CN109615312B - Abnormality detection method, device, electronic device and storage medium in business execution - Google Patents
Abnormality detection method, device, electronic device and storage medium in business execution Download PDFInfo
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Abstract
本公开提供了一种业务执行中的异常排查方法、装置、电子设备和存储介质。本公开用于系统性能优化中的漏洞修复效率优化。该方法包括：接收用户的操作指令，其中所述操作指令是针对所述业务中的一个操作的指令；按照所述操作指令中的操作对应的预设检验规则，检验所述操作指令，产生检验结果日志；响应于业务执行中出现异常的报告，基于异常的种类查找所述检验结果日志，定位所述检验结果日志中与该异常的种类对应的异常日志段；将所述异常日志段输入第一机器学习模型，由所述第一机器学习模型输出异常原因。本公开实施例减少了运维资源占用，又保障了业务执行中的高效异常排查。
The present disclosure provides a method, device, electronic device and storage medium for troubleshooting anomalies in business execution. The present disclosure is used for optimizing vulnerability repair efficiency in system performance optimization. The method includes: receiving an operation instruction from a user, wherein the operation instruction is an instruction for an operation in the business; inspecting the operation instruction according to a preset inspection rule corresponding to the operation in the operation instruction, and generating an inspection result log; in response to a report of an abnormality in business execution, searching the inspection result log based on the type of abnormality, locating the abnormal log segment corresponding to the type of abnormality in the inspection result log; inputting the abnormal log segment into a first machine learning model, and the first machine learning model outputs the cause of the abnormality. The embodiments of the present disclosure reduce the occupation of operation and maintenance resources, and ensure efficient abnormality troubleshooting in business execution.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for troubleshooting in service execution, an electronic device, and a storage medium.
Background
There are a large number of repeated daily work tasks in the daily IT operation and maintenance work of the unit. Some of these tasks are complex and cumbersome in number, some depend heavily on the execution order, and some require waiting for various conditions to be available for execution. Although the technology of IT operation and maintenance management is continuously advancing, IT operation and maintenance personnel are not really liberated, and most of the works such as system opening and closing, system updating and upgrading, emergency operation and the like of many enterprises are manually operated at present. Even if the system is simply changed or the software is copied and pasted, the operation and maintenance personnel often need to log in each device one by one to manually change. Especially in the case of cloud platforms, big data and mass devices, the workload is big and conceivable. Such changing and checking operations often occur daily in IT operation and maintenance, which occupies a large amount of operation and maintenance resources.
In the prior art, an abnormality investigation scheme in efficient service execution for reducing the occupation of operation and maintenance resources is urgently needed.
Disclosure of Invention
The present disclosure is directed to an anomaly investigation scheme in efficient business execution that reduces the occupancy of operation and maintenance resources.
According to a first aspect of the present disclosure, there is provided an anomaly investigation method in service execution, the service including a sequence of a plurality of operations that are consecutively executed, each operation corresponding to a preset check rule, the method comprising:
receiving an operation instruction of a user, wherein the operation instruction is an instruction for one operation in the service;
According to a preset checking rule corresponding to the operation in the operation instruction, checking the operation instruction to generate a checking result log;
Responding to the report of the abnormality in the business execution, searching the inspection result log based on the type of the abnormality, and positioning an abnormality log section corresponding to the type of the abnormality in the inspection result log;
Inputting the abnormal log segment into a first machine learning model, and outputting an abnormal reason by the first machine learning model, wherein the first machine learning model is trained by inputting each abnormal log segment sample in an abnormal log segment sample set into the first machine learning model, wherein each abnormal log segment sample is labeled with a known label of the abnormal reason of the abnormal log segment sample in advance, the first machine learning model outputs the abnormal reason, and if the abnormal reason output by the first machine learning model is inconsistent with the label of the abnormal log segment sample, the first machine learning model is adjusted to enable the abnormal reason output by the first machine learning model to be consistent with the label of the abnormal log segment sample.
In one embodiment, after inputting the anomaly log segment into a first machine learning model, outputting the cause of the anomaly by the first machine learning model, the method further comprises:
and searching an abnormal reason and solution comparison table based on the abnormal reason, and acquiring a solution corresponding to the abnormal reason.
In one embodiment, the checking the operation instruction according to a preset checking rule corresponding to the operation in the operation instruction, and generating a checking result log includes:
acquiring an operation name in the operation instruction;
Searching a comparison table of operation names and preset check rules, and acquiring the preset check rules corresponding to the operation names, wherein the preset check rules comprise conditions which are required to be met by each field in an operation instruction;
Checking whether each field in the operation instruction meets the condition required to be met by the field in the preset checking rule;
and generating a test result log according to the test result.
In one embodiment, in response to the report of the occurrence of the abnormality in the service execution, searching the inspection result log based on the type of the abnormality, and locating an abnormality log segment corresponding to the type of the abnormality in the inspection result log, including:
identifying the type of the abnormality from the report of the abnormality in the service execution;
Searching an abnormal type and an abnormal keyword comparison table to obtain keywords corresponding to the abnormal type;
locating the keywords in the test result log;
And positioning an abnormal log segment corresponding to the keyword in the inspection result log based on a preset criterion and serving as an abnormal log segment corresponding to the type of the abnormality.
In one embodiment, the predetermined criteria includes taking a template phrase segment that appears in the context of the keyword and is closest to the keyword as an exception log segment corresponding to the keyword, the template phrase segment being selected from a predetermined template phrase Duan Ku.
In one embodiment, the anomaly cause and solution look-up table is pre-established based on a second machine learning model, which is pre-established by:
Inputting each abnormality cause sample in an abnormality cause sample library into a second machine learning model, wherein each abnormality cause sample is labeled with a known label of a solution of the abnormality cause sample in advance, the second machine learning model outputs a judged solution, and if the judged solution of the second machine learning model is inconsistent with the label of the abnormality cause sample, the second machine learning model is adjusted so that the judged solution of the second machine learning model is consistent with the label of the abnormality cause sample.
In one embodiment, after searching the exception reason and the solution comparison table based on the exception reason and acquiring the solution corresponding to the exception reason, the method further includes:
searching a script corresponding to the solution in a script library established in advance based on the acquired solution;
Executing the script corresponding to the solution.
According to a second aspect of the present disclosure, there is provided an abnormality checking apparatus in execution of a service including a sequence of a plurality of operations that are consecutively executed, each operation corresponding to a preset check rule, the apparatus comprising:
A receiving unit configured to receive an operation instruction of a user, wherein the operation instruction is an instruction for one operation in the service;
The checking unit is used for checking the operation instruction according to a preset checking rule corresponding to the operation in the operation instruction, and generating a checking result log;
The positioning unit is used for responding to the report of the abnormality in the service execution, searching the inspection result log based on the type of the abnormality and positioning an abnormality log section corresponding to the type of the abnormality in the inspection result log;
An abnormality cause obtaining unit configured to input the abnormality log section into a first machine learning model, and output an abnormality cause by the first machine learning model, wherein the first machine learning model is trained by inputting each abnormality log section sample in a set of abnormality log section samples into the first machine learning model, each abnormality log section sample being previously labeled with a label of the known abnormality cause of the abnormality log section sample, the first machine learning model outputting the abnormality cause, and if the abnormality cause output by the first machine learning model does not coincide with the label of the abnormality log section sample, adjusting the first machine learning model so that the abnormality cause output by the first machine learning model coincides with the label of the abnormality log section sample.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
And a memory configured to store the executable instructions.
A processor configured to execute the executable instructions stored in the memory to perform the method described above.
According to a fourth aspect of the present disclosure, there is provided a computer readable program medium storing computer program instructions which, when executed by a computer, cause the computer to perform the method described above.
In the embodiment of the disclosure, the business is regarded as a sequence of a plurality of operations that are performed consecutively, and a preset check rule is set for each operation. Thus, after receiving an operation instruction of a user aiming at one operation in the sequence, the operation instruction is checked according to a preset check rule corresponding to the operation in the operation instruction, and a check result log is generated. Once an abnormality occurs in the execution of the business, the inspection result log can be searched based on the type of the abnormality, and an abnormality log section corresponding to the type of the abnormality in the inspection result log can be positioned. And inputting the abnormal log segment into a first machine learning model, and outputting the abnormal reason by the first machine learning model. The process is completely executed by the machine, the intervention of operation and maintenance personnel is not needed, the occupation of operation and maintenance resources is reduced, and the automation of abnormality investigation in service execution is realized.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Fig. 1 illustrates a flowchart of a method of troubleshooting in service execution according to an example embodiment of the present disclosure.
Fig. 2 illustrates a flowchart of a method of troubleshooting in service execution according to an example embodiment of the present disclosure.
Fig. 3 shows a specific flowchart of step 120 according to an example embodiment of the present disclosure.
Fig. 4 shows a specific flowchart of step 130 according to an example embodiment of the present disclosure.
Fig. 5 shows a block diagram of an abnormality troubleshooting device in service execution according to an example embodiment of the present disclosure.
Fig. 6 shows a hardware device diagram of an electronic device according to an example embodiment of the present disclosure.
Fig. 7 illustrates a computer-readable storage medium diagram according to an example embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein, but rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
As shown in fig. 1, in an embodiment, a method for troubleshooting during service execution is provided. A business here refers to a complete activity undertaken in an organization (business, institution, organization, team, etc.). For example, an insurance business in an insurance company is a business that it engages in, including a complete set of flows, such as user documentations, > quotes, > application for insurance, > order submittal, > invocation of financial system billing, > customer payments, > invocation of financial accounts, > underwriting. An exception refers to a situation that the system is inconsistent with normal operation during service execution, such as "recording failure" when "user records" and so on. The abnormality troubleshooting method is a method of ascertaining the cause of an abnormality when the abnormality occurs. The method is performed by a system server of the unit.
In one embodiment, the service includes a sequence of a plurality of consecutively performed operations. Each specific action in the service is called an operation. For example, the application service is a sequence of continuously performed operations such as user logging, quoting, applying for application, submitting an order, invoking a financial system order, customer payment, invoking a financial account, and underwriting. Each operation corresponds to a preset checking rule. The preset checking rule is a preset rule for checking the operation instruction. An operation instruction is an instruction for performing an operation. For example, in a user entry form, there is a requirement for each field in the user entry form. For example, whether the name field is for Chinese or Chinese plus pinyin entry. Or, the name field in the operation instruction of the corresponding list is Chinese or Chinese plus pinyin. If the operation instruction does not meet the preset checking rule, the operation instruction cannot pass the checking, but is recorded in a log. This is described in detail below.
As shown in fig. 1, the method includes:
Step 110, receiving an operation instruction of a user, wherein the operation instruction is an instruction for one operation in the service;
step 120, according to a preset inspection rule corresponding to the operation in the operation instruction, inspecting the operation instruction to generate an inspection result log;
Step 130, responding to the report of the abnormality in the service execution, searching the inspection result log based on the type of the abnormality, and positioning an abnormality log segment corresponding to the type of the abnormality in the inspection result log;
And 140, inputting the abnormal log segment into a first machine learning model, and outputting an abnormal reason by the first machine learning model, wherein the first machine learning model is trained by inputting each abnormal log segment sample in an abnormal log segment sample set into the first machine learning model, wherein each abnormal log segment sample is labeled with a label of the known abnormal reason of the abnormal log segment sample in advance, the first machine learning model outputs the abnormal reason, and if the abnormal reason output by the first machine learning model is inconsistent with the label of the abnormal log segment sample, the first machine learning model is adjusted to enable the abnormal reason output by the first machine learning model to be consistent with the label of the abnormal log segment sample.
These steps are described in detail below.
In step 110, an operation instruction of a user is received, wherein the operation instruction is an instruction for one operation in the service.
For example, for an operation instruction to record this operation, the operation instruction may first have an operation name field indicating that the operation to be performed by the operation instruction is to record the list. In addition, the operation instruction may be to carry specific content fields of the record, such as a name field, a name of the applicant, an age field, an age of the applicant, and the like. Different operations have different operation instructions.
In step 120, the operation instruction is checked according to a preset check rule corresponding to the operation in the operation instruction, and a check result log is generated.
In one embodiment, as shown in FIG. 3, step 120 includes:
step 1201, obtaining an operation name in the operation instruction;
Step 1202, searching a comparison table of operation names and preset check rules, and obtaining the preset check rules corresponding to the operation names, wherein the preset check rules comprise conditions to be met by each field in an operation instruction;
Step 1203, checking whether each field in the operation instruction meets the condition that the field in the predetermined checking rule needs to meet;
and 1204, generating a test result log according to the test result.
As indicated above, one field of the operation instruction is an operation name field, and thus, in step 1201, the operation name is read from the operation name field.
The operation name and preset check rule comparison table in step 1202 is a preset table storing operation names and corresponding check rules. The check rules are specific to the fields in the operation instruction, and the content of each field is different, so that different preset check rules corresponding to the operation names are required to be stored in the operation name and preset check rule comparison table.
For example, for the name field, the check rule may be Chinese+Pinyin. For the date of birth field, the verification rule may be that xxxx-yy-zz, where xxxx represents year, yy represents month, and zz represents day.
In step 1203, for each field in the operation instruction, it is checked whether it meets the corresponding preset check rule acquired in step 1202. For example, "month 8 of 2017, 21" is not in compliance with a corresponding inspection rule of xxxx-yy-zz.
In step 1204, since each operation of the computer is recorded in the log, the checking action also generates a log, i.e., a check result log, which stores the checking time, whether the checking was passed, the field failed, and which predetermined checking rule the field does not conform to.
In step 130, in response to the report of the occurrence of the abnormality in the business execution, the inspection result log is searched based on the type of the abnormality, and an abnormality log segment corresponding to the type of the abnormality in the inspection result log is located.
During the execution of the service, any one of the operations may be abnormal. An exception refers to a situation where a service cannot be executed or is not executed as well as normal execution. For example, in an application business in which a user records > quotation > application insurance > submitting an order > invoking a financial system opening > customer payment > invoking a financial accounting > underwriting such a flow, abnormalities of various links such as recording abnormality, submitting an order abnormality, payment abnormality and the like may occur. When the business is executed, an abnormal report is generated once the abnormality occurs, and the type, the occurrence time and the like of the abnormality are indicated. The types of the anomalies include the types of anomalies that occur when the business system is stopped, the business system cannot log in, the recording of a ticket is abnormal, etc.
In one embodiment, as shown in FIG. 4, step 130 includes:
step 1301, identifying the type of the abnormality from the report of the abnormality in the service execution;
Step 1302, searching an abnormal category and an abnormal keyword comparison table to obtain keywords corresponding to the abnormal category;
Step 1303, locating the keywords in the test result log;
step 1304, positioning an abnormal log segment corresponding to the keyword in the inspection result log based on a predetermined criterion as an abnormal log segment corresponding to the type of the abnormality based on the keyword positioned in the inspection result log.
The report of the occurrence of the abnormality in step 1301 may be reported to an abnormality troubleshooting module of the server when the abnormality is detected by a service execution module of the server. The report includes the kind of abnormality, occurrence time, and the like. Thus, the kind of abnormality can be identified from the report.
In step 1302, the anomaly keyword is a signature feature determined for each category of anomaly that may appear in the test results log when the anomaly of that category occurs. For the type of "payment anomaly", the corresponding transaction result log may have anomaly keywords such as "account name error", "password error", "no account", etc. Thus, these keywords may be obtained and then used to locate the exception log segments in the inspection result log.
After obtaining the keyword, in step 1303, the test result log is searched for with the keyword, and the keyword is located.
Then, in step 1304, an exception log segment corresponding to the keyword in the inspection result log may be located as an exception log segment corresponding to the type of the exception based on the keyword located in the inspection result log and based on a predetermined criterion.
The exception log segment is a portion of the inspection result log that may contain data needed to troubleshoot the cause of the exception.
In one embodiment, the predetermined criteria includes taking a segment of the inspection result log that includes the keyword as an exception log segment of the inspection result log that corresponds to the keyword. It is possible that the segment is the segment where the keyword is located, and the abnormality cause is obtained by subsequent processing using it as an abnormality log segment.
In one embodiment, the predetermined criteria includes taking a template phrase segment that appears in the context of the keyword and is closest to the keyword as an exception log segment corresponding to the keyword, the template phrase segment being selected from a predetermined template phrase Duan Ku.
The segment where the keyword is located does not necessarily reflect the cause of the abnormality, and may be the cause that some segments before and after the segment can reflect the abnormality. These segments often have some common characteristics, and the common parts can be made into template speech segments. Once some template segments appear in the context of the keywords (not necessarily the segment in which the keywords are located), they are likely to reflect the cause of the anomaly. Therefore, a template utterance appearing in the context of the keyword and closest to the keyword may be used as an anomaly log segment corresponding to the keyword, the template utterance being selected from a predetermined template utterance Duan Ku. The distance between the template speech segment and the keyword refers to the number of characters spaced between the last character of the template speech segment and the first character of the keyword if the template speech segment is in front of the keyword, the distance between the template speech segment and the keyword refers to the number of characters spaced between the last character of the keyword and the first character of the template speech segment if the template speech segment is behind the keyword, and the distance between the template speech segment and the keyword is 0 if the keyword is in the template speech segment.
After the anomaly log segment is located, the anomaly log segment may be input into a first machine learning model, which outputs the cause of the anomaly.
The first machine learning model is trained in advance by inputting each anomaly log segment sample in a set of anomaly log segment samples into the first machine learning model. The abnormal log segment sample is an abnormal log segment extracted from the historical test result log, and the extraction method can be referred to as shown in fig. 4. An abnormal log segment sample set is a set composed of a large number of abnormal log segment samples. Each log segment sample is previously labeled with a known label of the cause of the anomaly of the log segment sample, which can be labeled sample by an expert. And if the abnormality reason output by the first machine learning model is inconsistent with the label of the abnormality log section sample, adjusting the coefficient in the first machine learning model to ensure that the abnormality reason output by the first machine learning model is consistent with the label of the abnormality log section sample. After a large number of sample training, the first machine learning model can generate an output of the cause of the anomaly after receiving any anomaly log segments.
As shown in FIG. 2, in one embodiment, the method further includes, after step 140, step 150 of searching the exception reason and the solution comparison table based on the exception reason, and obtaining a solution corresponding to the exception reason.
The abnormality cause and solution map is a table storing various abnormality causes and corresponding solutions. In step 140, in determining the cause of the abnormality, the table may be searched to obtain a solution corresponding to the cause of the abnormality.
The advantage of this embodiment is that not only the cause of the abnormality can be automatically determined, but also the solution of the abnormality can be automatically found.
In one embodiment, the abnormality cause and solution look-up table is pre-established based on a second machine learning model pre-established by inputting each abnormality cause sample in the abnormality cause sample library into the second machine learning model, each abnormality cause sample being pre-labeled with a known label for the solution of the abnormality cause sample, the second machine learning model outputting a determined solution, and if the determined solution of the second machine learning model does not agree with the label for the abnormality cause sample, adjusting the second machine learning model such that the determined solution of the second machine learning model agrees with the label for the abnormality cause sample.
The abnormality cause sample may be an abnormality cause obtained as a sample according to the procedure of fig. 1 when a user encounters an abnormality at the time of execution of a service. The abnormality cause sample library is a library including a large number of abnormality cause samples, each of which is determined by an expert as a solution, and is labeled with a label of the solution.
In one embodiment, the present disclosure is able to find not only the solution of the anomaly, but also to automatically execute the solution. As shown in fig. 2, the method further includes, after step 150:
step 160, searching a script corresponding to the solution in a script library established in advance based on the acquired solution;
step 170, executing the script corresponding to the solution.
In an embodiment, for each solution listed in the exception cause and solution lookup table, a script that was previously programmed by the programmer for that solution is stored in a database. Once the solution is obtained by looking up the exception cause and solution lookup table, the script that passed the solution can be found from the database (e.g., script stored in correspondence with solution ID, also the correspondence between cause and solution ID stored in the solution lookup table). The script is then loaded into memory for execution, and as a result of script execution, the solution is executed.
As shown in fig. 5, the embodiment of the present disclosure further discloses an abnormality checking apparatus in service execution, where the service includes a sequence of a plurality of operations that are continuously executed, each operation corresponding to a preset check rule, and the apparatus includes:
a receiving unit 210, configured to receive an operation instruction of a user, where the operation instruction is an instruction for one operation in the service;
A checking unit 220, configured to check the operation instruction according to a preset checking rule corresponding to an operation in the operation instruction, and generate a checking result log;
a locating unit 230, configured to, in response to a report of an abnormality occurring in service execution, find the inspection result log based on a type of the abnormality, and locate an abnormality log segment corresponding to the type of the abnormality in the inspection result log;
an anomaly cause obtaining unit 240, configured to input the anomaly log segment into a first machine learning model, and output an anomaly cause by the first machine learning model, where the first machine learning model is trained by inputting each anomaly log segment sample in a set of anomaly log segment samples into the first machine learning model, each anomaly log segment sample being previously labeled with a known label of the anomaly cause of the anomaly log segment sample, the first machine learning model outputting the anomaly cause, and if the anomaly cause output by the first machine learning model does not agree with the label of the anomaly log segment sample, adjusting the first machine learning model so that the anomaly cause output by the first machine learning model agrees with the label of the anomaly log segment sample.
In one embodiment, the apparatus further comprises:
a solution obtaining unit (not shown) for searching the abnormality cause and the solution comparison table based on the abnormality cause and obtaining the solution corresponding to the abnormality cause.
In one embodiment, the verification unit 220 is further configured to:
acquiring an operation name in the operation instruction;
Searching a comparison table of the operation names and preset check rules, and acquiring the preset check rules corresponding to the operation names, wherein the preset check rules comprise conditions which are required to be met by each field in the operation instruction;
Checking whether each field in the operation instruction meets the condition required to be met by the field in the preset checking rule;
and generating a test result log according to the test result.
In one embodiment, the positioning unit 230 is further configured to:
identifying the type of the abnormality from the report of the abnormality in the service execution;
Searching an abnormal type and an abnormal keyword comparison table to obtain keywords corresponding to the abnormal type;
locating the keywords in the test result log;
And positioning an abnormal log segment corresponding to the keyword in the inspection result log based on a preset criterion and serving as an abnormal log segment corresponding to the type of the abnormality.
In one embodiment, the predetermined criteria includes taking a template phrase segment that appears in the context of the keyword and is closest to the keyword as an exception log segment corresponding to the keyword, the template phrase segment being selected from a predetermined template phrase Duan Ku.
In one embodiment, the anomaly cause and solution look-up table is pre-established based on a second machine learning model, which is pre-established by:
Inputting each abnormality cause sample in an abnormality cause sample library into a second machine learning model, wherein each abnormality cause sample is labeled with a known label of a solution of the abnormality cause sample in advance, the second machine learning model outputs a judged solution, and if the judged solution of the second machine learning model is inconsistent with the label of the abnormality cause sample, the second machine learning model is adjusted so that the judged solution of the second machine learning model is consistent with the label of the abnormality cause sample.
In one embodiment, the apparatus further comprises:
The script searching unit is used for searching a script corresponding to the solution in a script library established in advance based on the acquired solution;
and the script execution unit is used for executing the script corresponding to the solution.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects that may be referred to herein collectively as a "circuit," module "or" system.
An electronic device 400 according to such an embodiment of the invention is described below with reference to fig. 6. The electronic device 400 shown in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 6, the electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to, at least one processing unit 410 described above, at least one memory unit 420 described above, and a bus 430 that connects the various system components, including memory unit 420 and processing unit 410.
Wherein the storage unit stores program code that is executable by the processing unit 410 such that the processing unit 410 performs steps according to various exemplary embodiments of the present invention described in the above-described "exemplary methods" section of the present specification. For example, the processing unit 410 may perform the process as shown in fig. 1.
The storage unit 420 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 430 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 400, and/or any device (e.g., router, modem, etc.) that enables the electronic device 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 450. Also, electronic device 400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 over the bus 430. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 400, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
Referring to fig. 7, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind 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., connected via the Internet using an Internet service provider).
Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
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| CN111949994A (en) * | 2020-08-19 | 2020-11-17 | 北京紫光展锐通信技术有限公司 | Vulnerability analysis method and system, electronic device and storage medium |
| CN112116330B (en) * | 2020-09-28 | 2024-05-28 | 中国银行股份有限公司 | Automatic workflow error queue processing method and device |
| CN115129679A (en) * | 2021-03-29 | 2022-09-30 | 戴尔产品有限公司 | Service request remediation through machine learning-based identification of critical areas of log files |
| CN115130111B (en) * | 2022-07-07 | 2023-04-25 | 广州宇中网络科技有限公司 | System operation vulnerability restoration method based on AI analysis and big data service system |
| CN116644045A (en) * | 2023-05-29 | 2023-08-25 | 中国银行股份有限公司 | Batch job task processing method and device, server and storage medium |
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