Cloud network monitoring data acquisition method and device, storage medium and program product

Abstract

The invention provides a cloud network monitoring data acquisition method and device, a storage medium and a program product, and relates to the technical field of cloud computation.In the embodiment, damage time corresponding to a damaged monitoring index is determined in monitoring indexes obtained through calculation on the basis of operation data of cloud network equipment, and the damage time corresponding to the damaged monitoring index is determined; according to the method, the operation data corresponding to the damaged time is calculated again, and the monitoring indexes obtained through recalculation and the undamaged monitoring indexes are used as the monitoring data of the cloud network equipment to be sent to the user terminal, so that data missing caused by data damage is avoided, the accuracy of state judgment of the cloud network equipment can be improved, and the user experience is improved. And the stability of the cloud network service is improved. Moreover, the calculation is carried out based on the damage time, the calculation of total data is not needed, and the calculation resources can be saved on the premise of ensuring the accuracy of the calculation result.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, and in particular to a method, device, storage medium and program product for acquiring cloud network monitoring data. Background Technology

[0002] Cloud network users need real-time device monitoring metrics to analyze business operations, and providing stable and accurate monitoring metrics is one of the key objectives of cloud network services. In a cloud computing environment, monitoring metrics are characterized by complex logic, large scale, and strong correlations, making them prone to data gaps or calculation errors. In actual business operations, it is a common problem for users to misjudge device status even when their devices are functioning correctly due to incorrect monitoring metric calculations.

[0003] Currently, streaming computing is typically used to calculate monitoring metrics in real time based on the collected operational data of cloud network devices. To ensure the real-time performance of streaming computing, it is usually necessary to discard damaged data that is missing or has calculation errors. This results in the loss of historical data that cannot be recovered permanently, affecting the accuracy of users' judgment of the status of cloud network devices and consequently impacting the stability of cloud network services. Summary of the Invention

[0004] This application provides a method, device, storage medium, and program product for acquiring cloud network monitoring data to improve the stability of cloud network services.

[0005] In a first aspect, embodiments of this application provide a method for acquiring cloud network monitoring data, including:

[0006] Based on the operational data of cloud network devices, at least one primary monitoring indicator is determined.

[0007] Determine the time of damage corresponding to the damaged monitoring indicator from at least one first monitoring indicator data;

[0008] Determine the target operational data corresponding to the time of damage from the operational data, and based on the target operational data, determine at least one second monitoring indicator data;

[0009] At least one undamaged monitoring indicator from the first monitoring indicator data and at least one second monitoring indicator data are sent to the user terminal as monitoring data of the cloud network device.

[0010] Secondly, embodiments of this application provide a cloud network monitoring data acquisition device, comprising:

[0011] The first indicator determination module is used to determine at least one first monitoring indicator data based on the operation data of cloud network devices.

[0012] The damage time determination module is used to determine the damage time corresponding to the damaged monitoring indicator from at least one first monitoring indicator data.

[0013] The second indicator determination module is used to determine the target operating data corresponding to the damaged time in the operating data, and to determine at least one second monitoring indicator data based on the target operating data.

[0014] The data transmission module is used to send at least one undamaged monitoring indicator and at least one second monitoring indicator data from at least one first monitoring indicator data to the user terminal as monitoring data of the cloud network device.

[0015] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the methods described above when executing the computer program.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the methods described above.

[0017] Fifthly, embodiments of this application provide a computer program product, which includes a computer program that, when executed by a processor, implements any of the methods described above.

[0018] Compared with the prior art, this application has the following advantages:

[0019] This application provides a method, device, storage medium, and program product for acquiring cloud network monitoring data. First, based on the operational data of the cloud network device, at least one first monitoring indicator is determined. Then, the damage time corresponding to the damaged monitoring indicator is determined from the at least one first monitoring indicator. The target operational data corresponding to the damaged time is determined from the operational data, and based on the target operational data, at least one second monitoring indicator is determined. Finally, the undamaged monitoring indicators and at least one second monitoring indicator from the at least one first monitoring indicator are sent to the user terminal as monitoring data of the cloud network device. In this embodiment, the damage time corresponding to the damaged monitoring indicator is determined from the monitoring indicators calculated based on the operational data of the cloud network device. The operational data corresponding to the damaged time is recalculated, and the recalculated monitoring indicators and the undamaged monitoring indicators are sent to the user terminal as monitoring data of the cloud network device. This avoids data loss due to data damage, improves the accuracy of cloud network device status judgment, and thus improves the stability of cloud network services. Moreover, calculation based on the damage time does not require calculating the full amount of data, saving computing resources while ensuring the accuracy of the calculation results.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0021] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0022] Figure 1 This is a schematic diagram illustrating an application scenario of a cloud network monitoring data acquisition method according to an embodiment of this application.

[0023] Figure 2 This is a flowchart illustrating a cloud network monitoring data acquisition method according to an embodiment of this application.

[0024] Figure 3 This is a flowchart illustrating a cloud network monitoring data acquisition method according to an embodiment of this application.

[0025] Figure 4 This is a structural block diagram of a cloud network monitoring data acquisition device according to an embodiment of this application.

[0026] Figure 5 This is a block diagram of an electronic device used to implement embodiments of this application. Detailed Implementation

[0027] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0028] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0029] 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. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0030] Figure 1 This is a schematic diagram illustrating an application scenario of the cloud network monitoring data acquisition method provided in this application. The method in this embodiment can be applied to cloud network servers. The stream computing module and batch computing module can be implemented using two threads, thereby achieving a unified stream and batch architecture. The specific processing procedure is as follows:

[0031] Obtain operational data from cloud network devices, such as Figure 1 The device data shown is processed in real time using a stream computing module to obtain the first monitoring indicator data, such as... Figure 1 The online data shown is stored in a monitoring data database. The data processing methods include at least one of the following: filtering, enrichment, transformation, or aggregation. Specifically, filtering refers to filtering the operational data according to preset filtering conditions. Enrichment refers to supplementing the operational data with user identifiers, service identifiers, etc. Transformation refers to performing format conversion, field content conversion, mathematical operations, etc., on the operational data. Aggregation refers to merging the operational data according to time granularity, etc.

[0032] A pre-constructed data corruption feature library is included, containing data corruption information. This information includes multiple corruption determination rules and multiple corruption types, such as... Figure 1 The data shows abnormalities in volume, latency, and fields. The data corruption detection module, based on a data corruption feature library, performs real-time corruption detection on the first monitoring indicator data within the monitoring data. Specifically, based on at least one corruption judgment rule, it identifies the corrupted monitoring indicator and its corresponding corruption type within at least one first monitoring indicator data.

[0033] Based on the time corresponding to the damaged monitoring indicators, the time of damage is determined. Specifically, the data collection time and / or data processing time corresponding to the damaged monitoring indicators are statistically analyzed to obtain the time of damage. The data collection time is the time for collecting the operating data used to calculate the damaged monitoring indicators, and the data processing time is the time for calculating the damaged monitoring indicators using the operating data.

[0034] The batch processing module consumes the time period that needs to be recalculated from the data corruption detection module, such as... Figure 1The system displays the damaged time period, and then extracts the target running data for this time period from the original input running data, using it as input for the batch calculation module. The batch calculation module re-runs the target running data for data processing to obtain the correct monitoring data, i.e., the second monitoring indicator data, such as... Figure 1 The self-healing data shown is used to supplement the online monitoring data and output it to the user terminal along with the real-time data from the stream computing module, realizing the entire process of self-healing of damaged monitoring data.

[0035] In this embodiment, a unified stream-batch architecture is applied, using stream computing modules and batch computing modules to process real-time computing and data self-healing scenarios respectively, achieving asynchronous damage self-healing and improving the real-time performance and accuracy of monitoring data computation. The data compensation link of the batch computing module and the real-time data link of the stream computing module do not interfere with each other, achieving efficient and accurate data self-healing. Moreover, calculations are based on the time of damage, eliminating the need to compute the entire dataset, thus saving computational resources. By constructing a data damage feature library and a data damage detection module, the connection and collaborative work between the stream computing module and the batch computing module are realized, further improving the efficiency of data self-healing.

[0036] This application provides a method for acquiring cloud network monitoring data. The method in this embodiment can be applied to servers, platforms, devices, etc. with computing and processing capabilities. The server can be a server cluster or a single server, a server deployed in the cloud, or a local server.

[0037] like Figure 2 The diagram shown is a flowchart of a cloud network monitoring data acquisition method according to an embodiment of this application, including:

[0038] Step S201: Based on the operating data of the cloud network device, determine at least one first monitoring indicator data.

[0039] Cloud network devices refer to network devices used in a cloud computing environment, such as Elastic Compute Service (ECS), routers, and gateway devices. Operational data includes, but is not limited to, network traffic, bandwidth, packet loss rate, and connection count. Primary monitoring metrics can be obtained by processing operational data, including but not limited to: bandwidth utilization, number of abnormal requests, and number of normal requests.

[0040] The data processing methods include at least one of the following: filtering, enrichment, transformation, or aggregation. Specifically, filtering refers to filtering the running data according to preset filtering conditions. Enrichment refers to supplementing the running data with user identifiers, service identifiers, etc. Transformation refers to performing format conversion, field content conversion, mathematical operations, etc., on the running data. Aggregation refers to merging the running data according to time granularity, etc.

[0041] Step S202: Determine the time of damage corresponding to the damaged monitoring indicator from at least one first monitoring indicator data.

[0042] The damaged monitoring indicators include, but are not limited to, those indicating missing data, data computation latency exceeding thresholds, or abnormal data fields. Damaged monitoring indicators can be obtained by detecting damage to the first monitoring indicator data. The time of damage can be a single moment or a period of time.

[0043] Step S203: Determine the target operating data corresponding to the damaged time in the operating data, and determine at least one second monitoring indicator data based on the target operating data.

[0044] For damaged monitoring indicators, recalculation is performed to achieve data self-healing. The target operational data corresponding to the time of damage is acquired, reprocessed, and self-healed data, i.e., the second monitoring indicator data, is obtained.

[0045] Step S204: Send at least one undamaged monitoring indicator and at least one second monitoring indicator data from at least one first monitoring indicator data to the user terminal as monitoring data of the cloud network device.

[0046] The undamaged monitoring indicators and the recalculated monitoring indicators are sent together to the user terminal, thereby ensuring the integrity of the monitoring data and making it easier for users to judge the status of cloud network devices based on the monitoring data.

[0047] The cloud network monitoring data acquisition method provided in this application first determines at least one first monitoring indicator based on the operating data of the cloud network device; then, it determines the damage time corresponding to the damaged monitoring indicator from the at least one first monitoring indicator; it determines the target operating data corresponding to the damaged time from the operating data, and determines at least one second monitoring indicator based on the target operating data; finally, it sends the undamaged monitoring indicator and the at least one second monitoring indicator from the at least one first monitoring indicator as monitoring data of the cloud network device to the user terminal. In this embodiment, the damage time corresponding to the damaged monitoring indicator is determined from the monitoring indicators calculated based on the operating data of the cloud network device. The operating data corresponding to the damaged time is recalculated, and the recalculated monitoring indicators and the undamaged monitoring indicators are sent to the user terminal as monitoring data of the cloud network device. This avoids data loss caused by data damage, improves the accuracy of cloud network device status judgment, and thus improves the stability of cloud network services. Moreover, calculation based on the damage time does not require calculating the full amount of data, which can save computing resources while ensuring the accuracy of the calculation results.

[0048] The following describes the specific implementation process of each step above through various implementation methods:

[0049] In one implementation, step S202, determining the damage time corresponding to the damaged monitoring indicator from at least one first monitoring indicator data, includes: using stream computing to determine the damage time corresponding to the damaged monitoring indicator from at least one first monitoring indicator data.

[0050] In practical applications, stream computing engines can be used to process real-time operational data from cloud network devices, calculate primary monitoring metrics, and perform damage detection to obtain damage monitoring metrics and damage duration. The characteristics of stream computing in data processing are real-time and continuous operation; it can immediately process and analyze continuously arriving operational data to obtain real-time results.

[0051] In this embodiment, stream computing is used for real-time data damage detection, which facilitates immediate recovery after data damage is detected, minimizing the time that monitoring data is visible to users on the cloud network.

[0052] In one implementation, step S203, determining the target operating data corresponding to the damaged time in the operating data, and determining at least one second monitoring indicator data based on the target operating data, includes: using a batch calculation method to determine the target operating data corresponding to the damaged time in the operating data, and determining at least one second monitoring indicator data based on the target operating data.

[0053] In practical applications, a batch computing approach is adopted. Based on the time of damage, the target running data is extracted from the original input running data, recalculated, and the correct monitoring data is obtained. Then, the data is output in batches and supplemented into the online monitoring data. This data is sent to the user terminal along with the real-time data output by the stream computing approach, realizing the entire process of self-healing of damaged monitoring data.

[0054] If the streaming method is used to recalculate, it is easy to cause problems such as jitter in the streaming job, unstable window, and duplicate output, which will result in data corruption that cannot be completely eliminated.

[0055] In this embodiment, a batch processing method is used, which does not have real-time capability. Data compensation can be performed asynchronously based on the time boundary of the damage, ensuring that the correct monitoring data is calculated. Moreover, it can avoid the impact of allocating computing resources to compensate for historical data on the real-time performance of new data calculation.

[0056] In one implementation, step S202, determining the damage time corresponding to the damaged monitoring indicator from at least one first monitoring indicator data, includes: step S2021, determining the damaged monitoring indicator from at least one first monitoring indicator data based on preset data damage information; step S2022, determining the damage time based on the time corresponding to the damaged monitoring indicator.

[0057] A data damage feature library is pre-constructed, which includes data damage information. Based on the data damage information, the first monitoring indicator data is checked sequentially to determine whether there is a damaged monitoring indicator. If a damaged monitoring indicator is detected, the time corresponding to multiple damaged monitoring indicators is aggregated and statistically analyzed to obtain the damage time.

[0058] In this embodiment, by calculating the time of data loss, it is convenient to recalculate the monitoring indicators based on the time of data loss. Calculation based on the time of loss does not require calculating the full amount of data, which can save computing resources while ensuring the accuracy of the calculation results.

[0059] In one implementation, step S2021, based on preset data damage information, determines a damaged monitoring indicator from at least one first monitoring indicator data, including: based on at least one damage judgment rule, determining a damaged monitoring indicator from at least one first monitoring indicator data, and the damage type corresponding to the damaged monitoring indicator.

[0060] Data damage information includes at least one damage determination rule and at least one damage type.

[0061] For example, the first damage assessment rule is a decrease of a% in data volume per unit time, corresponding to a decrease in data volume, indicating that the stream processing module has stopped working. In other words, if the amount of data input to the batch processing module is the same per unit time, and the amount of data output for the first monitoring indicator decreases by a%, it indicates data loss.

[0062] The second damage assessment rule is increased data latency. The corresponding damage type is a real-time data latency exceeding 10 minutes in the stream computing output, indicating insufficient computing resources in the stream computing module to handle the real-time data volume. Data latency can be calculated based on the timestamps of the input batch computing module's running data and the time of outputting the first monitoring indicator data. Data latency exceeding a preset threshold indicates data corruption.

[0063] Damage determination rule three: If the first monitoring indicator data field is abnormal, the corresponding damage type is an increase in the null value rate of the user identifier (b).

[0064] It should be noted that the damage determination rules and damage types can be configured according to specific needs, and this application does not impose any restrictions.

[0065] In one implementation, step S2022, determining the damage time based on the time corresponding to the damage monitoring indicator, includes: statistically analyzing the data collection time and / or data processing time corresponding to the damage monitoring indicator to obtain the damage time.

[0066] In practical applications, if damaged monitoring indicators are detected, the data acquisition time and / or data processing time corresponding to the damaged indicators can be statistically analyzed to obtain the damage time. The data acquisition time refers to the time it takes to collect the operational data used to calculate the damaged monitoring indicators, i.e., the timestamp carried by the operational data. The data processing time is the time it takes to calculate the damaged monitoring indicators using the operational data, i.e., the time it takes for the batch calculation module to output the monitoring indicators.

[0067] For example, if monitoring metric C1 is abnormal, it is considered damaged data. If the data collection time of monitoring metric C1 falls within time period A, then the damaged time is time period A. If the delay of monitoring metric C2 exceeds a preset threshold, it is also considered damaged data. Therefore, based on the data processing time of monitoring metric C2, the preset time period is extrapolated backward to obtain the damaged data time. If there are multiple authorized monitoring metrics, the data collection times of multiple damaged monitoring metrics can be statistically analyzed and aggregated to obtain the damaged time.

[0068] In one implementation, step S203, determining at least one second monitoring indicator data based on the target operating data, includes: processing the target operating data to obtain at least one second monitoring indicator data; the data processing method includes at least one of the following: filtering, enrichment, transformation, or aggregation.

[0069] The target operational data refers to the operational data corresponding to the time of the incident. This operational data is reprocessed to obtain accurate monitoring data, using the same data processing method as the previous calculation of the first monitoring indicator data. The data processing methods include at least one of the following: filtering, enrichment, transformation, or aggregation. Specifically, filtering refers to filtering the operational data according to preset filtering conditions. Enrichment refers to supplementing the operational data with user identifiers, service identifiers, etc. Transformation refers to performing format conversion, field content conversion, mathematical operations, etc., on the operational data. Aggregation refers to merging the operational data according to time granularity, etc.

[0070] This application provides a method for acquiring cloud network monitoring data. The method in this embodiment can be applied to servers, platforms, devices, etc. with computing and processing capabilities. The server can be a server cluster or a single server, a server deployed in the cloud, or a local server.

[0071] like Figure 3 The diagram shown is a flowchart of a cloud network monitoring data acquisition method according to an embodiment of this application, including:

[0072] Step S301: Based on the operating data of the cloud network device, determine at least one first monitoring indicator data.

[0073] Cloud network devices refer to network devices used in a cloud computing environment, such as Elastic Compute Service (ECS), routers, and gateway devices. Operational data includes, but is not limited to, network traffic, bandwidth, packet loss rate, and connection count. Primary monitoring metrics can be obtained by processing operational data, including but not limited to: bandwidth utilization, number of abnormal requests, and number of normal requests.

[0074] The data processing methods include at least one of the following: filtering, enrichment, transformation, or aggregation. Specifically, filtering refers to filtering the running data according to preset filtering conditions. Enrichment refers to supplementing the running data with user identifiers, service identifiers, etc. Transformation refers to performing format conversion, field content conversion, mathematical operations, etc., on the running data. Aggregation refers to merging the running data according to time granularity, etc.

[0075] Step S302: Based on at least one damage determination rule, determine the damage monitoring indicator and the damage type corresponding to the damage monitoring indicator from at least one first monitoring indicator data.

[0076] A data damage feature library is pre-constructed, containing data damage information, including at least one damage determination rule and at least one damage type. Based on the data damage information, the first monitoring indicator data is sequentially examined to determine whether any monitoring indicators are damaged.

[0077] Step S303: Statistically analyze the data collection time and / or data processing time corresponding to the damaged monitoring indicators to obtain the damage time.

[0078] If a damaged monitoring indicator is detected, the data acquisition time and / or data processing time corresponding to the damaged indicator can be statistically analyzed to obtain the damage time. The data acquisition time refers to the time it takes to collect the operational data used to calculate the damaged monitoring indicator, i.e., the timestamp carried by the operational data. The data processing time refers to the time it takes to calculate the damaged monitoring indicator using the operational data, i.e., the time it takes for the batch calculation module to output the monitoring indicator.

[0079] Step S304: Determine the target operating data corresponding to the damaged time in the operating data, process the target operating data to obtain at least one second monitoring indicator data.

[0080] The data processing methods include at least one of the following: filtering, enrichment, transformation, or aggregation.

[0081] Step S305: Send at least one undamaged monitoring indicator and at least one second monitoring indicator data from at least one first monitoring indicator data to the user terminal as monitoring data of the cloud network device.

[0082] The undamaged monitoring indicators and the recalculated monitoring indicators are sent together to the user terminal, thereby ensuring the integrity of the monitoring data and making it easier for users to judge the status of cloud network devices based on the monitoring data.

[0083] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a cloud network monitoring data acquisition device. For example... Figure 4 The diagram shown is a structural block diagram of a cloud network monitoring data acquisition device according to an embodiment of this application. The device includes:

[0084] The first indicator determination module 401 is used to determine at least one first monitoring indicator data based on the operating data of cloud network devices.

[0085] Damage time determination module 402 is used to determine the damage time corresponding to the damaged monitoring indicator from at least one first monitoring indicator data;

[0086] The second indicator determination module 403 is used to determine the target operating data corresponding to the damaged time in the operating data, and to determine at least one second monitoring indicator data based on the target operating data.

[0087] The data sending module 404 is used to send at least one undamaged monitoring indicator and at least one second monitoring indicator data from at least one first monitoring indicator data to the user terminal as monitoring data of the cloud network device.

[0088] The cloud network monitoring data acquisition device provided in this application first determines at least one first monitoring indicator data based on the operating data of the cloud network device; then, it determines the damage time corresponding to the damaged monitoring indicator from the at least one first monitoring indicator data; it determines the target operating data corresponding to the damaged time from the operating data, and determines at least one second monitoring indicator data based on the target operating data; finally, it sends the undamaged monitoring indicator and the at least one second monitoring indicator data from the at least one first monitoring indicator data to the user terminal as monitoring data of the cloud network device. In this embodiment, the damage time corresponding to the damaged monitoring indicator is determined from the monitoring indicators calculated based on the operating data of the cloud network device. The operating data corresponding to the damaged time is recalculated, and the recalculated monitoring indicator and the undamaged monitoring indicator are sent to the user terminal as monitoring data of the cloud network device. This avoids data loss caused by data damage, improves the accuracy of cloud network device status judgment, and thus improves the stability of cloud network services. Moreover, calculation based on the damage time does not require calculating the full amount of data, which can save computing resources while ensuring the accuracy of the calculation results.

[0089] In one implementation, the damage time determination module 402 is used to: determine the damage time corresponding to the damaged monitoring indicator from at least one first monitoring indicator data using a stream computing method.

[0090] In one implementation, the second indicator determination module 403 is used to: determine the target operating data corresponding to the damaged time in the operating data using a batch calculation method, and determine at least one second monitoring indicator data based on the target operating data.

[0091] In one implementation, the damage time determination module 402 is used to: determine a damaged monitoring indicator from at least one first monitoring indicator data based on preset data damage information; and determine the damage time based on the time corresponding to the damaged monitoring indicator.

[0092] In one implementation, the data damage information includes at least one damage determination rule and at least one damage type; when the damage time determination module 402 determines the damaged monitoring indicator from at least one first monitoring indicator data based on the preset data damage information, it is used to: determine the damaged monitoring indicator and the damage type corresponding to the damaged monitoring indicator from at least one first monitoring indicator data based on at least one damage determination rule.

[0093] In one implementation, when determining the damage time based on the time corresponding to the damage monitoring indicator, the damage time determination module 402 is used to: statistically analyze the data acquisition time and / or data processing time corresponding to the damage monitoring indicator to obtain the damage time, wherein the data acquisition time is the time for collecting the running data used to calculate the damage monitoring indicator, and the data processing time is the time for calculating the damage monitoring indicator using the running data.

[0094] In one implementation, when the second indicator determination module 403 determines at least one second monitoring indicator data based on the target operating data, it is used to: process the target operating data to obtain at least one second monitoring indicator data; the data processing method includes at least one of the following: filtering, enrichment, transformation or aggregation.

[0095] The functions of each module in the embodiments of this application can be found in the corresponding descriptions in the above methods, and they have corresponding beneficial effects, which will not be repeated here.

[0096] Figure 5 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 5 As shown, the electronic device includes a memory 510 and a processor 520. The memory 510 stores a computer program that can run on the processor 520. When the processor 520 executes the computer program, it implements the method described in the above embodiments. The number of memories 510 and processors 520 can be one or more.

[0097] The electronic device also includes:

[0098] The communication interface 530 is used to communicate with external devices and exchange and transmit data.

[0099] If the memory 510, processor 520, and communication interface 530 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0100] Optionally, in a specific implementation, if the memory 510, processor 520 and communication interface 530 are integrated on a single chip, the memory 510, processor 520 and communication interface 530 can communicate with each other through an internal interface.

[0101] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0102] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the method provided in this application.

[0103] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0104] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0105] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0106] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0107] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0108] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0109] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0110] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0111] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0112] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0114] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for acquiring cloud network monitoring data, characterized in that, include: Based on the operational data of cloud network devices, at least one primary monitoring indicator is determined. Determine the time of damage corresponding to the damaged monitoring indicator from the at least one first monitoring indicator data; Determine the target operational data corresponding to the damaged time from the operational data, and determine at least one second monitoring indicator data based on the target operational data; The undamaged monitoring indicators from the at least one first monitoring indicator data and the at least one second monitoring indicator data are sent to the user terminal as monitoring data of the cloud network device.

2. The method according to claim 1, characterized in that, Determining the time of damage corresponding to the damaged monitoring indicator from the at least one first monitoring indicator data includes: Using stream computing, the time of damage corresponding to the damaged monitoring indicator is determined from the at least one first monitoring indicator data.

3. The method according to claim 1, characterized in that, The step involves determining the target operational data corresponding to the damaged time from the operational data, and based on the target operational data, determining at least one second monitoring indicator data, including: Using a batch calculation method, the target operating data corresponding to the damaged time is determined from the operating data, and based on the target operating data, at least one second monitoring indicator data is determined.

4. The method according to any one of claims 1-3, characterized in that, Determining the time of damage corresponding to the damaged monitoring indicator from the at least one first monitoring indicator data includes: Based on preset data damage information, a damaged monitoring indicator is determined from the at least one first monitoring indicator data. The time of damage is determined based on the time corresponding to the damaged monitoring indicators.

5. The method according to claim 4, characterized in that, The data damage information includes at least one damage determination rule and at least one damage type; the step of determining the damage monitoring indicator from the at least one first monitoring indicator data based on the preset data damage information includes: Based on the at least one damage determination rule, a damaged monitoring indicator and the damage type corresponding to the damaged monitoring indicator are determined from the at least one first monitoring indicator data.

6. The method according to claim 4, characterized in that, Determining the time of damage based on the time corresponding to the damaged monitoring indicator includes: The damage time is obtained by statistically analyzing the data collection time and / or data processing time corresponding to the damaged monitoring indicators. The data collection time is the time for collecting the operating data used to calculate the damaged monitoring indicators, and the data processing time is the time for calculating the damaged monitoring indicators using the operating data.

7. The method according to any one of claims 1-3, characterized in that, The determination of at least one second monitoring indicator based on the target operational data includes: The target operational data is processed to obtain at least one second monitoring indicator data; the data processing method includes at least one of the following: Filtration, enrichment, conversion, or polymerization.

8. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method of any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1-7.

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