CN114710401B - Abnormality positioning method and device - Google Patents
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Abstract
The disclosure relates to an anomaly locating method and device, and relates to the technical field of computers. Wherein the method comprises the following steps: dividing the service to be monitored into a plurality of monitoring grades according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, association relations exist among the monitoring nodes of different monitoring levels, and the monitoring levels comprise a module monitoring level and at least one statistic monitoring level; generating monitoring data of the service to be monitored according to the module monitoring nodes of the module monitoring level and the operation data on the statistic monitoring nodes of the statistic monitoring level; and determining the target abnormal node according to the abnormal data in the monitoring data. Therefore, in the process of locating the abnormality of the service to be monitored, different monitoring grades are divided by the service to be monitored, the abnormality can be comprehensively analyzed in multiple dimensions, the abnormal nodes are quickly located according to the abnormal data in the monitoring data, and the user experience is improved.
Description
Technical Field
The disclosure relates to the field of computer technology, and in particular, to an anomaly locating method and device.
Background
In the related art, a monitoring system monitors abnormal business by collecting operation data of monitoring indexes corresponding to different nodes in the business operation process, presetting alarm conditions corresponding to each monitoring index and analyzing the operation data of each monitoring index and the corresponding alarm conditions.
However, when the monitoring index gives an alarm, a technician processes each alarm event, and needs means such as auxiliary log and action link analysis to locate the problem, the monitoring data of the monitoring system only can find the problem, and the effect of quickly locating the abnormality cannot be achieved by monitoring the specific point generated by the data locating problem.
Disclosure of Invention
The disclosure provides an anomaly locating method and device, which at least solve the problem that a monitoring large disc cannot locate a problem in the related art. The technical scheme of the present disclosure is as follows:
according to a first aspect of an embodiment of the present disclosure, there is provided an anomaly locating method, including: dividing the service to be monitored into a plurality of monitoring grades according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, an association relationship exists among the monitoring nodes of different monitoring levels, and the plurality of monitoring levels comprise a module monitoring level and at least one statistic monitoring level; generating monitoring data of the service to be monitored according to the module monitoring level and the operation data on the monitoring nodes for counting the monitoring level; and determining a target abnormal node according to the abnormal data in the monitoring data.
In some embodiments, the generating the monitoring data of the service to be monitored according to the operation data on the monitoring nodes of the module monitoring level and the statistic monitoring level includes: acquiring module operation data of module monitoring indexes when the service to be monitored is executed on a module monitoring node of the module monitoring level; and according to the association relation, aggregating the module operation data on the statistic monitoring nodes of the statistic monitoring level to generate the monitoring data of the service to be monitored.
In some embodiments, the determining the target abnormal node according to the abnormal data in the monitoring data includes: acquiring abnormal data in the monitoring data; determining an abnormal monitoring node corresponding to the abnormal data; and responding to the abnormal monitoring node as the statistical monitoring node, determining a target module monitoring node related to the statistical monitoring node according to the association relation, and determining the target module monitoring node as a target abnormal node.
In some embodiments, in response to the anomaly monitoring node being the module monitoring node, the anomaly monitoring node is determined to be a target anomaly node.
In some embodiments, the acquiring the abnormal data in the monitoring data includes: determining an abnormal condition; and determining the abnormal data with the abnormality according to the monitoring data and the abnormal condition.
In some embodiments, the monitoring data includes a unique identification field of a corresponding monitoring node, wherein the determining the abnormal monitoring node to which the abnormal data corresponds includes: acquiring a target unique identification field in the abnormal data; determining a target monitoring node according to the target unique identification field; and determining the target monitoring node as the abnormal monitoring node corresponding to the abnormal data.
According to a second aspect of the embodiments of the present disclosure, there is provided an abnormality locating device including: the processing unit is used for dividing the service to be monitored into a plurality of monitoring grades according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, an association relationship exists among the monitoring nodes of different monitoring levels, and the plurality of monitoring levels comprise a module monitoring level and at least one statistic monitoring level; the data acquisition unit is used for generating the monitoring data of the service to be monitored according to the module monitoring grade and the operation data on the monitoring nodes for counting the monitoring grade; and the target determining unit is used for determining a target abnormal node according to the abnormal data in the monitoring data.
In some embodiments, the data acquisition unit includes: the first data acquisition module is used for acquiring module operation data of the module monitoring index when the service to be monitored is executed on the module monitoring node of the module monitoring level; and the second data acquisition module is used for aggregating the module operation data on the statistical monitoring nodes of the statistical monitoring level according to the association relation to generate the monitoring data of the service to be monitored.
In some embodiments, the targeting unit comprises: the abnormal data acquisition module is used for acquiring abnormal data in the monitoring data; the node determining module is used for determining an abnormal monitoring node corresponding to the abnormal data; the first target determining module is used for responding to the abnormal monitoring node as the statistical monitoring node, determining a target module monitoring node related to the statistical monitoring node according to the association relation, and determining the target module monitoring node as a target abnormal node.
In some embodiments, the targeting unit further comprises: and the second target determining module is used for determining the abnormal monitoring node as a target abnormal node in response to the abnormal monitoring node as the module monitoring node.
In some embodiments, the abnormal data acquisition module includes: an abnormal condition determination submodule for determining an abnormal condition; and the data determination submodule is used for determining the abnormal data with the abnormality according to the monitoring data and the abnormal condition.
In some embodiments, the monitoring data includes a unique identification field of a corresponding monitoring node, wherein the node determination module includes: a field acquisition sub-module, configured to acquire a target unique identification field in the abnormal data; a target node determining sub-module, configured to determine a target monitoring node according to the target unique identification field; and the abnormal node determining submodule is used for determining that the target monitoring node is the abnormal monitoring node corresponding to the abnormal data.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement the anomaly locating method as described in the first aspect above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the anomaly localization method as described in the first aspect above.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the anomaly localization method as described in the first aspect above.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
according to the abnormality positioning method provided by the embodiment of the disclosure, the service to be monitored is divided into a plurality of monitoring levels according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, association relations exist among the monitoring nodes of different monitoring levels, and the monitoring levels comprise a module monitoring level and at least one statistic monitoring level; generating monitoring data of the service to be monitored according to the module monitoring nodes of the module monitoring level and the operation data on the statistic monitoring nodes of the statistic monitoring level; and determining the target abnormal node according to the abnormal data in the monitoring data. Therefore, in the process of locating the abnormality of the service to be monitored, different monitoring grades are divided by the service to be monitored, the abnormality can be comprehensively analyzed in multiple dimensions, the abnormal nodes are quickly located according to the abnormal data in the monitoring data, and the user experience is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.
FIG. 1 is a flowchart illustrating an anomaly localization method in accordance with an exemplary embodiment;
FIG. 2 illustrates a flowchart of S2 in an anomaly localization method, according to an exemplary embodiment;
FIG. 3 illustrates a flowchart of S3 in an anomaly localization method, according to an exemplary embodiment;
FIG. 4 is a block diagram of an anomaly locating device, according to an example embodiment;
fig. 5 is a block diagram showing a data acquisition unit in an abnormality locating device according to an exemplary embodiment;
FIG. 6 is a block diagram showing a target determining unit in an abnormality locating device according to an exemplary embodiment;
FIG. 7 is a block diagram illustrating an anomaly data acquisition module in an anomaly locating device, according to an exemplary embodiment;
FIG. 8 is a block diagram illustrating a node determination module in an anomaly locating device, according to an example embodiment;
Fig. 9 illustrates a block diagram of an electronic device, according to an example embodiment.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
Throughout the specification and claims, the term "comprising" is to be interpreted as an open, inclusive meaning, i.e. "comprising, but not limited to, unless the context requires otherwise. In the description of the present specification, the term "some embodiments" or the like is intended to indicate that a particular feature, structure, material, or characteristic associated with the embodiment or example is included in at least one embodiment or example of the present disclosure. The schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
The terms "first" and "second" are used below for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
It should be noted that, the abnormality locating method according to the embodiment of the present disclosure may be performed by the abnormality locating device according to the embodiment of the present disclosure, where the abnormality locating device may be implemented by software and/or hardware, and the abnormality locating device may be configured in an electronic device, where the electronic device may install and run an abnormality locating program. The electronic device may include, but is not limited to, a smart phone, a tablet computer, a computer, and other hardware devices with various operating systems.
FIG. 1 is a flowchart illustrating an anomaly localization method in accordance with an exemplary embodiment.
As shown in fig. 1, the anomaly locating method provided in the embodiment of the present disclosure includes, but is not limited to, the following steps:
s1: dividing the service to be monitored into a plurality of monitoring grades according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, association relations exist among the monitoring nodes of different monitoring levels, and the monitoring levels comprise a module monitoring level and at least one statistic monitoring level.
In the embodiment of the disclosure, according to the content of the service to be monitored, the service is divided into a plurality of monitoring levels according to a preset rule. The preset rules may be divided according to the flow of the service to be monitored, or divided according to the functions of the service departments corresponding to the service to be monitored, or other rules may be selected according to the needs.
In the embodiment of the disclosure, the service to be monitored is divided into a plurality of monitoring levels according to a preset rule, and two or more monitoring levels may be divided. Wherein the plurality of monitoring levels includes a module monitoring level and at least one statistical monitoring level, for example, including a module monitoring level and a plurality of statistical monitoring levels.
In the embodiment of the disclosure, each monitoring level includes at least one monitoring index of at least one monitoring node, and an association relationship exists between monitoring nodes of different monitoring levels.
It may be appreciated that in the embodiment of the present disclosure, the service to be monitored is divided into a plurality of monitoring levels according to a preset rule, including a module monitoring level and at least one statistical monitoring level, where the module monitoring level corresponds to one or more monitoring nodes, and the statistical monitoring level corresponds to one or more monitoring nodes.
Taking an e-commerce-local living transaction as an example, a module monitoring level is set, and corresponding monitoring nodes include, for example: transaction node, merchant node and operation node, wherein, the monitor index of transaction node includes, for example: the order quantity index, refund quantity index, order quantity index, complaint quantity index, etc., and the monitoring indexes of the merchant node include, for example: the monitoring indexes of the operation node include, for example: odds ratio index, merchant residence index, etc.
Under the condition that the monitoring node of the module monitoring level is a transaction node, the monitoring node of the statistic monitoring level with the association relation with the transaction node comprises, for example: applet nodes, web page nodes, application APP nodes, etc., the monitoring metrics of the applet nodes include, for example: the monitoring indexes of the web page node include, for example: the monitoring indexes of the application APP node comprise, for example, a webpage order quantity index, a webpage refund quantity index, a webpage order quantity index, a webpage complaint quantity index and the like: an application APP order quantity index, an application APP refund quantity index, an application APP order quantity index, an application APP complaint quantity index and the like.
It should be noted that the foregoing examples are merely illustrative, and not meant to be a specific limitation on the protection scheme of the embodiments of the present disclosure, and the monitoring level may be three or more.
S2: and generating monitoring data of the service to be monitored according to the module monitoring nodes of the module monitoring level and the operation data on the statistic monitoring nodes of the statistic monitoring level.
In the embodiment of the disclosure, in the case that the service to be monitored is divided into a plurality of monitoring levels according to a preset rule, where the plurality of monitoring levels includes a module monitoring level and at least one statistic monitoring level, the monitoring data of the service to be monitored may be generated according to the operation data on the module monitoring node of the module monitoring level and the statistic monitoring node of the statistic monitoring level.
The method comprises the steps of generating monitoring data of a service to be monitored according to a module monitoring grade and operation data on monitoring nodes for counting the monitoring grade, acquiring the operation data on the counting monitoring nodes, generating the operation data of the module monitoring nodes according to the association relation between the counting monitoring nodes and the module monitoring nodes, and further generating the monitoring data of the service to be monitored according to the operation data of the module monitoring nodes.
In the embodiment of the present disclosure, the monitoring data may include relevant information of the module monitoring node and/or the statistics monitoring node, for example: including the identity of the module monitoring node and/or the statistics monitoring node, etc.
S3: and determining the target abnormal node according to the abnormal data in the monitoring data.
It may be appreciated that, in the embodiment of the present disclosure, the monitoring data may include relevant information of the module monitoring node and/or the statistical monitoring node, for example, including: the identity of the module monitoring node and/or the statistics monitoring node, etc.
In the embodiment of the disclosure, in the process of monitoring the service to be monitored according to the monitoring data, the abnormal data in the monitoring data can be obtained by presetting the abnormal condition when the monitoring data meets the abnormal condition. And then determining the target abnormal node according to the related information including the module monitoring node and/or the statistic monitoring node carried in the abnormal data.
By implementing the embodiment of the disclosure, the service to be monitored is divided into a plurality of monitoring levels according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, association relations exist among the monitoring nodes of different monitoring levels, and the monitoring levels comprise a module monitoring level and at least one statistic monitoring level; generating monitoring data of the service to be monitored according to the module monitoring nodes of the module monitoring level and the operation data on the statistic monitoring nodes of the statistic monitoring level; and determining the target abnormal node according to the abnormal data in the monitoring data. Therefore, in the process of locating the abnormality of the service to be monitored, different monitoring grades are divided by the service to be monitored, the abnormality can be comprehensively analyzed in multiple dimensions, the abnormal nodes are quickly located according to the abnormal data in the monitoring data, and the user experience is improved.
As shown in fig. 2, in some embodiments, S2: generating monitoring data of the service to be monitored according to the module monitoring nodes of the module monitoring level and the operation data on the statistic monitoring nodes of the statistic monitoring level, including but not limited to the following steps:
s21: and acquiring module operation data of module monitoring indexes when the service to be monitored is executed on the module monitoring nodes of the module monitoring level.
In the embodiment of the disclosure, a service to be monitored is divided into a plurality of monitoring levels according to a preset rule, wherein the monitoring levels comprise a module monitoring level and at least one statistics monitoring level, the module monitoring level comprises at least one module monitoring node, the statistics monitoring level comprises at least one statistics monitoring node, and an association relationship exists between the module monitoring level and the statistics monitoring level.
In the embodiment of the disclosure, on a module monitoring node of a module monitoring level, module operation data of a module monitoring index is obtained when a service to be monitored is executed.
In the embodiment of the disclosure, the collection of the module operation data is performed based on the SQL statement, and it may be understood that the SQL statement may include information of a module monitoring node of a module monitoring level, where the module operation data of a module monitoring index of the module monitoring node may be in a format of the SQL statement.
In the above embodiment, taking the service to be monitored as an e-commerce-local living transaction as an example, in the embodiment of the present disclosure, in the executing process of the service to be monitored, data of monitoring indexes of each node is collected through a preset interface, the preset interface is preset, the data obtained through the preset interface may be in a unified SQL statement, and the SQL statement may include multiple parts, where a part of the SQL statement is specific to the monitoring indexes of the node, and another part of the SQL statement may be common to the monitoring indexes of the same node, and may also include other contents and the like.
In the embodiment of the disclosure, module monitoring indexes corresponding to module monitoring nodes of a module monitoring level are collected through a preset interface, and module operation data when a service to be monitored is executed includes relevant information of the module monitoring nodes of the module monitoring level.
S22: and according to the association relation, aggregating the module operation data on the statistical monitoring nodes of the statistical monitoring level to generate the monitoring data of the service to be monitored.
In the embodiment of the disclosure, after module monitoring indexes corresponding to module monitoring nodes of a module monitoring level are collected through a preset interface, module operation data of a service to be monitored is aggregated on statistical monitoring nodes of the statistical monitoring level according to an association relationship between the module monitoring nodes and the statistical monitoring nodes after the module operation data of the service to be monitored is executed, and the monitoring data of the service to be monitored is generated.
In the embodiment of the disclosure, according to the module operation data of different module monitoring nodes, summary aggregation is performed according to the association relation, the data sources are subjected to uniform closing-up, and the monitoring data of the service to be monitored is generated, so that the monitoring data of the service to be monitored can be collected more comprehensively in multiple dimensions, the collection of the preset interfaces and the aggregation according to the association relation are adopted, the cost of the monitoring node data access of multiple monitoring levels of the service to be monitored can be reduced, the user can conveniently obtain the monitoring data of the service to be monitored, and the user experience is improved.
As shown in fig. 3, in some embodiments, S3: determining a target abnormal node according to abnormal data in the monitoring data, including but not limited to the following steps:
s31: and acquiring abnormal data in the monitoring data.
In the embodiment of the disclosure, the abnormal data in the monitoring data may be real-time data generated when the service performance of the service to be monitored is affected in the execution process of the service to be monitored, and it is conceivable that the real-time data is different from the historical data generated in the normal execution process of the service to be monitored, and the abnormal data which is different from the historical data in the real-time data may be obtained by comparison.
In some embodiments, obtaining anomaly data in the monitored data includes: determining an abnormal condition; and determining abnormal data with abnormality according to the monitoring data and the abnormal conditions.
In the embodiment of the disclosure, an abnormal condition can be set in advance for the monitoring data of the service to be monitored, and the abnormal data in the monitoring data can be obtained under the condition that the monitoring data reaches the preset abnormal condition.
The method comprises the steps of setting an abnormal condition for monitoring data of a service to be monitored in advance, wherein the set abnormal condition can be threshold data of a monitoring node and a corresponding monitoring index related to the service performance when the service performance is affected when the service to be monitored is executed, so that the execution of the service to be monitored is not affected when the real-time data of the corresponding monitoring index is smaller than the threshold data and the monitoring data does not reach the abnormal condition when the service to be monitored is executed, and the service to be monitored is executed smoothly at the moment.
S32: and determining an abnormal monitoring node corresponding to the abnormal data.
It may be appreciated that, in the embodiment of the present disclosure, the monitoring data may include information about the module monitoring node and/or the statistical monitoring node, for example: including the identity of the module monitoring node and/or the statistics monitoring node, etc. After the abnormal data is obtained, the abnormal monitoring node can be determined according to the identification of the module monitoring node and/or the statistic monitoring node carried in the abnormal data.
In some embodiments, the monitoring data includes a unique identification field of a corresponding monitoring node, wherein determining an anomaly monitoring node corresponding to the anomaly data includes: acquiring a target unique identification field in abnormal data; determining a target monitoring node according to the target unique identification field; and determining the target monitoring node as an abnormal monitoring node corresponding to the abnormal data.
In the embodiment of the disclosure, a target unique field in the abnormal data is determined under the condition that the abnormal data is acquired, and the target monitoring node can be determined according to the target unique field, so that the target monitoring node can be determined to be the abnormal monitoring node corresponding to the abnormal data.
In the embodiment of the present disclosure, the monitoring data may include related information of the module monitoring node and/or the statistic monitoring node, for example: the method comprises the steps of obtaining abnormal data in monitoring data by means of identification of module monitoring nodes and/or statistic monitoring nodes, and further obtaining target unique identification characters in the abnormal data, wherein the target unique identification characters are used for indicating specific module monitoring nodes and/or specific statistic monitoring nodes.
Based on the above, in the embodiment of the disclosure, under the condition of determining the target unique identification character, the target monitoring node corresponding to the target unique identification character can be further determined, and the target monitoring node is determined to be the abnormal monitoring node corresponding to the abnormal data, so that the abnormal positioning is realized.
S33: and responding to the abnormal monitoring node as a statistical monitoring node, determining a target module monitoring node related to the statistical monitoring node according to the association relation, and determining the target module monitoring node as a target abnormal node.
In the embodiment of the disclosure, when the abnormal monitoring node is determined according to the unique identification field in the abnormal data, the type of the determined abnormal monitoring node can be further identified, and the corresponding monitoring level of the determined abnormal monitoring node can be identified, so that the target abnormal node can be further determined.
In some embodiments, in response to the anomaly monitoring node being a module monitoring node, the anomaly monitoring node is determined to be a target anomaly node.
In the embodiment of the disclosure, under the condition that the abnormal monitoring node is determined to be the module monitoring node, the abnormal monitoring node is determined to be the target abnormal node.
In the embodiment of the disclosure, under the condition that the abnormal monitoring node is determined to be the statistical monitoring node, determining a target module monitoring node related to the statistical monitoring node according to the association relation, and determining the target module monitoring node to be the target abnormal node. Therefore, the abnormal positioning in the embodiment of the disclosure can be positioned to the statistical monitoring node, the abnormal positioning is more accurate, the user can update and maintain the abnormal node in time, and the user experience is improved.
Fig. 4 is a block diagram of an abnormality locating device 1 according to an exemplary embodiment.
As shown in fig. 4, the abnormality locating device 1 includes: a processing unit 11, a data acquisition unit 12, and a target determination unit 13.
A processing unit 11, configured to divide a service to be monitored into a plurality of monitoring levels according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, association relations exist among the monitoring nodes of different monitoring levels, and the monitoring levels comprise a module monitoring level and at least one statistic monitoring level.
The data acquisition unit 12 is configured to generate monitoring data of the service to be monitored according to the module monitoring level and the operation data on the monitoring nodes for counting the monitoring level.
A target determining unit 13 for determining a target abnormal node according to the abnormal data in the monitoring data.
By implementing the embodiment of the present disclosure, the processing unit 11 is configured to divide a service to be monitored into a plurality of monitoring levels according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, association relations exist among the monitoring nodes of different monitoring levels, and the monitoring levels comprise a module monitoring level and at least one statistic monitoring level; the data acquisition unit 12 is configured to generate monitoring data of a service to be monitored according to the module monitoring node of the module monitoring level and the operation data on the statistic monitoring node of the statistic monitoring level; a target determining unit 13 for determining a target abnormal node according to the abnormal data in the monitoring data. Therefore, in the process of locating the abnormality of the service to be monitored, different monitoring grades are divided by the service to be monitored, the abnormality can be comprehensively analyzed in multiple dimensions, the abnormal nodes are quickly located according to the abnormal data in the monitoring data, and the user experience is improved.
As shown in fig. 5, in some embodiments, the data acquisition unit 12 includes: a first data acquisition module 121 and a second data acquisition module 122.
The first data obtaining module 121 is configured to obtain, on a module monitoring node of a module monitoring level, module operation data of a module monitoring index when a service to be monitored is executed.
And the second data obtaining module 122 is configured to aggregate the module operation data on the statistical monitoring nodes of the statistical monitoring level according to the association relationship, and generate monitoring data of the service to be monitored.
As shown in fig. 6, in some embodiments, the target determining unit 13 includes: an abnormal data acquisition module 131, a node determination module 132, and a node determination module 132.
The abnormal data obtaining module 131 is configured to obtain abnormal data in the monitoring data.
The node determining module 132 is configured to determine an anomaly monitoring node corresponding to the anomaly data.
The first target determining module 133 is configured to determine, in response to the abnormal monitoring node being a statistical monitoring node, a target module monitoring node related to the statistical monitoring node according to the association relationship, and determine the target module monitoring node to be a target abnormal node.
Referring again to fig. 6, the target determining unit 13 further includes: the second target determining module 134 is configured to determine, in response to the anomaly monitoring node being a module monitoring node, that the anomaly monitoring node is a target anomaly node.
As shown in fig. 7, in some embodiments, the abnormal data acquisition module 131 includes: the abnormal condition determination submodule 1311 and the data determination submodule 1312.
An abnormal condition determination submodule 1311 for determining an abnormal condition.
The data determination submodule 1312 is configured to determine, according to the monitored data and the abnormal condition, abnormal data in which an abnormality occurs.
As shown in fig. 8, in some embodiments, the monitoring data includes a unique identification field of the corresponding monitoring node, wherein the node determination module 132 includes: a field acquisition submodule 1321, a target node determination submodule 1322 and an abnormal node determination submodule 1323.
The field acquisition submodule 1321 is configured to acquire a target unique identification field in the exception data.
The target node determining submodule 1322 is configured to determine a target monitoring node according to the target unique identification field.
The abnormal node determining submodule 1323 is configured to determine that the target monitoring node is an abnormal monitoring node corresponding to the abnormal data.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
The beneficial effects obtained by the abnormality locating device provided in the embodiment of the present disclosure are the same as those obtained by the abnormality locating method provided in the above example, and are not described here again.
Fig. 9 is a block diagram of an electronic device 100 for an anomaly localization method, according to an example embodiment.
By way of example, the electronic device 100 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
As shown in fig. 9, the electronic device 100 may include one or more of the following components: a processing component 101, a memory 102, a power supply component 103, a multimedia component 104, an audio component 105, an input/output (I/O) interface 106, a sensor component 107, and a communication component 108.
The processing component 101 generally controls overall operation of the electronic device 100, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 101 may include one or more processors 1011 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 101 may include one or more modules that facilitate interactions between the processing component 101 and other components. For example, the processing component 101 may include a multimedia module to facilitate interaction between the multimedia component 104 and the processing component 101.
The memory 102 is configured to store various types of data to support operations at the electronic device 100. Examples of such data include instructions for any application or method operating on the electronic device 100, contact data, phonebook data, messages, pictures, videos, and the like. The Memory 102 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as SRAM (Static Random-Access Memory), EEPROM (Electrically Erasable Programmable Read Only Memory, electrically erasable programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), ROM (Read-Only Memory), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The power supply assembly 103 provides power to the various components of the electronic device 100. Power supply components 103 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for electronic device 100.
The multimedia component 104 comprises a touch-sensitive display screen providing an output interface between the electronic device 100 and the user. In some embodiments, the Touch display screen may include an LCD (Liquid Crystal Display ) and a TP (Touch Panel). The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 104 includes a front-facing camera and/or a rear-facing camera. When the electronic device 100 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 105 is configured to output and/or input audio signals. For example, the audio component 105 includes a MIC (Microphone) configured to receive external audio signals when the electronic device 100 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 102 or transmitted via the communication component 108. In some embodiments, the audio component 105 further comprises a speaker for outputting audio signals.
The I/O interface 2112 provides an interface between the processing component 101 and a peripheral interface module, which may be a keyboard, click wheel, button, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 107 includes one or more sensors for providing status assessment of various aspects of the electronic device 100. For example, the sensor assembly 107 may detect an on/off state of the electronic device 100, a relative positioning of components, such as a display and keypad of the electronic device 100, a change in position of the electronic device 100 or a component of the electronic device 100, the presence or absence of a user's contact with the electronic device 100, an orientation or acceleration/deceleration of the electronic device 100, and a change in temperature of the electronic device 100. The sensor assembly 107 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. The sensor assembly 107 may also include a photosensor, such as a CMOS (Complementary Metal Oxide Semiconductor ) or CCD (Charge-coupled Device) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 107 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 108 is configured to facilitate communication between the electronic device 100 and other devices in a wired or wireless manner. The electronic device 100 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication component 108 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 108 further includes an NFC (Near Field Communication ) module to facilitate short range communications. For example, in the NFC module, it may be implemented based on RFID (Radio Frequency Identification ) technology, irDA (Infrared Data Association, infrared data association) technology, UWB (Ultra Wide Band) technology, BT (Bluetooth) technology, and other technologies.
In an exemplary embodiment, the electronic device 100 may be implemented by one or more ASICs (Application Specific Integrated Circuit, application specific integrated circuits), DSPs (Digital Signal Processor, digital signal processors), digital Signal Processing Devices (DSPDs), PLDs (Programmable Logic Device, programmable logic devices), FPGAs (Field Programmable Gate Array, field programmable gate arrays), controllers, microcontrollers, microprocessors, or other electronic elements for performing the anomaly localization method described above.
It should be noted that, the implementation process and the technical principle of the electronic device in this embodiment refer to the foregoing explanation of the abnormality locating method in the embodiment of the disclosure, and are not repeated herein.
The electronic device provided in the embodiments of the present disclosure may perform the anomaly locating method described in some embodiments above, and the beneficial effects of the anomaly locating method are the same as those of the anomaly locating method described above, and are not described herein again.
In order to implement the above-described embodiments, the present disclosure also proposes a storage medium.
Wherein the instructions in the storage medium, when executed by the processor of the electronic device, enable the electronic device to perform the anomaly localization method as described previously. For example, the storage medium may be ROM (Read Only Memory Image, read Only Memory), RAM (Random Access Memory ), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, optical data storage device, and the like.
To achieve the above embodiments, the present disclosure also provides a computer program product which, when executed by a processor of an electronic device, enables the electronic device to perform the anomaly localization method as described previously.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. An anomaly locating method, comprising:
dividing the service to be monitored into a plurality of monitoring grades according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, an association relationship exists among the monitoring nodes of different monitoring levels, and the plurality of monitoring levels comprise a module monitoring level and at least one statistic monitoring level;
Generating monitoring data of the service to be monitored according to the module monitoring level and the operation data on the monitoring nodes for counting the monitoring level;
determining a target abnormal node according to the abnormal data in the monitoring data;
the generating the monitoring data of the service to be monitored according to the module monitoring level and the operation data on the monitoring nodes of the statistic monitoring level comprises the following steps:
acquiring module operation data of module monitoring indexes when the service to be monitored is executed on a module monitoring node of the module monitoring level, wherein the module operation data is collected based on SQL sentences;
according to the association relation, the module operation data are aggregated on the statistic monitoring nodes of the statistic monitoring level, and the monitoring data of the service to be monitored are generated;
the determining the target abnormal node according to the abnormal data in the monitoring data comprises the following steps:
acquiring abnormal data in the monitoring data;
determining an abnormal monitoring node corresponding to the abnormal data;
responding to the abnormal monitoring node as the statistical monitoring node, determining a target module monitoring node related to the statistical monitoring node according to the association relation, and determining the target module monitoring node as a target abnormal node;
And responding to the abnormal monitoring node as the module monitoring node, and determining the abnormal monitoring node as a target abnormal node.
2. The method of claim 1, wherein the obtaining the anomaly data in the monitored data comprises:
determining an abnormal condition;
and determining the abnormal data with the abnormality according to the monitoring data and the abnormal condition.
3. The method of claim 1, wherein the monitoring data includes a unique identification field of a corresponding monitoring node, wherein the determining an anomaly monitoring node to which the anomaly data corresponds comprises:
acquiring a target unique identification field in the abnormal data;
determining a target monitoring node according to the target unique identification field;
and determining the target monitoring node as the abnormal monitoring node corresponding to the abnormal data.
4. An abnormality locating device, characterized by comprising:
the processing unit is used for dividing the service to be monitored into a plurality of monitoring grades according to a preset rule; each monitoring level comprises at least one monitoring index of at least one monitoring node, an association relationship exists among the monitoring nodes of different monitoring levels, and the plurality of monitoring levels comprise a module monitoring level and at least one statistic monitoring level;
The data acquisition unit is used for generating the monitoring data of the service to be monitored according to the module monitoring grade and the operation data on the monitoring nodes for counting the monitoring grade;
the target determining unit is used for determining a target abnormal node according to the abnormal data in the monitoring data;
the data acquisition unit includes:
the first data acquisition module is used for acquiring module operation data of module monitoring indexes when the service to be monitored is executed on the module monitoring node of the module monitoring level, wherein the module operation data is collected based on SQL sentences;
the second data acquisition module is used for aggregating the module operation data on the statistical monitoring nodes of the statistical monitoring level according to the association relation to generate the monitoring data of the service to be monitored;
the target determination unit includes:
the abnormal data acquisition module is used for acquiring abnormal data in the monitoring data;
the node determining module is used for determining an abnormal monitoring node corresponding to the abnormal data;
the first target determining module is used for responding to the abnormal monitoring node as the statistical monitoring node, determining a target module monitoring node related to the statistical monitoring node according to the association relation, and determining the target module monitoring node as a target abnormal node;
And the second target determining module is used for determining the abnormal monitoring node as a target abnormal node in response to the abnormal monitoring node as the module monitoring node.
5. The apparatus of claim 4, wherein the anomalous data acquisition module comprises:
an abnormal condition determination submodule for determining an abnormal condition;
and the data determination submodule is used for determining the abnormal data with the abnormality according to the monitoring data and the abnormal condition.
6. The apparatus of claim 4, wherein the monitoring data comprises a unique identification field of a corresponding monitoring node, wherein the node determination module comprises:
a field acquisition sub-module, configured to acquire a target unique identification field in the abnormal data;
a target node determining sub-module, configured to determine a target monitoring node according to the target unique identification field;
and the abnormal node determining submodule is used for determining that the target monitoring node is the abnormal monitoring node corresponding to the abnormal data.
7. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
Wherein the processor is configured to execute the instructions to implement the anomaly localization method of any one of claims 1 to 3.
8. A storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the anomaly localization method of any one of claims 1 to 3.
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