CN114723072B - Exporter combination method, system, equipment and storage medium - Google Patents
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Abstract
The invention provides an Exporter combination method, a system, equipment and a storage medium, which can adopt a machine learning method, train the relativity between an operation and maintenance scene and operation and maintenance indexes by inputting historical operation index data of the corresponding operation scene into a target model until reaching target training conditions, thereby obtaining a plurality of target operation and maintenance indexes related to the corresponding operation and maintenance scene, wherein the plurality of target operation and maintenance indexes respectively correspond to a plurality of exporters, and thus the plurality of exporters can be combined, so as to construct an operation and maintenance index system of the corresponding operation and maintenance scene. The machine learning means is used for rapidly determining the exporters related to the corresponding operation and maintenance scene, so that automatic combination is realized, and compared with the manual means of the related technology, the automatic combination scheme for the exporters is rapid and efficient and high in accuracy.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to an Exporter combining method, system, device, and storage medium.
Background
Prometheus is becoming a de facto standard for cloud protoplasm as a new generation of open source monitoring systems. Typically Prometheus Exporter is bundled with the monitoring target, in promethaus, the Exporter is responsible for collecting data, which is collected from the Agent end (Exporter) based on the Pull mode of operation. Prometheus Exporter exposes the Endpoint of the monitoring data collection to Prometheus Server through the form of HTTP service, and Prometheus Server can obtain the monitoring data to be collected by accessing the Endpoint provided by the Exporter.
In Prometaus, each exoter is independent of the others. For example, the API Server is concerned about the request number, delay, kube-state-metrics collection Pod, deployment and other resource meta information, the Node Exporter collection various machine index information such as CPU, memory, disk, and Process Exporter collection progress index information.
Thus, the more exporters that are present in the Prometaus monitoring system, the greater the operating and maintenance pressures will be.
It should be noted that the information disclosed in the foregoing background section is only for enhancement of understanding of the background of the invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an Exporter combination method, a system, equipment and a storage medium, which overcome the difficulties in the prior art and can improve the Exporter combination efficiency.
The embodiment of the invention provides an Exporter combination method, which comprises the following steps:
acquiring historical operation and maintenance index data of an operation and maintenance scene;
inputting historical operation and maintenance index data of the operation and maintenance scene into a target model, and training the correlation between the operation and maintenance scene and the operation and maintenance indexes until a target training condition is reached, so as to obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
And determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters.
Optionally, the target model is a recurrent neural network, and the input of the historical operation and maintenance index data of the operation and maintenance scene into the target model includes:
dividing historical operation and maintenance index data of an operation and maintenance scene into a plurality of operation and maintenance indexes;
and sequentially inputting the historical operation and maintenance indexes of the plurality of operation and maintenance indexes into the target model.
Optionally, the historical operation and maintenance index data of the operation and maintenance scene is divided into a plurality of operation and maintenance indexes, including:
performing operation and maintenance scene correlation analysis on historical operation and maintenance index data of the operation and maintenance scene by adopting factor analysis so as to divide the historical operation and maintenance index data into a plurality of operation and maintenance indexes;
the historical operation and maintenance indexes of the multiple operation and maintenance indexes are sequentially input into the target model, and the method comprises the following steps:
and sequencing the historical operation and maintenance index data of the plurality of operation and maintenance indexes from low to high according to the correlation analysis result, and sequentially inputting the historical operation and maintenance index data into the target model according to the sequencing.
Optionally, inputting historical operation and maintenance index data of the operation and maintenance scene into the target model includes:
generating an operation and maintenance scene matrix according to the different operation and maintenance scene information;
Generating an operation and maintenance index matrix by using historical operation and maintenance index data of the operation and maintenance scene;
and inputting the operation and maintenance scene matrix and the operation and maintenance index matrix into the target model.
Optionally, acquiring historical operation and maintenance index data of the operation and maintenance scene includes:
receiving a first input of a user;
and responding to the first input of the user, obtaining the requirement information of the operation and maintenance scene submitted by the user, and obtaining historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
Optionally, the Exporter combining method further includes:
and calling an operation and maintenance index system of the operation and maintenance scene, and collecting operation and maintenance index data through a plurality of exporters which are combined together and providing the operation and maintenance index data for a user.
Optionally, the Exporter combining method further includes:
and under the condition that the operation and maintenance index updating indication fed back by the user is obtained, updating an operation and maintenance index system of the operation and maintenance scene according to the operation and maintenance index updating indication.
Optionally, the Exporter combining method further includes:
receiving a second input from the user;
and responding to the second input of the user, acquiring the problem information of the operation and maintenance scene submitted by the user, positioning the operation and maintenance indexes of the problem in the operation and maintenance index system according to the problem information of the operation and maintenance scene, processing the problem, and feeding back the problem processing result to the user.
Optionally, acquiring historical operation and maintenance index data according to the requirement information of the operation and maintenance scene includes:
and under the condition that an operation and maintenance index system of the operation and maintenance scene constructed in advance is not obtained according to the requirement information of the operation and maintenance scene, acquiring historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
Optionally, the Exporter combining method further includes:
under the condition that an operation and maintenance index system of an operation and maintenance scene constructed in advance is obtained according to the requirement information of the operation and maintenance scene, the operation and maintenance index system of the operation and maintenance scene is called, operation and maintenance index data are collected through a plurality of exporters which are combined together, and the operation and maintenance index data are provided for a user.
Optionally, the Exporter combining method further includes:
under the condition that an update instruction of the exors running in the operation and maintenance scene is obtained, a new version of the plurality of exors combined together is pulled in response to the update instruction, and the plurality of exors combined together are updated by one key through the new version.
The embodiment of the disclosure also provides an Exporter combining system, which comprises:
the unified management module is used for managing and updating each Exporter and each operation and maintenance index;
the index system construction module is used for training a target model according to historical operation and maintenance index data of the operation and maintenance scene until a target training condition is reached, obtaining a plurality of target operation and maintenance indexes related to the operation and maintenance scene, and constructing an operation and maintenance index system of the operation and maintenance scene by combining a plurality of exporters corresponding to the plurality of target operation and maintenance indexes;
And the index system monitoring module is used for monitoring the operation and maintenance index system of the operation and maintenance scene.
The embodiment of the disclosure also provides an Exporter combining system, which comprises:
the acquisition module acquires historical operation and maintenance index data of the operation and maintenance scene;
the training module inputs historical operation and maintenance index data of the operation and maintenance scene into the target model, trains the correlation between the operation and maintenance scene and the operation and maintenance indexes until reaching the target training condition, and obtains a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
the construction module is used for determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters.
The embodiment of the invention also provides an Exporter combination device, which comprises:
a processor;
a memory having stored therein executable instructions of a processor;
wherein the processor is configured to perform the steps of the exor composition method described above via execution of executable instructions.
The embodiment of the invention also provides a computer readable storage medium for storing a program, which when executed implements the steps of the above-mentioned Exporter combination method.
The invention aims to provide an Exporter combination method, a system, equipment and a storage medium, which can adopt a machine learning method, train the correlation between an operation and maintenance scene and operation and maintenance indexes by inputting historical operation index data of the corresponding operation scene into a target model until reaching target training conditions, thereby obtaining a plurality of target operation and maintenance indexes relevant to the corresponding operation and maintenance scene, wherein the plurality of target operation and maintenance indexes respectively correspond to a plurality of exporters, and thus the plurality of exporters can be combined, so as to construct an operation and maintenance index system of the corresponding operation and maintenance scene.
By adopting the embodiment of the disclosure, the machine learning means can be used for rapidly determining the exors related to the corresponding operation and maintenance scene, so that automatic combination is realized, and compared with the manual means of the related technology, the exor combination scheme of the embodiment of the disclosure is rapid and efficient and has high accuracy.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings.
FIG. 1 is a schematic diagram of the structure of an Exporter's composition system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the structure of Prometaus based on the exoter assembly system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of the exor assembly system according to the present invention;
FIG. 4 is a flow chart of one of the embodiments of the exor composition method of the present invention;
FIG. 5 is a flow chart of a second embodiment of the exor composition method of the present invention;
FIG. 6 is a flow chart of a third embodiment of the exor composition method of the present invention;
FIG. 7 is a flow chart of a fourth embodiment of the exor composition method of the present invention;
FIG. 8 is a flow chart of a fifth embodiment of the exor composition method of the present invention;
FIG. 9 is a flow chart of a sixth embodiment of the exor composition method of the present invention;
FIG. 10 is a flowchart of an exor update process based on an exor composition system provided by an embodiment of the present disclosure;
FIG. 11 is a block diagram of one embodiment of an Exporter's composition system of the present invention;
FIG. 12 is a block diagram of a second embodiment of an Exporter's composition system according to the present invention;
FIG. 13 is a block diagram of a third embodiment of an Exporter's composition system according to the present invention;
FIG. 14 is a block diagram of a fourth embodiment of an Exporter's composition system according to the present invention;
FIG. 15 is a block diagram of a fifth embodiment of the exor composition system of the present invention;
FIG. 16 is a block diagram of a sixth embodiment of an Exporter's composition system according to the present invention;
FIG. 17 is a schematic diagram of the operation of the exor composition recommendation system of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware forwarding modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
Furthermore, the flow shown in the drawings is merely illustrative and not necessarily all steps are included. For example, some steps may be decomposed, some steps may be combined or partially combined, and the order of actual execution may be changed according to actual situations. The use of the terms "first," "second," and the like in the description herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. It should be noted that, without conflict, the embodiments of the present invention and features in different embodiments may be combined with each other.
Through searching and finding related technologies, the related technologies have proposed to combine some exporters, so that management such as consistent resource control and version upgrade can be performed on the combined exporters.
There are two types of related art Exporter combining schemes:
1. pulling a plurality of Exporter processes through a main process;
2. telegraf is used to support multiple types of inputs.
In practice, both schemes are found to require manual intervention and are poorly adaptive, resulting in lower exoter combining efficiency.
Aiming at the technical problems existing in the related technology, the embodiment of the disclosure provides an exor combination scheme, and the invention adopts a machine learning method, and trains the correlation between the operation and maintenance scene and the operation and maintenance indexes by inputting the historical operation index data of the corresponding operation scene into a target model until reaching a target training condition, thereby obtaining a plurality of target operation and maintenance indexes related to the corresponding operation and maintenance scene, wherein the plurality of target operation and maintenance indexes respectively correspond to a plurality of exors, so that the plurality of exors can be combined, and an operation and maintenance index system of the corresponding operation and maintenance scene is constructed.
By adopting the embodiment of the disclosure, the machine learning means can be used for rapidly determining the exors related to the corresponding operation and maintenance scene, so that automatic combination is realized, and compared with the manual means of the related technology, the exor combination scheme of the embodiment of the disclosure is rapid and efficient and has high accuracy.
By utilizing the scheme of the embodiment of the disclosure, a comprehensive operation and maintenance index system can be built aiming at operation and maintenance scenes with different requirements, the comprehensive operation and maintenance index system is written into a DB after summarized and screened, and then unified collection, updating and management are carried out on the comprehensive operation and maintenance index system, so that 1 pair of N centralized operation is realized, and therefore, the centralized management can be carried out on the exporters with the corresponding operation and maintenance scenes combined together, and the operation and maintenance burden is reduced.
Fig. 1 is a block diagram of an Exporter combining system according to an embodiment of the present invention. Referring to fig. 1, the exporter's composition system includes the following modules:
the unified management module 110 is configured to manage and update each Exporter and operation and maintenance index;
the index system construction module 120 is configured to train the target model according to the historical operation and maintenance index data of the operation and maintenance scene until reaching a target training condition, obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene, and construct an operation and maintenance index system of the operation and maintenance scene by combining a plurality of exporters corresponding to the plurality of target operation and maintenance indexes;
the index system monitoring module 130 is configured to monitor an operation and maintenance index system of the operation and maintenance scene.
The exor combination system of the embodiment of the disclosure can construct a corresponding operation and maintenance index system aiming at operation and maintenance scenes with different requirements, so that 1-pair N centralized operation can be carried out on operation and maintenance indexes of the corresponding operation and maintenance scenes and a plurality of exors corresponding to the operation and maintenance indexes, and operation and maintenance burden is reduced.
Referring to fig. 2, the Exporter combined system provided in the embodiment of the present disclosure performs unified management on each Exporter by interacting with Prometheus Server, and obtains operation and maintenance index data collected by the Exporter, specifically may collect the operation and maintenance index data by calling a metrics data interface provided by the Exporter.
In the embodiment of the present disclosure, the unified management module 110 is specifically configured to manage and update versions of each Exporter, and manage and update all operation and maintenance indexes. For example, as shown in fig. 3, the unified management module 310 is specifically configured to:
connecting a network;
version detection is carried out on the Exporter;
downloading the new version;
updating the system of the Exporter; and
And improving the performance of the Exporter.
Specifically, the index system construction module 320 specifically is configured to:
receiving requirement scene information submitted by a user;
and constructing a corresponding operation and maintenance index system according to the requirement scene information, feeding back to a user, and updating and monitoring. In the embodiment of the disclosure, in the process of using the operation and maintenance index system, a user is allowed to select updating, for example, when corresponding operation and maintenance index updating information submitted by the user is received, the operation and maintenance index system of a corresponding operation and maintenance scene is updated, for example, operation and maintenance indexes are added or deleted, and the operation and maintenance index system is iterated and perfected in advance.
Specifically, the index system monitoring module 330 is specifically configured to:
the method comprises the steps of performing index monitoring on corresponding exors based on a constructed operation and maintenance index system for user application scenes or user feedback, and performing real-time monitoring on the operation states and operation carding of operation and maintenance indexes acquired by various exors;
when the operation and maintenance problem is detected, submitting system analysis, submitting hidden danger, and processing feedback information of the monitoring result.
By using the embodiment of the disclosure, under the condition of automatically constructing the operation and maintenance index system aiming at different operation and maintenance scenes, a user can automatically call the operation and maintenance index system according to the operation and maintenance scenes without manual setting, multiplexing of the operation and maintenance index system is realized, and operation and maintenance monitoring efficiency is improved.
Under the condition, the monitoring operation and maintenance environments can be timely and separately deployed on different machines, the operation and maintenance index system of the corresponding operation and maintenance scene is monitored, and the efficiency is improved.
Fig. 4 is a flowchart of an exor combining method provided by an embodiment of the present disclosure, and as shown in fig. 4, the exor combining method includes the following steps:
step 410: acquiring historical operation and maintenance index data of an operation and maintenance scene;
step 420: inputting historical operation and maintenance index data of the operation and maintenance scene into a target model, and training the correlation between the operation and maintenance scene and the operation and maintenance indexes until a target training condition is reached, so as to obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
Step 430: and determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters.
The embodiment of the disclosure adopts a machine learning method to determine the correlation between the corresponding operation and maintenance scene and each operation and maintenance index, and rapidly determines the target operation and maintenance index related to the current operation and maintenance scene and the Exporter for monitoring and collecting the target operation and maintenance index, so as to automatically combine multiple exporters.
In the embodiment of the disclosure, the historical operation and maintenance index data of different operation and maintenance scenes can be input into the target model, so that an operation and maintenance index system of the different operation and maintenance scenes can be constructed, and the efficiency of an Exporter combination scheme is higher.
In the disclosed embodiment, the object model is a recurrent neural network (Recurrent Neural Network, RNN), which is a type of recurrent neural network (recursive neural network) that takes sequence data as input, performs recursion (recovery) in the evolution direction of the sequence, and all nodes (circulation units) are chained.
In this case, inputting historical operation and maintenance index data of the operation and maintenance scene into the target model includes:
Dividing historical operation and maintenance index data of an operation and maintenance scene into a plurality of operation and maintenance indexes;
and sequentially inputting the historical operation and maintenance indexes of the plurality of operation and maintenance indexes into the target model.
The cyclic neural network is used for processing the sequence data, and the embodiment obtains the sequence data by dividing the types of the operation and maintenance indexes in advance and reducing the dimension of the historical operation and maintenance index data, so that the training efficiency and the accuracy of the target model are improved.
Referring to fig. 5, where X1 and X2 represent historical operation and maintenance index data input at different moments, here, not only X1 and X2, but also input at other moments, not shown, will not be described in detail herein;
h 0 、h 1 、h 2 ……h T representing a hidden layer, wherein the hidden layer corresponds to an output hidden state and corresponds to an operation and maintenance index 1 and an index 2 … … index T respectively;
f w the activation function is characterized.
Wherein, the hidden state of each hidden layer output is used for generating the output of the moment and also participates in the calculation of the hidden state of the next moment.
Fig. 6 is a flowchart of an exor combining method according to another embodiment of the present disclosure, and as shown in fig. 6, the exor combining method includes the following steps:
step 610: acquiring historical operation and maintenance index data of an operation and maintenance scene;
step 620: performing operation scene correlation analysis on historical operation and maintenance index data of the operation and maintenance scene by adopting factor analysis so as to divide the historical operation and maintenance index data into a plurality of operation and maintenance indexes;
Step 630: sequencing historical operation and maintenance index data of a plurality of operation and maintenance indexes from low to high according to a correlation analysis result, sequentially inputting a target model according to the sequencing, and training the correlation between an operation and maintenance scene and the operation and maintenance indexes until a target training condition is reached, so as to obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
step 640: and determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters.
Where factor analysis attempts to identify a base variable (or factor) to interpret a correlation pattern embodied in a set of observed variables. Factor analysis is used for data dimension reduction, the purpose of which is to identify a few operation and dimension indicators to explain the variance observed in most dominant variables, which characterizes the importance level, i.e., the importance level, of the corresponding operation and dimension indicators.
For example, when factor concentration is performed by using factor analysis, the operation and maintenance indexes can be divided into four types, namely delay, flow, error number, saturation, and the like, which are taken as examples and are not limited solely.
Fig. 7 is a flowchart of an exor combining method according to another embodiment of the present disclosure, and as shown in fig. 7, the exor combining method includes the following steps:
Step 710: carrying out data cleaning and normalization on historical operation and maintenance index data of different operation and maintenance scenes;
step 720: generating an operation and maintenance scene matrix according to the different operation and maintenance scene information;
step 730: generating an operation and maintenance index matrix by using historical operation and maintenance index data of a corresponding operation and maintenance scene, wherein the operation and maintenance scene matrix and the operation and maintenance index matrix form a training sample;
step 740: carrying out correlation analysis on the operation and maintenance index matrix by adopting factor analysis, and sequencing from low to high according to the importance degree;
step 750: 70% of training samples are extracted, and RNNs are trained according to the sequence from low importance level to high importance level;
step 760: taking the rest 30% of the training samples to perform model test until reaching the target training conditions, and obtaining a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
step 770: and determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters.
Fig. 8 is a diagram of an exor combining method according to another embodiment of the present disclosure, as shown in fig. 8, where the exor combining method includes the following steps:
step 810: receiving a first input of a user;
step 820: responding to a first input of a user, obtaining requirement information of an operation and maintenance scene submitted by the user, and obtaining historical operation and maintenance index data according to the requirement information of the operation and maintenance scene;
Step 830: inputting historical operation and maintenance index data of the operation and maintenance scene into a target model, and training the correlation between the operation and maintenance scene and the operation and maintenance indexes until a target training condition is reached, so as to obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
step 840: and determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters.
According to the embodiment of the disclosure, the operation and maintenance index system of the requirement operation and maintenance scene can be automatically constructed according to the requirement of the user, so that manual intervention is reduced, and the adaptivity is improved.
In this embodiment, the first input may be a text input, or an action to a corresponding control in the system interface.
In this case, the Exporter combining method may further include:
and calling an operation and maintenance index system of the operation and maintenance scene, and collecting operation and maintenance index data through a plurality of exporters which are combined together and providing the operation and maintenance index data for a user.
In the embodiment, the user can directly call the operation and maintenance index system of the operation and maintenance scene, so that the monitoring operation and maintenance environments are deployed on different machines in time separately, and the operation and maintenance efficiency is improved.
The embodiment of the disclosure also allows the user to customize the operation and maintenance index system of the operation and maintenance scene. And under the condition that the operation and maintenance index system of the current operation and maintenance scene is provided for a user and the operation and maintenance index updating instruction fed back by the user is obtained, updating the operation and maintenance index system of the operation and maintenance scene according to the operation and maintenance index updating instruction.
The operation and maintenance index update instruction may be to add or delete an operation and maintenance index in the operation and maintenance index system, which is not limited herein.
In an embodiment of the present disclosure, acquiring historical operation and maintenance index data according to requirement information of an operation and maintenance scene includes:
and under the condition that an operation and maintenance index system of the operation and maintenance scene constructed in advance is not obtained according to the requirement information of the operation and maintenance scene, acquiring historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
Under the condition that the demand information of the operation and maintenance scene submitted by the user is received, whether a corresponding operation and maintenance index system is built in advance can be determined, and if a target model is trained based on the historical operation and maintenance index data of the corresponding operation and maintenance scene, the operation and maintenance index system is built automatically.
In the embodiment of the disclosure, under the condition that an operation and maintenance index system of an operation and maintenance scene constructed in advance is obtained according to the requirement information of the operation and maintenance scene, the operation and maintenance index system of the operation and maintenance scene is called, and operation and maintenance index data are collected through multiple exporters combined together and provided for a user.
The embodiment can realize the multiplexing of the operation and maintenance index system of the corresponding operation and maintenance scene without reconstruction, and improves the operation and maintenance efficiency of the system.
In the embodiment of the disclosure, under the condition of constructing the operation and maintenance index system of the operation and maintenance scene in advance, the operation states and operation carding of all operation and maintenance indexes of the operation and maintenance scene can be monitored in real time.
Specifically, fig. 9 is a flowchart of an exor combining method provided by an embodiment of the present disclosure, and as shown in fig. 9, the exor combining method may include the following steps:
step 910: receiving a first input of a user;
step 920: responding to a first input of a user, obtaining requirement information of an operation and maintenance scene submitted by the user, and obtaining historical operation and maintenance index data according to the requirement information of the operation and maintenance scene;
step 930: inputting historical operation and maintenance index data of the operation and maintenance scene into a target model, and training the correlation between the operation and maintenance scene and the operation and maintenance indexes until a target training condition is reached, so as to obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
step 940: determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of an operation and maintenance scene by combining the plurality of exporters;
step 950: invoking an operation and maintenance index system of the operation and maintenance scene, and collecting operation and maintenance index data through a plurality of exporters which are combined together, and providing the operation and maintenance index data for a user;
step 960: receiving a second input from the user;
step 970: and responding to the second input of the user, acquiring the problem information of the operation and maintenance scene submitted by the user, positioning and processing the problem operation and maintenance indexes in the operation and maintenance index system according to the problem information of the operation and maintenance scene, and feeding back the problem processing result to the user.
The embodiment provides index monitoring service for users, so that system analysis is carried out on operation and maintenance scene problem information submitted by the users, and related hidden danger submitting processing is carried out and fed back to the users.
The embodiment of the disclosure can realize all the exor one-key updating of the corresponding operation and maintenance scene.
Specifically, under the condition that an update instruction for the version of the exor of the operation and maintenance scene is obtained, in response to the update instruction, pulling new versions of multiple exors combined together, and carrying out one-key update on the multiple exors combined together by utilizing the new versions.
The method can realize unified update management of the exporters in the same operation and maintenance scene, and improves operation and maintenance efficiency.
The update command may be generated by triggering a corresponding control of the system interface by the user, and the triggering action may be touch input or a clicking action of the peripheral device, which is not limited herein.
In further embodiments, the update instruction may be automatically triggered if the system automatically detects a new version.
Fig. 10 is a flowchart of an exor update process based on an exor combination system according to an embodiment of the disclosure, where the method includes the following steps:
step 1010: the system detects an update version of the Exporter;
Step 1020: creating yaml updated resources;
step 1030: whether to configure the new information of the Exporter;
step 1040: if yes, the Exporter is updated normally, the updating is completed, and a new version is started;
step 1050: if not, the service rolls back to the appointed version, detects the abnormality and processes, and reconfigures the update file to update.
Wherein creating a yaml update resource includes creating a yaml file:
apiVersion: api version information;
kined: specifying a type of creating the resource object;
metadata: nested fields inside the metadata define the names, namespaces, etc. of the resource objects;
spec: the specification defines what characteristics the resource should possess, relying on the controller to ensure that the performance can meet the user's desired state;
status: the current state of the resource is displayed, and K8s is the current state is ensured to be infinitely close to the target state so as to meet the user expectations.
FIG. 11 is a block diagram of one embodiment of an Exporter's composition system of the present invention. As shown in FIG. 11, the exor composition system of the present invention includes, but is not limited to:
an acquisition module 1110 for acquiring historical operation and maintenance index data of an operation and maintenance scene;
the training module 1120 inputs historical operation and maintenance index data of the operation and maintenance scene into the target model, trains correlation between the operation and maintenance scene and the operation and maintenance index until reaching a target training condition, and obtains a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
The construction module 1130 determines a plurality of corresponding exporters according to the plurality of target operation indexes, and constructs an operation index system of the operation scene by combining the plurality of exporters.
The implementation principle of the above modules is referred to the related description in the exobler combining method, and will not be repeated here.
The exor combination system of the embodiment of the disclosure adopts a machine learning method to determine the correlation between the corresponding operation and maintenance scene and each operation and maintenance index, and rapidly determines the target operation and maintenance index related to the current operation and maintenance scene and monitors and collects the target operation and maintenance index, so as to automatically combine multiple exors.
In the embodiment of the disclosure, the historical operation and maintenance index data of different operation and maintenance scenes can be input into the target model, so that an operation and maintenance index system of the different operation and maintenance scenes can be constructed, and the efficiency of an Exporter combination scheme is higher.
Optionally, the target model is a recurrent neural network, and the training module 1120 is specifically configured to:
dividing historical operation and maintenance index data of an operation and maintenance scene into a plurality of operation and maintenance indexes;
and sequentially inputting the historical operation and maintenance indexes of the plurality of operation and maintenance indexes into the target model.
Optionally, the training module 1120 is specifically further configured to:
performing operation and maintenance scene correlation analysis on historical operation and maintenance index data of the operation and maintenance scene by adopting factor analysis so as to divide the historical operation and maintenance index data into a plurality of operation and maintenance indexes;
and sequencing the historical operation and maintenance index data of the plurality of operation and maintenance indexes from low to high according to the correlation analysis result, and sequentially inputting the historical operation and maintenance index data into the target model according to the sequencing.
Optionally, the training module 1120 is specifically configured to:
generating an operation and maintenance scene matrix according to the different operation and maintenance scene information;
generating an operation and maintenance index matrix by using historical operation and maintenance index data of the operation and maintenance scene;
and inputting the operation and maintenance scene matrix and the operation and maintenance index matrix into the target model.
Optionally, the acquiring module 1110 is specifically configured to:
receiving a first input of a user;
and responding to the first input of the user, obtaining the requirement information of the operation and maintenance scene submitted by the user, and obtaining historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
Optionally, the acquiring module 1110 is specifically configured to:
and under the condition that an operation and maintenance index system of the operation and maintenance scene constructed in advance is not obtained according to the requirement information of the operation and maintenance scene, acquiring historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
Optionally, compared to fig. 11, the exor combination system shown in fig. 12 further includes the following modules:
the first calling module 1210 calls the operation and maintenance index system of the operation and maintenance scene under the condition that the operation and maintenance index system of the operation and maintenance scene constructed in advance is obtained according to the requirement information of the operation and maintenance scene, and collects operation and maintenance index data through multiple exporters combined together and provides the operation and maintenance index data for a user.
Optionally, compared to fig. 11, the exor combination system shown in fig. 13 further includes the following modules:
the second calling module 1310 calls the operation and maintenance index system of the operation and maintenance scene, and collects operation and maintenance index data through multiple exporters combined together and provides the operation and maintenance index data to the user.
Optionally, compared to fig. 13, the exor combination system shown in fig. 14 further includes the following modules:
the updating module 1410 updates the operation and maintenance index system of the operation and maintenance scene according to the operation and maintenance index update instruction when the operation and maintenance index update instruction fed back by the user is obtained.
Optionally, compared to fig. 13, the exor combination system shown in fig. 15 further includes the following modules:
a receiving module 1510 that receives a second input of a user;
the problem processing module 1520 obtains the operation and maintenance scene problem information submitted by the user in response to the second input of the user, locates and processes the problem operation and maintenance indexes in the operation and maintenance index system according to the operation and maintenance scene problem information, and feeds back the problem processing result to the user.
Optionally, compared to fig. 11, the exor combination system shown in fig. 16 further includes the following modules:
the one-key update module 1610, in response to the update instruction, pulls new versions of the plurality of exporters combined together, and performs one-key update on the plurality of exporters combined together using the new versions, in case of obtaining the update instruction for the exporters running in the operation and maintenance scenario.
The embodiment of the invention also provides an Exporter combination device which comprises a processor. A memory having stored therein executable instructions of a processor. Wherein the processor is configured to execute the steps of the Exporter's composition method via execution of executable instructions.
As shown above, the exor combination device in the embodiment of the disclosure adopts the machine learning method to determine the correlation between the corresponding operation and maintenance scene and each operation and maintenance index, and rapidly determines the target operation and maintenance index related to the current operation and maintenance scene and the exor for monitoring and collecting the target operation and maintenance index, so as to automatically combine multiple exors.
In the embodiment of the disclosure, the historical operation and maintenance index data of different operation and maintenance scenes can be input into the target model, so that an operation and maintenance index system of the different operation and maintenance scenes can be constructed, and the efficiency of an Exporter combination scheme is higher.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" platform.
Fig. 17 is a schematic structural diagram of the exor composition device of the present invention. An electronic device 1700 according to such an embodiment of the invention is described below with reference to fig. 17. The electronic device 1700 shown in fig. 17 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 17, the electronic device 1700 is in the form of a general purpose computing device. The components of electronic device 1700 may include, but are not limited to: at least one processing unit 1710, at least one storage unit 1720, a bus 1730 connecting the different platform components (including the storage unit 1720 and the processing unit 1710), a display unit 1740, and the like.
Wherein the storage unit stores program code that can be executed by the processing unit 1710, such that the processing unit 1710 performs the steps according to various exemplary embodiments of the present invention described in the above-described exor combination method section of the present specification. For example, the processing unit 1710 may perform the steps as shown in fig. 4-9.
Storage unit 1720 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 1721 and/or cache memory unit 1722, and may further include read only memory unit (ROM) 1723.
Storage unit 1720 may also include a program/utility 1724 having a set (at least one) of program modules 1725, such program modules 1725 including, but not limited to: processing systems, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 1730 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, a graphics accelerator port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 1700 may also communicate with one or more external devices 1770 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 500, and/or any device (e.g., router, modem, etc.) that enables the electronic device 1700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1750.
Also, electronic device 1700 can communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, for example, the Internet, through network adapter 1760. Network adapter 1760 may communicate with other modules of electronic device 1700 via bus 1730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage platforms, and the like.
The embodiment of the invention also provides a computer readable storage medium for storing a program, and the steps of the Exporter combination method implemented when the program is executed. In some possible embodiments, the aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the above-mentioned exor combination method section of this specification, when the program product is run on the terminal device.
A program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out processes of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In summary, the present invention aims to provide an Exporter combining method, a system, a device and a storage medium, which adopt a machine learning method to determine the correlation between a corresponding operation and maintenance scene and each operation and maintenance index, and rapidly determine the target operation and maintenance index related to the current operation and maintenance scene and monitor and acquire the target operation and maintenance index, so as to automatically combine multiple exporters. In the embodiment of the disclosure, the historical operation and maintenance index data of different operation and maintenance scenes can be input into the target model, so that an operation and maintenance index system of the different operation and maintenance scenes can be constructed, and the efficiency of an Exporter combination scheme is higher.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (14)
1. An Exporter combining method, comprising:
acquiring historical operation and maintenance index data of an operation and maintenance scene;
inputting historical operation and maintenance index data of the operation and maintenance scene into a target model, and training the correlation between the operation and maintenance scene and the operation and maintenance indexes until a target training condition is reached, so as to obtain a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes, and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters;
the target model is a cyclic neural network, and the inputting the historical operation and maintenance index data of the operation and maintenance scene into the target model comprises the following steps:
dividing the historical operation and maintenance index data of the operation and maintenance scene into a plurality of operation and maintenance indexes;
And sequentially inputting the historical operation and maintenance indexes of the plurality of operation and maintenance indexes into a target model.
2. The exor composition method according to claim 1, wherein dividing the historical operation and maintenance index data of the operation and maintenance scene into a plurality of operation and maintenance indexes comprises:
performing operation and maintenance scene correlation analysis on historical operation and maintenance index data of the operation and maintenance scene by adopting factor analysis so as to divide the historical operation and maintenance index data into a plurality of operation and maintenance indexes;
the historical operation and maintenance indexes of the plurality of operation and maintenance indexes are sequentially input into a target model, and the method comprises the following steps:
and sequencing the historical operation and maintenance index data of the multiple operation and maintenance indexes from low to high according to the correlation analysis result, and sequentially inputting the historical operation and maintenance index data into the target model according to the sequencing.
3. The exor composition method according to claim 1, wherein inputting historical operation and maintenance index data of the operation and maintenance scene into a target model comprises:
generating an operation and maintenance scene matrix according to the different operation and maintenance scene information;
generating an operation and maintenance index matrix by utilizing the historical operation and maintenance index data of the operation and maintenance scene;
and inputting the operation and maintenance scene matrix and the operation and maintenance index matrix into the target model.
4. The exor composition method according to claim 1, wherein obtaining historical operation and maintenance index data of an operation and maintenance scene comprises:
Receiving a first input of a user;
and responding to the first input of the user, obtaining the requirement information of the operation and maintenance scene submitted by the user, and obtaining the historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
5. The exor composition method according to claim 4, wherein the exor composition method further comprises:
and calling an operation and maintenance index system of the operation and maintenance scene, and collecting operation and maintenance index data through the plurality of exporters which are combined together and providing the operation and maintenance index data for the user.
6. The exor composition method according to claim 5, wherein the exor composition method further comprises:
and under the condition that the operation and maintenance index updating instruction fed back by the user is obtained, updating an operation and maintenance index system of the operation and maintenance scene according to the operation and maintenance index updating instruction.
7. The exor composition method according to claim 5, wherein the exor composition method further comprises:
receiving a second input of the user;
and responding to the second input of the user, acquiring the problem information of the operation and maintenance scene submitted by the user, positioning and processing the problem operation and maintenance indexes in the operation and maintenance index system according to the problem information of the operation and maintenance scene, and feeding back the problem processing result to the user.
8. The exor composition method according to claim 4, wherein obtaining the historical operation and maintenance index data according to the requirement information of the operation and maintenance scene comprises:
and under the condition that an operation and maintenance index system of the operation and maintenance scene constructed in advance is not obtained according to the requirement information of the operation and maintenance scene, acquiring the historical operation and maintenance index data according to the requirement information of the operation and maintenance scene.
9. The exor composition method according to claim 8, wherein the exor composition method further comprises:
and under the condition that an operation and maintenance index system of the operation and maintenance scene constructed in advance is obtained according to the requirement information of the operation and maintenance scene, calling the operation and maintenance index system of the operation and maintenance scene, collecting operation and maintenance index data through the plurality of exporters which are combined together, and providing the operation and maintenance index data for a user.
10. The exor composition method according to claim 1, wherein the exor composition method further comprises:
and under the condition that an update instruction of the exors running in the operation and maintenance scene is obtained, pulling new versions of the plurality of exors combined together in response to the update instruction, and carrying out one-key update on the plurality of exors combined together by utilizing the new versions.
11. An Exporter's composition system, comprising:
the unified management module is used for managing and updating each Exporter and each operation and maintenance index;
the system comprises an index system construction module, a target model generation module and a target analysis module, wherein the index system construction module is used for training a target model according to historical operation and maintenance index data of an operation and maintenance scene until a target training condition is reached, obtaining a plurality of target operation and maintenance indexes related to the operation and maintenance scene, and constructing an operation and maintenance index system of the operation and maintenance scene by combining a plurality of exporters corresponding to the plurality of target operation and maintenance indexes;
the index system monitoring module is used for monitoring an operation and maintenance index system of the operation and maintenance scene;
the target model is a cyclic neural network, and the index system construction module is specifically configured to:
dividing the historical operation and maintenance index data of the operation and maintenance scene into a plurality of operation and maintenance indexes;
and sequentially inputting the historical operation and maintenance indexes of the plurality of operation and maintenance indexes into a target model.
12. An Exporter's composition system, comprising:
the acquisition module acquires historical operation and maintenance index data of the operation and maintenance scene;
the training module inputs the historical operation and maintenance index data of the operation and maintenance scene into a target model, trains the correlation between the operation and maintenance scene and the operation and maintenance indexes until reaching a target training condition, and obtains a plurality of target operation and maintenance indexes related to the operation and maintenance scene;
The construction module is used for determining a plurality of corresponding exporters according to the plurality of target operation and maintenance indexes and constructing an operation and maintenance index system of the operation and maintenance scene by combining the plurality of exporters;
the target model is a cyclic neural network, and the training module is specifically configured to:
dividing the historical operation and maintenance index data of the operation and maintenance scene into a plurality of operation and maintenance indexes;
and sequentially inputting the historical operation and maintenance indexes of the plurality of operation and maintenance indexes into a target model.
13. An Exporter's composite device, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the exor combining method of any of claims 1 to 10 via execution of the executable instructions.
14. A computer readable storage medium storing a program, wherein the program when executed by a processor implements the steps of the Exporter's composition method according to any one of claims 1 to 10.
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Application publication date: 20220708 Assignee: Tianyiyun Technology Co.,Ltd. Assignor: CHINA TELECOM Corp.,Ltd. Contract record no.: X2024110000020 Denomination of invention: Exporter combination methods, systems, devices, and storage media Granted publication date: 20231003 License type: Common License Record date: 20240315 |