WO2020140624A1 - Procédé pour extraire des données d'un journal, et dispositif associé - Google Patents

Procédé pour extraire des données d'un journal, et dispositif associé Download PDF

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Publication number
WO2020140624A1
WO2020140624A1 PCT/CN2019/118038 CN2019118038W WO2020140624A1 WO 2020140624 A1 WO2020140624 A1 WO 2020140624A1 CN 2019118038 W CN2019118038 W CN 2019118038W WO 2020140624 A1 WO2020140624 A1 WO 2020140624A1
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Prior art keywords
log
data
type
updated
extraction information
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PCT/CN2019/118038
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English (en)
Chinese (zh)
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陈珍妮
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平安科技（深圳）有限公司
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Publication of WO2020140624A1 publication Critical patent/WO2020140624A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/17—Details of further file system functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/18—File system types
    • 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

Definitions

  • the present application relates to the field of artificial intelligence technology, and in particular, to a method and related equipment for extracting data from logs.
  • the corresponding data is obtained by searching the corresponding data in the database table of the system.
  • the data stored in the database table is not complete.
  • the database table only includes data such as the final result processed by the system. The inventor realized that the collected data depends on the data stored in the database table. If the corresponding data to be collected is not saved in the database table, the data needs to be collected from other channels. The efficiency of data acquisition is low, and the data obtained is not complete.
  • the present application provides a method and apparatus for extracting data from a log.
  • a method for extracting data from a log includes: performing log update on a running system New monitoring; if the log update is monitored, the updated log is identified through the neural network model to determine the log type of the updated log; the data extraction information search corresponding to the log type in the configuration file is performed, and the data extraction The information indicates a data item for data extraction from the log of the log type; extracting data corresponding to the data item from the updated log according to the found data extraction information.
  • an apparatus for extracting data from a log includes: a monitoring module configured to: perform log update monitoring on a running system; an identification module configured to: if a log update is monitored, then Recognize the updated log through the neural network model to determine the log type of the updated log; the search module is configured to: search for data extraction information corresponding to the log type in the configuration file, and the data extraction information indicates A data item for data extraction from the log of the log type; an extraction module configured to: extract data corresponding to the data item from the updated log according to the found data extraction information.
  • an electronic device includes: a processor; and a memory, where the computer-readable instructions are stored on the memory, and the computer-readable instructions are executed by the processor to implement the following steps:
  • a computer non-volatile readable storage medium has stored thereon a computer program, and when the computer program is executed by a processor, the following steps are implemented: performing log update monitoring on the running system; if When the log update is monitored, the updated log is identified through the neural network model to determine the log type of the updated log; the data extraction information corresponding to the log type is searched in the configuration file, and the data extraction information indicates from Data items for data extraction in the log of the log type; extracting data corresponding to the data items from the updated log according to the found data extraction information.
  • the method of the present application through log update monitoring, identification of the log type of the updated log, search of data extraction information defined by the log type, and extracting pairs from the updated log according to the data extraction information
  • the data should be extracted from the log to obtain data related to system operation, real-time collection of system operation data, and data integrity is ensured.
  • the deep learning method is used to identify the type of the local chronicle, which improves the recognition efficiency and accuracy, and ensures the efficiency and real-time performance of data extraction.
  • FIG. 1 is a block diagram of a server according to an exemplary embodiment
  • FIG. 2 is a flowchart of a method for extracting data from a log according to an exemplary embodiment
  • FIG. 3 is a flowchart of step S 130 of the embodiment corresponding to FIG. 2;
  • FIG. 4 is a flowchart of steps before step S130 of the embodiment corresponding to FIG. 2;
  • FIG. 5 is a flowchart of steps before step S150 of the embodiment corresponding to FIG. 2;
  • step S170 of the embodiment corresponding to FIG. 2 is a flowchart of steps after step S170 of the embodiment corresponding to FIG. 2;
  • FIG. 7 is a flowchart of step S430 of the embodiment corresponding to FIG. 6;
  • FIG. 8 is a block diagram of a device for extracting data from a log according to an exemplary embodiment
  • Fig. 9 is a block diagram of an electronic device according to an exemplary embodiment.
  • Fig. 1 is a block diagram of a server according to an exemplary embodiment.
  • a server with this hardware structure can be used to perform the method of extracting data from the log of the present application, wherein the system runs on the server to provide services for each terminal of the system, thereby generating logs during the operation of the system, and the server can The generated log is subjected to data extraction according to the method of this application.
  • the main body of the method of extracting data from the log of the present application is not limited to the server shown in FIG. 1, the main body of the method of the present application may also be a device with logic operation processing capabilities, such as a desktop computer, a laptop computer, and The server cluster, cloud server, etc. composed of multiple servers are not specifically limited herein.
  • server is only an example adapted to the present application, and cannot be considered as providing any limitation on the scope of use of the present application.
  • the server cannot also be interpreted as requiring or having to have one or more components in the exemplary server 200 shown in FIG.
  • the server 200 includes: a power supply 210, an interface 230, at least one memory 250, and at least one central processor ( CPU, Central Processing Units) 270.
  • CPU Central Processing Unit
  • the power supply 210 is used to provide an operating voltage for each hardware device on the server 200.
  • the interface 230 includes at least one wired or wireless network interface 231, at least one serial-to-parallel conversion interface 233, at least one input-output interface 235, and at least one USB interface 237, etc., for communicating with external devices, such as data with the terminal 100 transmission.
  • the memory 250 may be a read-only memory, a random access memory, a magnetic disk, or an optical disk.
  • the resources stored on the memory 250 include an operating system 251, application programs 253, and data 255.
  • the storage method may be temporary storage. Or permanent storage.
  • the operating system 251 is used to manage and control the hardware devices and application programs 253 on the server 200 to implement the calculation and processing of the massive data 255 by the central processor 270, which may be Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM , FreeBSDTM, etc.
  • the application program 253 is a computer program that completes at least one specific job based on the operating system 251, and may include at least one module (not shown in FIG. 2), and each module may separately include a series of computers for the server 200. Readable instructions. Data 255 can be stored on disk Logs etc.
  • the central processor 270 may include one or more processors, and is configured to communicate with the memory 250 through a bus for computing and processing the massive data 255 in the memory 250.
  • the server 200 applicable to the present application will complete the method of extracting data from the log by the central processor 270 reading a series of computer-readable instructions stored in the memory 250.
  • the server 200 may be one or more application specific integrated circuits (Applicati on Specific Integrated Circuit, ASIC for short), digital signal processor, digital signal processing equipment, programmable logic device, field Programming gate arrays, controllers, microcontrollers, microprocessors or other electronic components are implemented to perform the following method of extracting data from the log. Therefore, the implementation of this application is not limited to any specific hardware circuit, software, or a combination of both.
  • ASIC Application specific integrated circuits
  • Fig. 2 is a flow chart showing a method for extracting data from a log according to an exemplary embodiment. The method may be executed by the server shown in FIG. 1, and may include the following steps:
  • Step S110 Perform log update monitoring on the running system.
  • the running system is, for example, a system that provides services for various program clients, such as a trading system, a valuation system, a fund system, etc. in a financial company, or an application program running on a terminal device
  • the system continuously performs logic processing during operation, for example, receiving a request initiated by the client, performing request processing according to the request initiated by the client, and issuing instructions to the client.
  • logic processing for example, receiving a request initiated by the client, performing request processing according to the request initiated by the client, and issuing instructions to the client.
  • the logic performed The treatment is also different. Therefore, during the logical processing of the system, a log is generated according to the logical processing performed, for example, after receiving a request initiated by a client, a log of the received request is generated, or after a request is processed, a log of the result of the request processing is generated, After the client login is successful, the user login log is generated.
  • the corresponding log storage unit is configured in the system, so that the logs generated during the operation of the system are stored in the configured log storage unit, so that the log can be updated in the log storage unit Monitoring, that is, whether a new log is stored in the log storage unit for the emergency over the year, the new log is a log newly generated by the running system.
  • Step S130 If the log update is monitored, the updated log is identified through the neural network model to determine the log type of the updated log. [0041] In response to different logical operations performed by the system, logs corresponding to different log types are generated, wherein logs with different log types have different formats of logs on the one hand and different data contained in the logs on the other hand. For example, for example, in a system, logs are generated for user login and user request processing results, where the logs generated for user login (called login type logs) are:
  • the log generated for user request processing success (called the request processing success type log) is:
  • the user login time and the logged-in user included therein are the carried in the login type log data.
  • request processing success type log including the time "20180 904-14:00" when the user initiated the request, the user "Amy” who initiated the request, the request type “product new”, the request processing result "success”, and the system response time "2.2 seconds"
  • the time included in the log by the user to initiate the request, the user who initiated the request, the type of request, the request processing result, and the time of the system response are the data carried in the log.
  • the updated log can be identified through the neural network model to determine the log type of the updated log, that is, the updated log is identified by deep learning.
  • the neural network model After training, the neural network model performs feature extraction on the updated log, and then predicts the label of the updated log according to the extracted feature, thereby determining the log type of the updated log.
  • the neural network model used may be a convolutional neural network model, a recursive neural network model, or a recurrent neural network model, which is not specifically limited here.
  • Step S150 Search for data extraction information corresponding to the log type in the configuration file, and the data extraction information indicates a data item for data extraction from the log of the log type.
  • the configuration file includes data extraction information configured for the log corresponding to the log type for which data extraction is required. Therefore, the configuration file may include one or more sets of data extraction information, where one set of data extraction information corresponds to a log type. In the data extraction information corresponding to each log type, data items to be extracted from the log corresponding to the log type are configured. For example, the log in the above example, if you log from the login type in the above example: [0050] 20180904-11:21: User jenny logged into the system
  • the logged-in time and the logged-in user are data items that need to be extracted, and in the log, the logged-in time “20180904-11:21” corresponds to the logged-in data item
  • the data of the login user "jenny" is the data corresponding to the data item of the login user.
  • Step S170 Extract data corresponding to the data item from the updated log according to the found data extraction information.
  • the data extraction information corresponding to each log type indicates one or more data items that require data extraction. Therefore, the data corresponding to the data item is extracted from the updated log according to the found data extraction information. Thus, data collection from the log is realized.
  • the operation-related data realizes the real-time collection of system operation data. Furthermore, the deep learning method is used to identify the log types, which improves the identification efficiency and accuracy, and further ensures the efficiency and real-time performance of data extraction.
  • the system log contains all the information related to the operation of the system, the data extracted from the log guarantees the integrity of the extracted data relative to the method obtained from the database or indirectly.
  • step S130 includes:
  • Step S131 Construct a feature vector of the updated log.
  • the feature vector may be constructed based on the text of the updated log. Because the format of logs of different log types is different, the feature vectors constructed for logs of different log types are also different. The constructed feature vector reflects the characteristics of the updated log.
  • the login type log mentioned above such as
  • 20180904-11:21 User jenny logged into the system [0060] and the request processing success type log, such as
  • YYYYYYY-YY User YY initiated a YYYY request, processing YY, response time YY seconds, except the position occupied by the Y symbol is, the rest of the log type is the same of.
  • the configured keywords and the location of the keywords are fixed.
  • the login type log after the specific login user XX is "Login to the system", so that when constructing the feature vector of a log of a certain log type, the feature vector is constructed according to the keyword in the log and the location of the keyword. That is, the keyword search is performed in the updated log, and the position of the keyword in the updated log is obtained, thereby constructing the feature vector of the updated log.
  • step S131 it further includes segmenting the updated log, and then constructing a feature vector of the updated log according to the encoding corresponding to each word.
  • Step S132 Perform classification prediction on the feature vector to obtain a type label corresponding to the updated log.
  • Step S133 Determine the log type of the updated log according to the type tag.
  • step S132 the classification prediction is performed according to the constructed feature vector, that is, the probability of each type label of the feature vector is predicted, and then the probability of predicting each type of label is traversed, and the type label with the maximum probability is used as the The type tag corresponding to the update log. Therefore, the log type of the updated log is determined according to the obtained type log.
  • the method further includes:
  • Step S210 Acquire a plurality of sample logs, and acquire a sample label marked for each sample log.
  • Step S220 training the neural network model through several sample logs and corresponding type tags.
  • Step S230 When the neural network model converges, the training of the neural network model ends.
  • the neural network model predicts the type label of the sample log for each sample log. If the predicted type label is inconsistent with the sample label marked on the sample log, the neural network model is adjusted Type parameters until the predicted type label is consistent with the sample label. Repeat this process for each sample log.
  • a prediction accuracy test is performed on the neural network model, that is, several test logs are input into the neural network model, the neural network model predicts the type label of each test log, and the type label of each test log Compared with the type label marked on the test log, if they are consistent, the Bem neural network model predicts the test log accurately, if not, the neural network model predicts the test log incorrectly, so that the neural network is statistically obtained
  • the training of the neural network model, and the neural network model after the training is used to identify the updated log in step S130.
  • step S150 it further includes:
  • Step S310 Acquire a template log of the same type as the log corresponding to the log to be extracted.
  • Step S320 In the template log, replace the data corresponding to the data item with the variable configured for the data item, and obtain data extraction information corresponding to the log type according to the replaced template log configuration.
  • Step S330 a configuration file is formed from the data extraction information corresponding to each log type.
  • the template log may be any log of the log type. Corresponding to the situation where data needs to be extracted from logs of multiple log types, correspondingly, a template log of each log type is obtained.
  • the log format is the same, in which there are the same parts, for example, the keywords in the log, and the position of the keywords are the same, but different
  • the part is only a few, such as the data corresponding to the data items that need to be extracted.
  • Metrics.login.pattern %timestamp%: SP%usemame% logged into the system
  • timestamp is a variable configured for the data item of login time
  • username is a variable configured for the data item of login user.
  • the template log is used to replace the data corresponding to the data item with the variables configured for the data item. That is equivalent to assigning the data corresponding to the data item to the variable configured for the data item.
  • the second line in the data extraction information defines the output variable, that is, the variable corresponding to the data item needs to be extracted as the output variable, so that when data extraction is performed according to the data extraction information, the corresponding data item in the log can be obtained data.
  • the configuration file may be configured for logs of multiple log types, data extraction information is configured for each log type, and the data extraction information corresponding to each log type constitutes a configuration file.
  • the corresponding identification is configured for the data extraction information of each log type, and the identification of the data extraction information and the log type are created Association, so that after identifying the log type of the updated log by identifying in step S130, the data extraction information identifier associated with the log type can be directly searched, so as to quickly find the data extraction information corresponding to the log type.
  • the method further includes:
  • Step S410 Search the data table corresponding to the log type.
  • Step S430 Write the extracted data to a data table to store the data.
  • the extracted data is different, so that each log type is configured with a corresponding data table for storing the data extracted from the log of the log type. And write the data to the corresponding data table to realize the storage of the extracted data. Therefore, when performing analysis processing, the analysis is performed directly based on the data stored in the data table, and the analysis results are obtained, for example, information such as user login volume, system processing success volume, and system processing failure volume are obtained.
  • step S430 includes:
  • Step S431 Locate the data field associated with the data item in the data table.
  • Step S432 Write the data corresponding to the data item into the table unit configured for the data field.
  • the data extracted for the log of each log type may be data of one data item, or data of multiple data items. Therefore, for the case where the extracted data is data of multiple data items, a data field is configured for each data item in the data table, and the data item is associated with the data field, thereby writing the extracted data to the data table During data, the data field associated with the data item is located and searched, and then the data of the data item is written into the table unit configured as the data field. Further, in the data table, data is written line by line, that is, after data is written in one line in the data table, the next extracted data is written in the next line of the line, and so on.
  • FIG. 8 is a block diagram of an apparatus for extracting data from logs according to an exemplary embodiment.
  • the apparatus may be deployed in the server 200 shown in FIG. 1 and execute any of the above method embodiments. All or part of the method of extracting data from the log.
  • the device includes but is not limited to: a monitoring module 110, an identification module 130, a search module 150, and an extraction module 170, wherein: the monitoring module 110 is configured to: perform log update monitoring on the running system.
  • An identification module 130 which is connected to the monitoring module 110, is configured to: if a log update is monitored, identify the updated log through a neural network model to determine the log type of the updated log.
  • a search module 150 which is connected to the identification module 130, is configured to: search for data extraction information corresponding to the log type in the configuration file, and the data extraction information indicates data items to be extracted from the log of the log type.
  • Extraction module 170 which is connected to the search module 150, and is configured to: extract data corresponding to the data item from the updated log according to the found data extraction information.
  • the recognition module 130 includes: a feature vector construction unit configured to: construct the updated feature vector of the log.
  • the classification prediction unit is configured to: perform classification prediction on the feature vector to obtain the type label corresponding to the updated log.
  • the log type determination unit is configured to: determine the log type of the updated log according to the type label.
  • the device for extracting data from the log further includes the following module, which performs the corresponding steps before the identification module is executed: a sample log acquisition module configured to: acquire a plurality of sample logs, and acquire the same The sample label marked in this log.
  • the training module is configured to: train the neural network model through several sample logs and corresponding type labels.
  • the end of training module is configured to: end the training of the neural network model when the neural network model converges.
  • the device for extracting data from the log further includes the following module, which performs the corresponding step before the search module is executed: a template log acquisition module configured to: acquire a log corresponding to the log to be extracted Template logs with the same log type.
  • the data extraction information generating module is configured to: replace the data corresponding to the data item with the variables configured for the data item in the template log, and obtain the data extraction information corresponding to the log type according to the template log configuration after the replacement.
  • the configuration file generation module is configured as follows: the configuration file is composed of data extraction information corresponding to each log type.
  • the apparatus for extracting data from the log further includes: a data table search module configured to: perform a search for a data table corresponding to the log type.
  • the data writing module is configured to: write the extracted data into a data table for data storage.
  • the data writing module includes: a data field positioning unit configured to: locate the data field associated with the data item in the data table.
  • Write unit configured to: map data items The data is written to the table cell configured for the data field.
  • the present application also provides an electronic device, which can be used in the server 200 shown in FIG. 1 to perform all of the methods for extracting data from logs shown in any of the above method embodiments Or some steps.
  • the slave electronic device 1000 includes: a processor 1001; and a memory 1002, where the computer readable instructions are stored on the memory 1002, and when the computer readable instructions are executed by the processor 1001, the method of any of the above method implementations is implemented .
  • the executable instruction when executed by the processor 1001, the method in any of the above embodiments is implemented.
  • the executable instructions are, for example, computer-readable instructions.
  • the processor 1001 executes the processor reads the computer-readable instructions stored in the memory through the communication line/bus 1003 connected to the memory.
  • a computer non-volatile readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the slave log in any of the above method embodiments is implemented The method of extracting data.
  • the computer non-volatile readable storage medium includes, for example, a memory 250 of a computer program, and the above instructions can be executed by the central processor 270 of the server 200 to implement the above method of extracting data from the log.

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Abstract

L'invention concerne un procédé et un appareil pour extraire des données d'un journal, se rapportant au domaine technique de l'intelligence artificielle. Le procédé consiste à : surveiller une mise à jour de journal du système en fonctionnement (S110) ; si la mise à jour de journal est détectée, identifier le journal mis à jour au moyen d'un modèle de réseau neuronal pour déterminer le type de journal du journal mis à jour (S130) ; rechercher, dans un fichier de configuration, des informations d'extraction de données correspondant au type de journal, les informations d'extraction de données indiquant un élément de données où les données sont extraites du journal du type de journal (S150) ; et extraire du journal mis à jour, selon les informations d'extraction de données trouvées, les données correspondant à l'élément de données (S170). Selon le procédé, les données requises sont extraites du système en fonctionnement en temps réel, et l'efficacité est élevée.
PCT/CN2019/118038 2019-01-04 2019-11-13 Procédé pour extraire des données d'un journal, et dispositif associé WO2020140624A1 (fr)

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CN109783459A (zh) * 2019-01-04 2019-05-21 平安科技（深圳）有限公司 从日志中提取数据的方法、装置及计算机可读存储介质

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