Self-adaptive monitoring period adjusting method and device based on hotspot identification

Abstract

The invention discloses a self-adaptive monitoring period adjusting method and device based on hotspot recognition, and relates to the field of computer information technology and data monitoring, and the method comprises the steps: obtaining the monitoring data of a monitoring object under a current collection interval and the monitoring state of the monitoring object under a current monitoring period; when the monitoring state of the monitoring object meets a preset monitoring period replacement condition, calculating a hotspot score of the monitoring object according to the monitoring data; generating a recommended monitoring period according to the hotspot score of the monitoring object, and executing a monitoring action based on the recommended monitoring period; the hotspot score of the monitoring object is used for evaluating the importance and activeness of the monitoring object. The invention provides an intelligent monitoring period adjusting technology which does not need manual intervention, can sense data changes and adapt to service priorities, and solves the problems of resource waste, response lag and manual configuration rigidity of a fixed period scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer information technology and data monitoring, in particular to a self-adaptive monitoring period adjustment method and device based on hot spot identification. BACKGROUND

[0002] In modern IT systems, Internet of Things and business operations, continuous monitoring of key indicators is an important means to ensure system stability and business health. At present, the mainstream monitoring scheme mostly adopts a fixed period polling mechanism, that is, the administrator pre-sets unified or type-based monitoring intervals (such as 1 minute / time, 5 minutes / time), and the system collects data and detects abnormalities at a fixed frequency. The existing technology has the following defects: 1. Resource waste: For slowly changing or long-term stable data, frequent monitoring of fixed period will generate a large amount of invalid data, occupying network bandwidth, computing resources and storage space.

[0003] 2. Response delay: In the business peak scenario (such as e-commerce promotion, flash sale activities) or data burst fluctuation (such as Internet of Things device fault alarm triggering), fixed long period monitoring cannot timely capture data changes. For example, during the promotion of a certain e-commerce platform, the order data updates more than one thousand times per second, but the fixed 5-minute monitoring period causes the order anomaly to be discovered 4 minutes and 30 seconds later, directly causing tens of thousands of yuan of transaction loss.

[0004] 3. Configuration rigidity: In complex systems, the monitoring objects often reach thousands (such as database tables, interfaces, device indicators), and the administrator needs to set the monitoring period one by one based on experience, which not only has a large configuration workload, but also cannot adapt to data dynamic changes (such as a business table changing from low-frequency update to high-frequency update).

[0005] If the above problems are handled manually, there are several pain points: Pain point 1: high maintenance cost, which requires manual adjustment of monitoring strategies; Pain point 2: delayed response, manual adjustment has a lag; Pain point 3: high configuration complexity, requiring high experience of operation and maintenance personnel. SUMMARY

[0006] The purpose of the present application is to provide a self-adaptive monitoring period adjustment method and device based on hot spot identification, and to propose an intelligent monitoring period adjustment technology that does not require manual intervention, can perceive data changes and adapt to business priorities, thereby solving the problems of resource waste, response lag and manual configuration rigidity of the fixed period scheme.

[0007] To achieve the above purpose, the present application provides the following solutions: In a first aspect, the present application provides a self-adaptive monitoring period adjustment method based on hot spot identification, comprising: The monitoring data of the monitoring object and the monitoring state of the monitoring object under a current monitoring period are acquired; the monitoring data includes dynamic behavior data, business value data, historical alarm data, and basic attribute data; When the monitoring state of the monitoring object meets preset monitoring period replacement conditions, a hotspot score of the monitoring object is calculated according to the monitoring data; a recommended monitoring period is generated according to the hotspot score of the monitoring object, and a monitoring action is performed based on the recommended monitoring period; the hotspot score of the monitoring object is used to evaluate the importance and activity of the monitoring object.

[0008] In a second aspect, the present application provides an adaptive monitoring period adjustment device based on hotspot identification, comprising: The data acquisition module is configured to acquire monitoring data of the monitoring object and a monitoring state of the monitoring object under a current monitoring period; the monitoring data includes dynamic behavior data, business value data, historical alarm data, and basic attribute data. The monitoring period adjustment module is configured to, when the monitoring state of the monitoring object meets preset monitoring period replacement conditions, calculate a hotspot score of the monitoring object according to the monitoring data; generate a recommended monitoring period according to the hotspot score of the monitoring object, and perform a monitoring action based on the recommended monitoring period; and use the hotspot score of the monitoring object to evaluate the importance and activity of the monitoring object.

[0009] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the adaptive monitoring period adjustment method based on hotspot identification.

[0010] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the adaptive monitoring period adjustment method based on hotspot identification.

[0011] In a fifth aspect, the present application provides a computer program product comprising a computer program executable by a processor to implement the adaptive monitoring period adjustment method based on hotspot identification.

[0012] According to the specific embodiments provided by the present application, the present application has the following technical effects: The application provides a hotspot identification-based adaptive monitoring period adjustment method and device, wherein the method comprises the following steps: obtaining monitoring data of a monitoring object and a monitoring state of the monitoring object under a current monitoring period; the monitoring data comprises dynamic behavior data, service value data, historical alarm data and basic attribute data; when the monitoring state of the monitoring object meets preset monitoring period replacement conditions, calculating a hotspot score of the monitoring object according to the monitoring data; generating a recommended monitoring period according to the hotspot score of the monitoring object, and performing a monitoring action based on the recommended monitoring period; and the hotspot score of the monitoring object is used to evaluate the importance and activity of the monitoring object. In the application, when the monitoring state of the monitoring object meets the preset monitoring period replacement conditions, the recommended monitoring period is generated according to the hotspot score of the monitoring object, the adjustment of the monitoring period does not need manual intervention, is automatically adapted to the change of data characteristics, has high automation, and through the adaptive adjustment of the monitoring period, the invalid monitoring of stable data can be reduced, the resource consumption is reduced, and the resource utilization is improved. The monitoring period of the hotspot data can be shortened to seconds in the application, the response delay is reduced by 80%, the timeliness of monitoring is greatly enhanced, and the application is suitable for various monitoring scenes, including system monitoring, service monitoring, database monitoring and the like. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0014] Figure 1 An application environment diagram of a hotspot identification-based adaptive monitoring period adjustment method in an embodiment of the present application; Figure 2 A flowchart of a hotspot identification-based adaptive monitoring period adjustment method provided in an embodiment of the present application; Figure 3 A technical concept diagram of a hotspot identification-based adaptive monitoring period adjustment method provided in an embodiment of the present application; Figure 4 A functional module diagram of a hotspot identification-based adaptive monitoring period adjustment device provided in an embodiment of the present application; Figure 5 A structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] With reference to the drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0016] The above-mentioned purposes, features and advantages of the present application will be more apparent and understandable. The present application will be further described in detail below with reference to the drawings and specific embodiments.

[0017] The adaptive monitoring period adjustment method based on hotspot identification provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 The terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be separately arranged, or integrated on the server 104, or placed on a cloud or other servers. The terminal 102 can send the monitoring data (including dynamic behavior data, business value data, historical alarm data and basic attribute data) of the monitoring object and the monitoring state of the monitoring object under the current monitoring period to the server 104. After receiving the monitoring data of the monitoring object and the monitoring state of the monitoring object under the current monitoring period, the server 104 judges whether the monitoring state of the monitoring object meets the preset monitoring period replacement condition. When it meets, the hotspot score of the monitoring object is calculated according to the monitoring data; the recommended monitoring period is generated according to the hotspot score of the monitoring object, and the monitoring action is executed based on the recommended monitoring period; the hotspot score of the monitoring object is used to evaluate the importance and activity of the monitoring object. The server 104 can feed back the recommended monitoring period obtained to the terminal 102. In addition, in some embodiments, the adaptive monitoring period adjustment method based on hotspot identification can also be realized by the server 104 or the terminal 102 alone, such as the terminal 102 directly performing adaptive monitoring period adjustment based on hotspot identification on the monitoring data of the monitoring object and the monitoring state of the monitoring object under the current monitoring period, or the server 104 obtaining the monitoring data of the monitoring object and the monitoring state of the monitoring object under the current monitoring period from the data storage system and performing adaptive monitoring period adjustment based on hotspot identification.

[0018] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The server 104 can be realized by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0019] In an exemplary embodiment, as shown in Figure 2 and Figure 3As shown, an adaptive monitoring period adjustment method based on hotspot identification is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or both. In the embodiments of the present application, the method is applied to Figure 1 The server 104 in the system 100 is taken as an example to illustrate the method, which includes the following steps 201 to 202.

[0020] In step 201, the monitoring data of the monitoring object and the monitoring state of the monitoring object under the current monitoring period are obtained, as shown in Table 1 and Table 3. The monitoring data includes dynamic behavior data, business value data, historical alarm data and basic attribute data. Table 2 shows an example of the monitoring data of the monitoring object. These monitoring data are collected at a preset collection interval.

[0021] Table 1 Monitoring data of the monitoring object

[0022] Table 2 Monitoring data of the monitoring object on a certain day

[0023] The collection strategy of the monitoring data in the present application is as follows: (1) Dynamic interval: positively correlated with the monitoring period (e.g., 10 seconds monitoring period corresponds to 10 seconds collection interval).

[0024] (2) Incremental collection: only collect the changed part of large volume data (>1GB) to reduce data transmission and storage pressure.

[0025] (3) Abnormal supplementary collection: when the characteristic data changes suddenly, the collection interval is automatically reduced to 1 / 3 of the original collection interval.

[0026] The collection of the monitoring data is the basis for subsequent quantification and scoring. The core goal is to comprehensively and efficiently capture the "dynamic characteristics" and "business value" of the monitoring object, and avoid the deviation of hotspot judgment due to the lack of features. The characteristic data of the monitoring object is continuously collected from multiple data sources, and the collection interval is dynamically adjusted according to the current monitoring period.

[0027] In step 202, when the monitoring state of the monitoring object meets the preset monitoring period replacement condition, the hotspot score of the monitoring object is calculated according to the monitoring data; the recommended monitoring period is generated according to the hotspot score of the monitoring object, and the monitoring action is performed based on the recommended monitoring period; the hotspot score of the monitoring object is used to evaluate the importance and activity of the monitoring object. As shown in Table 3, the preset monitoring period replacement condition can be that the n statistical period false negative rate is greater than the false negative rate warning threshold or the false positive rate is greater than the false positive rate warning threshold, or the CPU occupancy rate is greater than the preset occupancy rate or the storage increment is greater than the preset storage increment in a continuous preset time period, or the business satisfaction feedback of m times of investigation is less than the preset satisfaction score.

[0028] Table 3 Monitoring state of monitoring object

[0029] The preset monitoring period replacement condition shown in Table 3 is only an example, and can be set according to requirements.

[0030] By implementing the steps 201 to 202 described above, the application obtains monitoring data of the monitoring object in multiple dimensions. When the monitoring state of the monitoring object meets the preset monitoring period replacement condition, the hot spot score of the monitoring object can be calculated according to the monitoring data, so as to determine the importance and activity of the monitoring object, and a recommended monitoring period is generated in combination with the hot spot score of the monitoring object, so as to realize adaptive adjustment of the monitoring period. The monitoring period adjustment of the application does not require manual intervention, can automatically adapt to changes in data characteristics, has high automation, and through adaptive adjustment of the monitoring period, invalid monitoring of stable data can be reduced, optimal allocation of monitoring resources is realized, resource consumption is significantly reduced while ensuring monitoring effect, and resource utilization is improved. In the application, the monitoring period of hot spot data can be shortened to seconds, the response delay is reduced by 80%, the timeliness of monitoring is greatly enhanced, and the application is suitable for various monitoring scenarios, including system monitoring scenarios, business monitoring scenarios, database monitoring scenarios, and the like.

[0031] In another exemplary embodiment of the application, the adaptive monitoring period adjustment method based on hot spot identification further includes: when the monitoring state of the monitoring object meets a preset monitoring period fine-tuning condition, fine-tuning the current monitoring period, and performing a monitoring action based on the fine-tuned monitoring period.

[0032] For a situation that does not meet the preset monitoring period replacement condition but exceeds the warning threshold in the monitoring state, it can be used as the preset monitoring period fine-tuning condition. For example, the preset monitoring period fine-tuning condition can be: a false negative rate of less than n statistical periods > false negative rate warning threshold or false positive rate > false positive rate warning threshold, or: CPU occupancy rate in less than a preset time period > preset occupancy rate or storage increment > preset storage increment, or: less than m times of survey business satisfaction feedback is less than a preset satisfaction score. When the survey business satisfaction appears "single unqualified", the monitoring period is fine-tuned instead of being directly replaced. After fine-tuning, the situation is observed for 1 statistical period. If the satisfaction degree rises, the fine-tuned period is maintained; if it is still unqualified, a new period is directly generated by triggering the "replacement condition".

[0033] Taking the preset monitoring period replacement condition shown in Table 2 as an example, the fine-tuned monitoring period is explained. When the resource consumption exceeds the warning threshold, but does not reach the situation of 1 hour, after exceeding the warning threshold, the current monitoring period is directly fine-tuned, and the medium / low / extreme low hot spot object period is extended by 20%-30% (such as 30 minutes→36 minutes).

[0034] Monitoring status can also include alarm response time. When the alarm response time exceeds the preset time threshold, the monitoring cycle can be directly adjusted to shorten the cycle of high / medium-high hotspot objects by 10%-20% (e.g., 5 seconds → 4 seconds).

[0035] When a statistical period has a false negative rate of >5% or a false positive rate of >10%, the current monitoring period is fine-tuned, and the monitoring operation is performed using the fine-tuned monitoring period. If the false negative rate of >5% or the false positive rate of >10% occurs again, a recommended monitoring period is generated and replaced with the current monitoring period.

[0036] When this application detects that the status of the monitored object is not up to standard, it can choose to fine-tune the current monitoring cycle and perform monitoring operations based on the fine-tuned monitoring cycle. If the monitoring status still fails to meet the standard after fine-tuning, it can choose to calculate a new monitoring cycle to replace it.

[0037] In another exemplary embodiment of this application, step 202, calculating the hotspot score of the monitored object based on the monitoring data, specifically includes: (1) Calculate the data change frequency of the monitored object based on the number of data changes and the amount of data; the number of data changes includes the number of data additions, the number of data modifications, and the number of data deletions.

[0038] The core objective of data change frequency quantification is to transform raw change data into standardized "activity indicators," eliminate the impact of differences in dimensions (such as different data volumes or different statistical periods), and provide a unified input dimension for hotspot scoring.

[0039] The number of data changes (number of additions, number of modifications, number of deletions) is standardized using Min-Max and mapped to the [0,1] interval. The statistical period is set to 10 minutes to 1 hour according to the data stability.

[0040] The formula for calculating the data change frequency of the monitored object is as follows: In the formula, Indicates the frequency of data changes; This indicates the number of times the data has been added in a standardized manner. Indicates the number of standardized data modifications; Indicates the number of times data has been deleted in a standardized manner; This represents the standardized amount of data; Indicates the statistical period (unit: seconds).

[0041] Taking the monitored objects in Table 2 as an example, the quantitative results of the data change frequency based on the collected data (statistical period = 10 minutes = 600 seconds) are shown in Table 4.

[0042] Table 4. Examples of Quantification Results for Data Variation Frequency

[0043] (2) Calculate the access popularity of the monitored object based on the access frequency and access peak of the monitored object.

[0044] (3) Quantify the business weight level and core link identifier of the monitored object to obtain the quantified value of the business weight level and the quantified value of the core link identifier.

[0045] (4) Standardize the historical alarm data of the monitored object to obtain standardized alarm data.

[0046] (5) Calculate the standardized data growth rate based on the amount of data of the monitored object.

[0047] (6) Calculate the hotspot score of the monitored object based on the data change frequency, access popularity, business weight level quantification value, core link identifier quantification value, standardized alarm data, and standardized data growth rate. Table 5 shows an example of hotspot score calculation.

[0048] Among them, the hot spot rating of the monitored objects The calculation formula is: In the formula, Indicates the frequency of data changes; This indicates the access popularity (frequency × peak value × 0.001). This represents the quantified value of the business weight level (Level 1 → 0.2 to Level 5 → 1.0). This represents standardized alarm data, such as the number of alarms in the past 30 days. Indicates the rate of growth of standardized data; This indicates the core link identifier quantization value (Yes → 0.1, No → 0.02). to This indicates dynamic weights.

[0049] Table 5 Example of Hotspot Rating Calculation

[0050] When the monitoring status of a monitored object meets the preset monitoring cycle replacement conditions, the weights in the hotspot scoring formula of the monitored object need to be dynamically adjusted. to The value of , and the specific weight adjustment formula (based on gradient descent) is as follows: in, i =1 to 6; This represents the weight values ​​before normalization; These are the normalized weight values, and the sum of all weights is 1. This indicates the weight value before adjustment; This indicates the false alarm rate or missed detection rate. This indicates the warning threshold for either the false alarm rate or the missed detection rate. The learning rate is set to 0.01-0.1; the larger the bias, the lower the learning rate. The larger; This refers to the partial derivative of the monitoring performance indicator with respect to the weight (e.g., the partial derivative is negative when the false negative rate is positively correlated with the weight).

[0051] In another exemplary embodiment of this application, for monitoring cycle decision-making, this application adopts a "segmented adaptive + scenario calibration" decision-making mechanism based on hotspot scoring under dynamic weights, combined with business scenario characteristics and resource constraints, to achieve accurate matching and smooth adjustment of the monitoring cycle, ensuring both the timeliness of hotspot data and avoiding resource waste. Monitoring cycle decision-making rule: based on hotspot scoring. Using dynamic weights and business scenario tags (such as major sales events and routine maintenance) as the core input, a piecewise function is used to calculate the monitoring period. Recommended monitoring cycle The calculation formula is: Among them, "dynamic weight scenario" refers to dynamically determining the 6 weight values ​​in the hot spot scoring formula; "business scenario tag" will affect the value of λ in the recommendation monitoring cycle formula.

[0052] Table 6 shows the relevant calculation parameter definitions and value examples for the recommended monitoring period, and Table 7 shows the monitoring period adjustment example generated based on the above monitoring period decision rules (dynamic weight + promotional scenario).

[0053] Table 6. Definitions and Value Examples of Relevant Calculation Parameters for Recommended Monitoring Periods

[0054] When the monitoring status of the monitored object meets the preset monitoring cycle replacement conditions, the formula for calculating the recommended monitoring cycle needs to be dynamically adjusted. The value, for example, the underreporting rate failing to meet the standard for two statistical periods: Lower the threshold by 5%-10% (e.g., from 0.85 to 0.8) to expand coverage of high-risk areas; false alarm rates failed to meet standards for two consecutive statistical periods. The threshold will be increased by 5%-10% (e.g., from 0.8 to 0.85), tightening the criteria for determining high-profile topics. Additionally, the hot topic scoring formula will include... During the upgrade, Increase by 5%-10% (e.g., 0.8 → 0.85).

[0055] Table 7 Examples of monitoring cycle adjustments generated based on the above monitoring cycle decision rules. In this application, when the monitoring status of the monitored object meets the preset monitoring cycle replacement conditions, the weight values ​​in the hotspot score calculation formula and the parameters in the recommended monitoring cycle calculation formula are dynamically calculated first. The value is then used to generate a recommended monitoring cycle. This application constructs a closed-loop mechanism of "execution-evaluation-optimization-iteration," which is guided by monitoring effectiveness and business feedback, and dynamically optimizes the decision rules and weight coefficients of the monitoring cycle to ensure that the system adapts to data characteristics and business changes in the long term.

[0056] In this application, an adaptive adjustment of monitoring frequency based on data dynamic characteristics and business importance is achieved through a data hotspot scoring model based on multi-dimensional features, a dynamic adaptive monitoring cycle decision algorithm, and a closed-loop feedback model parameter optimization mechanism.

[0057] This application also provides an application scenario in which the above-described adaptive monitoring cycle adjustment method based on hotspot identification is applied. Specifically, the adaptive monitoring cycle adjustment method based on hotspot identification provided in this embodiment can be applied in a database monitoring scenario. This scenario includes a data acquisition stage and a monitoring cycle adaptive adjustment stage; the data acquisition stage is used to collect monitoring data of the monitored object and the monitoring status of the monitored object under the current monitoring cycle; the monitoring cycle adaptive adjustment stage is used to adaptively adjust the monitoring cycle of the monitored object based on the collected monitoring data and monitoring status. The adaptive monitoring cycle adjustment method based on hotspot identification provided in this embodiment belongs to the monitoring cycle adaptive adjustment stage.

[0058] Based on the same inventive concept, this application also provides an adaptive monitoring cycle adjustment device based on hotspot identification for implementing the aforementioned adaptive monitoring cycle adjustment method based on hotspot identification. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the adaptive monitoring cycle adjustment device based on hotspot identification provided below can be found in the limitations of the adaptive monitoring cycle adjustment method based on hotspot identification described above, and will not be repeated here.

[0059] In one exemplary embodiment, such as Figure 4 As shown, an adaptive monitoring cycle adjustment device based on hotspot identification is provided, comprising: The data acquisition module M1 is used to acquire the monitoring data of the monitored objects and the monitoring status of the monitored objects in the current monitoring period; the monitoring data includes dynamic behavior data, business value data, historical alarm data and basic attribute data.

[0060] The monitoring cycle adjustment module M2 is used to calculate the hotspot score of the monitored object based on the monitoring data when the monitoring status of the monitored object meets the preset monitoring cycle replacement conditions; generate a recommended monitoring cycle based on the hotspot score of the monitored object; and execute monitoring actions based on the recommended monitoring cycle; the hotspot score of the monitored object is used to evaluate the importance and activity of the monitored object.

[0061] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores adaptive monitoring cycle adjustment data based on hotspot identification. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an adaptive monitoring cycle adjustment method based on hotspot identification.

[0062] Those skilled in the art will understand that Figure 5 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0065] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0066] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0067] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An adaptive monitoring cycle adjustment method based on hotspot identification, characterized in that, include: Obtain the monitoring data of the monitored object and the monitoring status of the monitored object in the current monitoring period; Monitoring data includes dynamic behavior data, business value data, historical alarm data, and basic attribute data; When the monitoring status of the monitored object meets the preset monitoring cycle replacement conditions, the hot spot score of the monitored object is calculated based on the monitoring data. Recommended monitoring cycles are generated based on the hotspot ratings of the monitored objects, and monitoring actions are executed based on the recommended monitoring cycles; the hotspot ratings of the monitored objects are used to assess the importance and activity level of the monitored objects.

2. The adaptive monitoring cycle adjustment method based on hotspot identification according to claim 1, characterized in that, The dynamic behavior data includes the number of data changes, access frequency, and access peak of the monitored object; The business value data includes the business weight level and core link identifier of the monitored object; The historical alarm data includes the number of alarms within a preset historical time period; The basic attribute data includes the data volume of the monitored object; The monitoring status includes monitoring effectiveness, resource consumption, business satisfaction feedback, and alarm response time.

3. The adaptive monitoring cycle adjustment method based on hotspot identification according to claim 2, characterized in that, The hotspot score of the monitored object is calculated based on the monitoring data, specifically including: The data change frequency of the monitored object is calculated based on the number of data changes and the amount of data; the number of data changes includes the number of times data is added, the number of times data is modified, and the number of times data is deleted. Calculate the access popularity of the monitored object based on its access frequency and peak access frequency. The business weight level and core link identifier of the monitored object are numerically quantified to obtain the quantified value of the business weight level and the quantified value of the core link identifier. The historical alarm data of the monitored objects is standardized to obtain standardized alarm data; Calculate the standardized data growth rate based on the data volume of the monitored object; The hotspot score of the monitored object is calculated based on the frequency of data changes, access popularity, business weight level quantification value, core link identifier quantification value, standardized alarm data, and standardized data growth rate.

4. The adaptive monitoring cycle adjustment method based on hotspot identification according to claim 3, characterized in that, The formula for calculating the data change frequency of the monitored object is: In the formula, Indicates the frequency of data changes; This indicates the number of times the data has been added in a standardized manner. Indicates the number of standardized data modifications; Indicates the number of times data has been deleted in a standardized manner; This represents the standardized amount of data; Indicates the statistical period.

5. The adaptive monitoring cycle adjustment method based on hotspot identification according to claim 3, characterized in that, The formula for calculating the hotspot score of the monitored object is as follows: In the formula, Indicates hot topic ratings; Indicates the frequency of data changes; Indicates visit popularity; This represents the quantified value of the business weight level; This represents standardized alarm data; Indicates the rate of growth of standardized data; This represents the quantized value of the core link identifier; to Indicates the weight.

6. The adaptive monitoring cycle adjustment method based on hotspot identification according to claim 1, characterized in that, The recommended formula for calculating the monitoring period is: In the formula, Indicates the recommended monitoring period; Indicates the minimum monitoring period; Indicates the intermediate baseline monitoring period; Indicates the maximum monitoring period; Indicates popularity rating; This indicates the threshold for high popularity ratings; This indicates the threshold for a moderately popular topic rating; This indicates a low hotspot rating threshold; This indicates dynamic weights.

7. The adaptive monitoring cycle adjustment method based on hotspot identification according to claim 1, characterized in that, The adaptive monitoring cycle adjustment method based on hotspot identification further includes: when the monitoring status of the monitored object meets the preset monitoring cycle fine-tuning conditions, fine-tuning the current monitoring cycle, and performing monitoring actions based on the fine-tuned monitoring cycle.

8. An adaptive monitoring cycle adjustment device based on hotspot identification, characterized in that, include: The data acquisition module is used to acquire the monitoring data of the monitored object at the current acquisition interval and the monitoring status of the monitored object at the current monitoring period. Monitoring data includes dynamic behavior data, business value data, historical alarm data, and basic attribute data; The monitoring cycle adjustment module is used to calculate the hot spot score of the monitored object based on the monitoring data when the monitoring status of the monitored object meets the preset monitoring cycle replacement conditions. Recommended monitoring cycles are generated based on the hotspot ratings of the monitored objects, and monitoring actions are executed based on the recommended monitoring cycles; the hotspot ratings of the monitored objects are used to assess the importance and activity level of the monitored objects.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the adaptive monitoring cycle adjustment method based on hotspot identification as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the adaptive monitoring cycle adjustment method based on hotspot identification as described in any one of claims 1-7.

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