Performance measurement method and device, cloud-native platform and computer equipment

Abstract

The application relates to an efficiency measurement method, device, cloud native platform and computer equipment. The method is applied to a cloud native platform used for measuring the efficiency of at least one platform to be measured. The method comprises the following steps: obtaining a configuration file corresponding to the platform to be measured, and obtaining platform data of the platform to be measured according to the configuration file; performing format uniform processing on the platform data according to a preset standard protocol to obtain formatted measurement data; establishing an association relationship on multiple dimensions on the formatted measurement data to obtain target measurement data; and performing transportation on the target measurement data according to a target efficiency measurement model to obtain efficiency measurement data. The efficiency measurement method provided by the application can make the obtained platform data more complete, so that more accurate efficiency measurement data can be obtained.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a performance measurement method, apparatus, cloud-native platform, and computer equipment. Background Technology

[0002] Effectiveness is the ability to generate greater business value more efficiently, with higher quality, more reliably, and sustainably. It is an abstract process for measuring and evaluating R&D operations and efficiency. Metrics are the data products of effectiveness practices. For enterprises, acquiring effectiveness measurement data and conducting effectiveness metric analysis during the R&D process is crucial.

[0003] In traditional technologies, performance measurement is microservice-based through Platform as a Service (PaaS) platforms, performance measurement indicator systems are built, performance measurement data is analyzed, and performance measurement data is determined.

[0004] However, traditional technologies obtain scattered data when performing performance measurement data analysis through PaaS platforms, resulting in inaccurate performance measurement data. Summary of the Invention

[0005] Therefore, it is necessary to provide a performance measurement method, device, cloud-native platform, and computer equipment that can improve the integration and integrity of the acquired data, thereby making the obtained performance measurement data more accurate, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a performance measurement method applied to a cloud-native platform, which is used to measure the performance of at least one platform to be measured. The method includes:

[0007] Obtain the configuration file corresponding to the platform to be measured, and obtain the platform data of the platform to be measured based on the configuration file;

[0008] The platform data is processed according to a preset standard protocol to obtain formatted measurement data;

[0009] Establish relationships between formatted metric data across multiple dimensions to obtain target metric data;

[0010] The target performance measurement data is obtained by performing calculations on the target performance measurement model.

[0011] In one embodiment, platform data of the platform to be measured is obtained according to the configuration file, including:

[0012] Generate the data interface corresponding to the platform to be measured based on the configuration file;

[0013] Obtain platform data of the platform to be measured through the data interface.

[0014] In one embodiment, the method further includes:

[0015] Monitor for the presence of new configuration files;

[0016] If a new configuration file exists, then the new platform data for the measurement platform corresponding to the new configuration file will be obtained.

[0017] In one embodiment, the formatted metric data includes metric data across multiple dimensions; establishing relationships between the formatted metric data across multiple dimensions yields the target metric data, including:

[0018] Based on the formatted metric data, establish the initial relationships between the metric data of each dimension in the formatted metric data;

[0019] The initial correlation is corrected to obtain the target metric data.

[0020] In one embodiment, the initial association is corrected to obtain target metric data, including:

[0021] Obtain corrected metric data; corrected metric data is used to determine the degree of association of the initial association.

[0022] If the correlation does not reach the preset correlation threshold, the initial correlation is corrected based on the corrected measurement data to obtain the target correlation.

[0023] Based on the target association and the corresponding metric data, determine the target metric data.

[0024] In one embodiment, the target performance measurement model includes multiple streaming execution operators that perform calculations on the target performance measurement data according to the target performance measurement model to obtain performance measurement data, including:

[0025] Based on the container cluster management system, various streaming execution operators are used to batch process the target metric data to obtain performance metric data.

[0026] In one embodiment, the method further includes:

[0027] Obtain user identity information;

[0028] Based on the identity information, obtain the target effectiveness measurement data corresponding to the identity information, and display the target effectiveness measurement data.

[0029] Secondly, one embodiment of this application provides a cloud-native platform, including: a data collector, a protocol engine, and a model engine;

[0030] The data collector is used to obtain platform data of the platform to be measured according to the configuration file corresponding to the platform to be measured connected to the cloud-native platform, and send the platform data to the protocol engine.

[0031] The protocol engine is used to process platform data in a unified format according to a preset standard protocol to obtain the number of formatted metrics, establish correlations between the formatted metric data in multiple dimensions to obtain the target metric data, and send the target metric data to the model engine.

[0032] The model engine is used to perform calculations on the target performance measurement data based on the target performance measurement model to obtain performance measurement data.

[0033] Thirdly, one embodiment of this application provides a performance measurement device applied to a cloud-native platform, the cloud-native platform being used to measure the performance of at least one platform to be measured, the device comprising:

[0034] The acquisition module is used to obtain the configuration file corresponding to the platform to be measured, and to obtain the platform data of the platform to be measured based on the configuration file;

[0035] The processing module is used to process platform data in a unified format according to a preset standard protocol to obtain formatted measurement data;

[0036] The construction and maintenance module is used to establish relationships between formatted metric data across multiple dimensions to obtain target metric data;

[0037] The determination module is used to perform calculations on the target measurement data according to the target effectiveness measurement model to obtain effectiveness measurement data.

[0038] Fourthly, one embodiment of this application provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method provided in the first aspect above.

[0039] The aforementioned performance measurement method, apparatus, cloud-native platform, and computer equipment are described above. The method is applied to a cloud-native platform, enabling the measurement of the performance of at least one platform to be measured through the cloud-native platform within the computer equipment. The method involves obtaining the configuration file corresponding to the platform to be measured and acquiring platform data based on the configuration file; formatting and unifying the platform data according to a preset standard protocol to obtain formatted measurement data; establishing correlations between the formatted measurement data across multiple dimensions to obtain target measurement data; and performing calculations on the target measurement data according to a target performance measurement model to obtain performance measurement data. In this embodiment, by establishing correlations between multi-dimensional measurement data within the formatted measurement data to obtain target measurement data, the scattered nature of the obtained target measurement data can be avoided, thereby improving the accuracy of the performance measurement data obtained using the target measurement data and thus more objectively representing the performance of the platform to be measured. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the structure of a computer device in one embodiment;

[0041] Figure 2 This is a flowchart illustrating the steps of a performance measurement method in one embodiment;

[0042] Figure 3 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0043] Figure 4 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0044] Figure 5 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0045] Figure 6 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0046] Figure 7 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0047] Figure 8 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0048] Figure 9 This is a flowchart illustrating the steps of a performance measurement method in another embodiment;

[0049] Figure 10 This is a schematic diagram of the structure of a cloud-native platform in one embodiment;

[0050] Figure 11This is a schematic diagram of the structure of a performance measurement service platform based on a cloud-native platform in one embodiment;

[0051] Figure 12 This is a schematic diagram of multiple dimensions corresponding to platform data in one embodiment;

[0052] Figure 13 This is a schematic diagram of a performance measurement service architecture based on a cloud-native platform in one embodiment;

[0053] Figure 14 This is a schematic diagram of the performance measurement device in one embodiment. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] Before detailing the technical solutions of the embodiments of this application, the technical background and evolution of the embodiments of this application will be introduced first. Efficiency is the ability to interact with superior business value more efficiently, with higher quality, more reliably, and sustainably; it is an abstract process for measuring and evaluating R&D operations and efficiency. Metrics are data products of efficiency practices. For enterprises, acquiring efficiency measurement data and conducting efficiency indicator analysis during the R&D process is crucial. The combination of efficiency analysis and efficiency practices complement each other, realizing an effective efficiency data operation system to help enterprises improve and enhance R&D efficiency.

[0056] Traditional performance measurement has evolved through several stages: the first stage involves manual creation and analysis, where basic performance measurement data is manually recorded and stored, and then used computing tools or self-made applications to generate simple data reports and analyses. The second stage involves intelligent digital applications, built through DevOps platforms. This requires setting up basic measurement indicators, binding and deploying data sources and collection tools, and applying simple data models for statistical analysis. However, traditional performance measurement data analysis suffers from scattered and incomplete data, leading to inaccurate performance measurement data that fails to objectively represent enterprise performance. To address this, this application provides a performance measurement method.

[0057] The technical solutions involved in the embodiments of this application will be described below in conjunction with the application scenarios.

[0058] The performance measurement method provided in this application can be applied to computer equipment, and the internal structure of the computer equipment can be as follows: Figure 1As shown. The computer device includes a processor, memory, communication interface, display screen, and input devices connected by a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a performance measurement method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.

[0059] In one embodiment, such as Figure 2 As shown, a performance measurement method is provided. This embodiment uses the application of this method to a computer device as an example for illustration. It can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and can be implemented through the interaction between the terminal and the server.

[0060] Cloud-native platforms are built into computer devices, and performance measurement methods are applied to these platforms. By utilizing the cloud-native platforms within these devices, the performance of at least one platform can be measured. These cloud-native platforms can connect to one or more platforms to measure their performance. Different measurement platforms can correspond to different data sources, which may include Jenkins, Azure, and Extra. In other words, the cloud-native platform can acquire data from different data sources to measure the performance of the corresponding platform.

[0061] In this embodiment, the method includes the following steps:

[0062] Step 200: Obtain the configuration file corresponding to the platform to be measured, and obtain the platform data of the platform to be measured based on the configuration file.

[0063] The configuration file corresponding to the platform to be measured refers to the information corresponding to the data source in the platform, that is, the environment settings and files required for the computer device to obtain platform data from the platform to be measured. The configuration file corresponding to the platform to be measured can be entered by the user and stored on the computer device, or it can be generated through CICD and sent to the computer device. CICD is an abbreviation for Continuous Integration and Continuous Deployment. This embodiment does not limit the specific method for obtaining the configuration file corresponding to the platform to be measured, as long as its function can be achieved.

[0064] In an optional embodiment, assuming the data source of the platform to be measured is Azure, the configuration file may include the name of the platform to be measured: Azure; the path of the platform to be measured: http: / / azure.com; the name of the data source interface of the platform to be measured; and at least one of the following: specific platform data information to be obtained.

[0065] After obtaining the configuration file of the platform to be measured, the computer device can obtain the platform data of the platform to be measured based on the information in the configuration file. The platform data of the platform to be measured refers to the data of the platform to be measured that needs to be measured for performance.

[0066] Step 210: Process the platform data according to the preset standard protocol to obtain formatted measurement data.

[0067] A preset standard protocol is a standard protocol pre-stored in a computer device that is required for processing platform data. The standard protocol may include a standard format that requires the platform data to be formatted and standardized. This embodiment does not limit the specific content of the preset standard protocol, as long as its function can be achieved. Optionally, the preset standard protocol is a protocol in Metrics (standard protocol library).

[0068] After acquiring platform data from the platform to be measured, the computer equipment uses a preset standard protocol to format and unify the platform data. In other words, the computer equipment extracts key information from the platform data acquired from the platform to be measured, and sets the extracted key information into the standard format specified in the preset standard protocol, thus obtaining formatted measurement data.

[0069] In an optional embodiment, the preset standard protocol includes the definition, collection method, classification, priority, and health status of platform data. The computer device labels and processes the platform data according to the definition, collection method, classification, priority, and health status to obtain formatted measurement data.

[0070] Step 220: Establish relationships between the formatted metric data across multiple dimensions to obtain the target metric data.

[0071] After obtaining formatted metric data, the computer device establishes relationships between the formatted metric data across multiple dimensions to obtain the target metric data. The platform data acquired by the computer device from the platform to be metric includes multi-dimensional data, with different dimensions possessing different characteristics or attributes. The computer device establishes relationships between formatted metric data with different characteristics or attributes to obtain the target metric data.

[0072] Step 230: Calculate the target measurement data according to the target effectiveness measurement model to obtain effectiveness measurement data.

[0073] The target performance measurement model can be pre-stored in a computer device. After obtaining the target performance measurement data, the computer device uses the target performance measurement model to perform calculations on the target performance measurement data, thereby obtaining the performance measurement data. This embodiment does not limit the type or structure of the target performance measurement model, as long as it can perform calculations on the target performance measurement data.

[0074] In one optional embodiment, different metric data correspond to different performance measurement models. This can be due to different types of metric data corresponding to different performance measurement models, or different functional metrics corresponding to different performance measurement models. For example, if the metric data is used to predict risk, the corresponding performance measurement model is a predictive model; if the metric data is used to determine product quality, the corresponding performance measurement model is a quality assessment model. The computer device pre-stores the correspondence between metric data and performance measurement models. After obtaining the target metric data, the computer device determines the target performance measurement model based on the target metric data and the correspondence between the metric data and the performance measurement models.

[0075] In an optional embodiment, a performance measurement model database is stored in a computer device. After obtaining the target measurement data, the computer device can search the performance measurement model database for a performance measurement model that can perform calculations on the target measurement data, i.e., the target performance measurement model.

[0076] In an optional embodiment, the target performance measurement model can be any of a deep learning network model, a machine learning model, a neural network, and an AI model.

[0077] The performance measurement method provided in this application is applied to a cloud-native platform, which is used to measure the performance of at least one platform to be measured. The method involves obtaining the configuration file corresponding to the platform to be measured and acquiring the platform data based on the configuration file; formatting and unifying the platform data according to a preset standard protocol to obtain formatted measurement data; establishing correlations between the formatted measurement data across multiple dimensions to obtain target measurement data; and performing calculations on the target measurement data according to a target performance measurement model to obtain performance measurement data. In this embodiment, by establishing correlations between multi-dimensional measurement data within the formatted measurement data to obtain target measurement data, the scattered nature of the obtained target measurement data can be avoided, thereby improving the accuracy of the performance measurement data obtained using the target measurement data and thus more objectively representing the performance of the platform to be measured.

[0078] Furthermore, in this embodiment, the platform data of the platform to be measured can be obtained through the configuration file corresponding to the platform to be measured. This eliminates the need to bind the cloud-native platform to the platform to be measured; as long as the configuration file for the platform to be measured exists within the cloud-native platform, it is sufficient. This improves the efficiency of obtaining the platform data. Moreover, by formatting and unifying the platform data according to a preset standard protocol, this embodiment converts all platform data into formatted measurement data of the same format. This facilitates subsequent processing of the formatted measurement data, thereby improving the efficiency of determining performance measurement data.

[0079] In an optional embodiment, the performance measurement data obtained by the computer device is visualized data, that is, the correlation between the measurement data of each dimension in the target measurement data is displayed through various diagrams, such as bar charts, histograms, etc.

[0080] In another optional embodiment, after obtaining performance measurement data, the computer device can perform trend analysis, file comparison, correlation analysis, detailed analysis, etc., based on the performance measurement data to form analysis conclusions; it can also perform risk prediction and anomaly identification based on the performance measurement data to promptly identify problems existing in the platform to be measured.

[0081] In one embodiment, after the computer device obtains the configuration file corresponding to the platform to be measured, such as Figure 3 As shown, this describes an implementation method for obtaining platform data of the platform to be measured based on a configuration file. The steps of this implementation method include:

[0082] Step 300: Generate the data interface corresponding to the platform to be measured based on the configuration file.

[0083] After obtaining the configuration file of the platform to be measured, the computer device generates the corresponding data interface for the platform to be measured based on the information in the configuration file. That is, the application program interface (API) that enables data transmission between the cloud-native platform of the platform to be measured and the computer device.

[0084] In an optional embodiment, if the configuration file includes the data interface corresponding to the platform to be measured, that is, if the platform to be measured has an API interface for external access, then the data interface in the configuration file, i.e. the data interface corresponding to the platform to be measured, can be obtained directly.

[0085] In one optional embodiment, each platform to be measured is equipped with a file server or database. The computer device can obtain platform data from the file server or database through the edge service. The edge service can be integrated into the cloud-native platform of the computer device, deployed independently, or integrated with the cloud-native platform in the same Kubernetes (Container Registry) cluster.

[0086] Step 310: Obtain platform data of the platform to be measured through the data interface.

[0087] Once the computer device obtains the data interface, it can use this interface to acquire data from the platform to be measured, i.e., the platform data of the platform to be measured.

[0088] In this embodiment, the data interface generated according to the configuration file of the platform to be measured can establish communication between the platform to be measured and the cloud-native platform in the computer device. Through this data interface, the platform data of the platform to be measured can be obtained, which can improve the efficiency of obtaining the platform to be measured.

[0089] In one embodiment, such as Figure 4 As shown, the steps of this performance measurement method further include:

[0090] Step 400: Monitor for the existence of new configuration files.

[0091] During operation, computer devices monitor for new configuration files. New configuration files correspond to new metric platforms.

[0092] If the computer does not detect a new configuration file, monitoring will continue.

[0093] In one alternative embodiment, the computer device may periodically monitor for the existence of new configuration files, or it may monitor for the existence of new configuration files in real time.

[0094] Step 410: If a new configuration file exists, obtain the new platform data of the measurement platform corresponding to the new configuration file.

[0095] If a computer device detects that a new configuration file exists, it means that there is a measurement platform corresponding to the new configuration file that needs to be measured for performance. The computer device then obtains the platform data of the new measurement platform corresponding to the new configuration file based on the new configuration file.

[0096] In an optional embodiment, the computer device generates a new data interface corresponding to the new measurement platform based on the new configuration file; and obtains the platform data of the new measurement platform through the new data interface.

[0097] In another optional embodiment, the computer device can respond to a user's trigger operation, obtain a new configuration file corresponding to the new measurement platform, and obtain new platform data based on the new configuration file. That is, the user inputs a new platform access request to the computer device, and the computer device responds to the access request by obtaining new platform data. The user can input either the name of the new measurement platform or the new configuration file corresponding to the new measurement platform. If the user inputs the name of the new measurement platform, the computer device obtains the corresponding new configuration file based on the name of the new measurement platform.

[0098] In this embodiment, once a new configuration file is detected through real-time monitoring, the platform data of the corresponding new measurement platform can be obtained based on the new configuration file. In other words, the cloud-native platform in this embodiment can simultaneously acquire platform data from multiple measurement platforms, enabling the evaluation of the performance of multiple measurement platforms. This improves the practicality and reliability of the performance measurement method.

[0099] In one embodiment, such as Figure 5 As shown, this involves a method for unifying the format of platform data according to a preset standard protocol to obtain formatted measurement data. The steps of this method include:

[0100] Step 500: Obtain the standard format corresponding to the preset standard protocol; the standard format includes time series, measurement field and label field.

[0101] When computer equipment needs to format platform data, it determines the corresponding standard format according to a preset standard protocol. The standard format corresponding to the preset standard protocol includes time series, metric fields, and tag fields. The time series represents the events corresponding to the platform data, the metric fields represent the key information of the platform data, and the tag fields represent the tags corresponding to the key information of the platform data.

[0102] Step 510: Format and unify the platform data according to the standard format to obtain formatted measurement data.

[0103] After determining the standard format, the computer equipment formats and unifies the platform data according to the standard format. In other words, the format of the acquired platform data is converted into the standard format to obtain formatted measurement data.

[0104] In an optional embodiment, assuming that the acquired platform data is the number of defects that occurred 5 times when the application was tested during a certain period of time, the platform data will be formatted and unified according to the standard format corresponding to the preset standard protocol, and the formatted measurement data will be: a certain period of time, number of defects: 5 times; where the time series is a certain period of time, the label field is the number of defects, and the measurement field is 5 times.

[0105] In this embodiment, the platform data is formatted and uniformly processed according to the standard format corresponding to the preset standard protocol. This ensures that the platform data of the platform to be measured is stored in the same format, which facilitates the subsequent processing of the formatted measurement data and improves the efficiency of determining performance measurement data.

[0106] In one embodiment, the formatted metric data includes metric data across multiple dimensions. Multiple data interfaces corresponding to the platform to be measured, generated according to the configuration file, can be used to obtain metric data for one dimension of the platform. This results in multiple dimensions of platform data. By unifying the format of the platform data, the resulting formatted metric data also includes metric data across multiple dimensions.

[0107] In this case, such as Figure 6 As shown, this describes an implementation method for establishing relationships between formatted metric data across multiple dimensions to obtain target metric data. The steps of this implementation method include:

[0108] Step 600: Based on the formatted metric data, establish the initial correlation between the metric data of each dimension in the formatted metric data.

[0109] Quantitative data from multiple dimensions within formatted quantitative data may have interrelationships. Therefore, after obtaining the formatted quantitative data, the computer device establishes initial interrelationships between these multiple dimensions. This embodiment does not limit the specific method for establishing these initial interrelationships, as long as the functionality is achieved.

[0110] In an optional embodiment, the computer device may select to format any two dimensions of the measurement data, assuming that there is a correlation between the two dimensions of the measurement data, that is, to establish a correlation between the two dimensions of the measurement data.

[0111] In an optional embodiment, it is assumed that the data source in the platform to be measured is data related to the application development process. The platform data obtained may include the application developers, application development time, application testers, number of tests conducted, application defect rate, and application launch time. The application development process and the application testing process are two dimensions of measurement data. The initial association established by the computer equipment may be the relationship between the application developers and the number of tests conducted, the application development time, and the application testers.

[0112] Step 610: Correct the initial correlation to obtain the target metric data.

[0113] After obtaining the initial correlations between the metric data of each dimension in the formatted metric data, the computer device corrects these initial correlations to obtain the target metric data. That is, it checks the accuracy of the established initial correlations based on the formatted metric data; if the initial correlations are accurate, the initial correlations and the corresponding metric data are determined as the target metric data; if the initial correlations are inaccurate, the initial correlations are corrected, and the corrected initial correlations and the corresponding metric data are determined as the target metric data. The correction of the initial correlations can be done by using metric data from other dimensions in the formatted metric data besides the metric data corresponding to the initial correlations; or it can be metric data obtained from other metric platforms and processed through a unified format. This embodiment does not limit the specific method for correcting the initial correlations, as long as the function is achieved.

[0114] In this embodiment, by correcting the initial correlation between the measurement data of each dimension in the formatted measurement data, the target measurement data can be obtained, thus obtaining more accurate target measurement data, and consequently, more accurate performance measurement data.

[0115] In one embodiment, such as Figure 7 As shown, this describes an implementation method for correcting initial associations based on formatted metric data to obtain target metric data. The steps of this implementation method include:

[0116] Step 700: Obtain the corrected metric data. The corrected metric data is used to determine the degree of association of the initial association.

[0117] The computer device acquires corrected measurement data. Corrected measurement data can be measurement data from dimensions other than those corresponding to the initial association relationship in the formatted measurement data, or it can be measurement data from newly formatted measurement data obtained from a new measurement platform after format unification processing. This embodiment does not limit the specific method for acquiring corrected measurement data, as long as the function can be achieved.

[0118] After receiving the corrected metric data, the computer device calculates the correlation degree between each dimension in the initial association relationship based on the corrected metric data. The correlation degree of the initial association relationship is used to characterize the probability that there is a correlation between the metric data of each dimension in the initial association relationship.

[0119] The computer device stores a preset association threshold. After obtaining the association degree of the initial association relationship, the computer device compares the association degree with the preset association threshold. If the association degree exceeds the preset association threshold, it means that the initial association relationship is accurate, that is, the initial association relationship between the established measurement data of each dimension is accurate.

[0120] Step 710: If the correlation degree does not reach the preset correlation threshold, the initial correlation relationship is corrected according to the correction measurement data to obtain the target correlation relationship.

[0121] If the computer device determines that the degree of association of the initial association does not reach the preset association threshold, it means that the initial association is inaccurate. Then, the initial association is corrected using the correction metric data to obtain the target association.

[0122] The initial association relationship can be corrected based on the corrected metric data. This can be done by establishing an association between the corrected metric data and the metric data with a high degree of correlation in the initial association relationship, thereby obtaining the target metric data.

[0123] Step 720: Determine the target measurement data based on the target association relationship and the corresponding measurement data.

[0124] After obtaining the target association, the computer device determines the target association and the corresponding measurement data as the target measurement data.

[0125] In this embodiment, the correlation degree of the initial correlation is determined based on the corrected measurement data. If the correlation degree does not reach a preset correlation threshold, the initial correlation is corrected based on the corrected measurement data to obtain the target correlation, thereby obtaining the target measurement data. This ensures the accuracy of the final target measurement data, thereby improving the accuracy of the final determined performance measurement data.

[0126] In an optional embodiment, the platform to be measured includes multiple data interfaces. The platform data obtained from the first data interface is project data, and the platform data obtained from the second data interface is product data. The obtained platform data is formatted and standardized according to a preset standard protocol to obtain formatted project measurement data and formatted product measurement data. An initial association relationship is established between the formatted project measurement data and the formatted product measurement data. The platform data obtained from the third data interface is personnel data, which is formatted and standardized to obtain formatted personnel measurement data. The formatted personnel measurement data is used as corrected measurement data to correct the initial association relationship. Assume that the established initial association relationship is the relationship between the formatted project measurement data and product A in the formatted product measurement data; and the relationship between the formatted project measurement data and personnel C. Based on the formatted personnel measurement data, it is determined that personnel C is responsible for product B corresponding to the formatted product measurement data. Therefore, it is considered that the formatted project measurement data corresponds to product B in the formatted product measurement data. That is, the corrected association relationship (target association relationship) is the relationship between the formatted project measurement data and product B in the formatted product measurement data.

[0127] In one embodiment, the target performance measurement model includes multiple streaming execution operators, involving an implementation method of performing operations on target performance measurement data according to the target performance measurement model to obtain performance measurement data. This implementation method includes:

[0128] Based on the container cluster management system, various streaming execution operators are used to batch process the target metric data to obtain performance metric data.

[0129] Container Registry (Kubernetes) is a system for automatically deploying, scaling, and managing containerized applications. It is deployed on computer devices. After receiving target metric data, the computer device inputs this data into various streaming operators within the target performance metric model. These streaming operators can simultaneously process the received target metric data to obtain the performance metric data.

[0130] In this embodiment, the target metric data is batch-processed using multiple streaming execution operators in the target performance model, which improves the efficiency and accuracy of processing the target metric data.

[0131] In one embodiment, such as Figure 8 As shown, the steps of this performance measurement method further include:

[0132] Step 800: Obtain the user's identity information.

[0133] When a user needs to view or manipulate target metric data, they will enter their corresponding identity information to log in to the cloud-native platform. The computer device receives the identity information entered by the user. The user's identity information may include username, password, department, and job title, etc. This embodiment does not limit the specific content of the user's identity information, as long as the function can be achieved.

[0134] Step 810: Based on the identity information, obtain the target effectiveness measurement data corresponding to the identity information, and display the target effectiveness measurement data.

[0135] Different identity information corresponds to different target performance measurement data. In other words, different users can view different target performance measurement data and can perform different operations on the target performance measurement data.

[0136] After obtaining the user's identity information, the computer device determines the target performance measurement data corresponding to the identity information and displays the target performance measurement data on the computer device's screen so that the user can view and operate it.

[0137] In this embodiment, target performance measurement data corresponding to the user's input identity information is obtained and displayed. This ensures that different users have different permissions when viewing or operating target performance measurement data, guaranteeing that users can only search for and operate target performance measurement data within their permissions, thereby ensuring the security of target performance measurement data.

[0138] In one embodiment, the performance measurement method further includes the following steps:

[0139] The platform data of the platform to be measured and the platform data of the new measurement platform are stored in different partitions of the columnar storage database.

[0140] Computer devices include columnar storage databases, which differ from traditional relational databases, where data is stored row-by-row in tables. Columnar storage databases can include HBase, ClickHouse, Druid, and HP Vertica.

[0141] After acquiring platform data from the platform to be measured, the computer device stores it in a columnar storage database. Simultaneously, if the computer device detects a new configuration file, it also stores the new platform data from the corresponding measurement platform in the columnar storage database. This allows for the storage of large amounts of data and improves storage efficiency. Furthermore, platform data from different measurement platforms are stored isolated within the columnar storage database, i.e., in different partitions. This prevents data from different measurement platforms from becoming mixed up, thereby avoiding accidental operations.

[0142] In an optional embodiment, when storing platform data in a columnar database, the computer device also encrypts and backs up the platform data to ensure the security, integrity, and availability of the stored data.

[0143] In one optional embodiment, the columnar storage database can be integrated into a cloud-native platform or deployed independently as a cluster. Integrating the columnar storage database into a cloud-native platform facilitates unified management and maintenance and reduces the overhead of network access and transmission between services.

[0144] Please see Figure 9 One embodiment of this application provides a performance measurement method, the steps of which include:

[0145] Step 900: Obtain the configuration file corresponding to the platform to be measured;

[0146] Step 910: Generate the data interface corresponding to the platform to be measured according to the configuration file;

[0147] Step 920: Obtain the platform data corresponding to the platform to be measured through the data interface;

[0148] Step 930: Obtain the standard format corresponding to the preset standard protocol; the standard format includes time series, metric field, and label field;

[0149] Step 940: According to the standard format, the platform data is formatted and unified to obtain formatted measurement data;

[0150] Step 950: Based on the formatted metric data, establish the initial correlation between the metric data of each dimension in the formatted metric data;

[0151] Step 960: Determine the degree of association of the initial association based on the obtained corrected metric data;

[0152] Step 970: If the correlation degree does not reach the preset correlation threshold, the initial correlation relationship is corrected according to the correction measurement data to obtain the target correlation relationship;

[0153] Step 980: Determine the target measurement data based on the target association relationship and the corresponding measurement data.

[0154] Step 990: Based on the container cluster management system, use the various streaming execution operators in the target performance measurement model to batch process the target measurement data to obtain performance measurement data.

[0155] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0156] One embodiment of this application provides a cloud-native platform, such as... Figure 10 As shown, the cloud-native platform 10 includes a data collector 11, a protocol engine 12, and a model engine 13.

[0157] The data collector 11 is used to obtain platform data of the platform to be measured according to the configuration file corresponding to the platform to be measured connected to the cloud-native platform 10, and send the platform data to the protocol engine 12.

[0158] Data collectors can be integrated into cloud-native platforms or deployed independently as clusters. Integrating data collectors into cloud-native platforms facilitates unified management and maintenance, and reduces the overhead of network access and transmission between services. Data collectors can also be integrated with columnar storage databases within the same cluster, on the same node, and in the same namespace, further reducing the overhead of network access and transmission between services.

[0159] Protocol engine 12 is used to process platform data in a unified format according to a preset standard protocol to obtain the number of formatted metrics, establish correlations between the formatted metric data in multiple dimensions to obtain the target metric data, and send the target metric data to model engine 13.

[0160] Model Engine 13 is used to perform calculations on target measurement data based on the target performance measurement model to obtain performance measurement data.

[0161] For a detailed description of how performance measurement data is determined based on the data collector 11, protocol engine 12, and model engine 13 in the cloud-native platform 10, please refer to the description in the embodiments of the performance measurement method described above. It will not be repeated here.

[0162] In an optional embodiment, the cloud-native platform 10 includes a data collector management module. The data collector management model is used to manage the data collectors 11 within the cloud-native platform 10. For each metric platform, there is a corresponding data collector; that is, when a new metric platform requiring measurement is added to the cloud-native platform 10, the data collector management module generates a new data collector, which is then used to collect platform data from the added metric platform. This improves the scalability and maintainability of the cloud-native platform.

[0163] In an optional embodiment, a cloud-native platform-based performance measurement service platform, such as Figure 11 As shown in the diagram. The performance measurement data obtained through the performance measurement model can include process performance measurement data and structural performance measurement data. Process performance measurement data refers to the performance measurement data obtained during the process of deriving result performance measurement data from target performance measurement data. The performance measurement dashboard includes a data visualization dashboard to display the obtained performance measurement data.

[0164] In an optional embodiment, the performance metrics dashboard can display data from different dimensions. These dimensions include... Figure 12 As shown. Measurement data at the user scenario level can include domain experts, department heads, R&D managers, and frontline R&D personnel; at the business delivery level, measurement data can include delivery efficiency, delivery quality, and delivery capability; at the R&D activity level, measurement data can include data from each stage of the requirements phase, analysis phase, design phase, development phase, testing phase, deployment phase, and maintenance phase; and at the intelligent prediction level, measurement data includes trend comparisons, anomaly predictions, and risk predictions.

[0165] In an optional embodiment, the performance measurement service architecture based on a cloud-native platform is as follows: Figure 13 As shown, after the protocol engine performs format unification processing on the platform data, it stores the formatted metric data in the Kafka middleware. The model engine can retrieve the formatted metric data from the Kafka middleware when processing it. The model engine performs batch processing of the formatted metric data using multiple job pods in Kubernetes. The final performance metric data is stored in a columnar storage database, and the model engine can also retrieve performance metric data from the columnar storage database for further calculations.

[0166] In one optional embodiment, the cloud-native platform includes a performance measurement platform and a data acquisition platform, with different permission systems used to manage access permissions for the performance measurement platform and the data acquisition platform. That is, users accessing the performance measurement platform do not have permission to access the data acquisition platform, and vice versa. This improves the security of the data corresponding to the performance measurement platform and the data corresponding to the data acquisition platform.

[0167] In an optional embodiment, building and deploying a cloud-native platform-based performance measurement platform may include: installing a Kubernetes (K8S) environment; installing the K8S management tool (Helm); and installing the data collector, protocol engine, and model engine from the cloud-native platform-based performance measurement platform using Helm. The cloud-native platform can be installed offline or online. The columnar storage database (ClickHouse) can be deployed independently as a cluster or integrated into the data collector. The ClickHouse chart (the chart of the columnar storage database) is used as a sub-image of the data collector chart, and Helm automatically recognizes the data according to the chart structure.

[0168] When deploying a cloud-native performance measurement platform, HAProxy or Nginx is used to balance the load when the data collector communicates with the columnar storage database (ClickHouse). When using Kafka and ClickHouse for storage, NFS (Network File System) or MinIO is used for remote storage.

[0169] Based on the same inventive concept, this application also provides a performance measurement device for implementing the performance measurement method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more performance measurement device embodiments provided below can be found in the limitations of the performance measurement method described above, and will not be repeated here.

[0170] In one embodiment, such as Figure 14 As shown, a performance measurement device 20 is provided, applied to a cloud-native platform. The cloud-native platform is used to measure the performance of at least one platform to be measured. The performance measurement device 20 includes: an acquisition module 21, a processing module 22, a maintenance module 23, and a determination module 24, wherein:

[0171] The acquisition module 21 is used to acquire the configuration file corresponding to the platform to be measured, and to acquire the platform data of the platform to be measured based on the configuration file.

[0172] The processing module 22 is used to process the platform data in a unified format according to a preset standard protocol to obtain formatted measurement data.

[0173] The building and maintenance module 23 is used to establish relationships between formatted measurement data in multiple dimensions to obtain target measurement data.

[0174] The determination module 24 is used to perform calculations on the target measurement data according to the target effectiveness measurement model to obtain effectiveness measurement data.

[0175] In one embodiment, the acquisition module 21 is specifically used to generate the data interface corresponding to the platform to be measured according to the configuration file; and to acquire the platform data of the platform to be measured through the data interface.

[0176] In one embodiment, the performance measurement device 20 further includes a monitoring module. The monitoring module is used to monitor whether a new configuration file exists; if a new configuration file exists, it obtains new platform data of the measurement platform corresponding to the new configuration file.

[0177] In one embodiment, the processing module 22 is specifically used to obtain the standard format corresponding to the preset standard protocol; the standard format includes time series, metric field and label field; according to the standard format, the platform data is formatted and unified to obtain formatted metric data.

[0178] In one embodiment, the construction and maintenance module 23 includes a construction unit and a correction unit. The construction unit is used to establish the initial correlation between the measurement data of each dimension in the formatted measurement data according to the formatted measurement data; the correction unit is used to correct the initial correlation to obtain the target measurement data.

[0179] In one embodiment, the correction unit is specifically used to obtain correction measurement data; the correction measurement data is used to determine the correlation degree of the initial correlation; if the correlation degree does not reach the preset correlation threshold, the initial correlation is corrected according to the correction measurement data to obtain the target correlation; the target measurement data is determined according to the target correlation and the measurement data corresponding to the target correlation.

[0180] In one embodiment, the determining module 24 is specifically used to perform batch processing of target metric data using various streaming execution operators based on a container cluster management system to obtain performance metric data.

[0181] In one embodiment, the performance measurement device 20 further includes a display module. The display module is used to obtain the user's identity information; based on the identity information, obtain the target performance measurement data corresponding to the identity information, and display the target performance measurement data.

[0182] In one embodiment, the performance measurement device 20 further includes a storage module. The storage module is used to store the platform data of the platform to be measured and the platform data of the new measurement platform in different partitions of a columnar storage database, respectively.

[0183] Each module in the aforementioned performance measurement device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0184] In one embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 1 As shown. Those skilled in the art will understand that... Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0185] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0186] Obtain the configuration file corresponding to the platform to be measured, and obtain the platform data of the platform to be measured based on the configuration file;

[0187] The platform data is processed according to a preset standard protocol to obtain formatted measurement data;

[0188] Establish relationships between formatted metric data across multiple dimensions to obtain target metric data;

[0189] The target performance measurement data is obtained by performing calculations on the target performance measurement model.

[0190] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0191] Obtain the configuration file corresponding to the platform to be measured, and obtain the platform data of the platform to be measured based on the configuration file;

[0192] The platform data is processed according to a preset standard protocol to obtain formatted measurement data;

[0193] Establish relationships between formatted metric data across multiple dimensions to obtain target metric data;

[0194] The target performance measurement data is obtained by performing calculations on the target performance measurement model.

[0195] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0196] Obtain the configuration file corresponding to the platform to be measured, and obtain the platform data of the platform to be measured based on the configuration file;

[0197] The platform data is processed according to a preset standard protocol to obtain formatted measurement data;

[0198] Establish relationships between formatted metric data across multiple dimensions to obtain target metric data;

[0199] The target performance measurement data is obtained by performing calculations on the target performance measurement model.

[0200] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0201] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0202] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for measuring performance, characterized in that, Applied to a cloud-native platform, the method for measuring the performance of at least one platform to be measured includes: Obtain the configuration file corresponding to the platform to be measured, and obtain the platform data of the platform to be measured based on the configuration file; the configuration file corresponding to the platform to be measured includes the name, path and data source interface of the platform to be measured; The platform data is processed according to a preset standard protocol to obtain formatted measurement data; The formatted metric data is correlated across multiple dimensions to obtain the target metric data; The target measurement data is calculated based on the target effectiveness measurement model to obtain effectiveness measurement data; The step of establishing correlations between the formatted metric data across multiple dimensions to obtain the target metric data includes: Establish the initial relationships between the measurement data of each dimension in the formatted measurement data; The initial association relationship is corrected based on formatted metric data of other dimensions besides the metric data corresponding to the initial association relationship, to obtain the target metric data; The process of formatting and unifying the platform data according to a preset standard protocol to obtain formatted measurement data includes: Obtain the standard format corresponding to the preset standard protocol; the standard format includes time series, metric field, and label field; The platform data is formatted and standardized according to the standard format to obtain the formatted metric data; The step of modifying the initial association relationship based on formatted metric data of other dimensions besides the metric data corresponding to the initial association relationship to obtain the target metric data includes: establishing an association relationship between the formatted metric data of the other dimensions and the metric data with the highest correlation in the initial association relationship to obtain the target metric data.

2. The method according to claim 1, characterized in that, The step of obtaining the platform data of the platform to be measured according to the configuration file includes: Generate the data interface corresponding to the platform to be measured based on the configuration file; The platform data of the platform to be measured is obtained through the data interface.

3. The method according to claim 1, characterized in that, The method further includes: Monitor for the presence of new configuration files; If the new configuration file exists, then based on the new configuration file, obtain the new platform data of the measurement platform corresponding to the new configuration file.

4. The method according to claim 1, characterized in that, The step of correcting the initial association to obtain the target metric data includes: Obtain corrected metric data; the corrected metric data is used to determine the degree of association of the initial association. If the correlation degree does not reach the preset correlation threshold, the initial correlation relationship is corrected according to the corrected measurement data to obtain the target correlation relationship; The target measurement data is determined based on the target association relationship and the measurement data corresponding to the target association relationship.

5. The method according to any one of claims 1-3, characterized in that, The target performance measurement model includes multiple streaming execution operators. The step of processing the target performance measurement data according to the target performance measurement model to obtain performance measurement data includes: Based on the container cluster management system, the target metric data is batch-processed using the various streaming execution operators to obtain the performance metric data.

6. The method according to any one of claims 1-3, characterized in that, The method further includes: Obtain user identity information; Based on the identity information, obtain the target performance measurement data corresponding to the identity information, and display the target performance measurement data.

7. The method according to any one of claims 1-3, characterized in that, The performance measurement data is visualized data, and the method further includes: The relationships between the formatted metric data of each dimension in the target metric data are illustrated using a diagram.

8. A cloud-native platform, characterized in that, include: Data collector, protocol engine, and model engine; The data collector is used to obtain platform data of the platform to be measured according to the configuration file corresponding to the platform to be measured connected to the cloud-native platform, and send the platform data to the protocol engine. The configuration file corresponding to the platform to be measured includes the name, path, and data source interface of the platform to be measured; The protocol engine is used to process the platform data in a unified format according to a preset standard protocol to obtain formatted measurement data, and to establish correlations between the formatted measurement data in multiple dimensions to obtain target measurement data. And send the target metric data to the model engine; The model engine is used to perform calculations on the target measurement data according to the target performance measurement model to obtain performance measurement data; The process of establishing correlations between the formatted metric data across multiple dimensions to obtain target metric data includes: establishing initial correlations between metric data in each dimension of the formatted metric data; and revising the initial correlations based on formatted metric data in other dimensions besides those corresponding to the initial correlations to obtain the target metric data. The platform data is processed according to a preset standard protocol to obtain formatted measurement data, including: obtaining the standard format corresponding to the preset standard protocol; the standard format includes time series, measurement field and label field; and processing the platform data according to the standard format to obtain the formatted measurement data. The step of modifying the initial association relationship based on formatted metric data of other dimensions besides the metric data corresponding to the initial association relationship to obtain the target metric data includes: establishing an association relationship between the formatted metric data of the other dimensions and the metric data with the highest correlation in the initial association relationship to obtain the target metric data.

9. A performance measurement device, characterized in that, Applied to a cloud-native platform, the cloud-native platform is used to measure the performance of at least one platform to be measured, the apparatus includes: The acquisition module is used to acquire the configuration file corresponding to the platform to be measured, and to acquire the platform data of the platform to be measured based on the configuration file; the configuration file corresponding to the platform to be measured includes the name, path and data source interface of the platform to be measured; The processing module is used to obtain the standard format corresponding to a preset standard protocol; the standard format includes time series, metric field and label field; and to perform formatted and unified processing on the platform data according to the standard format to obtain formatted metric data. The construction and maintenance module is used to establish correlations between the formatted measurement data in multiple dimensions to obtain the target measurement data. The determination module is used to perform calculations on the target measurement data according to the target effectiveness measurement model to obtain effectiveness measurement data; The construction and maintenance module includes an establishment unit and a correction unit. The establishment unit is used to establish the initial correlation between the measurement data of each dimension in the formatted measurement data. The correction unit is used to establish the correlation between the formatted measurement data of other dimensions besides the measurement data corresponding to the initial correlation and the measurement data with the highest correlation in the initial correlation, so as to obtain the target measurement data.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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