1. An alarm processing method, comprising:

acquiring alarm data of a data system, wherein the data system comprises at least one functional layer;

determining data proportions from each functional layer in the at least one functional layer in the alarm data based on the alarm data;

and determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system.

2. The method of claim 1, wherein determining an alarm root cause for the data system based on the alarm data, the data proportions, and a pre-trained root cause analysis model comprises:

inputting the data proportion into a fault functional layer recognition module of the root cause analysis model, and outputting a functional layer with a fault in the data system;

and inputting the alarm data from the functional layer with the fault into a root cause determination module of the root cause analysis model, and outputting the alarm root cause of the data system.

3. The method of claim 1, wherein determining a proportion of data from each of the at least one functional layer in the alarm data based on the alarm data comprises:

acquiring a data tag in the alarm data, wherein the data tag is used for identifying the corresponding relation between the alarm data and the functional layer;

and determining the proportion of data from each functional layer in the at least one functional layer in the alarm data based on the data label.

4. The method of claim 1, wherein prior to determining the proportion of data from each of the at least one functional layer in the alarm data based on the alarm data, further comprising:

determining a root cause judgment result of the data system according to the alarm data and the root cause judgment logic tree;

and determining the alarm root cause of the data system according to the root cause judgment result.

5. The method of claim 1, wherein machine learning the root cause analysis model based on sets of sample alarm data for the data system comprises:

marking an alarm root cause for at least one group of first sample alarm data based on a root cause judgment logic tree to obtain marked first sample alarm data, wherein the first sample alarm data is one of the plurality of groups of sample alarm data;

labeling an alarm root cause for at least one group of second sample alarm data based on a predetermined labeling result to obtain labeled second sample alarm data, wherein the second sample alarm data is one of the plurality of groups of sample alarm data, and the second sample alarm data cannot determine the alarm root cause through a root cause judgment logic tree;

and training a machine learning model by using the labeled first sample alarm data and the labeled second sample alarm data to obtain the root cause analysis model.

6. The method of claim 5, wherein the machine learning model comprises: and (3) a multiple logistic regression model.

7. The method of claim 1, wherein in the event that the alarm data comprises first alarm data and second alarm data, obtaining alarm data for a data system comprises:

acquiring the first alarm data from an alarm log of the data system;

and acquiring the second alarm data through a monitoring plug-in of the data system.

8. An alarm processing apparatus, comprising:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring alarm data of a data system, and the data system comprises at least one functional layer;

the first determining module is used for determining the data proportion of each functional layer in the at least one functional layer in the alarm data based on the alarm data;

and the second determination module is used for determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system.

9. A computer-readable storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the alert processing method of any one of claims 1 to 6.

10. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the alarm handling method according to any one of claims 1 to 6 when running.

Background

In the field of intelligent operation and maintenance, the root cause analysis of a data system only has some theoretical processing schemes, and does not have a formed engineering scheme. In the actual use process, the existing technology is difficult to fall to the ground in the aspect of engineering, the root cause analysis is directly carried out by using a ready mainstream algorithm, the actual effect is difficult to achieve, and the existing technology is even completely unavailable in the initial stage.

In the existing alarm method for processing the data system, a unified alarm design is not provided, in addition, in a large amount of multi-terminal alarm data, a plurality of alarms caused by mutual dependence exist, after the alarms are sent to each person, relevant persons need to carry out testing and deduction according to actual experience, manual root positioning is carried out, problems are not processed quickly enough, and problems caused by human factors also exist.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the invention provides an alarm processing method, an alarm processing device, a computer readable storage medium and a processor, which are used for at least solving the technical problem that the judgment of the root cause of the alarm of a data system depends on manual experience.

According to an aspect of an embodiment of the present invention, an alarm processing method is provided, including: acquiring alarm data of a data system, wherein the data system comprises at least one functional layer; determining data proportions from each functional layer in the at least one functional layer in the alarm data based on the alarm data; and determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system.

Optionally, determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model includes: inputting the data proportion into a fault functional layer recognition module of the root cause analysis model, and outputting a functional layer with a fault in the data system; and inputting the alarm data from the functional layer with the fault into a root cause determination module of the root cause analysis model, and outputting the alarm root cause of the data system.

Optionally, determining, based on the alarm data, a data proportion from each functional layer of the at least one functional layer in the alarm data, includes: acquiring a data tag in the alarm data, wherein the data tag is used for identifying the corresponding relation between the alarm data and the functional layer; and determining the proportion of data from each functional layer in the at least one functional layer in the alarm data based on the data label.

Optionally, before determining, based on the alarm data, a data proportion from each functional layer of the at least one functional layer in the alarm data, the method further includes: determining a root cause judgment result of the data system according to the alarm data and the root cause judgment logic tree; and determining the alarm root cause of the data system according to the root cause judgment result.

Optionally, the obtaining the root cause analysis model by performing machine learning based on a plurality of groups of sample alarm data of the data system includes: marking an alarm root cause for at least one group of first sample alarm data based on a root cause judgment logic tree to obtain marked first sample alarm data, wherein the first sample alarm data is one of the plurality of groups of sample alarm data; labeling an alarm root cause for at least one group of second sample alarm data based on a predetermined labeling result to obtain labeled second sample alarm data, wherein the second sample alarm data is one of the plurality of groups of sample alarm data, and the second sample alarm data cannot determine the alarm root cause through a root cause judgment logic tree; and training a machine learning model by using the labeled first sample alarm data and the labeled second sample alarm data to obtain the root cause analysis model.

Optionally, the machine learning model comprises: and (3) a multiple logistic regression model.

Optionally, in a case that the alarm data includes the first alarm data and the second alarm data, acquiring alarm data of the data system includes: acquiring the first alarm data from an alarm log of the data system; and acquiring the second alarm data through a monitoring plug-in of the data system.

According to another aspect of the embodiments of the present invention, there is also provided an alarm processing apparatus, including: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring alarm data of a data system, and the data system comprises at least one functional layer; the first determining module is used for determining the data proportion of each functional layer in the at least one functional layer in the alarm data based on the alarm data; and the second determination module is used for determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system.

According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute any one of the above alarm processing methods.

According to still another aspect of the embodiments of the present invention, there is further provided a processor, where the processor is configured to execute a program, where the program executes any one of the alarm processing methods described above when running.

In the embodiment of the invention, the mode of acquiring the alarm data of the data system is adopted, the data proportion of each functional layer in at least one functional layer in the alarm data is determined based on the alarm data, the alarm root cause of the data system is determined based on the alarm data, the data proportion and a pre-trained root cause analysis model, and the purpose of analyzing the alarm data of the data system and further determining the root cause of the alarm is achieved, so that the technical effect of intelligently judging the root cause of the alarm of the data system is realized, and the technical problem that the judgment of the root cause of the alarm of the data system depends on artificial experience is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

FIG. 1 is a flow chart of an alarm processing method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a CDP system root cause decision logic tree provided in accordance with an alternative embodiment of the present invention;

fig. 3 is a block diagram of an alarm processing apparatus according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

In accordance with an embodiment of the present invention, an alarm handling method embodiment is provided, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.

Fig. 1 is a schematic flow chart of an alarm processing method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:

step S102, alarm data of a data system is obtained, wherein the data system comprises at least one functional layer. Alternatively, the data system may be a system for comprehensively processing data, and includes functions of collecting, processing, storing, distributing, and the like of data, the more the functions of the data system are, the more complicated the processed data are, the more difficult the analysis of the reason of the alarm thereof is, and when a certain specific service of the data system is in a problem, multiple alarms in the data system may be triggered, which may cause difficulty in the root cause analysis of the alarm. The functional layer may be one functional module in the data system, or may be a general name of a plurality of functional modules in the data system, which is determined according to the specific architecture of the data system.

Step S104, determining the data proportion of each functional layer in at least one functional layer in the alarm data based on the alarm data. The alarm data is derived from the data system, and therefore, by analyzing and judging the alarm data, which functional layer of the data system the alarm data comes from can be determined. Furthermore, by performing statistics and summarization on the sources of the alarm data, the proportion of the alarm data from each functional layer in a batch of alarm data to all the alarm data of the batch can be determined, and the proportion is used as one of the bases for subsequent root cause analysis.

Optionally, before determining the data proportion from each functional layer in the at least one functional layer in the alarm data, the alarm data in the determined time window may be selected as the analysis basis. For example, in a time period of two minutes in a certain duration, if the alarm amount of the data system suddenly increases greatly and exceeds a predetermined threshold, it may be determined that the data system has a fault, and at this time, the alarm data in the predetermined time window of the two minutes may be acquired as the basic data for performing the subsequent root cause analysis.

And S106, determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system. The root cause analysis model is a machine learning model, and the root cause analysis model can be obtained by pre-training, for example, sample alarm data of a data system can be uploaded to a big data platform at ordinary times, the training of the root cause analysis model is completed in the big data platform, the model is stored in the platform, and the trained model is directly called when the root cause analysis is subsequently carried out. Optionally, the results obtained after each root cause analysis using the model may also be uploaded to a big data platform for further iterative optimization of the root cause analysis model.

In this step, each set of sample alarm data used for training the root cause analysis model may include a batch of alarm data of the data system, a data ratio of the batch of alarm data from each functional layer of the data system, and a root cause of the data system causing the batch of alarm data. Because the sample alarm data is preprocessed in a more detailed mode, the root cause analysis model obtained by training the sample alarm data has more accurate analysis capability and faster analysis speed.

Through the steps, the mode of acquiring the alarm data of the data system is adopted, the data proportion of each functional layer in at least one functional layer in the alarm data is determined based on the alarm data, the alarm root cause of the data system is determined based on the alarm data, the data proportion and the pre-trained root cause analysis model, and the purpose of analyzing the alarm data of the data system and further determining the alarm root cause is achieved, so that the technical effect of intelligently judging the alarm root cause of the data system is achieved, and the technical problem that the judgment of the alarm root cause of the data system depends on artificial experience is solved.

As an alternative implementation, the Data system described in this embodiment may be a client Data Platform (CDP system). The CDP system is a data comprehensive management platform from business, can collect all client data and store the data in a unified data platform which can be accessed by multiple departments, and all the departments of an enterprise can use the data easily. Meanwhile, the CDP system is built for business personnel drive, not IT personnel, and a business team can directly operate on the CDP system without depending on the IT personnel.

As can be seen from the above description, the CDP platform can be used for processing the whole flow of data from collection to output application, and can be directly operated by non-IT professionals, so that the related modules are numerous and large in system, and are relatively laborious to maintain.

In this optional embodiment, the alarm data of the CDP system including at least one functional layer may be acquired. For example, the functional layers of the CDP system may include an application layer, a data access layer, a data processing layer, a platform dependent layer, a hardware layer, and the like, and each layer may be further subdivided by taking the category as a main basis. According to different layering modes, the proportion of alarm data from each functional layer of the CDP system can be determined, and the specific position of the failure of the CDP system can be conveniently determined.

In addition, a root cause analysis model suitable for the CDP system can be trained in advance aiming at the CDP system and a functional layer layering mode corresponding to the CDP system, and then the root cause of the alarm causing the CDP system to generate the alarm data is determined by using the root cause analysis model based on the alarm data and the data proportion.

As an alternative embodiment, in the case that the alarm data includes the first alarm data and the second alarm data, the first alarm data may be obtained from an alarm log of the data system; and acquiring second alarm data through a monitoring plug-in of the data system.

For example, when the data system is a CDP system, the alarm data of the CDP system may be acquired in a plurality of ways: firstly, the alarm log of the CDP system can be directly read, and the alarm log records are summarized; secondly, interfaces of various alarm plug-ins of the CDP system can be called, alarm data can be obtained from the alarm plug-ins, and the alarm plug-ins can adopt Alertmanager, Grafana or Zabbix and the like; thirdly, the alarm monitoring function module of the CDP system can be directly butted to obtain alarm data. Aiming at the various alarm data sources, a unified interface can be compiled, and data of various alarm data sending ends are summarized through the unified interface, so that subsequent alarm root cause analysis is facilitated.

As an optional embodiment, a data tag in the alarm data may be obtained, where the data tag is used to identify a correspondence between the alarm data and the functional layer; and determining the proportion of data from each functional layer in the at least one functional layer in the alarm data based on the data label. In order to facilitate the determination of the proportion of the alarm data respectively generated by each functional layer of the data system, the alarm data can be labeled when the alarm data are collected, and the functional layer from which the alarm data come is noted, otherwise, when the alarm data are all collected, the sources of the alarm data are difficult to distinguish.

As an alternative embodiment, the root cause analysis model can be trained as follows: firstly, based on a root cause judgment logic tree, marking an alarm root cause for at least one group of first sample alarm data to obtain marked first sample alarm data, wherein the first sample alarm data is one of a plurality of groups of sample alarm data; then, based on a predetermined labeling result, labeling a root cause of alarm for at least one group of second sample alarm data to obtain labeled second sample alarm data, wherein the second sample alarm data is one of a plurality of groups of sample alarm data, and the second sample alarm data cannot determine the root cause of alarm through a root cause judgment logic tree; and finally, training a machine learning model by using the labeled first sample alarm data and the labeled second sample alarm data to obtain a root cause analysis model.

In this alternative embodiment, the root cause judgment logic tree may be processing logic written through practical experience of years of maintenance work on the same data system and the upstream and downstream relationships of the system with other systems. For example, by continuous improvement and tuning, most alarm root causes of the CDP system can be judged according to the root cause judgment logic tree of the CDP system.

Fig. 2 is a schematic diagram of a CDP system root cause judgment logic tree according to an alternative embodiment of the present invention, and as shown in fig. 2, the alarm data of the CDP system is processed according to the CDP system root cause judgment logic tree, so that multi-step judgment can be performed, and the root cause of the alarm of the CDP system is finally judged. For example, after the alarm data occurs in the CDP system, it is first determined whether the front-end service of the CDP system and the back-end service of the CDP system are abnormal according to the alarm data. Taking the CDP system back-end service as an example, when finding that the CDP system back-end service is normal, further judging whether the ETL data stream depended on by the CDP system back-end service is normal; when the rear-end service of the CDP system is found to be abnormal, whether the server is abnormal or the software is abnormal is further judged, and specific alarm information or alarm logs related to the abnormality are packaged and sent to a subsequent processing unit.

Through the CDP system root cause judgment logic tree, the alarm root cause of the sample alarm data can be determined and marked in the sample alarm data for subsequent model training.

As an alternative embodiment, the machine learning model may comprise a multiple logistic regression model. Logistic regression is generally used to classify problems, does not require a linear relationship between dependent variables and independent variables, and can handle multiple types of relationships. According to actual verification, the multivariate logistic regression model is very suitable for the root cause analysis problem of the data system, and the optimal processing result of the alarm root cause analysis can be generated.

As an optional embodiment, before determining the data proportion from each functional layer in at least one functional layer in the alarm data based on the alarm data, the root cause determination result of the data system may be determined according to the alarm data and the root cause determination logic tree, and then the alarm root cause of the data system may be determined according to the root cause determination result.

For example, for the CDP system, since the CDP system root cause judgment logic tree itself can analyze and judge the alarm root cause of the CDP system to some extent, before inputting the alarm data of the CDP system into the root cause analysis model, the root cause judgment logic tree can be used to analyze and judge the alarm data of the CDP system to obtain the judgment result. When the judgment result is that the alarm root cause of the CDP system can be determined according to the root cause judgment logic tree, the alarm root cause of the system can be directly output or the comprehensive consideration can be carried out by combining the result of the root cause analysis model; when the judgment result is that the alarm root cause of the CDP system can not be determined, the root cause analysis model is required to be relied on to judge the alarm root cause of the CDP system.

As an alternative embodiment, the alarm root cause of the data system is determined based on the alarm data, the data proportion and the pre-trained root cause analysis model, and the following method can be adopted: inputting the data proportion into a fault functional layer recognition module of a root cause analysis model, and outputting a functional layer with a fault in a data system; and inputting the alarm data from the functional layer with the fault into a root cause determination module of the root cause analysis model, and outputting the alarm root cause of the data system.

In this optional embodiment, the failure functional layer identification module of the root cause analysis model may directly identify which layer the failed functional layer in the data system is, that is, from which functional layer the root cause of the alarm should be determined. For example, generally, when a service in a specific functional layer of a data system has a problem, the proportion of alarm data from the specific functional layer in alarm data sent by the entire data system should be the highest, so that the functional layer with the highest alarm data proportion can be determined to be the functional layer with the fault. For another example, when a service problem occurs in a specific functional layer of the data system, a specific rule may occur in the distribution of alarm data from each functional layer in the alarm data of the entire data system, and accordingly, which functional layer has a failure may be determined. In this optional embodiment, the determination of the functional layer with the fault is performed by determining the functional layer with the fault of the root cause analysis model by the module, so that the accuracy of identifying the root cause of the alarm can be improved.

In addition, based on the functional layer with the fault, which is obtained by the judgment, the optional embodiment may input only the alarm data from the functional layer into the root cause analysis model. The method can shield the interference of alarm data of other functional layers, can reduce the operation amount of the root cause analysis model, saves the operation amount consumption of the root cause analysis data processing, accelerates the data processing speed of the whole root cause analysis process, outputs the alarm root cause of the data system as soon as possible, and is favorable for operation and maintenance personnel to quickly respond and maintain and adjust the service of faults in the whole system.

Example 2

According to an embodiment of the present invention, there is also provided an alarm processing apparatus for implementing the alarm processing method, and fig. 3 is a block diagram of a structure of the alarm processing apparatus according to the embodiment of the present invention, as shown in fig. 3, the alarm processing apparatus includes: the acquisition module 32, the first determination module 34, and the second determination module 36, which will be described below.

An obtaining module 32, configured to obtain alarm data of a data system, where the data system includes at least one functional layer;

a first determining module 34, connected to the obtaining module 32, for determining, based on the alarm data, a data proportion of each functional layer in the alarm data from at least one functional layer;

and a second determining module 36, connected to the first determining module 34, for determining the root cause of the alarm of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by performing machine learning based on a plurality of groups of sample alarm data of the data system.

It should be noted here that the acquiring module 32, the first determining module 34 and the second determining module 36 correspond to steps S102 to S106 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1.

Example 3

An embodiment of the present invention may provide a computer device, and optionally, in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.

The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the alarm processing method and apparatus in the embodiments of the present invention, and the processor executes various functional applications and data processing by operating the software programs and modules stored in the memory, that is, the alarm processing method described above is implemented. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring alarm data of a data system, wherein the data system comprises at least one functional layer; determining the proportion of data from each functional layer in at least one functional layer in the alarm data based on the alarm data; and determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system.

Optionally, the processor may further execute the program code of the following steps: determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the alarm root cause comprises the following steps: inputting the data proportion into a fault functional layer recognition module of a root cause analysis model, and outputting a functional layer with a fault in a data system; and inputting the alarm data from the functional layer with the fault into a root cause determination module of the root cause analysis model, and outputting the alarm root cause of the data system.

Optionally, the processor may further execute the program code of the following steps: determining a data proportion from each functional layer in at least one functional layer in the alarm data based on the alarm data, comprising: acquiring a data tag in the alarm data, wherein the data tag is used for identifying the corresponding relation between the alarm data and the functional layer; and determining the proportion of data from each functional layer in the at least one functional layer in the alarm data based on the data label.

Optionally, the processor may further execute the program code of the following steps: before determining the proportion of data from each functional layer in at least one functional layer in the alarm data based on the alarm data, the method further comprises the following steps: determining a root cause judgment result of the data system according to the alarm data and the root cause judgment logic tree; and determining the alarm root cause of the data system according to the root cause judgment result.

Optionally, the processor may further execute the program code of the following steps: the method for obtaining the root cause analysis model by machine learning of the multi-group sample alarm data based on the data system comprises the following steps: marking an alarm root cause for at least one group of first sample alarm data based on the root cause judgment logic tree to obtain marked first sample alarm data, wherein the first sample alarm data is one of a plurality of groups of sample alarm data; labeling alarm root causes for at least one group of second sample alarm data based on a predetermined labeling result to obtain labeled second sample alarm data, wherein the second sample alarm data is one of a plurality of groups of sample alarm data, and the second sample alarm data cannot determine the alarm root causes through a root cause judgment logic tree; and training a machine learning model by using the labeled first sample alarm data and the labeled second sample alarm data to obtain a root cause analysis model.

Optionally, the processor may further execute the program code of the following steps: the machine learning model includes: and (3) a multiple logistic regression model.

Optionally, the processor may further execute the program code of the following steps: under the condition that the alarm data comprises first alarm data and second alarm data, acquiring the alarm data of the data system, wherein the alarm data comprises the following steps: acquiring first alarm data from an alarm log of a data system; and acquiring second alarm data through a monitoring plug-in of the data system.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 4

Embodiments of the present invention also provide a computer-readable storage medium. Optionally, in this embodiment, the computer-readable storage medium may be configured to store the program code executed by the alarm processing method provided in embodiment 1.

Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network, or in any one of a group of mobile terminals.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: acquiring alarm data of a data system, wherein the data system comprises at least one functional layer; determining the proportion of data from each functional layer in at least one functional layer in the alarm data based on the alarm data; and determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the root cause analysis model is obtained by machine learning based on a plurality of groups of sample alarm data of the data system.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: determining the alarm root cause of the data system based on the alarm data, the data proportion and a pre-trained root cause analysis model, wherein the alarm root cause comprises the following steps: inputting the data proportion into a fault functional layer recognition module of a root cause analysis model, and outputting a functional layer with a fault in a data system; and inputting the alarm data from the functional layer with the fault into a root cause determination module of the root cause analysis model, and outputting the alarm root cause of the data system.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: determining a data proportion from each functional layer in at least one functional layer in the alarm data based on the alarm data, comprising: acquiring a data tag in the alarm data, wherein the data tag is used for identifying the corresponding relation between the alarm data and the functional layer; and determining the proportion of data from each functional layer in the at least one functional layer in the alarm data based on the data label.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: before determining the proportion of data from each functional layer in at least one functional layer in the alarm data based on the alarm data, the method further comprises the following steps: determining a root cause judgment result of the data system according to the alarm data and the root cause judgment logic tree; and determining the alarm root cause of the data system according to the root cause judgment result.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: the method for obtaining the root cause analysis model by machine learning of the multi-group sample alarm data based on the data system comprises the following steps: marking an alarm root cause for at least one group of first sample alarm data based on the root cause judgment logic tree to obtain marked first sample alarm data, wherein the first sample alarm data is one of a plurality of groups of sample alarm data; labeling alarm root causes for at least one group of second sample alarm data based on a predetermined labeling result to obtain labeled second sample alarm data, wherein the second sample alarm data is one of a plurality of groups of sample alarm data, and the second sample alarm data cannot determine the alarm root causes through a root cause judgment logic tree; and training a machine learning model by using the labeled first sample alarm data and the labeled second sample alarm data to obtain a root cause analysis model.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: the machine learning model includes: and (3) a multiple logistic regression model.

Optionally, in this embodiment, the computer readable storage medium is configured to store program code for performing the following steps: under the condition that the alarm data comprises first alarm data and second alarm data, acquiring the alarm data of the data system, wherein the alarm data comprises the following steps: acquiring first alarm data from an alarm log of a data system; and acquiring second alarm data through a monitoring plug-in of the data system.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.