WO2020000738A1 - Gaussian distribution-based timed task abnormality monitoring method, electronic device, and medium - Google Patents

Gaussian distribution-based timed task abnormality monitoring method, electronic device, and medium Download PDF

Info

Publication number
WO2020000738A1
WO2020000738A1 PCT/CN2018/108209 CN2018108209W WO2020000738A1 WO 2020000738 A1 WO2020000738 A1 WO 2020000738A1 CN 2018108209 W CN2018108209 W CN 2018108209W WO 2020000738 A1 WO2020000738 A1 WO 2020000738A1
Authority
WO
WIPO (PCT)
Prior art keywords
current
ratio
timing task
task
probability
Prior art date
Application number
PCT/CN2018/108209
Other languages
French (fr)
Chinese (zh)
Inventor
刘洪晔
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2020000738A1 publication Critical patent/WO2020000738A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored

Definitions

  • the present application relates to the field of computer technology, and in particular, to a method, an electronic device, and a readable storage medium for monitoring a timing task abnormality based on a Gaussian distribution.
  • the purpose of the present application is to provide a method, an electronic device, and a readable storage medium for timing task abnormality monitoring based on a Gaussian distribution, so as to automatically and accurately determine an abnormal timing task.
  • a first aspect of the present application provides an electronic device, the electronic device includes a memory and a processor, and the memory stores a Gaussian distribution-based timing task abnormality monitoring system that can be run on the processor. , When the Gaussian distribution-based timing task abnormality monitoring system is executed by the processor, the following steps are implemented:
  • a probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
  • the second aspect of the present application further provides a method for monitoring abnormality of a scheduled task based on Gaussian distribution.
  • the method for monitoring abnormality of a scheduled task based on Gaussian distribution includes:
  • a probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
  • the third aspect of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a timing task abnormality monitoring system based on Gaussian distribution, and the timing task based on Gaussian distribution.
  • the abnormality monitoring system may be executed by at least one processor, so that the at least one processor executes the steps of the method for monitoring abnormality of a timing task based on the Gaussian distribution as described above.
  • the Gaussian-based timed task abnormality monitoring method, electronic device, and readable storage medium proposed in the present application collect various preset parameter data of the current timed task monitored and calculate each of the current timed task according to a preset calculation formula.
  • the superimposed value of the appearance probability value of the preset parameter data in the Gaussian distribution probability space of the preset normal timing task analyzing whether the monitored current timing task is abnormal based on the probability superposition value. Because it can obtain the probability value in the probability space corresponding to the Gaussian distribution according to the preset parameter data of the current timing task, and automatically determine whether the current timing task is abnormal based on the superposition value of each probability value, intelligent calculation can be used to determine the timing task. Whether it is abnormal or not requires manual analysis and judgment by the operating personnel, which saves labor and time costs and improves efficiency.
  • FIG. 1 is a schematic diagram of an operating environment of a preferred embodiment of a timing task abnormality monitoring system based on Gaussian distribution;
  • FIG. 2 is a schematic flowchart of an embodiment of a method for monitoring a timing task abnormality based on a Gaussian distribution in this application.
  • the application provides a timing task abnormality monitoring system based on Gaussian distribution.
  • FIG. 1 is a schematic diagram of an operating environment of a preferred embodiment of a timing task abnormality monitoring system 10 based on a Gaussian distribution of the present application.
  • the timing task abnormality monitoring system 10 based on the Gaussian distribution is installed and runs in the electronic device 1.
  • the electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a display 13.
  • FIG. 1 only shows the electronic device 1 with components 11-13, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
  • the memory 11 is at least one type of readable computer storage medium.
  • the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1.
  • the memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in hard disk, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, Flash card, etc.
  • the memory 11 may further include both an internal storage unit of the electronic device 1 and an external storage device.
  • the memory 11 is configured to store application software installed on the electronic device 1 and various types of data, such as program codes of the timing task abnormality monitoring system 10 based on a Gaussian distribution.
  • the memory 11 may also be used to temporarily store data that has been output or will be output.
  • the processor 12 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to run program codes or process data stored in the memory 11, such as Execute the timing task abnormality monitoring system 10 and the like based on the Gaussian distribution.
  • CPU central processing unit
  • microprocessor or other data processing chip, configured to run program codes or process data stored in the memory 11, such as Execute the timing task abnormality monitoring system 10 and the like based on the Gaussian distribution.
  • the display 13 may be an LED display, a liquid crystal display, a touch-type liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like.
  • the display 13 is used to display information processed in the electronic device 1 and to display a visual user interface, such as various preset parameter data of the current timing task, and a determination result of whether the current timing task is abnormal.
  • the components 11-13 of the electronic device 1 communicate with each other through a system bus.
  • the Gaussian distribution-based timing task abnormality monitoring system 10 includes at least one computer-readable instruction stored in the memory 11, and the at least one computer-readable instruction may be executed by the processor 12 to implement the embodiments of the present application.
  • Step S1 Collect various preset parameter data of the current timing task monitored
  • Step S2 Calculate a probability superposition value of each preset parameter data of the current timing task according to a preset calculation formula; the probability superposition value is a value of each preset parameter data of the current timing task in a preset normal timing task. Superimposed value of occurrence probability value in Gaussian distribution probability space;
  • Step S3 Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
  • each preset parameter data may include the current CPU usage rate of a scheduled task, or the ratio of the current CPU usage rate to the memory usage rate allocated for the scheduled task in advance; the current memory footprint of the scheduled task, or the current memory usage rate Ratio to disk I / O usage; the current execution time of the scheduled task, or the ratio of the current execution time to the historical average execution time; the ratio of the current cpu usage ratio to the historical cpu usage ratio of the timed task; the timed task's The ratio of the current memory usage ratio to the historical usage ratio, and so on.
  • Each parameter has a probability space with Gaussian distribution.
  • the probability value in the probability space corresponding to the Gaussian distribution can be obtained according to each preset parameter data, and according to each Probability value to automatically determine whether the current timing task is abnormal.
  • various monitoring parameters such as the CPU occupancy rate can be integrated, and dynamic intelligent calculation can be used to determine whether the timing task is abnormal, without the need for manual analysis and judgment by the operator.
  • the timing task abnormality monitoring system collects various preset parameter data of the current timing task (such as CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task), and can obtain There are two optional implementations of the numerical points corresponding to each preset parameter data: 1.
  • each preset parameter data can be directly used as the corresponding numerical point, for example, the CPU usage rate of the current scheduled task is 0.05, The corresponding value point is 0.05. 2.
  • the preset parameter data can also be converted into the corresponding attribute value points according to the preset segment conversion method, for example, converted into value points in a segmented manner, and here is preset
  • the parameter is "execute time" for example, if the execution time is 0-5 minutes, the identifier is 1; the 5-10 minutes is 2; the 10-17 minutes is 3; the 18-25 minutes is 4; The mark for 25-30 minutes is 5; the mark for 30 minutes and more is 6.
  • a suitable implementation manner 1 or 2 may be selected to convert the numerical points, which is not limited herein.
  • a preset archive database of past historical data stores historical case information that has been used to make timing task abnormality judgments, and finds all normal timing tasks in historical cases.
  • abnormal monitoring is performed on the current scheduled task, there are the following formulas:
  • a1, a2 to an are the 1,2 to n preset parameters in the current scheduled task to be monitored (for example, selecting several more important attributes such as CPU, memory, and disk I / O related to the current scheduled task Occupancy rate, execution time, etc.)
  • the attribute value points after conversion, A1, A2 to An are the first, second to nth preset attribute information corresponding to all the normal scheduled tasks in the preset archive database (the extracted current scheduled tasks).
  • the same attributes, such as the CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task, are a set of reference attribute value points after conversion.
  • mean (A1) is the mean of A1, which determines the position of the probability map of the Gaussian distribution (that is, the normal distribution).
  • Mean ((a1-mean (A1)) ⁇ 2) is the value of a1 and mean (A1). The standard deviation is also the magnitude of the Gaussian (ie, normal) distribution.
  • Gauss (an, mean (An), mean ((an-mean (An)) ⁇ 2)) is the nth preset parameter attribute value point in the current timing task to be monitored at the corresponding nth reference attribute value point
  • the occurrence probability value in the Gaussian distribution probability space of the set, P is the superposition value of the appearance probability value of the 1, 2, ... n preset parameter attribute value points in the current timing task to be monitored; the final calculated P value is The probability that the current scheduled task to be monitored is a normal scheduled task.
  • a reasonable preset probability threshold may be set in advance. If the calculated probability that the currently scheduled task to be monitored is a normal scheduled task, that is, the P value is less than the preset probability threshold, it is judged that the current task needs to be monitored.
  • the timing task is not similar to the normal timing task, that is, it is automatically determined that the currently scheduled task to be monitored is an abnormal timing task; if the P value is greater than the preset probability threshold, the currently scheduled task to be monitored is similar to the normal timing task, that is, automatically determined
  • the currently scheduled task to be monitored is a normal scheduled task.
  • timing tasks that record historical data can use the formula:
  • the preset probability threshold can also be adjusted by the user according to the needs of different application scenarios. For example, in the scenario where the execution of a scheduled task is more stringent, the threshold can be appropriately increased. In scenarios where the execution of a timed task is not too strict, the threshold can be lowered appropriately. More flexible and practical.
  • the preset parameter data of the current timing task that is monitored is collected; the occurrence probability of each preset parameter data of the current timing task in the Gaussian distribution probability space of the preset normal timing task is calculated according to a preset calculation formula.
  • Superimposed value of the value analyze whether the monitored current timing task is abnormal based on the probability superimposed value. Because it can obtain the probability value in the probability space corresponding to the Gaussian distribution according to the preset parameter data of the current timing task, and automatically determine whether the current timing task is abnormal based on the superposition value of each probability value, intelligent calculation can be used to determine the timing task. Whether it is abnormal or not requires manual analysis and judgment by the operating personnel, which saves labor and time costs and improves efficiency.
  • the method further includes:
  • the current timing task and each preset parameter data of the current timing task may be sent via SMS, WeChat, QQ message, email, and other methods.
  • Early warning information is sent to preset terminals, such as mobile phones and handheld terminals of operators, to remind operators to deal with abnormal timing tasks in a timely manner.
  • an early warning may also be performed in combination with ranking judgment, for example, each of the current scheduled tasks to be monitored is monitored.
  • the occurrence probability values of the preset parameter attribute value points in the Gaussian distribution probability space of the set of corresponding reference attribute value points are sorted in descending order.
  • the parameters that are ranked in the lower order in the order may appear in the current scheduled task to be monitored.
  • Abnormal parameters, the parameters that may appear abnormal are sent to the operator for early warning.
  • an abnormal probability threshold corresponding to each preset parameter may also be set. If there is a preset parameter attribute value point, a probability of occurrence in a Gaussian distribution probability space of a set of corresponding reference attribute value points If the value is higher than its corresponding abnormal probability threshold, then this parameter is sent to the operator for early warning.
  • FIG. 2 is a schematic flowchart of an embodiment of a method for monitoring timing task abnormality based on Gaussian distribution.
  • the method for monitoring timing task abnormality based on Gaussian distribution includes the following steps:
  • Step S10 Collect various preset parameter data of the monitored current scheduled task
  • Step S20 Calculate a probability superposition value of each preset parameter data of the current timing task according to a preset calculation formula; the probability superposition value is a value of each preset parameter data of the current timing task in a preset normal timing task. Superimposed value of occurrence probability value in Gaussian distribution probability space;
  • Step S30 Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
  • each preset parameter data may include the current CPU usage rate of a scheduled task, or the ratio of the current CPU usage rate to the memory usage rate allocated for the scheduled task in advance; the current memory footprint of the scheduled task, or the current memory usage rate Ratio to disk I / O usage; the current execution time of the scheduled task, or the ratio of the current execution time to the historical average execution time; the ratio of the current cpu usage ratio to the historical cpu usage ratio of the timed task; the timed task's The ratio of the current memory usage ratio to the historical usage ratio, and so on.
  • Each parameter has a probability space with Gaussian distribution.
  • the probability value in the probability space corresponding to the Gaussian distribution can be obtained according to each preset parameter data, and according to each Probability value to automatically determine whether the current timing task is abnormal.
  • various monitoring parameters such as the CPU occupancy rate can be integrated, and dynamic intelligent calculation can be used to determine whether the timing task is abnormal, without the need for manual analysis and judgment by the operator.
  • the timing task abnormality monitoring system collects various preset parameter data of the current timing task (such as CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task), and can obtain There are two optional implementations of the numerical points corresponding to each preset parameter data: 1.
  • each preset parameter data can be directly used as the corresponding numerical point, for example, the CPU usage rate of the current scheduled task is 0.05, The corresponding value point is 0.05. 2.
  • the preset parameter data can also be converted into the corresponding attribute value points according to the preset segment conversion method, for example, converted into value points in a segmented manner, and here is preset
  • the parameter is "execute time" for example, if the execution time is 0-5 minutes, the identifier is 1; the 5-10 minutes is 2; the 10-17 minutes is 3; the 18-25 minutes is 4; The mark for 25-30 minutes is 5; the mark for 30 minutes and more is 6.
  • a suitable implementation manner 1 or 2 may be selected to convert the numerical points, which is not limited herein.
  • the preset archive database of past historical data stores historical case information that has been used to make timing task abnormality judgments, and finds all normal timing tasks in historical cases.
  • abnormal monitoring is performed on the current scheduled task, there are the following formulas:
  • a1, a2 to an are the 1,2 to n preset parameters in the current scheduled task to be monitored (for example, selecting several more important attributes such as CPU, memory, and disk I / O related to the current scheduled task Occupancy rate, execution time, etc.)
  • the attribute value points after conversion, A1, A2 to An are the first, second to nth preset attribute information corresponding to all the normal scheduled tasks in the preset archive database (the extracted current scheduled tasks).
  • the same attributes, such as the CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task, are a set of reference attribute value points after conversion.
  • mean (A1) is the mean of A1, which determines the position of the probability map of the Gaussian distribution (that is, the normal distribution).
  • Mean ((a1-mean (A1)) ⁇ 2) is the value of a1 and mean (A1). The standard deviation is also the magnitude of the Gaussian (ie, normal) distribution.
  • Gauss (an, mean (An), mean ((an-mean (An)) ⁇ 2)) is the nth preset parameter attribute value point in the current timing task to be monitored at the corresponding nth reference attribute value point
  • the occurrence probability value in the Gaussian distribution probability space of the set, P is the superposition value of the appearance probability value of the 1, 2, ... n preset parameter attribute value points in the current timing task to be monitored; the final calculated P value is The probability that the current scheduled task to be monitored is a normal scheduled task.
  • a reasonable preset probability threshold may be set in advance. If the calculated probability that the currently scheduled task to be monitored is a normal scheduled task, that is, the P value is less than the preset probability threshold, it is judged that the current task needs to be monitored.
  • the timing task is not similar to the normal timing task, that is, it is automatically determined that the currently scheduled task to be monitored is an abnormal timing task; if the P value is greater than the preset probability threshold, the currently scheduled task to be monitored is similar to the normal timing task, that is, automatically determined
  • the currently scheduled task to be monitored is a normal scheduled task.
  • timing tasks that record historical data can use the formula:
  • the preset probability threshold can also be adjusted by the user according to the needs of different application scenarios. For example, in the scenario where the execution of a scheduled task is more stringent, the threshold can be appropriately increased. In scenarios where the execution of a timed task is not too strict, the threshold can be lowered appropriately. More flexible and practical.
  • the preset parameter data of the current timing task that is monitored is collected; the occurrence probability of each preset parameter data of the current timing task in the Gaussian distribution probability space of the preset normal timing task is calculated according to a preset calculation formula.
  • Superimposed value of the value analyze whether the monitored current timing task is abnormal based on the probability superimposed value. Because it can obtain the probability value in the probability space corresponding to the Gaussian distribution according to the preset parameter data of the current timing task, and automatically determine whether the current timing task is abnormal according to the superposition value of each probability value, the intelligent calculation can be used to determine the timing task. Whether it is abnormal or not requires manual analysis and judgment by the operating personnel, which saves labor and time costs and improves efficiency.
  • the method further includes:
  • the current timing task and each preset parameter data of the current timing task may be sent via SMS, WeChat, QQ message, email, and other methods.
  • Early warning information is sent to preset terminals, such as mobile phones and handheld terminals of operators, to remind operators to deal with abnormal timing tasks in a timely manner.
  • an early warning may also be performed in combination with ranking judgment, for example, each of the current scheduled tasks to be monitored is monitored.
  • the occurrence probability values of the preset parameter attribute value points in the Gaussian distribution probability space of the set of corresponding reference attribute value points are sorted in descending order.
  • the parameters that are ranked in the lower order in the order may appear in the current scheduled task to be monitored.
  • Abnormal parameters, the parameters that may appear abnormal are sent to the operator for early warning.
  • an abnormal probability threshold corresponding to each preset parameter may also be set. If there is a preset parameter attribute value point, a probability of occurrence in a Gaussian distribution probability space of a set of corresponding reference attribute value points If the value is higher than its corresponding abnormal probability threshold, then this parameter is sent to the operator for early warning.
  • the present application also provides a computer-readable storage medium that stores a Gaussian distribution-based timing task abnormality monitoring system.
  • the Gaussian-based timing task abnormality monitoring system may be processed by at least one processor. Execution, so that the at least one processor executes the steps of the method for monitoring a timing task abnormality based on the Gaussian distribution in the foregoing embodiment, and the specific implementation process of steps S10, S20, and S30 of the method for monitoring the timing task abnormality based on a Gaussian distribution As mentioned above, it will not be repeated here.
  • the technical solution of this application that is essentially or contributes to the existing technology can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, The optical disc) includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Debugging And Monitoring (AREA)

Abstract

A Gaussian distribution-based timed task abnormality monitoring method, an electronic device, and a readable storage medium. The method comprises: acquiring preset parameter data of a monitored current timed task (S10); calculating a probability superposition value of the preset parameter data of the current timed task according to a preset calculation formula (S20), the probability superposition value being a superposition value of probability values of the preset parameter data of the current timed task occurring in a Gaussian distribution probability space of a preset normal timed task; and analyzing, on the basis of the probability superposition value, whether the monitored current timed task is abnormal (S30). According to the present method, whether the timed task is abnormal is intelligently determined, without manual analysis and determination by an operator, thereby saving labor and time costs, and improving efficiency.

Description

基于高斯分布的定时任务异常监控方法、电子装置及介质Method, electronic device and medium for monitoring timing task abnormality based on Gaussian distribution
优先权申明Declaration of priority
本申请基于巴黎公约申明享有2018年06月29日递交的申请号为CN 201810694778.5、名称为“基于高斯分布的定时任务异常监控方法、电子装置及介质”中国专利申请的优先权，该中国专利申请的整体内容以参考的方式结合在本申请中。Based on the Paris Convention, this application claims the priority of a Chinese patent application filed on June 29, 2018 with an application number of CN201810694778.5 and a name of "Gaussian-based timed task abnormality monitoring method, electronic device and medium". This Chinese patent application The entire contents of the are incorporated herein by reference.
技术领域Technical field
本申请涉及计算机技术领域，尤其涉及一种基于高斯分布的定时任务异常监控方法、电子装置及可读存储介质。The present application relates to the field of computer technology, and in particular, to a method, an electronic device, and a readable storage medium for monitoring a timing task abnormality based on a Gaussian distribution.
背景技术Background technique
现有技术中对定时任务进行异常监控时，一般是简单的对资源指标设定阈值，若超过阈值，则向运营人员发布预警提示，由运营人员人工分析来综合判断该定时任务是否异常，这种判断异常的方式不够准确，且需依靠人工分析判断，提高了人工和时间成本，效率低下。In the prior art, when abnormal monitoring is performed on a timing task, it is generally simple to set a threshold for the resource index. If the threshold is exceeded, an early warning prompt is issued to the operator, and the operator analyzes it to determine whether the timing task is abnormal. This method of judging anomalies is not accurate enough, and requires manual analysis and judgment, which increases labor and time costs and is inefficient.
发明内容Summary of the invention
本申请的目的在于提供一种基于高斯分布的定时任务异常监控方法、电子装置及可读存储介质，旨在自动准确地判断出异常定时任务。The purpose of the present application is to provide a method, an electronic device, and a readable storage medium for timing task abnormality monitoring based on a Gaussian distribution, so as to automatically and accurately determine an abnormal timing task.
为实现上述目的，本申请第一方面提供一种电子装置，所述电子装置包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的基于高斯分布的定时任务异常监控***，所述基于高斯分布的定时任务异常监控***被所述处理器执行时实现如下步骤：To achieve the above object, a first aspect of the present application provides an electronic device, the electronic device includes a memory and a processor, and the memory stores a Gaussian distribution-based timing task abnormality monitoring system that can be run on the processor. , When the Gaussian distribution-based timing task abnormality monitoring system is executed by the processor, the following steps are implemented:
采集监控的当前定时任务的各个预设参数数据；Collect various preset parameter data of the monitored current scheduled task;
根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；A probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
基于所述概率叠加值分析监控的当前定时任务是否异常。Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
此外，为实现上述目的，本申请第二方面还提供一种基于高斯分布的定时任务异常监控方法，所述基于高斯分布的定时任务异常监控方法包括：In addition, in order to achieve the foregoing object, the second aspect of the present application further provides a method for monitoring abnormality of a scheduled task based on Gaussian distribution. The method for monitoring abnormality of a scheduled task based on Gaussian distribution includes:
采集监控的当前定时任务的各个预设参数数据；Collect various preset parameter data of the monitored current scheduled task;
根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参 数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；A probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
基于所述概率叠加值分析监控的当前定时任务是否异常。Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
进一步地，为实现上述目的，本申请第三方面还提供一种计算机可读存储介质，所述计算机可读存储介质存储有基于高斯分布的定时任务异常监控***，所述基于高斯分布的定时任务异常监控***可被至少一个处理器执行，以使所述至少一个处理器执行如上述的基于高斯分布的定时任务异常监控方法的步骤。Further, in order to achieve the above object, the third aspect of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a timing task abnormality monitoring system based on Gaussian distribution, and the timing task based on Gaussian distribution. The abnormality monitoring system may be executed by at least one processor, so that the at least one processor executes the steps of the method for monitoring abnormality of a timing task based on the Gaussian distribution as described above.
本申请提出的基于高斯分布的定时任务异常监控方法、电子装置及可读存储介质，通过采集监控的当前定时任务的各个预设参数数据；根据预设计算公式计算出所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；基于所述概率叠加值分析监控的当前定时任务是否异常。由于能根据当前定时任务的各个预设参数数据获取其在对应高斯分布的概率空间中的概率值，并依据各个概率值的叠加值来自动判断当前定时任务是否异常，实现智能计算判定出定时任务是否异常，无需由运营人员人工分析判断，节约了人工和时间成本，提高了效率。The Gaussian-based timed task abnormality monitoring method, electronic device, and readable storage medium proposed in the present application collect various preset parameter data of the current timed task monitored and calculate each of the current timed task according to a preset calculation formula. The superimposed value of the appearance probability value of the preset parameter data in the Gaussian distribution probability space of the preset normal timing task; analyzing whether the monitored current timing task is abnormal based on the probability superposition value. Because it can obtain the probability value in the probability space corresponding to the Gaussian distribution according to the preset parameter data of the current timing task, and automatically determine whether the current timing task is abnormal based on the superposition value of each probability value, intelligent calculation can be used to determine the timing task. Whether it is abnormal or not requires manual analysis and judgment by the operating personnel, which saves labor and time costs and improves efficiency.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本申请基于高斯分布的定时任务异常监控***较佳实施例的运行环境示意图；FIG. 1 is a schematic diagram of an operating environment of a preferred embodiment of a timing task abnormality monitoring system based on Gaussian distribution;
图2为本申请基于高斯分布的定时任务异常监控方法一实施例的流程示意图。FIG. 2 is a schematic flowchart of an embodiment of a method for monitoring a timing task abnormality based on a Gaussian distribution in this application.
具体实施方式detailed description
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。基于本申请中的实施例，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例，都属于本申请保护的范围。In order to make the purpose, technical solution, and advantages of the present application clearer, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
需要说明的是，在本申请中涉及“第一”、“第二”等的描述仅用于描述目的，而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本申请要求的保护范围之内。It should be noted that the descriptions related to "first", "second", etc. in this application are for descriptive purposes only, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated . Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but must be based on those that can be realized by a person of ordinary skill in the art. When the combination of technical solutions conflicts or cannot be achieved, such a combination of technical solutions should be considered nonexistent. Is not within the scope of protection claimed in this application.
本申请提供一种基于高斯分布的定时任务异常监控***。请参阅图1，是本申请基于高斯分布的定时任务异常监控***10较佳实施例的运行环境示意图。The application provides a timing task abnormality monitoring system based on Gaussian distribution. Please refer to FIG. 1, which is a schematic diagram of an operating environment of a preferred embodiment of a timing task abnormality monitoring system 10 based on a Gaussian distribution of the present application.
在本实施例中，所述的基于高斯分布的定时任务异常监控***10安装并运行于电子装置1中。该电子装置1可包括，但不仅限于，存储器11、处理器12及显示器13。图1仅示出了具有组件11-13的电子装置1，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。In this embodiment, the timing task abnormality monitoring system 10 based on the Gaussian distribution is installed and runs in the electronic device 1. The electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a display 13. FIG. 1 only shows the electronic device 1 with components 11-13, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
所述存储器11为至少一种类型的可读计算机存储介质，所述存储器11在一些实施例中可以是所述电子装置1的内部存储单元，例如该电子装置1的硬盘或内存。所述存储器11在另一些实施例中也可以是所述电子装置1的外部存储设备，例如所述电子装置1上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，所述存储器11还可以既包括所述电子装置1的内部存储单元也包括外部存储设备。所述存储器11用于存储安装于所述电子装置1的应用软件及各类数据，例如所述基于高斯分布的定时任务异常监控***10的程序代码等。所述存储器11还可以用于暂时地存储已经输出或者将要输出的数据。The memory 11 is at least one type of readable computer storage medium. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in hard disk, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, Flash card, etc. Further, the memory 11 may further include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 is configured to store application software installed on the electronic device 1 and various types of data, such as program codes of the timing task abnormality monitoring system 10 based on a Gaussian distribution. The memory 11 may also be used to temporarily store data that has been output or will be output.
所述处理器12在一些实施例中可以是一中央处理器(Central Processing Unit,CPU)，微处理器或其他数据处理芯片，用于运行所述存储器11中存储的程序代码或处理数据，例如执行所述基于高斯分布的定时任务异常监控***10等。In some embodiments, the processor 12 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to run program codes or process data stored in the memory 11, such as Execute the timing task abnormality monitoring system 10 and the like based on the Gaussian distribution.
所述显示器13在一些实施例中可以是LED显示器、液晶显示器、触控式液晶显示器以及OLED(Organic Light-Emitting Diode，有机发光二极管)触摸器等。所述显示器13用于显示在所述电子装置1中处理的信息以及用于显示可视化的用户界面，例如当前定时任务的各个预设参数数据、当前定时任务是否异常的判断结果等。所述电子装置1的部件11-13通过***总线相互通信。In some embodiments, the display 13 may be an LED display, a liquid crystal display, a touch-type liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display 13 is used to display information processed in the electronic device 1 and to display a visual user interface, such as various preset parameter data of the current timing task, and a determination result of whether the current timing task is abnormal. The components 11-13 of the electronic device 1 communicate with each other through a system bus.
基于高斯分布的定时任务异常监控***10包括至少一个存储在所述存储器11中的计算机可读指令，该至少一个计算机可读指令可被所述处理器12执行，以实现本申请各实施例。The Gaussian distribution-based timing task abnormality monitoring system 10 includes at least one computer-readable instruction stored in the memory 11, and the at least one computer-readable instruction may be executed by the processor 12 to implement the embodiments of the present application.
其中，上述基于高斯分布的定时任务异常监控***10被所述处理器12执行时实现如下步骤：Wherein, when the foregoing timing task abnormality monitoring system 10 based on the Gaussian distribution is executed by the processor 12, the following steps are implemented:
步骤S1，采集监控的当前定时任务的各个预设参数数据；Step S1: Collect various preset parameter data of the current timing task monitored;
步骤S2，根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现 概率值的叠加值；Step S2: Calculate a probability superposition value of each preset parameter data of the current timing task according to a preset calculation formula; the probability superposition value is a value of each preset parameter data of the current timing task in a preset normal timing task. Superimposed value of occurrence probability value in Gaussian distribution probability space;
步骤S3，基于所述概率叠加值分析监控的当前定时任务是否异常。Step S3: Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
本实施例中预先将对定时任务进行异常监控的观测数据整合为若干具有代表性的参数即采集的各个预设参数数据。例如，各个预设参数数据可包括定时任务的当前cpu使用率，或当前cpu使用率与预先为该定时任务分配的内存使用率的比值；该定时任务的当前内存占用大小，或当前内存使用率与磁盘I/O使用率的比值；该定时任务的当前执行时间，或当前执行时间与历史平均执行时间的比值；该定时任务的当前cpu使用比例与历史cpu使用比例的比值；该定时任务的当前内存使用比例与历史使用比例的比值，等等。每个参数都存在着一个高斯分布的概率空间，采集到当前定时任务的各个预设参数数据后，可根据各个预设参数数据获取其在对应高斯分布的概率空间中的概率值，并依据各个概率值来自动判断当前定时任务是否异常。本实施例中能整合出如cpu占用率等各个监控参数，并动态智能计算判定出定时任务是否异常，无需由运营人员人工分析判断。In this embodiment, the observation data for abnormal monitoring of the timing task is integrated into several representative parameters, that is, each preset parameter data collected. For example, each preset parameter data may include the current CPU usage rate of a scheduled task, or the ratio of the current CPU usage rate to the memory usage rate allocated for the scheduled task in advance; the current memory footprint of the scheduled task, or the current memory usage rate Ratio to disk I / O usage; the current execution time of the scheduled task, or the ratio of the current execution time to the historical average execution time; the ratio of the current cpu usage ratio to the historical cpu usage ratio of the timed task; the timed task's The ratio of the current memory usage ratio to the historical usage ratio, and so on. Each parameter has a probability space with Gaussian distribution. After collecting the preset parameter data of the current timing task, the probability value in the probability space corresponding to the Gaussian distribution can be obtained according to each preset parameter data, and according to each Probability value to automatically determine whether the current timing task is abnormal. In this embodiment, various monitoring parameters such as the CPU occupancy rate can be integrated, and dynamic intelligent calculation can be used to determine whether the timing task is abnormal, without the need for manual analysis and judgment by the operator.
具体地，由于异常定时任务和正常定时任务在执行时比起来一定会具有较大差异，而正常定时任务在执行时的情况都存在相似性。因此，若将各个定时任务中的若干具有代表性的参数(如与定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)转换成各个数值点，则在一定区域内，在历史数据记录的若干正常定时任务中每个出现的点空间内周围存在着一个高斯分布的概率空间。基于此原理，本实施例中，定时任务异常监控***采集当前定时任务的各个预设参数数据(如与定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)，并可获取各个预设参数数据对应的数值点，有以下两种可选的实施方式：1、可直接将各个预设参数数据的值作为对应的数值点，例如，当前定时任务的cpu使用率为0.05，则对应的数值点为0.05。2、还可按预设分段转换方式将预设参数数据转换成对应的属性数值点，例如，以分段的方式来转换成数值点，在此以预设参数为“执行时间”来举例说明，如执行时间为0-5分钟的标识为1；5-10分钟的标识为2；10-17分钟的标识为3；18-25分钟的标识为4；25-30分钟的标识为5；30分钟及以上的标识为6。根据不同监控参数的特征，可选择合适的实施方式1或2来进行数值点的转换，在此不做限定。Specifically, since the abnormally-timed task and the normal-timed task must have a large difference when executed, the situations of the normal-timed task when executed are similar. Therefore, if several representative parameters (such as CPU, memory, disk I / O occupancy rate, execution time, etc.) related to each timing task are converted into various numerical points, in a certain area, the There is a probability space around the Gaussian distribution in the point space of each normal timing task recorded in historical data records. Based on this principle, in this embodiment, the timing task abnormality monitoring system collects various preset parameter data of the current timing task (such as CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task), and can obtain There are two optional implementations of the numerical points corresponding to each preset parameter data: 1. The value of each preset parameter data can be directly used as the corresponding numerical point, for example, the CPU usage rate of the current scheduled task is 0.05, The corresponding value point is 0.05. 2. The preset parameter data can also be converted into the corresponding attribute value points according to the preset segment conversion method, for example, converted into value points in a segmented manner, and here is preset The parameter is "execute time" for example, if the execution time is 0-5 minutes, the identifier is 1; the 5-10 minutes is 2; the 10-17 minutes is 3; the 18-25 minutes is 4; The mark for 25-30 minutes is 5; the mark for 30 minutes and more is 6. According to the characteristics of different monitoring parameters, a suitable implementation manner 1 or 2 may be selected to convert the numerical points, which is not limited herein.
在过往历史数据的预设归档数据库中保存有曾经进行过定时任务异常判断的历史案例信息，找出历史案例中的所有正常定时任务。在对当前定时任务进行异常监控时，有如下公式：A preset archive database of past historical data stores historical case information that has been used to make timing task abnormality judgments, and finds all normal timing tasks in historical cases. When abnormal monitoring is performed on the current scheduled task, there are the following formulas:
P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2))*Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*…Gauss (an，mean(An)，mean((an-mean(An))^2))P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * … Gauss (an, mean (An), mean ((an-mean (An)) ^ 2))
其中，a1，a2至an为需监控的当前定时任务中第1，2至n个预设参数(例如，选择几个较为重要的属性如与当前定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)转换后的属性数值点，A1，A2至An为预设归档数据库中所有正常定时任务对应的第1，2至n个预设属性信息(与提取的当前定时任务中相同的属性，如与定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)转换后的参考属性数值点的集合。公式中，mean(A1)为求A1的均值，决定了高斯分布(即正态分布)概率图的位置，mean((a1-mean(A1))^2)为求a1与mean(A1)的标准差，也是高斯分布(即正态分布)的分布的幅度。Gauss(an，mean(An)，mean((an-mean(An))^2))为需监控的当前定时任务中第n个预设参数属性数值点在对应的第n个参考属性数值点的集合的高斯分布概率空间中的出现概率值，P为需监控的当前定时任务中第1，2…n个预设参数属性数值点的出现概率值的叠加值；最终计算得到的P值即为需监控的当前定时任务为正常定时任务的概率。Among them, a1, a2 to an are the 1,2 to n preset parameters in the current scheduled task to be monitored (for example, selecting several more important attributes such as CPU, memory, and disk I / O related to the current scheduled task Occupancy rate, execution time, etc.) The attribute value points after conversion, A1, A2 to An are the first, second to nth preset attribute information corresponding to all the normal scheduled tasks in the preset archive database (the extracted current scheduled tasks The same attributes, such as the CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task, are a set of reference attribute value points after conversion. In the formula, mean (A1) is the mean of A1, which determines the position of the probability map of the Gaussian distribution (that is, the normal distribution). Mean ((a1-mean (A1)) ^ 2) is the value of a1 and mean (A1). The standard deviation is also the magnitude of the Gaussian (ie, normal) distribution. Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) is the nth preset parameter attribute value point in the current timing task to be monitored at the corresponding nth reference attribute value point The occurrence probability value in the Gaussian distribution probability space of the set, P is the superposition value of the appearance probability value of the 1, 2, ... n preset parameter attribute value points in the current timing task to be monitored; the final calculated P value is The probability that the current scheduled task to be monitored is a normal scheduled task.
由于在一定区域内，在历史数据记录的若干正常定时任务中每个出现的点空间内周围存在着一个高斯分布的概率空间，即正常定时任务间存在相似性，可利用当前需监控的定时任务是否与历史数据记录的正常定时任务相似来判断当前需监控的定时任务是否异常。也即当前需监控的定时任务中各个数值点出现在历史数据记录的若干正常定时任务中每个点附近空间位置的概率越高，则当前需监控的定时任务与正常定时任务的相似度越高。具体在公式中，各个点的概率值叠加得到的参数P越高，则当前需监控的定时任务为正常定时任务的可能性越大。Because in a certain area, there is a probability space with a Gaussian distribution around the point space of each of the several normal timing tasks recorded in historical data recording, that is, there is similarity between normal timing tasks, and the current timing tasks that need to be monitored can be used Whether it is similar to the normal timing task recorded in historical data to determine whether the timing task currently being monitored is abnormal. That is, the higher the probability that each numerical point in the current scheduled task that appears in the historical data records near each point in several normal scheduled tasks, the higher the similarity between the currently scheduled task and the normal scheduled task. . Specifically in the formula, the higher the parameter P obtained by superposing the probability values of each point, the higher the probability that the currently scheduled task to be monitored is a normal scheduled task.
因此，本实施例中可预先设定一个合理的预设概率阈值，若计算得到的当前需监控的定时任务为正常定时任务的概率即P值小于该预设概率阈值，则判断当前需监控的定时任务与正常定时任务不相似，即自动判断当前需监控的定时任务为异常定时任务；若P值大于该预设概率阈值，则判断当前需监控的定时任务与正常定时任务相似，即自动判断当前需监控的定时任务为正常定时任务。Therefore, in this embodiment, a reasonable preset probability threshold may be set in advance. If the calculated probability that the currently scheduled task to be monitored is a normal scheduled task, that is, the P value is less than the preset probability threshold, it is judged that the current task needs to be monitored. The timing task is not similar to the normal timing task, that is, it is automatically determined that the currently scheduled task to be monitored is an abnormal timing task; if the P value is greater than the preset probability threshold, the currently scheduled task to be monitored is similar to the normal timing task, that is, automatically determined The currently scheduled task to be monitored is a normal scheduled task.
在预先设定概率阈值时，可将历史数据记录的若干定时任务(包括异常定时任务和正常定时任务)利用公式：When the probability threshold is set in advance, several timing tasks (including abnormal timing tasks and normal timing tasks) that record historical data can use the formula:
P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2))*Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*…Gauss(an，mean(An)，mean((an-mean(An))^2))来不断训练及验证概率阈值的合理性，在达到一定准确性后，即可设定好一合理的预设概率阈值。当然，在后续实际应用过程中，该预设概率阈值也可 以被用户根据不同应用场景的需要进行调整，如在对定时任务的执行要求较严格的场景中，可适当调高该阈值；在对定时任务的执行要求不太严格的场景中，可适当调低该阈值。更加灵活、实用。P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * … Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) to continuously train and verify the reasonableness of the probability threshold. After reaching a certain accuracy, you can set a reasonable Preset probability threshold. Of course, in the subsequent actual application process, the preset probability threshold can also be adjusted by the user according to the needs of different application scenarios. For example, in the scenario where the execution of a scheduled task is more stringent, the threshold can be appropriately increased. In scenarios where the execution of a timed task is not too strict, the threshold can be lowered appropriately. More flexible and practical.
本实施例通过采集监控的当前定时任务的各个预设参数数据；根据预设计算公式计算出所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；基于所述概率叠加值分析监控的当前定时任务是否异常。由于能根据当前定时任务的各个预设参数数据获取其在对应高斯分布的概率空间中的概率值，并依据各个概率值的叠加值来自动判断当前定时任务是否异常，实现智能计算判定出定时任务是否异常，无需由运营人员人工分析判断，节约了人工和时间成本，提高了效率。In this embodiment, the preset parameter data of the current timing task that is monitored is collected; the occurrence probability of each preset parameter data of the current timing task in the Gaussian distribution probability space of the preset normal timing task is calculated according to a preset calculation formula. Superimposed value of the value; analyze whether the monitored current timing task is abnormal based on the probability superimposed value. Because it can obtain the probability value in the probability space corresponding to the Gaussian distribution according to the preset parameter data of the current timing task, and automatically determine whether the current timing task is abnormal based on the superposition value of each probability value, intelligent calculation can be used to determine the timing task. Whether it is abnormal or not requires manual analysis and judgment by the operating personnel, which saves labor and time costs and improves efficiency.
在一可选的实施例中，在上述图1的实施例的基础上，所述基于高斯分布的定时任务异常监控***10被所述处理器12执行时，还包括：In an optional embodiment, based on the above-mentioned embodiment of FIG. 1, when the Gaussian distribution-based timing task abnormality monitoring system 10 is executed by the processor 12, the method further includes:
若判断所述当前定时任务为异常定时任务，则发送包含所述当前定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端，以提醒运营人员及时处理异常定时任务。If it is determined that the current timing task is an abnormal timing task, send an early warning prompt message including the current timing task and each preset parameter data of the current timing task to a preset terminal to remind an operator to handle the abnormal timing task in time .
本实施例中，智能计算判定出当前定时任务为异常定时任务时，可通过短信、微信、QQ消息、邮件等方式发送包含所述当前定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端如运营人员的手机、手持终端等，以提醒运营人员及时处理异常定时任务。In this embodiment, when the intelligent calculation determines that the current timing task is an abnormal timing task, the current timing task and each preset parameter data of the current timing task may be sent via SMS, WeChat, QQ message, email, and other methods. Early warning information is sent to preset terminals, such as mobile phones and handheld terminals of operators, to remind operators to deal with abnormal timing tasks in a timely manner.
进一步地，本实施例中，除了通过设置概率阈值的方式来判定当前需监控的定时任务是否为异常定时任务之外，还可结合排序判断进行预警，例如，将需监控的当前定时任务中各个预设参数属性数值点在对应的参考属性数值点的集合的高斯分布概率空间中的出现概率值按高低顺序进行排序，排序中排在后几位的参数为需监控的当前定时任务中可能出现异常的参数，则将可能出现异常的参数发送至向运营人员以进行预警提示。或者，在另一种实施方式中，还可设定各个预设参数对应的异常概率阈值，若有预设参数属性数值点在对应的参考属性数值点的集合的高斯分布概率空间中的出现概率值高于其对应的异常概率阈值，则将该参数发送至向运营人员以进行预警提示。Further, in this embodiment, in addition to determining whether the currently scheduled task to be monitored is an abnormally scheduled task by setting a probability threshold, an early warning may also be performed in combination with ranking judgment, for example, each of the current scheduled tasks to be monitored is monitored. The occurrence probability values of the preset parameter attribute value points in the Gaussian distribution probability space of the set of corresponding reference attribute value points are sorted in descending order. The parameters that are ranked in the lower order in the order may appear in the current scheduled task to be monitored. Abnormal parameters, the parameters that may appear abnormal are sent to the operator for early warning. Alternatively, in another implementation manner, an abnormal probability threshold corresponding to each preset parameter may also be set. If there is a preset parameter attribute value point, a probability of occurrence in a Gaussian distribution probability space of a set of corresponding reference attribute value points If the value is higher than its corresponding abnormal probability threshold, then this parameter is sent to the operator for early warning.
如图2所示，图2为本申请基于高斯分布的定时任务异常监控方法一实施例的流程示意图，该基于高斯分布的定时任务异常监控方法包括以下步骤：As shown in FIG. 2, FIG. 2 is a schematic flowchart of an embodiment of a method for monitoring timing task abnormality based on Gaussian distribution. The method for monitoring timing task abnormality based on Gaussian distribution includes the following steps:
步骤S10，采集监控的当前定时任务的各个预设参数数据；Step S10: Collect various preset parameter data of the monitored current scheduled task;
步骤S20，根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；Step S20: Calculate a probability superposition value of each preset parameter data of the current timing task according to a preset calculation formula; the probability superposition value is a value of each preset parameter data of the current timing task in a preset normal timing task. Superimposed value of occurrence probability value in Gaussian distribution probability space;
步骤S30，基于所述概率叠加值分析监控的当前定时任务是否异常。Step S30: Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
本实施例中预先将对定时任务进行异常监控的观测数据整合为若干具有代表性的参数即采集的各个预设参数数据。例如，各个预设参数数据可包括定时任务的当前cpu使用率，或当前cpu使用率与预先为该定时任务分配的内存使用率的比值；该定时任务的当前内存占用大小，或当前内存使用率与磁盘I/O使用率的比值；该定时任务的当前执行时间，或当前执行时间与历史平均执行时间的比值；该定时任务的当前cpu使用比例与历史cpu使用比例的比值；该定时任务的当前内存使用比例与历史使用比例的比值，等等。每个参数都存在着一个高斯分布的概率空间，采集到当前定时任务的各个预设参数数据后，可根据各个预设参数数据获取其在对应高斯分布的概率空间中的概率值，并依据各个概率值来自动判断当前定时任务是否异常。本实施例中能整合出如cpu占用率等各个监控参数，并动态智能计算判定出定时任务是否异常，无需由运营人员人工分析判断。In this embodiment, the observation data for abnormal monitoring of the timing task is integrated into several representative parameters, that is, each preset parameter data collected. For example, each preset parameter data may include the current CPU usage rate of a scheduled task, or the ratio of the current CPU usage rate to the memory usage rate allocated for the scheduled task in advance; the current memory footprint of the scheduled task, or the current memory usage rate Ratio to disk I / O usage; the current execution time of the scheduled task, or the ratio of the current execution time to the historical average execution time; the ratio of the current cpu usage ratio to the historical cpu usage ratio of the timed task; the timed task's The ratio of the current memory usage ratio to the historical usage ratio, and so on. Each parameter has a probability space with Gaussian distribution. After collecting the preset parameter data of the current timing task, the probability value in the probability space corresponding to the Gaussian distribution can be obtained according to each preset parameter data, and according to each Probability value to automatically determine whether the current timing task is abnormal. In this embodiment, various monitoring parameters such as the CPU occupancy rate can be integrated, and dynamic intelligent calculation can be used to determine whether the timing task is abnormal, without the need for manual analysis and judgment by the operator.
具体地，由于异常定时任务和正常定时任务在执行时比起来一定会具有较大差异，而正常定时任务在执行时的情况都存在相似性。因此，若将各个定时任务中的若干具有代表性的参数(如与定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)转换成各个数值点，则在一定区域内，在历史数据记录的若干正常定时任务中每个出现的点空间内周围存在着一个高斯分布的概率空间。基于此原理，本实施例中，定时任务异常监控***采集当前定时任务的各个预设参数数据(如与定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)，并可获取各个预设参数数据对应的数值点，有以下两种可选的实施方式：1、可直接将各个预设参数数据的值作为对应的数值点，例如，当前定时任务的cpu使用率为0.05，则对应的数值点为0.05。2、还可按预设分段转换方式将预设参数数据转换成对应的属性数值点，例如，以分段的方式来转换成数值点，在此以预设参数为“执行时间”来举例说明，如执行时间为0-5分钟的标识为1；5-10分钟的标识为2；10-17分钟的标识为3；18-25分钟的标识为4；25-30分钟的标识为5；30分钟及以上的标识为6。根据不同监控参数的特征，可选择合适的实施方式1或2来进行数值点的转换，在此不做限定。Specifically, since the abnormally-timed task and the normal-timed task must have a large difference when executed, the situations of the normal-timed task when executed are similar. Therefore, if several representative parameters (such as CPU, memory, disk I / O occupancy rate, execution time, etc.) related to each timing task are converted into various numerical points, in a certain area, the There is a probability space around the Gaussian distribution in the point space of each normal timing task recorded in historical data records. Based on this principle, in this embodiment, the timing task abnormality monitoring system collects various preset parameter data of the current timing task (such as CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task), and can obtain There are two optional implementations of the numerical points corresponding to each preset parameter data: 1. The value of each preset parameter data can be directly used as the corresponding numerical point, for example, the CPU usage rate of the current scheduled task is 0.05, The corresponding value point is 0.05. 2. The preset parameter data can also be converted into the corresponding attribute value points according to the preset segment conversion method, for example, converted into value points in a segmented manner, and here is preset The parameter is "execute time" for example, if the execution time is 0-5 minutes, the identifier is 1; the 5-10 minutes is 2; the 10-17 minutes is 3; the 18-25 minutes is 4; The mark for 25-30 minutes is 5; the mark for 30 minutes and more is 6. According to the characteristics of different monitoring parameters, a suitable implementation manner 1 or 2 may be selected to convert the numerical points, which is not limited herein.
在过往历史数据的预设归档数据库中保存有曾经进行过定时任 务异常判断的历史案例信息，找出历史案例中的所有正常定时任务。在对当前定时任务进行异常监控时，有如下公式：The preset archive database of past historical data stores historical case information that has been used to make timing task abnormality judgments, and finds all normal timing tasks in historical cases. When abnormal monitoring is performed on the current scheduled task, there are the following formulas:
P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2))*Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*…Gauss(an，mean(An)，mean((an-mean(An))^2))P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * … Gauss (an, mean (An), mean ((an-mean (An)) ^ 2))
其中，a1，a2至an为需监控的当前定时任务中第1，2至n个预设参数(例如，选择几个较为重要的属性如与当前定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)转换后的属性数值点，A1，A2至An为预设归档数据库中所有正常定时任务对应的第1，2至n个预设属性信息(与提取的当前定时任务中相同的属性，如与定时任务相关的CPU、内存、磁盘I/O占用率、执行时间等)转换后的参考属性数值点的集合。公式中，mean(A1)为求A1的均值，决定了高斯分布(即正态分布)概率图的位置，mean((a1-mean(A1))^2)为求a1与mean(A1)的标准差，也是高斯分布(即正态分布)的分布的幅度。Gauss(an，mean(An)，mean((an-mean(An))^2))为需监控的当前定时任务中第n个预设参数属性数值点在对应的第n个参考属性数值点的集合的高斯分布概率空间中的出现概率值，P为需监控的当前定时任务中第1，2…n个预设参数属性数值点的出现概率值的叠加值；最终计算得到的P值即为需监控的当前定时任务为正常定时任务的概率。Among them, a1, a2 to an are the 1,2 to n preset parameters in the current scheduled task to be monitored (for example, selecting several more important attributes such as CPU, memory, and disk I / O related to the current scheduled task Occupancy rate, execution time, etc.) The attribute value points after conversion, A1, A2 to An are the first, second to nth preset attribute information corresponding to all the normal scheduled tasks in the preset archive database (the extracted current scheduled tasks The same attributes, such as the CPU, memory, disk I / O occupancy rate, execution time, etc. related to the timing task, are a set of reference attribute value points after conversion. In the formula, mean (A1) is the mean of A1, which determines the position of the probability map of the Gaussian distribution (that is, the normal distribution). Mean ((a1-mean (A1)) ^ 2) is the value of a1 and mean (A1). The standard deviation is also the magnitude of the Gaussian (ie, normal) distribution. Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) is the nth preset parameter attribute value point in the current timing task to be monitored at the corresponding nth reference attribute value point The occurrence probability value in the Gaussian distribution probability space of the set, P is the superposition value of the appearance probability value of the 1, 2, ... n preset parameter attribute value points in the current timing task to be monitored; the final calculated P value is The probability that the current scheduled task to be monitored is a normal scheduled task.
由于在一定区域内，在历史数据记录的若干正常定时任务中每个出现的点空间内周围存在着一个高斯分布的概率空间，即正常定时任务间存在相似性，可利用当前需监控的定时任务是否与历史数据记录的正常定时任务相似来判断当前需监控的定时任务是否异常。也即当前需监控的定时任务中各个数值点出现在历史数据记录的若干正常定时任务中每个点附近空间位置的概率越高，则当前需监控的定时任务与正常定时任务的相似度越高。具体在公式中，各个点的概率值叠加得到的参数P越高，则当前需监控的定时任务为正常定时任务的可能性越大。Because in a certain area, there is a probability space with a Gaussian distribution around the point space of each of the several normal timing tasks recorded in historical data recording, that is, there is similarity between normal timing tasks, and the current timing tasks that need to be monitored can be used Whether it is similar to the normal timing task recorded in historical data to determine whether the timing task currently being monitored is abnormal. That is, the higher the probability that each numerical point in the current scheduled task that appears in the historical data records near each point in several normal scheduled tasks, the higher the similarity between the currently scheduled task and the normal scheduled task. . Specifically in the formula, the higher the parameter P obtained by superposing the probability values of each point, the higher the probability that the currently scheduled task to be monitored is a normal scheduled task.
因此，本实施例中可预先设定一个合理的预设概率阈值，若计算得到的当前需监控的定时任务为正常定时任务的概率即P值小于该预设概率阈值，则判断当前需监控的定时任务与正常定时任务不相似，即自动判断当前需监控的定时任务为异常定时任务；若P值大于该预设概率阈值，则判断当前需监控的定时任务与正常定时任务相似，即自动判断当前需监控的定时任务为正常定时任务。Therefore, in this embodiment, a reasonable preset probability threshold may be set in advance. If the calculated probability that the currently scheduled task to be monitored is a normal scheduled task, that is, the P value is less than the preset probability threshold, it is judged that the current task needs to be monitored. The timing task is not similar to the normal timing task, that is, it is automatically determined that the currently scheduled task to be monitored is an abnormal timing task; if the P value is greater than the preset probability threshold, the currently scheduled task to be monitored is similar to the normal timing task, that is, automatically determined The currently scheduled task to be monitored is a normal scheduled task.
在预先设定概率阈值时，可将历史数据记录的若干定时任务(包括异常定时任务和正常定时任务)利用公式：When the probability threshold is set in advance, several timing tasks (including abnormal timing tasks and normal timing tasks) that record historical data can use the formula:
P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2)) *Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*…Gauss(an，mean(An)，mean((an-mean(An))^2))来不断训练及验证概率阈值的合理性，在达到一定准确性后，即可设定好一合理的预设概率阈值。当然，在后续实际应用过程中，该预设概率阈值也可以被用户根据不同应用场景的需要进行调整，如在对定时任务的执行要求较严格的场景中，可适当调高该阈值；在对定时任务的执行要求不太严格的场景中，可适当调低该阈值。更加灵活、实用。P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * … Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) to continuously train and verify the reasonableness of the probability threshold. After reaching a certain accuracy, you can set a reasonable Preset probability threshold. Of course, in the subsequent actual application process, the preset probability threshold can also be adjusted by the user according to the needs of different application scenarios. For example, in the scenario where the execution of a scheduled task is more stringent, the threshold can be appropriately increased. In scenarios where the execution of a timed task is not too strict, the threshold can be lowered appropriately. More flexible and practical.
本实施例通过采集监控的当前定时任务的各个预设参数数据；根据预设计算公式计算出所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；基于所述概率叠加值分析监控的当前定时任务是否异常。由于能根据当前定时任务的各个预设参数数据获取其在对应高斯分布的概率空间中的概率值，并依据各个概率值的叠加值来自动判断当前定时任务是否异常，实现智能计算判定出定时任务是否异常，无需由运营人员人工分析判断，节约了人工和时间成本，提高了效率。In this embodiment, the preset parameter data of the current timing task that is monitored is collected; the occurrence probability of each preset parameter data of the current timing task in the Gaussian distribution probability space of the preset normal timing task is calculated according to a preset calculation formula. Superimposed value of the value; analyze whether the monitored current timing task is abnormal based on the probability superimposed value. Because it can obtain the probability value in the probability space corresponding to the Gaussian distribution according to the preset parameter data of the current timing task, and automatically determine whether the current timing task is abnormal according to the superposition value of each probability value, the intelligent calculation can be used to determine the timing task. Whether it is abnormal or not requires manual analysis and judgment by the operating personnel, which saves labor and time costs and improves efficiency.
在一可选的实施例中，在上述实施例的基础上，该方法还包括：In an optional embodiment, based on the above embodiment, the method further includes:
若判断所述当前定时任务为异常定时任务，则发送包含所述当前定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端，以提醒运营人员及时处理异常定时任务。If it is determined that the current timing task is an abnormal timing task, send an early warning prompt message including the current timing task and each preset parameter data of the current timing task to a preset terminal to remind an operator to handle the abnormal timing task in time .
本实施例中，智能计算判定出当前定时任务为异常定时任务时，可通过短信、微信、QQ消息、邮件等方式发送包含所述当前定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端如运营人员的手机、手持终端等，以提醒运营人员及时处理异常定时任务。In this embodiment, when the intelligent calculation determines that the current timing task is an abnormal timing task, the current timing task and each preset parameter data of the current timing task may be sent via SMS, WeChat, QQ message, email, and other methods. Early warning information is sent to preset terminals, such as mobile phones and handheld terminals of operators, to remind operators to deal with abnormal timing tasks in a timely manner.
进一步地，本实施例中，除了通过设置概率阈值的方式来判定当前需监控的定时任务是否为异常定时任务之外，还可结合排序判断进行预警，例如，将需监控的当前定时任务中各个预设参数属性数值点在对应的参考属性数值点的集合的高斯分布概率空间中的出现概率值按高低顺序进行排序，排序中排在后几位的参数为需监控的当前定时任务中可能出现异常的参数，则将可能出现异常的参数发送至向运营人员以进行预警提示。或者，在另一种实施方式中，还可设定各个预设参数对应的异常概率阈值，若有预设参数属性数值点在对应的参考属性数值点的集合的高斯分布概率空间中的出现概率值高于其对应的异常概率阈值，则将该参数发送至向运营人员以进行预警提示。Further, in this embodiment, in addition to determining whether the currently scheduled task to be monitored is an abnormally scheduled task by setting a probability threshold, an early warning may also be performed in combination with ranking judgment, for example, each of the current scheduled tasks to be monitored is monitored. The occurrence probability values of the preset parameter attribute value points in the Gaussian distribution probability space of the set of corresponding reference attribute value points are sorted in descending order. The parameters that are ranked in the lower order in the order may appear in the current scheduled task to be monitored. Abnormal parameters, the parameters that may appear abnormal are sent to the operator for early warning. Alternatively, in another implementation manner, an abnormal probability threshold corresponding to each preset parameter may also be set. If there is a preset parameter attribute value point, a probability of occurrence in a Gaussian distribution probability space of a set of corresponding reference attribute value points If the value is higher than its corresponding abnormal probability threshold, then this parameter is sent to the operator for early warning.
此外，本申请还提供一种计算机可读存储介质，所述计算机可读 存储介质存储有基于高斯分布的定时任务异常监控***，所述基于高斯分布的定时任务异常监控***可被至少一个处理器执行，以使所述至少一个处理器执行如上述实施例中的基于高斯分布的定时任务异常监控方法的步骤，该基于高斯分布的定时任务异常监控方法的步骤S10、S20、S30等具体实施过程如上文所述，在此不再赘述。In addition, the present application also provides a computer-readable storage medium that stores a Gaussian distribution-based timing task abnormality monitoring system. The Gaussian-based timing task abnormality monitoring system may be processed by at least one processor. Execution, so that the at least one processor executes the steps of the method for monitoring a timing task abnormality based on the Gaussian distribution in the foregoing embodiment, and the specific implementation process of steps S10, S20, and S30 of the method for monitoring the timing task abnormality based on a Gaussian distribution As mentioned above, it will not be repeated here.
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。It should be noted that, in this article, the terms "including", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, It also includes other elements not explicitly listed, or elements inherent to such a process, method, article, or device. Without more restrictions, an element limited by the sentence "including a ..." does not exclude that there are other identical elements in the process, method, article, or device that includes the element.
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件来实现，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。Through the description of the foregoing implementation manners, those skilled in the art can clearly understand that the methods in the foregoing embodiments can be implemented by using software plus a necessary universal hardware platform, and of course, can also be implemented by hardware, but in many cases the former is Better implementation. Based on such an understanding, the technical solution of this application that is essentially or contributes to the existing technology can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, The optical disc) includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the embodiments of the present application.
以上参照附图说明了本申请的优选实施例，并非因此局限本申请的权利范围。上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。另外，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and thus do not limit the scope of rights of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the superiority or inferiority of the embodiments. In addition, although the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than here.
本领域技术人员不脱离本申请的范围和实质，可以有多种变型方案实现本申请，比如作为一个实施例的特征可用于另一实施例而得到又一实施例。凡在运用本申请的技术构思之内所作的任何修改、等同替换和改进，均应在本申请的权利范围之内。Those skilled in the art can implement this application without departing from the scope and essence of this application. For example, the features of one embodiment can be used in another embodiment to obtain another embodiment. Any modification, equivalent replacement and improvement made within the application of the technical concept of this application shall fall within the scope of rights of this application.

Claims (20)

  1. 一种电子装置，其特征在于，所述电子装置包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的基于高斯分布的定时任务异常监控***，所述基于高斯分布的定时任务异常监控***被所述处理器执行时实现如下步骤：An electronic device, characterized in that the electronic device includes a memory and a processor, and the memory stores a Gaussian distribution-based timing task abnormality monitoring system that can be run on the processor. When the timing task abnormality monitoring system is executed by the processor, the following steps are implemented:
    采集监控的当前定时任务的各个预设参数数据；Collect various preset parameter data of the monitored current scheduled task;
    根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；A probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
    基于所述概率叠加值分析监控的当前定时任务是否异常。Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
  2. 如权利要求1所述的电子装置，其特征在于，所述根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值的步骤包括：The electronic device according to claim 1, wherein the step of calculating a probability superposition value of each preset parameter data of the current timing task according to a preset calculation formula comprises:
    将采集的各个预设参数数据按预设分段转换方式转换成对应的属性数值点，并将转换得到的各个属性数值点代入如下公式：The collected preset parameter data is converted into corresponding attribute value points according to the preset segment conversion method, and each attribute value point obtained by the conversion is substituted into the following formula:
    P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2))*Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*Gauss(an，mean(An)，mean((an-mean(An))^2))P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * Gauss (an, mean (An), mean ((an-mean (An)) ^ 2))
    其中，a1至an为所述当前定时任务中第1至n个预设参数数据转换后的属性数值点，A1至An为预设归档数据库中所有正常定时任务对应的第1至n个预设参数数据转换后的参考属性数值点的集合；Wherein, a1 to an are attribute value points after conversion of the first to n preset parameter data in the current timing task, and A1 to An are the first to n presets corresponding to all normal timing tasks in the preset archive database. The set of reference attribute value points after the parameter data conversion;
    Gauss(an，mean(An)，mean((an-mean(An))^2))为所述当前定时任务中第n个预设参数数据转换后的属性数值点在对应的第n个参考属性数值点的集合的高斯分布概率空间中的出现概率值，P为所述当前定时任务中第1至n个预设参数数据转换后的属性数值点的出现概率值的叠加值。Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) is the attribute value of the nth preset parameter data converted in the current timing task at the corresponding nth reference The occurrence probability value in the Gaussian distribution probability space of the set of attribute value points, P is a superimposed value of the appearance probability value of the attribute value points after the conversion of the first to n preset parameter data in the current timing task.
  3. 如权利要求2所述的电子装置，其特征在于，所述基于所述概率叠加值分析监控的当前定时任务是否异常的步骤包括：The electronic device according to claim 2, wherein the step of analyzing and monitoring whether the current timing task is abnormal based on the probability superposition value comprises:
    若P大于预设概率阈值，则判断所述当前定时任务为正常定时任务；If P is greater than a preset probability threshold, determining that the current timing task is a normal timing task;
    若P小于或等于预设概率阈值，则判断所述当前定时任务为异常定时任务。If P is less than or equal to a preset probability threshold, it is determined that the current timing task is an abnormal timing task.
  4. 如权利要求3所述的电子装置，其特征在于，所述基于高斯分布的定时任务异常监控***被所述处理器执行时，还实现如下步骤：The electronic device according to claim 3, wherein when the timing task abnormality monitoring system based on Gaussian distribution is executed by the processor, the following steps are further implemented:
    若判断所述当前定时任务为异常定时任务，则发送包含所述当前 定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端，以提醒运营人员及时处理异常定时任务。If it is determined that the current timing task is an abnormal timing task, send an early warning prompt message including the current timing task and each preset parameter data of the current timing task to a preset terminal to remind an operator to handle the abnormal timing task in time .
  5. 如权利要求1所述的电子装置，其特征在于，所述预设参数数据包括：The electronic device according to claim 1, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  6. 如权利要求2所述的电子装置，其特征在于，所述预设参数数据包括：The electronic device according to claim 2, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  7. 如权利要求3所述的电子装置，其特征在于，所述预设参数数据包括：The electronic device according to claim 3, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  8. 如权利要求4所述的电子装置，其特征在于，所述预设参数数据包括：The electronic device according to claim 4, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  9. 一种基于高斯分布的定时任务异常监控方法，其特征在于，所述基于高斯分布的定时任务异常监控方法包括：A method for monitoring abnormality of a scheduled task based on a Gaussian distribution, characterized in that the method for monitoring abnormality of a scheduled task based on a Gaussian distribution includes:
    采集监控的当前定时任务的各个预设参数数据；Collect various preset parameter data of the monitored current scheduled task;
    根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；A probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
    基于所述概率叠加值分析监控的当前定时任务是否异常。Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
  10. 如权利要求9所述的基于高斯分布的定时任务异常监控方法，其特征在于，所述根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值的步骤包括：The method for monitoring a timing task abnormality based on a Gaussian distribution according to claim 9, wherein the step of calculating a probability superposition value of each preset parameter data of the current timing task according to a preset calculation formula comprises:
    将采集的各个预设参数数据按预设分段转换方式转换成对应的属性数值点，并将转换得到的各个属性数值点代入如下公式：The collected preset parameter data is converted into corresponding attribute value points according to the preset segment conversion method, and each attribute value point obtained by the conversion is substituted into the following formula:
    P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2))*Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*Gauss(an，mean(An)，mean((an-mean(An))^2))P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * Gauss (an, mean (An), mean ((an-mean (An)) ^ 2))
    其中，a1至an为所述当前定时任务中第1至n个预设参数数据转换后的属性数值点，A1至An为预设归档数据库中所有正常定时任务对应的第1至n个预设参数数据转换后的参考属性数值点的集合；Wherein, a1 to an are attribute value points after conversion of the first to n preset parameter data in the current timing task, and A1 to An are the first to n presets corresponding to all normal timing tasks in the preset archive database. The set of reference attribute value points after the parameter data conversion;
    Gauss(an，mean(An)，mean((an-mean(An))^2))为所述当前定时任务中第n个预设参数数据转换后的属性数值点在对应的第n个参考属性数值点的集合的高斯分布概率空间中的出现概率值，P为所述当前定时任务中第1至n个预设参数数据转换后的属性数值点的出现概率值的叠加值。Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) is the attribute value of the nth preset parameter data converted in the current timing task at the corresponding nth reference The occurrence probability value in the Gaussian distribution probability space of the set of attribute value points, P is a superimposed value of the appearance probability value of the attribute value points after the conversion of the first to n preset parameter data in the current timing task.
  11. 如权利要求10所述的基于高斯分布的定时任务异常监控方法，其特征在于，所述基于所述概率叠加值分析监控的当前定时任务是否异常的步骤包括：The method for monitoring timing task abnormality based on Gaussian distribution according to claim 10, wherein the step of analyzing and monitoring whether the current timing task is abnormal based on the probability superposition value comprises:
    若P大于预设概率阈值，则判断所述当前定时任务为正常定时任务；If P is greater than a preset probability threshold, determining that the current timing task is a normal timing task;
    若P小于或等于预设概率阈值，则判断所述当前定时任务为异常定时任务。If P is less than or equal to a preset probability threshold, it is determined that the current timing task is an abnormal timing task.
  12. 如权利要求11所述的基于高斯分布的定时任务异常监控方法，其特征在于，还包括：The method for monitoring a timing task abnormality based on a Gaussian distribution according to claim 11, further comprising:
    若判断所述当前定时任务为异常定时任务，则发送包含所述当前定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端，以提醒运营人员及时处理异常定时任务。If it is determined that the current timing task is an abnormal timing task, send an early warning prompt message including the current timing task and each preset parameter data of the current timing task to a preset terminal to remind an operator to handle the abnormal timing task in time .
  13. 如权利要求9所述的基于高斯分布的定时任务异常监控方法，其特征在于，所述预设参数数据包括：The method for monitoring anomaly of a timing task based on Gaussian distribution according to claim 9, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  14. 如权利要求10所述的基于高斯分布的定时任务异常监控方法，其特征在于，所述预设参数数据包括：The method for monitoring timing task abnormality based on Gaussian distribution according to claim 10, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  15. 如权利要求11所述的基于高斯分布的定时任务异常监控方法，其特征在于，所述预设参数数据包括：The method for monitoring anomaly of a timing task based on Gaussian distribution according to claim 11, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  16. 如权利要求12所述的基于高斯分布的定时任务异常监控方法，其特征在于，所述预设参数数据包括：The method for monitoring anomaly of a timing task based on Gaussian distribution according to claim 12, wherein the preset parameter data comprises:
    所述当前定时任务的当前cpu使用率与预先为所述当前定时任务分配的内存使用率的比值、当前内存使用率与磁盘I/O使用率的比值、当前执行时间与历史平均执行时间的比值、当前cpu使用比例与历史cpu使用比例的比值、当前内存使用比例与历史内存使用比例的比值中的至少一个。A ratio of a current cpu usage rate of the current scheduled task to a memory usage rate previously allocated for the current scheduled task, a ratio of the current memory usage to a disk I / O usage rate, a ratio of the current execution time to the historical average execution time At least one of the ratio of the current cpu usage ratio to the historical cpu usage ratio, and the ratio of the current memory usage ratio to the historical memory usage ratio.
  17. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有基于高斯分布的定时任务异常监控***，所述基于高斯分布的定时任务异常监控***被处理器执行时实现如下步骤：A computer-readable storage medium, characterized in that the computer-readable storage medium stores a timing task abnormality monitoring system based on Gaussian distribution, and the timing task abnormality monitoring system based on Gaussian distribution is implemented as follows when executed by a processor: step:
    采集监控的当前定时任务的各个预设参数数据；Collect various preset parameter data of the monitored current scheduled task;
    根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值；所述概率叠加值为所述当前定时任务的各个预设参数数据在预设正常定时任务的高斯分布概率空间中的出现概率值的叠加值；A probability superposition value of each preset parameter data of the current timing task is calculated according to a preset calculation formula; the probability superposition value is a Gaussian distribution probability of each preset parameter data of the current timing task in a preset normal timing task Superimposed value of the probability of occurrence in space;
    基于所述概率叠加值分析监控的当前定时任务是否异常。Analyze whether the monitored current timing task is abnormal based on the probability superposition value.
  18. 如权利要求17所述的计算机可读存储介质，其特征在于，所述根据预设计算公式计算出所述当前定时任务的各个预设参数数据的概率叠加值的步骤包括：The computer-readable storage medium of claim 17, wherein the step of calculating a probability superimposed value of each preset parameter data of the current timing task according to a preset calculation formula comprises:
    将采集的各个预设参数数据按预设分段转换方式转换成对应的属性数值点，并将转换得到的各个属性数值点代入如下公式：The collected preset parameter data is converted into corresponding attribute value points according to the preset segment conversion method, and each attribute value point obtained by the conversion is substituted into the following formula:
    P＝Gauss(a1，mean(A1)，mean((a1-mean(A1))^2))*Gauss(a2，mean(A2)，mean((a2-mean(A2))^2))*Gauss(an，mean(An)，mean((an-mean(An))^2))P = Gauss (a1, mean (A1), mean ((a1-mean (A1)) ^ 2)) * Gauss (a2, mean (A2), mean ((a2-mean (A2)) ^ 2)) * Gauss (an, mean (An), mean ((an-mean (An)) ^ 2))
    其中，a1至an为所述当前定时任务中第1至n个预设参数数据转换后的属性数值点，A1至An为预设归档数据库中所有正常定时 任务对应的第1至n个预设参数数据转换后的参考属性数值点的集合；Wherein, a1 to an are attribute value points after conversion of the first to n preset parameter data in the current timing task, and A1 to An are the first to n presets corresponding to all normal timing tasks in the preset archive database. The set of reference attribute value points after the parameter data conversion;
    Gauss(an，mean(An)，mean((an-mean(An))^2))为所述当前定时任务中第n个预设参数数据转换后的属性数值点在对应的第n个参考属性数值点的集合的高斯分布概率空间中的出现概率值，P为所述当前定时任务中第1至n个预设参数数据转换后的属性数值点的出现概率值的叠加值。Gauss (an, mean (An), mean ((an-mean (An)) ^ 2)) is the attribute value of the nth preset parameter data converted in the current timing task at the corresponding nth reference The occurrence probability value in the Gaussian distribution probability space of the set of attribute value points, P is a superimposed value of the appearance probability value of the attribute value points after the conversion of the first to n preset parameter data in the current timing task.
  19. 如权利要求18所述的计算机可读存储介质，其特征在于，所述基于所述概率叠加值分析监控的当前定时任务是否异常的步骤包括：The computer-readable storage medium of claim 18, wherein the step of analyzing and monitoring whether the current timing task is abnormal based on the probability superposition value comprises:
    若P大于预设概率阈值，则判断所述当前定时任务为正常定时任务；If P is greater than a preset probability threshold, determining that the current timing task is a normal timing task;
    若P小于或等于预设概率阈值，则判断所述当前定时任务为异常定时任务。If P is less than or equal to a preset probability threshold, it is determined that the current timing task is an abnormal timing task.
  20. 如权利要求19所述的计算机可读存储介质，其特征在于，还包括：The computer-readable storage medium of claim 19, further comprising:
    若判断所述当前定时任务为异常定时任务，则发送包含所述当前定时任务及所述当前定时任务的各个预设参数数据的预警提示信息至预设终端，以提醒运营人员及时处理异常定时任务。If it is determined that the current timing task is an abnormal timing task, send an early warning prompt message including the current timing task and each preset parameter data of the current timing task to a preset terminal to remind an operator to handle the abnormal timing task in time .
PCT/CN2018/108209 2018-06-29 2018-09-28 Gaussian distribution-based timed task abnormality monitoring method, electronic device, and medium WO2020000738A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810694778.5A CN109101390B (en) 2018-06-29 2018-06-29 Timed task abnormity monitoring method based on Gaussian distribution, electronic device and medium
CN201810694778.5 2018-06-29

Publications (1)

Publication Number Publication Date
WO2020000738A1 true WO2020000738A1 (en) 2020-01-02

Family

ID=64845179

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/108209 WO2020000738A1 (en) 2018-06-29 2018-09-28 Gaussian distribution-based timed task abnormality monitoring method, electronic device, and medium

Country Status (2)

Country Link
CN (1) CN109101390B (en)
WO (1) WO2020000738A1 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109857618B (en) * 2019-02-02 2022-07-08 中国银行股份有限公司 Monitoring method, device and system
CN110008082B (en) * 2019-03-16 2022-06-17 平安科技（深圳）有限公司 Abnormal task intelligent monitoring method, device, equipment and storage medium
CN110569114B (en) * 2019-08-15 2023-03-31 平安普惠企业管理有限公司 Service processing method, device, equipment and storage medium
CN114998839B (en) * 2022-07-06 2023-01-31 北京原流科技有限公司 Data management method and system based on hierarchical distribution

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103592545A (en) * 2013-11-22 2014-02-19 国家电网公司 Transformer temperature rise abnormity monitoring and diagnosis method based on probability statistics
CN104423532A (en) * 2013-09-06 2015-03-18 ***通信集团公司 Power supply management method, device and system of mobile terminal
CN106383951A (en) * 2016-09-20 2017-02-08 北京理工大学 Fault diagnosis method and system for electric driven traffic tool
CN107103658A (en) * 2017-04-25 2017-08-29 哈尔滨工业大学 Aerial engine air passage method for detecting abnormality based on deep learning and Gaussian Profile

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB201215649D0 (en) * 2012-09-03 2012-10-17 Isis Innovation System monitoring
CN104376504B (en) * 2014-11-06 2017-10-27 国家电网公司 A kind of distribution system probabilistic reliability appraisal procedure based on analytic method
CN105071983B (en) * 2015-07-16 2017-02-01 清华大学 Abnormal load detection method for cloud calculation on-line business
CN107197473B (en) * 2017-06-15 2020-05-15 三星电子（中国）研发中心 Terminal abnormal state determination method and device
CN107526667B (en) * 2017-07-28 2020-04-28 阿里巴巴集团控股有限公司 Index abnormality detection method and device and electronic equipment
CN107704953A (en) * 2017-09-20 2018-02-16 河海大学 The short-term wind-electricity power probability density Forecasting Methodology of EWT quantile estimate forests

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104423532A (en) * 2013-09-06 2015-03-18 ***通信集团公司 Power supply management method, device and system of mobile terminal
CN103592545A (en) * 2013-11-22 2014-02-19 国家电网公司 Transformer temperature rise abnormity monitoring and diagnosis method based on probability statistics
CN106383951A (en) * 2016-09-20 2017-02-08 北京理工大学 Fault diagnosis method and system for electric driven traffic tool
CN107103658A (en) * 2017-04-25 2017-08-29 哈尔滨工业大学 Aerial engine air passage method for detecting abnormality based on deep learning and Gaussian Profile

Also Published As

Publication number Publication date
CN109101390B (en) 2021-08-24
CN109101390A (en) 2018-12-28

Similar Documents

Publication Publication Date Title
WO2020000738A1 (en) Gaussian distribution-based timed task abnormality monitoring method, electronic device, and medium
CN111835582B (en) Configuration method and device of Internet of things inspection equipment and computer equipment
CN111352921A (en) ELK-based slow query monitoring method and device, computer equipment and storage medium
JP5913145B2 (en) Log visualization device, method, and program
US20150143335A1 (en) System and method for identifying, analyzing and integrating risks associated with source code
US11042525B2 (en) Extracting and labeling custom information from log messages
CN109254901B (en) A kind of Monitoring Indexes method and system
CN111415107B (en) Power distribution network area image generation method, device, computer equipment and storage medium
CN109670091B (en) Metadata intelligent maintenance method and device based on data standard
CN113505044B (en) Database warning method, device, equipment and storage medium
CN110069925B (en) Software monitoring method, system and computer readable storage medium
CN113949652B (en) User abnormal behavior detection method and device based on artificial intelligence and related equipment
CN113051308A (en) Alarm information processing method, equipment, storage medium and device
CN107094086A (en) A kind of information acquisition method and device
WO2018122890A1 (en) Log analysis method, system, and program
CN116205396A (en) Data panoramic monitoring method and system based on data center
CN112463530A (en) Anomaly detection method and device for micro-service system, electronic equipment and storage medium
CN113438123B (en) Network flow monitoring method and device, computer equipment and storage medium
CN111221890A (en) Automatic monitoring and early warning method and device for general indexes
CN109784634A (en) Coverage division methods, electronic device and readable storage medium storing program for executing
CN113806343A (en) Assessment method and system for data quality of Internet of vehicles
CN109687999A (en) A kind of association analysis method of alarm failure, device and equipment
CN110324588B (en) Video analysis warning event information storage method based on dictionary structure
CN110850955A (en) Terminal position information processing method and device, computing equipment and storage medium
WO2016206241A1 (en) Data analysis method and apparatus

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18924185

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 29.03.2021)

122 Ep: pct application non-entry in european phase

Ref document number: 18924185

Country of ref document: EP

Kind code of ref document: A1