Cluster state evaluation method, storage medium, electronic equipment and program product

Abstract

The embodiment of the invention provides a cluster state evaluation method, a storage medium, electronic equipment and a program product, and relates to the field of computers.The method comprises the steps that a state evaluation request triggered for a cluster is obtained, and the state evaluation request carries template request parameters and data request parameters; based on the data request parameter, acquiring assessment data of the cluster, the assessment data being cluster data conforming to a data range indicated by the data request parameter; under the condition that an evaluation template matched with the template request parameter is obtained, a plurality of evaluation elements included in the evaluation template are obtained, and one evaluation element corresponds to one evaluation style; according to each evaluation style, multiple evaluation results are generated for the evaluation data, one evaluation result corresponds to one evaluation element, and the multiple evaluation results are used for evaluating the running state of the cluster. According to the embodiment of the invention, the technical problem of relatively low cluster state evaluation efficiency in related technologies is solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computers, and in particular to a cluster status assessment method and storage medium, electronic device, and program product. Background Art

[0002] In the existing cluster operation status assessment method, the process of obtaining and analyzing cluster status data is highly dependent on manual operation, which not only consumes a lot of time and resources, but also easily introduces subjective errors, resulting in low efficiency of cluster operation status assessment.

[0003] Therefore, there is a technical problem in the related art that the evaluation efficiency of the cluster's operating status is low. Summary of the Invention

[0004] The embodiments of the present application provide a cluster status assessment method and storage medium, an electronic device, and a program product to at least solve the technical problem of low efficiency in assessing the operating status of a cluster in related technologies.

[0005] According to an embodiment of the present application, a cluster status assessment method is provided, comprising: obtaining a status assessment request triggered on the cluster, wherein the status assessment request carries a template request parameter and a data request parameter; based on the data request parameter, obtaining assessment data of the cluster, wherein the assessment data is cluster data that conforms to a data range indicated by the data request parameter; upon obtaining an assessment template that matches the template request parameter, obtaining multiple assessment elements included in the assessment template, wherein one assessment element corresponds to one assessment style; and generating multiple assessment results for the assessment data according to each assessment style, wherein one assessment result corresponds to one assessment element, and the multiple assessment results are used to assess the operating status of the cluster.

[0006] According to another embodiment of the present application, a cluster status evaluation device is provided, including: a first acquisition unit, used to obtain a status evaluation request triggered on the cluster, wherein the status evaluation request carries a template request parameter and a data request parameter; a second acquisition unit, used to obtain evaluation data of the cluster based on the data request parameter, wherein the evaluation data is cluster data that conforms to the data range indicated by the data request parameter; a third acquisition unit, used to, when an evaluation template matching the template request parameter is obtained, obtain multiple evaluation elements included in the evaluation template, wherein one evaluation element corresponds to one evaluation style; an evaluation unit, used to generate multiple evaluation results for the evaluation data according to each evaluation style, wherein one evaluation result corresponds to one evaluation element, and the multiple evaluation results are used to evaluate the operating status of the cluster.

[0007] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any of the above method embodiments when running.

[0008] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0009] The embodiments provided herein enable flexible application of corresponding evaluation templates by receiving status evaluation requests carrying template request parameters. These templates include evaluation elements and styles of interest to the user, making evaluation results more tailored to actual needs and enhancing the flexibility of cluster operational status evaluation. Based on the data request parameters, cluster evaluation data that meets the specified data range is automatically acquired, avoiding the tedious process of manual data collection. Automated data acquisition ensures the immediacy and integrity of data, reducing data processing time and human error. Based on the evaluation data, corresponding evaluation results can be generated according to the evaluation style of each evaluation element. This not only significantly improves the automation level of cluster status evaluation, but also enables diversified evaluation methods for a single piece of evaluation data (multiple evaluation results, corresponding to multiple evaluation styles), enhancing the flexibility and comprehensiveness of evaluation reports. This achieves the technical effect of comprehensively improving the efficiency of cluster operational status evaluation, resolving the technical issue of low efficiency in cluster operational status evaluation in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a hardware structure diagram of a cluster status assessment method according to an embodiment of the present application;

[0011] Figure 2 is a flow chart of a cluster status assessment method according to an embodiment of the present application;

[0012] Figure 3 This is a flowchart of a method for generating an artificial intelligence platform cluster operation report according to an embodiment of the present application;

[0013] Figure 4 This is a processing flow of a method for generating an artificial intelligence platform cluster operation report according to an embodiment of the present application;

[0014] Figure 5 This is a structural block diagram of a cluster status assessment device according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device. Taking running on a computer terminal as an example, Figure 1 This is a hardware structure block diagram of a computer terminal of a cluster status evaluation method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. The computer terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0018] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the cluster status assessment method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0019] Transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a computer terminal's communications provider. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0020] As an optional solution, this embodiment provides a cluster status assessment method, such as Figure 2 As shown, the specific steps include:

[0021] S202: Obtain a status assessment request triggered on the cluster, where the status assessment request carries template request parameters and data request parameters;

[0022] S204, obtaining cluster evaluation data based on the data request parameters, wherein the evaluation data is cluster data that meets the data range indicated by the data request parameters;

[0023] S206 , when an evaluation template matching the template request parameters is obtained, obtaining multiple evaluation elements included in the evaluation template, wherein one evaluation element corresponds to one evaluation style;

[0024] S208 , generating multiple evaluation results for the evaluation data according to each evaluation style, wherein one evaluation result corresponds to one evaluation element, and the multiple evaluation results are used to evaluate the operation status of the cluster.

[0025] Optionally, in this embodiment, the status assessment request is an instruction issued by a user or an automated system to request the generation of a detailed cluster status assessment report. The request typically carries template request parameters and data request parameters to indicate the structure of the report and the scope of the required data.

[0026] Optionally, in this embodiment, the template request parameter specifies a template style for generating the report. This template defines the types of elements in the report (e.g., labels, tables, charts), layout, and possible intelligent analysis rules, ensuring that the report content is comprehensive and meets the user's specific needs.

[0027] Optionally, in this embodiment, the data request parameters specify the time range, cluster nodes, focus indicators and other specific requirements of the data required for evaluation, ensuring that the acquired data accurately reflects the cluster status and time period that the user wants to evaluate.

[0028] Optionally, in this embodiment, the evaluation data is historical or real-time operational data obtained from the cluster according to the data request parameters and conforming to a specific data range. Evaluation data is the basis for generating evaluation results, and its accuracy directly affects the credibility of the evaluation report.

[0029] Optionally, in this embodiment, the evaluation template is a predefined structure used to guide the content, layout, and presentation of the evaluation report. The evaluation template includes multiple user-defined evaluation elements and their styles to ensure that the report generation conforms to standard specifications while reflecting personalized needs.

[0030] Optionally, in this embodiment, the evaluation elements are the basic building blocks of the report, and each element represents a specific type of information display, such as labels for text descriptions, tables for data listings, and charts (bar charts, line charts, pie charts, etc.) for intuitive data visualization.

[0031] Optionally, in this embodiment, the evaluation style is the form of expression and rules associated with the evaluation elements, such as color coding of data, layout design of charts, highlighting method of abnormal data, etc. The evaluation style enhances the readability of the evaluation results and the efficiency of information transmission.

[0032] Optionally, in this embodiment, the evaluation results are various evaluation information generated from the evaluation data according to the evaluation style. Each result corresponds to an evaluation element, which aims to comprehensively reflect the operating status of the cluster within a specific time period, including but not limited to performance indicators, usage trends, abnormal situations, etc.

[0033] Optionally, in this embodiment, a status assessment request containing template request parameters and data request parameters is received. The request can be initiated manually by the user through the user interface or automatically triggered by the system based on a pre-defined timed assessment policy. The template request parameters and data request parameters together define the report's appearance and content boundaries, serving as an important basis for subsequent data acquisition and report generation.

[0034] Extract the required data from the cluster's monitoring system or logs based on the time range, data type, and specific metrics specified in the data request parameters. This evaluation data is filtered and pre-processed to ensure that only data points that match the request parameters are included, facilitating subsequent analysis and presentation.

[0035] Once the data is acquired, the system searches for an assessment template that matches the template request parameters. Once the correct template is found, it extracts all predefined assessment elements from the template and combines them with the assessment data to generate specific assessment results. This diversity of assessment elements ensures that the report fully and meticulously reflects the cluster status.

[0036] After defining all evaluation elements and their styles, we analyze the evaluation data and generate corresponding evaluation results based on the style rules for each evaluation element. For example, if a style rule specifies that a performance metric should be marked when it exceeds a threshold, this will be visually highlighted in the evaluation results. The generation of evaluation results takes into account in-depth data analysis and intelligent perception, presenting the evaluation's key points and findings in a more intuitive and effective manner.

[0037] As you can see, this embodiment allows for highly customized report styles and content based on user needs, while also automatically acquiring data from the cluster and performing in-depth analysis. This approach not only reduces the manual workload on operations and maintenance personnel and improves the efficiency of generating assessment reports, but also, through the application of intelligent analysis rules, enhances the report's responsiveness to cluster anomalies.

[0038] Through the embodiments provided by the present application, by receiving a status assessment request carrying template request parameters, the corresponding assessment template can be flexibly applied. The assessment template includes the assessment elements and styles of interest to the user, making the assessment results more closely aligned with actual needs and enhancing the flexibility of cluster operation status assessment. Based on the data request parameters, cluster assessment data that meets the specified data range is automatically obtained, avoiding the tedious process of manual data collection. Through automated data acquisition, the immediacy and integrity of the data are ensured, reducing the time consumption and human errors in data processing. Based on the assessment data, corresponding assessment results can be generated according to the assessment style of each assessment element. This not only greatly improves the automation level of cluster status assessment, but also achieves diversified assessment methods for a piece of assessment data (multiple assessment results, corresponding to multiple assessment styles), enhancing the flexibility and comprehensiveness of the assessment report, and achieving the technical effect of comprehensively improving the efficiency of cluster operation status assessment.

[0039] As an optional solution, after generating multiple evaluation results for the evaluation data according to each evaluation style, the method further includes:

[0040] The multiple evaluation results are integrated to obtain an evaluation report for evaluating the operation status of the cluster, wherein a first arrangement order of the multiple evaluation results on the evaluation report corresponds to a second arrangement order of the multiple evaluation elements on the evaluation template.

[0041] Optionally, in this embodiment, the first arrangement order is the order in which the evaluation results are displayed in the generated evaluation report. This order generally follows the arrangement logic of the evaluation elements in the evaluation template to ensure the coherence of the report content and the convenience of user reading.

[0042] Optionally, in this embodiment, the second arrangement order is the arrangement order of the evaluation elements defined in the evaluation template, which determines the order of the contents of each part in the evaluation report, and helps to clarify the report structure and rationalize the content organization.

[0043] Optionally, in this embodiment, after completing the specific analysis and result generation of the assessment data according to each assessment style, the next important step is to integrate these scattered assessment results to construct a complete assessment report. This process involves orderly combining all assessment results according to the order of elements in the predefined assessment template to ensure the logical and visual coherence of the report content.

[0044] The integration process involves more than just physically merging the data. More importantly, it ensures that the presentation of the assessment results matches their definition in the assessment template. This means that the presentation format of each assessment result in the assessment report (e.g., label, table, or chart) is consistent with the layout in the assessment template. This approach allows the assessment report to clearly and intuitively display all aspects of the cluster's operational status, making it easy for users to quickly access and understand it.

[0045] For example, if the evaluation elements in an evaluation template are arranged in the following order: CPU usage (label), memory usage (table), and network traffic (bar chart), then the generated evaluation report will display these three evaluation results in the same order: a label showing CPU usage, a table showing memory usage, and finally a bar chart showing changes in network traffic. This integration based on the preset template order helps users quickly locate the information of interest, improving the practical value of the report and user experience.

[0046] Through the embodiments provided in this application, the evaluation report not only brings together in-depth analysis results for various key indicators of the cluster, but also ensures that the organizational logic and visual presentation of the report content are perfectly aligned with the design intent of the original template, thereby preserving the integrity of the evaluation information and the user-customized visual style to the greatest extent, and providing a cluster status evaluation solution that is both professional and personalized.

[0047] As an optional solution, after integrating multiple evaluation results to obtain an evaluation report for evaluating the operating status of the cluster, the method further includes:

[0048] The first evaluation content in the evaluation report is displayed in a first display style, and the second evaluation content in the evaluation report is displayed in a second display style, wherein the first evaluation content is the evaluation content that meets the expected abnormal conditions in multiple evaluation results, and the second evaluation content is the evaluation content other than the first evaluation content in multiple evaluation results, and the first display style is different from the second display style.

[0049] Optionally, in this embodiment, the first evaluation content is information in the evaluation report that is identified as meeting expected abnormal conditions based on the evaluation results. Such content generally indicates that the cluster operation status is abnormal or deviates from the normal range, requiring special attention from operation and maintenance personnel or managers.

[0050] Optionally, in this embodiment, the second evaluation content includes additional evaluation information in the evaluation report, excluding the first evaluation content, namely, cluster status data within normal operating ranges. This content provides a comprehensive perspective on cluster performance. While not highlighted as an abnormality, it is equally important to the overall evaluation.

[0051] Optionally, in this embodiment, the first display style is a visual presentation method specifically used to highlight the first assessment content. Typically, the first display style uses more eye-catching colors, bold fonts, special symbols, or background highlights to make the abnormal information particularly conspicuous in the report and facilitate quick location.

[0052] Optionally, in this embodiment, the second display style is a visual style used to display the second assessment content, which is significantly different from the first display style. The design of the second display style focuses on the clear presentation of information and the aesthetics of the overall report, using conventional fonts, colors, and layout to ensure smooth and professional reading of the report content.

[0053] Optionally, in this embodiment, after the assessment report is assembled, the system identifies and marks all first assessment content, i.e., those assessment results that meet the expected abnormal conditions. The system then uses a predefined first display style to specially process these abnormal information, making them more prominent in the report. The first display style may include, but is not limited to: using red highlighting, bold text, adding warning icons, etc., to ensure that operations personnel can quickly notice abnormal conditions when reviewing the report.

[0054] For normal operating status assessment results outside of the first assessment, the system displays them in the second display style. This second display style is more conventional than the first, aiming to provide clear information presentation while maintaining overall visual harmony in the report. This may include using default fonts, standard color coding, and appropriate spacing to ensure that all assessment information is presented reasonably and professionally in the report, allowing users to fully understand the cluster's operating status.

[0055] The embodiments provided herein efficiently and intuitively convey abnormal and normal information in the assessment report through differentiated display styles. By employing different first and second display styles, the present invention significantly enhances the information differentiation of the assessment report, enabling users to quickly identify which indicators require special attention and which data represent the normal operation of the cluster.

[0056] As an optional solution, before displaying the first evaluation content in the evaluation report in the first display style and displaying the second evaluation content in the evaluation report in the second display style, the method further includes:

[0057] Get the parameter type and the parameter threshold associated with the parameter type carried in the status assessment request;

[0058] determining an expected abnormal condition according to the parameter type and the parameter threshold, wherein the expected abnormal condition is used to indicate that the parameter type is greater than the parameter threshold;

[0059] A first evaluation content that meets the expected abnormal condition is determined from the multiple evaluation results.

[0060] Optionally, in this embodiment, the parameter type is a performance indicator category associated with a specific evaluation result in the evaluation report, such as CPU usage, memory usage, network transmission rate, etc. These parameter types define the type and focus of the evaluation content.

[0061] Optionally, in this embodiment, the parameter threshold is a numerical limit associated with the parameter type, which is used to determine whether the evaluation result is abnormal. For example, if the parameter threshold of CPU usage is set to 85%, then any usage exceeding 85% will be considered abnormal.

[0062] Optionally, in this embodiment, before generating and displaying the assessment report, the system first needs to parse the status assessment request to identify the parameter types and thresholds that the user is interested in. This information is typically obtained through the template request parameters and data request parameters in the request, which define the structure of the assessment report and the data screening criteria.

[0063] The system automatically generates expected anomaly conditions based on the received parameter type and the user-specified parameter threshold. An expected anomaly condition is a logical judgment rule used to identify which metrics in the evaluation results are abnormal. For example, if the parameter type is CPU usage and the parameter threshold is 85%, the expected anomaly condition is "CPU usage greater than 85%."

[0064] Based on the defined expected abnormal conditions, the system screens the multiple integrated assessment results and identifies all assessment information that meets the conditions, which is the first assessment content. This process automatically completes the labeling and classification of abnormal data, ensuring that the abnormal information can be highlighted and analyzed in detail later.

[0065] The embodiments provided in this application ensure that assessment reports are not only efficiently generated but also visually distinguish between abnormal and normal indicators, significantly improving the efficiency and accuracy of operations and maintenance personnel and managers in assessing cluster operating status. The parameter type and threshold settings allow users to fine-tune the boundaries of anomalies based on their needs and experience, enabling the system to more intelligently detect and highlight anomalies, facilitating rapid response and resolution of issues, and maintaining stable cluster operation.

[0066] As an optional solution, multiple evaluation results are generated for the evaluation data according to each evaluation style, including at least one of the following:

[0067] In a case where the plurality of evaluation elements include a label evaluation element, generating a label evaluation result corresponding to the label evaluation element for the evaluation data, the plurality of evaluation results including the label evaluation result;

[0068] In a case where the plurality of evaluation elements include a tabular evaluation element, generating a tabular evaluation result corresponding to the tabular evaluation element for the evaluation data, the plurality of evaluation results including the tabular evaluation result;

[0069] In a case where the plurality of evaluation elements include a graphic evaluation element, a graphic evaluation result corresponding to the graphic evaluation element is generated for the evaluation data, and the plurality of evaluation results include the graphic evaluation result.

[0070] Optionally, in this embodiment, when the evaluation element is a label, the generated evaluation result usually includes a brief text description for intuitively displaying a specific evaluation indicator or operating status, such as an overview of cluster health.

[0071] Optionally, in this embodiment, when the evaluation element is a table, the evaluation result is a detailed data set presented in the form of rows and columns, which is convenient for users to view and compare performance indicators and status data of multiple nodes or multiple time points in the cluster, such as CPU usage, memory usage, etc.

[0072] Optionally, in this embodiment, when the evaluation element is a graph, the evaluation results are intuitively presented in the form of charts, including bar charts, line charts, pie charts, etc., which are used to show the distribution, trends and proportions of the evaluation data, making complex data clear at a glance, such as the changing trend of cluster resource utilization over time.

[0073] Optionally, in this embodiment, if the assessment template includes a label assessment element, the next step in the method is to process and generate corresponding label assessment results. This process typically involves extracting key metric values ​​from the assessment data and converting them into labels, such as "Cluster Average CPU Usage: 82%." Label assessment results are concise and clear, quickly conveying key information. They are suitable for use at the beginning or summary of a report, providing users with an overview of the current status of the cluster.

[0074] When a tabular evaluation element is defined in an evaluation template, the system selects appropriate data sets from the evaluation data based on the data request parameters and organizes them into tabular evaluation results. Tabular evaluation results provide a detailed overview of cluster performance metrics and status data, allowing users to conduct in-depth analysis and comparisons. The system ensures that the table formatting conforms to the template requirements, including specific column headers, row data sorting, and possible intelligent analysis tags (such as highlighting outliers).

[0075] If the assessment template uses graphical assessment elements, such as line graphs, bar graphs, or pie charts, the system will generate corresponding graphical assessment results based on the assessment data. This process involves statistical analysis and graphical presentation of the data. The system maps the assessment data to the X-axis, Y-axis, or other dimensions of the chart to generate an intuitive graphical presentation. It also applies user-defined intelligent analysis rules (such as marking points on the chart that fall outside the normal range). This makes the graphical assessment results not only intuitive but also highlights important or abnormal information.

[0076] Through the embodiments provided in this application, corresponding evaluation results are generated based on different types of evaluation elements (labels, tables, and graphs). This process ensures that the evaluation report can present the evaluation data in the most appropriate form based on the user's specific needs and preferences. Optionally, label evaluation results provide a way to quickly browse the report, allowing users to have a preliminary understanding of the overall health status of the cluster; table evaluation results provide a detailed view of the data, suitable for data comparison and historical trend analysis; graphical evaluation results display the distribution and changes of the evaluation data in an intuitive visual form, which helps to quickly grasp key trends and anomalies. By flexibly using different types of evaluation elements, not only the readability of the report and the efficiency of information transmission are improved, but also the professionalism and comprehensiveness of the evaluation report are ensured.

[0077] As an optional solution, when the multiple evaluation elements include a graphic evaluation element, generating a graphic evaluation result corresponding to the graphic evaluation element for the evaluation data includes at least one of the following:

[0078] In the case where the graphic evaluation element is a line chart element, generating a line chart evaluation result corresponding to the line chart element for the evaluation data, the graphic evaluation result including the line chart evaluation result;

[0079] In a case where the graphic evaluation element is a pie chart element, generating a pie chart evaluation result corresponding to the pie chart element for the evaluation data, the graphic evaluation result including the pie chart evaluation result;

[0080] In the case where the graphic evaluation element is a bar chart element, a bar chart evaluation result corresponding to the bar chart element is generated for the evaluation data, and the graphic evaluation result includes the bar chart evaluation result.

[0081] Optionally, in this embodiment, when the graphical evaluation element is set to a line graph, the system generates a dynamic trend chart based on the evaluation data. The line graph evaluation results generally show the trend of a specific indicator over time, such as the fluctuation of cluster resource usage.

[0082] Optionally, in this embodiment, when the graphical evaluation element is a pie chart, the system generates an evaluation result that displays the proportion of each component in the overall image. Pie chart evaluation results are often used to reflect cluster resource allocation, such as the CPU usage of different nodes.

[0083] Optionally, in this embodiment, when a bar chart element is used for the evaluation, the generated evaluation results are in the form of a bar chart that intuitively displays the comparative relationship between the data. The bar chart evaluation results are suitable for displaying the performance indicator differences between different time periods or different nodes, such as the average memory usage of each node.

[0084] Optionally, in this embodiment, if the evaluation template includes a line graph element, the system generates a line graph evaluation result based on the evaluation data. This process involves mapping the time series data in the dataset to the line graph's X-axis (time) and Y-axis (indicator value), and drawing a continuous line to illustrate the indicator's changing trend over time. The system ensures that the generated line graph evaluation result meets the display requirements set by the user in the template, such as the time accuracy of the X-axis and the numerical range of the Y-axis. It may also apply intelligent analysis rules, such as special marking of points exceeding the threshold, so that the line graph evaluation result is not only intuitive but also highlights abnormal data points.

[0085] If a pie chart element is defined in the assessment template, the system will generate a pie chart assessment result based on the assessment data. This process involves mapping the categorical data and their proportions in the dataset into a pie chart, with each category corresponding to a pie slice, whose size reflects the category's proportion of the overall data. The design of the pie chart assessment result follows the user's requirements in the template, such as the display of category labels and the pie chart color scheme, to ensure accurate information transmission and a beautiful report.

[0086] If the assessment template includes a bar chart element, the system will generate a bar chart assessment result, displaying the comparison or distribution of the assessment data. Generating a bar chart assessment result involves mapping the categorical or temporal data in the dataset to the individual bars of the bar chart, with the height or length of the bars reflecting the numerical value of the data. Based on the template requirements, the system will set appropriate category labels, value axis ranges, and bar styles for the bar chart assessment result. It may also apply intelligent analysis rules, such as highlighting bars with abnormal values, to enhance information visualization and anomaly detection capabilities.

[0087] Through the embodiments provided in this application, the assessment report not only provides an intuitive view of various types of data, but also selects the most appropriate chart type for presentation based on the characteristics of the data and user preferences, thereby improving the report's readability and the depth of data analysis. Whether displaying time series trends, the proportion of each component, or comparing data across different categories, line charts, pie charts, and bar charts can convey key information from the assessment data in the most intuitive way, helping users quickly gain insight into the details of the cluster's operating status and providing strong data support for subsequent operation and maintenance decisions and troubleshooting.

[0088] As an optional solution, based on the data request parameters, obtain cluster evaluation data, including:

[0089] In the case where the data request parameter includes a time request sub-parameter, determining the first data generated by the cluster within the time range indicated by the time request sub-parameter as the evaluation data; or

[0090] In a case where the data request parameter includes a node request sub-parameter, the second data generated by the target node indicated by the node request sub-parameter in the cluster is determined as the evaluation data.

[0091] Optionally, in this embodiment, the time request subparameter is a subparameter within the data request parameter that specifies the time range for the evaluation data. It includes a start time and an end time, and is used to limit the time window of the data in the evaluation report, ensuring that the report reflects the cluster's operating status within the specified time period.

[0092] Optionally, in this embodiment, the first data is evaluation data collected by the system within a specified time range according to a time request sub-parameter when the data request parameter includes the sub-parameter, and is used to generate report content reflecting changes in cluster operation status over time.

[0093] Optionally, in this embodiment, the node request sub-parameter is a sub-parameter in the data request parameter used to specify a target node. It allows the user or system to focus on the operating data of a specific node rather than the entire cluster when generating an evaluation report.

[0094] Optionally, in this embodiment, the second data is evaluation data of a specific target node collected by the system when the data request parameter includes a node request sub-parameter, and is used to generate report content reflecting the unique operating status of the node.

[0095] Optionally, in this embodiment, when the data request parameters include a time request sub-parameter, the system first parses the start and end time parameter values, and then retrieves the first data generated in the cluster during that time period. This process involves filtering historical data and monitoring real-time data to ensure that the evaluation report reflects the actual cluster operation status within the time window of interest to the user. For example, if the time request sub-parameter is the previous week, the system will collect data such as CPU usage, memory usage, and network traffic for all nodes during that week to generate an operation status evaluation report for the previous week.

[0096] If the data request parameter includes the node request sub-parameter, the system will focus on the specific target node and collect secondary data generated by that node during the assessment. This step allows for more targeted report generation, allowing users to gain in-depth understanding and analysis of the health of specific nodes.

[0097] The embodiments provided herein utilize the process of obtaining the first data based on the time request sub-parameter to ensure the timeliness of the assessment report content, enabling users to obtain operational status information within a specific period, helping to promptly identify and resolve cluster performance issues. Furthermore, obtaining the second data based on the node request sub-parameter enhances the personalization and targeting of the assessment report, enabling operations and maintenance personnel to conduct in-depth analysis of the operational status of a single or multiple specific nodes, providing data support for diagnosing and optimizing node-level issues.

[0098] As an optional solution, based on the data request parameters, obtain cluster evaluation data, including:

[0099] In a case where the data request parameter includes a time request sub-parameter and a node request sub-parameter, third data generated by the target node in the cluster within the time range is determined as the evaluation data.

[0100] Optionally, in this embodiment, when the data request parameters include a time request sub-parameter and a node request sub-parameter, the next step in the evaluation report generation method is to collect and determine the third data. This process involves accurately acquiring the operating data of a specific node within a specified time period. The system first identifies the time request sub-parameter, determines the time range for data collection, and then locates the target node indicated by the node request sub-parameter.

[0101] The system filters the target node's historical and real-time data streams for operational metrics within a set timeframe, such as CPU usage, memory usage, disk I / O, and network bandwidth. This filtered data is known as the third-party data. By accurately locating the target node at the time and node location, the third-party data provides detailed operational status of the target node within a specific timeframe, which is crucial for in-depth analysis of node-level performance issues, resource bottlenecks, or root causes of failures.

[0102] Through the embodiments provided in this application, through the combined use of time request sub-parameters and node request sub-parameters, multi-dimensional customization of evaluation report generation is achieved, which greatly improves the support strength of operation and maintenance decision-making and the efficiency of problem solving. At the same time, it also demonstrates the user-centricity of the present invention and its powerful function of providing refined operation and maintenance assistance.

[0103] As an optional solution, it is characterized in that, before obtaining the status assessment request triggered on the cluster, the method further includes:

[0104] At least two evaluation elements are sequentially combined to generate at least two evaluation templates, wherein different evaluation templates contain different numbers of evaluation elements or different combinations of the evaluation elements;

[0105] Establishing a one-to-one mapping relationship between at least two template request parameters and at least two evaluation templates;

[0106] Before obtaining the multiple assessment elements included in the assessment template, the method further includes:

[0107] An evaluation template that matches the template request parameters is determined from the mapping relationship.

[0108] Optionally, in this embodiment, before generating an assessment report, the system predefines multiple assessment templates. Each template consists of at least two assessment elements combined in a specific order and format. This process may involve permutations and combinations of multiple assessment elements to ensure that different assessment templates meet the needs of different scenarios, such as resource utilization efficiency analysis and system stability assessment. The system carefully designs the structure and content of each assessment template based on the intended report type, presentation requirements, and data analysis objectives.

[0109] To enable users to call assessment templates based on specific needs, the system needs to establish a mapping mechanism between template request parameters and assessment templates. This typically requires defining a unique template request parameter for each assessment template, which contains the template's identification information and any customization requirements. For example, an assessment template focused on node performance might be assigned a specific template request parameter, instructing the system to call that template when generating a report.

[0110] When a user or the system triggers a status assessment request, it carries a template request parameter, which instructs the system which assessment template to use for report generation. Before processing the request, the system parses the template request parameter based on a pre-defined mapping relationship to determine the matching assessment template. This process ensures that subsequent data acquisition, analysis, and report generation steps are carried out according to the user-specified template, meeting the user's customization and professional requirements.

[0111] Through the embodiments provided in this application, the design of the evaluation template takes into account the diversity of evaluation elements and the flexibility of combination, allowing users to generate reports focusing on different aspects and structures based on specific concerns and data display preferences. The mapping between template request parameters and evaluation templates provides users with a mechanism to select specific templates, ensuring the accuracy and customization level of report generation. When a user initiates a status evaluation request, the system achieves seamless connection from user needs to report generation logic by parsing the template request parameters and determining the corresponding evaluation template. This series of steps not only improves the efficiency of generating evaluation reports, but also ensures the diversification and personalization of report content and format, meeting users' diverse needs for the depth and breadth of cluster status evaluation.

[0112] As an optional solution, the method further includes:

[0113] In a case where the status assessment request carries a period request parameter and the template request parameter only includes a first template request parameter, obtaining a first assessment template matched by the first template request parameter, and obtaining a plurality of first assessment elements included in the first assessment template;

[0114] A plurality of first evaluation results corresponding to the plurality of first evaluation elements are generated for the evaluation data a plurality of times according to the number of cycles indicated by the cycle request parameter.

[0115] Optionally, in this embodiment, the periodic request parameter is a parameter in the status assessment request, which is used to indicate the frequency of generating the assessment report, such as daily, weekly, monthly, etc.

[0116] Optionally, in this embodiment, when the status assessment request carries a periodic request parameter and the template request parameter only contains the first template request parameter, the system will parse the first template request parameter in the request to determine the first assessment template that matches it. Subsequently, the system will identify all included first assessment elements from the first assessment template, and these elements will guide the subsequent data acquisition and assessment result generation process. For example, the first assessment template may contain a resource utilization table, an abnormal event statistics label, and a node status bar chart. The system will prepare the corresponding data and analysis logic based on the definitions of these elements.

[0117] After determining the first assessment template, the system will periodically repeat the process of data acquisition and assessment result generation according to the number of cycles specified in the periodic request parameters. For example, if the periodic request parameters indicate that a report should be generated once a week, the system will obtain the latest status data from the cluster at a fixed time each week, as defined by the first assessment element, and generate the corresponding first assessment results. This process will continue until the total number of times specified in the periodic request parameters is met. In this way, the system can automatically and regularly generate assessment reports with the same structure and content, thereby helping users to continuously monitor and analyze the operating status of the cluster and promptly identify potential problems and trend changes.

[0118] Through the embodiments provided in this application, when a status assessment request automatically triggered by a user or system includes a periodic request parameter, and the template request parameter specifies only one assessment template (i.e., only includes the first template request parameter), the system will automatically identify and lock this specific assessment template, while understanding all first assessment elements contained in the template. Subsequently, the system will automatically execute the process of data acquisition and assessment result generation on a regular basis according to the period and number of times set in the periodic request parameter. This mechanism ensures that the assessment report can be automatically updated within the user-defined time interval without manual intervention, greatly saving the time and energy of operation and maintenance personnel, while ensuring the timeliness and consistency of the report.

[0119] As an optional solution, the method further includes:

[0120] When the status assessment request carries a periodic request parameter and the template request parameter includes a first template request parameter and a second template request parameter, obtaining a first assessment template matched by the first template request parameter and a plurality of first assessment elements included in the first assessment template, and obtaining a second assessment template matched by the second template request parameter and a plurality of second assessment elements included in the second assessment template;

[0121] At the number of cycles indicated by the cycle request parameter, multiple first evaluation results corresponding to multiple first evaluation elements and multiple second evaluation results corresponding to multiple second evaluation elements are generated for the evaluation data multiple times, wherein the evaluation results corresponding to every two times in the number of cycles are multiple first evaluation results and second evaluation results.

[0122] Optionally, in this embodiment, when a status assessment request includes both a periodicity request parameter and specifies both a first template request parameter and a second template request parameter, the system first parses the two template request parameters to determine the matching first and second assessment templates. Next, the system further identifies all assessment elements (first and second assessment elements) contained in each of the first and second assessment templates. These elements define the report's content structure, including the data presentation type, format, and analysis rules.

[0123] After the periodic request parameter and the two template request parameters are determined, the system will generate a composite evaluation report containing the first evaluation result and the second evaluation result according to the indication of the periodic number. Specifically, in each periodic evaluation data acquisition process, the system will generate a first evaluation result according to the first evaluation template and a second evaluation result according to the second evaluation template, and integrate them into the same periodic comprehensive evaluation report. This processing method is particularly suitable for scenarios that require simultaneous monitoring and analysis of multiple cluster state data, ensuring that each report generation contains all the key information users care about, improving the comprehensiveness and timeliness of operation and maintenance decisions.

[0124] Through the embodiments provided in the present application, when the state evaluation request contains not only the periodic request parameter, but also two template request parameters (i.e. the first and second template request parameters), the system will perform multi-level data collection and report generation according to these parameters. First, the system will identify and obtain the first evaluation template and the second evaluation template to ensure that the content of the subsequent report can cover the two different dimensions of data specified by the user. Then, within each period, the system will generate corresponding first evaluation results and second evaluation results for the evaluation data according to the evaluation elements in the two templates, and integrate them into a periodic comprehensive evaluation report. This process not only embodies the efficiency and automation characteristics of the evaluation report generation method, but also demonstrates its high customization and data integration capabilities. By alternating or simultaneously presenting the results of different templates and evaluation elements in each period, the system can provide a comprehensive and dynamically updated cluster state view, significantly enhancing the monitoring and analysis capabilities of operation and maintenance personnel on the health status of the cluster, and thus improving the accuracy and efficiency of operation and maintenance decisions.

[0125] As an optional solution, after obtaining the evaluation data of the cluster based on the data request parameter, the method further includes:

[0126] performing data verification on the evaluation data;

[0127] in the case where the data verification determines that the evaluation data includes missing data, obtaining a proportion of the missing data in the evaluation data;

[0128] in the case where the proportion is less than a preset proportion threshold, performing a data filling operation matching the type of the missing data according to the type of the missing data.

[0129] Optionally, in this embodiment, after obtaining the evaluation data, the system will perform comprehensive data verification to check the integrity, accuracy and compliance of the data. The verification process includes but is not limited to field integrity verification, record integrity verification, data accuracy verification, etc., to ensure that each piece of data meets the preset data quality standards.

[0130] If missing data is found during the data verification process, the system will further calculate the proportion of missing data in the assessment data. This calculation is crucial because it directly affects whether the assessment report can be generated and how to handle the missing data.

[0131] When the proportion of missing data is determined to be below a preset threshold, the system performs data filling operations based on the type of missing data. Data filling strategies may include linear interpolation, mean value filling, nearest neighbor filling, etc. The specific strategy will be selected based on the nature of the missing data (such as time series data or statistical data) and contextual information to maximize the authenticity and completeness of the data.

[0132] For example, for time series data, if the missing data points are continuous data points for a certain period of time, the system can use linear interpolation to infer the missing value based on the data points before and after the missing period; for statistical data, if a statistical data point is missing, the average or median of the past 7 days can be used to fill it in.

[0133] In the embodiments provided herein, if data validation identifies missing data in the assessment data, the system will further calculate its proportion in the overall data. If this proportion is below a preset threshold, indicating that the impact of the missing data on overall data quality is within a controllable range, the system will automatically perform data filling based on the type of missing data, using a preset filling strategy to supplement the missing data points, thereby maintaining the continuity and accuracy of the assessment report generation.

[0134] As an optional solution, the aforementioned cluster status assessment method can be used to generate AI platform cluster operation reports. With the rapid development of AI technology, the scale and complexity of AI platform clusters are constantly increasing, making the monitoring and analysis of cluster operation status crucial. As an important vehicle for reflecting cluster operation status, cluster operation reports can provide key information to operations and maintenance personnel and managers, helping them to promptly identify problems and optimize cluster performance.

[0135] At present, there are many deficiencies in the way of generating cluster operation reports of artificial intelligence platforms. On the one hand, traditional report generation mostly relies on manual production. Operation and maintenance personnel need to tediously extract data from the platform and then organize it into reports according to a certain format. This not only consumes a lot of time and energy, but is also prone to data errors or inconsistent formats due to human operations. On the other hand, some existing tools for automatic report generation often have fixed report templates. Users cannot flexibly define the element types and data sets contained in the report according to their own needs, making it difficult to meet reporting needs in different scenarios. In addition, there is insufficient support for the function of generating reports on a scheduled basis, and it is impossible to automatically generate regular reports such as weekly, monthly, and annual reports. There is also a lack of intelligent perception and prominent display of data anomalies, which brings inconvenience to operation and maintenance work.

[0136] In order to solve the above-mentioned defects, this embodiment provides a method for generating a cluster operation report for an artificial intelligence platform based on the above-mentioned cluster status assessment method. Through this method, the definition of cluster report templates, data generation, report export, and configuration generation of scheduled reports can be realized, which can greatly improve the flexibility of cluster operation report definition, as well as the efficiency and accuracy of report generation.

[0137] Optionally, in this embodiment, a smart report template is defined. The template includes report element types (including labels, tables, bar charts, line charts, and pie charts), datasets, and smart analysis rules. Smart analysis rules can be user-defined, for example, to mark specific indicators when they exceed a preset threshold. The datasets are derived from the AI ​​platform's report statistics function and can be selected by the user as needed.

[0138] Optionally, in this embodiment, dynamic data sets are acquired and analyzed. When a user triggers a report export function or a scheduled task, the module retrieves the corresponding report data set from the AI ​​platform's report statistics function based on the data set defined in the report template, the user-selected data range (e.g., a specific time range), and intelligent analysis rules. During data acquisition, not only is the data integrity and accuracy verified, but if missing or anomalies are detected, prompts are issued or pre-set processing methods (e.g., default values ​​are applied) is applied. Furthermore, the data is deeply analyzed based on intelligent analysis rules to identify key information such as abnormal fluctuations and trend changes in the data and generate analysis tags.

[0139] Optionally, in this embodiment, adaptive report generation and export are performed. After obtaining the corresponding report data set and analysis tags, the module fills the data and analysis tags according to the element type and layout defined in the report template. For label elements, in addition to filling in the corresponding data description text, if there is an analysis tag, it will be displayed together. For table elements, the data is sorted and filled in according to the row and column structure, and abnormal data cells are marked with special colors. For chart elements such as bar charts, line charts, and pie charts, corresponding graphics are automatically generated according to the data, and abnormal data points are highlighted with special symbols. After filling is completed, the report layout is automatically adjusted according to the data volume and element layout to ensure clear and beautiful content, and then the report is converted to PDF format, and an export function is provided. The user can choose to save the report to a specified local path.

[0140] Optionally, in this embodiment, intelligent scheduled task management is provided. A definable intelligent scheduled task function is provided, and users can set the execution cycle (such as weekly, monthly, and annually), execution time, and corresponding report template and data range of the scheduled task according to their needs. This module has the ability to intelligently adjust tasks, and can automatically optimize the task execution time based on the historical report generation situation and the cluster operation load, avoiding the execution of tasks during periods of high cluster load. At the same time, the dynamic data set acquisition and analysis module and the adaptive report generation and export module are automatically triggered according to the set time to complete the scheduled generation of cluster operation reports, and the notification method of the task execution results can be configured (such as email notification, platform message notification), informing the user that the report has been generated and the storage location. If there are abnormal analysis marks in the report, they will be highlighted in the notification.

[0141] To further illustrate, a flowchart of a method for generating an artificial intelligence platform cluster operation report is shown as follows: Figure 3 As shown, the specific steps include: intelligent report template definition, dataset acquisition and analysis, report generation and export, and scheduled task management. Optionally, the intelligent report template definition step forms the foundation of the entire process, providing report presentation formats, including labels, tables, line charts, bar charts, pie charts, and more, for subsequent data visualization. The dataset acquisition and analysis step first reads the report template to define the data presentation requirements; then, it acquires the data required for the report from the reporting service; and finally, it analyzes the acquired data to provide data support for report generation. The report generation and export step first populates the analyzed dataset into the report template; then, it automatically formats the report for a standardized and aesthetically pleasing format; then, it generates a PDF report; and finally, it outputs the generated report to a specified path, completing the entire report generation process. The scheduled task management step manages report generation tasks. You can define the task execution cycle to determine the frequency of report generation; define associated report templates to specify the templates used by the task; and define task notification methods to provide notifications regarding task execution.

[0142] Optionally, in this embodiment, an intelligent report template definition function is developed to implement the definition of report templates. The definition content includes the report name, report description, and report data. The report data is defined in order from top to bottom. When the report is generated, the corresponding report data set is obtained according to the definition order to complete the report data generation. Each part of the report data includes: data type, title, data source, data set and other attributes. Depending on the data type, the attribute columns that need to be defined will also be dynamically displayed. The specific attribute information that needs to be defined for different data types is shown in Table 1:

[0143] Table 1

[0144]

[0145] You can set intelligent analysis rules for the aforementioned column attributes, including data columns, indicator columns, X-axis, Y-axis, category, and value. For example, "Mark CPU usage > 90% as abnormal." When an abnormality is marked, the generated report will display it in the specified eye-catching font and color.

[0146] Optionally, in this embodiment, a data request is sent by calling the report statistics interface provided by the artificial intelligence platform, based on the data source and dataset identifiers in the report template and the user-selected data range parameters (such as the start time and end time). After the interface returns the data, the data acquisition module parses the data to check the field integrity and whether the data format meets the template requirements. If any required fields are missing, an error message is returned to the user. If the data format does not match, format conversion is performed, such as converting a string date to a date format. Finally, a dataset suitable for the corresponding data type is generated to populate the components in the subsequent report.

[0147] Specifically, this embodiment obtains the corresponding report data by calling the report statistics function interface of the artificial intelligence platform. The interface data is obtained by calling the corresponding interface in sequence according to the data type defined in the report.

[0148] Field integrity check: First, when defining the template, the required fields are marked. The system automatically compares the fields of the pulled dataset with the list of required fields. If there are missing fields, a prompt mechanism is immediately triggered - a warning window pops up on the interface, displaying: "Missing field: "Field name". This field is a core indicator of "Template name" and may affect the completeness of the report." It also provides "Complete Pull" (request data containing missing fields) or "Ignore and Continue" (mark missing fields).

[0149] Record integrity check: For time range type data, check the continuity and coverage of time series. Pull data by timestamp sorting, calculate the time interval of adjacent records, and if the interval exceeds the preset threshold (default 15 minutes), judge as "time segment missing".

[0150] For numerical fields, a reasonable value range is preset. For text fields, a legal format is preset, such as IP address matching according to regular expressions. Based on business logic, the constraint relationship of associated fields is preset, such as cluster accelerator card number = accelerator card used number + accelerator card available number. Real-time calculation is performed on the associated fields. If the equation is not established, it is marked as "logical exception".

[0151] For time series type data, linear interpolation method is used for filling, such as missing 10:00 data, which is filled with the average value of 9:00 to 11:00 data. For statistical data, the average or median of the last 7 days is used for filling. If the missing data ratio exceeds the preset threshold (default 10%, configurable), the system will pause processing and give a prompt, and the user can choose "continue processing", "re-pull data" or "manual input".

[0152] This embodiment can but not limited to use PDF generation library itext (an open source Java library for PDF document generation and operation) to build PDF document structure, and draw labels, tables and charts in PDF pages according to the element layout defined in the template. The specific cases are as follows:

[0153] For label elements, the itext library Paragraph object is used to generate label elements in PDF documents, and abnormal elements are marked.

[0154] For table elements, the itext library Table object is used to fill the parsed interface data to generate table elements in PDF documents, and the elements marked as abnormal are highlighted in cells.

[0155] For chart elements, according to the defined chart type, the parsed interface data is passed to the corresponding chart object through the JFreeChart library (an open source Java library for generating various statistical charts) to generate the corresponding chart (bar chart, line chart, pie chart), and the abnormal data points are set with special symbols and colors. Then the chart object is converted into a picture, which is used as an itext picture element for display.

[0156] After completing the construction of the document, the entire PDF document object is output and saved to the specified server directory for users to download and view through the interface.

[0157] This embodiment implements scheduled task management based on an intelligent task scheduling framework. Users configure the scheduled task's basic information (task name, period, time, etc.), the associated report template ID, and data range parameters on the interface. The system then stores this information in the task database. The scheduling framework periodically scans the tasks in the database. When the task execution time arrives, it triggers the execution thread, invoking the interfaces of the dynamic dataset acquisition and analysis module and the adaptive report generation and export module to complete report generation. Upon task completion, a notification message containing the report storage address and any exceptions (if any) is sent according to the user-configured notification method.

[0158] The definition of scheduled tasks is shown in Table 2:

[0159] Table 2

[0160]

[0161] It can be understood that this embodiment supports a highly customized intelligent report template definition mechanism, allowing users to flexibly configure report element types (labels, tables, bar charts, line charts, pie charts, etc.), data set sources and intelligent analysis rules (such as indicator threshold anomaly annotation), breaking through the limitations of traditional fixed templates and meeting personalized reporting needs in different scenarios.

[0162] This embodiment automatically acquires data through an interface and performs integrity (field, time series) and accuracy (numeric range, format, business logic) verification. It uses preset rules (such as linear interpolation and mean filling) to process missing or abnormal data, and combines intelligent analysis rules to identify data trends and anomalies, generate analysis tags, and improve data reliability and analysis depth.

[0163] This embodiment automatically fills in data based on the template, uses differentiated display for abnormal information (special colors for table cells, special symbols for abnormal points in charts), and automatically optimizes the layout based on the data volume and layout, and finally exports it to PDF format, achieving the aesthetics of the report and the efficiency of information transmission.

[0164] This embodiment supports custom report generation cycles (weekly / monthly / yearly), time, and templates. The core feature is that it can automatically adjust task execution times to avoid high-load periods based on the cluster's historical load data. At the same time, it can push reports and exception prompts in a timely manner through a notification mechanism (email, platform messages), thereby improving the stability of scheduled tasks and the efficiency of operation and maintenance response.

[0165] This embodiment deeply integrates the four modules of template definition, data processing, report generation, and timing management to form a full-process automation solution from "user needs" to "intelligent report output", solving the problems of low efficiency of traditional manual production, poor flexibility of fixed tools, and weak abnormality perception.

[0166] To further illustrate, the processing flow of a method for generating an artificial intelligence platform cluster operation report is as follows: Figure 4 As shown, the specific steps include: the user or scheduled task, as the trigger for report generation, first initiates a request to the report template to obtain the report template. This step determines the basic framework, including the report format and structure. After the report template returns the result, the user or scheduled task calls the report service interface to obtain the data required for report generation. After receiving the relevant request, the report service populates the report data into the report data, preparing the data content for subsequent full report generation. After receiving the result of the data populated by the report service, the report template interacts with the document generation process to assemble the report template and the populated data to generate the document. Finally, the document generation process returns the document information (and document status) to the user or scheduled task, informing the user or scheduled task of the report generation result (success or failure, as well as the generated document).

[0167] Through the embodiments provided in this application, the efficiency and quality of report generation are greatly improved: user template definition time is reduced through intelligent template recommendation, dynamic data acquisition and analysis eliminates the need for manual analysis, and adaptive typesetting ensures the aesthetics of the report, which greatly improves the overall efficiency and quality of report generation, allowing operation and maintenance personnel to focus on problem solving. Significantly enhance report flexibility: dynamic data analysis can automatically identify key information, making reports more in line with actual needs and possessing intelligent decision-making support capabilities. Effectively guarantee report accuracy and anomaly perception capabilities: data is directly acquired from the platform to reduce manual errors, and intelligent analysis rules can promptly detect data anomalies and highlight them, making it easier for users to quickly detect cluster operation problems. Realize intelligent optimization of scheduled report generation: intelligent scheduled task management can automatically avoid high-load periods of the cluster to ensure smooth task execution, while providing timely reminders for abnormal reports to improve operation and maintenance response speed.

[0168] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present application.

[0169] A cluster state evaluation apparatus is also provided in the embodiments, which is configured to implement the above embodiments and preferred embodiments, and details of which have been described above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0170] Figure 5 is a structural block diagram of a cluster state evaluation apparatus according to an embodiment of the present application, as shown in Figure 5 the apparatus comprises:

[0171] a first obtaining unit 502 configured to obtain a state evaluation request triggered for a cluster, wherein the state evaluation request carries a template request parameter and a data request parameter;

[0172] a second obtaining unit 504 configured to obtain evaluation data of the cluster based on the data request parameter, wherein the evaluation data is cluster data that meets a data range indicated by the data request parameter;

[0173] a third obtaining unit 506 configured to obtain a plurality of evaluation elements included in an evaluation template in a case where the evaluation template matching the template request parameter is obtained, wherein one evaluation element corresponds to one evaluation style;

[0174] an evaluation unit 508 configured to generate a plurality of evaluation results for the evaluation data according to each evaluation style, wherein one evaluation result corresponds to one evaluation element, and the plurality of evaluation results are used to evaluate a running state of the cluster.

[0175] As an optional solution, the apparatus further comprises:

[0176] a consolidation module configured to consolidate the plurality of evaluation results after the plurality of evaluation results are generated for the evaluation data according to each evaluation style, to obtain an evaluation report used to evaluate the running state of the cluster, wherein a first arrangement order of the plurality of evaluation results on the evaluation report corresponds to a second arrangement order of the plurality of evaluation elements on the evaluation template.

[0177] As an optional solution, the apparatus further comprises:

[0178] a display module configured to display first evaluation content in the evaluation report in a first display style and second evaluation content in the evaluation report in a second display style after the plurality of evaluation results are consolidated to obtain the evaluation report used to evaluate the running state of the cluster, wherein the first evaluation content is evaluation content in the plurality of evaluation results that meets an expected abnormal condition, the second evaluation content is evaluation content in the plurality of evaluation results other than the first evaluation content, and the first display style is different from the second display style.

[0179] As an optional solution, the apparatus further comprises:

[0180] The first obtaining module is configured to obtain a parameter type and a parameter threshold associated with the parameter type carried in the state evaluation request before displaying the first evaluation content in the evaluation report in the first display style and displaying the second evaluation content in the evaluation report in the second display style;

[0181] The first determining module is configured to determine an expected abnormal condition according to the parameter type and the parameter threshold before displaying the first evaluation content in the evaluation report in the first display style and displaying the second evaluation content in the evaluation report in the second display style, wherein the expected abnormal condition is used to indicate that the parameter type is greater than the parameter threshold;

[0182] The second determining module is configured to determine the first evaluation content meeting the expected abnormal condition from the plurality of evaluation results before displaying the first evaluation content in the evaluation report in the first display style and displaying the second evaluation content in the evaluation report in the second display style.

[0183] As an optional solution, the evaluation unit 508 comprises at least one of the following:

[0184] The first evaluation module is configured to generate a label evaluation result corresponding to a label evaluation element for the evaluation data in a case where the plurality of evaluation elements comprises the label evaluation element, and the plurality of evaluation results comprises the label evaluation result;

[0185] The second evaluation module is configured to generate a table evaluation result corresponding to a table evaluation element for the evaluation data in a case where the plurality of evaluation elements comprises the table evaluation element, and the plurality of evaluation results comprises the table evaluation result;

[0186] The third evaluation module is configured to generate a graph evaluation result corresponding to a graph evaluation element for the evaluation data in a case where the plurality of evaluation elements comprises the graph evaluation element, and the plurality of evaluation results comprises the graph evaluation result.

[0187] As an optional solution, the third evaluation module comprises at least one of the following:

[0188] The first evaluation submodule is configured to generate a line graph evaluation result corresponding to a line graph element for the evaluation data in a case where the graph evaluation element is the line graph element, and the graph evaluation result comprises the line graph evaluation result;

[0189] The second evaluation submodule is configured to generate a pie chart evaluation result corresponding to a pie chart element for the evaluation data in a case where the graph evaluation element is the pie chart element, and the graph evaluation result comprises the pie chart evaluation result;

[0190] The third evaluation submodule is configured to generate a histogram evaluation result corresponding to the histogram element for the evaluation data when the graphic evaluation element is a histogram element, wherein the graphic evaluation result includes the histogram evaluation result.

[0191] As an optional solution, the second obtaining unit 504 includes:

[0192] A third determining module is configured to, when the data request parameter includes a time request sub-parameter, determine the first data generated by the cluster within a time range indicated by the time request sub-parameter as the evaluation data; or

[0193] The fourth determining module is configured to determine, when the data request parameter includes a node request sub-parameter, second data generated by a target node indicated by the node request sub-parameter in the cluster as evaluation data.

[0194] As an optional solution, the second obtaining unit 504 further includes:

[0195] The fifth determining module is configured to determine, when the data request parameter includes a time request sub-parameter and a node request sub-parameter, third data generated by the target node in the cluster within a time range as evaluation data.

[0196] As an optional solution, the device further includes:

[0197] a combining module, configured to, before obtaining a status evaluation request triggered on a cluster, sequentially combine at least two evaluation elements to generate at least two evaluation templates, wherein different evaluation templates contain different numbers of evaluation elements or different combinations of the evaluation elements;

[0198] An establishment module is used to establish a one-to-one mapping relationship between at least two template request parameters and at least two evaluation templates before obtaining a status evaluation request triggered on the cluster;

[0199] The device also includes:

[0200] The sixth determining module is configured to determine, from the mapping relationship, an evaluation template that matches the template request parameters before obtaining the multiple evaluation elements included in the evaluation template.

[0201] As an optional solution, the device further includes:

[0202] A second acquisition module is configured to, when the status evaluation request carries a period request parameter and the template request parameter only includes a first template request parameter, obtain a first evaluation template that matches the first template request parameter, and obtain a plurality of first evaluation elements included in the first evaluation template;

[0203] The first generating module is configured to generate a plurality of first evaluation results corresponding to a plurality of first evaluation elements for the evaluation data multiple times according to the number of cycles indicated by the cycle request parameter.

[0204] As an optional solution, the device further includes:

[0205] a third acquisition module, configured to, when the status assessment request carries a period request parameter and the template request parameter includes a first template request parameter and a second template request parameter, obtain a first assessment template matched by the first template request parameter and a plurality of first assessment elements included in the first assessment template, and obtain a second assessment template matched by the second template request parameter and a plurality of second assessment elements included in the second assessment template;

[0206] The second generation module is used to generate multiple first evaluation results corresponding to multiple first evaluation elements and multiple second evaluation results corresponding to multiple second evaluation elements for the evaluation data multiple times within the number of cycles indicated by the cycle request parameter, wherein the evaluation results corresponding to every two times in the number of cycles are multiple first evaluation results and second evaluation results.

[0207] As an optional solution, the device further includes:

[0208] A verification module is used to verify the evaluation data after obtaining the evaluation data of the cluster based on the data request parameters;

[0209] A fourth acquisition module is configured to, after acquiring the evaluation data of the cluster based on the data request parameters, acquire a proportion of the missing data in the evaluation data if it is determined through data verification that the evaluation data includes missing data;

[0210] The filling module is used to obtain the evaluation data of the cluster based on the data request parameters, and then perform data filling operations that match the type of missing data when the proportion is less than a preset proportion threshold.

[0211] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail here.

[0212] Those skilled in the art can clearly understand that the method according to the above-mentioned embodiments can be realized by means of software on a general hardware platform, and of course, can also be realized by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk) and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device) to execute the methods of the various embodiments of the present application.

[0213] It should be noted that the above-mentioned modules can be realized by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: all the modules are located in the same processor; or the modules are located in different processors in any combination.

[0214] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is configured to execute the steps in any of the above-mentioned method embodiments when running.

[0215] In an example embodiment, the above-mentioned computer readable storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0216] The embodiments of the present application also provide an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program to execute the steps in any of the above-mentioned method embodiments.

[0217] In an example embodiment, the above-mentioned electronic device can also include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0218] The embodiments of the present application also provide a computer program product, which includes a non-volatile computer readable storage medium, the non-volatile computer readable storage medium stores a computer program product, and the computer program is executed by a processor to realize the steps in the method of the various embodiments of the present application.

[0219] The specific examples in the present embodiment can refer to the examples described in the above-mentioned embodiments and example embodiments, which will not be described herein again.

[0220] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0221] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A cluster status assessment method, characterized in that: include: Obtaining a status assessment request triggered on the cluster, wherein the status assessment request carries a template request parameter and a data request parameter; Based on the data request parameter, obtaining evaluation data of the cluster, wherein the evaluation data is cluster data that meets the data range indicated by the data request parameter; When an evaluation template matching the template request parameters is obtained, multiple evaluation elements included in the evaluation template are obtained, wherein one evaluation element corresponds to one evaluation style; According to each evaluation pattern, a plurality of evaluation results are generated for the evaluation data, wherein one evaluation result corresponds to one evaluation element, and the plurality of evaluation results are used to evaluate the operating status of the cluster.

2. The method according to claim 1, characterized in that After generating a plurality of evaluation results for the evaluation data according to each evaluation pattern, the method further includes: The multiple evaluation results are integrated to obtain an evaluation report for evaluating the operating status of the cluster, wherein a first arrangement order of the multiple evaluation results on the evaluation report corresponds to a second arrangement order of the multiple evaluation elements on the evaluation template.

3. The method according to claim 2, characterized in that After integrating the multiple evaluation results to obtain an evaluation report for evaluating the operating status of the cluster, the method further includes: The first evaluation content in the evaluation report is displayed in a first display style, and the second evaluation content in the evaluation report is displayed in a second display style, wherein the first evaluation content is the evaluation content in the multiple evaluation results that meets the expected abnormal conditions, and the second evaluation content is the evaluation content in the multiple evaluation results other than the first evaluation content, and the first display style is different from the second display style.

4. The method according to claim 3, characterized in that Before displaying the first evaluation content in the evaluation report in the first display style and displaying the second evaluation content in the evaluation report in the second display style, the method further includes: Obtaining a parameter type carried in the status assessment request and a parameter threshold associated with the parameter type; determining the expected abnormal condition according to the parameter type and the parameter threshold, wherein the expected abnormal condition is used to indicate that the parameter type is greater than the parameter threshold; The first evaluation content that meets the expected abnormal condition is determined from the multiple evaluation results.

5. The method according to claim 1, wherein Generating multiple evaluation results for the evaluation data according to each evaluation pattern includes at least one of the following: In a case where the plurality of evaluation elements include a label evaluation element, generating a label evaluation result corresponding to the label evaluation element for the evaluation data, the plurality of evaluation results including the label evaluation result; In a case where the plurality of evaluation elements include a table evaluation element, generating a table evaluation result corresponding to the table evaluation element for the evaluation data, the plurality of evaluation results including the table evaluation result; In a case where the plurality of evaluation elements include a graphic evaluation element, a graphic evaluation result corresponding to the graphic evaluation element is generated for the evaluation data, and the plurality of evaluation results include the graphic evaluation result.

6. The method according to claim 5, characterized in that In the case where the multiple evaluation elements include a graphic evaluation element, generating a graphic evaluation result corresponding to the graphic evaluation element for the evaluation data includes at least one of the following: In a case where the graphic evaluation element is a line graph element, generating a line graph evaluation result corresponding to the line graph element for the evaluation data, the graphic evaluation result including the line graph evaluation result; In a case where the graphic evaluation element is a pie chart element, generating a pie chart evaluation result corresponding to the pie chart element for the evaluation data, the graphic evaluation result including the pie chart evaluation result; In a case where the graphic evaluation element is a bar chart element, a bar chart evaluation result corresponding to the bar chart element is generated for the evaluation data, and the graphic evaluation result includes the bar chart evaluation result.

7. The method according to claim 1, characterized in that The acquiring, based on the data request parameters, the evaluation data of the cluster includes: In a case where the data request parameter includes a time request sub-parameter, determining the first data generated by the cluster within the time range indicated by the time request sub-parameter as the evaluation data; or In a case where the data request parameter includes a node request sub-parameter, second data generated by a target node indicated by the node request sub-parameter in the cluster is determined as the evaluation data.

8. The method according to claim 7, characterized in that The acquiring the evaluation data of the cluster based on the data request parameter further includes: In a case where the data request parameter includes the time request sub-parameter and the node request sub-parameter, third data generated by the target node in the cluster within the time range is determined as the evaluation data.

9. The method according to any one of claims 1 to 8, characterized in that Before obtaining the status assessment request triggered on the cluster, the method further includes: At least two evaluation elements are sequentially combined to generate at least two evaluation templates, wherein different evaluation templates contain different numbers of evaluation elements or different combinations of the evaluation elements; Establishing a one-to-one mapping relationship between at least two template request parameters and the at least two evaluation templates; Before obtaining the multiple evaluation elements included in the evaluation template, the method further includes: The evaluation template that matches the template request parameters is determined from the mapping relationship.

10. The method according to any one of claims 1 to 8, characterized in that The method further comprises: In a case where the status assessment request carries a period request parameter and the template request parameter includes only a first template request parameter, obtaining a first assessment template that matches the first template request parameter, and obtaining a plurality of first assessment elements included in the first assessment template; A plurality of first evaluation results corresponding to the plurality of first evaluation elements are generated for the evaluation data multiple times according to the number of cycles indicated by the cycle request parameter.

11. The method according to any one of claims 1 to 8, characterized in that The method further comprises: When the status assessment request carries a periodic request parameter and the template request parameter includes a first template request parameter and a second template request parameter, obtaining a first assessment template matched by the first template request parameter and a plurality of first assessment elements included in the first assessment template, and obtaining a second assessment template matched by the second template request parameter and a plurality of second assessment elements included in the second assessment template; At the number of cycles indicated by the cycle request parameter, multiple first evaluation results corresponding to the multiple first evaluation elements and multiple second evaluation results corresponding to the multiple second evaluation elements are generated for the evaluation data multiple times, wherein the evaluation results corresponding to every two times in the number of cycles are the multiple first evaluation results and the second evaluation results.

12. The method according to any one of claims 1 to 8, characterized in that After obtaining the evaluation data of the cluster based on the data request parameters, the method further includes: Performing data verification on the evaluation data; When it is determined through the data verification that the evaluation data includes missing data, obtaining a proportion of the missing data in the evaluation data; When the proportion is less than a preset proportion threshold, a data filling operation matching the type of the missing data is performed according to the type of the missing data.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 12 are implemented.

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

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