Method, device and equipment for detecting newly-added ora fault in real time and medium

Abstract

The invention discloses a method, a device and equipment for detecting a newly added ora fault in real time and a medium. The method comprises the following steps: monitoring standardized data written into a database in real time; according to the low-code training statement, obtaining target writing time corresponding to the current standardized data, a predefined time dimension and a first time interval and a second time interval corresponding to a comparison period; obtaining a target real-time data set of which the write-in time is within the first time interval, and obtaining a target historical fault data set of which the write-in time is within the second time interval; and respectively inputting a target real-time data set and a target historical fault data set into the newly-added fault detection model, and when a newly-added error code is detected, storing the newly-added error code into an error code table. According to the embodiment of the invention, the historical data base line is compared with the real-time data, so that the problems of hysteresis, strong dependence on manual intervention, insufficient flexibility, insufficient log utilization and the like of traditional database fault detection are solved.

Description

Technical Field

[0001] The present invention relates to the field of database technology, and in particular to a method, device, equipment and medium for real-time detection of newly added ora faults. Background Art

[0002] In the current IT information system field, database failures can seriously affect production efficiency and related businesses. Baseline comparison technology can detect database anomalies by comparing historical data and real-time data.

[0003] However, traditional monitoring rules have a certain lag and rely on rules or manually defined thresholds to detect database anomalies. At the same time, fixed polling cycles are set (such as detection every 5 or 10 minutes), which makes the system unable to capture and respond to faults in real time, especially in the early stages of a fault, and the best time to handle the fault may be missed. Summary of the invention

[0004] Based on this, the present invention provides a method, device, equipment and medium for real-time detection of new ora faults to solve the problem that traditional database fault detection has lag and cannot capture and respond to faults in real time.

[0005] In a first aspect, an embodiment of the present invention provides a method, apparatus, device and medium for real-time detection of new ora failures, the method comprising:

[0006] When a real-time monitoring condition for an oracle database is met, real-time monitoring of standardized data written to the oracle database;

[0007] Whenever the current standardized data is monitored, the target write time corresponding to the current standardized data, the predefined time dimension, and the first time interval and the second time interval corresponding to the comparison period are obtained according to the low-code training statements extracted from the pre-built new fault detection model;

[0008] The second time interval is before the first time interval, and the time span of the second time interval is greater than that of the first time interval;

[0009] Acquire a target real-time data set whose writing time is within a first time interval in an Oracle database, and acquire a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set;

[0010] The target real-time data set and the target historical fault data set are respectively input into the newly added fault detection model, and when a newly added error code is detected by the newly added fault detection model, the newly added error code is stored in the error code table.

[0011] In a second aspect, an embodiment of the present invention provides a device for detecting new ora faults by baseline comparison, the device comprising:

[0012] A standardized data acquisition module, used for monitoring the standardized data written into the Oracle database in real time when the real-time monitoring conditions of the Oracle database are met;

[0013] A time interval acquisition module is used to obtain the first time interval and the second time interval corresponding to the target write time, the predefined time dimension and the comparison period corresponding to the current standardized data according to the low-code training statements extracted from the pre-built new fault detection model whenever the current standardized data is monitored;

[0014] The second time interval is before the first time interval, and the time span of the second time interval is greater than that of the first time interval;

[0015] A data set acquisition module is used to acquire a target real-time data set whose writing time is within a first time interval in an Oracle database, and acquire a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set;

[0016] A new error code detection module is added, which is used to input the target real-time data set and the target historical fault data set into the new fault detection model respectively, and when the new error code is detected by the new fault detection model, the new error code is stored in the error code table.

[0017] In a third aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for real-time detection of new ora failures described in any embodiment of the present invention.

[0021] In a fourth aspect, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for real-time detection of new ora failures as described in any embodiment of the present invention when executed.

[0022] The technical solution of the embodiment of the present invention fully utilizes the real-time information and comprehensiveness of the log data by collecting the log data of the Oracle database in real time and performing real-time detection. By comparing the historical data baseline with the real-time data, it automatically determines whether there are new fault error codes, greatly reduces the dependence on manual intervention, realizes real-time capture and response of faults, and effectively solves the problems of traditional database fault detection methods such as lag, strong dependence on manual intervention, insufficient flexibility, and insufficient log utilization.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a method for real-time detection of new ora faults provided according to Embodiment 1 of the present invention;

[0026] Figure 2 It is a reference diagram of an error code table applicable to the embodiment of the present invention;

[0027] Figure 3 is a flowchart of another method for real-time detection of new ora failures provided according to Embodiment 2 of the present invention;

[0028] Figure 4 It is a structural diagram of a method and device for real-time detection of newly added ora faults provided according to Embodiment 3 of the present invention;

[0029] Figure 5 The invention is a structural diagram of an electronic device for implementing a method for real-time detection of newly added ora faults according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 This is a flowchart of a method for real-time detection of new Oracle faults provided in the first embodiment of the present invention. This embodiment can be applied to the situation of detecting whether there are new faults in the Oracle database. The method can be executed by a device for detecting new Oracle faults by baseline comparison. The device can be implemented in the form of hardware and / or software. The device can be configured in an existing database monitoring and management system. Figure 1 As shown, the method includes:

[0034] S110 . When a real-time monitoring condition for an oracle database is met, the standardized data written into the oracle database is monitored in real time.

[0035] The embodiment of the present invention can collect massive amounts of oracle raw data through log probes and ODBC open database interconnection, extract time information from the collected raw data, and convert the extracted time information into a unified time format to facilitate subsequent data acquisition based on the time information.

[0036] S120. Whenever the current standardized data is monitored, based on the low-code training statements extracted from the pre-built new fault detection model, obtain the target write time corresponding to the current standardized data, the predefined time dimension, and the first time interval and the second time interval corresponding to the comparison period.

[0037] The second time interval is before the first time interval.

[0038] The pre-built new fault detection model is a model built on a low-code platform for detecting new error codes. The current standardized data is real-time data monitored in real time. Whenever real-time data is written to the new fault detection model, the target write time of the current standardized data is the data write time at the current time node. Specifically, in the new fault detection model, since it is necessary to detect new error codes, the time range for data acquisition is predefined in the low-code training statement. Among them, the specific time period for data acquisition can be determined according to the time dimension. For example, if the time dimension is one hour, it is necessary to obtain all data from one hour before the target write time corresponding to the current standardized data, and the predefined comparison period is used to determine the specific day's historical data before the current standardized data, that is, the historical data within the second time interval.

[0039] Optionally, before the low-code training statements extracted from the pre-built new fault detection model are added, the following can also be included:

[0040] Extract the target error code generation statement from the original training model generated based on the low-code platform;

[0041] Introduce the comparison cycle parameter into the target error code generation statement and write the historical baseline generation statement;

[0042] Combine the target error code generation statement and the historical baseline generation statement, introduce the new error code verification statement, and write the low-code training statement;

[0043] Write low-code training statements into the original training model to obtain a new fault detection model.

[0044] In an embodiment of the present invention, the beaver storage engine library component in Logeasy can be used to extract historical data and use it for model training. The original training model generated on the low-code platform is used to generate a target error code. The target error code generation statement for generating the target error code can be: [starttime="-1h

[0045] "endtime="now"appname:oralce|stats count()as cnt_1h by json.code]; where starttime is the start time and endtime is the end time. When the dynamic parameter in endtime is "now", it means up to the current time, that is, the writing time of the current standardized data. "-1h" is the time dimension. If the writing time of the standardized data is [2022 / 05 / 11-23:59:59], then when the time dimension is one hour, the starting point of the first time interval is [2022 / 05 / 11-22:59:59].

[0046] The historical baseline generation statement used to generate the historical baseline error code can be:

[0047] [starttime="-1h-1d"endtime="-1h"appname:oracle|stats count()as cnt_1d by json.code]; Compared with the target error code generation statement, the historical baseline generation statement introduces the dynamic parameter "1d", which means the day before the current day, that is, the comparison period. Similarly, if the writing time of the standardized data is [2022 / 05 / 11-23:59:59], when the time dimension is one hour and the comparison period is one day, the starting point of the second time interval is [2022 / 05 / 10-22:59:59].

[0048] The new error code verification statement can be: [where empty(cnt_1d)|fields json.code|renamejson.code as "new error code"]. Connect the above target error code generation statement, historical baseline generation statement, and new error code verification statement to get:

[0049] {starttime="-1h"endtime="now"appname:oralce|stats count()as cnt_1hby json.code|join type=left json.code[[starttime="-1h-1d"endtime="-1h"appname:oracle|stats count()as cnt_1d by json.code]]|where empty(cnt_1d)|fields json.code|rename json.code as"New error code"} low-code training statement.

[0050] S130, obtaining a target real-time data set whose writing time is within a first time interval in an Oracle database, and obtaining a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set.

[0051] By extracting the time dimension in the low-code training statement and obtaining the writing time of the current standardized data, the starting point of the first time interval can be obtained, where the target real-time data set is the collection of all real-time data without fault detection, and the historical fault data set is the collection of all historical fault data obtained by extracting abnormal data. The historical fault data set can be used to generate historical baseline error codes, and the target real-time data set can be used to generate target error codes.

[0052] Furthermore, before acquiring the target historical fault data set with a writing time in the second time interval from the pre-established historical fault data set, the method may further include:

[0053] Parsing each log message written into the oracle database according to a preset parsing rule to obtain standardized data consisting of key-value pairs in a key:value format;

[0054] Traversing all key-value pair data in each of the standardized data, and identifying problematic key-value pairs whose value carries error code characteristics in the key-value pair data;

[0055] All the problem key-value pairs are collected to construct the historical fault data set.

[0056] The process of constructing the historical fault data set first requires parsing all log information, that is, to determine the separators between fields and field values ​​and between fields through certain parsing rules, so as to identify fields in the key:value format. Among them, for historical fault data, its value carries error code features and is different from the normal vlaue value. Therefore, the field containing the value of the error code feature can be extracted by time series feature extraction or frequency domain feature extraction, and all fields in the key:value (error code feature) format are aggregated to construct the historical fault data set.

[0057] S140, inputting the target real-time data set and the target historical fault data set into the newly added fault detection model respectively, and when a newly added error code is detected by the newly added fault detection model, storing the newly added error code in an error code table.

[0058] In a specific example, if the historical baseline error code is Ora-12511 and the target error code is Ora-12510, it means that the target error code is a newly added error code. The target error code is stored as a newly added error code in the error code table. The error code table refers to Figure 2 .

[0059] Optionally, when a new error code is detected by the new fault detection model, storing the new error code in an error code table may include:

[0060] Generate a target error code according to the target real-time data set through the newly added fault detection model, and generate a historical baseline error code according to the historical fault data set;

[0061] By using the newly added fault detection model, the target error code is compared with the historical baseline error code;

[0062] By adding a fault detection model, if it is determined that the target error code is inconsistent with the historical baseline error code, the target error code is stored in the error code table as a newly added error code.

[0063] Further, generating a target error code according to a target real-time data set through the newly added fault detection model, and generating a historical baseline error code according to the historical fault data set may include:

[0064] Generate a target error code corresponding to the current standardized data based on a target error code generation statement in the newly added fault detection model;

[0065] Generate a historical baseline error code corresponding to the target historical fault data based on a historical baseline generation statement in the newly added fault detection model;

[0066] The target error code is compared with the historical baseline error code through the newly added fault detection model, including:

[0067] Based on the newly added error code verification statement in the newly added fault detection model, the target error code and the historical baseline error code are matched for field consistency.

[0068] It can be seen from the above embodiments that the low-code training statements in the new fault detection model include target error code generation statements, historical baseline generation statements and new error code verification statements. Therefore, after the target error code and the historical baseline error code are generated, the field verification is performed through the new error code verification statement. If the target error code is the same as the historical baseline error code, it is considered that no new error code is generated. At this time, the target error code does not need to be stored repeatedly in the error code table as a historical baseline error code.

[0069] The embodiments of the present invention receive the log data of the Oracle database in real time and perform real-time detection, thereby making full use of the real-time information and comprehensiveness in the log data. By comparing the historical data baseline with the real-time data, it is automatically determined whether there are new fault error codes, thereby greatly reducing the reliance on manual intervention, achieving real-time capture and response to faults, and effectively solving the problems of the traditional database fault detection method, such as the lag, strong reliance on manual intervention, insufficient flexibility, and insufficient log utilization.

[0070] Embodiment 2

[0071] Figure 3 This is a flow chart of another method for real-time detection of new ora faults provided in Embodiment 2 of the present invention. This embodiment is refined based on the above embodiment. Accordingly, Figure 3 As shown, the method specifically includes:

[0072] S310 . When a real-time monitoring condition for an oracle database is met, the standardized data written into the oracle database is monitored in real time.

[0073] S320. Whenever the current standardized data is monitored, extract the predefined time dimension and comparison period in the low-code training statement, where the time dimension is in hours and the comparison period is in days.

[0074] In the target error code generation statement, the dynamic parameter of starttime is the time dimension. The time dimension specifies the acquisition of data within the specified time range, starttime = "-1h"; in the historical baseline generation statement, the dynamic parameter of starttime includes the time dimension and the comparison period, such as "-1h-1d". The comparison period specifies the acquisition of data on the specified date.

[0075] S330. Perform regular expression matching on the current standardized data and the low-code training statement to obtain the target writing time corresponding to the current standardized data.

[0076] From the above embodiments, it can be seen that the target error code generation statement in the low-code training statement has a dynamic parameter of endtime of "now". By performing regular matching of "now" with the time information in the standardized data, the timestamp information of the current standardized data can be obtained, that is, the target writing time of the standardized data. Replace "now" with the target writing time to acquire data.

[0077] S340. Extract the writing date and writing hour from the target writing time.

[0078] Since the target writing time is extracted from the standardized data, the target writing time follows the standardized time format, such as: [2022 / 05 / 11-23:59:59], the writing date is [2022 / 05 / 11], and the writing hour is [23:59:59].

[0079] S350: construct a first time interval according to the writing hour and the time dimension, and construct a second time interval according to the writing date and the comparison period.

[0080] Since the first time interval is the acquisition time of the target real-time data set, there is no need to refer to the comparison cycle parameters. In the target error code generation statement, since the unit of the time dimension is hours and it is the data of the day, the start time of the first time interval of the current standardized data can be calculated by extracting the writing hour in the current standardized data writing time. The target writing time is used as the end time of the first time interval to obtain the complete first time interval.

[0081] The second time interval is the time when the historical fault data set is acquired, so it is necessary to refer to the comparison cycle. In the historical baseline generation statement, since the unit of the comparison cycle is day, when the acquired data is not data of the same day, it is necessary to extract the writing date in the writing time and combine the comparison cycle to calculate the start time of the second time interval of the target historical fault data. The end time also needs to be calculated based on the target writing time of the standardized data according to the comparison cycle and time dimension, so as to obtain the complete second time interval.

[0082] S360, obtaining a target real-time data set whose writing time is within a first time interval in an Oracle database, and obtaining a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set.

[0083] S370, inputting the target real-time data set and the target historical fault data set into the newly added fault detection model respectively, and when a newly added error code is detected by the newly added fault detection model, storing the newly added error code in an error code table.

[0084] Optionally, the method may further include:

[0085] The time dimension in the low-code training statement is dynamically updated through a scheduled task.

[0086] As time goes by, the data and fault conditions in the system will change. Therefore, the time dimension needs to be adjusted regularly to maintain its accuracy and reliability. The dynamic parameters of Starttime and Endtime are automatically updated through scheduled tasks to obtain a new time interval. Based on the new time interval, a new target error code and historical baseline error code are generated to ensure the accuracy and reliability of detection.

[0087] The technical solution of the embodiment of the present invention mainly explains the determination of the time interval by refining the overall solution. Specifically, the time interval is calculated by the write time, time dimension and comparison period. Overall, the embodiment of the present invention uses low-code modeling functions to perform model training, and by parsing the error code features in the log data, it achieves accurate positioning and analysis of database failures, which not only improves the efficiency of fault handling, but also provides data support for subsequent fault prevention and optimization. At the same time, by regularly updating the dynamic parameters in the model, it can adapt to changes in the database and fault conditions, and improve the flexibility and accuracy of detection.

[0088] Embodiment 3

[0089] Figure 4 This is a schematic diagram of the structure of a device for detecting new ora faults by baseline comparison provided in Embodiment 3 of the present invention. Figure 4 As shown, the device comprises:

[0090] The standardized data acquisition module 410 is used to monitor the standardized data written into the Oracle database in real time when the real-time monitoring condition of the Oracle database is met;

[0091] The time interval acquisition module 420 is used to acquire the target write time corresponding to the current standardized data, the predefined time dimension and the first time interval and the second time interval corresponding to the comparison period according to the low-code training statement extracted from the pre-built new fault detection model whenever the current standardized data is monitored;

[0092] The second time interval is before the first time interval, and the time span of the second time interval is greater than that of the first time interval;

[0093] The data set acquisition module 430 is used to acquire a target real-time data set whose writing time is within a first time interval in an Oracle database, and acquire a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set;

[0094] The newly added error code detection module 440 is used to input the target real-time data set and the target historical fault data set into the newly added fault detection model respectively, and when the newly added error code is detected by the newly added fault detection model, the newly added error code is stored in the error code table.

[0095] In the embodiments of the present invention, unique beneficial effects are achieved.

[0096] Optionally, based on the above embodiments, a new error code detection module 440 may include:

[0097] A baseline error code generating unit, configured to generate a target error code according to a target real-time data set through the newly added fault detection model, and to generate a historical baseline error code according to the historical fault data set;

[0098] A baseline error code comparison unit, used to compare the target error code with the historical baseline error code through the newly added fault detection model;

[0099] A new fault detection unit is added, which is used to store the target error code as a new error code in the error code table if it is determined that the target error code is inconsistent with the historical baseline error code through a new fault detection model.

[0100] Optionally, on the basis of the above embodiments, it further includes a historical fault data set construction unit, which is used to parse each log information written into the oracle database according to a preset parsing rule before acquiring a target historical fault data set with a writing time within a second time interval from a pre-established historical fault data set, to obtain standardized data consisting of key-value pairs in a key:value format;

[0101] Traversing all key-value pair data in each of the standardized data, and identifying problematic key-value pairs whose value carries error code characteristics in the key-value pair data;

[0102] All the problem key-value pairs are collected to construct the historical fault data set.

[0103] Optionally, based on the above embodiments, the time interval acquisition module 420 may further include:

[0104] A parameter extraction unit, used to extract a predefined time dimension and a comparison period in a low-code training statement, wherein the time dimension is in hours and the comparison period is in days;

[0105] A regular matching unit, used to perform regular matching on the current standardized data and the low-code training statement to obtain a target write time corresponding to the current standardized data;

[0106] A writing time extraction unit, used to extract the writing date and the writing hour from the target writing time;

[0107] The time interval construction unit is used to construct a first time interval according to the writing hour and the time dimension, and to construct a second time interval according to the writing date and the comparison period.

[0108] Optionally, on the basis of the above embodiments, it further includes a new fault detection model construction unit, which is used to extract a target error code generation statement from an original training model generated based on a low-code platform before extracting a low-code training statement from a pre-built new fault detection model;

[0109] Introduce the comparison cycle parameter into the target error code generation statement and write the historical baseline generation statement;

[0110] Combine the target error code generation statement and the historical baseline generation statement, introduce the new error code verification statement, and write the low-code training statement;

[0111] Write low-code training statements into the original training model to obtain a new fault detection model.

[0112] Optionally, based on the above embodiments, the baseline error code generating unit may also be used to:

[0113] Generate a target error code corresponding to the current standardized data based on a target error code generation statement in the newly added fault detection model;

[0114] A historical baseline error code corresponding to the target historical fault data is generated based on a historical baseline generation statement in the newly added fault detection model.

[0115] Optionally, based on the above embodiments, the baseline error code comparison unit may also be used to:

[0116] Based on the newly added error code verification statement in the newly added fault detection model, the target error code and the historical baseline error code are matched for field consistency.

[0117] Optionally, based on the above embodiments, it also includes a dynamic update unit, which is used to dynamically update the time dimension in the low-code training statement through a scheduled task.

[0118] The device for detecting new ora faults by baseline comparison provided by the embodiment of the present invention can execute the method for detecting new ora faults in real time provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0119] Embodiment 4

[0120] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0121] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0122] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0123] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for detecting new ora faults in real time.

[0124] That is, when the real-time monitoring conditions for the Oracle database are met, the standardized data written to the Oracle database is monitored in real time;

[0125] Whenever the current standardized data is monitored, the target write time corresponding to the current standardized data, the predefined time dimension, and the first time interval and the second time interval corresponding to the comparison period are obtained according to the low-code training statements extracted from the pre-built new fault detection model;

[0126] The second time interval is before the first time interval, and the time span of the second time interval is greater than that of the first time interval;

[0127] Acquire a target real-time data set whose writing time is within a first time interval in an Oracle database, and acquire a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set;

[0128] The target real-time data set and the target historical fault data set are respectively input into the newly added fault detection model, and when a newly added error code is detected by the newly added fault detection model, the newly added error code is stored in the error code table.

[0129] In some embodiments, a method for detecting newly added ora faults in real time may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for detecting newly added ora faults in real time described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a method for detecting newly added ora faults in real time by any other appropriate means (e.g., by means of firmware).

[0130] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0132] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0135] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for real-time detection of new ora failures, characterized in that: include: When a real-time monitoring condition for an oracle database is met, real-time monitoring of standardized data written to the oracle database; Whenever the current standardized data is monitored, the target write time corresponding to the current standardized data, the predefined time dimension, and the first time interval and the second time interval corresponding to the comparison period are obtained according to the low-code training statements extracted from the pre-built new fault detection model; Wherein, the second time interval is before the first time interval; Acquire a target real-time data set whose writing time is within a first time interval in an Oracle database, and acquire a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set; The target real-time data set and the target historical fault data set are respectively input into the newly added fault detection model, and when a newly added error code is detected by the newly added fault detection model, the newly added error code is stored in the error code table.

2. The method according to claim 1, characterized in that: When a new error code is detected by the new fault detection model, the new error code is stored in the error code table, including: Generate a target error code according to the target real-time data set through the newly added fault detection model, and generate a historical baseline error code according to the historical fault data set; By using the newly added fault detection model, the target error code is compared with the historical baseline error code; By adding a fault detection model, if it is determined that the target error code is inconsistent with the historical baseline error code, the target error code is stored in the error code table as a newly added error code.

3. The method according to claim 2, characterized in that In the pre-established historical fault data set, before obtaining the target historical fault data set whose writing time is in the second time interval, the method further includes: Parsing each log message written into the oracle database according to a preset parsing rule to obtain standardized data consisting of key-value pairs in a key:value format; Traversing all key-value pair data in each of the standardized data, and identifying problematic key-value pairs whose value carries error code characteristics in the key-value pair data; All the problem key-value pairs are collected to construct the historical fault data set.

4. The method according to claim 1, characterized in that According to the low-code training statements extracted from the pre-built new fault detection model, the target write time corresponding to the current standardized data, the first time interval and the second time interval corresponding to the predefined time dimension and the comparison period are obtained, including: In the low-code training statement, a predefined time dimension and a comparison period are extracted, wherein the time dimension is in hours and the comparison period is in days; Regularly match the current standardized data with the low-code training statement to obtain the target write time corresponding to the current standardized data; In the target writing time, extract the writing date and writing hour respectively; A first time interval is constructed based on the writing hour and the time dimension, and a second time interval is constructed based on the writing date and the comparison period.

5. The method according to claim 3, characterized in that: Before adding a new fault detection model based on low-code training statements extracted from the pre-built model, it also includes: Extract the target error code generation statement from the original training model generated based on the low-code platform; Introduce the comparison cycle parameter into the target error code generation statement and write the historical baseline generation statement; Combine the target error code generation statement and the historical baseline generation statement, introduce the new error code verification statement, and write the low-code training statement; Write low-code training statements into the original training model to obtain a new fault detection model.

6. The method according to claim 5, characterized in that By using the newly added fault detection model, a target error code is generated according to the target real-time data set, and a historical baseline error code is generated according to the historical fault data set, including: Generate a target error code corresponding to the current standardized data based on a target error code generation statement in the newly added fault detection model; Generate a historical baseline error code corresponding to the target historical fault data based on a historical baseline generation statement in the newly added fault detection model; The target error code is compared with the historical baseline error code through the newly added fault detection model, including: Based on the newly added error code verification statement in the newly added fault detection model, the target error code and the historical baseline error code are matched for field consistency.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes: The time dimension in the low-code training statement is dynamically updated through a scheduled task.

8. A device for detecting new ora faults by baseline comparison, characterized in that: include: A standardized data acquisition module, used for monitoring the standardized data written into the Oracle database in real time when the real-time monitoring conditions of the Oracle database are met; A time interval acquisition module is used to obtain the first time interval and the second time interval corresponding to the target write time, the predefined time dimension and the comparison period corresponding to the current standardized data according to the low-code training statements extracted from the pre-built new fault detection model whenever the current standardized data is monitored; The second time interval is before the first time interval, and the time span of the second time interval is greater than that of the first time interval; A data set acquisition module is used to acquire a target real-time data set whose writing time is within a first time interval in an Oracle database, and acquire a target historical fault data set whose writing time is within a second time interval in a pre-established historical fault data set; A new error code detection module is added, which is used to input the target real-time data set and the target historical fault data set into the new fault detection model respectively, and when the new error code is detected by the new fault detection model, the new error code is stored in the error code table.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for real-time detection of new ora failures according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for real-time detection of new ora failures according to any one of claims 1 to 7 when executed.

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