CN116974869A

CN116974869A - Index data monitoring method and device, electronic equipment and storage medium

Info

Publication number: CN116974869A
Application number: CN202310849363.1A
Authority: CN
Inventors: 苏亚; 张凌昕; 李之涵; 马茗; 郭君健
Original assignee: Beijing Dajia Internet Information Technology Co Ltd
Current assignee: Beijing Dajia Internet Information Technology Co Ltd
Priority date: 2023-07-11
Filing date: 2023-07-11
Publication date: 2023-10-31

Abstract

The present disclosure relates to an index data monitoring method, apparatus, electronic device, storage medium and computer program product. The method comprises the following steps: acquiring index data of a monitoring index at a plurality of time points, and determining abnormal index data in the index data; determining first abnormality information representing the abnormality degree of the abnormality index data itself and second abnormality information representing the abnormality degree of the plurality of the index data as a whole; and obtaining data abnormal information of the monitoring index based on the first abnormal information and the second abnormal information, and executing index alarm processing aiming at the monitoring index based on the data abnormal information. The method and the device can comprehensively determine the abnormal condition of the monitoring index by combining the data abnormal information including the first abnormal information and the second abnormal information and execute index alarm processing, thereby effectively improving the accuracy of monitoring index alarm.

Description

Index data monitoring method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the field of computer technology, and in particular, to a method and device for monitoring index data, an electronic device and a storage medium.

Background

With the development of computer technology, the monitoring index can be timely subjected to index data acquisition, and the change condition of the index data is monitored and analyzed to determine whether the monitoring index is abnormal or not.

In the related art, a prediction algorithm is generally used to predict a monitoring index to obtain a predicted value of a time sequence of the monitoring index, and then, point-by-point judgment and monitoring are performed on data of each moment of the monitoring index: the actual value and the predicted value of the monitoring index at the moment are compared, when the deviation between the actual value and the predicted value is large, the monitoring index at the moment is judged to be abnormal, and the relevant alarm processing is executed aiming at the abnormality of the monitoring index at the moment.

However, the above manner often results in a large amount of false alarm information, such as any slight jitter for the monitored data, and alarm processing is performed, which has a problem of low alarm accuracy.

Disclosure of Invention

The disclosure provides an index data monitoring method, an index data monitoring device, electronic equipment and a storage medium, so as to at least solve the problem of low alarm accuracy in the related art. The technical scheme of the present disclosure is as follows:

according to a first aspect of an embodiment of the present disclosure, there is provided an index data monitoring method, including:

Acquiring index data of a monitoring index at a plurality of time points, and determining abnormal index data in the index data;

determining first abnormality information representing the abnormality degree of the abnormality index data itself and second abnormality information representing the abnormality degree of the plurality of the index data as a whole;

and obtaining data abnormal information of the monitoring index based on the first abnormal information and the second abnormal information, and executing index alarm processing aiming at the monitoring index based on the data abnormal information.

In an exemplary embodiment, the determining the first anomaly information characterizing the anomaly degree of the anomaly index data itself includes:

determining the data abnormality degree of the abnormality index data according to the difference between the abnormality index data and the reference index data corresponding to the abnormality index data for each abnormality index data;

and counting the abnormal degree of each data, and obtaining the first abnormal information representing the abnormal degree of the abnormal index data according to the counting result.

In an exemplary embodiment, the determining the data anomaly degree of the anomaly index data according to the difference between the anomaly index data and the reference index data corresponding to the anomaly index data includes:

Determining the reference index data of the abnormal index data corresponding to a time point, wherein the reference index data comprises prediction index data of the abnormal index data at the corresponding time point and a data fluctuation range of the prediction index data;

obtaining the deviation degree of the abnormal index data relative to the predicted index data based on the difference value of the abnormal index data and the predicted index data;

and obtaining the data abnormality degree of the abnormality index data based on the ratio of the deviation degree of the abnormality index data to the data fluctuation range of the prediction index data.

In an exemplary embodiment, the counting the degree of abnormality of each data includes at least one of the following:

acquiring the data abnormality degree which is larger than a threshold value in the data abnormality degrees, summing the data abnormality degrees which are larger than the threshold value, and taking the sum result as a first statistical result;

determining an average value corresponding to each data abnormality degree to obtain a second statistical result;

determining at least one target abnormality degree from the data abnormality degrees, and obtaining a third statistical result according to an average value and/or a maximum value corresponding to the at least one target abnormality degree; the time points of the abnormal index data corresponding to the target abnormal degrees are all later than the time points of other abnormal index data, and the other abnormal index data are abnormal index data except the abnormal index data corresponding to the target abnormal degrees in the abnormal index data.

In an exemplary embodiment, the determining the second anomaly information characterizing the anomaly degree of the plurality of the index data overall includes:

determining the data quantity corresponding to the abnormal index data and determining the data quantity corresponding to a plurality of index data;

and determining the second abnormality information representing the whole abnormality degree of the plurality of index data according to the ratio of the data quantity corresponding to the abnormality index data to the data quantity corresponding to the plurality of index data.

In an exemplary embodiment, the data anomaly information includes third anomaly information characterizing the index data recovery trend;

and obtaining the data anomaly information of the monitoring index based on the first anomaly information and the second anomaly information, wherein the data anomaly information comprises the following steps:

if the duration ranges corresponding to the time points are larger than a preset duration threshold value, acquiring the change trend corresponding to the target index data in the index data; the time point of each target index data is later than the time points of other index data, wherein the other index data is index data except the target index data in the index data;

Determining comparison results between the plurality of target index data and prediction index data corresponding to the plurality of target index data; the comparison result represents that the plurality of target index data are larger than the prediction index data corresponding to the plurality of target index data, or the plurality of target index data are smaller than the prediction index data corresponding to the plurality of target index data;

and obtaining the third abnormal information according to the change trend and the comparison result.

In an exemplary embodiment, the acquiring the index data of the monitoring index at a plurality of time points includes:

determining monitoring indexes under a plurality of sub-dimensions corresponding to the monitoring indexes, and acquiring index data of the monitoring indexes under each sub-dimension at a plurality of time points to obtain a plurality of index data of the monitoring indexes under each sub-dimension;

the performing, based on the data anomaly information, an index alert process for the monitoring index includes:

determining an abnormality level corresponding to the monitoring index in each sub-dimension based on the data abnormality information of the monitoring index in each sub-dimension;

and determining a target abnormal grade with the highest abnormal degree from a plurality of abnormal grades, and executing index alarm processing aiming at the monitoring index according to an abnormal response strategy corresponding to the target abnormal grade.

In an exemplary embodiment, after the performing of the index alert process for the monitor index based on the data anomaly information, further includes:

if the monitoring index has corresponding configuration information for suspending the index alarm processing, determining a suspension time range of the index alarm processing and a triggering condition for restoring the index alarm processing according to the configuration information;

and pausing the index alarm processing until the triggering condition is met in the future pause time range, and continuously executing the index alarm processing aiming at the monitoring index.

In an exemplary embodiment, said suspending said indicator alert process until said trigger condition is met within said future suspension time range, continuing to perform indicator alert process for said monitored indicator, comprising:

and if the trigger condition is a first trigger condition, suspending the index alarm processing of the monitoring index in the future suspension time range until the abnormal grade upgrading of the monitoring index is determined, and continuing to execute the index alarm processing.

If the triggering condition is a second triggering condition, in the future pause time range, stopping the index alarm processing of the monitoring index until the monitoring index in the first sub-dimension corresponding to the monitoring index is abnormally upgraded or the monitoring index in the second sub-dimension is abnormal, and continuously executing the index alarm processing aiming at the monitoring index;

the monitoring indexes correspond to monitoring indexes in a plurality of sub-dimensions, the monitoring indexes in the first sub-dimension are abnormal monitoring indexes in the plurality of sub-dimensions, and the monitoring indexes in the second sub-dimension are monitoring indexes in which no abnormality occurs in the monitoring indexes in the plurality of sub-dimensions.

In an exemplary embodiment, said suspending said indicator alert process until said trigger condition is met within said future suspension time range, continuing to perform said indicator alert process, comprising:

if the triggering condition is a third triggering condition, acquiring an abnormal reason corresponding to the monitoring index;

and pausing the index alarm processing within the future pause time range until the abnormal reason of the monitoring index is changed, and continuously executing the index alarm processing aiming at the monitoring index.

According to a second aspect of the embodiments of the present disclosure, there is provided an index data monitoring apparatus, including:

an index data acquisition unit configured to perform acquisition of index data of a monitoring index at a plurality of time points, and to determine abnormal index data among a plurality of the index data;

an abnormality information acquisition unit configured to execute first abnormality information that determines an abnormality degree characterizing the abnormality index data itself and second abnormality information of abnormality degrees of a plurality of the index data as a whole;

and an alarm unit configured to perform data abnormality information based on the first abnormality information and the second abnormality information, obtain the monitoring index, and perform index alarm processing for the monitoring index based on the data abnormality information.

According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the index data monitoring method of any one of the above.

According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform the index data monitoring method as set forth in any one of the above.

According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising instructions therein, which when executed by a processor of an electronic device, enable the electronic device to perform the index data monitoring method as set forth in any one of the preceding claims.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

in the disclosure, on one hand, the abnormal index data can be analyzed separately to obtain the first abnormal information, on the other hand, the whole of the plurality of index data can be concerned to determine the second abnormal information, so that the problem that other index data in a plurality of continuous time points are ignored only by analyzing the abnormal index data is avoided, and further, the abnormal condition of the monitoring index can be comprehensively determined and the index alarm processing is executed by combining the data abnormal information including the first abnormal information and the second abnormal information, thereby effectively improving the accuracy of the monitoring index alarm.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.

Fig. 1 is an application environment diagram illustrating an index data monitoring method according to an exemplary embodiment.

FIG. 2 is a flow chart illustrating a method of index data monitoring according to an exemplary embodiment.

FIG. 3 is a flowchart illustrating steps for determining first anomaly information, according to an exemplary embodiment.

FIG. 4 is a flowchart illustrating steps for determining the degree of data anomalies, according to one exemplary embodiment.

FIG. 5 is a flowchart illustrating a process for index data according to an exemplary embodiment.

Fig. 6 is a block diagram illustrating an index data monitoring apparatus according to an exemplary embodiment.

Fig. 7 is a block diagram of an electronic device, according to an example embodiment.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

It should be further noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present disclosure are information and data authorized by the user or sufficiently authorized by each party.

The index data monitoring method provided by the disclosure can be applied to an application environment shown in fig. 1, wherein the application environment can comprise an index data monitoring end and an index data providing end, and the index data monitoring end can communicate with the index data providing end through a network to acquire index data of a monitoring index provided by the index data providing end.

In the present disclosure, an index data monitoring terminal may communicate with an index data providing terminal, acquire index data of a monitoring index at a plurality of time points, and determine abnormal index data in the plurality of index data, and then may determine first abnormality information characterizing abnormality degrees of the abnormal index data itself and second abnormality information of abnormality degrees of the plurality of index data as a whole, obtain data abnormality information of the monitoring index based on the first abnormality information and the second abnormality information, and perform index alarm processing for the monitoring index based on the data abnormality information.

The index data providing end can be a monitored object, and the monitored object can be a terminal, a server or a network service by way of example; of course, the index data providing end may also be a server for collecting index data and forwarding the index data to the index data monitoring end.

The index data monitoring end can be a server or a system consisting of a terminal and a server, and the index data monitoring end can be provided with a corresponding data storage system, and the data storage system can store data which needs to be processed by the index data monitoring end, such as index data of monitoring indexes.

It can be appreciated that the terminal in the present disclosure may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like; the portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. It should be understood that the application environment shown in fig. 1 is only one example of the application scenario of the present disclosure, for example, the index data monitoring end and the index data providing end may be integrated in the same device, and the index data providing end may transmit the index data of the monitored index to the index data monitoring end in the device for processing after acquiring the index data of the monitored index.

Fig. 2 is a flowchart of an index data monitoring method according to an exemplary embodiment, and as shown in fig. 2, an example of an index data monitoring method used for an index data monitoring terminal is described, which includes the following steps.

In step S210, index data of the monitoring index at a plurality of time points is acquired, and abnormal index data among the plurality of index data is determined.

Wherein the monitored index is a monitored index, which may be information reflecting characteristics of the monitored object, for example, if the monitored object is a network service, in an example, the monitored index may include a click through rate; the index data is specific index content of the monitoring index, such as the numerical value of the monitoring index at the corresponding time point.

In practical application, the monitoring index can be subjected to data acquisition to obtain index data of the monitoring index at different time points, wherein the index data at different time points can form a time sequence, and the time sequence can represent the change condition of the index data along with time. In this step, the index data monitoring end may acquire index data of the monitoring index at a plurality of time points, and determine that abnormal index data exists in the plurality of index data, so as to obtain abnormal index data.

In an alternative embodiment, the step S210 may be triggered to be performed when an abnormality is detected in the index data of the monitoring index, for example, after determining the abnormality starting time, the index data between the abnormality starting time and the current time may be acquired, where the abnormality starting time is the time when an abnormality is detected in the index data of the monitoring index. It is understood that, after determining that there is an abnormality in the index data, the index data at a plurality of time points may be collected in the range between the abnormality start time and the current time, and thus the abnormality index data from the abnormality start time to the current time among the plurality of index data may be determined. By the method, when the monitoring index starts to be abnormal, the subsequent index data change condition of the monitoring index can be monitored in time.

In step S220, first abnormality information indicating the degree of abnormality of the abnormality index data itself and second abnormality information indicating the degree of abnormality of the entire plurality of index data are determined.

The first anomaly information may reflect a characteristic of an anomaly degree of the anomaly index data itself, which can represent a degree of deviation of the anomaly index data from a normal value or a preset value.

The second abnormality information may reflect the degree of abnormality of the whole of the plurality of index data acquired over a period of time, for example, the index data a is acquired over a period of time ₁ 、a ₂ ……a _n Wherein there is abnormality index data a ₂ The abnormal index data is significantly deviated from the normal value, and the other index data is not abnormal, so that the degree of abnormality of the whole of the plurality of index data may be low.

In a specific implementation, after obtaining the plurality of index data and determining the abnormal index data in the plurality of index data, the abnormal index data and the whole plurality of index data may be counted to obtain first abnormal information of the abnormal index data and second abnormal information of the plurality of index data.

In other words, in this embodiment, the abnormal index data may be analyzed separately to determine the degree of abnormality of the abnormal index data, and meanwhile, the whole of the plurality of index data may be focused, so that the condition that other index data in a plurality of continuous time points are ignored by analyzing only the abnormal index data is avoided, and thus the whole change condition of the index data of the monitoring index in a plurality of time points may be obtained.

In step S230, data abnormality information of the monitoring index is obtained based on the first abnormality information and the second abnormality information, and an index alarm process for the monitoring index is performed based on the data abnormality information.

The data anomaly information may reflect characteristics of anomalies in the index data.

The index alert process may be a process operation that prompts monitoring for anomalies in the index. In one example, the index alert process may include at least two of the following: recording abnormality of the monitoring index (such as at least one of occurrence time, duration time and specific abnormality index data) in a preset file, sending prompt information in the group, and sending the prompt information to a target user account; wherein, the form of the prompt information can comprise at least one of the following: text, vibration, sound.

Specifically, after the first abnormality information and the second abnormality information are obtained, the data abnormality information of the monitor index may be determined based on the first abnormality information and the second abnormality information. The data abnormality information may reflect abnormality conditions in various aspects of the index data, such as that the abnormality is slight or that the abnormality is particularly serious, and after the data abnormality information is obtained, the index alarm processing for the monitoring index may be performed based on the data abnormality information.

In the index data monitoring method, index data of the monitoring index at a plurality of time points can be obtained, abnormal index data in the plurality of index data can be determined, and then first abnormal information representing the abnormal degree of the abnormal index data and second abnormal information representing the abnormal degree of the whole plurality of index data can be determined. In the disclosure, on one hand, the abnormal index data can be analyzed separately to obtain the first abnormal information, on the other hand, the whole of the plurality of index data can be concerned to determine the second abnormal information, so that the problem that other index data in a plurality of continuous time points are ignored only by analyzing the abnormal index data is avoided, and further, the abnormal condition of the monitoring index can be comprehensively determined and the index alarm processing is executed by combining the data abnormal information including the first abnormal information and the second abnormal information, thereby effectively improving the accuracy of the monitoring index alarm.

In one embodiment, the index monitoring method monitors a plurality of monitoring indexes in the platform, and the alarm accuracy of the monitoring indexes is greatly improved from 38.63% to 83%. In addition, by the method and the device, false alarms or invalid alarms can be effectively reduced, false alarms are avoided, and in an embodiment, 80% of alarm information can be reduced, so that resources required to be consumed after the alarm information is received are greatly reduced.

In an exemplary embodiment, as shown in fig. 3, in step S220, determining first abnormality information characterizing the degree of abnormality of the abnormality index data itself may include the steps of:

in step S310, for each piece of abnormality index data, the degree of data abnormality of the abnormality index data is determined from the difference between the abnormality index data and the reference index data corresponding to the abnormality index data.

The reference index data may be index data obtained under the assumption of normal conditions, and the reference index data may be index data obtained by evaluation prediction.

Specifically, when a plurality of index data are acquired, the abnormal index data among the plurality of index data may be determined, and the plurality of abnormal index data may be one or more. For each piece of abnormal index data, the reference index data corresponding to the abnormal index data can be obtained, and the data abnormality degree of the abnormal index data can be determined according to the difference between the abnormal index data and the reference index data.

In step S320, the degree of abnormality of each data is counted, and first abnormality information characterizing the degree of abnormality of the abnormality index data itself is obtained according to the counted result.

After determining the data abnormality degree of each abnormal index data, one or more modes of statistics can be performed on each data abnormality degree, corresponding statistical results are obtained, and each statistical result can be further used as first abnormal information of the abnormal index data.

In this embodiment, by determining the data anomaly degree of each anomaly index data and counting the anomaly degree of each data to determine the first anomaly information, the first anomaly information is prevented from being determined only by part of the anomaly index data, so that the first anomaly information can accurately reflect the overall anomaly degree of the anomaly index data.

In an exemplary embodiment, as shown in fig. 4, in step S310, determining the degree of data abnormality of the abnormality index data according to the difference between the abnormality index data and the reference index data corresponding to the abnormality index data may include the steps of:

in step S410, reference index data of the abnormality index data corresponding to the time point is determined, the reference index data including prediction index data of the abnormality index data at the corresponding time point and a data fluctuation range of the prediction index data.

As an example, the reference index data corresponding to the abnormal index data may be index data obtained by evaluating and predicting a time point corresponding to the abnormal index data. The corresponding time point may be a time point when the abnormal index data is collected, and a time point when a time interval between the time point when the abnormal index data is collected and the time point when the abnormal index data is collected is in a preset range is obtained.

The prediction index data may be a specific determined data value, which may be index data having the highest probability among a plurality of possible index data in the prediction process.

In practical application, the index data of the monitoring index at a plurality of time points can be predicted to obtain the predicted index data of each time point, and it can be understood that the predicted index data is a predicted value with uncertainty and can have a fluctuation range, so that the data fluctuation range of the predicted index data can be determined during the prediction, and the reference index data including the sudden-buckling predicted index data and the predicted index data fluctuation range can be obtained.

In an example, the data fluctuation range may be determined by a difference between an upper threshold and the predictor data or a difference between the predictor data and a lower threshold, wherein the upper threshold is a maximum value possible for the predictor data and the lower threshold is a minimum value possible for the predictor data.

In this step, after determining the abnormal index data, the reference index data of the time point corresponding to the abnormal index data may be determined, and in an alternative embodiment, when the index data of a plurality of time points are acquired, the reference index data corresponding to the index data of each time point may be output correspondingly, for example, the reference index data may be associated with the acquired index data according to how the format is:

[ Tnow, actual value, predicted value, upper threshold, lower threshold ]

Wherein Tnow is the moment of collecting index data, the actual value is the index data actually collected at present, and the predicted value is the predicted index data.

In step S420, the degree of deviation of the abnormality index data from the prediction index data is obtained based on the difference between the abnormality index data and the prediction index data.

After the prediction index data is obtained, a difference between the abnormality index data and the prediction index data may be determined, and the difference may be determined as a degree of deviation of the abnormality index data with respect to the prediction index data.

In step S430, the data abnormality degree of the abnormality index data is obtained based on the ratio of the deviation degree of the abnormality index data to the data fluctuation range of the prediction index data.

Further, the degree of data abnormality of the abnormality index data can be obtained based on the ratio of the degree of deviation of the abnormality index data to the data fluctuation range of the prediction index data. Illustratively, the degree of data anomaly may be determined as follows:

the degree of abnormality of data at a certain point in time=the degree of deviation of the point in time/the abnormal section at the point in time,

= (index data-prediction index data)/(threshold-prediction index data)

In this embodiment, the data anomaly degree of the anomaly index data is obtained based on the ratio of the deviation degree of the anomaly index data to the data fluctuation range of the predictive index data, so that the relative anomaly degree of the anomaly index data relative to the predictive index data at the time point can be determined, and the anomaly degree of each anomaly index data can be accurately identified.

In an exemplary embodiment, in step S320, the statistics of the abnormal degree of each data may include at least one of the following statistical manners:

and acquiring the data abnormality degree which is larger than the threshold value in the data abnormality degrees, summing the data abnormality degrees which are larger than the threshold value, and taking the sum result as a first statistical result.

In practical application, after obtaining the abnormal degrees of each data, the abnormal degrees of each data can be screened to obtain the abnormal degrees of data, which are greater than a threshold value, in the abnormal degrees of each data, and the abnormal degrees of each data, which are greater than the threshold value, are summed, the summed result is used as a first statistical result, the first statistical result can describe the severity and duration of the abnormal index data abnormality at the same time, and in an example, when the first statistical result is greater than a preset threshold value (for example, 5), the condition for triggering the index alarm processing can be determined. In an alternative embodiment, the first statistical result may be determined according to the following formula, wherein the threshold may be set to 1:

first statistical result=sum (degree of data abnormality greater than 1 among respective degrees of data abnormality)

By counting the degree of data abnormality greater than the threshold value, slight single-point jitter can be filtered out, accumulation of finer abnormality is avoided, and the characteristic of abnormal index data actually causing monitoring index abnormality can be better reflected by the first counting result.

Of course, an average value corresponding to each data abnormality degree may be determined, and the second statistical result may be obtained. In addition, at least one target abnormality degree may be determined from the abnormality degrees of the data, and the third statistical result may be obtained according to an average value or a maximum value corresponding to the at least one target abnormality degree.

The time points of the abnormal index data corresponding to the abnormal degrees of the targets are later than the time points of other abnormal index data, and the other index data are index data except the abnormal index data corresponding to the abnormal degrees of the targets.

In other words, after obtaining a plurality of data abnormality degrees, at least one data abnormality degree closest to the current time point may be determined as the target abnormality degree, and for example, the data abnormality degree of each abnormality index data within the last 10 minutes may be acquired as the target abnormality degree. And then the maximum value or the corresponding average value of at least one target abnormality degree can be determined as a third statistical result.

In this embodiment, one or more manners may be adopted to count the abnormal degree of the data, so as to obtain a statistical result reflecting different characteristics of the abnormal index data, and effectively enrich the first abnormal information type, so that the first abnormal information can fully and multi-angle represent the abnormal degree of the abnormal data.

In an exemplary embodiment, in step S220, determining the second abnormality information characterizing the abnormality degree of the plurality of index data as a whole may include the steps of:

Determining the data quantity corresponding to the abnormal index data and determining the data quantity corresponding to the plurality of index data; and determining second abnormality information representing the abnormality degree of the whole of the plurality of index data according to the ratio of the data quantity corresponding to the abnormality index data to the data quantity corresponding to the plurality of index data.

Specifically, after determining the abnormal index data in the plurality of index data, data point statistics may be performed on the abnormal index data to determine the data amount of the abnormal index data existing in the plurality of index data, and data point statistics may also be performed on the plurality of index data to obtain the data amount corresponding to the plurality of index data.

Then, a ratio of the data amount corresponding to the abnormal index data to the data amount corresponding to the plurality of index data may be obtained, the ratio may be determined as second abnormal information representing the degree of abnormality of the whole of the plurality of index data, the second abnormal information obtained according to the ratio may also be referred to as an abnormal data ratio, and in an example, when the ratio is greater than a preset threshold (for example, 0.8), the index data in the time interval corresponding to the plurality of time points may be determined, and the whole has abnormality.

In this embodiment, the second anomaly information representing the anomaly degree of the whole of the plurality of index data is determined according to the ratio of the data amount corresponding to the anomaly index data to the data amount corresponding to the plurality of index data, so that the ratio of the anomaly index data in the plurality of index data can be rapidly determined, and the anomaly condition of the whole of the plurality of index data can be accurately quantified.

In an exemplary embodiment, the data anomaly information may further include third anomaly information characterizing a recovery trend of the index data, and before step S230, the method may further include the steps of:

in step S231, if the duration ranges corresponding to the time points are greater than the preset duration threshold, a change trend corresponding to the target index data in the index data is obtained.

As an example, the trend of the plurality of target index data may include an upward trend or a downward trend.

In a specific implementation, the plurality of index data may exhibit corresponding variation trends over time. In this step, after the index data corresponding to the multiple time points are obtained, a duration range corresponding to the multiple time points may be determined, and whether the duration range is greater than a preset duration threshold may be determined, if not, the index data collection may be continued, and if yes, multiple target index data in the multiple index data may be obtained, and a change trend of the multiple target index data may be determined.

The time point of each target index data is later than the time points of other index data, and the other index data is index data other than the plurality of target index data, in other words, a plurality of index data closest to the current time point among the plurality of index data may be determined as target index data.

In an alternative embodiment, when determining the change trend of the plurality of target index data, the differential sequence of the plurality of target index data may be acquired first, and positive and negative values of each differential sequence data point in the differential sequence may be determined.

If the proportion of the positive differential sequence data points to the total number of differential sequence data points is greater than a threshold value (for example, 80% of differential sequence data points in the differential sequence are all positive values), and the absolute value of the overall change of the plurality of target index data points is greater than a change threshold value, determining that the plurality of target index data are in an ascending trend. If the proportion of the difference sequence data points with negative values to the total number of the difference sequence data points is greater than a threshold value (for example, 80% of the difference sequence data points in the difference sequence are all negative values), and the absolute value of the overall change of the plurality of target index data points is greater than a change threshold value, determining that the plurality of target index data are in an ascending trend. The change threshold may be determined based on a data fluctuation range of the predictor data, and for example, the general determination of the data fluctuation range may be referred to as a change threshold (i.e., 0.5×|threshold-predictor|).

In step S232, a comparison result between the plurality of target index data and the prediction index data corresponding to the plurality of target index data is determined.

The comparison result represents that the plurality of target index data are larger than the prediction index data corresponding to the plurality of target index data, or represents that the plurality of target index data are smaller than the prediction index data corresponding to the plurality of target index data.

In addition, a plurality of target index data and prediction index data corresponding to each target index data can be obtained, and the prediction index data can be obtained by predicting index data corresponding to the target index data; and then the target index data and the prediction index data corresponding to the target index data can be compared to obtain a comparison result, and the comparison result can represent that the current value of the target index data is higher or lower.

In an alternative embodiment, an average value of the plurality of target index data may be determined, and an average value of the plurality of prediction index data may be determined, and based on a comparison result of the two average values, a comparison result between the plurality of target index data and the prediction index data corresponding to the plurality of target index data may be used.

Of course, other ways of comparison may be adopted, for example, each target index data and the corresponding prediction index data may be compared to obtain a plurality of comparison results, statistics may be performed on the plurality of comparison results, and the number of the first comparison results and the number of the second comparison results are determined, where the first comparison results represent that the target index data is greater than the number of the prediction index data, and the second comparison results represent that the target index data is less than the prediction index data, so that a greater number of comparison results may be used as final comparison results.

In some embodiments, when comparing the size between the target index data and the prediction index data, a portion thereof may be selected for comparison, for example, if the target index data is index data within the last 10 minutes, the target index data within the last 5 minutes and the corresponding prediction index data may be compared during the comparison.

In step S233, third abnormality information is obtained based on the change trend and the comparison result.

Wherein the third anomaly information may include that the index data is being restored to normal or that the index data is not restored to normal (i.e., remains in an anomaly state).

In practical application, after the change trend and the comparison result are obtained, the change trend and the comparison result are combined, whether the current index data is normal is determined, and third abnormal information representing the recovery trend of the index data is obtained.

Specifically, for example, if the comparison result indicates that the target index data is greater than the predicted index data and the change trend is a downward trend, the method indicates that the value of the current target index data is decreasing under the condition that the value of the target index data is higher, and can determine that the index data is recovering from being normal; or if the comparison result indicates that the target index data is smaller than the predicted index data and the change trend is an ascending trend, the method indicates that the value of the current target index data is ascending under the condition that the value of the target index data is low, and can determine that the index data is recovering to be normal. In other cases, it may be determined that the index data is not restored to normal.

After the third abnormality information is obtained, the data abnormality information may be determined based on the first abnormality information, the second abnormality information, and the third abnormality information.

Of course, other anomaly information may be combined to determine the data anomaly information. For example, in some embodiments, the data anomaly information may also include at least one of: whether the index data at the current time point is abnormal, the duration of the abnormality, the abnormality degree of a plurality of index data, the importance of the monitoring index and the duty ratio of the monitoring index data quantity.

Whether the index data of the current time point is abnormal or not can include yes or no, if the index data of the current time point is within the threshold range, the index data of the current time point is no, and if the index data of the current time point exceeds the threshold range, the index data of the current time point is yes.

The abnormality duration may be a time range corresponding from the abnormality start time to the current time point.

The degree of abnormality of the plurality of index data may be an average degree of abnormality of each index data (sum of degree of abnormality of data of the abnormality index data/data amount of each index data in the abnormality duration) in the abnormality duration.

The monitoring indicator importance may include importance or non-importance, e.g., monitoring indicators (e.g., click-through rate, download failure rate) associated with user equipment efficiency (which may characterize the user experience) are important.

The monitoring index data amount duty cycle may include a duty cycle that is large or a duty cycle that is small; for a multi-dimensional monitoring index, the ratio of each dimension combination in the plurality of sub-dimension monitoring indexes=the data amount of the dimension combination/the total data amount of the plurality of sub-dimension monitoring indexes, when the ratio is greater than the threshold x/the number of dimension combinations (for example, x=10), the ratio can be determined to be large, otherwise the ratio is small.

In this embodiment, by obtaining the third anomaly information representing the recovery trend of the index data according to the variation trend and the comparison result, it is possible to determine whether the monitoring index starts to recover when the monitoring index is abnormal, so that the degree of abnormality of the monitoring index can be further identified.

In an exemplary embodiment, in step S210, acquiring index data of a monitoring index at a plurality of time points may include the steps of:

determining monitoring indexes under a plurality of sub-dimensions corresponding to the monitoring indexes, and acquiring index data of the monitoring indexes under each sub-dimension at a plurality of time points to obtain a plurality of index data of the monitoring indexes under each sub-dimension.

Specifically, the monitoring index may have a plurality of monitoring indexes with sub-dimensions, in an example, the monitoring index under the sub-dimensions may be further finely divided for the same type of monitoring index, for example, for the monitored index katana ratio, the province may be used as a sub-dimension division standard, and the katana ratio of different provinces may be used as the monitored index under the plurality of sub-dimensions for the monitored index.

In practical application, after determining the monitoring index, the monitoring index of the monitoring index in a plurality of sub-dimensions can be determined, and for the monitoring index in each sub-dimension, the index data of the monitoring index in the sub-dimension at a plurality of time points can be obtained, so as to obtain a plurality of index data of the monitoring index in each sub-dimension.

In step S240, performing an index alert process for the monitoring index based on the data anomaly information may include:

determining an abnormal level corresponding to the monitoring index in each sub-dimension based on the data abnormal information of the monitoring index in each sub-dimension; and determining a target abnormal grade with the highest abnormal degree from the plurality of abnormal grades, and executing index alarm processing aiming at the monitoring index according to an abnormal response strategy corresponding to the target abnormal grade.

It is understood that, in obtaining a plurality of index data of the monitor index in each sub-dimension, abnormal index data among the plurality of index data may be determined. For the monitoring index in each sub-dimension, first abnormal information of abnormal index data representing the monitoring index in the sub-dimension and second abnormal information of the whole of a plurality of index data of the monitoring index in the sub-dimension can be determined, the first abnormal information and the second abnormal information can be combined to obtain data abnormal information of the monitoring index in the sub-dimension, and then, based on the data abnormal information of the monitoring index in the sub-dimension, an abnormal grade corresponding to the monitoring index in the sub-dimension can be determined.

In an example, the determining logic of the anomaly level may be as shown in table 1, specifically, when determining the anomaly level, the anomaly level of the monitoring indicator may be determined according to the content of table 2 and the plurality of data anomaly information determined currently, where the anomaly level may include P0, P1, P2, P3, P4 and P100, and the anomaly degree corresponding to each anomaly level decreases in turn.

TABLE 1

TABLE 2

Wherein "-" represents any value.

After obtaining the abnormal level corresponding to each sub-dimension monitoring index, determining a target abnormal level with the highest abnormal degree from the abnormal levels corresponding to each of the plurality of sub-dimension monitoring indexes, and executing index alarm processing aiming at the monitoring index according to an abnormal response strategy corresponding to the target abnormal level.

For example, for the anomaly level P0 with the highest anomaly degree, which indicates that the anomaly is very serious, the index alarm processing may be an urgent call processing for the relevant responsible person, the anomaly level P3 may be understood as an anomaly that needs attention but is not very important, and the index alarm processing may be sending it to the relevant alarm group for subsequent processing.

It may be understood that if the monitoring index is a monitoring index with a single dimension, for example, a monitoring index with no sub dimension is set, an anomaly level corresponding to the monitoring index may be determined based on data anomaly information of the monitoring index, and an index alarm process corresponding to the monitoring index may be performed according to an anomaly response policy corresponding to the anomaly level.

In this embodiment, on one hand, accurate and fine anomaly monitoring on multiple dimensions of a monitoring index is achieved by acquiring index data on multiple sub-dimensions of the monitoring index, on the other hand, by determining an anomaly level corresponding to the monitoring index on the basis of data anomaly information of the monitoring index on each sub-dimension, determining a target anomaly level with the highest anomaly degree from the multiple anomaly levels, and executing index alarm processing for the monitoring index according to an anomaly response strategy corresponding to the target anomaly level, the anomaly level grading of the monitoring index on each sub-dimension can be achieved, and by combining the anomaly levels on the multiple sub-dimensions, anomaly conditions of the monitoring index can be accurately identified, alarms are avoided on each sub-dimension monitoring index under the monitoring index, and less index alarm processing and resources consumed by related personnel for responding to the index alarm processing are effectively avoided.

In an exemplary embodiment, after step S240, the method may further include the steps of:

if the monitoring index has corresponding configuration information for suspending the index alarm processing, determining a suspension time range of the index alarm processing and a triggering condition for resuming the index alarm processing according to the configuration information; and in the future pause time range, pausing the index alarm processing until the triggering condition is met, and continuously executing the index alarm processing aiming at the monitoring index.

In a specific implementation, after the alarm processing is performed on the monitored index, since the acquisition of the index data of the monitored index can be continued, and the processing from step S210 to step S230 is performed again, the alarm processing on the monitored index may be triggered again due to the abnormality still existing in the newly acquired index data, but the interval between the execution time interval of the alarm processing on the monitored index and the execution time interval of the previous alarm processing on the monitored index may be shorter, so that the alarm is frequently performed and a large amount of invalid alarm information is backlogged, for example, for the monitored index with the same abnormality, the prompt information is continuously and frequently sent in a period of time.

After performing the index alert process for the monitoring index based on the data anomaly information, it may be further judged whether or not configuration information for the monitoring index for instructing suspension of the index alert process, which may also be referred to as a cancellation alert configuration of the index alert process, is set in advance.

If the monitoring index is not associated with the configuration information, the processing can be continued after the next detection of the alarm processing of the monitoring index to be executed. If the monitoring index has the configuration information, determining a pause time range Tdelay of the index alarm processing according to the configuration information, and then restoring the triggering condition of the index alarm processing again when the condition of the index alarm processing is met.

For example, the staff may set a future preset time (e.g., x minutes after the first performance of the index alert process, and a future y minutes calculated from the current point in time) as a temporary time range using the configuration account. For the triggering condition, the method can be determined by judging whether any one of the following alarm objects meets the preset condition: monitoring indexes, monitoring indexes under sub-dimensions and abnormal reasons of the monitoring indexes.

And further, in a future pause time range, the index alarm processing aiming at the monitoring index can be paused first until the triggering condition is met, and the index alarm processing aiming at the monitoring index is continuously executed.

In some alternative embodiments, in addition to suspending the processing of the indicator alarm, i.e. automatically alarm, according to the configuration information through the indicator data monitoring end, manual alarm may be performed by the staff.

The manual alarm can be used for the situation that the abnormality reasons of the monitoring indexes meet expectations, the abnormality of the monitoring indexes is predictable at present, a worker knows that the monitoring indexes are abnormal under the preset condition, for example, the abnormality of all monitoring indexes of a certain content distribution network (Content Delivery Network, CDN) is caused by the fault of the content distribution network, the relevant monitoring quality assurance is always in an abnormal state before the content distribution network is restored to be normal, and the worker can send an alarm elimination instruction to an index data monitoring end after knowing the situation, and pauses the index alarm processing.

In some alternative embodiments, the manual alarm support user inputs a pause time range Tsilence1 (such as 1 hour or 1 day) and selects an alarm object, wherein the alarm object of the manual alarm can be wrapped with a monitoring index and an alarm dimension, and the corresponding alarm result is: and in the pause time range from Tnow to Tnow+Tsense 1, the monitoring index or the warning dimension is not warned.

In this embodiment, the indicator alarm processing is suspended until the trigger condition is met in a future suspension time range, so that the indicator alarm processing for the monitoring indicator is continuously executed, on one hand, the indicator alarm processing can be suspended under the condition that the abnormal condition of the monitoring indicator does not change significantly, and invalid indicator alarm processing is reduced, on the other hand, the indicator alarm processing can be resumed when the trigger condition is met is monitored, prompt is timely performed, the personnel is prevented from missing related information, and the accuracy and the effectiveness of the monitoring indicator alarm processing are improved.

In an exemplary embodiment, in a future suspension time range, suspending the index alarm processing until the trigger condition is satisfied, continuing to perform the index alarm processing for the monitored index, including:

If the trigger condition is the first trigger condition, suspending the index alarm processing of the monitoring index in a future suspension time range until the abnormal grade upgrading of the monitoring index is determined, and continuing to execute the index alarm processing.

Specifically, if the trigger condition is the first trigger condition, it may be determined that the alarm target is a monitoring indicator, and in a future suspension time range, the indicator alarm processing on the monitoring indicator may be suspended until the abnormal level of the monitoring indicator itself is upgraded, and then the indicator alarm processing is continuously performed after the abnormal level upgrade of the monitoring indicator is determined.

Specifically, for example, when the trigger condition is the first trigger condition, in the pause time range from Tnow to tnow+tsilence2, a warning is issued after the abnormal level of the monitoring index is upgraded.

In this embodiment, by determining the abnormal level upgrade of the monitoring index and continuing to perform the index alarm processing, the monitoring index with further deteriorated abnormal level can be alarmed while invalid index alarm processing is reduced, so that the staff is reminded of timely attention, and the alarm timeliness and accuracy are improved.

If the trigger condition is the second trigger condition, in a future pause time range, stopping the index alarm processing of the monitoring index until the monitoring index in the first sub-dimension corresponding to the monitoring index is upgraded abnormally or the monitoring index in the second sub-dimension is abnormal, and continuing to execute the index alarm processing aiming at the monitoring index.

The monitoring indexes correspond to the monitoring indexes in the plurality of sub-dimensions, the monitoring indexes in the first sub-dimension are abnormal monitoring indexes in the plurality of sub-dimensions, and the monitoring indexes in the second sub-dimension are monitoring indexes in the plurality of sub-dimensions, wherein the abnormality does not occur.

If the trigger condition is the second trigger condition, the monitoring index with the alarm object as the sub-dimension can be determined, and the index alarm processing on the monitoring index can be suspended within the future suspension time range.

It may be understood that if the monitoring index corresponds to the monitoring index in the multiple sub-dimensions, whether the monitoring index performs the index alarm processing may be triggered by the monitoring index in the sub-dimensions, for example, after determining the target abnormality level from the abnormality levels of the monitoring index in the multiple sub-dimensions, the index alarm processing is performed according to the abnormality response policy corresponding to the target abnormality level, in other words, if the monitoring index has triggered the execution of the index alarm processing, the monitoring index in the first sub-dimension, in which abnormality has occurred, exists in the monitoring index in the multiple sub-dimensions corresponding to the monitoring index.

Furthermore, the index alarm processing for the monitoring index can be continuously executed under the condition that the monitoring index abnormal grade upgrading under the first sub-dimension corresponding to the monitoring index is determined or the monitoring index of the second sub-dimension is abnormal.

Specifically, for example, when the monitoring index is in the pause time range from Tnow to tnow+tsilence2, for the monitoring index in the abnormal sub-dimension a, if the monitoring index of the sub-dimension is abnormal but the abnormal level is unchanged, the index alarm processing is not performed, and when the monitoring index of the other sub-dimension which is not abnormal is abnormal, the index alarm processing is still performed in the pause time range from Tnow to tnow+tsilence 2; and/or, if the abnormal degree of the monitoring index of the child dimension isoa in Tnow to Tnow+Tfile service 2 is upgraded, carrying out index alarm processing. And in other cases, index alarm processing is not performed.

In the implementation, through upgrading the monitoring index abnormality level under the first sub-dimension corresponding to the monitoring index or abnormal monitoring index occurrence of the second sub-dimension, the index alarm processing aiming at the monitoring index is continuously executed, so that when the monitoring index abnormality under the sub-dimension with the abnormality has further worsened or other monitoring indexes of the sub-dimension with the abnormality newly found are stored, the monitoring index is timely alarmed, and the staff can be reminded of timely focusing on the monitoring index under the important sub-dimension, and the alarm accuracy is improved.

if the trigger condition is a third trigger condition, acquiring an abnormal reason corresponding to the monitoring index; and in the future pause time range, pausing the index alarm processing until the abnormal reason of the monitoring index is changed, and continuously executing the index alarm processing aiming at the monitoring index.

In practical application, after abnormal index data appear in the monitoring index, the abnormal reason of the monitoring index can be determined. If the trigger condition is the third trigger condition, the alarm object can be determined to be an abnormal cause, in a future pause time range, the alarm processing of the monitoring index can be paused until the abnormal cause of the monitoring index is changed, and then the alarm processing of the index is continuously executed.

In order that those skilled in the art may better understand the above steps, the embodiments of the present disclosure will be exemplified below by way of one example, but it should be understood that the embodiments of the present disclosure are not limited thereto.

As shown in fig. 5, a flowchart of processing index data is provided, and the index data monitoring end includes an online anomaly detection module, an alarm module and a attribution module.

The online abnormality detection module and the attribution module can be used for providing input information and serve as a processing basis of the alarm module: the online anomaly detection module may calculate its predicted value (predicted index data) and upper and lower thresholds for the monitor index and the monitor index for each sub-dimension, for example:

[ Tstart, actual value, predicted value, upper threshold, lower threshold ], [ Tnow, actual value, predicted value, upper threshold, lower threshold ]

The Tstart is the time when the monitoring index starts to be abnormal, whether the monitoring index is abnormal or not can be determined by judging whether the actual value exceeds an upper threshold or a lower threshold, and if so, the occurrence of the abnormality can be determined; tnow is the current time point.

The attribution module can determine an abnormality reason for the abnormality of the monitoring index.

In practical application, the alarm module may determine the abnormal level according to the index data and the abnormal index data at a plurality of time points and execute corresponding index alarm processing, and at the current moment Tnow sends alarm information, when the monitored index has a corresponding monitored index in a plurality of sub-dimensions, the abnormal level is the abnormal level with the largest abnormal degree in the monitored index in all the sub-dimensions, and is represented by an abnormal level Px, and in an example, the output information of the alarm module may be as follows:

[ current time Tnow, start time Tstart, IDi …, alarm level Px ]

Monitoring can be continued after the abnormality occurs, and if the index data has the abnormality but does not reach the lowest alarm level or the current moment is in alarm suppression (namely, alarm elimination processing is carried out according to configuration information) during the monitoring, alarm processing is not carried out; meanwhile, the monitoring index of the normal sub-dimension can be removed, and only the monitoring index [ IDi … ] under the still abnormal sub-dimension is reserved.

After the monitoring index is abnormally finished (i.e. the monitoring index is recovered to be normal), if the monitoring process has the execution of the index alarm processing, the index alarm processing can be recorded, and the recording format can be as follows: the last anomaly time Tend, the start time Tstart, the maximum alarm level for each sub-dimension in the interval.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

It should be understood that the same/similar parts of the embodiments of the method described above in this specification may be referred to each other, and each embodiment focuses on differences from other embodiments, and references to descriptions of other method embodiments are only needed.

Based on the same inventive concept, the embodiment of the disclosure also provides an index data monitoring device for implementing the index data monitoring method.

Fig. 6 is a block diagram of an index data monitoring device according to an exemplary embodiment. Referring to fig. 6, the apparatus includes an index data acquisition unit 601, an abnormality information acquisition unit 602, and an alarm unit 603.

An index data acquisition unit 601 configured to perform acquisition of index data of a monitoring index at a plurality of points in time, and to determine abnormal index data among a plurality of the index data;

an abnormality information acquisition unit 602 configured to execute first abnormality information that determines an abnormality degree characterizing the abnormality index data itself and second abnormality information of abnormality degrees of a plurality of the index data as a whole;

an alarm unit 603 configured to perform data abnormality information of the monitor index based on the first abnormality information and the second abnormality information, and perform index alarm processing for the monitor index based on the data abnormality information.

In an exemplary embodiment, the anomaly information acquisition unit 602 is configured to perform:

In an exemplary embodiment, the anomaly information acquisition unit 602 is configured to perform at least one of the following steps:

determining at least one target abnormality degree from the data abnormality degrees, and obtaining a third statistical result according to an average value and/or a maximum value corresponding to the at least one target abnormality degree; the time points of the abnormal index data corresponding to the target abnormal degrees are later than the time points of other abnormal index data, and the other abnormal index data are the abnormal index data except the abnormal index data corresponding to the target abnormal degrees in the abnormal index data.

In an exemplary embodiment, the data anomaly information includes third anomaly information characterizing the index data recovery trend; the alert unit 603 is further configured to perform:

In an exemplary embodiment, the index data obtaining unit 601 is configured to perform:

The alarm unit 603 is configured to perform:

In an exemplary embodiment, the apparatus further comprises:

a configuration information acquisition unit configured to execute, if the monitoring indicator has corresponding configuration information for suspending the indicator alarm processing, determining a suspension time range of the indicator alarm processing and a trigger condition for resuming the indicator alarm processing according to the configuration information;

and a warning unit configured to perform the suspension of the index warning processing until the trigger condition is satisfied within the suspension time range in the future, and to continue the execution of the index warning processing for the monitoring index.

In an exemplary embodiment, the alarm unit is configured to perform:

The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.

The respective modules in the index data monitoring device may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

Fig. 7 is a block diagram of an electronic device 700 for implementing a method of monitoring index data, according to an exemplary embodiment. For example, the electronic device 700 may be a server. Referring to fig. 7, the electronic device 700 includes a processing component 720 that further includes one or more processors, and memory resources represented by a memory 722, for storing instructions, such as applications, executable by the processing component 720. The application program stored in memory 722 may include one or more modules that each correspond to a set of instructions. Further, the processing component 720 is configured to execute instructions to perform the above-described methods.

The electronic device 700 may further include: the power component 724 is configured to perform power management of the electronic device 700, the wired or wireless network interface 726 is configured to connect the electronic device 700 to a network, and the input output (I/O) interface 728. The electronic device 700 may operate based on an operating system stored in memory 722, such as Windows Server, mac OS X, unix, linux, freeBSD, or the like.

In an exemplary embodiment, a computer-readable storage medium is also provided, such as memory 722, including instructions executable by a processor of electronic device 700 to perform the above-described method. The storage medium may be a computer readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

In an exemplary embodiment, a computer program product is also provided, comprising instructions therein, executable by a processor of the electronic device 700 to perform the above-described method.

It should be noted that the descriptions of the foregoing apparatus, the electronic device, the computer readable storage medium, the computer program product, and the like according to the method embodiments may further include other implementations, and the specific implementation may refer to the descriptions of the related method embodiments and are not described herein in detail.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An index data monitoring method, comprising:

2. The method according to claim 1, wherein said determining first abnormality information characterizing an abnormality degree of the abnormality index data itself includes:

3. The method according to claim 2, wherein the determining the degree of data abnormality of the abnormality index data based on the difference between the abnormality index data and the reference index data corresponding to the abnormality index data includes:

4. The method of claim 2, wherein said counting each of said data anomalies comprises at least one of:

determining at least one target abnormality degree from the data abnormality degrees, and obtaining a third statistical result according to an average value and/or a maximum value corresponding to the at least one target abnormality degree; the time points of the abnormal index data corresponding to the target abnormal degrees are all later than the time points of other abnormal index data, and the other abnormal index data are the abnormal index data except the abnormal index data corresponding to the target abnormal degrees in the abnormal index data.

5. The method of claim 1, wherein determining second anomaly information characterizing anomaly degree of a plurality of the index data as a whole comprises:

6. The method according to claim 1, wherein the data anomaly information includes third anomaly information characterizing the index data recovery trend;

before the data anomaly information of the monitoring index is obtained based on the first anomaly information and the second anomaly information, the method further comprises the following steps:

7. The method of claim 1, wherein the obtaining the index data of the monitoring index at a plurality of time points comprises:

8. The method according to any one of claims 1 to 7, further comprising, after the performing the index alert process for the monitor index based on the data abnormality information:

9. The method of claim 8, wherein said suspending said indicator alert process until said trigger condition is met within said future suspension time period continues to perform indicator alert processes for said monitored indicator, comprising:

10. The method of claim 8, wherein said suspending said indicator alert process until said trigger condition is met within said future suspension time period continues to perform indicator alert processes for said monitored indicator, comprising:

11. The method of claim 8, wherein the suspending the indicator alert process until the trigger condition is met within the future suspension time frame, continuing to perform the indicator alert process, comprises:

12. An index data monitoring device, characterized by comprising:

13. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

Wherein the processor is configured to execute the instructions to implement the index data monitoring method of any one of claims 1 to 11.

14. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the index data monitoring method of any one of claims 1 to 11.