CN114138601A - Service alarm method, device, equipment and storage medium - Google Patents
Service alarm method, device, equipment and storage medium Download PDFInfo
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- CN114138601A CN114138601A CN202111424320.6A CN202111424320A CN114138601A CN 114138601 A CN114138601 A CN 114138601A CN 202111424320 A CN202111424320 A CN 202111424320A CN 114138601 A CN114138601 A CN 114138601A
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- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F11/00—Error detection; Error correction; Monitoring
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- G06F11/3089—Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents
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Abstract
The present disclosure relates to a service alarm method, device, equipment and storage medium, the method comprising: acquiring relevant data of a current service index; determining a trend of the business indicator over a future period of time based on the correlated data and a predictive model; and performing service alarm based on the change trend and an alarm threshold value. According to the scheme, the change trend of the service index is obtained through the prediction model, the time when the service index reaches the alarm threshold value can be obtained in advance, the problem is found in advance, early warning is carried out, the time is saved, and the production efficiency and the system stability are improved.
Description
Technical Field
The present disclosure relates to the field of data prediction technologies, and in particular, to a method, an apparatus, a device, and a storage medium for service alarm.
Background
With the rapid development of the service, when the service index is abnormal, the alarm is rapidly given, which is a precondition for determining the reason of the abnormality and eliminating the abnormality.
At present, a monitoring and warning system is operated based on measures such as system monitoring, log monitoring, keyword monitoring and the like, and warning rules can be triggered to warn after the system fails. However, the existing monitoring and alarming means are all lagged, and belong to the following intervention, namely, after a problem occurs in the system, an alarming rule is triggered, and then related personnel are informed to investigate and solve the problem, so that the system is not lagged and has low efficiency.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the present disclosure provides a service alarm method, device, equipment, and storage medium, which analyze whether a change trend of a page number index is abnormal, and predict a future change trend, thereby finding a problem in advance and performing an accurate alarm.
In a first aspect, an embodiment of the present disclosure provides a service alarm method, including:
acquiring relevant data of a current service index;
determining a change trend of the required business index in a future period of time based on the relevant data and a prediction model;
and performing service alarm based on the change trend and an alarm threshold value.
In a second aspect, an embodiment of the present disclosure provides a service alarm device, including:
the relevant data acquisition module is used for acquiring relevant data of the current service index;
the change trend determining module is used for determining the change trend of the service index in a future period of time based on the relevant data and a prediction model;
and the service alarm module is used for carrying out service alarm based on the change trend and the alarm threshold value.
In a third aspect, an embodiment of the present disclosure provides a service alarm device, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of the first aspect.
In a fourth aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to the first aspect.
The service alarm method, device, equipment and storage medium provided by the embodiment of the disclosure comprise: acquiring relevant data of a current service index; determining a trend of the business indicator over a future period of time based on the correlated data and a predictive model; and performing service alarm based on the change trend and an alarm threshold value. According to the scheme, the change trend of the service index is obtained through the prediction model, the time when the service index reaches the alarm threshold value can be obtained in advance, the problem is found in advance, early warning is carried out, the time is saved, and the production efficiency and the system stability are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a service alarm method provided in the embodiment of the present disclosure;
FIG. 2 is a diagram of an internal structure of a prediction model provided in an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an application scenario provided by the embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a service alarm device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a service alarm device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
In general, an operation monitoring and warning system in production and life is based on means such as system monitoring, log monitoring, keyword monitoring and the like, and can trigger warning rules to perform service warning after obvious faults occur in the system. The monitoring and alarming means is lagged, and belongs to intervention after the incident, and related personnel are informed to troubleshoot and solve the problem after the alarming, so that the lag is avoided and the efficiency is lower. In view of the above problems, the embodiments of the present disclosure provide a service alarm method, which is also applicable to different application scenarios. For example: the service alarm method provided by the embodiment of the disclosure can be applied to an e-commerce system. The business warning method provided by the embodiment of the invention can predict how long the business reaches the configured warning threshold value after continuously falling through the combination of relevant data and real-time data and a prediction model, thereby informing relevant personnel to pay attention to the falling risk of the business in advance, avoiding online faults and further ensuring the system stability index of the service. The following steps are repeated: the service alarm method provided by the embodiment of the disclosure can be applied to online new services, and as the new services are not checked by real access on the line in a large quantity, some undetected problems may occur, for example, an exception that the return is empty due to a parameter problem belongs to an exception that is not an error, and a traditional monitoring strategy needs to be configured to trigger an alarm after the empty reaches a threshold. However, the service alarm method provided by the embodiment of the invention can be used for pre-calculating the growth curve of the null error in advance by combining real-time data with a prediction model, triggering the alarm rule in advance and giving an alarm, so that related personnel are informed to pay attention to the growth risk of the new service in advance.
It can be understood that the service alarm method provided by the embodiment of the present disclosure is not limited to the application scenario described above, and is only schematically illustrated here. The method is described below with reference to specific examples.
Fig. 1 is a flowchart of a service alarm method provided in the embodiment of the present disclosure. The method comprises the following specific steps:
s101, obtaining relevant data of the current service index.
Specifically, the current service index is a parameter for measuring a certain service, and may be, for example, an order quantity of the e-commerce system, or a quantity of the parameter returned to be empty. The related data of the current service index refers to the data volume related to the service index at the current moment; the related data of the current service index can be obtained by monitoring the service alarm system in real time, or the service system can send the obtained current service index to the service alarm system.
Optionally, the current service index includes at least one of the following: the number of times the service order volume, the parameter return are empty, and the number of defective products in the production line.
For example: when the service alarm method is applied to the e-commerce system, the current service index is a service order amount, and correspondingly, the relevant data of the current service index at least comprises one of the following data: the method comprises the steps that a user browses data of commodities, the times of logging in the e-commerce platform by the user, the frequency of logging in the e-commerce platform by the user, the time of last order placement by the user and the like.
Another example is: when the service alarm method is applied to new service online, the current service index is the number of times that the parameter is returned to be empty, and correspondingly, the related data of the current service index at least comprises one of the following data: the number of other types of parameters returned, the frequency of other types of parameters returned, the number of other types of parameter exceptions, and the like. The parameter returned to be null may be understood as that when a new service is on-line, after one of the functions or programs is run, the returned parameter is null, that is, no relevant content is returned.
For another example: when the service alarm method is applied to a production line, the current service index is the number of defective products in the production line, and accordingly, the relevant data of the current service index at least comprises one of the following data: the running time of the production line, the number of good products, the current time, etc.
And S102, determining the change trend of the current service index in a future period of time based on the relevant data and a prediction model.
Specifically, the relevant data of the current service index acquired in step S101 is input into the prediction model, the prediction model processes the relevant data, determines a change curve of the service index corresponding to the relevant data, and uses the change curve of the corresponding service index as a change trend of the current service index in a period of time in the future.
In this embodiment, a schematic structural diagram of a prediction model is provided, and as shown in fig. 2, the prediction model 21 includes modules such as feature extraction, model training, algorithm scheduling, data resource configuration, index configuration, algorithm configuration, alarm configuration, system log, and algorithm library. Inputting the offline data into a prediction model 21, screening the data by the prediction model 21 through feature extraction, model training, algorithm scheduling and the like, entering the links of data resource configuration, index configuration and algorithm configuration, and combining with an artificial intelligence algorithm in an algorithm library to obtain a change curve of a related service index.
S103, service alarm is carried out based on the change trend and the alarm threshold value.
In this embodiment, the variation trend may be a rising variation trend or a falling variation trend. The specific transformation trend may be set according to different service indexes, and this embodiment is not limited. The above-mentioned trend may be represented by a line graph or a numerical list, and the manner of representing the trend is not limited in this embodiment.
The alarm threshold may also be set according to different service indexes, specifically, may be set according to historical data, or may be set according to service requirements, which is not limited in this embodiment.
Specifically, when the change trend is a falling trend, the time required for falling to a first alarm threshold value in the change trend is determined, and if the required time is less than or equal to a preset time length, an alarm is given. And when the change trend is an ascending trend, determining the time required by the change trend to rise to a second alarm threshold, and if the required time is less than or equal to the preset time, giving an alarm. The first alarm threshold and the second alarm threshold may be alarm thresholds of the same service index.
The service alarm method provided by the embodiment of the disclosure comprises the following steps: acquiring relevant data of a current service index; determining a trend of the business indicator over a future period of time based on the correlated data and a predictive model; and performing service alarm based on the change trend and an alarm threshold value. According to the scheme, the change trend of the service index is obtained through the prediction model, the time when the service index reaches the alarm threshold value can be obtained in advance, the problem is found in advance, early warning is carried out, the time is saved, and the production efficiency and the system stability are improved.
In one possible embodiment, the determining a trend of the current traffic indicator over a future period of time based on the relevant data and a prediction model includes: inputting the relevant data into the predictive model; determining a variation curve of a business index corresponding to the relevant data in the prediction model; and taking the change curve corresponding to the determined service index as the change trend of the current service index in a period of time in the future.
In this embodiment, the predictive model may be trained or learned from historical first-pass data. The prediction model may be trained by using various algorithms, and this embodiment is not limited.
Optionally, the prediction model is trained by at least one of the following algorithms: long Short-Term Memory network (LSTM) algorithm, time series prediction algorithm and cubic exponential smoothing algorithm.
The Long Short-Term Memory Network (LSTM) is a time-cycle Neural Network, and is specially designed for solving the Long-Term dependence problem of a general RNN (Recurrent Neural Network), wherein the LSTM is the key point of successful application of the cycle Neural Network, and the cycle Neural Network adopting the LSTM structure has better performance than a standard cycle Neural Network on a plurality of tasks.
The time series prediction means that acquired data are arranged into a sequence according to time sequence, and the change direction and degree of the data are analyzed, so that the level which can be reached in a plurality of periods in the future is presumed.
The third exponential smoothing prediction method is to carry out third exponential smoothing on the basis of a second exponential smoothing value, similarly, the third exponential smoothing value is not directly used for prediction, but is prepared for solving a smoothing coefficient and establishing a prediction model, the third exponential smoothing method is almost suitable for analyzing application problems of all time sequences, and the second exponential smoothing prediction model is a special case of the third exponential smoothing prediction model, namely, a case when data presents a linear trend and curve curvature is zero.
Furthermore, after the prediction model is trained or learned according to the historical first-relation data, a change curve corresponding to the historical relevant data is obtained. And then, inputting the relevant data of the current service index collected in the step S101 into a prediction model, determining a change trend corresponding to the relevant data by the prediction model according to a change curve corresponding to the relevant data and historical relevant data, and taking the change curve corresponding to the determined service index as the change trend of the current service index in a period of time in the future.
In one possible embodiment, the performing traffic alarm based on the variation trend and the alarm threshold includes: determining the time length required for the real-time data of the service index to reach an alarm threshold value based on the change trend; and when the required time length is less than or equal to the preset time length, performing service alarm.
In this embodiment, when the relevant data of the current service index is obtained, the real-time data of the current service index is obtained. The real-time data may be obtained in the same manner as the related data, and the related data obtaining manner may be specifically referred to, which is not limited in this embodiment.
In one embodiment, the service alarm is performed by at least one of the following methods: editing the alarm information into information and sending the information to a third party terminal; editing the alarm information into a mail and sending the mail to a preset mailbox; carrying out voice communication with a third party terminal through a service alarm platform, and playing alarm information; and sending the alarm information to a third party terminal through the instant messaging application.
The third-party terminal can be one or more of a mobile intelligent terminal, a notebook computer, a desktop computer, a wearable intelligent device, a multimedia tablet and the like used by a worker.
For example: the warning information can be edited into a short message and sent to the mobile phone of the related staff; the following steps are repeated: or editing the alarm information into a mail box which is sent to the relevant staff; for another example: and the related staff can be informed by telephone through the service alarm platform. For another example: and sending the alarm information to an application program of a mobile phone of the related staff. It should be noted that the service alarm manner may adopt any one or a combination of the above, and this embodiment is not limited. Specifically, the alarm is given by adopting various modes, so that the working personnel can be ensured to receive the alarm information, and the omission of the alarm information is avoided. In addition, the alarm mode may also perform an alarm through an acousto-optic electric signal, which is only exemplary and not limiting in the embodiment.
Wherein, the alarm information comprises at least one of the following information: the current service index, the real-time data of the current service index, the time length required for the real-time data of the current service index to reach the alarm threshold, the follow-up modification suggestion and the like.
In an application example, the service alarm method provided by the embodiment of the disclosure can be applied to an e-commerce system. Collecting relevant data and real-time data of the order quantity, inputting the relevant data into a prediction model to obtain the change trend of the order quantity in a period of time in the future, and determining the time length required for the real-time data to reach an alarm threshold value based on the change trend; and when the required time is less than or equal to the preset time, performing service alarm. The service alarm method provided by the embodiment of the invention can predict how long the service reaches the configured alarm threshold value after continuously falling through the relevant data and the real-time data combined with the prediction model, thereby informing relevant personnel to pay attention to the falling risk of the service in advance, avoiding online faults and further ensuring the system stability index of the service.
As shown in fig. 3, it can be seen that at 11 months, 23 days, 10: 03 to 10: between 20, the data A and the data B have a phenomenon of rapid rise; in the conventional monitoring and warning system, warning is performed only when the data a and the data B reach the critical point of the highest value, but the service warning method provided by this embodiment has a rapid rising trend before the data a and the data B fall to the peak, and advanced warning can be performed according to the rapid rising trend. Compared with the traditional monitoring strategy, the alarm method disclosed by the embodiment of the disclosure can predict the abnormal condition of the system and alarm in advance, so that the production stability is ensured.
In an application example, the service alarm method provided by the embodiment of the present disclosure may be applied to a new service online. Collecting related data and real-time data of a new service, inputting the related data into a prediction model to obtain the change trend of the return times with null parameters in a period of time in the future, and determining the time length required by the real-time data to reach an alarm threshold value based on the change trend; and when the required time is less than or equal to the preset time, performing service alarm. The service alarm method provided by the embodiment of the invention can predict how long the service reaches the configured alarm threshold value after continuously rising through the relevant data and the real-time data combined with the prediction model, so that relevant personnel are informed to pay attention to the falling risk of the service in advance.
In an application example, the service alarm method provided by the embodiment of the disclosure can be applied to a product production line. Collecting relevant data and real-time data of product quality, inputting the relevant data into a prediction model to obtain the change trend of the defective products in a period of time in the future, and determining the time length required for the real-time data to reach an alarm threshold value based on the change trend; and when the required time is less than or equal to the preset time, performing service alarm. The service alarm method provided by the embodiment of the invention can predict how long the number of the defective products reaches the configured alarm threshold value after rising through the combination of the relevant data and the real-time data with the prediction model, so that relevant personnel are informed to pay attention to the rising risk of the service in advance, the product quality is ensured, and the production efficiency is improved.
Fig. 4 is a schematic structural diagram of a service alarm device according to an embodiment of the present disclosure. The service alarm device provided in the embodiment of the present disclosure may execute the processing procedure provided in the embodiment of the service alarm method, as shown in fig. 4, the service alarm device 40 includes: a related data acquisition module 41, a change trend determination module 42 and a service alarm module 43; the related data obtaining module 41 is configured to obtain a data amount related to the service indicator in a period of time before the current time; the change trend determining module 42 determines the change trend of the quality of service according to the acquired related data; the traffic alarm module 43 is configured to perform a traffic alarm based on the variation trend and the alarm threshold.
Optionally, the service alarm module 43 includes: a required time period determination unit 431 and a traffic alarm unit 432.
The required duration determining unit is used for determining the required duration for the real-time data of the service index to reach an alarm threshold value based on the change trend; and the service alarm unit is used for carrying out service alarm when the required time length is less than or equal to the preset time length.
Wherein the predictive model is trained by at least one of the following algorithms: a long and short term memory network LSTM algorithm, a time series prediction algorithm and a cubic exponential smoothing algorithm.
Optionally, the trend determining module includes: a relevant data input unit for inputting the relevant data into the prediction model; a transformation curve determining unit, configured to determine a transformation curve of a service index corresponding to the relevant data in the prediction model; and the change trend determining unit is used for taking the change curve corresponding to the determined service index as the change trend of the current service index in a period of time in the future.
Wherein the current service index includes at least one of the following:
the number of times the service order volume, the parameter return are empty, and the number of defective products in the production line.
Wherein the prediction model is trained using at least one of the following algorithms: a long and short term memory network algorithm, a time series prediction algorithm and a cubic exponential smoothing algorithm.
In one embodiment, the service alarm module 43 is configured to execute at least one of the following service alarm modes:
editing the alarm information into information and sending the information to a third party terminal;
editing the alarm information into a mail and sending the mail to a preset mailbox;
carrying out voice communication with a third party terminal through a service alarm platform, and playing alarm information;
and sending the alarm information to a third party terminal through the instant messaging application.
Wherein, the alarm information comprises at least one of the following information: the current service index, the real-time data of the current service index, the time length required for the real-time data of the current service index to reach the alarm threshold, the follow-up modification suggestion and the like.
The service alarm device in the embodiment shown in fig. 4 may be used to implement the technical solution of the above method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 5 is a schematic structural diagram of a service alarm device according to an embodiment of the present disclosure. The service alarm device provided in the embodiment of the present disclosure may execute the processing procedure provided in the embodiment of the service alarm method, as shown in fig. 5, the service alarm device 50 includes: memory 51, processor 52, computer program 53; wherein a computer program is stored in the memory 51 and is configured to execute the traffic alerting method as described above by the processor 52.
In addition, the embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the service alarm method described in the foregoing embodiment.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A traffic alarm method, characterized in that the method comprises:
acquiring relevant data of a current service index;
determining a trend of the current business indicator over a future period of time based on the relevant data and a predictive model;
and performing service alarm based on the change trend and an alarm threshold value.
2. The method of claim 1, wherein the alerting traffic based on the trend of change and an alert threshold comprises:
determining the time length required for the real-time data of the current service index to reach an alarm threshold value based on the change trend;
and when the required time length is less than or equal to the preset time length, performing service alarm.
3. The method of claim 1, wherein determining a trend of the current traffic indicator over a future period of time based on the correlation data and a predictive model comprises:
inputting the relevant data into the predictive model;
determining a variation curve of a business index corresponding to the relevant data in the prediction model;
and taking the change curve corresponding to the determined service index as the change trend of the current service index in a period of time in the future.
4. The method according to claim 1 or 2, wherein the current traffic indicator comprises at least one of:
the number of times the service order volume, the parameter return are empty, and the number of defective products in the production line.
5. The method of claim 1, wherein the predictive model is trained using at least one of the following algorithms:
a long and short term memory network algorithm, a time series prediction algorithm and a cubic exponential smoothing algorithm.
6. The method of claim 1, wherein performing the traffic alarm comprises at least one of:
editing the alarm information into information and sending the information to a third party terminal;
editing the alarm information into a mail and sending the mail to a preset mailbox;
carrying out voice communication with a third party terminal through a service alarm platform, and playing alarm information;
and sending the alarm information to a third party terminal through the instant messaging application.
7. The method of claim 6, wherein the alarm information comprises at least one of: the current service index, the real-time data of the current service index, the time length required for the real-time data of the current service index to reach the alarm threshold, the follow-up modification suggestion and the like.
8. A traffic alerting device, characterized in that the device comprises:
the relevant data acquisition module is used for acquiring relevant data of the current service index;
the change trend determining module is used for determining the change trend of the current service index in a future period of time based on the relevant data and a prediction model;
and the service alarm module is used for carrying out service alarm based on the change trend and the alarm threshold value.
9. A traffic alerting device comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN202111424320.6A CN114138601A (en) | 2021-11-26 | 2021-11-26 | Service alarm method, device, equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115967827A (en) * | 2022-12-06 | 2023-04-14 | 北京奇艺世纪科技有限公司 | Data index monitoring and alarming method, device, equipment and storage medium |
| CN116662122A (en) * | 2023-06-06 | 2023-08-29 | 长春师范大学 | Monitoring method, system, equipment and medium based on service monitoring |
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2021
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115967827A (en) * | 2022-12-06 | 2023-04-14 | 北京奇艺世纪科技有限公司 | Data index monitoring and alarming method, device, equipment and storage medium |
| CN116662122A (en) * | 2023-06-06 | 2023-08-29 | 长春师范大学 | Monitoring method, system, equipment and medium based on service monitoring |
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