Method and device for determining monitoring condition

Abstract

The embodiment of the invention provides a method and a device for determining a monitoring condition. The method comprises the following steps: acquiring monitoring data under a first-level index obtained by monitoring a monitoring object by a monitoring tool; the monitoring data is divided into N monitoring data sets according to the corresponding relation between the first-level indexes and the second-level indexes, N is an integer, each monitoring data set corresponds to one second-level index, and each piece of monitoring data in the monitoring data sets corresponds to one third-level index; and determining the monitoring condition of the monitoring tool on the monitoring object according to the N monitoring data sets. According to the invention, the problem that the monitoring condition of the monitoring tool cannot be known timely and comprehensively is solved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of communications, and in particular, to a method and device for determining a monitoring situation. Background Art

[0002] Monitoring tools are the core tools for us to monitor system anomalies. In daily operation and maintenance, due to incomplete monitoring management, imperfect monitoring strategies, missing monitoring indicators, invalid monitoring thresholds, etc., it is often impossible to accurately detect system anomalies. Due to low monitoring collection and calculation efficiency, it is impossible to detect system anomalies in a timely manner, affecting early detection and prevention of risks. Therefore, effective evaluation and optimization of the completeness, accuracy, and timeliness of monitoring have become a key link in the operation and maintenance management of data centers, and at the same time, it is a task that is both time-consuming and laborious, but the results are often not significant. Currently, for the optimization of monitoring management, it mainly relies on manually exporting or viewing operation and maintenance data from the monitoring system, and then performing subsequent statistics and analysis.

[0003] However, this method in the prior art has the following problems: (1) Lack of monitoring indicators for global evaluation: There are no accurate evaluation indicators and clear calculation rules in the monitoring field, and it is impossible to accurately and comprehensively evaluate the monitoring effect, resulting in the inability to actively discover monitoring problems in advance and only being able to discover monitoring deficiencies after an event occurs. (2) Missing centralized integration of monitoring data: The data of multi-dimensional monitoring systems and various monitoring types have not been centrally integrated and aggregated, and cannot comprehensively reflect monitoring problems. The monitoring data is extremely large and cannot be manually statistically analyzed and processed, seriously affecting the quality and efficiency of monitoring analysis; lack of time trend analysis, which is not convenient for continuously tracking monitoring problems. (3) Lack of multi-dimensional and accurate analysis and positioning: There is no multi-dimensional monitoring, multi-view, and multi-level drilling statistics, the data analysis is not thorough, and the problem positioning is not clear; (4) Lack of a rectification plan for continuous optimization: There is no rectification plan to be given according to the analyzed and located problems, and subsequent optimization is insufficient.

[0004] In view of the above problems, there is currently no effective solution. Summary of the Invention

[0005] Embodiments of the present invention provide a method and device for determining a monitoring situation, so as to at least solve the problem in the related art that it is impossible to timely and comprehensively understand the monitoring situation of monitoring tools.

[0006] According to an embodiment of the present invention, a method for determining a monitoring situation is provided, including: obtaining monitoring data under a primary indicator obtained by a monitoring tool for a monitoring object; dividing the monitoring data into N monitoring data sets according to the corresponding relationship between the primary indicator and the secondary indicator, where N is an integer, each monitoring data set corresponds to one of the secondary indicators, and each monitoring data in the monitoring data set corresponds to a tertiary indicator; determining the monitoring situation of the monitoring tool for the monitoring object according to the N monitoring data sets.

[0007] In an exemplary embodiment, determining the monitoring situation of the monitoring tool for the monitoring object according to the N monitoring data sets includes at least one of the following: determining whether the type of the tertiary indicator monitored by the monitoring tool for the monitoring object meets a first preset condition; determining whether the monitoring data of each of the tertiary indicators monitored by the monitoring tool for the monitoring object meets a second preset condition; determining whether the alarm thresholds of each of the tertiary indicators monitored by the monitoring tool for the monitoring object meet a third preset condition.

[0008] In an exemplary embodiment, determining whether the type of the tertiary indicator monitored by the monitoring tool for the monitoring object meets the first preset condition includes: determining the type of the tertiary indicator corresponding to the monitoring data in each of the monitoring data sets; determining that the first preset condition is met when the types of the tertiary indicators corresponding to all the monitoring data in the N monitoring data sets are consistent with a preset indicator type; otherwise, determining that the first preset condition is not met.

[0009] In an exemplary embodiment, the method further includes: when the first monitoring data set includes M monitoring data, determining the types of the tertiary indicators corresponding to the M monitoring data to obtain M indicator types, where M is an integer, and the N monitoring data sets include the first monitoring data set; when the M indicator types are not consistent with the preset indicator type, determining the missing indicator type among the M indicators as the target indicator type; adding the target indicator type to the target secondary indicator, where the first monitoring data set is the monitoring data set corresponding to the target secondary indicator.

[0010] In an exemplary embodiment, determining whether the monitoring data of each of the tertiary indicators monitored by the monitoring tool for the monitoring object meets the second preset condition includes: obtaining the preset threshold conditions of the tertiary indicators corresponding to each of the monitoring data in the N monitoring data sets; determining that the second preset condition is met when each of the monitoring data meets the corresponding preset threshold condition; otherwise, the second preset condition is not met.

[0011] In an exemplary embodiment, the method further includes: obtaining a preset threshold condition of a first target third-level indicator corresponding to first monitoring data included in a second monitoring data set, where the N monitoring data sets include the second monitoring data set; and determining the first target third-level indicator as a monitoring abnormal indicator when the first monitoring data does not meet the preset threshold condition.

[0012] In an exemplary embodiment, determining whether alarm thresholds of the third-level indicators for which the monitoring tool monitors the monitoring object meet a third preset condition includes: when an alarm event occurs for second monitoring data in a third monitoring data set, obtaining a first alarm threshold of a second target third-level indicator corresponding to the second monitoring data, where the N monitoring data sets include the third monitoring data set; and determining that the alarm threshold of the second target third-level indicator for which the monitoring tool monitors the monitoring object does not meet the third preset condition when the second monitoring data is less than the first alarm threshold.

[0013] In an exemplary embodiment, determining whether alarm thresholds of the third-level indicators for which the monitoring tool monitors the monitoring object meet a third preset condition further includes: obtaining third monitoring data in a fourth monitoring data set and a second alarm threshold of a third target third-level indicator corresponding to the third monitoring data; and determining that the alarm threshold of the third target third-level indicator for which the monitoring tool monitors the monitoring object does not meet the third preset condition when the third monitoring data is greater than or equal to the second alarm threshold and no alarm event occurs.

[0014] According to another embodiment of the present invention, there is provided a device for determining a monitoring situation, including: an obtaining module, configured to obtain monitoring data of a monitoring object monitored by a monitoring tool under a first-level indicator; a dividing module, configured to divide the monitoring data into N monitoring data sets according to a corresponding relationship between the first-level indicator and a second-level indicator, where N is an integer, each monitoring data set corresponds to one second-level indicator, and each monitoring data in the monitoring data set corresponds to a third-level indicator; and a determining module, configured to determine a monitoring situation of the monitoring object by the monitoring tool according to the N monitoring data sets.

[0015] According to still another embodiment of the present invention, there is further provided a computer-readable storage medium, in which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method described in any one of the above.

[0016] According to another embodiment of the present invention, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0017] According to another embodiment of the present invention, there is also provided a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0018] Through the present invention, monitoring data under a primary index obtained by a monitoring tool for a monitoring object is acquired; according to the correspondence between the primary index and the secondary index, the monitoring data is divided into N monitoring data sets, where N is an integer, each monitoring data set corresponds to a secondary index, and each monitoring data in the monitoring data set corresponds to a tertiary index; the monitoring situation of the monitoring tool for the monitoring object is determined based on the N monitoring data sets. Therefore, the problem of being unable to timely and comprehensively understand the monitoring situation of the monitoring tool can be solved, and the effect of being able to timely and comprehensively understand the monitoring situation of the monitoring tool can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a hardware structure block diagram of a mobile terminal for a method of determining a monitoring situation according to an embodiment of the present invention;

[0020] Figure 2 is a flowchart of a method of determining a monitoring situation according to an embodiment of the present invention;

[0021] Figure 3 is a system architecture diagram according to an embodiment of the present invention;

[0022] Figure 4 is an operational architecture schematic diagram according to an embodiment of the present invention;

[0023] Figure 5 is a structure block diagram of a device for determining a monitoring situation according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The embodiments of the present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence.

[0026] The method embodiments provided in the embodiments of the present application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1The following is a block diagram of the hardware of a mobile terminal for a method of determining a monitoring situation according to an embodiment of the present invention. As Figure 1 shown, the mobile terminal may include one or more ( Figure 1 only one is shown in Figure 1 ) processors 102 (the processor 102 may include, but is not limited to, processing devices such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Among them, the above mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown in Figure 1 is only schematic and does not limit the structure of the above mobile terminal. For example, the mobile terminal may further include more or fewer components than

[0027] shown in

[0028] , or have a different configuration from

[0029] shown in Figure 2 . Figure 2 The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method of determining a monitoring situation in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories may be connected to the mobile terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] Step S202: Obtain the monitoring data of the monitored object obtained by the monitoring tool under the primary indicator.

[0031] Step S204: According to the corresponding relationship between the primary indicator and the secondary indicator, divide the monitoring data into N monitoring data sets, where N is an integer, each monitoring data set corresponds to one secondary indicator, and each monitoring data in the monitoring data set corresponds to a tertiary indicator.

[0032] Step S206: Determine the monitoring situation of the monitoring tool for the monitored object based on the N monitoring data sets.

[0033] Optionally, the execution entity of the above steps can be a background processor, or other devices with similar processing capabilities, or a machine integrated with at least an image acquisition device and a data processing device. Among them, the image acquisition device can include graphic acquisition modules such as cameras, and the data processing device can include terminals such as computers and mobile phones, but is not limited thereto.

[0034] Through the above steps, the problem of being unable to timely and comprehensively understand the monitoring situation of the monitoring tool is solved, and the effect of being able to timely and comprehensively understand the monitoring situation of the monitoring tool is achieved.

[0035] In an exemplary embodiment, the monitoring data is divided into indicators of different levels, including: primary indicator, secondary indicator, and tertiary indicator. Among them, the primary indicators include: basic monitoring, log monitoring, application monitoring, service probing ability. The secondary indicators include: monitoring scale, monitoring quality, monitoring efficiency. The tertiary indicators under the monitoring scale include: number of hosts / instances, number of managed hosts / instances, number of associated policy hosts / instances, number of monitoring indicators, number of second-level monitoring indicators, number of minute-level monitoring indicators, number of key indicators, required number of key indicators, number of hosts with complete key indicators, number of instances with complete key indicators, number of key hosts with effective thresholds, number of key instances with effective thresholds. The tertiary indicators under the monitoring quality include: monitoring management rate, monitoring policy coverage rate, monitoring indicator completeness rate, threshold setting effectiveness rate. The tertiary indicators under the monitoring efficiency include: second-level collection rate, minute collection rate, average collection cycle. The division of the primary indicator, secondary indicator, and tertiary indicator is shown in Table 1 below.

[0036] Table 1

[0037]

[0038]

[0039] The proportions of the indicators A, B, and C in the table can be set according to the actual situation. As shown in Table 1 above, the monitoring data of the first-level indicators (basic monitoring / log monitoring / application monitoring / service probing ability) are obtained, and the monitoring data under the above first-level indicators are divided according to the second-level indicators (monitoring scale, monitoring quality, monitoring efficiency). In this embodiment, they are divided into three monitoring data sets corresponding to the three second-level indicators of monitoring scale, monitoring quality, and monitoring efficiency. The monitoring data set corresponding to the monitoring scale includes the data of the following third-level indicators: number of hosts / instances, number of managed hosts / instances, number of associated policy hosts / instances, number of monitoring indicators, number of second-level monitoring indicators, number of minute-level monitoring indicators, number of key indicators, required number of key indicators, number of hosts with complete key indicators, number of instances with complete key indicators, number of key hosts with valid thresholds, and number of key instances with valid thresholds. The monitoring data set corresponding to the monitoring quality includes the data of the following third-level indicators: monitoring management rate, monitoring policy coverage rate, monitoring indicator completeness rate, and valid threshold setting rate. The monitoring data set corresponding to the monitoring efficiency includes the data of the following third-level indicators: second-level collection rate, minute-level collection rate, and average collection period.

[0040] In an exemplary embodiment, determining the monitoring situation of the monitoring tool for the monitoring object according to the N monitoring data sets includes at least one of the following:

[0041] First, determining whether the types of the third-level indicators for which the monitoring tool monitors the monitoring object meet the first preset condition, including:

[0042] Determining the types of the third-level indicators corresponding to the monitoring data in each of the monitoring data sets; when the types of the third-level indicators corresponding to all the monitoring data in the N monitoring data sets are consistent with the preset indicator types, it is determined that the first preset condition is met; otherwise, it is determined that the first preset condition is not met.

[0043] For the monitoring object, the types of the third-level indicators that should be monitored (preset indicator types) can be preset in advance. For example, it is preset in advance that the third-level indicators that should be monitored under the second-level indicator monitoring scale for the monitoring object A are the number of minute-level monitoring indicators, the number of key indicators, and the required number of key indicators. If the monitoring data set corresponding to the monitoring scale includes the monitoring data of the above three indicators, then the third-level indicators monitored under the monitoring scale meet the preset conditions. And so on. If the types of the third-level indicators under all the second-level indicators are consistent with the preset indicator types, it means that the monitoring tool monitors according to the preset third-level indicator types and meets the first preset condition. Otherwise, it is determined that the monitoring tool does not monitor according to the preset third-level indicator types and does not meet the first preset condition.

[0044] In the above embodiments, by classifying the monitoring data according to different metrics, it is possible to check whether the tertiary metrics under each secondary metric meet the pre-set conditions, and it is possible to comprehensively and timely check whether the monitoring tool monitors the monitored object according to the pre-set tertiary metric types, and it is possible to timely discover the missing tertiary metric types.

[0045] For example, when the first monitoring data set includes M monitoring data, determine the types of the tertiary metrics corresponding to the M monitoring data to obtain M metric types, where M is an integer, and the N monitoring data sets include the first monitoring data set; when the M metric types are inconsistent with the preset metric types, determine the missing metric types among the M metrics as the target metric types; add the target metric types to the target secondary metric, where the first monitoring data set is the monitoring data set corresponding to the target secondary metric.

[0046] For example, it is pre-set that the tertiary metrics that should be monitored under the secondary metric monitoring scale of the monitored object A are the number of minute-level monitoring metrics, the number of key metrics, and the required number of key metrics. If the monitoring data set corresponding to the monitoring scale includes the lack of the number of minute-level monitoring metrics, then add the tertiary metric type of the number of minute-level monitoring metrics to the secondary metric monitoring scale. In this way, the missing monitoring types can be timely discovered and added to the corresponding secondary metrics in a timely manner, so that the monitoring tool can comprehensively monitor the monitored object according to the pre-set conditions.

[0047] Second, determining whether the monitoring data of each tertiary metric monitored by the monitoring tool for the monitored object meets the second preset condition includes:

[0048] Obtain the preset threshold conditions of the tertiary metrics corresponding to each monitoring data in the N monitoring data sets; when each monitoring data meets the corresponding preset threshold conditions, it is determined that the second preset condition is met; otherwise, the second preset condition is not met.

[0049] Threshold conditions can be pre-set for each tertiary metric. For example, for the tertiary metrics: monitoring acceptance rate, monitoring strategy coverage rate, monitoring metric completeness rate, and threshold setting effectiveness rate, threshold conditions can be pre-set. For example, the monitoring acceptance rate is greater than 80%, the monitoring strategy coverage rate is greater than 90%, etc. By setting threshold conditions for each tertiary metric, it is possible to check whether the monitoring data of each tertiary metric meets the preset conditions.

[0050] For example, obtain the preset threshold condition of the first target third-level indicator corresponding to the first monitoring data included in the second monitoring data set, where the N monitoring data sets include the second monitoring data set; in the case where the first monitoring data does not meet the preset threshold condition, determine the first target third-level indicator as a monitoring abnormal indicator.

[0051] In the above embodiment, by setting thresholds for the monitoring data of each third-level indicator, in the case where the monitoring data of a certain third-level indicator does not meet the preset threshold condition, the third-level indicator with monitoring anomalies can be discovered in a timely manner, and then the cause of the anomaly can be analyzed in a timely manner.

[0052] Third, determine whether the alarm thresholds of each of the third-level indicators for which the monitoring tool monitors the monitoring object meet the third preset condition, including:

[0053] In the case where an alarm event occurs for the second monitoring data in the third monitoring data set, obtain the first alarm threshold of the second target third-level indicator corresponding to the second monitoring data, where the N monitoring data sets include the third monitoring data set; in the case where the second monitoring data is less than the first alarm threshold, determine that the alarm threshold of the second target third-level indicator for which the monitoring tool monitors the monitoring object does not meet the third preset condition. In this embodiment, an alarm event occurs when the second monitoring data is less than the first alarm threshold, indicating that the alarm threshold set by the monitoring tool is relatively low.

[0054] Obtain the third monitoring data in the fourth monitoring data set and the second alarm threshold of the third target third-level indicator corresponding to the third monitoring data; in the case where the third monitoring data is greater than or equal to the second alarm threshold and no alarm event occurs, determine that the alarm threshold of the third target third-level indicator for which the monitoring tool monitors the monitoring object does not meet the third preset condition. In this embodiment, no alarm occurs when the third monitoring data is greater than or equal to the second alarm threshold, indicating that the alarm threshold set by the monitoring tool is relatively high.

[0055] In the above embodiment, through the monitoring data, it can be seen whether the alarm threshold set by the monitoring tool is reasonable, and unreasonable alarm thresholds can be discovered in a timely manner for timely modification.

[0056] This application proposes an analysis method and system applicable to monitoring management, designed according to the ideas of indexation, onlineization, standardization, multi-dimension, trend, and classification. By constructing monitoring evaluation indicators, generating monitoring data online, performing multi-dimensional analysis and positioning, and providing classification solutions, a closed-loop of discovery, analysis, and rectification of monitoring problems is formed, and it is applied to monitoring promotion and rectification after the system is implemented, achieving the goal of improving the quality and efficiency of monitoring management.

[0057] This application proposes a monitoring and evaluation analysis solution based on evaluation indicators, aiming to automatically calculate indicators according to operation data, locate and analyze problems based on multi-dimensional indicators, and give solutions according to the located problems, so as to achieve the goal of improving the stratification and automation of monitoring. It includes: establishing a hierarchical cloud monitoring indicator system according to monitoring requirements, forming a benchmark for continuous evaluation, quickly synchronizing monitoring data for a specified period, and supplementing statistical fields for statistical analysis; establishing monitoring indicator baselines for each monitoring type, and generating monitoring detail analysis based on the comparison with the baselines. Generate multi-dimensional monitoring reports and indicators according to multi-dimensional monitoring details to locate problems in various types of monitoring; generate trend charts according to monitoring indicators to facilitate tracking the rectification effect. Generate classified solutions according to automatically located problems.

[0058] The analysis method of cloud monitoring is designed according to the ideas of indexation, onlineization, trendification, multi-dimension, trendification, and classification. It establishes cloud monitoring indicators, connects various monitoring tools in the operation and maintenance platform, and automatically generates multi-dimensional, multi-view, and multi-level monitoring statistical reports, which are implemented in the operation index system. It is applied in the monitoring field for the 8 major product fields of cloud computing, middleware, storage, network, database, operation, operation and maintenance, and security, forming an overview view of cloud monitoring and achieving good results.

[0059] As Figure 3 shown, the system includes: 1. Monitoring and evaluation index module, where users maintain and manage index descriptions, calculation cycles, calculation formulas, evaluation rules, etc. of evaluation indicators; 2. Key index report module, which is used to maintain and manage monitoring index baselines for various types of monitoring, providing a basis for comparison, including monitoring types, resource types, monitoring indicators, alarm levels, index thresholds, collection rules, etc.; 3. Monitoring detail report module, which is used to query and manage monitoring detail data from the dimensions of product fields, monitoring types, and monitoring indicators; 4. Monitoring management report module, which generates multi-view reports based on monitoring data details, including operation index data and scores such as maintenance parties, domain, system views, resource type views, monitoring index views, etc., and conducts intelligent analysis to generate visual charts and time trend charts; based on multi-dimensional monitoring reports such as basic monitoring, log monitoring, application monitoring, and service dial testing, generate multi-dimensional classified and graded monitoring reports to comprehensively reflect the monitoring situation. 5. Monitoring rectification report module, which analyzes and summarizes all monitoring reports and detail data, and generates lists of unmanaged hosts, incomplete indicators, invalid thresholds, and monitoring timeout hosts in categories for key indicator non-compliance situations, forming an optimization and rectification plan.

[0060] The following will be described separately from four aspects: the index design of the solution and system, standardized key indicators, online monitoring details, multi-dimensional trend monitoring reports, and positioning analysis to generate solutions by classification.

[0061] 1) Index design

[0062] First, the monitoring area is divided into first-level indicators such as basic monitoring, log monitoring, application monitoring, and service dial test capabilities according to monitoring classifications; for each classification, secondary indicators are set from perspectives such as monitoring scale, quality, efficiency, and risk. The secondary indicators are further drilled down layer by layer into multiple tertiary indicators. Each tertiary indicator is designed in detail in accordance with the principles of completeness, applicability, objectivity, and forward-lookingness, including indicator descriptions, docking systems, data extraction rules, calculation cycles, calculation formulas, evaluation rules, etc., and has four characteristics: quantifiable, comparable, guiding, and scenario-based. The proportion of each level of indicator can be set according to management requirements, and an evaluation model is formed based on the indicator proportion, and statistics are made according to the time period and different views selected by the user. Among them, the tertiary indicators calculate scores according to the calculation rules, and the secondary indicators and first-level indicator scores are calculated layer by layer according to the evaluation model.

[0063] For example, the key evaluation indicators for cloud monitoring management are shown in Table 1 above, and the detailed definitions of the key indicators in the tertiary evaluation indicators are shown in Table 2 below (the indicator proportion in the evaluation model can be adjusted according to the phased management objectives):

[0064] Table 2

[0065]

[0066]

[0067]

[0068]

[0069] 2) Standardize key indicators and online monitoring details

[0070] Based on the above-mentioned monitoring evaluation indicators, this solution designs a data extraction plan for the indicators, including data sources, data fields, processing methods, etc., and conducts feasibility verification, and is implemented online through the operation of the indicator system. The system processing steps include:

[0071] First, the operation indicator system connects to the monitoring components of the operation and maintenance platform to establish a standardized key indicator baseline for each monitoring type as the basis for analysis and comparison. The key indicator report includes: monitoring type, resource type, monitoring indicator, alarm level, alarm suppression times, upper limit of the threshold of the main alarm indicator, lower limit of the threshold of the main alarm indicator, upper limit of the threshold of the secondary alarm indicator, lower limit of the threshold of the secondary alarm indicator, collection cycle, etc.; the data of the key indicator report can be manually entered and imported in batches.

[0072] Then, run the metric system to connect to the operation and maintenance platform for monitoring and the configuration management library component, and batch synchronize the detailed data monitored on T-1 day daily. By synchronizing the details in real time according to the update time, the consistency of the operation and maintenance data with the source system is ensured. The synchronized monitoring details include: host / instance name, whether it is managed, associated monitoring policy, number of monitoring indicators, number of second-level monitoring indicators, second-level collection rate, number of minute-level monitoring indicators, and minute collection rate. By querying relevant components such as monitoring and the configuration management library, supplement the associated fields according to the mapping relationship to ensure the integrity of the operation and maintenance data. For example, query the CMDB through the host to automatically map the business system, physical system, and deployment unit to which the monitoring object belongs, and query the domain system mapping table through the system to supplement the product domain, maintenance party, etc. to which it belongs. In addition, compare the monitored indicators that should be monitored and the actual monitored indicators in the key indicator baseline of this monitoring type, and supplement the analysis fields according to the comparison results, such as: required key indicator count, key indicator count, number of key indicators with invalid thresholds, etc.;

[0073] 3) Multi-dimensional trend reports

[0074] Based on the monitoring detailed data, generate indicators and reports with multiple views such as each domain, product, system, department, etc. according to the data extraction plan, calculation formula, and statistical rules defined by the monitoring evaluation indicators. According to the calculation rules, compare the results of each evaluation indicator with the target to calculate the score, and then calculate the scores of each dimension of this first-level indicator by weighting according to the weights of each third-level indicator to comprehensively evaluate the level of this type of monitoring ability.

[0075] Based on various monitoring statistical reports, generate multi-dimensional monitoring indicators and reports, and count the overall monitoring ability and related indicators to facilitate subsequent positioning of which type of monitoring and which type of indicators have problems.

[0076] Based on the data snapshots of each dimension (day, month, quarter, half-year, year) during a certain period, generate trend views, including indicator reports and time trend charts from different perspectives such as each module, product domain, department, etc.

[0077] Support statistical analysis of multiple types of monitoring tools, multiple levels of monitoring objects, and multiple types of monitoring.

[0078] Multiple types of monitoring tools: Relying on the evaluation of monitoring capabilities such as basic monitoring, log monitoring, application monitoring, and service dial testing, generate multi-dimensional monitoring reports to reflect the overall and various monitoring capabilities.

[0079] Multiple levels of monitoring objects: Based on the detailed data, according to the supplemented mapping fields, statistically count according to the object hierarchy from the perspectives of the maintenance party, department, product domain, and business system view, and cluster and count those with the same maintenance party, department, product domain, and business system. Such as Figure 4As shown, for the operation data of the operation and maintenance process, the maintenance party is divided into the platform side and the tenant side. One department includes multiple product areas, and one product area includes multiple business systems. Analyze layer by layer to facilitate the monitoring, analysis, and problem location of different objects.

[0080] For example: The operation data of the domain view is shown in Table 3. It is possible to drill down and analyze from the domain view data to locate that the low monitoring management rate, low completeness rate of monitoring indicators, and low effectiveness rate of threshold settings in the network domain result in low overall indicators.

[0081] Table 3

[0082]

[0083] The operation data of the system view is shown in Table 4. It is possible to further drill down and analyze from the domain view data. Multiple systems are set under the same domain. According to the indicators of different systems, it is possible to locate and analyze that the low completeness rate of monitoring indicators of the GWP system in the network domain results in the low completeness rate of monitoring indicators of the entire network domain (similarly for other domains).

[0084] Table 4

[0085]

[0086] Multiple monitoring types: It is possible to count the operation data according to the monitoring type for problem analysis and location. For basic monitoring, count and analyze according to the resource type. Under the resource type dimension, the monitoring report data of all resource types are normally displayed, and it is also possible to drill down for a certain resource type. For example: The operation data of the resource type view is shown in Table 5. By drilling down and analyzing from the resource type view, multiple monitoring types are set under the "host" type. According to the indicators of different monitoring types, it is possible to locate and analyze that the low completeness rate of monitoring indicators of Linux in the "host" type results in the low completeness rate of monitoring indicators of the entire host type. Similarly, for log monitoring, count and analyze according to the log type; for service probing, count and analyze according to the probing type, etc.

[0087] Table 5

[0088]

[0089] 4) Classification and implementable solutions

[0090] Accurately locate problems according to the monitoring evaluation indicators, and generate a list of issues to be rectified and optimized and classified rectification plans. For example:

[0091] 1) Monitoring management rate: Generate a list of unmanaged hosts, and according to the indicators, the rectification plan is to increase management.

[0092] 2) Completeness rate of monitoring indicators: Generate a list of hosts with incomplete indicators, and the rectification plans are to adjust the monitoring strategy, add monitoring indicators, adjust the indicator baseline, etc.

[0093] 3) Monitoring threshold effectiveness rate: Generate a list of hosts with ineffective thresholds. The rectification plan is to adjust the threshold type, upper and lower limits of the threshold, suppression times, aggregation rules, etc.

[0094] 4) Collection period, alarm generation duration: Generate a list of alarm timeouts. The positioning and rectification plan is to adjust the collection period, optimize the alarm generation duration, etc.

[0095] This application establishes global monitoring evaluation indicators, forms an evaluation model and mechanism for multi-dimensional monitoring management, generates centralized and complete monitoring data, improves the integrity, accuracy, and efficiency of data acquisition, conducts multi-dimensional perspective monitoring analysis, improves the analysis and positioning efficiency of monitoring problems, and automatically generates classified rectification plans, improving the solution effect of monitoring problems.

[0096] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), including several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0097] In this embodiment, a device for determining the monitoring situation is also provided. This device is used to implement the above embodiments and preferred implementation methods, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0098] Figure 5 is a structural block diagram of the device for determining the monitoring situation according to an embodiment of the present invention, as Figure 5 shown. The device includes:

[0099] An acquisition module 52, configured to acquire monitoring data of a monitoring object monitored by a monitoring tool under a primary indicator;

[0100] A division module 54, configured to divide the monitoring data into N monitoring data sets according to the corresponding relationship between the primary indicator and the secondary indicator, where N is an integer, each monitoring data set corresponds to one of the secondary indicators, and each monitoring data in the monitoring data set corresponds to a tertiary indicator;

[0101] A determination module 56, configured to determine the monitoring situation of the monitoring tool for the monitored object according to the N monitoring data sets.

[0102] In an exemplary embodiment, the above device is further configured to determine whether the types of the third-level indicators monitored by the monitoring tool for the monitored object meet a first preset condition; determine whether the monitoring data of each of the third-level indicators monitored by the monitoring tool for the monitored object meets a second preset condition; and determine whether the alarm thresholds of each of the third-level indicators monitored by the monitoring tool for the monitored object meet a third preset condition.

[0103] In an exemplary embodiment, the above device is further configured to determine the types of the third-level indicators corresponding to the monitoring data in each of the monitoring data sets; and determine that the first preset condition is met when the types of the third-level indicators corresponding to all the monitoring data in the N monitoring data sets are consistent with the preset indicator types; otherwise, determine that the first preset condition is not met.

[0104] In an exemplary embodiment, when the first monitoring data set includes M monitoring data, the above device is further configured to determine the types of the third-level indicators corresponding to the M monitoring data to obtain M indicator types, where M is an integer, and the N monitoring data sets include the first monitoring data set; when the M indicator types are inconsistent with the preset indicator types, determine the missing indicator types among the M indicators as target indicator types; and add the target indicator types to the target secondary indicators, where the first monitoring data set is the monitoring data set corresponding to the target secondary indicators.

[0105] In an exemplary embodiment, the above device is further configured to obtain the preset threshold conditions of the third-level indicators corresponding to each of the monitoring data in the N monitoring data sets; and determine that the second preset condition is met when each of the monitoring data meets the corresponding preset threshold conditions; otherwise, determine that the second preset condition is not met.

[0106] In an exemplary embodiment, the above device is further configured to obtain the preset threshold conditions of the first target third-level indicators corresponding to the first monitoring data included in the second monitoring data set, where the N monitoring data sets include the second monitoring data set; and determine the first target third-level indicators as monitoring abnormal indicators when the first monitoring data does not meet the preset threshold conditions.

[0107] In an exemplary embodiment, the above-mentioned device is further configured to, when an alarm event occurs in the second monitoring data in the third monitoring data set, obtain a first alarm threshold of a second target third-level indicator corresponding to the second monitoring data, where the N monitoring data sets include the third monitoring data set; when the second monitoring data is less than the first alarm threshold, determine that the alarm threshold of the second target third-level indicator for which the monitoring tool monitors the monitoring object does not meet the third preset condition.

[0108] In an exemplary embodiment, the above-mentioned device is further configured to obtain third monitoring data in a fourth monitoring data set and a second alarm threshold of a third target third-level indicator corresponding to the third monitoring data; when the third monitoring data is greater than or equal to the second alarm threshold and no alarm event occurs, determine that the alarm threshold of the third target third-level indicator for which the monitoring tool monitors the monitoring object does not meet the third preset condition.

[0109] It should be noted that the above-mentioned modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited thereto: the above-mentioned modules are all located in the same processor; or, the above-mentioned modules are respectively located in different processors in any combination form.

[0110] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method described in any one of the above.

[0111] In an exemplary embodiment, the above-mentioned computer-readable storage medium may include, but is not limited to: USB flash drives, read-only memories (ROM for short), random access memories (RAM for short), mobile hard disks, magnetic disks, or optical discs and other various media that can store computer programs.

[0112] An embodiment of the present invention further provides an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0113] In an exemplary embodiment, the above-mentioned electronic device may further include a transmission device and an input / output device, where the transmission device is connected to the above-mentioned processor, and the input / output device is connected to the above-mentioned processor.

[0114] Specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0115] An embodiment of the present invention further provides a computer program product, including a computer program, where when the computer program is executed by a processor, the steps of the methods described in various embodiments of the present application are implemented.

[0116] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a sequence different from that here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. In this way, the present invention is not limited to any specific combination of hardware and software.

[0117] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining a monitoring situation, characterized in that: include: Obtain monitoring data under the first-level indicators obtained by the monitoring tool on the monitored object; According to the correspondence between the first-level indicators and the second-level indicators, the monitoring data is divided into N monitoring data sets, where N is an integer, each monitoring data set corresponds to one second-level indicator, and each monitoring data in the monitoring data set corresponds to one third-level indicator; The monitoring status of the monitoring object by the monitoring tool is determined according to the N monitoring data sets.

2. The method according to claim 1, characterized in that: Determining the monitoring status of the monitoring object by the monitoring tool according to the N monitoring data sets includes at least one of the following: Determining whether the type of the third-level indicator monitored by the monitoring tool on the monitoring object meets a first preset condition; Determine whether the monitoring data of each of the third-level indicators monitored by the monitoring tool on the monitoring object meets a second preset condition; Determine whether the alarm threshold of each of the third-level indicators monitored by the monitoring tool on the monitored object meets the third preset condition.

3. The method according to claim 2, characterized in that Determining whether the type of the third-level indicator monitored by the monitoring tool on the monitoring object meets a first preset condition includes: Determine the type of the third-level indicator corresponding to the monitoring data in each of the monitoring data sets; When the types of the third-level indicators corresponding to all the monitoring data in the N monitoring data sets are consistent with the preset indicator types, it is determined that the first preset condition is met; otherwise, it is determined that the first preset condition is not met.

4. The method according to claim 3, characterized in that The method further comprises: In the case where the first monitoring data set includes M monitoring data, determining the types of the third-level indicators corresponding to the M monitoring data to obtain M indicator types, where M is an integer, and the N monitoring data sets include the first monitoring data set; In the case where the M indicator types are inconsistent with the preset indicator types, determining the indicator type missing from the M indicators as the target indicator type; The target indicator type is added to the target secondary indicator, wherein the first monitoring data set is a monitoring data set corresponding to the target secondary indicator.

5. The method according to claim 2, characterized in that: Determining whether the monitoring data of each of the third-level indicators monitored by the monitoring tool on the monitoring object meets the second preset condition includes: Obtaining a preset threshold condition of the third-level indicator corresponding to each of the monitoring data in the N monitoring data sets; When each of the monitoring data satisfies the corresponding preset threshold condition, it is determined that the second preset condition is met; otherwise, the second preset condition is not met.

6. The method according to claim 5, characterized in that The method further comprises: Obtaining a preset threshold condition of a first target third-level indicator corresponding to first monitoring data included in a second monitoring data set, wherein the N monitoring data sets include the second monitoring data set; When the first monitoring data does not satisfy the preset threshold condition, the first target level 3 indicator is determined as a monitoring abnormality indicator.

7. The method according to claim 2, characterized in that Determining whether the alarm threshold of each of the third-level indicators monitored by the monitoring tool on the monitoring object meets the third preset condition includes: When an alarm event occurs in the second monitoring data in the third monitoring data set, obtaining a first alarm threshold of the second target third-level indicator corresponding to the second monitoring data, wherein the N monitoring data sets include the third monitoring data set; When the second monitoring data is less than the first alarm threshold, it is determined that the alarm threshold of the second target third-level indicator monitored by the monitoring tool for the monitored object does not meet the third preset condition.

8. The method according to claim 2, characterized in that: Determining whether the alarm threshold of each of the third-level indicators monitored by the monitoring tool on the monitored object meets the third preset condition also includes: Acquire the third monitoring data in the fourth monitoring data set, and the second alarm threshold of the third target third-level indicator corresponding to the third monitoring data; When the third monitoring data is greater than or equal to the second alarm threshold and no alarm event occurs, it is determined that the alarm threshold of the third target third-level indicator monitored by the monitoring tool for the monitored object does not meet the third preset condition.

9. A device for determining monitoring conditions, characterized in that: include: An acquisition module is used to obtain monitoring data under the first-level indicator obtained by the monitoring tool from monitoring the monitored object; A division module, used for dividing the monitoring data into N monitoring data sets according to the correspondence between the first-level indicators and the second-level indicators, wherein N is an integer, each monitoring data set corresponds to one of the second-level indicators, and each monitoring data in the monitoring data set corresponds to one of the third-level indicators; A determination module is used to determine the monitoring status of the monitoring tool on the monitoring object based on the N monitoring data sets.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 8 when executed by a processor.

11. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 8 are implemented.

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