] calculating the data similarity between the archived system data and the current system data;

[0129] determining the execution time point corresponding to the system recovery request according to the data similarity and the recovery point priority.

[0130] It can be seen that, through the above optional embodiments, by obtaining the archived system data of the target recovery point and calculating the data similarity between the archived system data and the current system data, the execution time point of the system recovery request is determined in combination with the recovery point priority, thereby realizing accurate recovery execution time determination based on data similarity and priority, improving the efficiency and data consistency of system recovery, and reducing the risk of data loss in the recovery process.

[0131] As an optional embodiment, in the above steps, determining the execution time point corresponding to the system recovery request according to the data similarity and the recovery point priority comprises:

[0132] calculating a predicted recovery speed proportional to the data similarity;

[0133] calculating a recovery urgency parameter proportional to the recovery point priority;

[0134] The product of the predicted recovery speed and the recovery emergency parameter is calculated to obtain a recovery execution parameter;

[0135] A plurality of executable recovery time points corresponding to the user are determined, and for each executable recovery time point, a time difference value between the executable recovery time point and a current time point is calculated;

[0136] The ratio of the time difference value and the recovery execution parameter is calculated to obtain an execution priority corresponding to the executable recovery time point;

[0137] The executable recovery time point with the highest execution priority is determined as the execution time point corresponding to the system recovery request.

[0138] It can be seen that, through the above optional embodiments, the product of the predicted recovery speed proportional to the data similarity and the recovery emergency parameter proportional to the recovery point priority is calculated to obtain the recovery execution parameter, the time difference value between the executable recovery time point and the current time point is calculated in combination with the user, the ratio of the time difference value and the recovery execution parameter is calculated 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, so as to realize the precise recovery execution time optimization based on the recovery speed and the emergency degree, improve the system recovery efficiency and the data consistency, and reduce the recovery delay risk.

[0139] Embodiment two

[0140] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a system recovery system based on time similarity disclosed by the embodiments of the present application. Among them, Figure 2 The system recovery system based on time similarity described can be applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 2 indicated, the system recovery system based on time similarity can include:

[0141] The acquisition module 201 is configured to acquire current system data in response to a system recovery request of a user.

[0142] The analysis module 202 is configured to determine fault time information corresponding to the current system data based on a fault analysis algorithm.

[0143] The determination module 203 is configured to determine a target recovery point from a plurality of candidate recovery points based on a time similarity algorithm according to the fault time information.

[0144] The recovery module 204 is configured to determine a system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point.

[0145] It can be seen that the above embodiment of the application responds to the system recovery request to obtain current system data and determines fault time information by using a fault analysis algorithm, filters a target recovery point from candidate recovery points based on a time similarity algorithm, and formulates a system recovery strategy in combination with the current system data and the target recovery point, so that precise recovery point selection based on fault time and data analysis can be realized, system recovery efficiency and data integrity are improved, and the risk of recovery failure is reduced.

[0146] As an optional embodiment, the current system data includes at least one of system hardware information, system version information, system internal application data, system operation record data, and system user information.

[0147] It can be seen that, through the above optional embodiment, the content of the current system data is limited to comprehensively represent the relevant data features of the system to be recovered, so as to facilitate the subsequent determination of an accurate recovery strategy, assist in realizing precise recovery point selection based on fault time and data analysis, improve system recovery efficiency and data integrity, and reduce the risk of recovery failure.

[0148] As an optional embodiment, the analysis module determines the specific manner of determining the fault time information corresponding to the current system data based on the fault analysis algorithm, including:

[0149] Each data part of the current system data is input into a fault correlation prediction model to obtain a fault correlation degree corresponding to each data part; optionally, the fault correlation prediction model is trained by a training data set including a plurality of training data and corresponding fault correlation labels;

[0150] Data parts with a fault correlation degree greater than a preset degree threshold are filtered out to obtain a plurality of fault data;

[0151] The fault time information corresponding to the current system data is determined according to the data time points corresponding to the fault data.

[0152] It can be seen that, through the above optional embodiment, each data part of the current system data is input into a fault correlation prediction model to evaluate the fault correlation degree, the fault data exceeding the preset degree threshold is filtered out, and the fault time information is determined according to the data time points, so that precise fault time positioning based on data correlation analysis is realized, the accuracy and efficiency of system recovery strategy formulation are improved, and the risk of data loss and recovery delay is reduced.

[0153] As an optional embodiment, the analysis module determines the specific manner of determining the fault time information corresponding to the current system data according to the data time points corresponding to the fault data, including:

[0154] For each fault data, based on the data content, it is determined whether the fault data has time information;

[0155] If yes, the corresponding time information is determined as the data time point corresponding to the fault data;

[0156] If no, the relevant data that has data association or application internal communication with the fault data is determined in the current system data;

[0157] The time information corresponding to the relevant data is determined as the data time point corresponding to the fault data;

[0158] Based on the clustering algorithm, the centralized data set is screened out from all the fault data according to the data time point;

[0159] 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.

[0160] As can be seen, through the above optional embodiments, whether the time information is contained in each fault data is analyzed to determine the data time point directly, or when there is no time information, the time information of the relevant data extracted through data association or application internal communication is taken as the data time point, the centralized data set is screened out based on the clustering algorithm, and the average value of the data time points is calculated to determine the fault time information, so as to realize the precise fault time positioning based on content analysis and clustering, improve the accuracy and efficiency of the system recovery strategy, and reduce the risk of data loss in the recovery process.

[0161] As an optional embodiment, the specific way in which the analysis module screens out the centralized data set from all the fault data based on the clustering algorithm includes:

[0162] The objective function is set to be the total data amount of all the fault data in the calculated data set reaching the minimum;

[0163] The constraint conditions include that the time difference between the data time points corresponding to any two fault data in the calculated data set is less than a preset time difference threshold, and the fault type similarity between any two fault data in the calculated 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 labels;

[0164] According to the objective function and the constraint conditions, the optimal calculated data set is iteratively calculated from all the fault data based on a clustering programming algorithm, so as to obtain the centralized data set.

[0165] It can be seen that, through the above optional embodiments, by setting the target function to minimize the total data amount of the fault data in the data set, and ensuring 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.

[0166] As an optional embodiment, the specific manner in which the determining module determines the target recovery point from the plurality of candidate recovery points based on the time similarity algorithm includes:

[0167] For each candidate recovery point, the historical recovery record corresponding to the candidate recovery point is obtained;

[0168] The similarity of the time type between each record time point in the historical recovery record and the fault time information is calculated; optionally, the time type includes at least one of the year, month, date, holiday type, preset time period, preset user operation time type, and preset work task time type to which the corresponding time point belongs;

[0169] The average value of the similarity of the time type corresponding to all record time points is calculated to obtain the recovery point priority corresponding to the candidate recovery point;

[0170] The candidate recovery point with the highest recovery point priority is determined as the target recovery point.

[0171] It can be seen that, through the above optional embodiments, by obtaining the historical recovery record of each candidate recovery point and calculating the average value of the similarity of the time type between the record time point and the fault time information 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.

[0172] As an optional embodiment, the specific manner in which the recovery module determines the system recovery strategy corresponding to the system recovery request according to the current system data and the target recovery point includes:

[0173] The archived system data corresponding to the target recovery point is obtained;

[0174] The data similarity between the archived system data and the current system data is calculated;

[0175] The execution time point corresponding to the system recovery request is determined according to the data similarity and the recovery point priority.

[0176] It can be seen that, through the above optional embodiments, by acquiring the archived system data of the target recovery point and calculating the data similarity thereof with the current system data, and combining the recovery point priority to determine the execution time point of the system recovery request, the accurate recovery execution time determination based on the data similarity and the priority is realized, the efficiency and data consistency of system recovery are improved, and the data loss risk in the recovery process is reduced.

[0177] As an optional embodiment, the specific manner in which the recovery module determines the execution time point corresponding to the system recovery request according to the data similarity and the recovery point priority includes:

[0178] calculating a predicted recovery speed proportional to the data similarity;

[0179] calculating a recovery urgency parameter proportional to the recovery point priority;

[0180] calculating the product of the predicted recovery speed and the recovery urgency parameter to obtain a recovery execution parameter;

[0181] determining a plurality of executable recovery time points corresponding to the user, and for each executable recovery time point, calculating the time difference value between the executable recovery time point and the current time point;

[0182] calculating the ratio of the time difference value and the recovery execution parameter to obtain the execution priority corresponding to the executable recovery time point;

[0183] determining the executable recovery time point with the highest execution priority as the execution time point corresponding to the system recovery request.

[0184] It can be seen that, through the above optional embodiments, 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 to obtain a recovery execution parameter, combining the time difference value between the executable recovery time point of the user and the current time point, calculating the ratio of the time difference value and the recovery execution parameter as the execution priority, and selecting the executable recovery time point with the highest priority as the execution time point of the system recovery request, the accurate recovery execution time optimization based on the recovery speed and the urgency level is realized, the system recovery efficiency and data consistency are improved, and the recovery delay risk is reduced.

[0185] Embodiment Three

[0186] Please refer to Figure 3 , Figure 3 is another system recovery system based on time similarity disclosed in the embodiments of the application. Figure 3 The system recovery system based on time similarity described is applied in a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). As Figure 3As shown, the system recovery method based on time similarity can comprise:

[0187] a memory 301 storing executable program code;

[0188] a processor 302 coupled to the memory 301;

[0189] The processor 302 invokes the executable program code stored in the memory 301 to execute the steps of the system recovery method based on time similarity described in Embodiment One.

[0190] Embodiment Four

[0191] The embodiment of the application discloses a computer readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the system recovery method based on time similarity described in Embodiment One.

[0192] Embodiment Five

[0193] The embodiment of the application discloses a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the system recovery method based on time similarity described in Embodiment One.

[0194] The above describes specific embodiments of the present application, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order described in the embodiments, and still achieve the desired results. Also, the processes depicted in the figures do not necessarily have to be performed in the specific order described or in sequential order, but can be performed in other orders or concurrently. In certain implementations, multitasking and parallel processing can be advantageous.

[0195] The systems, apparatuses, modules or units illustrated by the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer can 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.

[0196] For the convenience of description, the above apparatus is described as various units respectively by functions. Of course, the functions of each unit can be implemented in one or more software and / or hardware in the implementation of the present specification.

[0197] Those skilled in the art will appreciate that embodiments of the present description can be readily used as a method, an apparatus (system) or a computer program product. Accordingly, embodiments of the present description can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer readable program code.

[0198] The present description is described in reference to flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present description. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0199] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks. Figure 1 one or more functions specified in the flow diagram and / or block diagram block or blocks.

[0201] In one typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0202] The memory can include non-persistent memory and / or persistent memory, such as flash memory, read-only memory (ROM), and / or the like, of computer- readable storage media. The memory is an example of computer-readable media.

[0203] Computer-readable media includes permanent and non-permanent, movable and non-movable 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0204] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0205] The specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The specification can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0206] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0207] Finally, it should be noted that: the embodiment of the application discloses a kind of system recovery method and system based on time similarity disclosed by only the preferred embodiment of the application, only for describing the technical solutions of the application, not for its limitation;Although the application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand;It can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features;And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A system recovery method based on time similarity, characterized in that, The method includes: In response to a user's system recovery request, retrieve current system data; Based on the fault analysis algorithm, the fault time information corresponding to the current system data is determined; Based on a time similarity algorithm, a target recovery point is determined from multiple candidate recovery points according to the fault time information, including: For each candidate recovery point, obtain the historical recovery record corresponding to that candidate recovery point; Calculate the similarity of time type between each record time point in the historical recovery record and the fault time information; the time type includes at least one of the following: year, month, date, 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 the recorded time points to obtain the priority of the recovery point corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point; Based on the current system data and the target recovery point, determine the system recovery strategy corresponding to the system recovery request, 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; Based on the data similarity and the recovery point priority, the execution time point corresponding to the system recovery request is determined.

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 the following: system hardware information, system version information, system application data, system operation log data, and system user information.

3. The system recovery method based on time similarity according to claim 1, characterized in that, The process of determining the fault time information corresponding to the current system data based on the fault analysis algorithm includes: Each data portion of the current system data is input into the fault correlation prediction model to obtain the fault correlation degree corresponding to each data portion; the fault correlation prediction model is trained using a training dataset that includes multiple training data and corresponding fault correlation labels. Filter out all data portions where the degree of correlation with the fault is greater than a preset threshold to obtain multiple fault data; Based on the data time point corresponding to the fault data, determine the fault time information corresponding to the current system data.

4. The system recovery method based on time similarity according to claim 3, characterized in that, The step of determining 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 aforementioned fault data, based on the data content, determine whether the fault data contains time information; If it exists, the corresponding time information will be determined as the data time point corresponding to the fault data; If not, identify relevant data in the current system data that is associated with the faulty data or has in-application communication; The time information corresponding to the relevant data is determined as the data time point corresponding to the fault data; Based on the clustering algorithm, a centralized data set is selected from all the fault data according to the data time point; Calculate the average of the data time points corresponding to all the fault data in the centralized data set 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 clustering algorithm-based method for selecting a centralized data set from all the fault data based on the data time points includes: The objective function is set to minimize the total amount of all fault data in the computation dataset. The constraints include that the time difference between any two fault data points in the calculation dataset is less than a preset time difference threshold, and the fault type similarity between any two fault data points in the calculation dataset 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 on a training dataset that includes multiple training data pairs and corresponding fault type similarity labels. Based on the objective function and the constraints, and using a clustering programming algorithm, the optimal set of computational data 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 step of determining the execution time point corresponding to the system recovery request based on the data similarity and the recovery point priority includes: Calculate the predicted recovery speed, which is proportional to the data similarity. Calculate the recovery urgency parameter, which is proportional to the recovery point priority; The product of the predicted recovery rate and the recovery urgency parameter is calculated to obtain the recovery execution parameters; 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 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.

7. A system recovery system based on time similarity, characterized in that, The system includes: The acquisition module is used to acquire current system data in response to a user's system recovery request; The analysis module is used to determine the fault time information corresponding to the current system data based on the fault analysis algorithm; The determination module is used to determine the target recovery point from multiple candidate recovery points based on the fault time information using a time similarity algorithm, including: For each candidate recovery point, obtain the historical recovery record corresponding to that candidate recovery point; Calculate the similarity of time type between each record time point in the historical recovery record and the fault time information; the time type includes at least one of the following: year, month, date, 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 the recorded time points to obtain the priority of the recovery point corresponding to the candidate recovery point; The candidate recovery point with the highest recovery point priority is determined as the target recovery point; The recovery module is used to determine the system recovery strategy 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; Based on the data similarity and the recovery point priority, the execution time point corresponding to the system recovery request is determined.

8. A system recovery system based on time similarity, characterized in that, The system includes: Memory containing 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 as described in any one of claims 1-6.

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