CN116149926A - Abnormality monitoring method, device, equipment and storage medium for business index - Google Patents

Abnormality monitoring method, device, equipment and storage medium for business index Download PDF

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CN116149926A
CN116149926A CN202210867542.3A CN202210867542A CN116149926A CN 116149926 A CN116149926 A CN 116149926A CN 202210867542 A CN202210867542 A CN 202210867542A CN 116149926 A CN116149926 A CN 116149926A
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index
sequence
detected
sample sequence
sample
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谭万辉
唐蠡
郭剑霓
吴海英
曾琳铖曦
蒋宁
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Mashang Xiaofei Finance Co Ltd
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Mashang Xiaofei Finance Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3051—Monitoring arrangements for monitoring the configuration of the computing system or of the computing system component, e.g. monitoring the presence of processing resources, peripherals, I/O links, software programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
    • G06F11/3072—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting

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Abstract

The embodiment of the application provides a method, a device, equipment and a storage medium for monitoring abnormality of a service index, wherein the method for monitoring abnormality of the service index comprises the following steps: acquiring an index element set corresponding to a service index of a service to be monitored; dividing an index element set into a sequence to be detected and a sample sequence; dividing the sample sequence into a plurality of first subsequences according to a first time period; performing processing for determining the index type based on the plurality of first subsequences to obtain the index type of the sample sequence; generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence. By adopting the embodiment of the application, the monitoring workload of the business index can be reduced, so that the abnormal monitoring efficiency is improved.

Description

Abnormality monitoring method, device, equipment and storage medium for business index
Technical Field
The present invention relates to the field of data processing, and in particular, to a method, an apparatus, a device, and a storage medium for monitoring abnormality of a service index.
Background
During the actual operation of the service, failures that occur randomly are very common. The occurrence of faults is often accompanied by negative effects of the faults on the various dimensions of the traffic. Each service is often preconfigured with a plurality of service indicators of different dimensions, each having respective characteristics. And carrying out abnormal monitoring on each service index so as to discover the influence range of faults on the service index in time, solve the influence of the faults in time and ensure the normal operation of the service.
As mentioned above, each service is often configured with a plurality of indexes with different dimensions, how to configure a corresponding monitoring flow for each service index specifically, which has large workload and many redundant operations. Therefore, how to reduce the monitoring workload of each service index for the service to be monitored, and improve the monitoring efficiency of the service index is becoming more and more important.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a storage medium for monitoring abnormality of a service index, which can reduce the monitoring workload of the service index, thereby improving the abnormality monitoring efficiency of the index.
In a first aspect, an embodiment of the present application provides a method for monitoring abnormality of a service indicator, including:
acquiring an index element set corresponding to a service index of a service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and the buried point data of the business to be monitored;
dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements, wherein the generation time of the index elements is close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
Dividing the sample sequence into a plurality of first sub-sequences according to a first time period;
performing processing for determining the index type based on the plurality of first subsequences to obtain the index type of the sample sequence;
generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
In a second aspect, an embodiment of the present application provides an anomaly monitoring device for a service indicator, including:
the acquisition unit is used for acquiring an index element set corresponding to the service index of the service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and the buried point data of the business to be monitored;
the first dividing unit is used for dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements, wherein the generation time of the index elements is close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
A second dividing unit for dividing the sample sequence into a plurality of first sub-sequences according to a first time period;
a processing unit, configured to perform processing for determining an index type based on the plurality of first sub-sequences, and obtain an index type of the sample sequence;
the output unit is used for generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
In a third aspect, an embodiment of the present application provides an anomaly monitoring device for a service indicator, including: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the anomaly monitoring method of the traffic indicator of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method for anomaly monitoring of a traffic indicator according to the first aspect.
It can be seen that, in the embodiment of the present application, when abnormal monitoring needs to be performed on a service to be monitored, firstly, an index element set corresponding to a service index of the service to be monitored is obtained; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and buried data of a business to be monitored; secondly, dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected; then, dividing the sample sequence into a plurality of first subsequences according to a first time period; then, the method is used for carrying out processing for determining the index types based on the plurality of first subsequences to obtain the index types of the sample sequence; and finally, generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence. Therefore, after the acquired index element set corresponding to the service index of the service to be monitored is divided into the sequence to be detected and the sample sequence, and the sample sequence is divided into a plurality of first subsequences, the index types of the service index of the service to be monitored can be determined through the characteristics of the first subsequences, so that the abnormal parameters corresponding to the sample sequence are generated by combining the index types under the condition that the index types are different, and the abnormal parameters of the sample sequence are generated according to the index types, the sequence to be detected and the sample sequence by determining the processing of the index types, the configuration workload of the monitoring flow of the service index is greatly reduced without configuring a special monitoring flow for each service index, the monitoring workload of each service index of the service to be monitored is reduced, and the generation modes of the service indexes of the same index type are the same, so that the abnormal conditions of each service index are favorably compared transversely.
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For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are required to be used in the description of the embodiments or of the prior art will be briefly described, it being obvious that the drawings in the description below are only some of the embodiments described in the present specification, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art;
fig. 1 is a process flow diagram of a method for monitoring abnormality of a business index according to an embodiment of the present application;
fig. 2 is a schematic frame diagram of a method for monitoring abnormality of a business index according to an embodiment of the present application;
fig. 3 is a schematic diagram of an abnormality monitoring device for a business index according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an abnormality monitoring device for a business index according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions in the embodiments of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
Random faults may affect services in different dimensions to different extents, for example, a part of service data is greatly abnormal and cannot work normally, a part of services are affected moderately, and can continue to work but have reduced effect, and another part of services have little effect. Services often have different service indicators in different dimensions, each having respective characteristics. The monitoring flow of the corresponding business index is specially configured for each business index, the workload of the configuration process is large, and redundant operation is more. In order to overcome the problem, the embodiment of the application provides an anomaly monitoring method for service indexes.
Fig. 1 is a process flow diagram of a method for monitoring abnormality of a business index according to an embodiment of the present application. The abnormality monitoring method of the business index of fig. 1 may be performed by an abnormality monitoring device of the business index, which may be a terminal device, such as a mobile phone, a notebook computer, an intelligent interaction device, etc.; alternatively, the anomaly monitoring device of the traffic index may also be a server, such as a stand-alone physical server, a server cluster, or a cloud server capable of cloud computing. The method for monitoring the abnormality of the business index provided in this embodiment specifically includes steps S102 to S110:
Step S102, acquiring an index element set corresponding to a service index of a service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on the business index generation rule and the buried data of the business to be monitored.
The embedded point is a record of user behavior data, based on business or product requirements, relevant codes are implanted into pages, positions, attributes and the like corresponding to each event of the user generating behavior in the product, statistics are reported through an acquisition tool, the acquired data can be used for analyzing the use condition of websites/Application programs (APP), the use habit of the user and the like, and a series of data products such as user portraits, user preferences, conversion paths and the like are extended.
The embedded point SDK (software development kit, data software development kit) is a dependent packet which is used by a service system and can perform standard data output at a key service node, and the service system needs to add the dependent packet into a core service code, so that the embedded point data is constructed in the process of operating the core service code. The core service code is a code which can be used for executing each preset service task when the service system runs.
The service to be monitored can be any service with monitoring requirements. The buried point data of the service to be monitored may refer to user data collected by an abnormal monitoring device of the service index during operation of the service to be monitored, which may reflect user behaviors related to the service to be monitored. For example, the service to be monitored may be an application form service, and the buried data includes, but is not limited to: the creation time of the application form, the application form number used to uniquely identify the application form, the approval start time and the approval end time of the application form, the approval result of the application form, and the like.
In specific implementation, a corresponding buried point data code can be added into a core service code of a service to be monitored by operating the buried point SDK. Furthermore, when the service to be monitored runs, namely, when the core service code added with the embedded point data code runs, the abnormal monitoring equipment of the service index can collect the embedded point data of the service to be monitored and output the embedded point data to the standard log catalog in a log form. And then, collecting the buried point logs through a log collecting component and transmitting the buried point logs to a distributed message system.
The log collection component may be a filecoat component, which is a lightweight log collection component and has the characteristics of robust performance, breakpoint recovery, concurrent reading, and multi-channel support. The distributed messaging system may be a kafka system, which is a distributed, partitioned, multi-copy, multi-subscriber distributed messaging system; the method has the characteristics of high throughput, low delay, expandability and high concurrency. The kafka system supports hundred thousand level message read-write and millisecond level delay; the message is persisted to the local disk and supports data backup to prevent data loss; allowing nodes in the cluster to fail and support thousands of clients to read and write simultaneously.
Furthermore, the abnormality monitoring device of the service index can read the buried point data of the service to be monitored from the buried point log through the distributed message system.
The service data is buried, different service scenes can be visualized in a rule configuration mode, and the data of the service indexes can be used for scenes such as alarm, operation analysis and the like. The embedded point data can be reused by adopting a mode of separating the embedded point from index calculation, and the business index can be calculated jointly with other embedded point data, so that the invasiveness to a business system is reduced.
In particular implementations, each service may be preconfigured with a corresponding service indicator generation rule that specifies how to generate a service indicator that may reflect a service condition of the corresponding service based on the buried data. The number of the service indexes of each service may be one or more. The business index generation rule can be applied to a business index monitoring system of a corresponding business.
For example, assume that the service to be monitored is an application form service, and the core service is represented by an application form quantity, an application form approval passing rate and an application form approval duration. And then the total index can be configured for the embedded points of the received application form, the passing rate index can be configured for the number of the embedded points of the examination and approval result and the embedded points of the received application form, the examination and approval duration index can be configured for the time of the embedded points of the examination and approval result and the embedded points of the received application form, and then the indexes are associated with the corresponding service domains in the service index monitoring system.
Furthermore, the abnormality monitoring device of the service index can read the buried point data of the service to be monitored from the buried point log through the distributed message system based on each service index configured in the service index generation rule; the buried data of the service to be monitored includes parameter values of the buried data parameters.
After the buried data is read, the abnormality monitoring device of the business index may generate a business index value of each business index according to the buried data and the business index generation rule.
In particularFor each index element, if the generation time is closer to the current time point, the position of the index element in the index element set is more forward; if the generating time is farther from the current time point, the position of the index element in the index element set is more backward. For example, the service to be monitored is an approval business, the embedded data of the service to be monitored includes approval result parameters of the application form, and the approval result parameters include n parameter values: approval result 1, approval result 2, approval result 3 … …, approval result n. The n parameter values are sequentially arranged according to the order of the generation time of the parameter values, the approval result 1 is the first approval result with the earliest generation time in the n parameter values, the generation time of the approval result 2 is located after the approval result 1, the generation time of the approval result 3 is located after the generation time of the approval result 2, and the generation time of the approval result n … … is located after the generation time of the approval result n-1. Based on the n parameter values of the approval result parameter and the approval passing rate calculation rule in the business index generation rule, m business index values of the business index approval passing rate can be calculated: s is S 1 、S 2 、S 3 ……S m-1 S and S m . The m business index values are sequentially arranged according to the sequence of the generation time of the business index values, S 1 For the first business index value S generated earliest 2 The generation time of (2) is at S 1 Thereafter, S 3 The generation time of (2) is at S 2 After that … … S m The generation time of (2) is at S m-1 After that, the process is performed. n and m may be any natural number.
After generating the plurality of business index values of each business index, each business index value may be determined as one index element to form an index element set including a plurality of index elements sequentially arranged in order of generation time.
Sequentially arranged according to the order of the generation time, may be a first arrangement order: the farther the generation time is from the current time point, the smaller the sequence number of the index element in the index element set is, the closer the generation time is from the current time point, and the larger the sequence number of the index element in the index element set is; the second arrangement may also be: the closer the generation time is to the current time point, the smaller the sequence number of the index element in the index element set is, and the farther the generation time is to the current time point, the larger the sequence number of the index element in the index element set is.
The "first" and "second" in the first arrangement order and the second arrangement order are merely for convenience in distinguishing two different orders, and do not have a practical meaning.
The index element set appearing hereinafter will be described collectively by taking the second arrangement order as an example. The embodiment of the first arrangement sequence may refer to the corresponding description part in the embodiment of the second arrangement sequence, which is not repeated.
The execution main body for executing the generated index element set can be an abnormality monitoring device of the business index or other electronic devices.
In one embodiment, before the step S102 is performed, the anomaly monitoring device or other electronic device for the traffic index may perform generating the index element set based on the traffic index generation rule and the buried data of the traffic to be monitored and store the generated index element set in the preset storage space. The obtaining of the index element set corresponding to the service index of the service to be monitored may be reading the index element set from a preset storage space.
In connection with this embodiment, step S102 may be replaced by reading the index element set from the preset storage space. Alternatively, step S102 may be replaced with: generating an index element set based on the service index generation rule and the buried point data of the service to be monitored and storing the index element set in a preset storage space; the index element set is read from a preset storage space.
In another embodiment, the obtaining of the index element set corresponding to the service index of the service to be monitored may be that the anomaly monitoring device of the service index performs the generation of the index element set based on the service index generation rule and the buried data of the service to be monitored. The steps in this embodiment may replace step S102.
In specific implementation, the buried point data may be read from the buried point data, where the buried point data includes a plurality of parameter values of the buried point parameter, and the plurality of parameter values are sequentially arranged according to the order of reading time; dividing the plurality of parameter values into a plurality of parameter value sets, each parameter value set comprising at least one parameter value; and generating corresponding index elements according to each parameter value group and the business index generation rule to form an index element set comprising a plurality of index elements which are sequentially arranged according to the sequence of the generation time.
In addition, in the implementation, considering that the service to be monitored can be continuously operated for a long time, the index element set can be continuously expanded in the operation process of the service to be detected, so that the number of index elements included in the index element set is larger than the first preset number of index elements required for meeting the abnormal monitoring requirement. The obtaining the index element set corresponding to the service index of the service to be monitored may be obtaining an index element subset formed by a second preset number of index elements in the index element set, where the index element subset includes a second preset number of index elements with generation time close to the current time point, and the second preset number of index elements are sequentially arranged according to the sequence of the generation time. The second preset number may be equal to the first preset number, and the second preset number may be greater than the first preset number. The terms "first" and "second" are used herein only for convenience in distinguishing between different amounts of action and not as a practical meaning.
In one embodiment, the anomaly monitoring device of the traffic index may generate an index element set by a link execution based on the traffic index generation rule and the buried data, calculate a new index element per minute, and then store the new index element in es (elasticsearch) and add it to the index element set.
The link is an open source stream processing framework developed by ASF (Apache Software Foundation ), the core of which is a distributed stream data stream engine written in Java and Scala. The flink executes any stream data program in a data parallel and pipeline manner, and the pipeline runtime system of the flink can execute batch processing and stream processing programs. Furthermore, the runtime itself of the flink also supports the execution of iterative algorithms.
The es is an open-source search and data analysis engine, and is also a distributed real-time document storage and full-text search engine; can be used for expanding hundreds of service nodes and supporting structured or unstructured data of PB level.
Step S104, dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is located after the generation time of the last index element in the sequence to be detected. The sequence to be detected may be a sequence of index elements for detecting whether the traffic index is abnormal. The sequence to be detected may include generating a preset number of index elements whose time is close to the current time point, i.e., the latest preset number of index elements. The preset number may be 5 or other values.
The index elements in the sequence to be detected are sequentially arranged according to the sequence of the generation time. Specifically, for each index element, if the generation time is closer to the current time point, the position of the index element in the sequence to be detected is more forward; the farther the generation time is from the current time point, the later the position of the index element in the sequence to be detected is.
The sample sequence may be a sequence of index elements from which a business index sample may be extracted. The sample sequence may include a plurality of index elements sequentially arranged in order of generation time. Specifically, for each index element, if the generation time is closer to the current time point, the position of the index element in the sample sequence is more advanced; the farther the generation time is from the current point in time, the later the position of the index element in the sample sequence.
The generation time of the first index element in the sample sequence may be located after the generation time of the last index element in the sequence to be detected.
Each index element in the index element set may correspond to a sequence number, for example: the index element set includes: a, a 1 ，a 2 ，a 3 ，a 4 ，……a L L is the number of index elements included in the index element set, and L can be any natural number. The index element sets are arranged according to a second arrangement sequence: raw materials The closer the time is to the current time point, the smaller the sequence number of the index element in the index element set, and the farther the time is to the current time point, the larger the sequence number of the index element in the index element set. a, a 1 Is numbered 1, a 1 Is numbered 1, a 2 Is numbered 2, a 3 Is numbered 3 … … a L Is given the number L.
The dividing the index element set into the sequence to be detected and the sample sequence may be determining a target sequence number in the index element set based on a preset number, and dividing the index element set into the sequence to be detected and the sample sequence according to the target sequence number. For example, if the preset number is 5, the target sequence number may be determined to be 5 according to the preset number, and the index element set is divided into two subsets according to the target sequence number 5: the first subset includes a 1 ，a 2 ，a 3 ，a 4 ，a 5 The first subset may be determined to be the sequence to be detected; the second subset includes a 6 ，a 7 ，a 8 ……a L The second subset may be determined as a sequence of samples.
Step S106, dividing the sample sequence into a plurality of first subsequences according to the first time period.
Illustratively, the sample sequence may include data for 28 days closest to the current point in time. The sample sequence may be a sample sequence corresponding to a core service indicator of the service to be monitored, where each indicator element in the sample sequence is an indicator value of the core service indicator. The first time period may be 7 days. The sample sequence is divided into a plurality of first sub-sequences according to the first time period, and may be that the sample sequence including index elements corresponding to 28 days is divided into 4 first sub-sequences, each of which includes a plurality of index elements corresponding to 7 days.
The time length corresponding to each first sub-sequence obtained by dividing is the same, and the time length is determined by the total time length corresponding to the sample sequence and the first time period. The number of index elements included in each first sub-sequence obtained through division is equal. There is no intervening index element between two adjacent first sub-sequences, e.g., the last index element of a first sub-sequence is adjacent to the first index element of a second first sub-sequence.
Step S108, processing for determining the index type is performed based on the plurality of first sub-sequences, and the index type of the sample sequence is obtained.
The traffic index may be divided into a periodic index and a volatility index according to the index's characteristics with respect to time. The index type of the sample sequence may be a periodic index or a fluctuation index.
For example, the index calculated by the statistics count includes that the application form amount, the cash amount and the like are all a periodic sequence, and 08:00-20 in the daytime of working days: the difference between 00 and 20:00-the next night 08:00 is significant and related to human work and rest time, the values generally increase from 08:00 and to around 11:00 to peak, then decrease, increase from 13:00 and to around 17:00 to peak, then decrease, and night continue to decrease and to around 04:00 to valley, appearing as a periodic sequence that varies daily; the power and time length indexes are not obviously related to time and are only influenced by the capacity of a service system, so that the power and time length indexes are expressed as a fluctuation sequence.
The process of determining the index type may be a process of obtaining the index type of the sample sequence.
Optionally, the index category of the sample sequence includes one of a periodicity index and a volatility index; performing a process of determining an index type based on the plurality of first sub-sequences to obtain an index type of the sample sequence, including: generating a differential sequence corresponding to each first subsequence according to the difference value between every two adjacent index elements in each first subsequence; calculating the standard deviation of the differential sequence corresponding to each first subsequence; determining standard deviation parameters of the sample sequence according to the standard deviation of the differential sequence corresponding to each first subsequence; if the standard deviation parameter is larger than a preset threshold value, determining the index type of the sample sequence as a periodic index; and if the standard deviation is smaller than or equal to the prediction threshold value, determining the index type of the sample sequence as the fluctuation index.
And generating a differential sequence corresponding to each first subsequence according to the difference value between two index elements adjacently arranged in each first subsequence.
For example, the first subsequence includes x 1 ,x 2 ……x a The differential sequence corresponding to the first sub-sequence is: x is x 1 -x 2 ,x 2 -x 3 ……x a-1 -x a 。
After the differential sequence corresponding to each first sub-sequence is obtained, calculating the standard deviation of the differential sequence corresponding to each first sub-sequence. The standard deviation can well reflect the discrete degree of the data, so that the degree of the sequence change in one period can be judged according to the standard deviation, the smaller the standard deviation is, the smaller the fluctuation of the first subsequence is, and on the contrary, the larger the fluctuation of the first subsequence is.
And determining the standard deviation parameter of the sample sequence according to the standard deviation of each differential sequence, wherein the standard deviation parameter of the sample sequence can be obtained by averaging one of the standard deviations, the median and the highest value of the differential sequence corresponding to each first subsequence.
Illustratively, the preset threshold may be 0.005.
Judging whether the standard deviation parameter is larger than a preset threshold value or not: if yes, determining the index type of the sample sequence as a periodic index; if not, determining the index type of the sample sequence as the fluctuation index.
By calculating the differential sequence corresponding to each first sub-sequence and calculating the standard deviation of each differential sequence, whether the fluctuation of each first sub-sequence is severe or not can be determined, and by calculating the standard deviation parameter of the sample sequence, the magnitude of the sequence variation degree of the whole sample sequence can be determined. If the numerical value of the standard deviation parameter is larger, the sequence change degree of the whole sample sequence is larger, and the index type of the sample sequence can be determined to be a periodic index under the condition; if the value of the standard deviation parameter is smaller, the sequence variation degree of the whole sample sequence is smaller, and the index type of the sample sequence can be determined to be a fluctuation index in the case. Accordingly, the index type of the sample sequence can be determined by comparing the standard deviation parameter with the preset threshold value.
In a specific implementation, generating a corresponding differential sequence according to each first sub-sequence in the plurality of first sub-sequences includes: normalizing the index elements in each first subsequence to obtain normalized first subsequences; and generating a differential sequence corresponding to the normalized first subsequence according to the difference value between any two index elements adjacently arranged in each normalized first subsequence.
Illustratively, a first subsequence includes x 1 ,x 2 ……x a A index elements are used. Normalizing the index elements in the first subsequence, which may be, for example, x 1 ,x 2 ……x a And (3) summing the element values to obtain A, and solving the ratio of each index element in the first subsequence to the A to obtain the normalized index element. The normalized first sub-sequence includes x 1 /A,x 2 /A……x a /A。
Through normalization processing, index elements in the first subsequence can be converted into a numerical value interval [0,1], but the original change trend is kept unchanged, and the data processing difficulty of the step of calculating the differential sequence can be reduced.
And generating a differential sequence corresponding to the normalized first subsequence according to the difference value between any two index elements adjacently arranged in each normalized first subsequence.
For example, the normalized first subsequence includes x 1 /A,x 2 /A……x a and/A, the differential sequence corresponding to the first subsequence is: x is x 1 /A-x 2 /A,x 2 /A–x 3 /A……x a-1 /A-x a /A。
The index value ranges of different business indexes are different, and by carrying out normalization processing on each first subsequence, the influence of the index value ranges on the calculation of standard deviation parameters can be reduced, and the data processing difficulty is reduced.
Step S110, generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
The abnormal parameter of the sample sequence may be a parameter for reflecting whether a service index of the service to be detected corresponding to the sample sequence is abnormal.
The abnormal monitoring result of the service to be detected may be a detection result of whether an abnormality occurs in a service index of the service to be detected. The outputting of the abnormal monitoring result of the service to be detected based on the abnormal parameter of the sample sequence may be determining and outputting the abnormal monitoring result of the service to be detected based on the comparison result of the abnormal parameter of the sample sequence and the preset abnormal threshold value.
Optionally, generating the abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence includes: if the index type is a periodic index, performing interval sampling processing on the sample sequence according to a second time period to obtain a plurality of second subsequences arranged according to the sequence of the generation time; the number of index elements included in each second sub-sequence is a preset number; determining a parity element set of each index element in the sequence to be detected, wherein the parity element set of any index element comprises the parity element corresponding to any index element determined from each second subsequence, and the parity element corresponding to any index element in any second subsequence refers to the index element with the same sequence number as that of any index element in any second subsequence; the sequence number of each index element in each second subsequence and the sequence number of each index element in the sequence to be detected are determined according to the generated time sequence; and generating abnormal parameters of the sample sequence based on the parity element set of each index element in the sequence to be detected.
The second time period may be 7 days, a month, or other predetermined time period, for example. The first time period and the second time period may have the same or different time periods, and there is no direct connection between the two, and the first and second are merely time periods that are used in two different steps. The first time period is used for dividing the sample sequence into a plurality of first sub-sequences, no interval exists between the adjacent first sub-sequences, the second time period is used for carrying out interval sampling processing on the sample sequence, the obtained second sub-sequences are provided with index elements of the interval between the adjacent second sub-sequences.
If the index type is a periodic index, performing interval sampling processing on the sample sequence according to a second time period to obtain a plurality of second subsequences arranged according to the sequence of the generation time, wherein the method specifically comprises the following steps:
firstly, dividing a sample sequence into a plurality of sub-sequences to be processed according to a second time period, wherein the time length corresponding to each sub-sequence to be processed is the same, and the time length is determined by the total time length corresponding to the sample sequence and the second time period. And the number of index elements included in each sub-sequence to be processed obtained through dividing is equal. There is no index element of the interval between two adjacent sub-sequences to be processed, e.g. the last index element of the first sub-sequence to be processed is adjacent to the first index element of the second sub-sequence to be processed.
And then, in each sub-sequence to be processed, generating a second sub-sequence corresponding to each sub-sequence to be processed based on the last k index elements of the sub-sequence to be processed, wherein k is greater than 1 and less than the number of index elements included in the sub-sequence to be processed and is used for indicating the number of index elements included in each second sub-sequence, namely the preset number.
For example, the sample sequence comprises 3 sub-sequences to be processed, each second sub-sequence comprising 10080 index elements. The sub-sequence 1 to be processed comprises the 1 st to 10080 th index elements, the sub-sequence 2 to be processed comprises the 10081 st to 20160 th index elements, and the sub-sequence 3 to be processed comprises the 20161 th to 30240 th index elements. And k=5, the second subsequence corresponding to the sub-sequence 1 to be processed comprises 10076-10080 index elements, the second subsequence corresponding to the sub-sequence 2 to be processed comprises 20156-20160 index elements, and the second subsequence corresponding to the sub-sequence 3 to be processed comprises 30236-30240 index elements. Of the plurality of second sub-sequences arranged in the order of generation time, there are 10080 index elements between two second sub-sequences arranged adjacently, and the 10080 index elements are interval index elements between the two second sub-sequences.
Or generating a second sub-sequence corresponding to each sub-sequence to be processed based on the first k index elements of the sub-sequence to be processed in each sub-sequence to be processed.
For example, the sample sequence comprises 3 sub-sequences to be processed, each second sub-sequence comprising 10080 index elements. The sub-sequence 1 to be processed comprises the 1 st to 10080 th index elements, the sub-sequence 2 to be processed comprises the 10081 st to 20160 th index elements, and the sub-sequence 3 to be processed comprises the 20161 th to 30240 th index elements. And k=5, the second subsequence corresponding to the sub-sequence 1 to be processed comprises the 1 st to 5 th index elements, the second subsequence corresponding to the sub-sequence 2 to be processed comprises the 10081 st to 10085 th index elements, and the second subsequence corresponding to the sub-sequence 3 to be processed comprises the 20161 th to 201665 th index elements. Of the plurality of second sub-sequences arranged in the order of generation time, there are 10080 index elements between two second sub-sequences arranged adjacently, and the 10080 index elements are interval index elements between the two second sub-sequences.
Or in each sub-sequence to be processed, generating a second sub-sequence corresponding to each sub-sequence to be processed based on a preset data selection rule and k.
The preset data selection rule may be configured with a sequence number of the first index element in each second sub-sequence in the corresponding sub-sequence to be processed, and further may generate a second sub-sequence corresponding to each sub-sequence to be processed according to the sequence number and k.
The number of index elements included in the sequence to be detected is the same as the number of index elements included in each second subsequence, and the number of index elements is the preset number. The sequence to be detected and the sample sequence can be adjacent, or the index elements of the interval can exist. In addition, the number of interval index elements between the sequence to be detected and the first and second sub-sequences may be the same as the number of interval index elements between the adjacent two second sub-sequences.
For example, the sample sequence comprises 4 second sub-sequences, the sequence to be detected comprising a number of index elements of 5Each second sub-sequence comprises 5 index elements. The sequence to be detected is separated from the first second subsequence by 10080 elements, the sequence to be detected is separated from the second subsequence by 20160 elements, the sequence to be detected is separated from the third second subsequence by 30240 elements, and the sequence to be detected is separated from the fourth subsequence by 40320 elements. The sequence number of each index element in the sequence to be detected is determined by the sequence of the generation time, for example, the sequence to be detected comprises x 1 ,x 2 ,x 3 ,x 4 ,x 5 X is then 1 Is numbered 1, x 2 Is numbered 2, x 3 Is numbered 3, x 4 Is numbered 4, x 5 Is numbered 5.
The sequence number of each index element in each second sub-sequence is determined by the order of the generation times. For example, a second sub-sequence comprises x 201 ,x 202 ,x 203 ,x 204 ,x 205 X is then 201 Is numbered 1, x 202 Is numbered 2, x 203 Is numbered 3, x 204 Is numbered 4, x 205 Is numbered 5.
And determining a parity element set of the b index element in the sequence to be detected, wherein the parity element set can comprise parity elements corresponding to the b index element in the sequence to be detected, which are determined from each second subsequence. The parity element corresponding to the b-th index element in the sequence to be detected in each second subsequence refers to the b-th index element in each second subsequence.
For example, the sequences to be detected include: x is x 1 ,x 2 ,x 3 ,x 4 ,x 5 The sample sequence comprises a second sub-sequence 1 and a second sub-sequence 2, the second sub-sequence 1 comprising: x is x 201 ,x 202 ,x 203 ,x 204 ,x 205 The second subsequence 2 includes: x is x 401 ,x 402 ,x 403 ,x 404 ,x 405 。x 1 、x 201 X 401 And the serial numbers of the two are 1, x can be determined 1 Comprises x 201 And x 401 ；x 2 、x 202 X 402 And the serial numbers of the two are 2, x can be determined 2 Comprises x 202 And x 402 ；x 3 、x 203 X 403 And the serial numbers of the two are 3, x can be determined 3 Comprises x 203 And x 403 ；x 4 、x 204 X 404 And the serial numbers of the two are all 4, x can be determined 4 Comprises x 204 And x 404 ；x 5 、x 205 X 405 And the number of (2) is 5, x can be determined 5 Comprises x 205 And x 405 。
Optionally, generating the abnormal parameter of the sample sequence based on the set of parity elements of each index element in the sequence to be detected includes: determining a cyclic ratio value of each index element in the sequence to be detected based on each index element in the set of parity elements of each index element in the sequence to be detected and each index element in the sequence to be detected; summing the ring ratio value of each index element in the sequence to be detected to obtain the abnormal parameters of the sample sequence.
Determining a cyclic ratio value of each index element in the sequence to be detected based on each index element in the set of index elements of each index element in the sequence to be detected and each index element in the sequence to be detected, wherein the cyclic ratio value of each index element in the sequence to be detected and each index element in the set of index elements of each index element can be calculated respectively to obtain a plurality of parity ratio values of the index element, the number of the parity ratio values is equal to the number of the parity elements in the set of parity elements, and each parity ratio value corresponds to one parity element; then, the average value of the plurality of the same-ratio values of the index element is obtained by carrying out average operation on the plurality of the same-ratio values of the index element, and the average value is determined as the ring ratio value of the index element. After the ring ratio value of each index element in the sequence to be detected is calculated, the ring ratio values can be summed to obtain the abnormal parameters of the sample sequence.
The ring ratio value may be used to determine whether the traffic indicator, the indicator type of which is a periodic indicator, is abnormal. By summing the ring ratio values of each index element in the sequence to be detected, the influence of the calculation errors of the ring ratio values of the individual index elements on the abnormal parameters can be reduced. Optionally, the sequence to be detected includes N index elements; the number of the plurality of second subsequences is M; the sequence to be detected comprises index elements with the sequence number of i, and the ring ratio value of the index elements with the sequence number of i is determined based on each index element with the sequence number of i and each index element with the sequence number of i in a parity element set of index elements with the sequence number of i, wherein i is more than 0 and less than or equal to N, and the method comprises the following steps: calculating the ratio between the index element with the sequence number i in the sequence to be detected and each parity element in the parity element set corresponding to the index element with the sequence number i to obtain M parity ratio values; m is a positive integer; and carrying out average operation on the M same-ratio values to obtain the ring ratio value of the index element with the sequence number i in the sequence to be detected.
And if the number of the plurality of second subsequences is M, the set of parity elements corresponding to each index element in the sequence to be detected comprises M parity elements. M is a positive integer.
Calculating the ratio of index element with the sequence number i in the sequence to be detected to each parity element in the parity element set corresponding to the index element with the sequence number i to obtain M parity ratio values, for example, calculating index element x with the sequence number 1 in the sequence to be detected 1 Index element x with sequence number 1 in first and second subsequences 201 To obtain a first ratio value x 1 /x 201 … … calculating index element x with sequence number 1 in sequence to be detected 1 Index element x with sequence number 1 in Mth second subsequence z Obtain the M-th homonymous ratio value x 1 /x z Where Z is a natural number, the value of Z may be determined by the value of M, e.g., where M is 2, then z=m×200+1=401, so the 2 nd odds ratio value is x 1 /x 401 。
And carrying out average operation on the M identical ratio values, namely calculating the average value of the M identical ratio values, and obtaining the ring ratio value of the index element with the sequence number i in the sequence to be detected.
Optionally, generating the abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence includes: if the index type is a fluctuation index, calculating the standard deviation of the sample sequence and the average value of the sample sequence based on the numerical values of a plurality of index elements in the sample sequence; and generating abnormal parameters of the sample sequence according to the standard deviation of the sample sequence, the average value of the sample sequence and each index element in the sequence to be detected.
If the index type is a fluctuation index, calculating the standard deviation of the sample sequence according to the element values of all index elements in the sample sequence, and calculating the average value of the sample sequence.
Furthermore, according to the standard deviation of the sample sequence and the average value of the sample sequence, whether each index element in the sequence to be detected accords with the normal distribution trend generated by the sample sequence can be determined, if so, the index element closest to the current time point of the service index is indicated to have no abnormality, namely, the service index at the current time point has no abnormality, and if not, the service index is indicated to have an abnormality.
In the implementation, since the magnitude orders of the service indexes are different, for example, the magnitude orders of the passing rate are hundred grades, and the duration indexes are 10 ten thousand grades (millisecond units), the sample sequence can be normalized before calculating the standard deviation of the sample sequence and the average value of the sample sequence. For example, the sample sequence may be normalized using a Z-score normalization method.
The Z-Score is standardized, two or more groups of data can be converted into a unitless Z-Score value through a calculation formula (element value-average value)/variance), so that the data standards are unified, the data comparability is improved, and the data interpretation is weakened.
Optionally, generating the abnormal parameter of the sample sequence according to the standard deviation of the sample sequence, the average value of the sample sequence and each index element in the sequence to be detected includes: calculating a first ratio corresponding to each index element in the sequence to be detected based on the average difference between each index element in the sequence to be detected and the sample sequence and the standard deviation of the sample sequence; and carrying out average calculation on the first ratio corresponding to each index element in the sequence to be detected to obtain the abnormal parameters of the sample sequence.
In the specific implementation, carrying out a difference operation on the average value of each index element in the sequence to be detected and the sample sequence to obtain a first difference value corresponding to each index element; and calculating the ratio of the absolute value of the first difference value corresponding to each index element to the standard deviation of the sample sequence to obtain the first ratio corresponding to each index element. And carrying out average calculation on the first ratio corresponding to each index element in the sequence to be detected to obtain the abnormal parameters of the sample sequence.
Optionally, the sequence to be detected includes N index elements; the sequence to be detected comprises index elements with the sequence number j, and a first ratio corresponding to the index elements with the sequence number j is calculated based on the average difference between the index elements with the sequence number j in the sequence to be detected and the sample sequence and the standard deviation of the sample sequence, and the method comprises the following steps: performing difference calculation on index elements with the sequence number of j in the sequence to be detected and an average value of the sample sequence to obtain a first difference value corresponding to the index elements with the sequence number of j; j is greater than 0 and less than or equal to N; and calculating the ratio of the absolute value of the first difference value corresponding to the index element with the sequence number j to the standard deviation of the sample sequence to obtain the first ratio corresponding to the index element with the sequence number j.
For example, the average value of the 1 st index element in the sequence to be detected and the sample sequence is subjected to difference to obtain a first difference value corresponding to the 1 st index element, the absolute value of the first difference value corresponding to the 1 st index element is calculated, and then the ratio of the absolute value to the standard deviation of the sample sequence is calculated to obtain a first ratio corresponding to the 1 st index element. The first ratio … … of the 2 nd index element in the sequence to be detected is obtained by the method, the first ratio of the N th index element in the sequence to be detected is obtained by the method, and the N first ratios are averaged to obtain the abnormal parameters of the sample sequence. The anomaly parameter can be used to reflect the degree of anomaly of the traffic indicator whose indicator type is a volatility indicator.
The data abnormality of the business index is analyzed through an algorithm, so that manual intervention can be reduced. The abnormality monitoring equipment of each minute of service index is used for analyzing whether the current latest element index calculates abnormality or not, and monitoring staff can timely obtain index abnormality data when a fault occurs, so as to judge the influence range of the fault.
Optionally, outputting an anomaly monitoring result of the service to be detected based on the anomaly parameter of the sample sequence includes: determining an abnormal grade to which the business index to be monitored belongs according to a comparison result of the abnormal parameter and a preset parameter threshold; and displaying the business index to be monitored according to the alarm color corresponding to the abnormal grade.
Determining an abnormal grade to which the business index to be monitored belongs according to a comparison result of the abnormal parameter and a preset parameter threshold; and displaying the business index to be monitored according to the alarm color corresponding to the abnormal grade.
In specific implementation, the absolute value of the abnormal parameter can be calculated, and then the abnormal grade of the business index to be monitored is determined according to the comparison result of the absolute value of the abnormal parameter and one or more preset thresholds.
In one embodiment, each anomaly level may be preconfigured with a corresponding alert color. After determining the abnormal grade to which the business index to be monitored belongs, the business index to be monitored can be displayed according to the alarm color corresponding to the abnormal grade, for example, the background color of the area for displaying the business index of the business to be monitored in the index monitoring page is the alarm color corresponding to the abnormal grade.
For example, when the absolute value of the anomaly parameter is smaller than 3, it may be determined that the anomaly level is 1 level, the traffic index of the traffic to be monitored on the index monitoring page is marked blue, when the absolute value of the anomaly parameter is greater than or equal to 3 and smaller than 6, it may be determined that the anomaly level is 2 level, the traffic index of the traffic to be monitored on the index monitoring page is marked yellow, when the absolute value of the anomaly parameter is greater than 6, it may be determined that the anomaly level is 3 level, and the traffic index of the traffic to be monitored on the index monitoring page is marked red.
Furthermore, when a plurality of business indexes of the business to be monitored are monitored through the abnormal monitoring method of the business indexes provided by the embodiment of the application and at least one business index is in fault, the background colors of the areas corresponding to the business indexes on the index monitoring page are different, so that the influence range of the current fault to the business to be monitored in different dimensions can be intuitively displayed before monitoring workers, time is saved, and the visual effect is obvious.
In other embodiments, each anomaly level may be preconfigured with a display mode. In each display mode, the area of the index monitoring page for displaying the corresponding service index may present a background color, or a blinking display effect, or other preconfigured display effects.
For example, when the absolute value of the abnormal parameter is smaller than 3, the abnormal grade may be determined to be 1 grade, the area of the index monitoring page, where the service index of the service is to be monitored, may be blue, when the absolute value of the abnormal parameter is greater than or equal to 3 and smaller than 6, the abnormal grade may be determined to be 2 grade, the area of the index monitoring page, where the service index of the service is to be monitored, may be determined to be 3 grade, when the absolute value of the abnormal parameter is greater than 6, the area of the index monitoring page, where the service index of the service is to be monitored, may be determined to be 3 grade, and the alarm animation may be played.
Furthermore, when a plurality of business indexes of the business to be monitored are monitored through the abnormal monitoring method of the business indexes provided by the embodiment of the application and at least one business index is in fault, the display effect of the area corresponding to each business index on the index monitoring page is different, the influence range of the current fault to the business to be monitored in different dimensions can be intuitively displayed in front of monitoring staff, time is saved, and the visual effect is obvious. In addition, the accurate influence range of faults on the business indexes of different dimensions is intuitively presented, and the maintenance sequence is determined, for example, the maintenance work of the business indexes with higher abnormal grades is preferentially processed. In addition, after obtaining the abnormal grade to which the business index to be monitored belongs, an alarm rule can be configured in advance, and if the alarm rule is triggered according to the abnormal grade, alarm information corresponding to the abnormal grade is generated to prompt the monitoring staff to correspond to the abnormal condition of the business index.
In addition, the abnormality monitoring method of the business index provided by the embodiment of the application is not only applied to an abnormality monitoring scene, but also applied to a business operation analysis scene. In the service operation analysis scene, the method for monitoring the abnormality of the service index may further include the following steps: displaying one or more of the following parameters for each of a plurality of business metrics on a metrics analysis page: the sequence to be detected, an element value average value of a plurality of index elements included in the sequence to be detected, a sum of ring ratio values of each index element in the sequence to be detected, and the like.
In another embodiment, the sample sequence may be labeled to generate sample data, and the One-Class SVM model is trained based on the sample data, so that the trained One-Class SVM model may be used for performing anomaly detection processing on a service indicator of a service to be monitored, to obtain an anomaly parameter of the service indicator, so as to determine whether the service indicator is abnormal.
In the embodiment of the anomaly monitoring method for service indicators shown in fig. 1, firstly, an indicator element set corresponding to a service indicator of a service to be monitored is obtained; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and buried data of a business to be monitored; secondly, dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected; then, dividing the sample sequence into a plurality of first subsequences according to a first time period; then, the method is used for carrying out processing for determining the index types based on the plurality of first subsequences to obtain the index types of the sample sequence; and finally, generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence. Therefore, after the acquired index element set corresponding to the service index of the service to be monitored is divided into the sequence to be detected and the sample sequence, and the sample sequence is divided into a plurality of first subsequences, the index types of the service index of the service to be monitored can be determined through the characteristics of the first subsequences, so that the abnormal parameters corresponding to the sample sequence are generated by combining the index types under the condition that the index types are different, and the abnormal parameters of the sample sequence are generated according to the index types, the sequence to be detected and the sample sequence by determining the processing of the index types, the configuration workload of the monitoring flow of the service index is greatly reduced without configuring a special monitoring flow for each service index, the monitoring workload of each service index of the service to be monitored is reduced, and the generation modes of the service indexes of the same index type are the same, so that the abnormal conditions of each service index are favorably compared transversely.
The embodiment of the application also provides another embodiment of the abnormality monitoring method of the business index, which is the same technical conception as the embodiment of the method. Fig. 2 is a schematic frame diagram of a method for monitoring abnormality of a traffic index according to an embodiment of the present application.
Referring to fig. 2, a distributed messaging system 201 reads embedded point data from embedded point logs of multiple business scenarios. The plurality of traffic scenarios includes traffic scenario 1, traffic scenario 2, and traffic scenario 3. After the buried data is obtained, index calculation is performed based on the buried data, a business index value of a business index of a business to be monitored is obtained, and the business index value is stored in the search server 202. The distributed messaging system 201 may be a kafka system.
The abnormality monitoring device of the traffic index acquires an index element set from the traffic index value of the traffic index of the traffic to be monitored stored in the search server 202. The search server 202 may be the ES described previously. This step corresponds to step S102 in the embodiment of fig. 1.
After the index element set is acquired, the index element set is divided into a sequence to be detected and a sample sequence. This step corresponds to step S104 in the embodiment of fig. 1.
As shown in fig. 2, algorithm analysis is performed according to the sequence to be detected and the sample sequence, and abnormal parameters of the sequence to be detected are calculated. In the specific implementation, algorithm analysis is performed according to the sequence to be detected and the sample sequence, and the abnormal parameters of the sequence to be detected are obtained through calculation, wherein the sample sequence can be divided into a plurality of first subsequences according to a first time period; performing processing for determining the index type based on the plurality of first subsequences to obtain the index type of the sample sequence; and generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence. After generating the abnormal parameters of the sample sequence, the abnormal monitoring results of the services to be detected may be output based on the abnormal parameters of the sample sequence, and then, each service to be detected is displayed in the index monitoring page 203 in a distinguishing manner based on the abnormal level corresponding to the abnormal monitoring result of each service to be detected. The steps described above correspond to steps S106 to S110 in the embodiment of fig. 1, and reference is made to the corresponding description of fig. 1.
Since the technical conception is the same, the description in this embodiment is relatively simple, and the relevant parts only need to refer to the corresponding descriptions of the method embodiments provided above.
In the foregoing embodiments, a method for monitoring an abnormality of a service indicator is provided, and a device for monitoring an abnormality of a service indicator is provided corresponding to the method, which will be described with reference to the accompanying drawings.
Fig. 3 is a schematic diagram of an abnormality monitoring device for a business index according to an embodiment of the present application.
The present embodiment provides an anomaly monitoring device 300 for a business index, including:
an obtaining unit 301, configured to obtain an index element set corresponding to a service index of a service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and buried data of a business to be monitored;
a first dividing unit 302, configured to divide the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
A second dividing unit 303, configured to divide the sample sequence into a plurality of first sub-sequences according to a first time period;
a processing unit 304, configured to perform a process of determining an index type based on the plurality of first sub-sequences, to obtain an index type of the sample sequence;
and an output unit 305, configured to generate an abnormal parameter of the sample sequence according to the index type, the sequence to be detected, and the sample sequence, and output an abnormal monitoring result of the service to be detected based on the abnormal parameter of the sample sequence.
Optionally, the index category of the sample sequence includes one of a periodicity index and a volatility index; the processing unit 304 performs the following steps when performing the processing of determining the index type based on the plurality of first sub-sequences to obtain the index type of the sample sequence:
generating a differential sequence corresponding to each first subsequence according to the difference value between every two adjacent index elements in each first subsequence;
calculating the standard deviation of the differential sequence corresponding to each first subsequence;
determining standard deviation parameters of the sample sequence according to the standard deviation of the differential sequence corresponding to each first subsequence;
if the standard deviation parameter is larger than a preset threshold value, determining the index type of the sample sequence as a periodic index;
And if the standard deviation is smaller than or equal to the prediction threshold value, determining the index type of the sample sequence as the fluctuation index.
Alternatively, the output unit 305 performs the following steps when generating an abnormal parameter of the sample sequence from the index type, the sequence to be detected, and the sample sequence:
if the index type is the periodic index, performing interval sampling processing on the sample sequence according to a second time period to obtain a plurality of second subsequences arranged according to the sequence of the generation time; the number of index elements included in each second sub-sequence is the preset number;
determining a parity element set of each index element in the sequence to be detected, wherein the parity element set of any index element comprises the parity element corresponding to any index element determined from each second subsequence, and the parity element corresponding to any index element in any second subsequence refers to the index element with the same sequence number as the sequence number of any index element in any second subsequence; the sequence number of each index element in each second subsequence and the sequence number of each index element in the sequence to be detected are determined according to the generated time sequence;
And generating abnormal parameters of the sample sequence based on the parity element set of each index element in the sequence to be detected.
Optionally, the output unit 305 performs the following steps when generating the abnormal parameters of the sample sequence based on the set of parity elements of each index element in the sequence to be detected:
determining a cyclic ratio value of each index element in the sequence to be detected based on each index element in the set of parity elements of each index element in the sequence to be detected and each index element in the sequence to be detected;
and summing the ring ratio value of each index element in the sequence to be detected to obtain the abnormal parameters of the sample sequence.
Optionally, the sequence to be detected includes N index elements; the number of the plurality of second subsequences is M; the sequence to be detected comprises index elements with the sequence number of i; the output unit 305 performs the following steps when determining the ring ratio value of index element with index number i based on each of the index elements with index number i and each of the index elements with index number i in the set of index elements with index number i:
calculating the ratio between the index element with the sequence number i in the sequence to be detected and each parity element in the parity element set corresponding to the index element with the sequence number i to obtain M parity ratio values; m is a positive integer; i is greater than 0 and less than or equal to N;
And carrying out average operation on the M same-ratio values to obtain the ring ratio value of the index element with the sequence number i in the sequence to be detected.
Alternatively, the output unit 305 performs the following steps when generating an abnormal parameter of the sample sequence from the index type, the sequence to be detected, and the sample sequence:
if the index type is the fluctuation index, calculating the standard deviation of the sample sequence and the average value of the sample sequence based on the numerical values of a plurality of index elements in the sample sequence;
and generating abnormal parameters of the sample sequence according to the standard deviation of the sample sequence, the average value of the sample sequence and each index element in the sequence to be detected.
Optionally, the output unit 305 performs the following steps when generating an abnormal parameter of the sample sequence according to a standard deviation of the sample sequence, an average value of the sample sequence, and each index element in the sequence to be detected:
calculating a first ratio corresponding to each index element in the sequence to be detected based on the average difference between each index element in the sequence to be detected and the sample sequence and the standard deviation of the sample sequence;
And carrying out average calculation on a first ratio corresponding to each index element in the sequence to be detected to obtain an abnormal parameter of the sample sequence.
Optionally, the sequence to be detected includes N index elements; the sequence to be detected includes index elements with sequence number j, and the output unit 305 performs the following steps when calculating a first ratio corresponding to the index elements with sequence number j based on an average difference between the index elements with sequence number j in the sequence to be detected and the sample sequence and a standard deviation of the sample sequence:
performing difference calculation on index elements with the sequence number j in the sequence to be detected and the average value of the sample sequence to obtain a first difference value corresponding to the index elements with the sequence number j; j is greater than 0 and less than or equal to N;
and calculating the ratio of the absolute value of the first difference value corresponding to the index element with the sequence number j to the standard deviation of the sample sequence to obtain the first ratio corresponding to the index element with the sequence number j.
Alternatively, the output unit 305 performs the following steps when outputting an abnormality monitoring result for the traffic to be detected based on the abnormality parameter of the sample sequence:
determining an abnormal grade to which the business index to be monitored belongs according to a comparison result of the abnormal parameter and a preset parameter threshold;
And displaying the business index to be monitored according to the alarm color corresponding to the abnormal grade.
The abnormality monitoring device for the business index provided by the embodiment of the application comprises an acquisition unit, a first dividing unit, a second dividing unit, a processing unit and an output unit, wherein the acquisition unit is used for acquiring an index element set corresponding to the business index of the business to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and buried data of a business to be monitored; the first dividing unit is used for dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected; a second dividing unit for dividing the sample sequence into a plurality of first sub-sequences according to the first time period; a processing unit, configured to perform processing for determining an index type based on the plurality of first sub-sequences, to obtain an index type of the sample sequence; the output unit is used for generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence. After the acquired index element set corresponding to the service index of the service to be monitored is divided into the sequence to be detected and the sample sequence, and the sample sequence is divided into a plurality of first subsequences, the index types of the service index of the service to be monitored can be determined through the characteristics of the first subsequences, so that the abnormal parameters corresponding to the sample sequence are generated by combining the index types under the condition that the index types are different, each index type corresponds to one abnormal parameter generation mode by determining the processing of the index types and generating the abnormal parameters of the sample sequence according to the index types, the sequence to be detected and the sample sequence, the configuration workload of the monitoring flow of the service index is greatly reduced, the special monitoring flow is not required to be configured for each service index, the monitoring workload of each service index of the service to be monitored is reduced, the generation modes of the service indexes of the same index type are the same, and the abnormal conditions of each service index are favorably compared transversely.
The embodiment of the present application further provides an abnormality monitoring device for a service indicator, where the abnormality monitoring device for a service indicator is used to execute the abnormality monitoring method for a service indicator provided above, and fig. 4 is a schematic structural diagram of the abnormality monitoring device for a service indicator provided in the embodiment of the present application, where the abnormality monitoring device for a service indicator corresponds to the abnormality monitoring method for a service indicator described above.
As shown in fig. 4, the abnormality monitoring device of the traffic index may have a relatively large difference due to different configurations or performances, and may include one or more processors 401 and a memory 402, where the memory 402 may store one or more storage applications or data. Wherein the memory 402 may be transient storage or persistent storage. The application program stored in memory 402 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in an anomaly monitoring device for a business metric. Still further, the processor 401 may be configured to communicate with the memory 402 to execute a series of computer executable instructions in the memory 402 on the anomaly monitoring device of the traffic index. The anomaly monitoring device of the traffic index may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input/output interfaces 405, one or more keyboards 406, etc.
In one particular embodiment, a business metric anomaly monitoring device includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the business metric anomaly monitoring device, and configured to be executed by one or more processors the one or more programs include computer-executable instructions for:
acquiring an index element set corresponding to a service index of a service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and buried data of a business to be monitored;
dividing an index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
Dividing the sample sequence into a plurality of first subsequences according to a first time period;
performing processing for determining the index type based on the plurality of first subsequences to obtain the index type of the sample sequence;
generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
The embodiment of the application also provides a computer readable storage medium based on the same technical concept, which corresponds to the anomaly monitoring method of the service index.
The computer readable storage medium provided in this embodiment is configured to store computer executable instructions, where the computer executable instructions when executed by a processor implement the following procedures:
acquiring an index element set corresponding to a service index of a service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and buried data of a business to be monitored;
dividing an index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements with the time close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
Dividing the sample sequence into a plurality of first subsequences according to a first time period;
performing processing for determining the index type based on the plurality of first subsequences to obtain the index type of the sample sequence;
generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
It should be noted that, in the present specification, the embodiments of the computer readable storage medium and the embodiments of the anomaly monitoring method for the traffic indicator in the present specification are based on the same inventive concept, so that the specific implementation of the embodiments may refer to the implementation of the corresponding method, and the repetition is omitted.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-readable storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
Embodiments of the application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is by way of example only and is not intended to limit the present disclosure. Various modifications and changes may occur to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. that fall within the spirit and principles of the present document are intended to be included within the scope of the claims of the present document.

Claims (12)

1. The method for monitoring the abnormality of the business index is characterized by comprising the following steps:
acquiring an index element set corresponding to a service index of a service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and the buried point data of the business to be monitored;
dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements, wherein the generation time of the index elements is close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
dividing the sample sequence into a plurality of first sub-sequences according to a first time period;
performing processing for determining the index type based on the plurality of first subsequences to obtain the index type of the sample sequence;
generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
2. The method of claim 1, wherein the index class of the sample sequence comprises one of a periodicity index and a volatility index; the processing for determining the index type based on the plurality of first sub-sequences, to obtain the index type of the sample sequence, includes:
generating a differential sequence corresponding to each first subsequence according to the difference value between every two adjacent index elements in each first subsequence;
calculating the standard deviation of the differential sequence corresponding to each first subsequence;
determining standard deviation parameters of the sample sequence according to standard deviations of the differential sequences corresponding to the first subsequences;
if the standard deviation parameter is larger than a preset threshold, determining the index type of the sample sequence as the periodic index;
and if the standard deviation is smaller than or equal to the prediction threshold value, determining the index type of the sample sequence as the fluctuation index.
3. The method according to claim 2, wherein generating the abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence comprises:
if the index type is the periodic index, performing interval sampling processing on the sample sequence according to a second time period to obtain a plurality of second subsequences arranged according to the sequence of the generation time; the number of index elements included in each second sub-sequence is the preset number;
Determining a parity element set of each index element in the sequence to be detected, wherein the parity element set of any index element comprises the parity element corresponding to any index element determined from each second subsequence, and the parity element corresponding to any index element in any second subsequence refers to the index element with the same sequence number as the sequence number of any index element in any second subsequence; the sequence number of each index element in each second subsequence and the sequence number of each index element in the sequence to be detected are determined according to the generated time sequence;
and generating abnormal parameters of the sample sequence based on the parity element set of each index element in the sequence to be detected.
4. A method according to claim 3, wherein generating the anomaly parameter for the sample sequence based on the set of co-located elements for each index element in the sequence to be detected comprises:
determining a cyclic ratio value of each index element in the sequence to be detected based on each index element in the set of parity elements of each index element in the sequence to be detected and each index element in the sequence to be detected;
And summing the ring ratio value of each index element in the sequence to be detected to obtain the abnormal parameters of the sample sequence.
5. The method according to claim 4, wherein the sequence to be detected comprises N index elements; the number of the plurality of second subsequences is M; the sequence to be detected comprises index elements with the sequence number of i, and the ring ratio value of the index elements with the sequence number of i is determined based on each index element with the sequence number of i and each index element with the sequence number of i in a parity element set of the index elements with the sequence number of i, and the method comprises the following steps:
calculating the ratio between the index element with the sequence number i in the sequence to be detected and each parity element in the parity element set corresponding to the index element with the sequence number i to obtain M parity ratio values; m is a positive integer; i is greater than 0 and less than or equal to N;
and carrying out average operation on the M equal ratio values to obtain the ring ratio value of the index element with the sequence number i in the sequence to be detected.
6. The method according to claim 2, wherein generating the abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence comprises:
if the index type is the fluctuation index, calculating the standard deviation of the sample sequence and the average value of the sample sequence based on the numerical values of a plurality of index elements in the sample sequence;
And generating abnormal parameters of the sample sequence according to the standard deviation of the sample sequence, the average value of the sample sequence and each index element in the sequence to be detected.
7. The method of claim 6, wherein generating the anomaly parameters for the sample sequence based on the standard deviation of the sample sequence, the average value of the sample sequence, and each index element in the sequence to be detected comprises:
calculating a first ratio corresponding to each index element in the sequence to be detected based on the average difference between each index element in the sequence to be detected and the sample sequence and the standard deviation of the sample sequence;
and carrying out average calculation on a first ratio corresponding to each index element in the sequence to be detected to obtain an abnormal parameter of the sample sequence.
8. The method according to claim 7, wherein the sequence to be detected comprises N index elements; the sequence to be detected includes index elements with a sequence number j, and the calculating of a first ratio corresponding to the index elements with a sequence number j based on an average difference between the index elements with a sequence number j in the sequence to be detected and the sample sequence and a standard deviation of the sample sequence includes:
Performing difference calculation on index elements with the sequence number j in the sequence to be detected and the average value of the sample sequence to obtain a first difference value corresponding to the index elements with the sequence number j; j is greater than 0 and less than or equal to N;
and calculating the ratio of the absolute value of the first difference value corresponding to the index element with the sequence number j to the standard deviation of the sample sequence to obtain the first ratio corresponding to the index element with the sequence number j.
9. The method according to claim 1, wherein outputting the anomaly monitoring result for the service to be detected based on the anomaly parameter of the sample sequence comprises:
determining the abnormal grade of the business index to be monitored according to the comparison result of the abnormal parameter and a preset parameter threshold;
and displaying the business index to be monitored according to the alarm color corresponding to the abnormal grade.
10. An anomaly monitoring device for a traffic index, the device comprising:
the acquisition unit is used for acquiring an index element set corresponding to the service index of the service to be monitored; the index element set comprises a plurality of index elements which are sequentially arranged according to the sequence of the generation time; the index element set is generated based on a business index generation rule and the buried point data of the business to be monitored;
The first dividing unit is used for dividing the index element set into a sequence to be detected and a sample sequence; the sequence to be detected comprises a preset number of index elements, wherein the generation time of the index elements is close to the current time point; the sample sequence comprises other index elements except the sequence to be detected in the index element set; the generation time of the first index element in the sample sequence is positioned after the generation time of the last index element in the sequence to be detected;
a second dividing unit for dividing the sample sequence into a plurality of first sub-sequences according to a first time period;
a processing unit, configured to perform processing for determining an index type based on the plurality of first sub-sequences, and obtain an index type of the sample sequence;
the output unit is used for generating abnormal parameters of the sample sequence according to the index type, the sequence to be detected and the sample sequence, and outputting an abnormal monitoring result of the service to be detected based on the abnormal parameters of the sample sequence.
11. An anomaly monitoring device for a traffic index, the device comprising:
a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to perform the business metric anomaly monitoring method of any one of claims 1-9.
12. A computer readable storage medium storing computer executable instructions which, when executed by a processor, implement the method of anomaly monitoring of traffic metrics according to any one of claims 1 to 9.
CN202210867542.3A 2022-07-22 2022-07-22 Abnormality monitoring method, device, equipment and storage medium for business index Pending CN116149926A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117237126A (en) * 2023-09-18 2023-12-15 广州美保科技有限公司 Insurance platform and insurance data processing method
CN117648232A (en) * 2023-12-11 2024-03-05 武汉天宝莱信息技术有限公司 Application program data monitoring method, device and storage medium

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117237126A (en) * 2023-09-18 2023-12-15 广州美保科技有限公司 Insurance platform and insurance data processing method
CN117648232A (en) * 2023-12-11 2024-03-05 武汉天宝莱信息技术有限公司 Application program data monitoring method, device and storage medium
CN117648232B (en) * 2023-12-11 2024-05-24 武汉天宝莱信息技术有限公司 Application program data monitoring method, device and storage medium

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