WO2020087829A1 - 数据趋势分析方法、***、计算机装置及可读存储介质 - Google Patents
数据趋势分析方法、***、计算机装置及可读存储介质 Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
Definitions
- the present application relates to the field of data processing, and in particular, to a data trend analysis method, system, computer device, and readable storage medium.
- the present application provides a data trend analysis method, system, computer device, and storage medium, which can analyze and judge the running state trend of the equipment in advance to provide early warning.
- An embodiment of the present application provides a data trend analysis method.
- the method includes:
- time series data of a monitoring object where the monitoring object includes one or more monitoring categories, and the time series data is a parameter data set of each monitoring category at different time nodes;
- warning information is output according to the attribute information of the extreme point of the monitoring category.
- An embodiment of the present application provides a data trend analysis system.
- the system includes:
- An acquisition module configured to acquire time series data of a monitoring object, wherein the monitoring object includes one or more monitoring categories, and the time series data is a parameter data set of each monitoring category at different time nodes;
- a generating module configured to generate a trend graph corresponding to each monitoring category according to the time series data
- the statistics module is used to obtain the extreme points included in each of the trend graphs through a statistical analysis of a preset trend analysis algorithm
- a judgment module configured to judge whether an abnormality occurs in the monitoring category corresponding to the trend graph according to each extreme point of the trend graph;
- the output module is configured to output corresponding warning information according to the attribute information of the extreme point of the monitoring category when the monitoring category is determined to be abnormal.
- An embodiment of the present application provides a computer device.
- the computer device includes a processor and a memory.
- the memory stores a plurality of computer-readable instructions.
- the processor is used to execute the computer-readable instructions stored in the memory. The steps of the data trend analysis method described earlier.
- An embodiment of the present application provides a non-volatile readable storage medium on which computer-readable instructions are stored.
- the computer-readable instructions are executed by a processor, the steps of the data trend analysis method described above are implemented.
- the above data trend analysis method, system, computer device and non-volatile readable storage medium draw a trend graph by acquiring time series data of one or more monitoring objects, and perform monitoring on each monitoring category of the monitoring objects according to the trend graph
- Trend analysis and status prediction can output corresponding warning information.
- the analysis and judgment of the running status trend of the monitored object can be realized, so that problems can be discovered in advance and alarm notification can be made in advance.
- FIG. 1 is a flowchart of steps in a data trend analysis method in an embodiment of the present application.
- FIG. 2 is a functional block diagram of a data trend analysis system in an embodiment of this application.
- FIG. 3 is a schematic diagram of a computer device in an embodiment of the application.
- the data trend analysis method of the present application is applied to one or more computer devices.
- the computer device is a device that can automatically perform numerical calculation and / or information processing according to a preset or stored instruction, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) , Programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded equipment, etc.
- ASIC Application Specific Integrated Circuit
- FPGA Field-Programmable Gate Array
- DSP Digital Signal Processor
- the computer device may be a computing device such as a desktop computer, a notebook computer, a tablet computer, and a server.
- the computer device can interact with the user through a keyboard, a mouse, a remote control, a touchpad, or a voice control device.
- FIG. 1 is a flowchart of steps of a preferred embodiment of the data trend analysis method of the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.
- the data trend analysis method specifically includes the following steps.
- Step S11 Obtain time series data of the monitoring object, where the monitoring target includes one or more monitoring categories, and the time series data is a parameter data set of each monitoring category on different time nodes.
- one or more of the monitoring objects can be connected to the network to obtain time series data of the monitoring objects.
- the monitoring object may be a server, a server cluster, or other electronic devices.
- the server or the server cluster may include several hardware resources (for example: CPU, memory, I / O interface, memory, etc.).
- the server or the server cluster may run different or the same operating system, database, application software, and system software. Understandably, the server cluster may be composed of multiple virtual machine managers (Virtual Machine Manager, VMM) and a number of physical nodes (Physical Node, PN), and multiple operating systems running on the VMM, through VMM resources Scheduling algorithms, these operating systems share physical machine resources.
- VMM Virtual Machine Manager
- PN Physical Node
- the monitoring object may include one or more monitoring categories.
- the parameter data of the corresponding type index output by each monitoring category may be obtained.
- the time series data of the monitoring object is a parameter data set of corresponding type indicators output by multiple monitoring categories at different time nodes.
- the monitoring objects may include system resource objects and / or business type objects, and the time series data of the monitoring objects may be received / acquired in real time or periodically.
- the time series data is read from the monitored object every preset time, or the monitored object uploads the time series data to the data trend analysis system every preset time.
- the monitoring object may include CPU, memory, hard disk, and other hardware monitoring categories, and may also include database, system software, and other software monitoring categories running on the server.
- the monitoring category is CPU, it can output the utilization rate (the percentage of time the processor executes non-idle threads), the interrupt rate (the number of times the device interrupts the processor per second), and the system call rate (the processor calls the operating system service routine Parameter rate data of other types of indicators;
- the monitoring category is memory, it can output the page missing rate (indicating that the processor requests an error in a page from the specified location of the memory) and other parameter data of such indicators;
- the monitoring category is hard disk
- it can output parameter data such as the average number of read and write requests (the hard disk is queued in the instance interval); when the monitoring category is a database, it can output parameter data such as data read and write performance.
- the business type When the business type is the monitoring object of the business class, it may include monitoring categories such as user login volume, user registration volume, and core transaction data. For example, when the monitoring category is user login volume, parameter data such as the number of users online can be output; when the monitoring category is user registration volume, parameter data such as the number of registered accounts can be output, and when the monitoring category is core transaction data , Can output parameter data of orders, click ads and other types of indicators.
- monitoring categories such as user login volume, user registration volume, and core transaction data.
- time series data of the monitored object after the time series data of the monitored object is obtained, it can also be stored locally to facilitate subsequent data analysis and reading.
- the time series data can be stored in the relational database by default, that is, the time t and the parameter data v of the type index in the time series data are stored as key-value pairs in the relational database.
- the relational database may be an RRD Tool database that is simply stored based on files, an opentsdb database built on the K / V database, and a mysql and postgresql database built on the relational database.
- the time series data when the requirements for data storage are high or the amount of data is relatively large, the time series data may be stored in a time series data type database to improve data reading and writing efficiency and reduce the storage space occupied by the data.
- the time series data database may include Elasticsearch, Crate.io, Solr databases based on Lucene, or Vertica and Actian databases based on columnar storage databases.
- Step S12 Generate a trend graph corresponding to each monitoring category according to the time series data.
- a trend graph corresponding to the monitored object can be generated according to the time series data to intuitively understand the status of the monitored object.
- the monitoring category When the monitoring category has a type indicator (for example, when the monitoring category is memory, it has a type index of page miss rate; or when the monitoring category is user login volume, it has a type indicator for the number of users online), the corresponding time series
- the parameter data v n of the corresponding type index is used as the value on the vertical axis (Y axis) of the trend graph, after that, the parameter information of the corresponding type index is passed Straight line or smooth curve connection, so that you can generate a trend graph corresponding to the monitoring category.
- the monitoring category is CPU, it has three types of indicators: utilization rate, interruption rate, and system call rate.
- the time series data is classified and split to obtain sub-time series X 1 , X 2 , and X 3 corresponding to each type of index, where X 1 corresponds to the type index of utilization rate, X 2 corresponds to the type index of interruption rate, and X 3 corresponds to Type indicators of system call rate, and then draw a trend graph corresponding to each type of indicators in the above manner.
- each time t n in the time series data can be taken as the point on the horizontal axis of the first trend graph, and the parameter information v n1 of the corresponding type indicator can be taken as the value on the vertical axis of the first trend graph, and then Then, the parameter information of the corresponding type indicator is connected by a straight line or a smooth curve, so that the first trend graph corresponding to the CPU utilization rate can be generated.
- each time t n in the time series data can be used as a point on the horizontal axis of the trend graph, and the parameter information v n2 of the corresponding type index can be used as the value on the vertical axis of the trend graph.
- the parameter information of the type indicator is connected by a straight line or a smooth curve, so that a second trend graph corresponding to the CPU interrupt rate can be generated.
- the trend graph whose monitoring type is CPU it may include the trend graphs corresponding to the three types of indicators of utilization rate, interruption rate, and system call.
- step S13 the extreme point included in each of the trend graphs is statistically obtained through a preset trend analysis algorithm.
- the extreme points included in each of the trend graphs can be obtained by statistically: first, randomly select a time node data and a previous node adjacent to the time node data from a trend graph Time node data, then calculate the trend slope between the time node data and the previous time node data, and then determine whether the calculated trend slope is greater than a preset threshold; when the trend slope is greater than the preset threshold , Determine that the time node data is an extreme point in the trend graph.
- the trend The slope can be calculated by the following mathematical formula:
- K m is the trend slope. If the trend slope K m > R, where R represents a preset threshold, then it can be determined that the time node data (v m , t m ) is an extreme point in the trend graph.
- the set of all extreme points in a trend graph can be represented as an extreme value set.
- the R value for different category indicators can be set differently. For example, according to the application system, the CPU utilization fluctuates within ⁇ 5%. If it is too low, the CPU utilization of the server is not high; if it is too high, the CPU may become the processing bottleneck of the system. Therefore, for the monitoring category is CPU, the preset threshold of the utilization type index can be set to [-5, 5]. For the CPU interrupt rate, in general, the lower the processor interrupt rate, the better; it should not exceed 1000 times per second; if the value of the interrupt rate increases significantly, it may indicate that there is a hardware problem, you need to check the network adapter that caused the interrupt , Disk or other hardware. Therefore, for the monitoring category CPU, the preset threshold of the type indication of the interrupt rate is 1000 times.
- Step S14 According to each extreme point of the trend graph, determine whether an abnormality occurs in the monitoring category corresponding to the trend graph.
- a comprehensive trend slope K corresponding to two or more time series data adjacent to an extreme point in a trend graph may be used to determine whether the monitoring category corresponding to the trend graph is abnormal. Specifically: first, randomly select an extreme point from a trend graph, and obtain data of at least two prior time nodes adjacent to the extreme point; secondly, calculate the data of the extreme point and the first time node respectively The first trend slope between, the second trend slope between the extreme point and the second time node data, wherein the first time node data is the data of the last time node that is close to the extreme point, The second time node data is the last time node data adjacent to the first time node data; furthermore, the standard deviation and the mean slope of the first trend slope and the second trend slope are calculated; furthermore , Based on the calculated standard deviation and mean slope, the comprehensive trend slope of the extreme point is calculated; finally, it is determined whether the comprehensive trend slope of the extreme point is within a preset range value; when the extreme point is integrated When the trend slope is not within the preset range value
- the corresponding time series data is (v m , t m ), so the two time series data adjacent to the extreme point are (v m-1 , t m- 1 ), (v m-2 , t m-2 ); the three time series data adjacent to the extreme point are (v m-1 , t m-1 ), (v m-2 , t m- 2 ), (v m-3 , t m-3 ).
- the following uses the data of the extreme point and the three adjacent time nodes as an example to illustrate:
- time series data (v m , t m ) and time series data (v m-1 , t m-1 ) is K m, m-1 ; time series data (v m , t m )
- the trend slope with time series data (v m-2 , t m-2 ) is K m, m-2 , time series data (v m , t m ) and time series data (v m-3 , t m -3 )
- the standard deviation K m, sd between the trend slopes K m, m-1 , K m, m-2 , K m, m-3 can be calculated by the following mathematical formula:
- K (K m, m-1 -O m ) / K m, sd * K m, m-1 + (K m, m-2 -O m ) / K m, sd * K m, m-2 + (K m, m-3 -O m ) / K m, sd * K m, m-3 ;
- the preset range [-c, c] can be set and adjusted according to actual use requirements.
- Step S15 When the monitoring category is determined to be abnormal, corresponding warning information is output according to the attribute information of the extreme point of the monitoring category.
- the extreme point in the extreme value set may include several maximum values and minimum values, that is, the attribute information of the extreme point may include a maximum value or a minimum value.
- the attribute information of the extreme point may include a maximum value or a minimum value.
- the warning information exceeding the upper limit of the threshold value can be output; if the attribute information of the corresponding extreme point is the minimum value, the low value can be output Warning information below the threshold.
- FIG. 2 is a functional block diagram of a preferred embodiment of the data trend analysis system of the present application.
- the data trend analysis system 10 may include an acquisition module 101, a generation module 102, a statistics module 103, a judgment module 104, and an output module 105.
- the acquiring module 101 is used to acquire time series data of a monitoring object, where the monitoring object includes one or more monitoring categories, and the time series data is a parameter data set of each monitoring category on different time nodes.
- the acquiring module 101 may be connected to one or more monitoring objects by accessing a network, and then acquiring time series data of the monitoring objects.
- the monitoring object may be a server, a server cluster, or other electronic devices.
- the server or the server cluster may include several hardware resources (for example: CPU, memory, I / O interface, memory, etc.).
- the server or the server cluster may run different or the same operating system, database, application software, and system software. Understandably, the server cluster may be composed of multiple virtual machine managers (Virtual Machine Manager, VMM) and a number of physical nodes (Physical Node, PN), multiple operating systems running on the VMM, through VMM resources Scheduling algorithms, these operating systems share physical machine resources.
- VMM Virtual Machine Manager
- PN Physical Node
- the monitoring object may include one or more monitoring categories.
- the parameter data of the corresponding type index output by each monitoring category may be obtained.
- the time series data of the monitoring object is a parameter data set of corresponding type indicators output by multiple monitoring categories at different time nodes.
- the monitoring objects may include system resource objects and / or business type objects, and the time series data of the monitoring objects may be received / acquired in real time or periodically.
- the time series data is read from the monitored object every preset time, or the monitored object uploads the time series data to the data trend analysis system every preset time.
- the monitoring object may include CPU, memory, hard disk and other hardware monitoring categories, and may also include database, system software and other software monitoring categories running on the server .
- the monitoring category is CPU, it can output the utilization rate (the percentage of time the processor executes non-idle threads), the interrupt rate (the number of times the device interrupts the processor per second), and the system call rate (the processor calls the operating system service routine Parameter rate data of other types of indicators;
- the monitoring category is memory, it can output the page missing rate (indicating that the processor requests an error in a page from the specified location of the memory) and other parameter data of such indicators;
- the monitoring category is hard disk
- it can output parameter data such as the average number of read and write requests (the hard disk is queued in the instance interval); when the monitoring category is a database, it can output parameter data such as data read and write performance.
- the business type When the business type is the monitoring object of the business class, it may include monitoring categories such as user login volume, user registration volume, and core transaction data. For example, when the monitoring category is user login volume, parameter data such as the number of users online can be output; when the monitoring category is user registration volume, parameter data such as the number of registered accounts can be output, and when the monitoring category is core transaction data , Can output parameter data of orders, click ads and other types of indicators.
- monitoring categories such as user login volume, user registration volume, and core transaction data.
- the acquisition module 101 after the acquisition module 101 acquires the time-series data of the monitored object, it can also be stored locally to facilitate subsequent data analysis and reading.
- the time series data can be stored in the relational database by default, that is, the time t and the parameter data v of the type index in the time series data are stored as key-value pairs in the relational database.
- the relational database may be an RRD Tool database that is simply stored based on files, an opentsdb database built on the K / V database, and a mysql and postgresql database built on the relational database.
- the time series data when the requirements for data storage are high or the amount of data is relatively large, the time series data may be stored in a time series data type database to improve data reading and writing efficiency and reduce the storage space occupied by the data.
- the time series data database may include Elasticsearch, Crate.io, Solr databases based on Lucene, or Vertica and Actian databases based on columnar storage databases.
- the generating module 102 is configured to generate a trend graph corresponding to each monitoring category according to the time series data.
- the generating module 102 can generate a trend graph corresponding to the monitored object according to the time series data to intuitively understand the status of the monitored object.
- the monitoring category When the monitoring category has a type indicator (for example, when the monitoring category is memory, it has a type index of page miss rate; or when the monitoring category is user login volume, it has a type indicator for the number of users online), the corresponding time series
- the parameter data v n of the corresponding type index is used as the value on the vertical axis (Y axis) of the trend graph, after that, the parameter information of the corresponding type index is passed Straight line or smooth curve connection, so that you can generate a trend graph corresponding to the monitoring category.
- the monitoring category is CPU, it has three types of indicators: utilization rate, interruption rate, and system call rate.
- the time series data is divided into categories to obtain sub-time series X 1 , X 2 and X 3 corresponding to each type of index, where X 1 corresponds to the type index of utilization rate, X 2 corresponds to the type index of interruption rate, and X 3 Type indicators of system call rate, and then draw a trend graph corresponding to each type of indicators in the above manner.
- each time t n in the time series data can be taken as the point on the horizontal axis of the first trend graph
- the parameter information v n1 of the corresponding type indicator can be taken as the value on the vertical axis of the first trend graph
- the parameter information of the corresponding type indicator is connected by a straight line or a smooth curve, so that the generating module 102 can generate a first trend graph corresponding to the CPU utilization.
- each time t n in the time series data can be used as a point on the horizontal axis of the trend graph, and the parameter information v n2 of the corresponding type index can be used as the value on the vertical axis of the trend graph.
- the parameter information of the type indicator is connected by a straight line or a smooth curve, so that the generating module 102 can generate a second trend graph corresponding to the CPU interrupt rate.
- the trend graph whose monitoring type is CPU it may include the trend graphs corresponding to the three types of indicators of utilization rate, interruption rate, and system call.
- the statistics module 103 is configured to obtain the extreme points included in each of the trend graphs by using a preset trend analysis algorithm.
- the statistical module 103 can statistically obtain the extreme points included in each of the trend graphs by first: randomly selecting a time node data and the time node data from a trend graph Adjacent last time node data, then calculate the trend slope between the time node data and the last time node data, and then determine whether the calculated trend slope is greater than a preset threshold; when the trend slope is greater than When the preset threshold is determined, it is determined that the time node data is an extreme point in the trend graph.
- the trend The slope can be calculated by the following mathematical formula:
- K m is the trend slope. If the trend slope K m > R, where R represents a preset threshold, then it can be determined that the time node data (v m , t m ) is an extreme point in the trend graph.
- the set of all extreme points in a trend graph can be represented as an extreme value set.
- the R value for different category indicators can be set differently. For example, according to the application system, the CPU utilization fluctuates within ⁇ 5%. If it is too low, the CPU utilization of the server is not high; if it is too high, the CPU may become the processing bottleneck of the system. Therefore, for the monitoring category is CPU, the preset threshold of the utilization type index can be set to [-5, 5]. For the CPU interrupt rate, in general, the lower the processor interrupt rate, the better; it should not exceed 1000 times per second; if the value of the interrupt rate increases significantly, it may indicate that there is a hardware problem, you need to check the network adapter that caused the interrupt , Disk or other hardware. Therefore, for the monitoring category CPU, the preset threshold of the type indication of the interrupt rate is 1000 times.
- the determining module 104 is used to determine whether an abnormality occurs in the monitoring category corresponding to the trend graph according to each extreme point of the trend graph.
- the determination module 104 is used to determine the comprehensive trend slope K corresponding to two or more time series data adjacent to the extreme point in a trend graph to determine the corresponding trend graph Whether the monitoring category is abnormal. Specifically: first, randomly select an extreme point from a trend graph, and obtain data of at least two prior time nodes adjacent to the extreme point; secondly, calculate the data of the extreme point and the first time node respectively The first trend slope between, the second trend slope between the extreme point and the second time node data, wherein the first time node data is the data of the last time node that is close to the extreme point, The second time node data is the last time node data adjacent to the first time node data; furthermore, the standard deviation and the mean slope of the first trend slope and the second trend slope are calculated; furthermore , Based on the calculated standard deviation and mean slope, the comprehensive trend slope of the extreme point is calculated; finally, it is determined whether the comprehensive trend slope of the extreme point is within a preset range value; when the extreme point is integrated When the trend slope is not within
- the corresponding time series data is (v m , t m ), so the two time series data adjacent to the extreme point are (v m-1 , t m- 1 ), (v m-2 , t m-2 ); the three time series data adjacent to this extreme point are (v m-1 , t m-1 ), (v m-2 , t m- 2 ), (v m-3 , t m-3 ).
- the following uses the data of the extreme point and the three adjacent time nodes as an example to illustrate:
- time series data (v m , t m ) and time series data (v m-1 , t m-1 ) is K m, m-1 ; time series data (v m , t m )
- the trend slope with time series data (v m-2 , t m-2 ) is K m, m-2 , time series data (v m , t m ) and time series data (v m-3 , t m -3 )
- the standard deviation K m, sd between the trend slopes K m, m-1 , K m, m-2 , K m, m-3 can be calculated by the following mathematical formula:
- K (K m, m-1 -O m ) / K m, sd * K m, m-1 + (K m, m-2 -O m ) / K m, sd * K m, m-2 + (K m, m-3 -O m ) / K m, sd * K m, m-3 ;
- the preset range [-c, c] can be set and adjusted according to actual use requirements.
- the output module 105 is configured to output corresponding warning information according to the attribute information of the extreme point of the monitoring category when the monitoring category is determined to be abnormal.
- the extreme point in the extreme value set may include several maximum values and minimum values, that is, the attribute information of the extreme point may include a maximum value or a minimum value.
- the attribute information of the extreme point may include a maximum value or a minimum value.
- the output module 105 may output warning information exceeding the upper limit of the threshold; if the attribute information of the corresponding extreme point is a minimum value Then, the output module 105 can output warning information that is lower than the lower threshold.
- FIG. 3 is a schematic diagram of a preferred embodiment of the computer device of the present application.
- the computer device 1 includes a memory 20, a processor 30, and computer readable instructions 40 stored in the memory 20 and executable on the processor 30, such as a data trend analysis program.
- the processor 30 executes the computer-readable instruction 40
- the steps in the above embodiment of the data trend analysis method are implemented, for example, steps S11 to S15 shown in FIG. 1.
- the processor 30 executes the computer-readable instructions 40
- the functions of the modules in the above embodiment of the data trend analysis system are implemented, for example, the modules 101 to 105 in FIG. 2.
- the computer-readable instructions 40 may be divided into one or more modules / units, the one or more modules / units are stored in the memory 20 and executed by the processor 30, To complete this application.
- the one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer-readable instructions 40 in the computer device 1.
- the computer-readable instructions 40 may be divided into an acquisition module 101, a generation module 102, a statistics module 103, a judgment module 104, and an output module 105 in FIG. For specific functions of each module, see Embodiment 2.
- the computer device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer and a cloud server.
- a person skilled in the art may understand that the schematic diagram is only an example of the computer device 1 and does not constitute a limitation on the computer device 1, and may include more or less components than the illustration, or a combination of certain components, or different Components, for example, the computer device 1 may also include input and output devices, network access devices, buses, and the like.
- the so-called processor 30 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
- the general-purpose processor may be a microprocessor or the processor 30 may also be any conventional processor, etc.
- the processor 30 is the control center of the computer device 1 and connects the entire computer device 1 using various interfaces and lines The various parts.
- the memory 20 may be used to store the computer-readable instructions 40 and / or modules / units, and the processor 30 executes or executes the computer-readable instructions and / or modules / units stored in the memory 20, and The data stored in the memory 20 is called to realize various functions of the computer device 1.
- the memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one function required application programs (such as sound playback function, image playback function, etc.); the storage data area may Data (such as audio data, phone book, etc.) created according to the use of the computer device 1 is stored.
- the memory 20 may include a high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart, Media, Card, SMC), and a secure digital (SD) Card, flash memory card (Flash), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
- non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart, Media, Card, SMC), and a secure digital (SD) Card, flash memory card (Flash), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
- the module / unit integrated in the computer device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a non-volatile readable storage medium.
- the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through computer-readable instructions.
- the computer-readable instructions can be stored in a non-volatile In reading the storage medium, when the computer-readable instructions are executed by the processor, the steps of the foregoing method embodiments may be implemented.
- the computer readable instructions include computer readable instruction codes, and the computer readable instruction codes may be in source code form, object code form, executable file, or some intermediate form, etc.
- the non-volatile readable medium may include: any entity or device capable of carrying the computer-readable instruction code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media.
- ROM Read-Only Memory
- RAM Random Access Memory
- electrical carrier signals telecommunications signals
- telecommunications signals and software distribution media.
- the content contained in the non-volatile readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions. For example, in some jurisdictions, according to legislation and patent practice, non- Volatile readable media does not include electrical carrier signals and telecommunication signals.
- the functional units in the embodiments of the present application may be integrated in the same processing unit, or each unit may exist alone physically, or two or more units may be integrated in the same unit.
- the above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software function modules.
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Abstract
一种数据趋势分析方法、***、计算机装置及可读存储介质。所述数据趋势分析方法包括:获取监控对象的时间序列数据(S11);根据所述时间序列数据生成对应于监控对象的每一监控类别的趋势图(S12);通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点(S13);根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常(S14);当所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息(S15)。该方法基于数据分析算法可实现对监控对象的运行状态趋势进行分析判断,进而可做到问题提前发现,提前进行告警通知。
Description
本申请要求于2018年10月31日提交中国专利局,申请号为201811289826.9发明名称为“数据趋势分析方法、***、计算机装置及可读存储介质”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
本申请涉及数据处理领域,尤其涉及一种数据趋势分析方法、***、计算机装置及可读存储介质。
进行设备故障的自动监测,已经成为保障设备正常运行的一种重要技术手段。当设备的某项参数超出预先设定的报警门限值时,设备可以发出相应的报警信息。然而,设备监控平台无法对监控对象的运行状态趋势进行分析判断,进而无法做到问题提前发现,无法提前告警通知。
本部分旨在为权利要求书及具体实施方式中陈述的本申请的实施方式提供背景或上下文。此处的描述不因为包括在本部分中就承认是现有技术。
发明内容
鉴于上述,本申请提供一种数据趋势分析方法、***、计算机装置及存储介质,其可以实现提前对设备运行状态趋势进行分析判断,以提前进行预警。
本申请一实施方式提供一种数据趋势分析方法,所述方法包括:
获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集;
根据所述时间序列数据生成对应于每一所述监控类别的趋势图;
通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点;
根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常;及
当所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
本申请一实施方式提供一种数据趋势分析***,所述***包括:
获取模块,用于获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集;
生成模块,用于根据所述时间序列数据生成对应于每一所述监控类别的趋势图;
统计模块,用于通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点;
判断模块,用于根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常;及
输出模块,用于在所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
本申请一实施方式提供一种计算机装置,所述计算机装置包括处理器及存储器,所述存储器上存储有若干计算机可读指令,所述处理器用于执行存储器中存储的计算机可读指令时实现如前面所述的数据趋势分析方法的步骤。
本申请一实施方式提供一种非易失性可读存储介质,其上存储有计算机可读指令,所述计算机可读指令被处理器执行时实现如前面所述的数据趋势分析方法的步骤。
上述数据趋势分析方法、***、计算机装置及非易失性可读存储介质,通过获取一个或者多个监控对象的时间序列数据来绘制趋势图,并根据趋势图对监控对象的每一监控类别进行趋势分析与状态预测,可以输出对应的警示信息,本案可实现对监控对象的运行状态趋势进行分析判断,进而可做到问题提前发现,提前进行告警通知。
为了更清楚地说明本申请实施方式的技术方案,下面将对实施方式描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本申请的一些实施方式,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1是本申请一实施例中数据趋势分析方法的步骤流程图。
图2为本申请一实施例中数据趋势分析***的功能模块图。
图3为本申请一实施例中计算机装置示意图。
为了能够更清楚地理解本申请的上述目的、特征和优点,下面 结合附图和具体实施方式对本申请进行详细描述。需要说明的是,在不冲突的情况下,本申请的实施方式及实施方式中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本申请,所描述的实施方式仅仅是本申请一部分实施方式,而不是全部的实施方式。基于本申请中的实施方式,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施方式,都属于本申请保护的范围。
除非另有定义,本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同。本文中在本申请的说明书中所使用的术语只是为了描述具体的实施方式的目的,不是旨在于限制本申请。
优选地,本申请的数据趋势分析方法应用在一个或者多个计算机装置中。所述计算机装置是一种能够按照事先设定或存储的指令,自动进行数值计算和/或信息处理的设备,其硬件包括但不限于微处理器、专用集成电路(Application Specific Integrated Circuit,ASIC)、可编程门阵列(Field-Programmable Gate Array,FPGA)、数字处理器(Digital Signal Processor,DSP)、嵌入式设备等。
所述计算机装置可以是桌上型计算机、笔记本电脑、平板电脑、服务器等计算设备。所述计算机装置可以与用户通过键盘、鼠标、遥控器、触摸板或声控设备等方式进行人机交互。
实施例一:
图1是本申请数据趋势分析方法较佳实施例的步骤流程图。根据不同的需求,所述流程图中步骤的顺序可以改变,某些步骤可以省略。
参阅图1所示,所述数据趋势分析方法具体包括以下步骤。
步骤S11、获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集。
在一实施方式中,可以通过接入网络来连接至一个或多个所述监控对象,进而获取所述监控对象的时间序列数据。所述监控对象可以是一服务器、一服务器集群或者其他电子设备。所述服务器或所述服务器集群可包括若干硬件资源(例如:CPU、内存、I/O接口、存储器等)。所述服务器或所述服务器集群可运行有不同或相同的操作***、数据库、运用软件、***软件。可以理解地,所述服务器集群可由多个运行有虚拟机管理器(Virtual Machine Manager,VMM)、若干物理节点(Physical Node,PN) 构成,VMM之上运行着多个操作***,通过VMM的资源调度算法,这些操作***共享物理机的资源。
所述监控对象可包括一个或多个监控类别。当对所述监控对象进行监控时,可获取每一所述监控类别输出的对应类型指标的参数数据。可以理解地,监控对象的时间序列数据为在不同时间节点上多个监控类别输出的对应类型指标的参数数据集。
在一实施方式中,所述监控对象可包括***资源对象及/或业务类型对象,可实时或周期性接收/获取监控对象的时间序列数据。例如,每隔一预设时间从所述监控对象读取所述时间序列数据,或者所述监控对象每隔一预设时间上传所述时间序列数据至数据趋势分析***。
例如,当服务器作为***资源的监控对象时,所述监控对象可包括CPU、内存、硬盘等硬件类的监控类别,还可包括运行于所述服务器中的数据库、***软件等软件类的监控类别。当监控类别为CPU时,可输出利用率(处理器执行非闲置线程时间的百分比)、中断率(每秒钟设备中断处理器的次数)、***调用率(处理器调用操作***服务例行程序的综合速率)等类型指标的参数数据;当监控类别为内存时,可输出页缺失率(表示处理器向内存指定的位置请求一页出现错误)等类型指标的参数数据;当监控类别为硬盘时,可输出读取和写入请求的平均数(为硬盘在实例间隔中列队)等类型指标的参数数据;当监控类别为数据库时,可输出数据读写性能等类型指标的参数数据。
当业务类型作为业务类的监控对象时,其可包括用户登陆量、用户注册量、核心交易数据等监控类别。例如,监控类别为用户登陆量时可输出用户在线数量等类型指标的参数数据;当监控类别为用户注册量时,可输出注册账号数量等类型指标的参数数据,当监控类别为核心交易数据时,可输出订单、点击广告等类型指标的参数数据。
可以理解地,所述时间序列数据可表示为监控类别在t时刻所对应的类型指标的参数数据v。因而,对于监控类别包含一个类型指标时,其对应的监控类别的时间序列数据可表示为:{X=(v
1,t
1),(v
2,t
2),...,(v
n,t
n)}其中n为自然数,(v
n,t
n)表示时间节点t
n的时间节点数据,t
n>t
n-1,即时间节点数据(v
n,t
n)为最新的时间节点数据;对于监控类别包含两个或两个以上的类型指标时,其监控类别的时间序列数据可表示为:X={X
1,X
2,…,X
m},其中,X
m可表示为:{X
m=(v
1m,t
1),(v
2m,t
2),…,(v
nm,t
n)},其中m表示类型指标的数量,n为自然数。
在一实施方式中,当获取监控对象的时间序列数据后,还可对 其进行本地化存储,以方便后续进行数据分析与读取。时间序列数据可默认存在关系型数据库中,即将时间序列数据中时刻t时刻及类型指标的参数数据v作为键值对存储于关系型数据库。其中,关系型数据库可以是直接基于文件的简单存储的RRD Tool数据库,基于K/V数据库构建的opentsdb数据库,基于关系型数据库构建mysql、postgresql数据库。
在本申请的其他实施方式中,当对数据存储要求较高或数据量比较大时,可以将时间序列数据存储于时间序列数据类数据库中,以提升数据读写效率和减少数据占用存储空间。其中,时间序列数据类数据库可包括基于Lucene构建的搜索引擎Elasticsearch,Crate.io,Solr数据库,或基于列式存储数据库的Vertica,Actian数据库。
步骤S12、根据所述时间序列数据生成对应于每一所述监控类别的趋势图。
在一实施方式中,可根据时间序列数据生成对应于监控对象的趋势图,以直观地对了解监控对象的状态。
当监控类别具有一类型指标(如监控类别为内存时,其具有页缺失率一个类型指标;或监控类别为用户登陆量时,其具有一用户在线数量的类型指标)时,其对应的时间序列数据可表示为{X=(v
1,t
1),(v
2,t
2),…,(v
n,t
n)},建立一XY坐标轴,并将时间序列数据中各时刻t
n作为趋势图在横轴(X轴)上的点,而对应的类型指标的参数数据v
n作为趋势图中纵轴(Y轴)上的值,之后,再将对应的类型指标的参数信息通过直线或平滑的曲线连接,如此,即可生成对应该监控类别趋势图。
当监控类别具有两个或多个类型指标时,对应的时间序列数据可表示为:X={X
1,X
2,…,X
m},其中,X
m可表示为:{X
m=(v
1m,t
1),(v
2m,t
2),…,(v
nm,t
n)}。例如,监控类别为CPU时,其具有利用率、中断率及***调用率三个类型指标,此时,获取的时间序列数据可表示为可X={X
1,X
2,X
3},对时间序列数据进行分类拆分,以得到对应每一类型指标的子时间序列X
1、X
2、X
3,其中X
1对应利用率的类型指标、X
2对应中断率的类型指标,X
3对应***调用率的类型指标,进而再按照上述方式绘制每一类型指标所对应的趋势图。
例如,对于CPU利用率的趋势图,对于X
1子时间序列而言,其可表示为{X
1=(v
11,t
1),(v
12,t
2),…,(v
n1,t
n)},可将时间序列数据中各时刻t
n作为第一趋势图的横轴上的点,而对应的类型指标的参数信息v
n1作为该第一趋势图中纵轴上的值,之后,再将对应的类型指标的参数信息通过直线或平滑的曲线连接,如此,即可生成对应于 CPU利用率的第一趋势图。同理,对于CPU中断率的趋势图,对于X
2子时间序列而言,其可表示为{X
2=(v
21,t
1),(v
22,t
2),…,(v
n2,t
n)},可将时间序列数据中各时刻t
n作为趋势图的横轴上的点,而对应的类型指标的参数信息v
n2作为趋势图中纵轴上的值,之后,再将对应的类型指标的参数信息通过直线或平滑的曲线连接,如此,即可生成对应于CPU中断率的第二趋势图。如此,对于监控类型为CPU的趋势图,其可包括利用率、中断率及***调用三个类型指标所分别对应的趋势图。
步骤S13,通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点。
在一实施方式中,可以通过以下方式统计得出每一所述趋势图中包含的极值点:首先从一趋势图中任意选取一时间节点数据及与所述时间节点数据相邻的上一时间节点数据,其次计算所述时间节点数据与所述上一时间节点数据之间的趋势斜率,再判断计算得到的趋势斜率是否大于预设阈值;当所述趋势斜率大于所述预设阈值时,判定所述时间节点数据为所述趋势图中的一极值点。
举例而言,从一趋势图中选取一时间节点数据(v
m,t
m)及与所述时间节点数据相邻的上一时间节点数据(v
m-1,t
m-1),其趋势斜率可以通过以下数学式计算得到:
K
m=|(V
m-V
m-1)/(t
m-t
m-1)|
其中,K
m为趋势斜率。如果趋势斜率K
m>R,其中R表示预设阈值,那么可以确定所述时间节点数据(v
m,t
m)为所述趋势图中的一极值点。
在一实施方式中,一趋势图中所有的极值点的集合可表示为极值集合。对于不同的类别指标的R值可设置为不同。例如,根据应用***情况而言,CPU的利用率在±5%范围内波动为宜。过低,则服务器CPU利用率不高;过高,则CPU可能成为***的处理瓶颈。因而,对于监控类别是CPU而言,其利用率的类型指标的预设阈值可设为[-5,5]。对于CPU的中断率而言,一般而言,处理器中断率越低越好;不宜超过1000次/秒;如果中断率的值显著增加,则表明可能存在硬件问题,需要检查引起中断的网络适配器、磁盘或其他硬件。因而,对于监控类别CPU而言,其中断率的类型指示的预设阈值为1000次。
步骤S14,根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常。
在一实施方式中,可将一趋势图中相邻于极值点的两个或两个以上的时间序列数据所对应的综合趋势斜率K来判断该趋势图对应的监控类别是否异常。具体地:首先从一趋势图中任意选取一 极值点,并获取与所述极值点相邻的至少两个在先时间节点数据;其次分别计算所述极值点与第一时间节点数据之间的第一趋势斜率,所述极值点与第二时间节点数据之间的第二趋势斜率,其中所述第一时间节点数据为与所述极值点临近的上一时间节点数据,所述第二时间节点数据为与所述第一时间节点数据临近的上一时间节点数据;再者,计算所述第一趋势斜率与所述第二趋势斜率的标准差及均值斜率;再者,根据计算得到的标准差及均值斜率计算得到所述极值点的综合趋势斜率;最后,判断所述极值点的综合趋势斜率是否位于预设范围值内;当所述极值点的综合趋势斜率不在所述预设范围值内时,判定所述趋势图对应的监控类别发生异常。
举例而言,对于一极值点,其对应的时间序列数据为(v
m,t
m),因而,相邻该极值点的两个时间序列数据分别为(v
m-1,t
m-1)、(v
m-2,t
m-2);相邻该极值点的三个时间序列数据分别为(v
m-1,t
m-1)、(v
m-2,t
m-2)、(v
m-3,t
m-3)。以下以极值点及其相邻的三个在先时间节点数据为例进行举例说明:
假设,时间序列数据(v
m,t
m)与时间序列数据(v
m-1,t
m-1)之间的趋势斜率为K
m,m-1;时间序列数据(v
m,t
m)与时间序列数据(v
m-2,t
m-2)之间的趋势斜率为K
m,m-2,时间序列数据(v
m,t
m)与时间序列数据(v
m-3,t
m-3)之间的趋势斜率为K
m,m-3;
趋势斜率K
m,m-1、K
m,m-2、K
m,m-3的均值斜率O
m可以通过以下数学式计算得到:O
m=(K
m,m-1+K
m,m-2+K
m,m-2)/3;
对于极值点(v
m,t
m)的综合趋势斜率K可以通过以下数学式计算得到:
K=(K
m,m-1-O
m)/K
m,sd*K
m,m-1+(K
m,m-2-O
m)/K
m,sd*K
m,m-2+(K
m,m-3-O
m)/K
m,sd*K
m,m-3;
通过判断综合趋势斜率K是否在预设范围[-c,c]内,当K在预设范围[-c,c]内时,则表示该监控类别的状态正常;当K不在预设范围[-c,c]内时,则表示该监控类别的状态异常。所述预设范围[-c,c]可以根据实际使用需求进行设定及调整。
步骤S15,当所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
在一实施方式中,所述极值集合中的极值点可包括若干极大值、极小值,即极值点的属性信息可包含极大值或极小值。具体而言, 两相邻时间序列数据(v
m,t
m)、(v
m-1,t
m-1),当趋势斜率K
n=(v
m-v
m-1)/(t
m-t
m-1)>R时,表示时间序列数据(v
m,t
m)对应为极大值;当趋势斜率K
n=(v
m-v
m-1)/(t
m-t
m-1)<-R时,表示时间序列数据(v
m,t
m)对应为极小值。当监控类别的状态异常时,若对应的极值点的属性信息为极大值,则可输出超出阈值上限的警示信息;若对应的极值点的属性信息为极小值,则可输出低于阈值下限的警示信息。
实施例二:
图2为本申请数据趋势分析***较佳实施例的功能模块图。
参阅图2所示,所述数据趋势分析***10可以包括获取模块101、生成模块102、统计模块103、判断模块104、输出模块105。
所述获取模块101用于获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集。
在一实施方式中,所述获取模块101可以通过接入网络来连接至一个或多个所述监控对象,进而获取所述监控对象的时间序列数据。所述监控对象可以是一服务器、一服务器集群或者其他电子设备。所述服务器或所述服务器集群可包括若干硬件资源(例如:CPU、内存、I/O接口、存储器等)。所述服务器或所述服务器集群可运行有不同或相同的操作***、数据库、运用软件、***软件。可以理解地,所述服务器集群可由多个运行有虚拟机管理器(Virtual Machine Manager,VMM)、若干物理节点(Physical Node,PN)构成,VMM之上运行着多个操作***,通过VMM的资源调度算法,这些操作***共享物理机的资源。
所述监控对象可包括一个或多个监控类别。当对所述监控对象进行监控时,可获取每一所述监控类别输出的对应类型指标的参数数据。可以理解地,监控对象的时间序列数据为在不同时间节点上多个监控类别输出的对应类型指标的参数数据集。
在一实施方式中,所述监控对象可包括***资源对象及/或业务类型对象,可实时或周期性接收/获取监控对象的时间序列数据。例如,每隔一预设时间从所述监控对象读取所述时间序列数据,或者所述监控对象每隔一预设时间上传所述时间序列数据至数据趋势分析***。
例如,当服务器作为***资源的监控对象时,所述监控对象可包括CPU、内存、硬盘等硬件类的监控类别,还可包括运行于所述服务器中的数据库、***软件等软件类的监控类别。当监控类别为CPU时,可输出利用率(处理器执行非闲置线程时间的百分比)、中断率(每秒钟设备中断处理器的次数)、***调用率(处理器调用操作***服务例行程序的综合速率)等类型指标的参数 数据;当监控类别为内存时,可输出页缺失率(表示处理器向内存指定的位置请求一页出现错误)等类型指标的参数数据;当监控类别为硬盘时,可输出读取和写入请求的平均数(为硬盘在实例间隔中列队)等类型指标的参数数据;当监控类别为数据库时,可输出数据读写性能等类型指标的参数数据。
当业务类型作为业务类的监控对象时,其可包括用户登陆量、用户注册量、核心交易数据等监控类别。例如,监控类别为用户登陆量时可输出用户在线数量等类型指标的参数数据;当监控类别为用户注册量时,可输出注册账号数量等类型指标的参数数据,当监控类别为核心交易数据时,可输出订单、点击广告等类型指标的参数数据。
可以理解地,所述时间序列数据可表示为监控类别在t时刻所对应的类型指标的参数数据v。因而,对于监控类别包含一个类型指标时,其对应的监控类别的时间序列数据可表示为:{X=(v
1,t
1),(v
2,t
2),...,(v
n,t
n)}其中n为自然数,(v
n,t
n)表示时间节点t
n的时间节点数据,t
n>t
n-1,即时间节点数据(v
n,t
n)为最新的时间节点数据;对于监控类别包含两个或两个以上的类型指标时,其监控类别的时间序列数据可表示为:X={X
1,X
2,…,X
m},其中,X
m可表示为:{X
m=(v
1m,t
1),(v
2m,t
2),…,(v
nm,t
n)},其中m表示类型指标的数量,n为自然数。
在一实施方式中,当所述获取模块101获取监控对象的时间序列数据后,还可对其进行本地化存储,以方便后续进行数据分析与读取。时间序列数据可默认存在关系型数据库中,即将时间序列数据中时刻t时刻及类型指标的参数数据v作为键值对存储于关系型数据库。其中,关系型数据库可以是直接基于文件的简单存储的RRD Tool数据库,基于K/V数据库构建的opentsdb数据库,基于关系型数据库构建mysql、postgresql数据库。
在本申请的其他实施方式中,当对数据存储要求较高或数据量比较大时,可以将时间序列数据存储于时间序列数据类数据库中,以提升数据读写效率和减少数据占用存储空间。其中,时间序列数据类数据库可包括基于Lucene构建的搜索引擎Elasticsearch,Crate.io,Solr数据库,或基于列式存储数据库的Vertica,Actian数据库。
所述生成模块102用于根据所述时间序列数据生成对应于每一所述监控类别的趋势图。
在一实施方式中,所述生成模块102可根据时间序列数据生成对应于监控对象的趋势图,以直观地对了解监控对象的状态。
当监控类别具有一类型指标(如监控类别为内存时,其具有页 缺失率一个类型指标;或监控类别为用户登陆量时,其具有一用户在线数量的类型指标)时,其对应的时间序列数据可表示为{X=(v
1,t
1),(v
2,t
2),…,(v
n,t
n)},建立一XY坐标轴,并将时间序列数据中各时刻t
n作为趋势图在横轴(X轴)上的点,而对应的类型指标的参数数据v
n作为趋势图中纵轴(Y轴)上的值,之后,再将对应的类型指标的参数信息通过直线或平滑的曲线连接,如此,即可生成对应该监控类别趋势图。
当监控类别具有两个或多个类型指标时,对应的时间序列数据可表示为:X={X
1,X
2,…,X
m},其中,X
m可表示为:{X
m=(v
1m,t
1),(v
2m,t
2),…,(v
nm,t
n)}。例如,监控类别为CPU时,其具有利用率、中断率及***调用率三个类型指标,此时,获取的时间序列数据可表示为可X={X
1,X
2,X
3},对时间序列数据进行分类拆分,以得到对应每一类型指标的子时间序列X
1、X
2、X
3,其中X
1对应利用率的类型指标、X
2对应中断率的类型指标,X
3对应***调用率的类型指标,进而再按照上述方式绘制每一类型指标所对应的趋势图。
例如,对于CPU利用率的趋势图,对于X
1子时间序列而言,其可表示为{X
1=(v
11,t
1),(v
12,t
2),…,(v
n1,t
n)},可将时间序列数据中各时刻t
n作为第一趋势图的横轴上的点,而对应的类型指标的参数信息v
n1作为该第一趋势图中纵轴上的值,之后,再将对应的类型指标的参数信息通过直线或平滑的曲线连接,如此,所述生成模块102即可生成对应于CPU利用率的第一趋势图。同理,对于CPU中断率的趋势图,对于X
2子时间序列而言,其可表示为{X
2=(v
21,t
1),(v
22,t
2),…,(v
n2,t
n)},可将时间序列数据中各时刻t
n作为趋势图的横轴上的点,而对应的类型指标的参数信息v
n2作为趋势图中纵轴上的值,之后,再将对应的类型指标的参数信息通过直线或平滑的曲线连接,如此,所述生成模块102即可生成对应于CPU中断率的第二趋势图。如此,对于监控类型为CPU的趋势图,其可包括利用率、中断率及***调用三个类型指标所分别对应的趋势图。
所述统计模块103用于通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点。
在一实施方式中,所述统计模块103可以通过以下方式统计得出每一所述趋势图中包含的极值点:首先从一趋势图中任意选取一时间节点数据及与所述时间节点数据相邻的上一时间节点数据,其次计算所述时间节点数据与所述上一时间节点数据之间的趋势斜率,再判断计算得到的趋势斜率是否大于预设阈值;当所述趋势斜率大于所述预设阈值时,判定所述时间节点数据为所述趋势 图中的一极值点。
举例而言,从一趋势图中选取一时间节点数据(v
m,t
m)及与所述时间节点数据相邻的上一时间节点数据(v
m-1,t
m-1),其趋势斜率可以通过以下数学式计算得到:
K
m=|(V
m-V
m-1)/(t
m-t
m-1)|
其中,K
m为趋势斜率。如果趋势斜率K
m>R,其中R表示预设阈值,那么可以确定所述时间节点数据(v
m,t
m)为所述趋势图中的一极值点。
在一实施方式中,一趋势图中所有的极值点的集合可表示为极值集合。对于不同的类别指标的R值可设置为不同。例如,根据应用***情况而言,CPU的利用率在±5%范围内波动为宜。过低,则服务器CPU利用率不高;过高,则CPU可能成为***的处理瓶颈。因而,对于监控类别是CPU而言,其利用率的类型指标的预设阈值可设为[-5,5]。对于CPU的中断率而言,一般而言,处理器中断率越低越好;不宜超过1000次/秒;如果中断率的值显著增加,则表明可能存在硬件问题,需要检查引起中断的网络适配器、磁盘或其他硬件。因而,对于监控类别CPU而言,其中断率的类型指示的预设阈值为1000次。
所述判断模块104用于根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常。
在一实施方式中,所述判断模块104用于可将一趋势图中相邻于极值点的两个或两个以上的时间序列数据所对应的综合趋势斜率K来判断该趋势图对应的监控类别是否异常。具体地:首先从一趋势图中任意选取一极值点,并获取与所述极值点相邻的至少两个在先时间节点数据;其次分别计算所述极值点与第一时间节点数据之间的第一趋势斜率,所述极值点与第二时间节点数据之间的第二趋势斜率,其中所述第一时间节点数据为与所述极值点临近的上一时间节点数据,所述第二时间节点数据为与所述第一时间节点数据临近的上一时间节点数据;再者,计算所述第一趋势斜率与所述第二趋势斜率的标准差及均值斜率;再者,根据计算得到的标准差及均值斜率计算得到所述极值点的综合趋势斜率;最后,判断所述极值点的综合趋势斜率是否位于预设范围值内;当所述极值点的综合趋势斜率不在所述预设范围值内时,判定所述趋势图对应的监控类别发生异常。
举例而言,对于一极值点,其对应的时间序列数据为(v
m,t
m),因而,相邻该极值点的两个时间序列数据分别为(v
m-1,t
m-1)、(v
m-2,t
m-2);相邻该极值点的三个时间序列数据分别为(v
m-1,t
m-1)、 (v
m-2,t
m-2)、(v
m-3,t
m-3)。以下以极值点及其相邻的三个在先时间节点数据为例进行举例说明:
假设,时间序列数据(v
m,t
m)与时间序列数据(v
m-1,t
m-1)之间的趋势斜率为K
m,m-1;时间序列数据(v
m,t
m)与时间序列数据(v
m-2,t
m-2)之间的趋势斜率为K
m,m-2,时间序列数据(v
m,t
m)与时间序列数据(v
m-3,t
m-3)之间的趋势斜率为K
m,m-3;
趋势斜率K
m,m-1、K
m,m-2、K
m,m-3的均值斜率O
m可以通过以下数学式计算得到:O
m=(K
m,m-1+K
m,m-2+K
m,m-2)/3;
对于极值点(v
m,t
m)的综合趋势斜率K可以通过以下数学式计算得到:
K=(K
m,m-1-O
m)/K
m,sd*K
m,m-1+(K
m,m-2-O
m)/K
m,sd*K
m,m-2+(K
m,m-3-O
m)/K
m,sd*K
m,m-3;
通过判断综合趋势斜率K是否在预设范围[-c,c]内,当K在预设范围[-c,c]内时,则表示该监控类别的状态正常;当K不在预设范围[-c,c]内时,则表示该监控类别的状态异常。所述预设范围[-c,c]可以根据实际使用需求进行设定及调整。
所述输出模块105用于在所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
在一实施方式中,所述极值集合中的极值点可包括若干极大值、极小值,即极值点的属性信息可包含极大值或极小值。具体而言,两相邻时间序列数据(v
m,t
m)、(v
m-1,t
m-1),当趋势斜率K
n=(v
m-v
m-1)/(t
m-t
m-1)>R时,表示时间序列数据(v
m,t
m)对应为极大值;当趋势斜率K
n=(v
m-v
m-1)/(t
m-t
m-1)<-R时,表示时间序列数据(v
m,t
m)对应为极小值。当监控类别的状态异常时,若对应的极值点的属性信息为极大值,则所述输出模块105可输出超出阈值上限的警示信息;若对应的极值点的属性信息为极小值,则所述输出模块105可输出低于阈值下限的警示信息。
图3为本申请计算机装置较佳实施例的示意图。
所述计算机装置1包括存储器20、处理器30以及存储在所述存储器20中并可在所述处理器30上运行的计算机可读指令40,例如数据趋势分析程序。所述处理器30执行所述计算机可读指令40时实现上述数据趋势分析方法实施例中的步骤,例如图1所示的步骤S11~S15。或者,所述处理器30执行所述计算机可读指令40时实现上述数据趋势分析***实施例中各模块的功能,例如图 2中的模块101~105。
示例性的,所述计算机可读指令40可以被分割成一个或多个模块/单元,所述一个或者多个模块/单元被存储在所述存储器20中,并由所述处理器30执行,以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机可读指令段,所述指令段用于描述所述计算机可读指令40在所述计算机装置1中的执行过程。例如,所述计算机可读指令40可以被分割成图2中的获取模块101、生成模块102、统计模块103、判断模块104、输出模块105。各模块具体功能参见实施例二。
所述计算机装置1可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。本领域技术人员可以理解,所述示意图仅仅是计算机装置1的示例,并不构成对计算机装置1的限定,可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件,例如所述计算机装置1还可以包括输入输出设备、网络接入设备、总线等。
所称处理器30可以是中央处理单元(Central Processing Unit,CPU),还可以是其他通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现成可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者所述处理器30也可以是任何常规的处理器等,所述处理器30是所述计算机装置1的控制中心,利用各种接口和线路连接整个计算机装置1的各个部分。
所述存储器20可用于存储所述计算机可读指令40和/或模块/单元,所述处理器30通过运行或执行存储在所述存储器20内的计算机可读指令和/或模块/单元,以及调用存储在存储器20内的数据,实现所述计算机装置1的各种功能。所述存储器20可主要包括存储程序区和存储数据区,其中,存储程序区可存储操作***、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等;存储数据区可存储根据计算机装置1的使用所创建的数据(比如音频数据、电话本等)等。此外,存储器20可以包括高速随机存取存储器,还可以包括非易失性存储器,例如硬盘、内存、插接式硬盘,智能存储卡(Smart Media Card,SMC),安全数字(Secure Digital,SD)卡,闪存卡(Flash Card)、至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
所述计算机装置1集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个非易失 性可读取存储介质中。基于这样的理解,本申请实现上述实施例方法中的全部或部分流程,也可以通过计算机可读指令来指令相关的硬件来完成,所述的计算机可读指令可存储于一非易失性可读存储介质中,所述计算机可读指令在被处理器执行时,可实现上述各个方法实施例的步骤。其中,所述计算机可读指令包括计算机可读指令代码,所述计算机可读指令代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述非易失性可读介质可以包括:能够携带所述计算机可读指令代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是,所述非易失性可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减,例如在某些司法管辖区,根据立法和专利实践,非易失性可读介质不包括电载波信号和电信信号。
在本申请所提供的几个实施例中,应该理解到,所揭露的计算机装置和方法,可以通过其它的方式实现。例如,以上所描述的计算机装置实施例仅仅是示意性的,例如,所述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式。
另外,在本申请各个实施例中的各功能单元可以集成在相同处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在相同单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用硬件加软件功能模块的形式实现。
对于本领域技术人员而言,显然本申请不限于上述示范性实施例的细节,而且在不背离本申请的精神或基本特征的情况下,能够以其他的具体形式实现本申请。因此,无论从哪一点来看,均应将实施例看作是示范性的,而且是非限制性的,本申请的范围由所附权利要求而不是上述说明限定,因此旨在将落在权利要求的等同要件的含义和范围内的所有变化涵括在本申请内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。此外,显然“包括”一词不排除其他单元或步骤,单数不排除复数。计算机装置权利要求中陈述的多个单元或计算机装置也可以由同一个单元或计算机装置通过软件或者硬件来实现。第一,第二等词语用来表示名称,而并不表示任何特定的顺序。
最后应说明的是,以上实施例仅用以说明本申请的技术方案而非限制,尽管参照较佳实施例对本申请进行了详细说明,本领域的普通技术人员应当理解,可以对本申请的技术方案进行修改或等同替换,而不脱离本申请技术方案的精神和范围。
Claims (20)
- 一种数据趋势分析方法,其特征在于,所述方法包括:获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集;根据所述时间序列数据生成对应于每一所述监控类别的趋势图;通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点;根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常;及当所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
- 如权利要求1所述的数据趋势分析方法,其特征在于,所述根据所述时间序列数据生成对应于每一所述监控类别的趋势图的步骤包括:将所述时间序列数据按照每一所述监控类别进行分类;建立一XY坐标轴,并将所述监控对象的第一监控类别的时间序列数据中每一时间节点作为所述趋势图在X轴上的点;及将每一所述时间节点对应的参数数据作为所述趋势图在Y轴上的值,以得到对应于所述第一监控类别的趋势图。
- 如权利要求1或2所述的数据趋势分析方法,其特征在于,所述通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点的步骤包括:从一趋势图中任意选取一时间节点数据及与所述时间节点数据相邻的上一时间节点数据;计算所述时间节点数据与所述上一时间节点数据之间的趋势斜率;判断计算得到的趋势斜率是否大于预设阈值;及当所述趋势斜率大于所述预设阈值时,判定所述时间节点数据为所述趋势图中的一极值点。
- 如权利要求3所述的数据趋势分析方法,其特征在于,所述时间节点数据与所述上一时间节点数据的趋势斜率可以通过以下数学式计算得到:K m=|(V m-V m-1)/(t m-t m-1)|;其中,K m为趋势斜率,t m为所述时间节点数据对应的时间节 点,t m-1为与t m相邻的上一时间节点,V m为时间节点t m对应的参数数据,V m-1为时间节点t m-1对应的参数数据。
- 如权利要求1或2所述的数据趋势分析方法,其特征在于,所述根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常的步骤包括:从一趋势图中任意选取一极值点,并获取与所述极值点相邻的至少两个在先时间节点数据,其中所述至少两个在先时间节点数据包括第一时间节点数据以及第二时间节点数据;分别计算所述极值点与所述第一时间节点数据之间的第一趋势斜率,所述极值点与所述第二时间节点数据之间的第二趋势斜率,其中所述第一时间节点数据为与所述极值点临近的上一时间节点数据,所述第二时间节点数据为与所述第一时间节点数据临近的上一时间节点数据;计算所述第一趋势斜率与所述第二趋势斜率的标准差及均值斜率;根据计算得到的标准差及均值斜率计算得到所述极值点的综合趋势斜率;判断所述极值点的综合趋势斜率是否位于预设范围值内;及当所述极值点的综合趋势斜率不在所述预设范围值内时,判定所述趋势图对应的监控类别发生异常。
- 如权利要求5所述的数据趋势分析方法,其特征在于,所述极值点的综合趋势斜率可通过以下数学式计算得到:K=(K m,m-1-O m)/K m,sd*K m,m-1+(K m,m-2-O m)/K m,sd*K m,m-2;其中,K为所述综合趋势斜率,K m,m-1为所述第一趋势斜率,K m,m-2为所述第二趋势斜率,O m为所述第一趋势斜率与所述第二趋势斜率的均值斜率,K m,sd为所述第一趋势斜率与所述第二趋势斜率的标准差。
- 如权利要求1所述的数据趋势分析方法,其特征在于,所述根据所述监控类别的极值点的属性信息输出对应的警示信息的步骤包括:当所述监控类别的极值点的属性信息为极大值时,输出超出阈值上限的警示信息;当所述监控类别的极值点的属性信息为极小值时,输出低于阈值下限的警示信息。
- 一种数据趋势分析***,其特征在于,所述***包括:获取模块,用于获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集;生成模块,用于根据所述时间序列数据生成对应于每一所述监控类别的趋势图;统计模块,用于通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点;判断模块,用于根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常;及输出模块,用于在所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
- 一种计算机装置,所述计算机装置包括处理器及存储器,所述存储器上存储有至少一个计算机可读指令,其特征在于,所述处理器用于执行存储器中存储的计算机可读指令时实现以下步骤:获取监控对象的时间序列数据,其中所述监控对象包括一个或者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集;根据所述时间序列数据生成对应于每一所述监控类别的趋势图;通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点;根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常;及当所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
- 如权利要求9所述的计算机装置,其特征在于,所述处理器在根据所述时间序列数据生成对应于每一所述监控类别的趋势图时,执行所述计算机可读指令以实现以下步骤:将所述时间序列数据按照每一所述监控类别进行分类;建立一XY坐标轴,并将所述监控对象的第一监控类别的时间序列数据中每一时间节点作为所述趋势图在X轴上的点;及将每一所述时间节点对应的参数数据作为所述趋势图在Y轴上的值,以得到对应于所述第一监控类别的趋势图。
- 如权利要求9或10所述的计算机装置,其特征在于,所述处理器在通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点时,执行所述计算机可读指令以实现以下步骤:从一趋势图中任意选取一时间节点数据及与所述时间节点数据相邻的上一时间节点数据;计算所述时间节点数据与所述上一时间节点数据之间的趋势斜率;判断计算得到的趋势斜率是否大于预设阈值;及当所述趋势斜率大于所述预设阈值时,判定所述时间节点数据为所述趋势图中的一极值点。
- 如权利要求11所述的计算机装置,其特征在于,所述时间节点数据与所述上一时间节点数据的趋势斜率可以通过以下数学式计算得到:K m=|(V m-V m-1)/(t m-t m-1)|;其中,K m为趋势斜率,t m为所述时间节点数据对应的时间节点,t m-1为与t m相邻的上一时间节点,V m为时间节点t m对应的参数数据,V m-1为时间节点t m-1对应的参数数据。
- 如权利要求9或10所述的计算机装置,其特征在于,所述处理器根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常时,执行所述计算机可读指令以实现以下步骤:从一趋势图中任意选取一极值点,并获取与所述极值点相邻的至少两个在先时间节点数据,其中所述至少两个在先时间节点数据包括第一时间节点数据以及第二时间节点数据;分别计算所述极值点与所述第一时间节点数据之间的第一趋势斜率,所述极值点与所述第二时间节点数据之间的第二趋势斜率,其中所述第一时间节点数据为与所述极值点临近的上一时间节点数据,所述第二时间节点数据为与所述第一时间节点数据临近的上一时间节点数据;计算所述第一趋势斜率与所述第二趋势斜率的标准差及均值斜率;根据计算得到的标准差及均值斜率计算得到所述极值点的综合趋势斜率;判断所述极值点的综合趋势斜率是否位于预设范围值内;及当所述极值点的综合趋势斜率不在所述预设范围值内时,判定所述趋势图对应的监控类别发生异常。
- 如权利要求9所述的计算机装置,其特征在于,所述处理器在根据所述监控类别的极值点的属性信息输出对应的警示信息时,执行所述计算机可读指令以实现以下步骤:当所述监控类别的极值点的属性信息为极大值时,输出超出阈值上限的警示信息;当所述监控类别的极值点的属性信息为极小值时,输出低于阈值下限的警示信息。
- 一种非易失性可读存储介质,其上存储有计算机可读指令,其特征在于,所述计算机可读指令被处理器执行时实现以下步骤:获取监控对象的时间序列数据,其中所述监控对象包括一个或 者多个监控类别,所述时间序列数据为在不同时间节点上每一所述监控类别的参数数据集;根据所述时间序列数据生成对应于每一所述监控类别的趋势图;通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点;根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常;及当所述监控类别被判定为发生异常时,根据所述监控类别的极值点的属性信息输出对应的警示信息。
- 如权利要求15所述的存储介质,其特征在于,所述根据所述时间序列数据生成对应于每一所述监控类别的趋势图时,所述计算机可读指令被处理器执行以实现以下步骤:将所述时间序列数据按照每一所述监控类别进行分类;建立一XY坐标轴,并将所述监控对象的第一监控类别的时间序列数据中每一时间节点作为所述趋势图在X轴上的点;及将每一所述时间节点对应的参数数据作为所述趋势图在Y轴上的值,以得到对应于所述第一监控类别的趋势图。
- 如权利要求15或16所述的存储介质,其特征在于,在所述通过预设趋势分析算法统计得出每一所述趋势图中包含的极值点时,所述计算机可读指令被处理器执行以实现以下步骤:从一趋势图中任意选取一时间节点数据及与所述时间节点数据相邻的上一时间节点数据;计算所述时间节点数据与所述上一时间节点数据之间的趋势斜率;判断计算得到的趋势斜率是否大于预设阈值;及当所述趋势斜率大于所述预设阈值时,判定所述时间节点数据为所述趋势图中的一极值点。
- 如权利要求17所述的存储介质,其特征在于,所述时间节点数据与所述上一时间节点数据的趋势斜率可以通过以下数学式计算得到:K m=|(V m-V m-1)/(t m-t m-1)|;其中,K m为趋势斜率,t m为所述时间节点数据对应的时间节点,t m-1为与t m相邻的上一时间节点,V m为时间节点t m对应的参数数据,V m-1为时间节点t m-1对应的参数数据。
- 如权利要求15或16所述的存储介质,其特征在于,所述根据每一所述趋势图的极值点判断与所述趋势图对应的监控类别是否发生异常时,所述计算机可读指令被处理器执行以实现以下 步骤:从一趋势图中任意选取一极值点,并获取与所述极值点相邻的至少两个在先时间节点数据,其中所述至少两个在先时间节点数据包括第一时间节点数据以及第二时间节点数据;分别计算所述极值点与所述第一时间节点数据之间的第一趋势斜率,所述极值点与所述第二时间节点数据之间的第二趋势斜率,其中所述第一时间节点数据为与所述极值点临近的上一时间节点数据,所述第二时间节点数据为与所述第一时间节点数据临近的上一时间节点数据;计算所述第一趋势斜率与所述第二趋势斜率的标准差及均值斜率;根据计算得到的标准差及均值斜率计算得到所述极值点的综合趋势斜率;判断所述极值点的综合趋势斜率是否位于预设范围值内;及当所述极值点的综合趋势斜率不在所述预设范围值内时,判定所述趋势图对应的监控类别发生异常。
- 如权利要求15所述的存储介质,其特征在于,在所述根据所述监控类别的极值点的属性信息输出对应的警示信息时,所述计算机可读指令被处理器执行以实现以下步骤:当所述监控类别的极值点的属性信息为极大值时,输出超出阈值上限的警示信息;当所述监控类别的极值点的属性信息为极小值时,输出低于阈值下限的警示信息。
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