Monitoring data processing method and device, equipment, storage medium and program product

Abstract

The invention provides a monitoring data processing method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring monitoring data of a to-be-monitored product; creating and executing a real-time calculation task according to a pre-configured monitoring index; consuming the monitoring data in real time through a real-time calculation task so as to add an attribute tag for the monitoring data according to the monitoring index, and storing the monitoring data added with the attribute tag to a time sequence database; dynamically loading index related information corresponding to a pre-configured monitoring index to a rule engine according to the monitoring data added with the attribute tag; performing query and calculation processing on the monitoring data added with the attribute label according to the index related information through a rule engine to generate monitoring index data; and storing the monitoring index data in a preset time sequence database. The processing accuracy of the monitoring data and the system development efficiency are improved.

Description

Technical Field

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

[0002] In a private cloud platform, it is necessary to continuously monitor the operating status of the underlying computing resources to obtain monitoring data, and generate business metrics based on the monitoring data to support operation and maintenance decisions and business optimization.

[0003] Existing methods for processing monitoring data typically involve customized front-end and back-end development based on metric documents. However, when developers are working on both front-end and back-end development, the front-end development relies on the back-end interface definition. Each product iteration requires the repeated development of data collection, calculation, and display logic, and the code implementation of complex business logic is prone to introducing calculation errors.

[0004] As a result, existing technologies suffer from poor system decoupling, low development efficiency, and insufficient accuracy in processing monitoring data. Summary of the Invention

[0005] This application provides a method, apparatus, device, storage medium, and program product for processing monitoring data, in order to improve the accuracy of monitoring data processing and enhance system development efficiency.

[0006] In a first aspect, embodiments of this application provide a method for processing monitoring data, including:

[0007] Obtain monitoring data for the product to be monitored;

[0008] Create real-time computing tasks based on pre-configured monitoring metrics;

[0009] By executing real-time computing tasks, monitoring data is consumed in real time to add attribute tags to the monitoring data according to monitoring indicators, and the monitoring data with added attribute tags is stored in a time-series database.

[0010] Based on the monitoring data after adding attribute tags, the relevant information of the pre-configured monitoring metrics will be dynamically loaded into the rule engine.

[0011] The rules engine queries and calculates the monitoring data after adding attribute tags based on the relevant information of the metrics to generate monitoring metric data.

[0012] Save the monitoring indicator data to a preset time series database.

[0013] In one possible implementation, acquiring monitoring data of the product to be monitored includes:

[0014] The monitoring data of the product to be monitored is collected using a data acquisition device.

[0015] The monitoring data is stored in a message queue.

[0016] In one possible implementation, the pre-configured monitoring metrics include tag configuration information;

[0017] Accordingly, by executing real-time computing tasks, monitoring data is consumed in real time to add attribute labels to the monitoring data according to monitoring metrics, and the monitoring data with added attribute labels is stored in a time-series database, including:

[0018] Monitoring data is retrieved from the message queue by executing real-time computing tasks;

[0019] Add corresponding attribute tags to the monitoring data based on the tag configuration information contained in the pre-configured monitoring metrics;

[0020] The monitoring data with added attribute tags is written to the time series database.

[0021] In one possible implementation, the attribute label includes product type and / or product name.

[0022] In one possible implementation, based on the monitoring data after adding attribute tags, the relevant information of the pre-configured monitoring metrics is dynamically loaded into the rule engine, including:

[0023] Based on the monitoring data after adding attribute tags, determine the relevant information of the pre-configured monitoring metrics; among which, the relevant information of the metrics includes the definition information and calculation rules of the pre-configured monitoring metrics.

[0024] Save the relevant information of the indicators to the indicator library of a relational database;

[0025] Dynamically load indicator-related information from the relational database indicator library into the rule engine.

[0026] In one possible implementation, before creating and executing the real-time computing task based on pre-configured monitoring metrics, the method further includes:

[0027] In response to the user's monitoring metric configuration operation, the monitoring metric configuration interface is displayed;

[0028] In response to user input and / or selection operations on the monitoring metric configuration interface, receive configuration information for the monitoring metrics.

[0029] Based on the configuration information, determine the pre-configured monitoring metrics.

[0030] In one possible implementation, the configuration information includes the type of monitoring metric, the topic information of the metric, the information to be collected, and the information to be calculated.

[0031] In one possible implementation, after saving the monitoring indicator data to a preset time-series database, the method further includes:

[0032] In response to a user's viewing action, obtain the monitoring metric data corresponding to the viewing action;

[0033] Render monitoring metric data to generate metric viewing information;

[0034] The preset visualization interface displays indicator information.

[0035] Secondly, embodiments of this application provide a monitoring data processing apparatus, comprising:

[0036] The acquisition module is used to acquire monitoring data of the products to be monitored.

[0037] Create a module to generate real-time computing tasks based on pre-configured monitoring metrics.

[0038] The consumption module is used to consume monitoring data in real time by executing real-time computing tasks, add attribute tags to the monitoring data according to monitoring indicators, and store the monitoring data with added attribute tags in the time series database.

[0039] Optionally, the attribute tags include product type and / or product name.

[0040] The loading module is used to dynamically load the relevant information of the pre-configured monitoring metrics into the rule engine based on the monitoring data after adding attribute tags.

[0041] The generation module is used by the rule engine to query and calculate the monitoring data after adding attribute tags based on the relevant information of the indicators, so as to generate monitoring indicator data.

[0042] The save module is used to save monitoring indicator data to a preset time series database.

[0043] In one possible implementation, the acquisition module can also be used for:

[0044] The monitoring data of the product to be monitored is collected using a data acquisition device.

[0045] The monitoring data is stored in a message queue.

[0046] In one possible implementation, the pre-configured monitoring metrics include tag configuration information; the consumption module can also be used for:

[0047] Monitoring data is retrieved from the message queue by executing real-time computing tasks;

[0048] Add corresponding attribute tags to the monitoring data based on the tag configuration information contained in the pre-configured monitoring metrics;

[0049] The monitoring data with added attribute tags is written to the time series database.

[0050] In one possible implementation, the loading module can also be used for:

[0051] Based on the monitoring data after adding attribute tags, determine the relevant information of the pre-configured monitoring metrics; among which, the relevant information of the metrics includes the definition information and calculation rules of the pre-configured monitoring metrics.

[0052] Save the relevant information of the indicators to the indicator library of a relational database;

[0053] Dynamically load indicator-related information from the relational database indicator library into the rule engine.

[0054] In one possible implementation, before creating and executing real-time computing tasks based on pre-configured monitoring metrics, the acquisition module may also be used for:

[0055] In response to the user's monitoring metric configuration operation, the monitoring metric configuration interface is displayed;

[0056] In response to user input and / or selection operations on the monitoring metric configuration interface, receive configuration information for the monitoring metrics.

[0057] Based on the configuration information, determine the pre-configured monitoring metrics.

[0058] Optionally, the configuration information includes the type of monitoring indicator, the topic information of the indicator, the information to be collected, and the information to be calculated.

[0059] In one possible implementation, after saving the monitoring indicator data to a preset time-series database, the monitoring data processing device further includes a display module, which can specifically be used for:

[0060] In response to a user's viewing action, obtain the monitoring metric data corresponding to the viewing action;

[0061] Render monitoring metric data to generate metric viewing information;

[0062] The preset visualization interface displays indicator information.

[0063] Thirdly, embodiments of this application provide a monitoring data processing device, including: a memory and a processor;

[0064] The memory stores the instructions that the computer executes;

[0065] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0066] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0067] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0068] The monitoring data processing method, apparatus, device, storage medium, and program product provided in this application's embodiments acquire monitoring data of the product to be monitored; create and execute real-time computing tasks according to pre-configured monitoring indicators; consume monitoring data in real time through the real-time computing tasks to add attribute tags to the monitoring data according to the monitoring indicators, and store the attribute-tagged monitoring data in a time-series database; dynamically load indicator-related information corresponding to the pre-configured monitoring indicators into a rule engine based on the attribute-tagged monitoring data; query and calculate the attribute-tagged monitoring data through the rule engine based on the indicator-related information to generate monitoring indicator data; and save the monitoring indicator data to a preset time-series database. Compared with the prior art, the method of this application automatically processes monitoring data by dynamically creating tasks for processing monitoring data; improves the accuracy and stability of monitoring data; automates and generalizes the monitoring process; and improves development efficiency. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0070] Figure 1 This application provides a schematic diagram of a monitoring data processing system architecture.

[0071] Figure 2 Flowchart of the method for processing monitoring data provided in this application Figure 1 ;

[0072] Figure 3 Flowchart of the method for processing monitoring data provided in this application Figure 2 ;

[0073] Figure 4Flowchart of the method for processing monitoring data provided in this application Figure 3 ;

[0074] Figure 5 A schematic diagram of the structure of the monitoring data processing device provided in this application;

[0075] Figure 6 A schematic diagram of the structure of the monitoring data processing device provided in this application.

[0076] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0077] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0078] It should be noted that all data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0079] With the rapid development of cloud computing, the Internet of Things, and big data analytics technologies, enterprise-level systems are placing higher demands on the real-time performance, accuracy, and flexibility of monitoring data. For example, private cloud platforms need to monitor underlying resources and generate business metrics to support operational decisions.

[0080] In existing technologies, product managers define monitoring metric requirements through documentation, and developers then analyze the document content item by item to write code to implement the data collection, calculation, and display logic. During this process, the front-end needs to rely on back-end interface documentation for customized development, resulting in long development cycles and high maintenance costs.

[0081] Furthermore, when dealing with complex business logic, manual coding by developers can easily introduce calculation errors, and the system has poor scalability, making it difficult to cope with changes in metric requirements brought about by product iterations.

[0082] Therefore, how to automate the monitoring data processing workflow to improve the flexibility and accuracy of monitoring data processing has become a core issue that current enterprise-level monitoring systems urgently need to address.

[0083] To address the aforementioned issues, the core concept of this application is as follows: By acquiring monitoring data of the product to be monitored, and based on pre-configured monitoring indicators, a real-time calculation task is created to add attribute tags to the monitoring data. The tagged monitoring data is then stored in a time-series database. Corresponding indicator-related information is dynamically loaded into the rule engine to generate monitoring indicator data, which is then stored in the time-series database. This method, by dynamically creating real-time calculation tasks and automatically processing monitoring data, improves the accuracy and stability of monitoring data processing, automates and generalizes the monitoring process, and enhances system development efficiency.

[0084] Optionally, Figure 1 This is a schematic diagram of a monitoring data processing system architecture provided in this application. Figure 1 As shown, the system architecture for processing monitoring data includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

[0085] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the above architecture. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0086] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface. The data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface. The data acquisition device 101 can be used to acquire monitoring data of the product to be monitored.

[0087] The processing device 102 can be used to create and execute real-time computing tasks based on pre-configured monitoring indicators; through real-time computing tasks, it consumes monitoring data in real time to add attribute tags to the monitoring data according to the monitoring indicators, and stores the monitoring data with added attribute tags in a time-series database; based on the monitoring data with added attribute tags, it dynamically loads the indicator-related information corresponding to the pre-configured monitoring indicators into the rule engine; through the rule engine, it queries and calculates the monitoring data with added attribute tags according to the indicator-related information to generate monitoring indicator data; and saves the monitoring indicator data to a preset time-series database.

[0088] The display device 103 can also be a touch screen or the screen of a terminal device, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0089] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0090] Figure 2 Flowchart of the method for processing monitoring data provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0091] S201. Obtain monitoring data of the product to be monitored.

[0092] In this embodiment, a data collector is used to collect monitoring data of the product to be monitored and send the monitoring data of the product to be monitored to the middleware cluster to decouple the front end and the back end, so as to ensure the reliability and stability of monitoring data transmission.

[0093] S202. Create a real-time computing task based on the pre-configured monitoring metrics.

[0094] In this embodiment, the existence of a real-time computing task is detected. If a real-time computing task exists, the indicator library is updated according to the pre-configured monitoring indicators. The indicator library can be a relational database.

[0095] If no real-time computing task exists, a real-time computing task will be created and executed based on the pre-configured monitoring metrics.

[0096] Optionally, based on pre-configured monitoring metrics, a real-time computing task can be created, including:

[0097] Based on the monitoring metric type and the topic information where the metric is located, establish a connection between the real-time computing task and the message queue to ensure that the real-time computing task only consumes monitoring data under the topic information where the metric is located.

[0098] For the information that needs to be collected, data extraction and format standardization are performed to obtain the information to be collected in a unified format; for the information that needs to be calculated, the pre-configured performance monitoring structured query language is parsed and combined with the information to be collected in a unified format to convert the pre-configured performance monitoring structured query language into real-time executable logic.

[0099] A stream computing framework is adopted to encapsulate the executable logic of real-time computing into executable real-time computing tasks, and to configure the running parameters of the real-time computing tasks, including parallelism and latency threshold.

[0100] S203. By executing real-time computing tasks, the monitoring data is consumed in real time to add attribute labels to the monitoring data according to the monitoring indicators, and the monitoring data with added attribute labels is stored in the time series database.

[0101] In this embodiment, after starting the real-time computing task, the monitoring data is consumed in real time. At the same time, attribute tags are added to the monitoring data according to the monitoring indicator type and the topic information corresponding to the monitoring indicator type. The monitoring data with added attribute tags is then stored in the time series database, thereby obtaining structured monitoring data.

[0102] S204. Based on the monitoring data after adding attribute tags, dynamically load the relevant information of the pre-configured monitoring metrics into the rule engine.

[0103] In this embodiment, the indicator-related information includes the monitoring indicator type, the topic information of the indicator, the information to be collected, the information to be calculated, the calculation rules, and the execution parameters. By configuring a listening or interface call mechanism, the indicator-related information is hot-loaded into the rule engine in real time, decoupling the rule configuration from the data flow processing and improving the system's flexibility.

[0104] S205. Through the rule engine, the monitoring data after adding attribute tags is queried and calculated based on the relevant information of the indicators to generate monitoring indicator data.

[0105] In this embodiment, a rule engine loaded with the latest pre-configured monitoring metrics and their corresponding metric information periodically initiates precise queries to the time-series database based on the metric information in each rule. It retrieves monitoring data with added attribute tags according to the data filtering conditions defined in the configuration, then parses and executes the corresponding performance monitoring query language expression, and processes it according to the calculation rules to generate monitoring metric data.

[0106] The rules engine transforms basic metric information into derived metrics with direct business semantics, providing directly usable structured monitoring metric data for monitoring, alerting, and visualization.

[0107] S206. Save the monitoring indicator data to the preset time series database.

[0108] In this embodiment, the monitoring indicator data is saved to a preset time-series database, establishing a unified data outlet. This allows the monitoring data and monitoring indicator data to coexist in the same preset time-series database, providing a single query source for the front end. It also supports data reuse, improving the front end rendering speed and query response performance.

[0109] Optionally, after saving the monitoring indicator data to a preset time-series database, the method further includes:

[0110] In response to a user's viewing action, retrieve the corresponding monitoring metric data.

[0111] Render the monitoring metric data to generate metric viewing information.

[0112] The preset visualization interface displays indicator information.

[0113] In this embodiment, the acquired monitoring indicator data is rendered to generate visualized indicator viewing data for users to view. This allows users to monitor the system status in real time based on the indicator viewing information and adjust response strategies in a timely manner, thereby improving the stability and reliability of the system.

[0114] The monitoring data processing method provided in this application acquires monitoring data of the product to be monitored, creates real-time calculation tasks based on pre-configured monitoring indicators, adds attribute tags to the monitoring data, and stores the tagged monitoring data in a time-series database. It then dynamically loads corresponding indicator-related information into a rule engine to generate monitoring indicator data, which is also stored in the time-series database. This method, by decoupling the front-end and back-end, improves the system's flexibility and maintainability. By dynamically creating real-time calculation tasks and automatically processing monitoring data, it improves the accuracy and stability of monitoring data processing, automates and generalizes the monitoring process, and enhances system development efficiency.

[0115] Figure 3 Flowchart of the method for processing monitoring data provided in this application Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the method for processing monitoring data is described in detail, which includes:

[0116] S301. Collect monitoring data of the product to be monitored through a data acquisition device.

[0117] In this embodiment, the data collector is deployed in the server, container, or network of the product to be monitored, and collects monitoring data of the product to be monitored at a preset frequency.

[0118] Optionally, the data collector can also collect monitoring data of the product to be monitored in response to the triggering of a collection event.

[0119] For example, if the product to be monitored is an elastic computing server or a physical machine, the monitoring data includes CPU utilization, memory utilization, disk I / O, and network traffic; if the product to be monitored is a database, the monitoring data includes the number of connections, query rate, cache hit rate, and master-slave latency; if the product to be monitored is an application service, the monitoring data includes the number of Hypertext Transfer Protocol requests, response events, and error code counts.

[0120] S302. Store the monitoring data in the message queue.

[0121] In this embodiment, the collected monitoring data is continuously sent as messages to the corresponding topic in the message queue, enabling the data collector to process the monitoring data asynchronously and providing a buffer for the monitoring data to prevent data loss; at the same time, it decouples the front end and back end, improving system development efficiency.

[0122] Optionally, before creating and executing real-time computing tasks based on pre-configured monitoring metrics, the following steps are also included:

[0123] S303. In response to the user's monitoring metric configuration operation, display the monitoring metric configuration interface.

[0124] In this embodiment, the configuration information of the monitoring indicators is displayed through a visual monitoring indicator configuration interface.

[0125] S304. Responding to user input and / or selection operations on the monitoring indicator configuration interface, receive configuration information for the monitoring indicators.

[0126] Optionally, the configuration information includes the type of monitoring indicator, the topic information of the indicator, the information to be collected, and the information to be calculated.

[0127] In this embodiment, the monitoring metrics include elastic computing servers, physical machines, relational databases, and non-relational databases. The information to be collected includes CPU utilization, memory utilization, disk utilization, and input / output.

[0128] S305. Based on the configuration information, determine the pre-configured monitoring indicators.

[0129] In this embodiment, in response to the user's input and / or selection operations on the monitoring indicator configuration interface, a structured configuration information object is generated and submitted to the backend service to realize semantic verification and persistence processing of the configuration information.

[0130] The monitoring data processing method provided in this application uses a data collector to capture monitoring data of the products to be monitored in real time and asynchronously send it to a message queue, thereby decoupling the production and consumption of monitoring data and preventing data loss. A visual monitoring indicator configuration interface is used to provide users with input and / or selection operations for configuration information, thus obtaining monitoring indicators. Automatic verification and persistence of monitoring indicators ensure their accuracy and traceability. This method separates physical data flow from logical control flow, enabling the system to dynamically adjust monitoring strategies according to business needs without modifying code or restarting services, significantly improving system maintainability, scalability, and development efficiency.

[0131] Figure 4 Flowchart of the method for processing monitoring data provided in this application Figure 3 ,like Figure 4 As shown, in this embodiment... Figure 3 Based on the embodiments, the method for processing monitoring data is described in detail, wherein the pre-configured monitoring indicators include tag configuration information; accordingly, the method includes:

[0132] S401. Obtain monitoring data from the message queue by executing real-time computing tasks.

[0133] In this embodiment, a real-time computing task is executed to continuously monitor and consume monitoring data in the message queue.

[0134] S402. Based on the tag configuration information contained in the pre-configured monitoring metrics, add corresponding attribute tags to the monitoring data.

[0135] In this embodiment, attribute tags refer to metadata used to identify the attributes of monitoring data.

[0136] Optionally, the attribute tags include product type and / or product name.

[0137] In this embodiment, by adding attribute tags, efficient retrieval of monitoring data is achieved, thereby improving the management efficiency and analysis capabilities of monitoring data.

[0138] S403. Write the monitoring data with added attribute tags into the time series database.

[0139] In this embodiment, the monitoring data after adding attribute tags is written to a time-series database to achieve persistence of the monitoring data after adding attribute tags.

[0140] S404. Based on the monitoring data after adding attribute tags, determine the relevant information of the pre-configured monitoring indicators; wherein, the relevant information of the indicators includes the definition information and calculation rules of the pre-configured monitoring indicators.

[0141] In this embodiment, the pre-configured monitoring indicator definition information includes the monitoring indicator type, the topic information corresponding to the monitoring indicator type, and the basic collection indicators; wherein, the monitoring indicator type includes elastic computing server, physical machine, relational database, and non-relational database; the basic collection indicators include CPU utilization, memory utilization, disk utilization, and network input / output.

[0142] The calculation rules include aggregation calculation rules, conditional filtering rules, and multi-indicator association rules. Aggregation calculation rules refer to rules that perform statistical calculations on monitoring data, such as calculating the average CPU utilization of physical machines. Conditional filtering rules refer to rules that filter monitoring data based on preset conditions, such as filtering records with CPU utilization greater than 90%. Multi-indicator association rules refer to calculation rules based on a combination of multiple monitoring indicators, such as calculating the ratio of CPU utilization to memory utilization.

[0143] S405. Save the relevant information of the indicator to the relational database indicator library.

[0144] In this embodiment, the relevant information of the indicators is saved to a relational database indicator library to avoid data loss and provide a stable data source.

[0145] S406: Dynamically load indicator-related information from the relational database indicator library into the rule engine.

[0146] In this embodiment, the rule engine refers to the component used to perform monitoring metric calculation and alarm judgment. By dynamically loading metric-related information from the relational database metric library into the rule engine, the rule engine service can be detected and loaded with the latest metric-related information in real time without restarting the rule engine service, ensuring that the calculated rules take effect in real time.

[0147] The monitoring data processing method provided in this application obtains monitoring data from a message queue by executing real-time computing tasks; it adds corresponding attribute tags to the monitoring data and writes the tagged monitoring data into a time-series database, providing a stable and reliable data source for the rule engine, while improving data retrieval and management efficiency. The definition information of monitoring indicators and calculation rules are persistently stored in a relational database and dynamically loaded by the rule engine to achieve real-time updates and effectiveness of the calculation rules, thus improving the system's real-time performance.

[0148] Figure 5 A schematic diagram of the structure of the monitoring data processing device provided in this application is shown below. Figure 5 As shown, the monitoring data processing device provided in this embodiment includes:

[0149] The acquisition module 501 is used to acquire monitoring data of the product to be monitored.

[0150] Create module 502 to create real-time computing tasks based on pre-configured monitoring metrics.

[0151] The consumption module 503 is used to consume monitoring data in real time by executing real-time computing tasks, add attribute tags to the monitoring data according to monitoring indicators, and store the monitoring data with added attribute tags in a time-series database.

[0152] Optionally, the attribute tags include product type and / or product name.

[0153] The loading module 504 is used to dynamically load the relevant information of the pre-configured monitoring metrics into the rule engine based on the monitoring data after adding attribute tags.

[0154] The generation module 505 is used to query and calculate the monitoring data after adding attribute tags based on the indicator-related information through the rule engine, so as to generate monitoring indicator data.

[0155] The storage module 506 is used to save monitoring indicator data to a preset time series database.

[0156] In one possible implementation, the acquisition module 501 can also be used for:

[0157] The monitoring data of the product to be monitored is collected using a data acquisition device.

[0158] The monitoring data is stored in a message queue.

[0159] In one possible implementation, the pre-configured monitoring metrics include tag configuration information; the consumer module 503 can also be used for:

[0160] Monitoring data is retrieved from the message queue by executing real-time computing tasks;

[0161] Add corresponding attribute tags to the monitoring data based on the tag configuration information contained in the pre-configured monitoring metrics;

[0162] The monitoring data with added attribute tags is written to the time series database.

[0163] In one possible implementation, the loading module 504 can also be used for:

[0164] Based on the monitoring data after adding attribute tags, determine the relevant information of the pre-configured monitoring metrics; among which, the relevant information of the metrics includes the definition information and calculation rules of the pre-configured monitoring metrics.

[0165] Save the relevant information of the indicators to the indicator library of a relational database;

[0166] Dynamically load indicator-related information from the relational database indicator library into the rule engine.

[0167] In one possible implementation, before creating and executing real-time computing tasks based on pre-configured monitoring metrics, module 501 can also be used for:

[0168] In response to the user's monitoring metric configuration operation, the monitoring metric configuration interface is displayed;

[0169] In response to user input and / or selection operations on the monitoring metric configuration interface, receive configuration information for the monitoring metrics.

[0170] Based on the configuration information, determine the pre-configured monitoring metrics.

[0171] Optionally, the configuration information includes the type of monitoring indicator, the topic information of the indicator, the information to be collected, and the information to be calculated.

[0172] In one possible implementation, after saving the monitoring indicator data to a preset time-series database, the monitoring data processing device further includes a display module, which can specifically be used for:

[0173] In response to a user's viewing action, obtain the monitoring metric data corresponding to the viewing action;

[0174] Render monitoring metric data to generate metric viewing information;

[0175] The preset visualization interface displays indicator information.

[0176] The monitoring data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0177] Figure 6 A schematic diagram of the structure of the monitoring data processing device provided in this application. Figure 6 As shown, the monitoring data processing device provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the monitoring data processing device further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0178] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0179] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0181] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0182] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0183] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0184] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0185] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0186] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0187] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0190] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0192] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for processing monitoring data, characterized in that, include: Obtain monitoring data for the product to be monitored; Create real-time computing tasks based on pre-configured monitoring metrics; By executing the real-time computing task, the monitoring data is consumed in real time, attribute tags are added to the monitoring data according to the monitoring indicators, and the monitoring data with added attribute tags is stored in the time series database. Based on the monitoring data after adding attribute tags, the relevant information of the pre-configured monitoring metrics is dynamically loaded into the rule engine; The rule engine queries and calculates the monitoring data after adding attribute tags based on the relevant information of the indicators to generate monitoring indicator data. The monitoring indicator data is saved to a preset time-series database.

2. The method according to claim 1, characterized in that, The acquisition of monitoring data for the product to be monitored includes: The monitoring data of the product to be monitored is collected using a data acquisition device. The monitoring data is stored in a message queue.

3. The method according to claim 2, characterized in that, The pre-configured monitoring metrics include tag configuration information; Accordingly, the step of consuming the monitoring data in real time by executing the real-time computing task, adding attribute tags to the monitoring data according to the monitoring indicators, and storing the monitoring data with added attribute tags in a time-series database includes: The monitoring data is obtained from the message queue by executing the real-time computing task; Based on the tag configuration information contained in the pre-configured monitoring metrics, add corresponding attribute tags to the monitoring data; The monitoring data with added attribute tags is written into the time series database.

4. The method according to claim 3, characterized in that, The attribute tags include product type and / or product name.

5. The method according to any one of claims 1 to 3, characterized in that, The step of dynamically loading the relevant information of the pre-configured monitoring metrics into the rule engine based on the monitoring data after adding attribute tags includes: Based on the monitoring data after adding attribute tags, determine the relevant information of the pre-configured monitoring indicators; wherein, the relevant information of the indicators includes the definition information and calculation rules of the pre-configured monitoring indicators; Save the relevant information of the aforementioned indicators to a relational database indicator library; Dynamically load indicator-related information from the relational database indicator library into the rule engine.

6. The method according to any one of claims 1 to 3, characterized in that, Before creating and executing real-time computing tasks based on pre-configured monitoring metrics, the process also includes: In response to the user's monitoring metric configuration operation, the monitoring metric configuration interface is displayed; In response to user input and / or selection operations on the monitoring metric configuration interface, receive configuration information for the monitoring metrics. Based on the configuration information, determine the pre-configured monitoring metrics.

7. The method according to claim 6, characterized in that, The configuration information includes the monitoring indicator type, the topic information of the indicator, the information to be collected, and the information to be calculated.

8. The method according to any one of claims 1 to 3, characterized in that, After saving the monitoring indicator data to a preset time-series database, the method further includes: In response to a user's viewing action, obtain the monitoring metric data corresponding to the viewing action; Render the monitoring indicator data to generate indicator viewing information; The indicator viewing information is displayed on the preset visual interface.

9. A device for processing monitoring data, characterized in that, include: The acquisition module is used to acquire monitoring data of the products to be monitored. Create a module to generate real-time computing tasks based on pre-configured monitoring metrics; The consumption module is used to consume the monitoring data in real time by executing the real-time computing task, add attribute tags to the monitoring data according to the monitoring indicators, and store the monitoring data with added attribute tags in the time series database. The loading module is used to dynamically load the relevant information of the pre-configured monitoring indicators into the rule engine based on the monitoring data after the attribute tags are added. The generation module is used to query and calculate the monitoring data after adding attribute tags based on the indicator-related information through the rule engine, so as to generate monitoring indicator data. The storage module is used to save the monitoring indicator data to a preset time-series database.

10. A device for processing monitoring data, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

12. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-8.

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