Server fault processing method and device and electronic equipment

Abstract

The invention provides a server fault processing method which can be applied to the technical field of servers. The server fault processing method comprises the steps that according to attribute information of a server, a to-be-processed exception of the server is matched with a plurality of exceptions in an exception database, at least one candidate exception is obtained, and the exception database comprises a plurality of exception data sets with corresponding exception types; determining a business association degree between the to-be-processed exception and the candidate exception according to the occurrence frequency of the at least one candidate exception in the plurality of exception data sets; determining the matching degree between the to-be-processed exception and the candidate exception according to the text similarity and the business association degree between the to-be-processed exception and the candidate exception; and under the condition that the matching degree meets a preset matching condition, determining candidate fault information corresponding to the candidate abnormality as target fault information so as to process the to-be-processed abnormality by using the target fault information. The invention further provides a server fault processing device and electronic equipment.

Description

Technical Field

[0001] This application relates to the technical field of servers, and more particularly to a server fault handling method, apparatus, and electronic device. Background Art

[0002] Servers play an important role in computer networks and provide various services to terminals. By reasonably configuring servers, various functions such as data storage and file sharing can be achieved. Since server anomalies are usually the result of the interweaving of multiple factors, it is difficult to accurately lock down server faults, and the fault handling efficiency is low. Summary of the Invention

[0003] This application provides a server fault handling method, apparatus, and electronic device.

[0004] According to a first aspect of this application, a server fault handling method is provided, including: matching a to-be-processed anomaly of a server with multiple anomalies in an anomaly database according to attribute information of the server to obtain at least one candidate anomaly, where the anomaly database includes multiple anomaly data sets each having a corresponding anomaly type; determining a business association degree between the to-be-processed anomaly and the candidate anomaly according to the occurrence frequency of the at least one candidate anomaly in the multiple anomaly data sets; determining a matching degree between the to-be-processed anomaly and the candidate anomaly according to the text similarity and the business association degree between the to-be-processed anomaly and the candidate anomaly; and when the matching degree meets a preset matching condition, determining candidate fault information corresponding to the candidate anomaly as target fault information so as to process the to-be-processed anomaly by using the target fault information.

[0005] A second aspect of this application provides a server fault handling apparatus, including: a matching module configured to match a to-be-processed anomaly of a server with multiple anomalies in an anomaly database according to attribute information of the server to obtain at least one candidate anomaly, where the anomaly database includes multiple anomaly data sets each having a corresponding anomaly type; a first determination module configured to determine a business association degree between the to-be-processed anomaly and the candidate anomaly according to the occurrence frequency of the at least one candidate anomaly in the multiple anomaly data sets; a second determination module configured to determine a matching degree between the to-be-processed anomaly and the candidate anomaly according to the text similarity and the business association degree between the to-be-processed anomaly and the candidate anomaly; and a third determination module configured to, when the matching degree meets a preset matching condition, determine candidate fault information corresponding to the candidate anomaly as target fault information so as to process the to-be-processed anomaly by using the target fault information.

[0006] A third aspect of the present application provides an electronic device, including: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0007] A fourth aspect of the present application further provides a computer-readable storage medium, on which computer programs or instructions are stored, and when the computer programs or instructions are executed by a processor, the steps of the above method are implemented.

[0008] A fifth aspect of the present application further provides a computer program product, including computer programs or instructions, and when the computer programs or instructions are executed by a processor, the steps of the above method are implemented.

[0009] According to an embodiment of the present application, according to the attribute information of the server, the to-be-processed exception is matched with multiple exceptions in the exception database to obtain at least one candidate exception. Since different types of exceptions have different impacts on business processing, the business association degree between the to-be-processed exception and the candidate exception is determined according to the occurrence frequency of the at least one candidate exception in multiple exception datasets, comprehensively evaluating the business association degree between the to-be-processed exception and the candidate exception from different exception types. At the same time, according to the business association degree and the text similarity between the to-be-processed exception and the candidate exception, the matching degree between the to-be-processed exception and the candidate exception is obtained. When the matching degree meets the preset matching condition, the candidate fault information corresponding to the candidate exception is determined as the target fault information, accurately locking the candidate exception that matches the to-be-processed exception in terms of attribute information, business, etc. The to-be-processed exception can be processed using the target fault information, improving the fault processing efficiency and reducing the impact on the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Through the following description of the embodiments of the present application with reference to the drawings, the above content and other objects, features and advantages of the present application will become clearer. In the drawings:

[0011] Figure 1 Schematically shows an application scenario diagram of the server fault processing method according to an embodiment of the present application;

[0012] Figure 2 Schematically shows a flowchart of the server fault processing method according to an embodiment of the present application;

[0013] Figure 3A Schematically shows a schematic diagram of the exception database according to an embodiment of the present application;

[0014] Figure 3BSchematically shows a schematic diagram of matching a to-be-processed exception with multiple exceptions in multiple exception datasets respectively according to a server identifier, configuration information, and exception information of the to-be-processed exception to obtain candidate exceptions according to an embodiment of the present application;

[0015] Figure 4 Schematically shows a flowchart of a server fault handling method according to another embodiment of the present application;

[0016] Figure 5 Schematically shows a structural block diagram of a server fault handling device according to an embodiment of the present application; and

[0017] Figure 6 Schematically shows a block diagram of an electronic device suitable for implementing the server fault handling method according to an embodiment of the present application. Detailed implementation manners

[0018] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present application. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0019] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0020] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0021] In cases where expressions similar to "at least one of A, B, and C, etc." are used, generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0022] Servers play an important role in modern computer networks, providing various services to clients. By reasonably configuring servers, various functions can be achieved, such as data storage, file sharing, etc. Since server anomalies are usually the result of the intertwining of multiple factors, it is difficult to accurately lock down server failures, and the efficiency of fault handling is low.

[0023] In view of this, embodiments of the present application provide a server fault handling method, including: matching the to-be-processed anomaly of the server with multiple anomalies in the anomaly database according to the attribute information of the server to obtain at least one candidate anomaly, where the anomaly database includes multiple anomaly data sets each having a corresponding anomaly type; determining the business correlation degree between the to-be-processed anomaly and the candidate anomaly according to the occurrence frequency of the at least one candidate anomaly in the multiple anomaly data sets; determining the matching degree between the to-be-processed anomaly and the candidate anomaly according to the text similarity and business correlation degree between the to-be-processed anomaly and the candidate anomaly; and when the matching degree meets a preset matching condition, determining the candidate fault information corresponding to the candidate anomaly as the target fault information, so as to use the target fault information to process the to-be-processed anomaly.

[0024] Figure 1 Schematically shows an application scenario diagram of the server fault handling method according to an embodiment of the present application.

[0025] As Figure 1 shown, the application scenario according to this embodiment may include a first terminal device 101, a second terminal device 102, a network 103, and a target server 104. The network 103 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, and the target server 104. The network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0026] Users can use the first terminal device 101 and the second terminal device 102 to interact with the target server 104 through the network 103 to receive or send messages, etc. Various communication client applications may be installed on the terminal device, such as the production system of the server, test management software, supply management software used by the server's supplier, user feedback software, etc. (only for example).

[0027] For example, the first terminal device 101 may have user feedback software, and the user feedback software can be used to feedback the situation of server anomalies (such as the anomaly description text of the to-be-processed anomaly) to the target server 103, so that the target server 103 can obtain server fault information in a timely manner.

[0028] For example, the second terminal device 102 may have a production system of the server. The production system of the server can be used to record the attribute information of the server. The attribute information may be the component configuration of the server, the software system version, the system compatibility information, etc. The attribute information can be sent to the target server 103 to analyze server failures.

[0029] For example, the second terminal device 102 has test management software. The test management software can be used to record the test information of the server. The test information may be hardware test information, software test information, etc. The test information can be sent to the target server 103 to analyze server failures. The attribute information may also include the test information of the server.

[0030] For example, the second terminal device 102 has supply management software. The supply management software is used to collect the component information of the server fed back by the supply manufacturer. The attribute information may also include the component information of the server.

[0031] Exemplarily, the abnormal situations of the server in production, testing, and original components can be collected from the first terminal 101 and the second terminal 102, and the abnormalities of the server can be described in text to form an abnormal database.

[0032] It should be noted that the server failure handling method provided by the embodiments of the present application can generally be executed by the target server 104. Correspondingly, the server failure handling device provided by the embodiments of the present application can generally be set in the target server 104. The server failure handling method provided by the embodiments of the present application can also be executed by a server or a server cluster different from the target server 104 and capable of communicating with the first terminal device 101, the second terminal device 102, and / or the target server 104. Correspondingly, the server failure handling device provided by the embodiments of the present application can also be set in a server or a server cluster different from the target server 104 and capable of communicating with the first terminal device 101, the second terminal device 102, and / or the target server 104.

[0033] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the target server in

[0034] Figure 2 are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and target servers.

[0035] As Figure 2 shown, the server failure handling method of this embodiment includes operations S210 to S240. This server failure handling method can be executed by the target server 104.

[0036] In operation S210, based on the attribute information of the server, match the to-be-processed exception of the server with multiple exceptions in the exception database to obtain at least one candidate exception.

[0037] According to an embodiment of the present application, the exception database includes multiple exception data sets each having a corresponding exception type.

[0038] For example, the exception database can be divided into multiple exception data sets according to the stage where the exception appears in the server. The exception type can be a production stage type, an after-sales stage type, a test stage type, etc.

[0039] For example, the exception database can be divided into multiple exception data sets according to the cause of the exception. The exception type can be a network exception type, an operating system exception type, a security exception type, etc.

[0040] For example, the exception database can be divided into multiple exception data sets according to the exception manifestation. The exception type can be a version compatibility type, a software inoperable type, a memory shortage type, etc.

[0041] According to an embodiment of the present application, the attribute information may include the type of the server, hardware information, software information, system information, etc.

[0042] For example, according to the type of the server, determine the reference exceptions of multiple reference servers of the same type from multiple exceptions in the exception database; perform a similarity match between the to-be-processed exception and the reference exceptions to obtain at least one candidate exception.

[0043] For example, according to the type of the server, determine the reference exceptions of multiple reference servers of the same type from multiple exceptions in the exception database; determine the occurrence stage of the to-be-processed exception, and determine the target exception data set from the exception database, where the exception database includes exception data sets of multiple stages; determine at least one candidate exception in the target exception data set from multiple reference exceptions.

[0044] In operation S220, determine the business association degree between the to-be-processed exception and the candidate exception according to the occurrence frequency of at least one candidate exception in multiple exception data sets.

[0045] According to an embodiment of the present application, since the same exception may occur in multiple stages, multiple hardware, multiple software, etc. of the server, that is, the same exception can appear multiple times in the same exception data set. For example, the exception can be a server downtime. The exception can appear in the exception data set of the application stage class, but the server downtime may be caused by an exception in the hardware configuration; the exception can also be caused by an incompatible component version resulting in server downtime. Therefore, the exception appears 2 times in the exception data set of the application stage class.

[0046] It should be noted that an exception can also have multiple exception types. For example, an exception such as "server downtime" can appear in an exception data set of the test phase category, an exception data set of the application phase category, etc. An exception such as "slow business response" can appear in a resource - type exception data set, a third - party service - type exception data set, etc.

[0047] According to an embodiment of the present application, the business relevance represents the degree of business similarity between the server corresponding to the exception to be processed and the candidate server corresponding to the candidate exception. Both the server and the candidate server can be used to execute business, and the business type can be data accounting, data analysis, interactive information processing, etc.

[0048] In operation S230, according to the text similarity and business relevance between the exception to be processed and the candidate exception, determine the matching degree between the exception to be processed and the candidate exception.

[0049] According to an embodiment of the present application, the text similarity can be determined based on the semantic similarity between the exception to be processed and the candidate exception; the text similarity can also be based on the similarity of attribute keyword fields between the exception to be processed and the candidate exception, etc.

[0050] For example, according to the attribute information of the exception to be processed, weight assignments are made to the text similarity and business relevance to obtain a text similarity weight and a business relevance weight. Based on the text similarity weight and the business relevance weight, a weighted sum of the text similarity and business relevance is calculated to obtain the matching degree. Therefore, matching results with high text similarity but no business relevance can be reduced, thereby improving the server exception handling efficiency.

[0051] For example, according to the attribute information of the exception to be processed, keyword fields are determined. For example, the attribute keyword fields can include "hardware configuration", "software configuration", "server classification", etc. According to multiple attribute keyword fields, the text description of the to - be - processed attributes corresponding to the exception to be processed and the text description of the candidate attributes corresponding to the candidate exception are respectively determined. Calculate the text similarity between the text description of the to - be - processed attributes and the text description of the candidate attributes. The text similarity and the business relevance can be multiplied to obtain the matching degree. Therefore, the results of the text similarity and business relevance can be combined to lock in the candidate exception that matches the exception to be processed, improving the processing efficiency of server exceptions.

[0052] In operation S240, when the matching degree meets the preset matching condition, determine the candidate fault information corresponding to the candidate exception as the target fault information, so as to use the target fault information to process the exception to be processed.

[0053] According to an embodiment of the present application, the preset matching condition may be that the matching degree is greater than a preset matching threshold. When the matching degree is greater than the preset matching threshold, the candidate fault information corresponding to the candidate anomaly is determined as the target fault information.

[0054] According to an embodiment of the present application, the candidate fault information may include information such as the anomaly handling process and anomaly solution of the candidate anomaly.

[0055] For example, when the number of target fault information is 1, the target fault information is used to process the anomaly to be processed.

[0056] For example, when the number of target fault information is multiple, the confidence level of each of the multiple target fault information is determined; the target fault information with the highest confidence level is used to process the fault information to be processed.

[0057] According to an embodiment of the present application, when the matching degree does not meet the preset matching condition, the anomaly to be processed can be handed over to manual review to promptly handle the anomaly of the server.

[0058] According to an embodiment of the present application, according to the attribute information of the server, the anomaly to be processed is matched with multiple anomalies in the anomaly database to obtain at least one candidate anomaly. Since different types of anomalies have different impacts on business processing, the business association degree between the anomaly to be processed and the candidate anomaly is determined according to the occurrence frequency of at least one candidate anomaly in multiple anomaly datasets, comprehensively evaluating the business association degree between the anomaly to be processed and the candidate anomaly from different anomaly types. At the same time, according to the business association degree and the text similarity between the anomaly to be processed and the candidate anomaly, the matching degree between the anomaly to be processed and the candidate anomaly is obtained. When the matching degree meets the preset matching condition, the candidate fault information corresponding to the candidate anomaly is determined as the target fault information, accurately locking the candidate anomaly that matches the anomaly to be processed in terms of attribute information, business, etc. The target fault information can be used to process the anomaly to be processed, which can improve the fault handling efficiency and reduce the impact on business processing.

[0059] According to an embodiment of the present application, the attribute information includes the server identifier and configuration information; matching the anomaly to be processed of the server with multiple anomalies in the anomaly database according to the attribute information of the server to obtain at least one candidate anomaly, including: respectively matching the anomaly to be processed with multiple anomalies according to the server identifier, configuration information, and anomaly information of the anomaly to be processed to obtain candidate anomalies.

[0060] According to an embodiment of the present application, the server identifier may be the model of the server, device code, etc. The configuration information may include system configuration, hardware configuration, and software configuration.

[0061] According to an embodiment of the present application, the abnormal information may be the server abnormal situation feedback by the user, or the information about the abnormal situation to be processed recorded by the server.

[0062] For example, according to the server identifier, multiple server abnormalities of the same server type are determined from the abnormal database; according to the abnormal information of the abnormal situation to be processed, multiple text abnormalities matching the text of the abnormal situation to be processed are determined from the multiple server abnormalities; according to the configuration information, at least one candidate abnormality is determined from the multiple text abnormalities.

[0063] According to an embodiment of the present application, by matching the abnormal situation to be processed with multiple abnormalities in the abnormal database from multiple dimensions such as server identifier, configuration information, and abnormal information, the abnormal range can be quickly narrowed down, the candidate abnormality can be accurately located, and the fault handling efficiency of the server can be improved.

[0064] According to an embodiment of the present application, the abnormal database includes: a function type data set, a configuration type data set, a component type data set, and a root cause type data set.

[0065] Figure 3A The schematic diagram of the abnormal database according to an embodiment of the present application is schematically shown.

[0066] As Figure 3A shown, the abnormal database includes a function type data set 310, a configuration type data set 320, a component type data set 330, and a root cause type data set 340.

[0067] The function type data set 310 can be divided into a security type abnormal subset, a device function type abnormal subset, etc. The function type data set can grasp the influence degree of the abnormal situation to be processed from a macroscopic level. For example, the security type abnormality can be processed preferentially to ensure the security of server data and operation.

[0068] The configuration type data set 320 can be divided into a hardware configuration type abnormal subset, a software configuration type abnormal subset, a network type abnormal, etc. The configuration type data set can make the positioning of the abnormal situation to be processed more accurate.

[0069] The component type data set 330 can be divided into a main processor type abnormal subset, a motherboard type abnormal subset, a cable type abnormal subset, etc. The component type data set can make the positioning of the abnormal situation to be processed more precise.

[0070] The root cause type data set 340 can be divided into a signal type abnormal subset, a software version type abnormal subset. The root cause type data set can directly determine the abnormal cause of the abnormal situation to be processed and accurately determine the target fault information related to the abnormal cause.

[0071] According to an embodiment of the present application, the exception database is divided into a function type dataset, a configuration type dataset, a component type dataset, and a root cause type dataset. The candidate exceptions matching the exception to be processed are accurately determined not only from multiple dimensions such as server identification, configuration information, and exception information, but also from the function types, configuration types, component types, and root cause types of different exceptions, so as to accurately determine the target fault information corresponding to the exception cause of the exception to be processed.

[0072] According to an embodiment of the present application, the exception information of the exception to be processed includes an exception log and an exception description text. According to the server identification, configuration information, and the exception information of the exception to be processed, the exception to be processed is respectively matched with multiple exceptions in multiple exception datasets to obtain candidate exceptions, including: determining at least one function exception from the function type dataset of the exception database according to the server identification, exception log, and exception description text; determining at least one configuration exception from the configuration type dataset of the exception database according to the matching result between the reference configuration information of at least one function exception and the configuration information; determining at least one component exception from the component type dataset of the exception database according to the matching result between the reference exception log of at least one configuration exception and the exception log; and determining at least one candidate exception from the root cause type dataset of the exception database according to the matching result between the reference exception description text of at least one component exception and the exception description text.

[0073] According to an embodiment of the present application, the exception log may be a log regarding the exception to be processed.

[0074] According to an embodiment of the present application, the exception description text may be the abnormal situation of the server feedback by the user. For example, the exception description text may be the exception feedback information sent by the user to the target server through the first terminal device. The exception feedback information may be "the server is down".

[0075] Figure 3B Schematically shows a schematic diagram of matching the exception to be processed with multiple exceptions in multiple exception datasets respectively according to the server identification, configuration information, and exception information of the exception to be processed to obtain candidate exceptions according to an embodiment of the present application.

[0076] As Figure 3B shown, at least one function exception is determined from the function type dataset 310 of the exception database according to the server identification, exception log, and exception description text. Specifically, it may be: determining at least one function exception from the function type dataset 310 of the exception database according to the server identification, preset keywords existing in the exception description text, and error codes in the exception log.

[0077] For example, in the abnormal description problem, if the preset keyword "system crash" appears and the system error code "0x0000007B" exists in the abnormal log, at least one functional abnormality is determined from the functional data set of the abnormal database. The functional abnormality has the same server type as the abnormal to be processed.

[0078] According to the matching result of the reference configuration information of at least one functional abnormality and the configuration information, at least one configuration abnormality is determined from the configuration data set 320 of the abnormal database. It may also include: determining the matching result of the reference configuration information of at least one functional abnormality and the configuration information based on configuration rules in the server identifier, error code, and configuration information. When the matching result of the reference configuration information of the functional abnormality and the configuration information indicates that the configuration types of the abnormal configuration corresponding to the functional abnormality and the to-be-processed abnormal configuration corresponding to the abnormal to be processed are the same, the functional abnormality that belongs to both the functional data set 310 and the configuration data set 320 is determined as a configuration abnormality. It should be noted that if no configuration abnormality occurs in the abnormal to be processed, at least one functional abnormality can be directly determined as a configuration abnormality.

[0079] For example, the functional abnormality can be "system crash", the server identifier can be the server model, the error code can be a common fault code related to the internal memory. Combining the error message of the internal memory module in the hardware configuration and the record of abnormal internal memory usage in the system configuration in the configuration information, it is determined that the abnormal to be processed belongs to a hardware configuration abnormality.

[0080] According to the matching result of the reference abnormal log of at least one configuration abnormality and the abnormal log, at least one component abnormality is determined from the component data set 330 of the abnormal database. It may also include: extracting the call chain pattern from the abnormal log and combining it with the software version and compatibility matrix information to determine the matching result of the reference abnormal log of the configuration abnormality and the abnormal log; when the matching result of the reference abnormal log of the configuration abnormality and the abnormal log indicates that the abnormal component corresponding to the configuration abnormality and the abnormal component corresponding to the abnormal to be processed are the same, the configuration abnormality that belongs to both the configuration data set 320 and the component data set 330 is determined as a component abnormality. It should be noted that if no component abnormality occurs in the abnormal to be processed, at least one configuration abnormality can be directly determined as a component abnormality.

[0081] For example, the configuration abnormality can be a software configuration abnormality. The call chain pattern of the abnormal log shows that the call of a software component (such as the operating system) is abnormal and the call times far exceed the normal level. At the same time, the software version of the server and other key components in the system show known compatibility problems in the compatibility matrix, and it is determined that the abnormal to be processed is a fault caused by the version compatibility problem of the operating system.

[0082] According to the matching result between the reference exception description text of at least one component exception and the exception description text, determine at least one candidate exception from the root cause class data set 340 of the exception database. It may further include: determining the matching result between the reference exception description text of the component exception and the exception description text based on the target key attribute information extracted from the exception log and the exception description text; when the matching result between the reference exception description text and the exception description text indicates that the description information is consistent, determine at least one component exception that belongs to both the component class data set 330 and the root cause class data set 340 as the candidate exception 350.

[0083] For example, the disk read / write error signal is frequently recorded in the exception log, and the exception description text mentions that the data read / write speed is extremely slow. The root cause exception may be a problem caused by disk aging, and the root cause class data set includes exceptions of multiple root cause classes.

[0084] According to the embodiment of the present application, determine at least one function exception from the function class data set of the exception database according to the server identifier, the exception log, and the exception description text; determine at least one configuration exception from the configuration class data set of the exception database according to the matching result between the reference configuration information of at least one function exception and the configuration information; determine at least one component exception from the component class data set of the exception database according to the matching result between the reference exception log of at least one configuration exception and the exception log; determine at least one candidate exception from the root cause class data set of the exception database according to the matching result between the reference exception description text of at least one component exception and the exception description text. Therefore, match the exception to be processed sequentially from the first-layer function class, the second-layer configuration class, the third-layer component class, and the fourth-layer root cause class, and also refine the root cause of the exception from different attribute combinations (such as server identifier, exception log, exception description text, configuration information, etc.), so as to improve the matching accuracy of the exception to be processed.

[0085] It should be noted that the collected exceptions are stored in different types of exception data sets of the exception database from the four dimensions of function type, configuration type, component type, and root cause type, so as to avoid omission of exception-related information. For example, the attribute information may include the exception source, exception code, stage at which the exception occurs, production batch, and so on. It can be automatically associated with the server production system according to the server identifier and batch to retrieve information such as the system configuration, hardware configuration, software configuration, and exception log of the server, providing data support for exception location.

[0086] For example, the source of an anomaly can be channels such as customer complaints, production, testing, or suppliers, providing a direction for subsequent analysis of the problem background. Tag the anomalies to ensure that each anomaly has a unique identifier, facilitating accurate tracking and management throughout the process. The occurrence stage clarifies in which specific stage such as design, production, testing, delivery, etc. the problem appears, helping to determine the timing of the problem. Customer information accurately locates the object affected by the anomaly, providing a reference for evaluating the importance of the anomaly. Server category and server identifier classify the anomalies, making the analysis more targeted. The occurrence time can precisely record the time when the anomaly occurs, providing a basis for tracing the development context of the anomaly. Anomaly batch information and the quantity of anomaly occurrence batches are used to count the scale of the anomalies, assisting in judging the prevalence of the anomalies. The anomaly description text can elaborate on the anomaly phenomenon in detail, enabling an intuitive understanding of the anomaly information. Save the anomaly log to provide raw data support for in-depth analysis of the anomalies to be processed.

[0087] During the establishment of the anomaly database, a data verification mechanism can be added. For the occurrence time in date format, automatically verify whether it conforms to the specified date format, such as "YYYY - MM - DD HH:MM:SS", and if not, prompt an input error; for numerical data such as the quantity of anomaly batches, set a reasonable value range, such as it must be an integer greater than or equal to 1, to avoid interference from invalid data input in the subsequent analysis process.

[0088] Aggregate the anomalies of servers in different business scenarios (such as production, testing, etc.) into the anomaly database, breaking data silos, and use multi-level classification in six dimensions (problem description text, problem log, software configuration, hardware configuration, system configuration, and server identifier) (the first layer is function category, the second layer is configuration category, the third layer is component category, and the fourth layer is root cause category) to locate the anomalies to be processed, thereby refining the root cause of the anomalies through different combinations of attributes.

[0089] For example, the server identifier and the abnormal batch information are automatically and closely associated with the production system to obtain comprehensive and key configuration information, component information, etc. System configuration includes software environment information. Version information is of great significance for determining whether there are compatibility or vulnerability issues in the software environment. Hardware configuration includes hardware information such as chassis, processor, motherboard, hard disk related components, fan, radiator, network card related components, adapter card, power supply unit, internal memory, etc., so as to quickly investigate the possibility of hardware failures; software configuration records the software versions of BIOS (Baseboard Management Controller), BMC (Baseboard Management Controller), CPLD (Complex Programmable Logic Device), and key components. Software version incompatibility is often an important root cause of server problems. The abnormal log contains key event records during the operation of the server, providing key clues for problem analysis.

[0090] In addition to being associated with the production system, it can also be associated with the operation and maintenance management system. The operation and maintenance management system stores historical data on the running state of the server, such as processor utilization rate, memory occupancy rate, network traffic, etc. By obtaining the historical data, it can assist in determining whether the problem is periodic or sporadic. For example, if the processor utilization rate frequently reaches the peak within a certain period and is accompanied by problems, it is necessary to focus on investigating hardware or software problems related to the processor.

[0091] According to an embodiment of the present application, the business association degree between the to-be-processed abnormal and the candidate abnormal is determined according to the occurrence frequency of at least one candidate abnormal in multiple abnormal data sets, including: based on the mapping relationship between the preset occurrence frequency and the correlation degree, the correlation degree between the to-be-processed abnormal and each of the multiple abnormal data sets is determined according to the occurrence frequency; based on the business attributes of the business processed by the server, the influence degree of the abnormal type of the to-be-processed abnormal on the business is determined; according to the correlation degree, influence degree, and type weight corresponding to each of the multiple abnormal types, the business association degree is determined.

[0092] According to an embodiment of the present application, the formula for the business association degree L is as follows:

[0093] (1)

[0094] i represents the number of abnormal types in the abnormal database. The i abnormal types respectively correspond to function type data sets, configuration type data sets, component type data sets, and root cause type data sets. Fi is the type weight, Ri is the correlation degree between the to-be-processed abnormal and the abnormal data set, and Pi is the influence degree of the abnormal type of the to-be-processed abnormal on the business.

[0095] According to an embodiment of the present application, the type weight can be dynamically adjusted according to multi-dimensional factors. For example, the type weight can be adjusted according to conditions such as the matching situation of abnormal logs, hardware configuration, software version, etc. Or the weight can be adjusted in combination with the occurrence frequency of different abnormal types. For frequently occurring abnormal types, the type weight is appropriately increased. For example, if a certain type of hardware abnormality occurs frequently, in the configuration class dataset, the type weight of the hardware-related attributes is appropriately increased to locate the abnormal to be processed more quickly.

[0096] For example, the type weights of the abnormal to be processed in the function class dataset, configuration class dataset, component class dataset, and root cause class dataset can be F1 = 0.2, F2 = 0.25, F3 = 0.3, and F4 = 0.25 respectively. Based on the mapping relationship between the preset occurrence frequency and the relevance, the relevance between the abnormal to be processed and the function class dataset, configuration class dataset, component class dataset, and root cause class dataset can be determined as R1 = 0.1, R2 = 0.05, R3 = 0.02, and R4 = 0.03 respectively. Based on the business attributes of the business processed by the server, the influence degrees of the abnormal to be processed on the function class dataset, configuration class dataset, component class dataset, and root cause class dataset can be determined as P1 = 0.8, P2 = 0.7, P3 = 0.6, and P4 = 0.7 respectively. The business relevance of the abnormal to be processed is calculated using formula (1).

[0097] Through the refined dynamic weight adjustment mechanism, combined with factors such as the occurrence frequency, the matching degree is calculated to provide a quantitative standard for abnormal location, further improving the efficiency and accuracy of abnormal location.

[0098] According to an embodiment of the present application, based on the mapping relationship between the preset occurrence frequency and the relevance, the relevance between the abnormal to be processed and each of the multiple abnormal datasets is determined according to the occurrence frequency. When the occurrence frequency increases or decreases, the relevance can be dynamically adjusted. Based on the business attributes of the business processed by the server, the influence degree of the abnormal type of the abnormal to be processed on the business is determined, and the impact of the server abnormality on the business can be accurately integrated. According to the relevance, influence degree, and type weight corresponding to each of the multiple abnormal types, the business relevance is determined. Therefore, the business relevance can not only be dynamically adjusted according to the business changes of the server and the change of the occurrence frequency of the abnormal type, the business influence degree, but also comprehensively evaluate the relevance between the abnormal to be processed and the business, and can realize dynamic abnormal location as the business of the server changes and the occurrence frequency of the abnormal type in the database changes.

[0099] According to an embodiment of the present application, according to the text similarity and business relevance between the abnormal to be processed and the candidate abnormal, the matching degree between the abnormal to be processed and the candidate abnormal is determined, including: the matching degree is calculated according to the business relevance, text similarity, and confidence of the candidate fault information, and the confidence is determined based on the historical processing results of the candidate fault information.

[0100] According to an embodiment of the present application, the formula for the matching degree T is as follows:

[0101] (2)

[0102] L is the business association degree, c is the text similarity, and u is the confidence level of the candidate fault information.

[0103] Determine the server identifier, exception description text, exception log, system configuration, hardware configuration, and software configuration as dimension fields. Determine the text similarity between the exception to be processed and the candidate exception on the dimension fields. For example, the candidate exception includes six dimension fields and the information of the six dimensions exists. While the system configuration is missing in the exception to be processed, the text similarity can be 5 / 6.

[0104] It should be noted that the text similarity can also be calculated by calculating the similarity of the dimension information between the exception to be processed and the candidate exception to obtain the text similarity.

[0105] According to an embodiment of the present application, by calculating the matching degree based on the business association degree, text similarity, and confidence level of the candidate fault information, the matching degree also covers the reliability of information such as the exception handling process and exception solution in the candidate fault information, improving the accuracy of exception handling.

[0106] According to an embodiment of the present application, the above method further includes: based on the scoring mapping relationship, determine the occurrence stage evaluation information, exception phenomenon evaluation information, and exception severity evaluation information of the exception to be processed according to the occurrence stage, exception description text, and exception severity of the exception to be processed; determine the exception urgency according to the occurrence stage evaluation information, exception phenomenon evaluation information, exception severity evaluation information, and time decay factor; determine the processing time of the exception to be processed according to the exception urgency.

[0107] According to an embodiment of the present application, the exception severity and exception urgency are key indicators for measuring the exception handling priority.

[0108] According to an embodiment of the present application, the exception urgency can consider not only evaluation items such as the occurrence stage, exception description text, and exception severity of the exception to be processed, but also evaluation items such as the order information of the server and the purchase object of the server. It can be set according to the actual situation.

[0109] According to an embodiment of the present application, the formula for the exception urgency q is as follows:

[0110] (3)

[0111] n is the number of evaluation items, t is the occurrence time of the exception to be processed, and βj(t) is the weight adjustment coefficient of the evaluation item. is the weight of the scoring item, and xj is the evaluation information. is the time decay factor.

[0112] According to the numerical range of the abnormal urgency, it can be divided into different levels to facilitate determining the processing time of the abnormal to be processed.

[0113] For example, the scoring items can be the occurrence stage, the abnormal description text, and the abnormal severity. The weights of the occurrence stage, the abnormal description text, and the abnormal severity are 0.4, 0.3, and 0.3 respectively. The evaluation information of the occurrence stage in server after-sales can be 10. The evaluation information of the abnormal description text representing the interface abnormality can be 4. The evaluation information of the abnormal severity representing very serious can be 10. Then, according to the occurrence time of the abnormal to be processed, the abnormal urgency is calculated according to formula (3).

[0114] It should be noted that the weights and evaluation information of the scoring items can be preset according to the actual situation, or data analysis can be performed on historical abnormal data to determine the weights and evaluation information of the scoring items.

[0115] According to the embodiments of the present application, the above method further includes: when the abnormal to be processed is of the target abnormal type, determining the number of times the abnormal to be processed appears on multiple reference servers in the same batch; when the number is greater than or equal to the preset threshold, determining the abnormal severity of the abnormal to be processed; when the number is less than the preset threshold, determining the judgment information for the abnormal number; and evaluating the abnormal to be processed according to the judgment information to obtain the abnormal severity.

[0116] According to the embodiments of the present application, the target abnormal type can be the device function type abnormal in the function type abnormal.

[0117] For example, the preset threshold can be 5. When the number is greater than or equal to 5, it is determined that the abnormal severity of the abnormal to be processed is the serious impact level and needs to be processed immediately. When the number is less than 5, the judgment information for the abnormal number can be the abnormal information source and the server identifier. When the abnormal information source is user feedback, it is determined that the abnormal severity of the abnormal to be processed is the serious impact level and needs to be processed immediately. When it is determined that the abnormal information source is the test terminal device, according to the mapping relationship between the preset server identifier and the abnormal severity, the abnormal severity corresponding to the server identifier is determined.

[0118] When the abnormal to be processed is the security abnormal in the function type abnormal, it is determined that the abnormal severity of the abnormal to be processed is the serious impact level and needs to be processed immediately.

[0119] When the abnormal to be processed is the user's suggestive feedback in the function type abnormal, it is determined that the abnormal severity of the abnormal to be processed is less affected and the processing priority is lower.

[0120] In the case where the exception to be processed is a performance - type exception among functional - type exceptions, determine the source of the exception information of the exception to be processed. When the source of the exception information is a test terminal device, determine the exception severity corresponding to the server identifier according to the mapping relationship between the preset server identifier and the exception severity. When the source of the exception information is a user terminal device, determine that the exception severity of the exception to be processed is less affected and the processing priority is low. When the source of the exception information is a production terminal device, determine that the exception severity of the exception to be processed is highly affected and needs to be processed preferentially. When the source of the exception information is other terminal devices, determine that the exception severity of the exception to be processed has a certain impact and needs to be processed as soon as possible.

[0121] In the case where the exception to be processed is a user - experience - type exception among functional - type exceptions, determine the exception severity corresponding to the server identifier according to the mapping relationship between the preset server identifier and the exception severity.

[0122] According to an embodiment of the present application, the exception severity can be automatically assigned through logical calculation from multi - dimensional factors such as the type of functional - type exceptions, the number of times the exception to be processed appears on multiple reference servers in the same batch, the source of the exception information, the server identifier, etc.

[0123] Figure 4 Schematically shows a flowchart of a server failure handling method according to another embodiment of the present application.

[0124] As Figure 4 shown, the server failure handling method according to another embodiment of the present application includes steps S410 - S460.

[0125] In operation S410, obtain the matching degree between the exception to be processed and the candidate exception.

[0126] In operation S420, determine whether the matching degree is greater than a preset matching threshold. If so, execute operation S430; if not, execute operation S460.

[0127] In operation S430, determine whether the number of candidate fault information is 1. If so, execute operation S440; if not, execute operation S450.

[0128] In operation S440, determine the candidate fault information as the target fault information.

[0129] The preset matching condition can be that the matching degree is greater than a preset matching threshold (such as 90%) and the number of candidate fault information is 1. In the case where the matching degree is greater than the preset matching threshold and the number of candidate fault information is 1, determine the candidate fault information corresponding to the candidate exception as the target fault information, so as to process the exception to be processed using the target fault information.

[0130] In operation S450, the confidence levels of multiple candidate fault messages are compared to obtain the target fault message with the highest confidence level.

[0131] In the case where the matching degree is greater than the preset matching threshold and there are multiple candidate fault messages, the confidence levels of the multiple candidate fault messages are compared to obtain the target fault message with the highest confidence level.

[0132] In operation S460, the exception to be processed is processed according to the processing strategy corresponding to the numerical range to which the matching degree belongs.

[0133] In the case where the matching degree is less than the preset matching threshold, determine the numerical range to which the matching degree belongs; process the exception to be processed according to the processing strategy corresponding to the numerical range.

[0134] For example, if the matching degree belongs to the first numerical range (such as 70% - 89%), the first processing strategy corresponding to the first numerical range may be to automatically transfer the exception to be processed to the person in charge assigned to the corresponding exception type for review. Notify the person in charge by email and attach the relevant data of the candidate exception, and allocate the processing time according to the exception urgency. If it is not completed within the processing time, it will be transferred to an expert for review.

[0135] For example, if the matching degree belongs to the second numerical range (50% - 69%), the second processing strategy corresponding to the second numerical range may be to start enhanced diagnosis, send a request to call a tool to capture additional logs, and locate the exception to be processed again to recalculate the reference matching degree. If the reference matching degree is greater than the matching degree, process according to the reference matching degree. In the case where the matching degree belongs to the second numerical range, in order to avoid the reduction of the matching degree caused by incomplete exception logs of the exception to be processed, additional logs can be captured to use the additional logs to locate the exception to be processed again to reduce the failure rate of matching and improve the efficiency of exception processing.

[0136] If the reference matching degree remains unchanged relative to the matching degree, transfer it to the person in charge of the corresponding exception type for in-depth analysis. Allocate the processing time according to the exception urgency. If it is not completed within the processing time, resolve it layer by layer.

[0137] For example, if the matching degree belongs to the third numerical range (0% - 50%), the third processing strategy corresponding to the third numerical range may be to start manual in-depth analysis.

[0138] A variety of flexible and highly targeted solution strategies are formulated according to the matching degree. When the matching degree ≥ 90% and the number of candidate fault information = 1, directly create the corresponding problem-solving task order and assign it to the responsible person to complete within the processing time to ensure that the exception can be quickly processed. When the matching degree ≥ 90% and the number of candidate fault information > 1, different processing methods are adopted according to whether multiple candidate fault information is consistent or mutually alternative, etc., and the optimal solution is scientifically selected. When the matching degree is in different numerical ranges, such as ≥ 70% - 89%, 50% - 69%, < 50%, etc., different processing strategies such as expert manual review process, enhanced diagnosis or manual in-depth analysis are respectively initiated to cope with various complex exceptions.

[0139] The processing results of the exceptions to be processed can be divided into 3 levels, for example, very good, general, and large adjustment.

[0140] In the case where the matching degree is greater than the preset matching threshold and the number of candidate fault information is 1, the candidate fault information corresponding to the candidate exception is determined as the target fault information. For the processing result of using the target fault information to process the exception to be processed is very good, increase the confidence of the target fault information by 10%; for the processing result is general, the confidence of the target fault information remains unchanged; for the processing result is large adjustment, reduce the confidence of the target fault information by 10%, and update the confidence of the target fault information in the exception database.

[0141] In the case where the matching degree is greater than the preset matching threshold and there are multiple candidate fault information, compare the confidence of multiple candidate fault information to obtain the target fault information with the highest confidence. For the processing result of using the target fault information to process the exception to be processed is general or large adjustment, reduce the confidence of the target fault information. For the processing result of using the target fault information to process the exception to be processed is very good, increase the confidence of the target fault information by 10%.

[0142] For the exception to be processed with the matching degree belonging to the first numerical range (such as 70% - 89%), if the processing result is very good, increase the confidence of the actual fault information of the exception to be processed by 10%; if the processing result is general, increase the confidence of the actual fault information of the exception to be processed by 5%; the confidence of the rest of the processing results remains unchanged. At the same time, comprehensively evaluate the actual fault information using the effect evaluation index system, and feedback the evaluation result to achieve continuous optimization. For example, if it is found that the processing time in the actual fault information is relatively long, the matching model can be further optimized in the exception location stage to improve the location accuracy, thereby shortening the problem-solving time.

[0143] For the to-be-processed exceptions whose matching degrees belong to the second numerical range and the third numerical range, update the actual fault information of the to-be-processed exceptions to the exception database. If there are other matching reference exceptions in the exception database, use the exception description text and exception log for re-verification to increase the matching degree. At the same time, comprehensively evaluate the actual fault information using the effect evaluation index system, and feedback the evaluation results to achieve continuous optimization. For example, if it is found that the to-be-processed exception has a greater impact on the business, more backup fault information can be considered to reduce the impact on the business.

[0144] According to an embodiment of the present application, update the relevant information in the exception database according to the processing results of the to-be-processed exceptions, so as to effectively track and feedback the exception processing results, continuously optimize the exception processing process, improve the effectiveness and reliability of subsequent problem exceptions, and reduce the processing cost of similar exceptions.

[0145] By comprehensively constructing an exception database from multiple exception types, and each exception in the exception database has six-dimensional information including exception description text, exception log, server identifier, software configuration, system configuration, and hardware configuration, so as to quickly obtain comprehensive and accurate exception basic information and improve the exception location efficiency. The newly added data verification mechanism ensures that the input data is accurate and standardized, and guarantees the reliability of the exception processing process from the source.

[0146] The exception database can be associated with multiple systems to obtain more comprehensive server attribute information. For example, the system can be the production terminal device of the server, the operation and maintenance terminal device, etc. The production terminal device can obtain production information, such as component information and hardware configuration information of the server. The operation and maintenance terminal device, etc. can obtain the server operation history data. The to-be-processed exception can be analyzed from two dimensions of production information and operation history. Based on the changes in indicators such as the usage rate of the processor and the memory occupancy rate, combined with the hardware configuration and software version information, the cause of the exception can be judged more accurately, and the accuracy and comprehensiveness of exception analysis can be improved.

[0147] Figure 5 Schematically shows a structural block diagram of a server fault processing device according to an embodiment of the present application.

[0148] As Figure 5 shown, the server fault processing device 500 of this embodiment includes a matching module 510, a first determination module 520, a second determination module 530, and a third determination module 540.

[0149] The matching module 510 is used to match the to-be-processed exceptions of the server with multiple exceptions in the exception database according to the attribute information of the server, and obtain at least one candidate exception. The exception database includes multiple exception data sets each having a corresponding exception type. In one embodiment, the matching module 510 can be used to perform the operation S210 described above, which will not be elaborated here.

[0150] The first determination module 520 is configured to determine the business association degree between the to-be-processed exception and the candidate exception according to the occurrence frequency of at least one candidate exception in multiple exception datasets. In an embodiment, the first determination module 520 may be configured to perform the operation S220 described above, which will not be elaborated here.

[0151] The second determination module 530 is configured to determine the matching degree between the to-be-processed exception and the candidate exception according to the text similarity and business association degree between the to-be-processed exception and the candidate exception. In an embodiment, the second determination module 530 may be configured to perform the operation S230 described above, which will not be elaborated here.

[0152] The third determination module 540 is configured to, when the matching degree meets a preset matching condition, determine the candidate fault information corresponding to the candidate exception as the target fault information, so as to process the to-be-processed exception by using the target fault information. In an embodiment, the third determination module 540 may be configured to perform the operation S240 described above, which will not be elaborated here.

[0153] According to an embodiment of the present application, the attribute information includes a server identifier and configuration information; the matching module 510 includes a matching sub-module. The matching sub-module is configured to match the to-be-processed exception with multiple exceptions respectively according to the server identifier, the configuration information, and the exception information of the to-be-processed exception, so as to obtain candidate exceptions.

[0154] According to an embodiment of the present application, the exception database includes: a function dataset, a configuration dataset, a component dataset, and a root cause dataset.

[0155] According to an embodiment of the present application, the exception information of the to-be-processed exception includes an exception log and an exception description text; the matching sub-module includes a first determination unit, a second determination unit, a third determination unit, and a fourth determination unit. The first determination unit is configured to determine at least one functional exception from the function dataset of the exception database according to the server identifier, the exception log, and the exception description text. The second determination unit is configured to determine at least one configuration exception from the configuration dataset of the exception database according to the matching result between the reference configuration information of at least one functional exception and the configuration information; the third determination unit is configured to determine at least one component exception from the component dataset of the exception database according to the matching result between the reference exception log of at least one configuration exception and the exception log; the fourth determination unit is configured to determine at least one candidate exception from the root cause dataset of the exception database according to the matching result between the reference exception description text of at least one component exception and the exception description text.

[0156] According to an embodiment of the present application, the first determination module 520 includes a first determination sub-module, a second determination sub-module, and a third determination sub-module. The first determination sub-module is configured to determine the relevance of the to-be-processed exception to each of the multiple exception data sets according to the occurrence frequency based on the mapping relationship between the preset occurrence frequency and the relevance; the second determination sub-module is configured to determine the influence degree of the exception type of the to-be-processed exception on the service based on the service attribute of the service processed by the server; the third determination sub-module is configured to determine the service relevance according to the relevance, influence degree, and type weight corresponding to each of the multiple exception types.

[0157] According to an embodiment of the present application, the second determination module 530 includes a calculation sub-module. The calculation sub-module is configured to calculate a matching degree according to the service relevance, text similarity, and confidence level of the candidate fault information, and the confidence level is determined based on the historical processing result of the candidate fault information.

[0158] According to an embodiment of the present application, the above-mentioned device further includes a fourth determination module, a fifth determination module, and a sixth determination module. The fourth determination module is configured to determine the occurrence stage evaluation information, exception phenomenon evaluation information, and exception severity evaluation information of the to-be-processed exception based on the scoring mapping relationship according to the occurrence stage, exception description, and exception severity of the to-be-processed exception; the fifth determination module is configured to determine the exception urgency according to the occurrence stage evaluation information, exception phenomenon evaluation information, exception severity evaluation information, and time decay factor; the sixth determination module is configured to determine the processing time of the to-be-processed exception according to the exception urgency.

[0159] According to an embodiment of the present application, the above-mentioned device further includes a seventh determination module, an eighth determination module, a ninth determination module, and an evaluation module. The seventh determination module is configured to determine the number of occurrences of the to-be-processed exception on multiple reference servers in the same batch when the to-be-processed exception is of a target exception type; the eighth determination module is configured to determine the exception severity of the to-be-processed exception when the number is greater than or equal to a preset threshold; the ninth determination module is configured to determine the judgment information for the number of exceptions when the number is less than the preset threshold; the evaluation module is configured to evaluate the to-be-processed exception according to the judgment information to obtain the exception severity.

[0160] According to an embodiment of the present application, any combination of the matching module 510, the first determination module 520, the second determination module 530, and the third determination module 540 can be implemented in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present application, at least one of the matching module 510, the first determination module 520, the second determination module 530, and the third determination module 540 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on a substrate, a system in a package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging circuits, etc., implemented by hardware or firmware, or implemented in any one or a suitable combination of the three implementation methods of software, hardware, and firmware. Alternatively, at least one of the matching module 510, the first determination module 520, the second determination module 530, and the third determination module 540 can be at least partially implemented as a computer program module, and when the computer program module is run, it can execute the corresponding functions.

[0161] Figure 6 A block diagram of an electronic device suitable for implementing the server fault handling method according to an embodiment of the present application is schematically shown.

[0162] As Figure 6 shown, the electronic device 600 according to an embodiment of the present application includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. The processor 601 can include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 601 can also include on-board memory for caching purposes. The processor 601 can include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present application.

[0163] In the RAM 603, various programs and data required for the operation of the electronic device 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present application by executing the programs in the ROM 602 and / or the RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to the embodiments of the present application by executing the programs stored in the one or more memories.

[0164] According to an embodiment of the present application, the electronic device 600 may further include an input / output (I / O) interface 605, and the input / output (I / O) interface 605 is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 606 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read from it can be installed into the storage section 608 as needed.

[0165] The present application also provides a computer-readable storage medium, which may be included in the device / device / system described in the above embodiments; or may exist separately without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present application is implemented.

[0166] According to an embodiment of the present application, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present application, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than the ROM 602 and RAM 603.

[0167] An embodiment of the present application also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program code is used to enable the computer system to implement the server fault handling method provided by the embodiment of the present application.

[0168] When the computer program is executed by the processor 601, the above functions defined in the system / apparatus of the embodiment of the present application are executed. According to an embodiment of the present application, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0169] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and is downloaded and installed through the communication part 609, and / or installed from the removable medium 611. The program code included in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0170] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present application are executed. According to an embodiment of the present application, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0171] In accordance with embodiments of the present application, program code for executing the computer programs provided by the embodiments of the present application may be written in any combination of one or more programming languages. Specifically, these computing programs may be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0172] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0173] Those skilled in the art can understand that the features described in the various embodiments of the present application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments of the present application can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present application.

[0174] The above describes the embodiments of the present application. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present application. Although the embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present application, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present application.

Claims

1. A server fault handling method, characterized in that, The method includes: Matching the to-be-processed exception of the server with multiple exceptions in an exception database according to the attribute information of the server to obtain at least one candidate exception, where the exception database includes multiple exception data sets each having a corresponding exception type; Determining the business association degree between the to-be-processed exception and the candidate exception according to the occurrence frequency of the at least one candidate exception in the multiple exception data sets; Determining the matching degree between the to-be-processed exception and the candidate exception according to the text similarity and the business association degree between the to-be-processed exception and the candidate exception; When the matching degree meets a preset matching condition, determining the candidate fault information corresponding to the candidate exception as the target fault information, so as to process the to-be-processed exception by using the target fault information.

2. The method according to claim 1, characterized in that The attribute information includes a server identifier and configuration information; The matching the to-be-processed exception of the server with multiple exceptions in an exception database according to the attribute information of the server to obtain at least one candidate exception includes: Matching the to-be-processed exception with the multiple exceptions respectively according to the server identifier, the configuration information and the exception information of the to-be-processed exception to obtain the candidate exception.

3. The method according to claim 1 or 2, characterized in that, The exception database includes: a function data set, a configuration data set, a component data set and a root cause data set.

4. The method according to claim 3, wherein The exception information of the to-be-processed exception includes an exception log and an exception description text; The matching the to-be-processed exception with multiple exceptions in multiple exception data sets respectively according to the server identifier, the configuration information and the exception information of the to-be-processed exception to obtain the candidate exception includes: Determining at least one function exception from the function data set of the exception database according to the server identifier, the exception log and the exception description text; Determining at least one of the configuration exceptions from the configuration data set of the exception database according to the matching result between the reference configuration information of the multiple function exceptions and the configuration information; Determining at least one component exception from the component data set of the exception database according to the matching result between the reference exception log of at least one of the configuration exceptions and the exception log; Determining the at least one candidate exception from the root cause data set of the exception database according to the matching result between the reference exception description text of at least one of the component exceptions and the exception description text.

5. The method according to claim 3, wherein The determining the business association degree between the to-be-processed exception and the candidate exception according to the occurrence frequency of the at least one candidate exception in the multiple exception data sets includes: Based on the mapping relationship between the preset occurrence frequency and the relevance, determining the relevance between the to-be-processed exception and each of the multiple exception data sets according to the occurrence frequency; Based on the business attribute of the business processed by the server, determining the influence degree of the exception type of the to-be-processed exception on the business; Determining the business association degree according to the relevance, the influence degree and the type weight corresponding to each of the multiple exception types.

6. The method according to claim 1, wherein Determining the matching degree between the to-be-processed exception and the candidate exception according to the text similarity and the business association degree between the to-be-processed exception and the candidate exception includes: Calculating the matching degree according to the business association degree, the text similarity, and the confidence level of the candidate fault information, where the confidence level is determined based on the historical processing results of the candidate fault information.

7. The method according to claim 2, characterized in that The method further includes: Based on a scoring mapping relationship, determining the occurrence stage evaluation information, the abnormal phenomenon evaluation information, and the abnormal severity evaluation information of the to-be-processed exception according to the occurrence stage, the exception description, and the abnormal severity of the to-be-processed exception; Determining the abnormal urgency according to the occurrence stage evaluation information, the abnormal phenomenon evaluation information, the abnormal severity evaluation information, and the time decay factor; Determining the processing time of the to-be-processed exception according to the abnormal urgency.

8. The method according to claim 7, wherein The method further includes: When the to-be-processed exception is of a target exception type, determining the number of occurrences of the to-be-processed exception on multiple reference servers in the same batch; When the number is greater than or equal to a preset threshold, determining the abnormal severity of the to-be-processed exception; When the number is less than the preset threshold, determining the judgment information for the number of exceptions; Evaluating the to-be-processed exception according to the judgment information to obtain the abnormal severity.

9. A server fault handling device, characterized in that, The device includes: A matching module, configured to match the to-be-processed exception of the server with multiple exceptions in an exception database according to the attribute information of the server, to obtain at least one candidate exception, where the exception database includes multiple exception data sets each having a corresponding exception type; A first determination module, configured to determine the business association degree between the to-be-processed exception and the candidate exception according to the occurrence frequency of the at least one candidate exception in the multiple exception data sets; A second determination module, configured to determine the matching degree between the to-be-processed exception and the candidate exception according to the text similarity and the business association degree between the to-be-processed exception and the candidate exception; A third determination module, configured to, when the matching degree meets a preset matching condition, determine the candidate fault information corresponding to the candidate exception as the target fault information, so as to process the to-be-processed exception by using the target fault information.

10. An electronic device, including: One or more processors; A memory for storing one or more computer programs, Characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

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