CN113672446B - Monitoring parameter determining method, device, equipment and storage medium - Google Patents
Monitoring parameter determining method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, equipment and a storage medium for determining monitoring parameters, which relate to the field of data processing, in particular to an information flow technology. The specific implementation scheme is as follows: acquiring a monitoring data sequence of a service to be monitored, and monitoring index data of the monitoring data sequence; dividing the monitoring data sequence by at least one time granularity to obtain at least two data fragments; determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments; and determining monitoring parameters according to the target fragments so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameters. The embodiment of the application provides a monitoring parameter determining method, a device, equipment and a storage medium, which realize automatic determination of abnormal monitoring parameters and further reduce the cost of manually adjusting the monitoring parameters.
Description
Technical Field
The embodiment of the application relates to the field of data processing, in particular to an information flow technology. Specifically, the embodiment of the application provides a monitoring parameter determining method, a monitoring parameter determining device, monitoring parameter determining equipment and a storage medium.
Background
The data anomaly monitoring is an important means for guaranteeing product service, and if the data anomaly monitoring is difficult to continuously and effectively operate, the overall service quality of the product can be seriously affected, and immeasurable loss is caused for enterprises.
Currently, in some scenes where the monitoring alarm strategy changes with time, the selection of the monitoring alarm parameters is mainly obtained through experience combing of experienced personnel. However, the threshold based on human experience construction is high, the energy of the self-service staff is seriously wasted, and the cost performance is low.
Disclosure of Invention
The embodiment of the application provides a monitoring parameter determining method, a monitoring parameter determining device, monitoring parameter determining equipment and a storage medium.
In a first aspect, an embodiment of the present application provides a method for determining a monitoring parameter, where the method includes:
acquiring a monitoring data sequence of a service to be monitored, and monitoring index data of the monitoring data sequence;
dividing the monitoring data sequence by at least one time granularity to obtain at least two data fragments;
determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments;
and determining monitoring parameters according to the target fragments so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameters.
In a second aspect, an embodiment of the present application provides a monitoring parameter determining apparatus, including:
the data acquisition module is used for acquiring a monitoring data sequence of the service to be monitored and monitoring index data of the monitoring data sequence;
the sequence dividing module is used for dividing the monitoring data sequence by at least one time granularity to obtain at least two data fragments;
the segment determining module is used for determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments;
and the parameter determining module is used for determining monitoring parameters according to the target fragments so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameters.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the embodiments of the present application.
In a fourth aspect, embodiments of the present application also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of any one of the embodiments of the present application.
The embodiment of the application realizes automatic determination of the abnormal monitoring parameters, thereby reducing the cost of manually adjusting the monitoring parameters.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is a flowchart of a method for determining monitoring parameters according to an embodiment of the present application;
FIG. 2 is a flowchart of another monitoring parameter determining method according to an embodiment of the present application;
FIG. 3 is a flowchart of yet another monitoring parameter determination method provided by an embodiment of the present application;
FIG. 4 is a flowchart of yet another monitoring parameter determination method provided by an embodiment of the present application;
FIG. 5 is a flowchart of yet another monitoring parameter determination method provided by an embodiment of the present application;
FIG. 6 is a flowchart of yet another monitoring parameter determination method provided by an embodiment of the present application;
FIG. 7 is a schematic structural diagram of a monitoring parameter determining apparatus according to an embodiment of the present application;
fig. 8 is a block diagram of an electronic device for implementing a method of determining a monitoring parameter according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a method for determining a monitoring parameter according to an embodiment of the present application. The embodiment can be suitable for the situation of carrying out abnormal monitoring on the business data. The method may be performed by a monitoring parameter determining means, which may be implemented in software and/or hardware. Referring to fig. 1, the method for determining monitoring parameters provided by the embodiment of the application includes:
s110, acquiring a monitoring data sequence of a service to be monitored and monitoring index data of the monitoring data sequence.
The service to be monitored refers to the service to be monitored.
The monitoring data sequence refers to a data sequence which is generated by the service to be monitored and needs to be monitored.
In one embodiment, the sequence of monitoring data may be obtained by ranking the generated monitoring data according to the time of generation.
The monitoring index data refers to data corresponding to the monitoring index.
In one embodiment, monitoring the metrics may include: at least one of missing information of the monitoring data, monitoring data values, a same ratio of the monitoring data, a ring ratio of the monitoring data, a same ratio difference of the monitoring data, a ring ratio difference of the monitoring data and a fluctuation slope of the monitoring data.
In one embodiment, the monitoring index may be determined based on the type of monitoring. For example, if the monitoring type is a breakpoint alarm, the monitoring indicator may be a monitoring data value.
S120, dividing the monitoring data sequence by at least one time granularity to obtain at least two data fragments.
Wherein, at least one time granularity can be set according to the requirement.
The specific time granularity may be 5 minutes, 10 minutes, 15 minutes, 30 minutes, or the like.
The data segment refers to a data segment obtained by dividing the monitoring data sequence.
S130, determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments.
Wherein the index distribution characteristic is a distribution characteristic of monitoring index data of the data segment.
In one embodiment, the index profile feature may be a mean, a maximum, a minimum, a standard deviation, a discrete coefficient, or a quantile value of the monitored index data, or the like.
The target segment refers to a data segment that can accurately describe the index distribution characteristics of the data segment.
And S140, determining monitoring parameters according to the target segments so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameters.
The monitoring parameters refer to parameters which need to be set during abnormal monitoring.
In one embodiment, the monitoring parameters may include an alarm threshold and a time granularity of monitoring.
According to the technical scheme, at least two data fragments are obtained by dividing the monitoring data sequence by at least one time granularity; determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments; and determining the monitoring parameters according to the target fragments, so that the automatic determination of the monitoring parameters is realized, and the cost of manually adjusting the monitoring parameters is reduced.
Fig. 2 is a flowchart of another monitoring parameter determining method according to an embodiment of the present application. In this embodiment, the step S130 is refined based on the above embodiment. Referring to fig. 2, S130 specifically includes:
s131, determining the index accuracy of the data segment according to the index distribution characteristics of the data segment.
Wherein, the index accuracy refers to the accuracy of monitoring the index data.
In one embodiment, the determining the index accuracy of the data segment according to the index distribution feature of the data segment includes:
and if the index distribution characteristic is the standard deviation of the monitoring index data, determining the index accuracy according to the standard deviation, wherein the larger the standard deviation is, the smaller the index accuracy is.
S132, determining the target segment from the at least two data segments according to the index accuracy.
In one embodiment, determining the target segment from the at least two data segments according to the index accuracy comprises:
and according to the index accuracy, taking the data segment with the highest standard definition in the at least two data segments as the target segment.
According to the technical scheme, the index accuracy of the data segment is determined according to the index distribution characteristics of the data segment; and determining the target segment from the at least two data segments according to the index accuracy, so as to realize the determination of the target segment based on the index distribution characteristics.
Fig. 3 is a flowchart of yet another monitoring parameter determining method according to an embodiment of the present application. The embodiment is based on the above embodiment, and specifically optimizes S130 to improve accuracy of the target segment. Referring to fig. 3, S130 specifically includes:
s133, determining the target segment from the at least two data segments according to the set attention dimension and the index distribution characteristics of the data segments.
The dimension of interest refers to an information dimension that needs to be focused in anomaly monitoring.
In one embodiment, the dimension of interest may be at least one of monitoring aging, allowable interference, and allowable loss.
The determining the target segment from the at least two data segments according to the set dimension of interest and the index distribution characteristics of the data segments comprises:
performing primary screening on the at least two data fragments according to the dimension of interest;
and determining the target fragment from the primary screening result according to the index distribution characteristics of the data fragment.
Further, the pre-screening the at least two data segments according to the dimension of interest includes:
if the dimension of interest is age of interest, matching a time length of interest with time granularity of the at least two data segments;
and filtering the at least two data fragments according to the matching result.
The term "monitoring time" refers to monitoring effectiveness that can occur over a period of time.
The duration of interest refers to the period of time during which utility can occur. The attention duration may be set according to actual needs.
Matching the attention duration with the time granularity of the at least two data fragments, namely respectively comparing the attention duration with the time granularity of each data fragment in the at least two data fragments.
In one embodiment, if the temporal granularity of a data segment is greater than the duration of interest, the data segment is filtered out.
Further, the pre-screening the at least two data segments according to the dimension of interest includes:
if the dimension of interest is an allowable loss amount, comparing the allowable loss amount with loss amounts of data segments in the at least two data segments;
and filtering the at least two data fragments according to the comparison result.
The allowable loss amount refers to the loss amount allowed to be generated by the service in the monitoring logic.
In one embodiment, filtering the at least two data segments according to the comparison result includes:
if the loss amount of the data segment is larger than the allowable loss amount, deleting the data segment.
According to the technical scheme, the target segment is determined from the at least two data segments according to the set attention dimension and the index distribution characteristics of the data segments, so that the target segment is determined. Because the embodiment of the application increases the determining factors of the dimension of interest, the embodiment of the application can improve the accuracy of the target segment.
Fig. 4 is a flowchart of yet another monitoring parameter determining method according to an embodiment of the present application. In this embodiment, the step S140 is further refined based on the above embodiment. Referring to fig. 4, the step S140 specifically includes:
s141, determining an alarm threshold according to the monitoring index data of the target segment.
The alarm threshold value refers to a threshold value for carrying out abnormal alarm in the abnormal monitoring process.
In one embodiment, the alarm threshold may be a maximum value of the index data triggering the alarm and/or a minimum value of the index data triggering the alarm.
In one embodiment, the determining the alarm threshold according to the monitored index data of the target segment includes:
sequencing the monitoring index data of the target fragments;
and determining the alarm threshold value from the monitoring index data of the target segment according to the sequencing result.
For example, the maximum value and the minimum value of the monitoring index data in the sorting result are used as alarm threshold values.
S142, determining the monitoring time granularity according to the time granularity of the target segment.
The monitoring time granularity refers to the minimum time unit for monitoring.
For example, if the monitoring time granularity is 5 minutes, abnormality determination is performed on the monitoring data every 5 minutes.
S143, taking the determined alarm threshold and the monitoring time granularity as the monitoring parameters.
According to the technical scheme, the alarm threshold value is determined according to the monitoring index data of the target segment; and determining the monitoring time granularity according to the time granularity of the target segment, thereby realizing the determination of the monitoring parameters.
In order to improve the accuracy of the alarm threshold, the determining the alarm threshold from the monitoring index data of the target segment according to the sequencing result includes:
filtering abnormal data in the monitoring index data of the target fragment according to the sequencing result;
and determining the alarm threshold value from the residual monitoring index data.
The abnormal data is undetected abnormal data. In one embodiment, the exception data may be some extreme data.
The remaining monitoring index data is monitoring index data in which abnormal data is filtered.
Based on the technical characteristics, the embodiment of the application determines the alarm threshold value from the residual monitoring index data by filtering the abnormal data in the monitoring index data, thereby improving the accuracy of the alarm threshold value.
In one embodiment, the determining the alarm threshold from the remaining monitoring index data includes:
sequencing the rest monitoring index data;
and taking the maximum value in the sequencing result as a maximum alarm threshold value and the minimum value in the sequencing result as a minimum alarm threshold value.
Fig. 5 is a flowchart of yet another monitoring parameter determining method according to an embodiment of the present application. The present embodiment is a further extended application to the scheme based on the above embodiment. Referring to fig. 5, after the at least one time granularity division is performed on the monitored data sequence to obtain at least two data segments, the method for determining the monitoring parameters provided by the embodiment of the present application further includes:
s150, grouping the obtained at least two data fragments according to the date and/or the time period to obtain at least one data group, wherein the duration of the time granularity is smaller than that of the time period.
Accordingly, the S130 includes:
s134, determining a target fragment of the data set from at least one data fragment in the data set according to the index distribution characteristics of the data fragments in the data set.
In one embodiment, the date may be a month, a day of the week, or a holiday.
A time period refers to the time between a certain point in the day to another point in time. For example, the time period may be the time between 7 a.m. and 10 a.m..
According to the technical scheme provided by the embodiment of the application, the data fragments are grouped according to the date and/or the time period, so that the monitoring parameters of different dates and/or time periods are determined.
Further, in practical application, the application of the monitoring parameter determined in the embodiment of the present application may be described as:
determining a target group from the data group according to the date to be monitored and/or the time period to be monitored;
taking the monitoring parameter of the target packet as a target parameter;
and carrying out abnormal monitoring on the service to be monitored according to the target parameters.
Wherein the target packet refers to a data set matching the date to be monitored and/or the time period to be monitored.
The target parameter is a monitored parameter of the target packet association time.
In one embodiment, the target parameter may be determined from a target fragment in the target packet.
Based on the technical characteristics, the embodiment of the application can realize automatic adjustment of the monitoring parameters aiming at different dates and/or time periods, thereby improving the accuracy of abnormal monitoring while reducing the cost of manually adjusting the monitoring parameters.
Fig. 6 is a schematic diagram illustrating an execution of another monitoring parameter determining method according to an embodiment of the present application. Referring to fig. 6, the execution sequence of the present embodiment is: basic dimensions, time aggregation/grouping, basic features, feature analysis, alarms remembers the order of execution of recommendations and traffic monitoring. This embodiment is an alternative to the embodiments described above. With continued reference to fig. 6, the method for determining the monitoring parameter provided by the embodiment of the application includes:
acquiring a historical monitoring data sequence of a service to be monitored and monitoring index data of the historical monitoring data sequence, wherein the historical monitoring data sequence is a monitoring data sequence of the service to be monitored which occurs in historical time;
determining a monitoring index of the historical monitoring data sequence according to the monitoring type, wherein the monitoring index can be at least one of the same ratio, the ring ratio, the same ratio difference, the ring ratio difference, the slope and the missing state (namely whether the state of the data exists) of the monitoring data;
based on an aggregation window, dividing the historical monitoring data sequence by at least one time granularity according to a time stamp to obtain at least two historical data segments;
aggregating the monitoring indexes of the historical data segments to obtain an index set;
grouping the historical data segments according to the date and time period to obtain at least one data group;
determining a distribution characteristic of the index set in each data set, wherein the distribution characteristic comprises: at least one of a mean, a maximum, a minimum, a standard deviation, a discrete coefficient, and a quantile value;
filtering historical data segments in each data group according to the set attention dimension;
determining a target data segment from the remaining historical data segments of each data set according to the distribution characteristics;
sequencing the monitoring index data of the target data segment, and filtering extreme values according to sequencing results;
determining an alarm threshold value of the target data segment based on the monitoring index according to the sequencing result of the residual monitoring index data;
and monitoring the data to be monitored of the current time of the service to be monitored according to the determined alarm threshold.
According to the technical scheme, the time sequence data set corresponding to at least one index is input, and the time sequence characteristics of the index are output through data basic characteristic analysis, time window analysis and date and period similarity analysis on the time sequence data. The alarm threshold value of the index is determined based on the time sequence characteristic of the index, so that the automatic determination of the alarm threshold value is realized, and the trouble of manual operation is avoided.
Fig. 7 is a schematic structural diagram of a monitoring parameter determining apparatus according to an embodiment of the present application. Referring to fig. 7, a monitoring parameter determining apparatus 700 provided in an embodiment of the present application includes: a data acquisition module 701, a sequence division module 702, a fragment determination module 703 and a parameter determination module 704.
The data acquisition module 701 is configured to acquire a monitoring data sequence of a service to be monitored and monitoring index data of the monitoring data sequence;
a sequence dividing module 702, configured to divide the monitored data sequence by at least one time granularity, so as to obtain at least two data segments;
a segment determining module 703, configured to determine a target segment from the at least two data segments according to the index distribution characteristics of the data segments;
and the parameter determining module 704 is configured to determine a monitoring parameter according to the target segment, so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameter.
According to the technical scheme, at least two data fragments are obtained by dividing the monitoring data sequence by at least one time granularity; determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments; and determining the monitoring parameters according to the target fragments, so that the automatic determination of the monitoring parameters is realized, and the cost of manually adjusting the monitoring parameters is reduced.
Further, the fragment determination module includes:
an accuracy determining unit, configured to determine an index accuracy of the data segment according to an index distribution feature of the data segment;
and the segment determining unit is used for determining the target segment from the at least two data segments according to the index accuracy.
Further, the accuracy determining unit includes:
and the accuracy determining subunit is used for determining the index accuracy according to the standard deviation if the index distribution characteristic is the standard deviation of the monitoring index data, wherein the larger the standard deviation is, the smaller the index accuracy is.
Further, the fragment determination module includes:
and the segment determining unit is used for determining the target segment from the at least two data segments according to the set attention dimension and the index distribution characteristics of the data segments.
Further, the fragment determination unit includes:
a primary screening subunit configured to perform primary screening on the at least two data segments according to the dimension of interest;
and the fragment determining subunit is used for determining the target fragment from the primary screening result according to the index distribution characteristics of the data fragment.
Further, the primary screening subunit is specifically configured to:
if the dimension of interest is age of interest, matching a time length of interest with time granularity of the at least two data segments;
and filtering the at least two data fragments according to the matching result.
Further, the primary screening subunit is specifically configured to:
if the dimension of interest is an allowable loss amount, comparing the allowable loss amount with loss amounts of data segments in the at least two data segments;
and filtering the at least two data fragments according to the comparison result.
Further, the parameter determining module includes:
the threshold determining unit is used for determining an alarm threshold according to the monitoring index data of the target segment;
the granularity determining unit is used for determining the monitoring time granularity according to the time granularity of the target segment;
and the parameter determining unit is used for taking the determined alarm threshold value and the monitoring time granularity as the monitoring parameters.
Further, the threshold determining unit includes:
a sequencing subunit, configured to sequence the monitoring index data of the target segment;
and the threshold value determining subunit is used for determining the alarm threshold value from the monitoring index data of the target segment according to the sequencing result.
Further, the threshold determining subunit includes:
the filter is used for filtering abnormal data in the monitoring index data of the target segment according to the sequencing result;
and the selector is used for determining the alarm threshold value from the residual monitoring index data.
Further, the selector is specifically configured to:
sequencing the rest monitoring index data;
and taking the maximum value in the sequencing result as a maximum alarm threshold value and the minimum value in the sequencing result as a minimum alarm threshold value.
Further, the apparatus further comprises:
the grouping module is used for grouping the obtained at least two data fragments according to the date and/or time period after the at least one time granularity of the monitoring data sequence is divided to obtain at least two data fragments, so as to obtain at least one data group, wherein the duration of the time granularity is smaller than the duration of the time period;
accordingly, the fragment determination module includes:
and the target segment determining unit is used for determining the target segment of the data group from at least one data segment in the data group according to the index distribution characteristics of the data segments in the data group.
Further, the apparatus further comprises:
the grouping determining module is used for determining target grouping from the data group according to the date to be monitored and/or the time period to be monitored after the monitoring parameters are determined according to the target segment;
and the target parameter determining module is used for taking the monitoring parameter of the target packet as a target parameter.
According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.
As shown in fig. 8, a block diagram of an electronic device of a monitoring parameter determining method according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 8, the electronic device includes: one or more processors 801, memory 802, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 801 is illustrated in fig. 8.
Memory 802 is a non-transitory computer readable storage medium provided by the present application. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the monitoring parameter determining method provided by the present application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to execute the monitoring parameter determining method provided by the present application.
The memory 802 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and program instructions/modules (e.g., the data acquisition module 701, the sequence division module 702, the segment determination module 703, and the parameter determination module 704 shown in fig. 7) corresponding to the monitoring parameter determination method in the embodiment of the present application. The processor 801 executes various functional applications of the server and data processing, i.e., implements the monitoring parameter determining method in the above-described method embodiment, by running non-transitory software programs, instructions, and modules stored in the memory 802.
Memory 802 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created by determining the use of the electronic device based on the monitored parameters, and the like. In addition, memory 802 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 802 may optionally include memory remotely located with respect to processor 801, which may be connected to the monitoring parameter determination electronics via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device that monitors the parameter determination method may further include: an input device 803 and an output device 804. The processor 801, memory 802, input devices 803, and output devices 804 may be connected by a bus or other means, for example in fig. 8.
The input device 803 may receive entered numeric or character information and generate key signal inputs related to monitoring parameter determination of user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, pointer stick, one or more mouse buttons, trackball, joystick, and like input devices. The output device 804 may include a display apparatus, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
The embodiment of the application realizes automatic determination of the abnormal monitoring parameters, thereby reducing the use of labor cost.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed embodiments are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (15)
1. A method for determining a monitoring parameter, comprising:
acquiring a monitoring data sequence of a service to be monitored, and monitoring index data of the monitoring data sequence;
dividing the monitoring data sequence by at least one time granularity to obtain at least two data fragments;
determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments;
determining monitoring parameters according to the target fragments so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameters;
wherein the determining the target segment from the at least two data segments according to the index distribution characteristics of the data segments includes:
performing primary screening on the at least two data fragments according to the dimension of interest;
determining the target fragment from the primary screening result according to the index distribution characteristics of the data fragment;
wherein the pre-screening the at least two data segments according to the dimension of interest comprises:
if the dimension of interest is age of interest, matching a time length of interest with time granularity of the at least two data segments;
and filtering the at least two data fragments according to the matching result.
2. The method of claim 1, wherein determining the target segment from the at least two data segments based on the index profile of the data segment comprises:
determining the index accuracy of the data segment according to the index distribution characteristics of the data segment;
and determining the target fragment from the at least two data fragments according to the index accuracy.
3. The method of claim 2, wherein determining the index accuracy of the data segment based on the index distribution characteristics of the data segment comprises:
and if the index distribution characteristic is the standard deviation of the monitoring index data, determining the index accuracy according to the standard deviation, wherein the larger the standard deviation is, the smaller the index accuracy is.
4. The method of claim 1, wherein the pre-screening the at least two data segments according to the dimension of interest comprises:
if the dimension of interest is an allowable loss amount, comparing the allowable loss amount with loss amounts of data segments in the at least two data segments;
and filtering the at least two data fragments according to the comparison result.
5. A method according to any one of claims 1-3, wherein said determining a monitoring parameter from said target segment comprises:
determining an alarm threshold according to the monitoring index data of the target segment;
determining monitoring time granularity according to the time granularity of the target segment;
and taking the determined alarm threshold value and the monitoring time granularity as the monitoring parameters.
6. The method of claim 5, wherein determining an alarm threshold based on the monitored indicator data for the target segment comprises:
sequencing the monitoring index data of the target fragments;
and determining the alarm threshold value from the monitoring index data of the target segment according to the sequencing result.
7. The method of claim 6, wherein determining the alarm threshold from the monitored indicator data of the target segment based on the ranking result comprises:
filtering abnormal data in the monitoring index data of the target fragment according to the sequencing result;
and determining the alarm threshold value from the residual monitoring index data.
8. The method of claim 7, wherein said determining the alarm threshold from the remaining monitored metric data comprises:
sequencing the rest monitoring index data;
and taking the maximum value in the sequencing result as a maximum alarm threshold value and the minimum value in the sequencing result as a minimum alarm threshold value.
9. A method according to any one of claims 1-3, wherein after said dividing of said monitored data sequence by at least one time granularity to obtain at least two data segments, the method further comprises:
grouping the obtained at least two data fragments according to the date and/or the time period to obtain at least one data group, wherein the duration of the time granularity is smaller than that of the time period;
correspondingly, the determining the target segment from the at least two data segments according to the index distribution characteristics of the data segments comprises:
and determining the target fragment of the data group from at least one data fragment in the data group according to the index distribution characteristics of the data fragments in the data group.
10. The method of claim 9, wherein after determining the monitoring parameter from the target segment, the method further comprises:
determining a target group from the data group according to the date to be monitored and/or the time period to be monitored;
taking the monitoring parameter of the target packet as the target parameter.
11. A monitoring parameter determining apparatus, comprising:
the data acquisition module is used for acquiring a monitoring data sequence of the service to be monitored and monitoring index data of the monitoring data sequence;
the sequence dividing module is used for dividing the monitoring data sequence by at least one time granularity to obtain at least two data fragments;
the segment determining module is used for determining a target segment from the at least two data segments according to the index distribution characteristics of the data segments;
the parameter determining module is used for determining monitoring parameters according to the target fragments so as to perform abnormal monitoring on the service to be monitored according to the monitoring parameters;
wherein the fragment determination module comprises:
a primary screening subunit configured to perform primary screening on the at least two data segments according to a dimension of interest;
a segment determining subunit, configured to determine the target segment from the primary screening result according to the index distribution feature of the data segment;
wherein the primary screening subunit is specifically configured to:
if the dimension of interest is age of interest, matching a time length of interest with time granularity of the at least two data segments;
and filtering the at least two data fragments according to the matching result.
12. The apparatus of claim 11, wherein the segment determination module comprises:
an accuracy determining unit, configured to determine an index accuracy of the data segment according to an index distribution feature of the data segment;
and the segment determining unit is used for determining the target segment from the at least two data segments according to the index accuracy.
13. The apparatus according to claim 12, wherein the accuracy determination unit comprises:
and the accuracy determining subunit is used for determining the index accuracy according to the standard deviation if the index distribution characteristic is the standard deviation of the monitoring index data, wherein the larger the standard deviation is, the smaller the index accuracy is.
14. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
15. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-10.
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