CN112115032B - Log generation method and device - Google Patents
Log generation method and device Download PDFInfo
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- CN112115032B CN112115032B CN202011052937.5A CN202011052937A CN112115032B CN 112115032 B CN112115032 B CN 112115032B CN 202011052937 A CN202011052937 A CN 202011052937A CN 112115032 B CN112115032 B CN 112115032B
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000003062 neural network model Methods 0.000 claims abstract description 29
- 238000012549 training Methods 0.000 claims abstract description 10
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Abstract
The embodiment of the application provides a log generation method and device, wherein the method comprises the following steps: acquiring target fault information of a target fault type of a system; inputting the target fault information of the target fault type into a neural network model to obtain a first log detail degree; the neural network model is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail degree; the first log detail represents the detail degree of log record fault information; determining a record item matching the first log detail; and generating a log corresponding to the target fault type according to the record item. According to the log generation method provided by the embodiment of the application, the corresponding log detail degree can be obtained according to the fault information, the log record item is determined according to the log detail degree, and the record item of the unfixed log can be solved, so that the recording problem of various faults can be solved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a log generating method and apparatus.
Background
In the current banking system, the log is the main module for recording the operation data and faults, record items are in the log to determine which data to record, and the record items of the log are fixed in the current banking system, namely, no matter the position or link of the module where the faults of the system occur, the record items of the log are unchanged. However, the content of the unified record item cannot completely solve diversified faults, the position of the fault occurrence, the link of the fault occurrence or the reason of the fault occurrence cannot be positioned timely, and the unified log record item also increases the difficulty for solving the faults of the system.
In summary, the problem of recording diversified faults cannot be solved by using unified log records in the existing bank system.
Disclosure of Invention
The embodiment of the application provides a log generation method and device, which can solve the problem of log record diversified faults.
The embodiment of the application provides a log generation method, which comprises the following steps:
acquiring target fault information of a target fault type of a system;
inputting the target fault information of the target fault type into a neural network model to obtain a first log detail degree; the neural network model is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail degree; the first log detail represents the detail degree of log record fault information;
determining a record item matching the first log detail;
and generating a log corresponding to the target fault type according to the record item.
Optionally, before determining the entry matching the first log detail, the method further includes:
acquiring a second log detail degree corresponding to the target fault type;
the second log detail level is replaced with the first log detail level.
Optionally, the method further comprises:
and sending the record item to a terminal device or a server.
Optionally, the target fault information includes one or more of:
the number of faults, the duration of the faults, the fault influence range and the fault resolution time.
Optionally, the target fault type is any one of the following:
system failure, user data failure, or program failure.
The application also provides a log generation device, which comprises:
the first acquisition unit is used for acquiring target fault information of a target fault type of the system;
the input unit is used for inputting the target fault information of the target fault type into the neural network model to obtain a first log detail degree; the neural network model is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail degree; the first log detail represents the detail degree of log record fault information;
a determining unit configured to determine a record item matching the first log detail;
and the generating unit is used for generating a log corresponding to the target fault type according to the record item.
Optionally, before determining the record item matching the first log detail, the determining unit further includes:
the second acquisition unit is used for acquiring a second log detail degree corresponding to the target fault type;
and the replacing unit is used for replacing the second log detail degree with the first log detail degree.
Optionally, the apparatus further includes:
and the sending unit is used for sending the record item to the terminal equipment or the server.
Optionally, the target fault information includes one or more of:
the number of faults, the duration of the faults, the fault influence range and the fault resolution time.
Optionally, the target fault type is any one of the following:
system failure, user data failure, or program failure.
Compared with the prior art, the application has at least the following advantages:
obtaining target fault information of a target fault type of a system, inputting the target fault information of the target fault type into a neural network model, and obtaining first log detail, wherein the neural network model is trained according to a corresponding relation between the historical fault information of the target fault type and log detail, the first log detail reflects the detail degree of log record fault information, a record item matched with the first log detail is determined, and a log corresponding to the target fault type is generated according to the record item. Therefore, according to the embodiment of the application, the first log detail degree corresponding to the target fault information of the target fault type is obtained by inputting the target fault information of the target fault type into the trained neural network model, the record item of the log is determined according to the first log detail degree, and the log corresponding to the target fault type is generated according to the record item of the log. According to the log generation method provided by the embodiment of the application, the corresponding log detail degree can be obtained according to the fault information, the log record item is determined according to the log detail degree, and the record item of the unfixed log can be solved, so that the recording problem of various faults can be solved.
Drawings
In order to more clearly illustrate the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below, and it will be apparent that the drawings in the following description are only embodiments of the present application and that other drawings may be obtained from the provided drawings without inventive effort to those skilled in the art.
Fig. 1 is a flow chart of a log generation method according to a first embodiment of the present application;
fig. 2 is a flow chart of a log generation method according to a second embodiment of the present application;
fig. 3 is a flow chart of a log generating method according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a log generating device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As known from the background art, the unified log record item of the existing banking system cannot meet the requirement of recording diversified faults.
Therefore, the embodiment of the application provides a log generation method, which is used for obtaining a first log detail degree corresponding to target fault information of a target fault type by inputting the target fault information of the target fault type into a trained neural network model, determining a record item of a log according to the first log detail degree, and generating the log corresponding to the target fault type according to the record item of the log. Therefore, according to the embodiment of the application, the first log detail degree corresponding to the target fault information of the target fault type is obtained by inputting the target fault information of the target fault type into the trained neural network model, the record item of the log is determined according to the first log detail degree, and the log corresponding to the target fault type is generated according to the record item of the log. According to the log generation method provided by the embodiment of the application, the corresponding log detail degree can be obtained according to the fault information, the log record item is determined according to the log detail degree, and the record item of the unfixed log can be solved, so that the recording problem of various faults can be solved.
As shown in fig. 1, a flow chart of a log generating method according to an embodiment of the present application is shown, and the method includes the following steps:
step 101: and acquiring target fault information of the target fault type of the system.
In the embodiment of the application, the bank system can record the running condition and the abnormal condition by utilizing the log in daily running, and the abnormal condition of the system can be considered to be faults of the system, and the fault types of the system are various and can be divided into the following categories: system failure, user data failure, or program failure. System failure refers to a failure of a banking system during operation, user data failure refers to a failure of stored data for a user, such as user data loss, and program failure refers to a failure of a program during operation, such as a program defect bug (bug). And targeted fault information statistics is carried out aiming at fault types, so that the method is beneficial to solving the faults by utilizing logs afterwards. The statistics of fault information for a certain type of fault type may be fault information, which may include one or more of the following: the method comprises the steps of fault times, fault duration, fault influence range and fault solution time, wherein the fault times are represented by times of occurrence of the fault in a bank system, the fault duration is represented by time of the fault duration, the fault influence range is represented by a range of influence of the fault on other modules or programs, and the fault solution time is represented by how long it takes to solve the fault.
Step 102: inputting the target fault information of the target fault type into a neural network model to obtain a first log detail, wherein the neural network model is trained according to the corresponding relation between the historical fault information of the target fault type and the log detail, and the first log detail represents the detail degree of the log record fault information.
In the embodiment of the application, fault information obtained for a certain fault type is input into the neural network model to obtain the log detail, the log detail represents the detail degree of the log recorded fault information, the log detail degree can be divided into a plurality of levels, for example, the first log detail degree represents that the detail degree of the log recorded fault information is at a first level. The neural network model is obtained through training, and the training process mainly uses the corresponding relation between the historical fault information of a certain fault type and the log detail degree.
Step 103: an entry matching the first log detail is determined.
In the embodiment of the application, the log details have the matched record items, and the contents of the record items matched with different log details are different. For example, the first log detail indicates that the detail level of the fault information recorded by the log is at a first level, the record item matched with the first log detail level has the largest recorded content, and the record item can include a fault itself, a data input link when the fault occurs, a data output link when the fault occurs, and a data circulation link of the fault itself.
Step 104: and generating a log corresponding to the target fault type according to the record item.
In an embodiment of the present application, the record item may include information of all fields within a certain range when the fault occurs, and the log may record all field information within the certain range when the fault occurs according to the content of the record item.
Optionally, before step 103, the following steps may be further included:
step 201: and obtaining the second log detail degree corresponding to the target fault type.
In the embodiment of the application, before determining the record item matched with the first log detail, the log detail corresponding to the current fault type can be further obtained and used as the second log detail; the entries matching the second log detail level are different from the entries matching the first log, i.e., the entries matching the second log detail level may be more than the entries matching the first log, and the entries matching the second log detail level may be less than the entries matching the first log.
Step 202: the second log detail level is replaced with the first log detail level.
In an embodiment of the present application, when the log entries do not meet the requirement, for example, the log entries are fewer, the log entries are to be added, and the entries matched with the second log detail level may be replaced with the entries matched with the first log detail level, that is, the second log detail level is replaced with the first log detail level.
According to the log generation method provided by the embodiment of the application, the target fault information of the target fault type of the system is acquired, the target fault information of the target fault type is input into a neural network model, and the first log detail is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail, the first log detail reflects the detail degree of the log record fault information, the record item matched with the first log detail is determined, and the log corresponding to the target fault type is generated according to the record item. Therefore, according to the embodiment of the application, the first log detail degree corresponding to the target fault information of the target fault type is obtained by inputting the target fault information of the target fault type into the trained neural network model, the record item of the log is determined according to the first log detail degree, and the log corresponding to the target fault type is generated according to the record item of the log. Therefore, according to the embodiment of the application, the first log detail degree corresponding to the target fault information of the target fault type is obtained by inputting the target fault information of the target fault type into the trained neural network model, the record item of the log is determined according to the first log detail degree, and the log corresponding to the target fault type is generated according to the record item of the log. According to the log generation method provided by the embodiment of the application, the corresponding log detail degree can be obtained according to the fault information, the log record item is determined according to the log detail degree, and the record item of the unfixed log can be solved, so that the recording problem of various faults can be solved.
As shown in fig. 3, a flow chart of another log generating method according to an embodiment of the present application is shown, and the method includes the following steps:
step 301: and acquiring target fault information of the target fault type of the system.
Step 302: inputting the target fault information of the target fault type into a neural network model to obtain a first log detail, wherein the neural network model is trained according to the corresponding relation between the historical fault information of the target fault type and the log detail, and the first log detail represents the detail degree of the log record fault information.
Step 303: an entry matching the first log detail is determined.
Step 304: and generating a log corresponding to the target fault type according to the record item.
The execution principle of steps 301 to 304 is the same as that of steps 101 to 104, and will not be described here again.
Step 305: and sending the record item to a terminal device or a server.
In the embodiment of the application, the content of the record item needs to be adjusted according to a certain fault type, the log of the content of the related record item is generated after adjustment, and the adjusted record item can be sent to the terminal equipment or the server so as to remind related personnel of solving the fault as soon as possible according to the log content, wherein the sending mode can be a WeChat mode or a short message mode.
According to the log generation method provided by the embodiment of the application, the target fault information of the target fault type of the system is acquired, the target fault information of the target fault type is input into a neural network model, and the first log detail is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail, the first log detail reflects the detail degree of the log record fault information, the record item matched with the first log detail is determined, and the log corresponding to the target fault type is generated according to the record item. Therefore, according to the embodiment of the application, the first log detail degree corresponding to the target fault information of the target fault type is obtained by inputting the target fault information of the target fault type into the trained neural network model, the record item of the log is determined according to the first log detail degree, and the log corresponding to the target fault type is generated according to the record item of the log. Therefore, according to the embodiment of the application, the first log detail degree corresponding to the target fault information of the target fault type is obtained by inputting the target fault information of the target fault type into the trained neural network model, the record item of the log is determined according to the first log detail degree, and the log corresponding to the target fault type is generated according to the record item of the log. According to the log generation method provided by the embodiment of the application, the corresponding log detail degree can be obtained according to the fault information, the log record item is determined according to the log detail degree, and the record item of the unfixed log can be solved, so that the recording problem of various faults can be solved.
Based on the log generating method provided by the embodiment of the present application, the embodiment of the present application further provides a log generating device 400, as shown in fig. 4, which is a schematic structural diagram of the log generating device provided by the embodiment of the present application, including:
a first obtaining unit 410, configured to obtain target fault information of a target fault type of the system;
an input unit 420, configured to input the target fault information of the target fault type into a neural network model, to obtain a first log detail; the neural network model is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail degree; the first log detail represents the detail degree of log record fault information;
a determining unit 430 for determining a record item matching the first log detail;
and the generating unit 440 is configured to generate a log corresponding to the target fault type according to the record item.
The log generating device further includes:
the second acquisition unit is used for acquiring a second log detail degree corresponding to the target fault type;
and the replacing unit is used for replacing the second log detail degree with the first log detail degree.
The log generating device further includes:
and the sending unit is used for sending the record item to the terminal equipment or the server.
The log generation device target fault information includes one or more of:
the number of faults, the duration of the faults, the fault influence range and the fault resolution time.
The target fault type of the log generating device is any one of the following:
system failure, user data failure, or program failure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the above-described system and unit may refer to the corresponding procedures in the foregoing method embodiments, which are not repeated here.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the division of the units is merely a logic service division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each service unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software business units.
The above embodiments are further described in detail for the purpose, technical solution and advantageous effects of the present application, and it should be understood that the above description is only an embodiment of the present application.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (10)
1. A method of log generation, the method comprising:
acquiring target fault information of a target fault type of a system;
inputting the target fault information of the target fault type into a neural network model to obtain a first log detail degree; the neural network model is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail degree; the first log detail represents the detail degree of log record fault information;
determining a record item matching the first log detail;
and generating a log corresponding to the target fault type according to the record item.
2. The method of claim 1, wherein prior to determining the entry matching the first log detail, the method further comprises:
acquiring a second log detail degree corresponding to the target fault type;
the second log detail level is replaced with the first log detail level.
3. The method according to any one of claims 1 or 2, further comprising:
and sending the record item to a terminal device or a server.
4. The method of claim 1, wherein the target fault information comprises one or more of:
the number of faults, the duration of the faults, the fault influence range and the fault resolution time.
5. The method of claim 1, wherein the target fault type is any one of:
system failure, user data failure, or program failure.
6. A log generating apparatus, the apparatus comprising:
the first acquisition unit is used for acquiring target fault information of a target fault type of the system;
the input unit is used for inputting the target fault information of the target fault type into the neural network model to obtain a first log detail degree; the neural network model is obtained through training according to the corresponding relation between the historical fault information of the target fault type and the log detail degree; the first log detail represents the detail degree of log record fault information;
a determining unit configured to determine a record item matching the first log detail;
and the generating unit is used for generating a log corresponding to the target fault type according to the record item.
7. The apparatus according to claim 6, wherein the determining unit, before determining the entry matching the first log detail, further comprises:
the second acquisition unit is used for acquiring a second log detail degree corresponding to the target fault type;
and the replacing unit is used for replacing the second log detail degree with the first log detail degree.
8. The apparatus according to any one of claims 6 or 7, further comprising:
and the sending unit is used for sending the record item to the terminal equipment or the server.
9. The apparatus of claim 6, wherein the target fault information comprises one or more of:
the number of faults, the duration of the faults, the fault influence range and the fault resolution time.
10. The apparatus of claim 6, wherein the target fault type is any one of:
system failure, user data failure, or program failure.
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| US11194692B2 (en) * | 2017-09-22 | 2021-12-07 | Nec Corporation | Log-based system maintenance and management |
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| JP2018180982A (en) * | 2017-04-14 | 2018-11-15 | 富士通株式会社 | Information processing device and log recording method |
| CN107870850A (en) * | 2017-08-25 | 2018-04-03 | 成都萌想科技有限责任公司 | A kind of efficient the Internet, applications log system |
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