Index monitoring method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the technical field of data analysis, and relates to an index monitoring method and device, computer equipment and a storage medium, and the method comprises the steps: reading a system database, and obtaining index information in a hive table of the system database; configuring monitoring information according to the index information; obtaining a historical index value corresponding to the monitoring information in the hive table; calculating an index threshold value according to the historical index value; collecting a current index value in real time; judging whether the current index value belongs to the index threshold range or not; if the current index value belongs to the index threshold range, continuously collecting the current index value; and if the current index value does not belong to the index threshold range, outputting an alarm signal. By analyzing the recent trend of the index, whether the index is abnormal or not is detected, an alarm is given after the index is abnormal, a data developer is notified in time, the problem is found in advance, the problem is solved in advance, and the influence on the downstream is relieved.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, applicable to the financial or medical fields, and in particular to an indicator monitoring method, device, computer equipment and storage medium. Background Art

[0002] The scale of operation and maintenance data is large and has grown explosively, and the categories of monitoring indicators have increased, such as system indicators (such as memory overload, etc.) and business indicators. However, the characteristics of indicators are numerous and complex, and the content included in indicator characteristics is becoming more and more abundant, such as stable trends, periodic changes, large fluctuations, etc.

[0003] There is an existing indicator monitoring method that uses traditional manual experience to set thresholds to monitor indicator data, and notifies users by generating alarms when the thresholds are exceeded.

[0004] However, the applicant found that the traditional indicator monitoring method has a long verification cycle, high maintenance cost, and leads to a large number of alarms and missed and false alarms, resulting in low accuracy and coverage of monitoring and early warning, and unable to detect and resolve faults in a timely and accurate manner. It can be seen that the traditional indicator monitoring method has problems with low monitoring accuracy and coverage. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose an indicator monitoring method, device, computer equipment and storage medium to solve the problems of low monitoring accuracy and coverage in traditional indicator monitoring methods.

[0006] In order to solve the above technical problems, the present application embodiment provides an indicator monitoring method, which adopts the following technical solution:

[0007] Read the system database and obtain indicator information in the hive table of the system database;

[0008] Configure monitoring information according to the indicator information;

[0009] Obtaining historical indicator values ​​corresponding to the monitoring information in the hive table;

[0010] Calculating an indicator threshold value according to the historical indicator value;

[0011] Collect current indicator values ​​in real time;

[0012] Determine whether the current indicator value is within the indicator threshold range;

[0013] If the current indicator value is within the indicator threshold range, continuously collect the current indicator value;

[0014] If the current indicator value does not fall within the indicator threshold range, an alarm signal is output.

[0015] Furthermore, the indicator threshold includes an upper limit of the indicator threshold and a lower limit of the indicator threshold, and the step of calculating the indicator threshold according to the historical indicator value specifically includes the following steps:

[0016] Performing a feature extraction operation on the historical indicator value to obtain a historical indicator feature;

[0017] Calculate the lower quartile Q1 and the upper quartile Q3 according to the historical indicator characteristics;

[0018] Calculating the interquartile range IQR according to the lower quartile Q1 and the upper quartile Q3, wherein the interquartile range IQR is expressed as IQR=Q3-Q1;

[0019] The upper limit of the indicator threshold and the lower limit of the indicator threshold are calculated according to the lower quartile Q1, the upper quartile Q3 and the interquartile range IQR, wherein the upper limit of the indicator threshold is expressed as Q3+k×IQR, and the lower limit of the indicator threshold is expressed as Q1-k×IQR, where k represents the sensitivity parameter of the monitoring information.

[0020] Furthermore, before the step of reading the system database and obtaining the indicator information from the hive table in the system database, the following steps are also included:

[0021] Obtain historical data on system metrics from dispersed data sources;

[0022] Performing data cleaning and data conversion operations on the historical data to obtain standard historical data;

[0023] The standard historical data is stored in the hive table.

[0024] Furthermore, after the step of collecting the current indicator value in real time, the following steps are also included:

[0025] An abnormality detection model based on machine learning is called, and an abnormality recognition operation is performed on the current indicator value in combination with a clustering algorithm to obtain an abnormality recognition result.

[0026] Furthermore, after the step of calling the anomaly detection model based on machine learning and performing an anomaly identification operation on the current indicator value in combination with a clustering algorithm to obtain an anomaly identification result, the following steps are also included:

[0027] If the abnormal recognition result has an abnormal pattern, performing an abnormal feature extraction operation on the abnormal pattern to obtain abnormal feature data;

[0028] Analyze the abnormal characteristic data to obtain the cause of the abnormality;

[0029] Reading a decision database, and obtaining a correction strategy corresponding to the abnormal cause in the decision database;

[0030] A correction operation is performed on the abnormal mode according to the correction strategy.

[0031] Furthermore, if the current indicator value does not fall within the indicator threshold range, the step of outputting an alarm signal specifically includes the following steps:

[0032] The historical indicator characteristics, the upper limit of the indicator threshold, and the lower limit of the indicator threshold are displayed in the form of a chart.

[0033] In order to solve the above technical problems, the embodiment of the present application also provides an indicator monitoring device, which adopts the following technical solution:

[0034] An indicator information acquisition module is used to read the system database and obtain indicator information in the hive table of the system database;

[0035] A monitoring information configuration module, used to configure monitoring information according to the indicator information;

[0036] A historical indicator acquisition module, used to obtain historical indicator values ​​corresponding to the monitoring information in the hive table;

[0037] An indicator threshold calculation module, used to calculate the indicator threshold according to the historical indicator value;

[0038] The current indicator collection module is used to collect the current indicator value in real time;

[0039] A current indicator judgment module is used to judge whether the current indicator value falls within the indicator threshold range;

[0040] A first judgment result module, configured to continuously collect the current indicator value if the current indicator value is within the indicator threshold range;

[0041] The second judgment result module is used to output an alarm signal if the current indicator value does not fall within the indicator threshold range.

[0042] Furthermore, the indicator threshold calculation module includes:

[0043] A feature extraction submodule is used to perform feature extraction operations on the historical indicator values ​​to obtain historical indicator features;

[0044] The quartile calculation submodule is used to calculate the lower quartile Q1 and the upper quartile Q3 according to the historical indicator characteristics;

[0045] An interquartile range calculation submodule, used for calculating the interquartile range IQR according to the lower quartile Q1 and the upper quartile Q3, wherein the interquartile range IQR is expressed as IQR=Q3-Q1;

[0046] The threshold calculation submodule is used to calculate the upper limit of the indicator threshold and the lower limit of the indicator threshold according to the lower quartile Q1, the upper quartile Q3 and the interquartile range IQR, wherein the upper limit of the indicator threshold is expressed as Q3+k×IQR, the lower limit of the indicator threshold is expressed as Q1-k×IQR, and k represents the sensitivity parameter of the monitoring information.

[0047] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0048] It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the indicator monitoring method as described above when executing the computer-readable instructions.

[0049] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0050] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the indicator monitoring method described above are implemented.

[0051] The present application provides an indicator monitoring method, including: reading a system database, obtaining indicator information in a hive table of the system database; configuring monitoring information according to the indicator information; obtaining historical indicator values ​​corresponding to the monitoring information in the hive table; calculating indicator thresholds according to the historical indicator values; collecting current indicator values ​​in real time; judging whether the current indicator value is within the indicator threshold range; if the current indicator value is within the indicator threshold range, continuously collecting the current indicator value; if the current indicator value is not within the indicator threshold range, outputting an alarm signal. Compared with the prior art, the present application analyzes the recent trend of the indicator, detects whether the indicator is abnormal, and issues an alarm after an abnormality, notifies the data developer in time, discovers, solves problems in advance, and reduces the impact on the downstream. In addition, the alarm threshold is continuously updated based on recent indicators, which can more accurately reflect the fluctuation of the indicator, without artificially defining or changing the threshold, and completes more accurate monitoring of the indicator at a lower cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0054] Figure 2 It is a flowchart of the implementation of the indicator monitoring method provided in the embodiment of the present application;

[0055] Figure 3 is a schematic diagram of an embodiment of an alarm threshold provided in an embodiment of the present application;

[0056] Figure 4 It is a structural schematic diagram of an indicator monitoring device provided in an embodiment of the present application;

[0057] Figure 5 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0061] like Figure 1As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0062] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0063] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0064] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0065] It should be noted that the indicator monitoring method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the indicator monitoring device is generally set in the server / terminal device.

[0066] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0067] Continue to refer Figure 2 , shows a flow chart of an embodiment of an indicator monitoring method according to the present application. The indicator monitoring method comprises: step S201, step S202, step S203, step S204, step S205, step S206, step S207 and step S208.

[0068] In step S201, the system database is read, and indicator information is obtained from the hive table of the system database.

[0069] In the embodiment of the present application, the indicators are usually stored in a hive table. To monitor the indicators, it is necessary to know the table and field where the indicators are stored.

[0070] In an embodiment of the present application, the indicator information may be "transaction indicator information or payment indicator information or business indicator information or purchase indicator information" related to a financial institution (such as a bank, etc.). The indicator information may also be medical indicator information related to a medical scenario, such as personal health records, prescriptions, examination reports and other indicator information. It should be understood that the examples of indicator information here are only for the convenience of understanding and are not used to limit the present application.

[0071] In step S202, monitoring information is configured according to the indicator information.

[0072] In the embodiment of the present application, after obtaining the indicator information in the hive table, it is necessary to configure the monitoring information according to the characteristics of the indicator. Specifically, the monitoring information may be:

[0073] (1) Configure the indicator cycle. A clear cycle can make the alarm more accurate. For example, the MTD indicator is a monthly cycle. This type of indicator is based on a natural month and increases every day from the beginning of the month to the end of the month.

[0074] (2) Configure data dependencies to ensure that monitoring tasks are executed after the indicators are calculated;

[0075] (3) Configure the monitoring sensitivity parameter k. The smaller the k value, the higher the monitoring sensitivity and the more sensitive it is to small fluctuations.

[0076] (4) Configure the monitoring scope, such as the time range and indicator dimension, to achieve more refined monitoring of the indicator.

[0077] In step S203, historical indicator values ​​corresponding to the monitoring information are obtained in the hive table.

[0078] In the embodiments of this application, see Figure 3 As shown, the indicator value is obtained from the indicator storage table based on the configuration information, and the threshold is calculated based on the recent indicator value and trend.

[0079] In step S204, the indicator threshold is calculated according to the historical indicator value.

[0080] In step S205, the current indicator value is collected in real time.

[0081] In step S206, it is determined whether the current indicator value is within the indicator threshold range.

[0082] In step S207, if the current indicator value is within the indicator threshold range, the current indicator value is continuously collected.

[0083] In step S208, if the current indicator value does not fall within the indicator threshold range, an alarm signal is output.

[0084] In the embodiment of the present application, whether to alarm is determined based on the upper and lower limits of the threshold, and the alarm will be notified to the designated person in the form of an email. The alarm email will contain some information of the alarm indicator, such as the indicator name, indicator value, etc., to facilitate faster problem location.

[0085] In an embodiment of the present application, an indicator monitoring method is provided, including: reading a system database, obtaining indicator information in a hive table of the system database; configuring monitoring information according to the indicator information; obtaining historical indicator values ​​corresponding to the monitoring information in the hive table; calculating indicator thresholds according to historical indicator values; collecting current indicator values ​​in real time; judging whether the current indicator value is within the indicator threshold range; if the current indicator value is within the indicator threshold range, continuously collecting the current indicator value; if the current indicator value is not within the indicator threshold range, outputting an alarm signal. Compared with the prior art, the present application analyzes the recent trend of the indicator, detects whether the indicator is abnormal, and issues an alarm after an abnormality, notifies the data developer in time, discovers, solves problems in advance, and reduces the impact on the downstream. In addition, the alarm threshold is continuously updated based on recent indicators, which can more accurately reflect the fluctuation of the indicator, without artificially setting or changing the threshold, and completes more accurate monitoring of the indicator at a lower cost.

[0086] In some optional implementations of the embodiments of the present application, the above-mentioned indicator threshold includes an upper limit of the indicator threshold and a lower limit of the indicator threshold, and the above-mentioned step of calculating the indicator threshold according to the historical indicator value specifically includes the following steps:

[0087] Perform feature extraction operations on historical indicator values ​​to obtain historical indicator features;

[0088] Calculate the lower quartile Q1 and upper quartile Q3 based on historical indicator characteristics;

[0089] The interquartile range IQR is calculated based on the lower quartile Q1 and the upper quartile Q3, where the interquartile range IQR is expressed as IQR = Q3-Q1;

[0090] The upper limit of the indicator threshold and the lower limit of the indicator threshold are calculated according to the lower quartile Q1, the upper quartile Q3 and the interquartile range IQR, where the upper limit of the indicator threshold is expressed as Q3+k×IQR, and the lower limit of the indicator threshold is expressed as Q1-k×IQR, where k represents the sensitivity parameter of the monitoring information.

[0091] In the embodiment of the present application, the process of calculating the indicator threshold may be:

[0092] ① Extract indicator features based on recent data and after eliminating periodicity;

[0093] ② Calculate the lower quartile Q1 and upper quartile Q3 based on recent indicator characteristics;

[0094] ③Calculate the interquartile range IQR = Q3-Q1;

[0095] ④ Obtain the upper limit of the indicator threshold Q3+k×IQR and the lower limit of the indicator threshold Q1-k×IQR.

[0096] In some optional implementations of the embodiments of the present application, before the step of reading the system database and obtaining the indicator information in the hive table of the system database, the following steps are also included:

[0097] Obtain historical data on system metrics from dispersed data sources;

[0098] Perform data cleaning and data conversion operations on historical data to obtain standard historical data;

[0099] Store standard historical data in the hive table.

[0100] In the embodiments of the present application, first of all, it is necessary to clarify which data sources contain the required historical data of system indicators. These data sources may include, but are not limited to, databases (such as MySQL, Oracle), log files, message queues (such as Kafka), cloud storage services (such as AWS S3), third-party APIs, etc.; then, according to the type and characteristics of the data source, select appropriate tools and techniques to extract data. For example, for a relational database, SQL queries can be used; for log files, log parsing tools may be needed; for message queues, messages need to be consumed, etc.; finally, the extracted data usually needs to be collected and integrated for subsequent processing. This may involve aggregating data from multiple data sources to a central location, such as the Hadoop Distributed File System (HDFS) or local storage.

[0101] In an embodiment of the present application, historical data of system indicators are obtained from scattered data sources, and the data are cleaned and converted and uniformly stored in a hive table to build an indicator historical data warehouse, providing a data basis for subsequent threshold calculation and anomaly detection.

[0102] In some optional implementations of the embodiments of the present application, after the step of calling the anomaly detection model based on machine learning and performing an anomaly identification operation on the current indicator value in combination with the clustering algorithm to obtain the anomaly identification result, the following steps are also included:

[0103] If there is an abnormal pattern in the abnormal recognition result, an abnormal feature extraction operation is performed on the abnormal pattern to obtain abnormal feature data;

[0104] Analyze the abnormal characteristic data to obtain the abnormal cause;

[0105] Reading a decision database, and obtaining a correction strategy corresponding to the abnormal cause in the decision database;

[0106] Correct the abnormal pattern according to the correction strategy.

[0107] In the embodiments of the present application, abnormal pattern recognition refers to first identifying abnormal patterns in the data through a preset algorithm or model during data monitoring or analysis. These abnormal patterns may appear as sudden changes in data, trends that do not meet expectations, values ​​outside the normal range, etc.

[0108] In the embodiments of the present application, abnormal feature extraction refers to the need to perform detailed feature extraction on these patterns once abnormal patterns are identified. Feature extraction refers to extracting key information or indicators that can describe the characteristics of abnormal patterns from abnormal patterns. These features may include the time, location, scope of impact, intensity of the abnormality, etc.

[0109] In the embodiments of the present application, abnormal feature data analysis refers to an in-depth analysis of the extracted abnormal feature data to reveal the reasons behind the abnormal pattern. This may involve a variety of methods such as data visualization, statistical analysis, and machine learning model prediction.

[0110] In the embodiment of the present application, abnormality cause determination refers to determining the specific cause of the abnormal pattern by analyzing abnormal feature data and combining business knowledge and experience. These causes may include system failure, human error, external environment changes, etc.

[0111] In the embodiment of the present application, after determining the cause of the abnormality, the decision database needs to be read next. The decision database is a database that stores correction strategies or solutions for different abnormal causes and scenarios; in the decision database, according to the determined abnormal cause, the corresponding correction strategy is searched and obtained. These strategies may include specific operation steps, systems or services that need to be called, parameters that need to be adjusted, etc.

[0112] In some optional implementations of the embodiments of the present application, an incremental learning approach may be used to continuously optimize the anomaly detection model and improve the detection accuracy and recall rate.

[0113] In the embodiments of the present application, by continuously optimizing and improving the above process, the efficiency and accuracy of exception handling can be improved, thereby ensuring the stable operation of the business.

[0114] In some optional implementations of the embodiments of the present application, the step of outputting an alarm signal if the current indicator value does not fall within the indicator threshold range specifically includes the following steps:

[0115] The historical indicator characteristics, the upper limit of the indicator threshold, and the lower limit of the indicator threshold are displayed in the form of a chart.

[0116] In the embodiment of the present application, the indicator characteristics and the upper and lower thresholds are plotted into a chart, which can clearly reflect the recent trend of the indicator and the alarm situation. The actual situation of the indicator is combined to determine whether the alarm is reasonable. If unreasonable alarms occur multiple times, the sensitivity of the monitoring needs to be adjusted.

[0117] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0118] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0119] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0120] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0121] Further references Figure 4 , as a response to the above Figure 2 The present application provides an embodiment of an indicator monitoring device, and the embodiment of the device is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0122] like Figure 4 As shown, the indicator monitoring device 200 of the embodiment of the present application includes:

[0123] The indicator information acquisition module 210 is used to read the system database and obtain the indicator information from the hive table of the system database;

[0124] A monitoring information configuration module 220, configured to configure monitoring information according to indicator information;

[0125] The historical indicator acquisition module 230 is used to obtain the historical indicator value corresponding to the monitoring information in the hive table;

[0126] An indicator threshold calculation module 240, used to calculate an indicator threshold according to historical indicator values;

[0127] The current indicator collection module 250 is used to collect the current indicator value in real time;

[0128] The current indicator determination module 260 is used to determine whether the current indicator value is within the indicator threshold range;

[0129] A first judgment result module 270 is used to continuously collect the current indicator value if the current indicator value is within the indicator threshold range;

[0130] The second judgment result module 280 is used to output an alarm signal if the current indicator value does not fall within the indicator threshold range.

[0131] In an embodiment of the present application, an indicator monitoring device 200 is provided, including: an indicator information acquisition module 210, which is used to read a system database and obtain indicator information in a hive table of the system database; a monitoring information configuration module 220, which is used to configure monitoring information according to the indicator information; a historical indicator acquisition module 230, which is used to obtain historical indicator values ​​corresponding to the monitoring information in the hive table; an indicator threshold calculation module 240, which is used to calculate the indicator threshold according to the historical indicator value; a current indicator collection module 250, which is used to collect the current indicator value in real time; a current indicator judgment module 260, which is used to judge whether the current indicator value falls within the indicator threshold range; a first judgment result module 270, which is used to continuously collect the current indicator value if the current indicator value falls within the indicator threshold range; and a second judgment result module 280, which is used to output an alarm signal if the current indicator value does not fall within the indicator threshold range. Compared with the existing technology, the present application analyzes the recent trends of indicators to detect whether the indicators are abnormal, and issues an alarm in case of an abnormality, promptly notifying the data developer, discovering, solving problems in advance, and reducing the impact on the downstream. In addition, the alarm threshold is continuously updated based on recent indicators, which can more accurately reflect the fluctuation of the indicators. There is no need to manually define or change the threshold, and more accurate monitoring of the indicators is completed at a lower cost.

[0132] In some optional implementations of the embodiments of the present application, the above-mentioned indicator threshold calculation module includes:

[0133] The feature extraction submodule is used to perform feature extraction operations on historical indicator values ​​to obtain historical indicator features;

[0134] The quartile calculation submodule is used to calculate the lower quartile Q1 and the upper quartile Q3 based on the historical indicator characteristics;

[0135] An interquartile range calculation submodule is used to calculate an interquartile range IQR according to a lower quartile Q1 and an upper quartile Q3, wherein the interquartile range IQR is expressed as IQR=Q3-Q1;

[0136] The threshold calculation submodule is used to calculate the upper limit of the indicator threshold and the lower limit of the indicator threshold according to the lower quartile Q1, the upper quartile Q3 and the interquartile range IQR, where the upper limit of the indicator threshold is expressed as Q3+k×IQR, the lower limit of the indicator threshold is expressed as Q1-k×IQR, and k represents the sensitivity parameter of the monitoring information.

[0137] To solve the above technical problems, the present application also provides a computer device. Figure 5 , Figure 5 This is a basic structural block diagram of a computer device according to an embodiment of the present application.

[0138] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0139] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0140] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 310 can be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 can also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Of course, the memory 310 can also include both the internal storage unit of the computer device 300 and its external storage device. In the embodiment of the present application, the memory 310 is generally used to store the operating system and various application software installed on the computer device 300, such as computer-readable instructions of the indicator monitoring method, etc. In addition, the memory 310 can also be used to temporarily store various data that have been output or are to be output.

[0141] The processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 320 is generally used to control the overall operation of the computer device 300. In the embodiment of the present application, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as computer-readable instructions for running the indicator monitoring method.

[0142] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0143] The computer equipment provided in this application analyzes the recent trends of indicators to detect whether the indicators are abnormal, and issues an alarm in case of an abnormality, promptly notifying the data developer, discovering, solving problems in advance, and reducing the impact on downstream. In addition, the alarm threshold is continuously updated based on recent indicators, and can more accurately reflect the fluctuation of the indicators. There is no need to manually define or change the threshold, and more accurate monitoring of the indicators is completed at a lower cost.

[0144] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the indicator monitoring method as described above.

[0145] The computer-readable storage medium provided by the present application detects whether an indicator is abnormal by analyzing the recent trend of the indicator, and issues an alarm in case of an abnormality, promptly notifying the data developer, discovering, solving problems in advance, and reducing the impact on the downstream. In addition, the alarm threshold is continuously updated based on recent indicators, and can more accurately reflect the fluctuation of the indicator. There is no need to manually define or change the threshold, and more accurate monitoring of the indicator is completed at a lower cost.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0147] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. An indicator monitoring method, characterized in that: The steps include: Read the system database, in which the hiv e Get indicator information from the table; Configure monitoring information according to the indicator information; In the hiv e Obtain the historical indicator value corresponding to the monitoring information in the table; Calculating an indicator threshold value according to the historical indicator value; Collect current indicator values ​​in real time; Determine whether the current indicator value is within the indicator threshold range; If the current indicator value is within the indicator threshold range, continuously collect the current indicator value; If the current indicator value does not fall within the indicator threshold range, an alarm signal is output.

2. The indicator monitoring method according to claim 1, characterized in that: The indicator threshold includes an upper limit of the indicator threshold and a lower limit of the indicator threshold. The step of calculating the indicator threshold according to the historical indicator value specifically includes the following steps: Performing a feature extraction operation on the historical indicator value to obtain a historical indicator feature; Calculate the lower quartile Q1 and the upper quartile Q3 according to the historical indicator characteristics; Calculating the interquartile range IQR according to the lower quartile Q1 and the upper quartile Q3, wherein the interquartile range IQR is expressed as IQR=Q3-Q1; The upper limit of the indicator threshold and the lower limit of the indicator threshold are calculated according to the lower quartile Q1, the upper quartile Q3 and the interquartile range IQR, wherein the upper limit of the indicator threshold is expressed as Q3+k×IQR, and the lower limit of the indicator threshold is expressed as Q1-k×IQR, where k represents the sensitivity parameter of the monitoring information.

3. The indicator monitoring method according to claim 1, characterized in that: Before the step of reading the system database and obtaining the indicator information in the hive table of the system database, the following steps are also included: Obtain historical data on system metrics from dispersed data sources; Performing data cleaning and data conversion operations on the historical data to obtain standard historical data; The standard historical data is stored in the hive table.

4. The indicator monitoring method according to claim 1, characterized in that: After the step of collecting the current indicator value in real time, the following steps are also included: An abnormality detection model based on machine learning is called, and an abnormality recognition operation is performed on the current indicator value in combination with a clustering algorithm to obtain an abnormality recognition result.

5. The indicator monitoring method according to claim 4, characterized in that: After the step of calling the anomaly detection model based on machine learning and performing an anomaly identification operation on the current indicator value in combination with a clustering algorithm to obtain an anomaly identification result, the following steps are also included: If the abnormal recognition result has an abnormal pattern, performing an abnormal feature extraction operation on the abnormal pattern to obtain abnormal feature data; Analyze the abnormal characteristic data to obtain the cause of the abnormality; Reading a decision database, and obtaining a correction strategy corresponding to the abnormal cause in the decision database; A correction operation is performed on the abnormal mode according to the correction strategy.

6. The indicator monitoring method according to claim 2, characterized in that: The step of outputting an alarm signal if the current indicator value does not fall within the indicator threshold range specifically comprises the following steps: The historical indicator characteristics, the upper limit of the indicator threshold, and the lower limit of the indicator threshold are displayed in the form of a chart.

7. An indicator monitoring device, characterized in that: include: An indicator information acquisition module is used to read the system database and obtain indicator information in the hive table of the system database; A monitoring information configuration module, used to configure monitoring information according to the indicator information; A historical indicator acquisition module, used to obtain historical indicator values ​​corresponding to the monitoring information in the hive table; An indicator threshold calculation module, used to calculate the indicator threshold according to the historical indicator value; The current indicator collection module is used to collect the current indicator value in real time; A current indicator judgment module is used to judge whether the current indicator value falls within the indicator threshold range; A first judgment result module, configured to continuously collect the current indicator value if the current indicator value is within the indicator threshold range; The second judgment result module is used to output an alarm signal if the current indicator value does not fall within the indicator threshold range.

8. The indicator monitoring device according to claim 7, characterized in that: The indicator threshold calculation module includes: A feature extraction submodule is used to perform feature extraction operations on the historical indicator values ​​to obtain historical indicator features; The quartile calculation submodule is used to calculate the lower quartile Q1 and the upper quartile Q3 according to the historical indicator characteristics; An interquartile range calculation submodule, used for calculating the interquartile range IQR according to the lower quartile Q1 and the upper quartile Q3, wherein the interquartile range IQR is expressed as IQR=Q3-Q1; The threshold calculation submodule is used to calculate the upper limit of the indicator threshold and the lower limit of the indicator threshold according to the lower quartile Q1, the upper quartile Q3 and the interquartile range IQR, wherein the upper limit of the indicator threshold is expressed as Q3+k×IQR, and the lower limit of the indicator threshold is expressed as Q1-k×IQR, where k represents the sensitivity parameter of the monitoring information.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the indicator monitoring method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the indicator monitoring method according to any one of claims 1 to 6 are implemented.

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