CN114168444B - Dynamic operation maintenance report repairing method based on monitoring big data - Google Patents
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Abstract
The invention discloses a dynamic operation maintenance report repairing method based on monitoring big data, which comprises the following steps: s1) acquiring historical monitoring data of a monitoring item, wherein the historical monitoring data is divided into a training set and a testing set; s2) establishing a Holt-windows model of the monitoring item by using the historical monitoring data; s3) predicting future data according to a Holt-windows model of the monitoring item, and determining an alarm threshold interval of the monitoring item; s4) carrying out threshold analysis on actual monitoring data based on an alarm threshold interval of the monitoring item, and judging whether the monitoring item is abnormal or not; if the abnormality occurs, alarm information is sent to the system. The dynamic operation maintenance report method based on the monitoring big data predicts and dynamically adjusts the threshold based on the historical data, and solves the problems that the conventional operation maintenance system scheme sets a fixed threshold based on manual experience, has poor timeliness and is difficult to dynamically adjust the alarm threshold according to actual conditions.
Description
Technical Field
The invention relates to the technical field of system operation and maintenance, in particular to a dynamic operation and maintenance report and repair method based on monitoring big data.
Background
The operation and maintenance of the system pay attention to ensuring the normal operation of the system, and the operation and maintenance of the system comprise two layers of meanings. With the rapid development of various hardware and software, the system structure is more and more complex, the operation and maintenance difficulty is more and more difficult, and the requirements on operation and maintenance personnel are higher and higher. At present, the operation and maintenance of the system also seriously depends on the experience of operation and maintenance personnel, and the operation and maintenance personnel with insufficient experience are very easy to configure errors, and report omission and false report occur, so that great loss is caused.
Aiming at the problems, a dynamic operation maintenance report method based on monitoring big data is needed.
Disclosure of Invention
The invention aims to provide a dynamic operation maintenance report method based on monitoring big data, which predicts and dynamically adjusts a threshold value based on historical data, and solves the problems that the conventional operation maintenance system scheme sets a fixed threshold value based on manual experience, has poor timeliness and is difficult to dynamically adjust an alarm threshold value according to actual conditions.
In order to achieve the above object, the present invention provides the following solutions:
A dynamic operation maintenance report repairing method based on monitoring big data comprises the following steps:
S1) acquiring historical monitoring data of a monitoring item, wherein the historical monitoring data is divided into a training set and a testing set;
s2) establishing a Holt-winter model of the monitoring item by using the historical monitoring data;
s3) predicting future data according to the Holt-windows model of the monitoring item, and determining an alarm threshold interval of the monitoring item;
S4) carrying out threshold analysis on actual monitoring data based on an alarm threshold interval of the monitoring item, and judging whether the monitoring item is abnormal or not; if the abnormality occurs, alarm information is sent to the system.
Optionally, step S1) is performed to obtain historical monitoring data of the monitoring item, where the historical monitoring data is divided into training set data and test set data, and specifically includes:
S101) determining a required monitoring host and a monitoring item of the monitoring host, and acquiring a key value and a monitoring item ID corresponding to the monitoring item;
s102) judging the type of the historical data corresponding to the monitoring item, and inquiring a data table corresponding to the data type in the Zabbix database through the name and the key value of the monitoring host to obtain the historical data of the monitoring item;
S103) dividing the acquired historical data of the monitoring item into a training set and a testing set, wherein the testing set is the data of the last h hours, and the rest data is the training set.
Optionally, step S2) establishes a Holt-windows model of the monitored item by using the historical monitored data, which specifically includes:
s201) initializing a horizontal smoothing value L 0, a trend smoothing value P 0, and a season smoothing value S k, and calculating the following formulas respectively:
Wherein: t is the length of the season; y (i) is the monitoring item data value at the moment i; y (T+i) is the data value of the monitoring item at the moment i in the next season; s is the number of seasons, and the number of the seasons, N is the number of the historical time series data of the monitoring items, and Y (z is T+k) is the data value of the monitoring items at the kth moment of the z+1th season;
S202) fitting a Holt-windows model of a monitoring item by using training set data, setting a horizontal smoothing coefficient alpha, a trend smoothing coefficient beta and a seasonal smoothing coefficient gamma initial value, and obtaining an optimal smoothing parameter by using an average absolute percentage error MAPE as an index by adopting a cross verification method;
s203), inputting optimal smoothing parameters, generating an optimal Holt-windows model of the monitoring item, fixing model parameters and storing the optimal Holt-windows model of the current monitoring item.
Optionally, step S3) predicts future data according to the Holt-windows model of the monitoring item, and determines an alarm threshold interval of the monitoring item, which specifically includes:
S301) calculating monitoring item prediction data of each moment in a fixed time interval by using an optimal Holt-windows model;
S302) creating a monitored item prediction data confidence interval for each moment using Brutlag algorithm:
wherein: m is a scale factor; d, predicting deviation; y t is a predicted value at time t; The confidence upper bound and the confidence lower bound of the time t are respectively;
Wherein: The predicted value and the actual value of the data of the monitoring item at the time T-T are respectively;
s303) setting the created confidence interval as an alarm threshold interval that triggers the monitoring item.
Optionally, after step S303), further includes:
s304) re-executing steps S301) to S303) for the next time interval, and obtaining a monitoring item alarm threshold interval at each moment in the next time interval.
Optionally, in step S4), the threshold analysis is performed on the actual monitoring data in the alarm threshold interval based on the monitoring item, so as to determine whether the monitoring item is abnormal, which specifically includes: when the value of the actual monitoring data is in a threshold value interval, judging that the monitoring item is not abnormal; and when the value of the actual monitoring data exceeds the threshold value interval, judging that the monitoring item is abnormal.
Optionally, the monitoring items include available memory in Zabbix, CPU idle time, CPU user utilization, idle disk space, idle index node, incoming network working flow eth0, number of processes, processor load per second, used disk space.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the dynamic operation and maintenance report repair method based on the monitoring big data, monitoring index historical data are firstly obtained from a Zabbix monitoring platform, and each monitoring index historical data is divided into a training set and a testing set; secondly, taking training set data as input of a Holt-windows model, initializing a horizontal smoothing value, a trend smoothing value and a season smoothing value of the Holt-windows, calculating an optimal horizontal smoothing coefficient, a trend smoothing coefficient and a season smoothing coefficient through cross verification, establishing an optimal Holt-windows model, inputting a test set into the established model, and evaluating the accuracy of the model; finally, predicting data which is not subjected to a certain period of time according to the selected optimal Holt-windows model, dynamically determining a threshold interval of a monitoring index by utilizing a predicted value and an algorithm of the model, judging the data exceeding the threshold interval as an abnormal value, and sending alarm information to a system; according to the invention, time sequence analysis is carried out according to the monitoring historical information, a historical information model is established, and the method is used for realizing reasonable monitoring alarm threshold setting and dynamic updating of the threshold, improving the problem of false alarm and missing report of a visual operation and maintenance system and reducing the defect of seriously depending on operation and maintenance personnel; the algorithm provided by the invention combines a large amount of historical information obtained by Zabbix monitoring, analyzes the time sequence based on a Holt-windows model, dynamically determines and updates a monitoring item threshold value in real time, and alarms an operation and maintenance person for abnormal conditions; in a word, the invention solves the problems that the conventional operation and maintenance system scheme sets a fixed threshold based on manual experience, has poor timeliness and is difficult to dynamically adjust an alarm threshold according to actual conditions.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, 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 flow chart of a method for reporting and repairing dynamic operation based on monitoring big data according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a dynamic operation maintenance report method based on monitoring big data, which predicts and dynamically adjusts a threshold value based on historical data, and solves the problems that the conventional operation maintenance system scheme sets a fixed threshold value based on manual experience, has poor timeliness and is difficult to dynamically adjust an alarm threshold value according to actual conditions.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Exponential smoothing is a classical prediction method framework that can quickly generate reliable prediction results and is applicable to a wide range of time sequences, and based on this great advantage, plays an important role in industrial applications, thus also motivating some very successful prediction methods. Based on the framework, a learner Holt and windows expands the exponential smoothing method for a plurality of times, and a Holt-windows model is provided for further capturing seasonal factors in time sequence prediction, so that prediction accuracy is improved. The Holt-windows model comprises a prediction equation and three smoothing equations, namely a horizontal smoothing equation, a trend smoothing equation and a seasonal smoothing equation, which jointly form a model for time series modeling.
Zabbix is a highly sophisticated network monitoring solution capable of monitoring numerous network parameters and server health, integrity. Zabbix uses a flexible alert mechanism that allows users to configure mail-based alerts for almost any event, allowing users to quickly respond to server problems. ZabbixAPI provided by the method provides a programming interface for Zabbix, is used for batch operation, third-party software integration and the like, and can conveniently acquire monitoring history information. In the basic Zabbix scheme, an alarm mechanism mainly depends on a manually set threshold value or condition, adaptability is limited, dependence on manual experience is strong, and the existence of a Zabbix programming interface provides possibility for a user to perform data analysis modeling based on monitoring history information and construct an automatic and intelligent alarm method.
As shown in fig. 1, the method for reporting and repairing dynamic operation based on monitoring big data provided by the embodiment of the invention comprises the following steps:
S1) acquiring historical monitoring data of a monitoring item, wherein the historical monitoring data is divided into a training set and a testing set; in this embodiment, there are 10004 pieces of history data, wherein 9884 pieces of training set data and 120 pieces of test set data;
s2) establishing a Holt-winter model of the monitoring item by using the historical monitoring data;
s3) predicting future data according to the Holt-windows model of the monitoring item, and determining an alarm threshold interval of the monitoring item;
S4) carrying out threshold analysis on actual monitoring data based on an alarm threshold interval of the monitoring item, and judging whether the monitoring item is abnormal or not; if the abnormality occurs, alarm information is sent to the system.
Step S1) is to obtain the history monitoring data of the monitoring item, wherein the history monitoring data is divided into training set data and testing set data, and the method specifically comprises the following steps:
S101) determining a required monitoring host and a monitoring item of the monitoring host, and acquiring a key value and a monitoring item ID corresponding to the monitoring item;
s102) judging the type of the historical data corresponding to the monitoring item, and inquiring a data table corresponding to the data type in the Zabbix database through the name and the key value of the monitoring host to obtain the historical data of the monitoring item;
S103) dividing the acquired historical data of the monitoring item into a training set and a testing set, wherein the testing set is the data of the last h hours, and the rest data is the training set.
Step S2) establishes a Holt-Winters model of the monitoring item by using the historical monitoring data, and the method specifically comprises the following steps:
S201) initializing a horizontal smoothing value L 0, a trend smoothing value P 0 and a season smoothing value S k, setting an initial value when the Holt-windows algorithm starts the first round of prediction, wherein the initial value has a larger influence on the initial stage of the time sequence, and the influence of the initial value on the prediction is gradually reduced after a plurality of time step iterations, and the general method for selecting the initial value is as follows:
wherein, the seasonal length T takes a value of 180; y (i) is the monitoring item data value at the moment i; y (180+i) is the data value of the monitoring item at the moment i in the next season; s is the number of seasons, and the number of the seasons, N is the number of the historical time series data of the monitoring item, wherein the value of n is 9884; y (z180+k) is the monitor item data value at the kth time of the z+1th season;
S202) fitting a Holt-windows model of a monitoring item by using training set data, setting a horizontal smoothing coefficient alpha, a trend smoothing coefficient beta and a seasonal smoothing coefficient gamma as initial values [0, 0], taking a value range as (0, 1), obtaining an optimal smoothing parameter by adopting a ten-fold cross validation method and taking an average absolute percentage error MAPE as an index, wherein the obtained MAPE index value is 2.66%, and the optimized smoothing systems are alpha=0.0148, beta=0.0007 and gamma= 0.2589 respectively;
s203) inputting optimal smoothing parameters, generating an optimal Holt-windows model of the monitoring item, fixing model parameters and storing the optimal Holt-windows model of the current monitoring item; 120 pieces of test set data are input into the model, predicted values are output, and compared with the prediction precision of other method models, and the result is shown in table 1, and the result shows that: the MAPE index value calculated by the model is 2.54%, and the verification model has higher precision.
Table 1 Holt-predictions accuracy vs. other method models
Step S3) predicts future data according to the Holt-windows model of the monitoring item, and determines an alarm threshold interval of the monitoring item, which specifically comprises the following steps:
s301) calculating monitoring item prediction data of each moment in a fixed time interval by using an optimal Holt-windows model; the fixed time interval is 2 hours, and 120 pieces of data are taken in total;
S302) creating a monitored item prediction data confidence interval for each moment using Brutlag algorithm:
Wherein: m is a scale factor, where m takes a value of 1.96; d, predicting deviation; y t is a predicted value at time t; The confidence upper bound and the confidence lower bound are respectively at the time t;
Wherein: The predicted value and the actual value of the data of the monitoring item at the time T-T are respectively;
s303) confidence interval to be created An alarm threshold interval set to trigger the monitoring item, taking t=9885 as an example, is calculated as (68.46, 76.89).
Optionally, after step S303), further includes:
S304) updating historical data by using 120 pieces of actual monitoring data corresponding to the current round of prediction, replacing 120 pieces of data with earliest time in the historical data, and updating the model parameters for the next round, namely re-executing the steps S301) to S303) aiming at the next time interval, and obtaining the monitoring item alarm threshold interval at each moment in the next time interval.
In step S4), the threshold analysis is performed on the actual monitoring data based on the alarm threshold interval of the monitoring item, and the judging whether the monitoring item is abnormal or not specifically includes: when the value of the actual monitoring data is in a threshold value interval, judging that the monitoring item is not abnormal; and when the value of the actual monitoring data exceeds the threshold value interval, judging that the monitoring item is abnormal.
The monitoring items comprise available memory in Zabbix, CPU idle time, CPU user utilization, idle disk space, idle index nodes, incoming network working flow eth0, process number, processor load per second and used disk space.
According to the dynamic operation and maintenance report repair method based on the monitoring big data, monitoring index historical data are firstly obtained from a Zabbix monitoring platform, and each monitoring index historical data is divided into a training set and a testing set; secondly, taking training set data as input of a Holt-windows model, initializing a horizontal smoothing value, a trend smoothing value and a season smoothing value of the Holt-windows, calculating an optimal horizontal smoothing coefficient, a trend smoothing coefficient and a season smoothing coefficient through cross verification, establishing an optimal Holt-windows model, inputting a test set into the established model, and evaluating the accuracy of the model; finally, predicting data which is not subjected to a certain period of time according to the selected optimal Holt-windows model, dynamically determining a threshold interval of a monitoring index by utilizing a predicted value and an algorithm of the model, judging the data exceeding the threshold interval as an abnormal value, and sending alarm information to a system; according to the invention, time sequence analysis is carried out according to the monitoring historical information, a historical information model is established, and the method is used for realizing reasonable monitoring alarm threshold setting and dynamic updating of the threshold, improving the problem of false alarm and missing report of a visual operation and maintenance system and reducing the defect of seriously depending on operation and maintenance personnel; the algorithm provided by the invention combines a large amount of historical information obtained by Zabbix monitoring, analyzes the time sequence based on a Holt-windows model, dynamically determines and updates a monitoring item threshold value in real time, and alarms an operation and maintenance person for abnormal conditions; in a word, the invention solves the problems that the conventional operation and maintenance system scheme sets a fixed threshold based on manual experience, has poor timeliness and is difficult to dynamically adjust an alarm threshold according to actual conditions.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (5)
1. A dynamic operation maintenance report repairing method based on monitoring big data is characterized by comprising the following steps:
S1) acquiring historical monitoring data of a monitoring item, wherein the historical monitoring data is divided into a training set and a testing set;
s2) establishing a Holt-winter model of the monitoring item by using the historical monitoring data;
s3) predicting future data according to the Holt-windows model of the monitoring item, and determining an alarm threshold interval of the monitoring item;
s4) carrying out threshold analysis on actual monitoring data based on an alarm threshold interval of the monitoring item, and judging whether the monitoring item is abnormal or not; if abnormal occurs, sending alarm information to the system;
step S1) is to obtain the history monitoring data of the monitoring item, wherein the history monitoring data is divided into training set data and testing set data, and the method specifically comprises the following steps:
S101) determining a required monitoring host and a monitoring item of the monitoring host, and acquiring a key value and a monitoring item ID corresponding to the monitoring item;
s102) judging the type of the historical data corresponding to the monitoring item, and inquiring a data table corresponding to the data type in the Zabbix database through the name and the key value of the monitoring host to obtain the historical data of the monitoring item;
s103) dividing the acquired historical data of the monitoring item into a training set and a testing set, wherein the testing set is the data of the last h hours, and the rest data is the training set;
Step S2) establishes a Holt-Winters model of the monitoring item by using the historical monitoring data, and the method specifically comprises the following steps:
s201) initializing a horizontal smoothing value L 0, a trend smoothing value P 0, and a season smoothing value S k, and calculating the following formulas respectively:
Wherein: t is the length of the season; y (i) is the monitoring item data value at the moment i; y (T+i) is the data value of the monitoring item at the moment i in the next season; s is the number of seasons, and the number of the seasons, N is the number of the historical time series data of the monitoring items, and Y (z is T+k) is the data value of the monitoring items at the kth moment of the z+1th season;
S202) fitting a Holt-windows model of a monitoring item by using training set data, setting a horizontal smoothing coefficient alpha, a trend smoothing coefficient beta and a seasonal smoothing coefficient gamma initial value, and obtaining an optimal smoothing parameter by using an average absolute percentage error MAPE as an index by adopting a cross verification method;
s203), inputting optimal smoothing parameters, generating an optimal Holt-windows model of the monitoring item, fixing model parameters and storing the optimal Holt-windows model of the current monitoring item.
2. The method for reporting and repairing dynamic operation and maintenance based on monitoring big data according to claim 1, wherein step S3) predicts future data according to the Holt-windows model of the monitoring item, and determines an alarm threshold interval of the monitoring item, specifically includes:
S301) calculating monitoring item prediction data of each moment in a fixed time interval by using an optimal Holt-windows model;
S302) creating a monitored item prediction data confidence interval for each moment using Brutlag algorithm:
wherein: m is a scale factor; d, predicting deviation; y t is a predicted value at time t; The confidence upper bound and the confidence lower bound of the time t are respectively;
Wherein: y t-T, The predicted value and the actual value of the data of the monitoring item at the time T-T are respectively;
s303) setting the created confidence interval as an alarm threshold interval that triggers the monitoring item.
3. The method for monitoring big data based dynamic operation and maintenance report according to claim 2, further comprising, after step S303):
s304) re-executing steps S301) to S303) for the next time interval, and obtaining a monitoring item alarm threshold interval at each moment in the next time interval.
4. The method for reporting and repairing dynamic operation and maintenance based on monitoring big data according to claim 2, wherein in step S4), the actual monitoring data is subjected to threshold analysis in the alarm threshold interval based on the monitoring item, and the method for judging whether the monitoring item is abnormal specifically comprises: when the value of the actual monitoring data is in a threshold value interval, judging that the monitoring item is not abnormal; and when the value of the actual monitoring data exceeds the threshold value interval, judging that the monitoring item is abnormal.
5. The method of any one of claims 1-4, wherein the monitoring items include available memory in Zabbix, CPU idle time, CPU user utilization, free disk space, free inodes, incoming network traffic eth0, number of processes, processor load per second, used disk space.
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| CN115311829B (en) * | 2022-10-12 | 2023-02-03 | 山东未来网络研究院(紫金山实验室工业互联网创新应用基地) | Accurate alarm method and system based on mass data |
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