Information presentation method

Abstract

The present disclosure provides an information display method, which comprises: obtaining target requirement information indicating an information display task; analyzing the target requirement information to obtain key information of a first to-be-processed indicator; configuring the first to-be-processed indicator according to the key information and indicator metadata of a plurality of preset indicators to obtain target indicator configuration information of the first to-be-processed indicator; generating indicator view data according to the target indicator configuration information; and rendering a page based on the indicator view data to display a visual chart corresponding to the first to-be-processed indicator. The target requirement information is automatically parsed and the adaptive indicator configuration and visual chart are generated, thereby improving the accuracy and efficiency of the indicator configuration, reducing the use threshold and technical cost of data visualization, improving the convenience and efficiency of information display, and maintaining the consistency between the display content and the target requirement information.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and in particular to an information display method. Background Technology

[0002] In the era of data-driven decision-making, businesses and organizations need to quickly extract key information from massive amounts of data to support operations, analysis, and decision-making. However, raw data is often complex in structure and lacks intuitiveness, making it difficult for business personnel to understand directly. To solve this problem, data visualization technology has emerged.

[0003] However, to achieve data visualization, relevant personnel need a deep understanding of the definitions and relationships of related database tables to complete the entire process from data extraction to visualization. This high technical barrier leads to a severe disconnect between business and development. Business personnel, lacking database knowledge, cannot extract data independently and must repeatedly rely on the technical team to write query scripts. This not only lengthens the response time to requirements but also causes distorted visualization results due to misunderstandings in the transmission of requirements. Furthermore, changes to the database structure (such as field renaming) require simultaneous modifications to all related queries, resulting in high maintenance costs and a high risk of system failures. Ultimately, this hinders the agility and accuracy of data-driven decision-making. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] One aspect of this disclosure proposes an information display method that can directly and accurately extract key information of the indicators to be processed based on actual display needs, avoiding interference from irrelevant information in the indicator processing process and ensuring the relevance of indicator analysis. By combining preset indicator metadata for information configuration, indicator definitions can be standardized, data standards can be unified, repetitive configuration work and human configuration errors can be reduced, and the standardization and efficiency of indicator configuration can be improved. Based on the configuration information, indicator view data can be automatically generated and page rendering can be completed, which can quickly build intuitive and clear visualization charts, realize the visualization and real-time display of data, and eliminate the need for users to understand the underlying data table structure and field definitions. This reduces the threshold and technical cost of using data visualization, improves the convenience and efficiency of information display, and maintains the consistency between the displayed content and the requirements.

[0006] The first aspect of this disclosure provides an information display method, the method comprising:

[0007] Obtain the target requirement information used to instruct the execution of the information display task; The target demand information is analyzed to obtain the key information of the first indicator to be processed; Based on the key information and the metadata of multiple preset indicators, the first indicator to be processed is configured to obtain the target indicator configuration information of the first indicator to be processed. Generate indicator view data based on the target indicator configuration information; The page is rendered based on the indicator view data to display a visual chart corresponding to the first indicator to be processed.

[0008] A second aspect of this disclosure provides an information display device, the device comprising: The first acquisition module is used to acquire target requirement information for instructing the execution of information display tasks; The first analysis module is used to analyze the target demand information to obtain key information of the first indicator to be processed; The configuration module is used to configure the first indicator to be processed based on the key information and the indicator metadata of multiple preset indicators, so as to obtain the target indicator configuration information of the first indicator to be processed. The generation module is used to generate indicator view data based on the target indicator configuration information; The first processing module is used to render the page based on the indicator view data to display the visualization chart corresponding to the first indicator to be processed.

[0009] A third aspect of this disclosure provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the information display method as proposed in the first aspect of this disclosure.

[0010] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the information display method as proposed in the first aspect of this disclosure.

[0011] A fifth aspect of this disclosure provides a computer program product that, when instructions in the computer program product are executed by a processor, performs an information display method as described in a first aspect of this disclosure.

[0012] The technical solutions provided by the above embodiments of this disclosure bring at least the following beneficial effects: The process involves: acquiring target requirement information to instruct the execution of information display tasks; analyzing the target requirement information to obtain key information for the first indicator to be processed; configuring the first indicator to be processed based on the key information and the indicator metadata of multiple preset indicators to obtain the target indicator configuration information for the first indicator to be processed; generating indicator view data based on the target indicator configuration information; and rendering the page based on the indicator view data to obtain a visual chart. This allows for the precise extraction of key information for the indicators to be processed based on actual display needs, avoiding interference from irrelevant information and ensuring the relevance of indicator analysis. By combining information configuration with preset indicator metadata, indicator definitions can be standardized, data standards unified, and repetitive configuration work and human error reduced, improving the standardization and efficiency of indicator configuration. Automatic generation of indicator view data and page rendering based on configuration information enables the rapid construction of intuitive and clear visual charts, achieving data visualization and real-time display without requiring users to understand the underlying data table structure and field definitions, lowering the barrier to entry and technical cost of data visualization, improving the convenience and efficiency of information display, and maintaining consistency between the displayed content and requirements. Furthermore, leveraging the powerful language understanding and computational capabilities of a large language model, the process achieves... The system intelligently analyzes and extracts key information from target requirements, automatically matching predefined indicator analysis metadata to determine the configuration information for target indicators. This eliminates the need for manual mapping between requirements and indicators, improving the efficiency and accuracy of indicator configuration, reducing the professional requirements for relevant personnel, and effectively minimizing configuration errors caused by human misunderstanding. Furthermore, it automatically detects anomalies based on the target values ​​of the indicators to be processed, quickly and accurately identifying abnormal values ​​to facilitate real-time monitoring of data quality and early warning of problems. When abnormal values ​​are found, it can also automatically perform anomaly attribution analysis, directly locating the root cause of the anomaly without requiring manual investigation, improving the efficiency and accuracy of problem localization, and providing a reliable basis for subsequent data correction, process optimization, and decision adjustments.

[0013] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0014] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart illustrating an information display method provided in an embodiment of the present disclosure; Figure 2 This is a flowchart illustrating another information display method provided in an embodiment of the present disclosure; Figure 3 This is a flowchart illustrating another information display method provided in an embodiment of the present disclosure; Figure 4 This is a flowchart illustrating another information display method provided in an embodiment of the present disclosure; Figure 5 This is a flowchart illustrating another information display method provided in an embodiment of the present disclosure; Figure 6 This is a schematic diagram of the structure of an information display device provided in an embodiment of the present disclosure; Figure 7 This is a schematic diagram of the structure of an electronic device shown in an exemplary embodiment of the present disclosure. Detailed Implementation

[0015] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0016] It should be noted that the acquisition, storage, use, and processing of data in this disclosed technical solution comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0017] It should also be noted that the information (including but not limited to user personal information, transaction information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0018] As enterprises comprehensively deepen the application of DevOps systems, the entire R&D process generates various types of data, covering projects, products, iterations, functional requirements, code submissions, test cases, defects, pipeline execution, work hours, personnel, and quality and safety aspects. This data is scattered across different systems, leading to difficulties for R&D teams in quantifying and evaluating performance, accurately identifying bottlenecks, and lacking reliable basis for decision-making. For example, it's impossible to quickly identify the specific reasons for delayed requirement delivery, or to issue timely warnings when defect rates suddenly rise, and managers struggle to intuitively understand the team's overall performance.

[0019] "Performance Insights" helps R&D teams intuitively grasp the actual situation of R&D performance. The current technology used is as follows: the front end uses the Vue framework and ECharts components to realize data visualization, provides data services by calling API (Application Programming Interface), and relies on manual configuration (including selecting datasets, chart types, indicator dimensions, indicator names, etc.) to complete the management of indicators and views.

[0020] However, its shortcomings include: First, the operation threshold is relatively high.

[0021] Manual configuration requires users to be thoroughly familiar with the definitions of DevOps database tables and their relationships, such as the binding logic between defect data and project dimensions, and the binding relationship between test cases and functional requirements. Ordinary business personnel, lacking understanding of DevOps database table definitions, must rely on technical personnel for support, leading to inefficient requirement response.

[0022] Second, there is insufficient exploitation of the value of data.

[0023] It only provides data visualization and lacks intelligent analysis capabilities such as anomaly detection and trend prediction. For example, it cannot automatically identify problems such as sudden increases in pipeline execution time or abnormal fluctuations in defect rates. The data remains at the "viewing" level and is insufficient to support in-depth performance optimization decisions.

[0024] In response to at least one of the aforementioned problems, this disclosure proposes an information display method.

[0025] The information display method of this disclosure embodiment is described below with reference to the accompanying drawings.

[0026] Figure 1 This is a flowchart illustrating an information display method provided in an embodiment of the present disclosure.

[0027] This disclosure illustrates the example of the information display method being configured in an information display device, which can be applied to any electronic device to enable the electronic device to perform information display functions.

[0028] Among them, electronic devices can be any device with computing capabilities, such as personal computers, mobile terminals, servers, etc. Mobile terminals can be hardware devices with various operating systems, touch screens and / or displays, such as mobile phones, tablets, personal digital assistants, wearable devices, etc.

[0029] like Figure 1 As shown, the information display method includes the following steps S110 to S140: Step S110: Obtain the target requirement information used to instruct the execution of the information display task.

[0030] The target requirement information can be used to instruct the execution of information display tasks; the information display tasks can be used to display relevant information. For example, the target requirement information could be "the correlation between the project defect rate and code submissions in the past 3 months".

[0031] In this embodiment of the disclosure, target requirement information for instructing the execution of an information display task can be obtained. As an example, target requirement information for instructing the execution of an information display task can be obtained in response to user input. The forms of input include, but are not limited to, keyboard input, voice input, mouse click input, etc. As another example, a target image can be acquired and image recognition can be performed on the target image to obtain the target requirement information for displaying the target image.

[0032] Step S120: Analyze the target demand information to obtain the key information of the first indicator to be processed.

[0033] The first metric to be processed refers to the metric object that can be used for information display, data presentation, or visual analysis. For example, it could be the defect rate, code submission volume, etc.

[0034] In order to obtain key information, in one possible implementation of the embodiments of this disclosure, such as Figure 2 As shown, the following steps S121 to S123 can be used: Step S121: Obtain indication information; wherein, the indication information is used to indicate the key information to be extracted.

[0035] The indication information can be used to indicate key information related to the first indicator to be processed. The indication information may include, but is not limited to: dataset, indicator name, indicator dimension, relationship type, chart type, time information, etc.

[0036] As an example, instruction information can be pre-set and saved to a relevant database, so that after obtaining the target requirement information, the instruction information can be directly retrieved from the relevant database.

[0037] As another example, user input can be responded to to obtain user input instructions. It should be noted that the explanations of input operations in any embodiment of this disclosure also apply to this embodiment, and will not be repeated here.

[0038] Step S122: Fill the instruction information and target requirement information into the corresponding positions of the first prompt word template to obtain the first prompt word.

[0039] As an example, suppose the indication information includes dataset, metric name, relationship type, chart type, and time information, and the target requirement is "correlation between project defect rate and code submission in the past 3 months". Filling the above information into the first prompt word template yields the following first prompt word:

[0040] Step S123: Input the first prompt word into the large language model and obtain the key information in response to the output of the large language model.

[0041] In this embodiment of the disclosure, the first prompt word can be input into an LLM (Large Language Model), the LLM can analyze the first prompt word, and in response to the output of the model, information corresponding to the indication information can be obtained, and this information can be used as key information.

[0042] Using the example above, the model output might be: JSON: Dataset: Metrics Names: Defect Rate, Code Submissions Metrics Dimensions: Project Chart type: Time information: the past 3 months Relationship type: Association Among them, "defect rate", "code submissions", "projects", "last 3 months", and "relationships" can be used as key information.

[0043] Therefore, by first obtaining instructional information to clarify the direction of key information extraction, and then generating prompt words based on the key information and inputting them into the LLM, the model can be guided to output key information according to the specified intention, thereby improving the targeting and accuracy of extraction, reducing interference from irrelevant information, and simplifying the prompt word construction process, thus improving the efficiency and controllability of key information extraction.

[0044] Step S130: Based on the key information and the metadata of multiple preset indicators, configure the information of the first indicator to be processed to obtain the target indicator configuration information of the first indicator to be processed.

[0045] The target indicator configuration information can be used to define various rules for the calculation, statistics, filtering, aggregation, and display of the first indicator to be processed. It should be noted that the target indicator configuration information may include, but is not limited to: the indicator information of the first indicator to be processed, the chart type corresponding to the first indicator to be processed, the relationship type between the first indicators to be processed, etc.

[0046] The indicator information may include, but is not limited to: the attribute information of the first indicator to be processed (such as indicator name, indicator dimension, time range, etc.), the calculation logic corresponding to the first indicator to be processed, the dataset from which the first indicator to be processed originates, etc.

[0047] The dataset can be used to store raw data related to the first metric to be processed. For example, for the first metric to be processed named "defect rate", the related dataset could be a "defect quantity dataset" or a "defect rate dataset", where the defect quantity data can be used to store the defect quantity and the defect rate dataset can be used to store the defect rate. For the first metric to be processed named "code submission quantity", the related dataset could be a "code submission dataset", where the code submission dataset can be used to store the code submission quantity.

[0048] The chart type can be used to indicate the visualization method used to perform the display task. It can be a graph, such as a line chart, scatter plot, bar chart, pie chart, area chart, etc., or it can be a data table or other forms.

[0049] Among them, the relationship type can be used to indicate the relationship between multiple first indicators to be processed.

[0050] The computational logic can be used to indicate the operational rules, formulas, or processing procedures used to calculate the corresponding first indicator to be processed.

[0051] Among them, the preset indicators can be pre-set.

[0052] Among them, the indicator metadata can be a predefined data set that includes information related to the corresponding preset indicator. This data set may include, for example, the specific values ​​of the corresponding preset indicator under multiple fields such as indicator name, calculation logic, data source, indicator dimension, time range, data type, and indicator description information.

[0053] The indicator description information can be used to describe the preset indicators corresponding to the corresponding indicator metadata.

[0054] It is understandable that in actual data processing and application scenarios, due to the diversity of data sources and the complexity of data definitions, a situation may arise where the extracted key information cannot be completely matched with the fields corresponding to the values ​​in the metadata of multiple preset indicators. For example, when the extracted key information is "defect rate," the metadata of the existing metadata of multiple preset indicators may not directly define metadata for a specific indicator named "defect rate," but rather define metadata for a related indicator with a slightly different name, such as metadata for an indicator named "defect data." Therefore, in this embodiment of the disclosure, in order to configure the information of the first indicator to be processed, in one example, the metadata of multiple preset indicators can be retrieved based on the key information to obtain the indicator metadata that matches the key information, and the target indicator configuration information can be determined based on the indicator metadata.

[0055] To obtain the target indicator configuration information for the first indicator to be processed, one possible implementation method is as follows: Figure 3 As shown, the following steps S131 to S133 can be used: Step S131: For any preset indicator, determine the matching degree information between the first indicator to be processed and the preset indicator based on the key information and the indicator metadata of the preset indicator.

[0056] The matching degree information can be used to indicate the degree of matching between the first indicator to be processed and the preset indicator. It should be noted that the matching degree information can include, but is not limited to, numerical matching degree values ​​and matching degree levels. Specifically, a scoring system can be used to represent the numerical matching degree of the statement; that is, in a scoring system, the matching degree information can be quantified into a specific numerical value with a certain range, such as 0 to 100, 0 to 10, etc. Alternatively, a rating system can be used to represent the rating degree of the statement; that is, in a rating system, the matching degree information can be divided into several discrete levels, such as "high," "medium," and "low," or using numerical levels such as 1, 2, 3, etc., or letter levels such as "A+," "A," "A-," "B+," etc.

[0057] As one possible implementation, such as Figure 4 As shown, the following steps S1311 to S1314 can be used: Step S1311: For any preset indicator, extract the target indicator information from the indicator metadata of the preset indicator.

[0058] The target indicator information can be used to indicate preset indicators. For example, the target indicator information can be the indicator name, indicator description, etc. of the preset indicator.

[0059] As an example, for any preset indicator, if the indicator metadata of the preset indicator includes the indicator name of the preset indicator, the indicator name of the preset indicator can be extracted from the indicator metadata of the preset indicator and used as the target indicator information.

[0060] As another example, for any preset indicator, if the indicator metadata of the preset indicator includes the indicator description information of the preset indicator, the indicator description information of the preset indicator can be extracted from the indicator metadata of the preset indicator and used as the target indicator information.

[0061] As another example, for any preset indicator, if the indicator metadata of the preset indicator includes the indicator name and indicator description information of the preset indicator, the indicator name and indicator description information of the preset indicator can be extracted from the indicator metadata of the preset indicator and used as the target indicator information.

[0062] Step S1312: Generate a first vector based on the target indicator information, and generate a second vector based on the key information.

[0063] As an example, when the target indicator information is the name of a preset indicator, the indicator name can be input into the text embedding model to obtain the semantic vector of the indicator name output by the text embedding model, and the semantic vector can be used as the first vector; or key information can be input into the text embedding model to obtain the semantic vector of the key information output by the text embedding model, and the semantic vector can be used as the second vector.

[0064] As another example, when the target indicator information is the indicator description information of a preset indicator, the indicator description information of the preset indicator can be input into the text embedding model to obtain the semantic vector of the indicator description information output by the text embedding model, and the semantic vector can be used as the first vector; or key information can be input into the text embedding model to obtain the semantic vector of the key information output by the text embedding model, and the semantic vector can be used as the second vector.

[0065] As another example, when the target indicator information is the indicator name and indicator description information of a preset indicator, the indicator name and indicator description information of the preset indicator can be concatenated to obtain concatenated information. Then, the concatenated information is input into the text embedding model to obtain the semantic vector output by the text embedding model, and this semantic vector is used as the first vector. Alternatively, key information can be input into the text embedding model to obtain the semantic vector of the key information output by the text embedding model, and this semantic vector is used as the second vector.

[0066] Step S1313: Calculate the similarity between the first vector and the second vector to obtain the similarity between the first vector and the second vector.

[0067] As an example, similarity algorithms, such as cosine similarity or Euclidean distance, can be used to calculate the similarity between the first vector and the second vector, thereby obtaining the similarity between the first vector and the second vector.

[0068] Step S1314: Determine the matching degree information between the first indicator to be processed and the preset indicator based on the similarity.

[0069] Among them, similarity can be positively correlated with matching degree, that is, the greater the similarity, the higher the matching degree, and vice versa.

[0070] As an example, the similarity between the first vector and the second vector can be determined as the matching degree information between the first indicator to be processed and the preset indicator.

[0071] As another example, a correspondence between similarity and matching degree can be established in advance. Then, after determining the similarity between the first vector and the second vector, the corresponding matching degree can be determined based on the above correspondence, and the matching degree can be used as the matching degree information between the first indicator to be processed and the preset indicator.

[0072] Therefore, by extracting target indicator information from the metadata of preset indicators and generating vectors for similarity calculation, the key information of the indicator to be processed can be quantitatively matched with the preset indicators, effectively avoiding the subjectivity and error caused by manual comparison and improving the objectivity and accuracy of the matching process. The vector similarity calculation method can efficiently process multi-dimensional and fine-grained target indicator information, realize intelligent matching at the complex semantic and feature levels, and improve matching efficiency and recognition accuracy. Furthermore, the matching degree information is determined based on the calculated similarity, which can provide reliable data support for subsequent target indicator metadata screening and indicator configuration, making indicator matching more in line with actual needs, reducing manual intervention, and helping to improve the adaptability and accuracy of indicator information configuration.

[0073] Step S132: Determine the target indicator metadata based on the matching degree information and the indicator metadata of multiple preset indicators.

[0074] As an example, assuming the matching information includes a matching value, the metadata of the preset metric with the highest matching value can be used as the target metric metadata.

[0075] As an example, assuming the matching information includes matching level, the metadata of the preset metric with the highest matching level can be used as the target metric metadata.

[0076] As another example, assuming the matching information is a matching score value, firstly, multiple preset indicators can be sorted in descending order of matching score value to obtain a sorted sequence. The metadata of the first set number (e.g., 3) preset indicators in the sorted sequence is then used as the first indicator metadata. Next, the first indicator metadata and key information are input into the corresponding positions in the second prompt word template to obtain the second prompt word. For example, the second prompt word is shown below:

[0077] Finally, the second prompt word is input into the large language model, and in response to the output of the large language model, the target metric metadata is obtained.

[0078] As another example, assuming the matching information is a matching level, firstly, multiple preset indicators can be sorted in descending order of matching level to obtain a sorted sequence. The metadata of the second set number (e.g., 3) preset indicators that are ranked first in the sorted sequence is used as the first indicator metadata. Then, the first indicator metadata and key information are input into the corresponding positions of the second prompt word template to obtain the second prompt word. Finally, the second prompt word is input into the large language model, and in response to the output of the large language model, the target indicator metadata is obtained.

[0079] It should be noted that the first and second preset quantities can be preset, and this disclosure does not restrict their values.

[0080] Step S133: Based on the target indicator metadata, determine the target indicator configuration information of the first indicator to be processed.

[0081] As an example, the target metric metadata can be used as the target metric configuration information for the first metric to be processed. For instance, the metric name, calculation logic, dataset indicated by the data source, and metric dimensions (such as project) and time information extracted from the target requirement information can be used as the target metric configuration information for the first metric to be processed.

[0082] Optionally, in some embodiments, when the key information includes a relationship type but not a chart type, the corresponding chart type can be determined based on the relationship type. As an example, a correspondence between relationship types and chart types can be established in advance and saved. Then, when a relationship type is extracted, the above correspondence is queried to determine the chart type corresponding to the relationship type, and the chart type is used as the target indicator configuration information, or the chart type is added to the target indicator configuration information of the first indicator to be processed.

[0083] Therefore, by calculating the matching degree information based on key information and the metadata of each preset indicator, it is possible to achieve accurate matching between the indicator to be processed and the existing indicator system, avoiding deviations caused by subjective judgment. Based on the matching degree, the metadata of the target indicator is filtered out, which can make full use of the definitions of existing standard indicators, maintain the consistency between the indicator to be processed and the overall indicator system, and reduce indicator redundancy and data ambiguity. Then, configuration is completed based on the metadata of the target indicator, which can effectively improve the efficiency, automation level and accuracy of indicator configuration, reduce the cost of manual configuration and maintenance difficulty, and make the subsequently generated indicator view data more standardized and unified, effectively improving the reliability and business applicability of the visualization chart.

[0084] Step S140: Generate indicator view data based on the target indicator configuration information.

[0085] The indicator view data can be the data required for front-end visualization rendering, including but not limited to the chart type of the chart to be displayed, the size of the rendering container, the text, the mapping relationship between the coordinate axis and the value of the first indicator to be processed, the coordinate axis unit, the font used for the coordinate axis name, etc.

[0086] As one possible implementation, if the target metric configuration information includes the attribute information of the first metric to be processed and the target dataset identifier, such as... Figure 5 As shown, the generation of indicator view data can be achieved by using the following steps S141 to S143: Step S141: Based on the attribute information of the first indicator to be processed, query the target dataset indicated by the target dataset identifier to extract the target original data that matches the attribute information of the first indicator to be processed from the original data of the target dataset.

[0087] The target dataset identifier can be used to uniquely identify the target dataset.

[0088] The target dataset can be used to store the raw data related to the first metric to be processed. It should be noted that the target dataset can directly store the values ​​of the first metric to be processed, and use these values ​​as the raw data. For example, the target dataset might store the following values ​​for the first metric to be processed: "Defect Rate": [{"Project": "Project A", "September": 0.22, "October": 1.23, "November": 0.89, "December": 1.05}, {"Project": "Project B", "September": 2.0, "October": 2.56, "November": 2.10, "December": 1.88}, {"Project": "Project C", "September": 3.01, "October": 3.11, "November": 2.78, "December": 2.45} Then the value of the first indicator to be processed mentioned above can be used as the original data; Alternatively, the target dataset can store the values ​​of the calculation factors for the first metric to be processed, and use these values ​​as the raw data. The calculation factors can be used to calculate the value of the first metric to be processed according to a certain calculation logic. For example, assuming the first metric to be processed is the defect rate, which is calculated from the calculation factors "defect rate" and "project workload," then the values ​​of the calculation factors for the first metric to be processed stored in the target dataset include: "Defect Quantity": [{"Project": "Project A", "September": 1, "October": 4, "November": 5, "December": 7}, {"Project": "Project B", "September": 2, "October": 1, "November": 2, "December": 6}, {"Project": "Project C", "September": 3, "October": 0, "November": 1, "December": 2} Project workload [{“Project”: “Project A”, “September”: 1000, “October”: 990, “November”: 890, “December”: 1200}, {"Project": "Project B", "September": 999, "October": 1120, "November": 1222, "December": 890}, {"Project": "Project C", "September": 880, "October": 1000, "November": 1101, "December": 1200} Then the value of the calculation factor of the first indicator to be processed can be used as the original data.

[0089] The attribute information may include, but is not limited to, indicator dimensions and time information.

[0090] As an example, suppose the first metric to be processed is the defect rate, the metric dimension is "project", and the time information is "last 3 months". If the target dataset stores raw data related to the defect rate, then the raw data related to the defect rate of the project within the last 3 months can be extracted from the target dataset, for example: “Project List”: [“Project A”, “Project B”, “Project C”]; "Month List": ["October", "November", "December"]; "Defect Quantity": [{"Project": "Project A", "October": 4, "November": 5, "December": 7}, {"Project": "Project B", "October": 1, "November": 2, "December": 6}, {"Project": "Project C", "October": 0, "November": 1, "December": 2} Project workload [{“Project”: “Project A”, “October”: 990, “November”: 890, “December”: 1200}, {"Project": "Project B", "October": 1120, "November": 1222, "December": 890}, {"Project": "Project C", "October": 1000, "November": 1101, "December": 1200} For example, it could be: “Project List”: [“Project A”, “Project B”, “Project C”]; "Month List": ["October", "November", "December"]; "Defect Rate": [{"Project": "Project A", "October": 1.23, "November": 0.89, "December": 1.05}, {"Project": "Project B", "October": 2.56, "November": 2.10, "December": 1.88}, {"Project": "Project C", "October": 3.11, "November": 2.78, "December": 2.45} Therefore, these data can be identified as the target raw data.

[0091] Step S142: Based on the target original data, perform numerical calculations on the first indicator to be processed to obtain the target value of the first indicator to be processed.

[0092] As one possible implementation, if the target indicator configuration information includes the calculation logic of the first indicator to be processed, the first indicator to be processed can be logically calculated based on the target original data according to the calculation logic of the first indicator to be processed to obtain the target value of the first indicator to be processed. For example, the calculation factors of the first indicator to be processed can be determined from the calculation logic; using the values ​​of the calculation factors in the target original data, the first indicator to be processed is calculated according to the calculation logic to obtain the target value of the first indicator to be processed. For instance, if the first indicator to be processed is the defect rate, and its corresponding calculation logic is (defect quantity / project workload), the calculation factors of the first indicator to be processed, defect quantity and project workload, can be determined from this calculation logic; then, using the values ​​of the defect quantity and project workload in the target original data, the first indicator to be processed is calculated according to the calculation logic to obtain the target value of the first indicator to be processed.

[0093] Therefore, it can automatically follow the calculation logic of the first indicator to be processed, and perform logical calculations based on the filtered target raw data. This effectively avoids interference from irrelevant data and deviations in calculation logic, improves the accuracy and reliability of indicator value determination, and provides a reliable and high-quality data foundation for subsequent indicator view transformation and data analysis decision-making.

[0094] As another possible implementation, when the target raw data is the value of the first indicator to be processed, the target raw data can be directly used as the target value of the first indicator to be processed.

[0095] It is understandable that there may be abnormal situations in the first indicator to be processed, such as sudden changes in the value of the first indicator to be processed or deviations from the normal range. Therefore, in one possible implementation of this disclosure embodiment, the first indicator to be processed is subjected to anomaly detection based on the target value of the first indicator to be processed to determine whether there is an abnormal value in the first indicator to be processed; in response to the existence of an abnormal value in the first indicator to be processed, anomaly attribution analysis is performed on the first indicator to be processed to obtain anomaly attribution results.

[0096] As an example, anomaly detection algorithms, such as the Isolation Forest algorithm and the LOF (Local Outlier Factor) algorithm, can be used to detect anomalies in the target value of the first indicator to be processed, and determine whether there are any abnormal values ​​in the first indicator to be processed. When there are abnormal values ​​in the first indicator to be processed, anomaly attribution analysis strategies can be used to perform anomaly attribution analysis on the first indicator to be processed and obtain the anomaly attribution results.

[0097] As one possible implementation, the anomaly attribution analysis strategy can be as follows: obtain at least one associated indicator related to the first indicator to be processed; determine the correlation coefficient between the first indicator to be processed and any associated indicator; based on the correlation coefficient between the first indicator to be processed and any associated indicator, identify a target associated indicator that is strongly related to the indicator to be processed, and determine the target associated indicator as the anomaly attribution result.

[0098] The correlation coefficient indicates the degree of correlation between the first indicator to be processed and its corresponding related indicator. It should be noted that the larger the absolute value of the correlation coefficient, the stronger the correlation between the first indicator to be processed and its corresponding related indicator.

[0099] To obtain at least one associated metric related to the first pending metric, as an example, a correspondence between the first pending metric and associated metrics can be established in advance and the correspondence can be saved. Then, after the first pending metric is determined, the above correspondence can be queried to determine the corresponding associated metrics. For example, assuming the first pending metric is the defect rate, the associated metrics may include code submissions, requirement changes, number of test cases, number of releases, etc.

[0100] To determine the correlation coefficient between the first indicator to be processed and any associated indicator, as an example, for any associated indicator, the value of the associated indicator at the time when the timestamp matches (or is the same as) the target value of the first indicator to be processed can be obtained; based on the target value of the first indicator to be processed and the value of the associated indicator at the time when the timestamp matches (or is the same as) the target value of the first indicator to be processed, the correlation coefficient between the first indicator to be processed and the corresponding associated indicator is calculated using the Pearson correlation coefficient algorithm, the Spearman rank correlation coefficient algorithm, or the Kendall correlation coefficient algorithm.

[0101] For example, assuming the first metric to be addressed is the defect rate, its corresponding related metrics could include code commit volume, requirement change volume, number of test cases, number of releases, etc. The correlation coefficient between the defect rate and each related metric can be calculated to identify strongly correlated target metrics, which can then be used as the cause of the anomaly. For instance, the anomaly attribution result might be: the defect rate anomaly is mainly strongly correlated with code commit volume and requirement change volume, suggesting that a surge in code commit volume and frequent requirement changes led to the increase in the defect rate.

[0102] Therefore, by introducing related indicators to calculate correlation coefficients for anomaly attribution, on the one hand, we can quantitatively analyze the influence of each related indicator on the first indicator to be processed, avoiding the subjectivity and one-sidedness caused by relying solely on human experience, and improving the accuracy and objectivity of anomaly location; on the other hand, we can quickly screen out strongly correlated indicators from a large number of related indicators, narrowing the scope of anomaly investigation, and achieving efficient and accurate location of the cause of indicator anomalies.

[0103] As another possible implementation, the anomaly attribution analysis strategy can also be as follows: obtain multiple analytical dimensions for statistical analysis of the first indicator to be processed; for any analytical dimension, based on the target original data, group the first indicator to be processed in the analytical dimension to obtain the indicator value of at least one group of the first indicator to be processed in the analytical dimension; based on the indicator value of at least one group of the first indicator to be processed in the analytical dimension, identify the abnormal group in at least one group; based on the abnormal grouping of the first indicator to be processed in each analytical dimension, determine the anomaly attribution result.

[0104] To obtain multiple analytical dimensions for the first indicator to be processed, as an example, a correspondence between the first indicator to be processed and the analytical dimensions can be established in advance and the correspondence can be saved. Then, after the first indicator to be processed is determined, the above correspondence can be queried to determine the corresponding analytical dimensions.

[0105] For example, assuming the first metric to be addressed is the defect rate, its corresponding analysis dimensions could include: project, personnel, department, version, environment, etc. The defect rate is then grouped and statistically analyzed according to these analysis dimensions to identify the target dimension and corresponding abnormal group within the set of outliers. For example: The grouped statistical results are as follows: By project: Project A has a defect rate of 0.32%, while other projects have a defect rate of 0.09%. By employee type: new employee group defect rate 0.40%, experienced employee group 0.07%; By version: V2.0 has a defect rate of 0.35%, while other versions have a defect rate of 0.08%. Based on the above grouping statistics, after grouping by project, it was determined that the outliers were concentrated in project A; after grouping by personnel type, it was determined that the outliers were concentrated in new employees; and after grouping by version, it was determined that the outliers were concentrated in version V2.0. Therefore, the anomaly attribution result can be: the defect rate anomalies are mainly concentrated in project A, the new employee group, and version V2.0, which is speculated to be due to the new version of the project being developed by new employees, leading to an increase in the defect rate.

[0106] Therefore, by grouping and statistically analyzing the indicators to be processed from multiple analytical dimensions and identifying anomalies, it is possible to achieve a refined positioning of indicator anomalies from multiple dimensions, avoiding the problem of vague causes of anomalies caused by analyzing only from an overall level; at the same time, based on the comprehensive determination of the attribution results of anomaly grouping under each dimension, the specific dimension that caused the indicator anomaly can be accurately identified, improving the depth and credibility of anomaly attribution and reducing ineffective investigations.

[0107] As another possible implementation, the anomaly attribution analysis strategy can also be as follows: when there are multiple target values, and each target value has a corresponding timestamp, based on the timestamps corresponding to the multiple target values, sort the multiple target values ​​in chronological order to obtain a time sorting sequence; based on the time sorting sequence, identify the time node when the value of the first indicator to be processed is an anomalous value; obtain at least one indicator association event related to the first indicator to be processed within a preset time period (e.g., the duration of the preset time period is 1 hour, 12 hours, etc.) that matches the time node; and generate anomaly attribution results based on at least one indicator association event related to the first indicator to be processed.

[0108] For example, assuming the first metric to be processed is the defect rate, the point in time when the defect rate suddenly changes (referred to as the time node in this application) is located. Then, combined with business events within a preset time period before and after that time point (referred to as metric-related events in this application), such as version releases, requirement deployments, personnel changes, system modifications, etc., the anomaly attribution result is determined. For example, the anomaly attribution result is: the defect rate suddenly changed on December 8th, which is highly correlated with the emergency deployment of V2.0, temporary requirement changes, and a reduction in test cases. It is speculated that the emergency version release caused the sudden increase in the defect rate.

[0109] Therefore, by constructing a time-series sequence by sorting the target values ​​of the indicators to be processed by timestamps, the specific time nodes when the indicators undergo abnormal changes can be accurately captured. Then, by combining the related events of the indicators within a preset time period before and after the time node for attribution analysis, the temporal relationship between indicator anomalies and corresponding event behaviors can be intuitively established, avoiding blind judgments that are detached from the actual scenario. At the same time, relying on time matching and event association, the key events that cause indicator anomalies can be quickly located, making the anomaly attribution process more in line with actual business logic, and the results more convincing and traceable, effectively improving the efficiency and accuracy of anomaly root cause location.

[0110] As another possible implementation, the anomaly attribution analysis strategy can also be as follows: obtain at least one associated indicator related to the first indicator to be processed, and obtain multiple analytical dimensions for statistical analysis of the first indicator to be processed; use each analytical dimension and each associated indicator as features; calculate the contribution of each feature to the abnormal value of the first indicator to be processed through a machine learning model; based on the contribution of each feature to the abnormal value of the first indicator to be processed, filter at least one associated indicator and multiple analytical dimensions to obtain target features; and determine the anomaly attribution result based on the target features.

[0111] For example, assuming the first metric to be processed is the defect rate, the analysis dimensions (such as project, personnel type, version, etc.) and related metrics (such as code submission volume, requirement change volume, etc.) are used as features. Machine learning models, such as decision trees and random forests, are used to calculate the contribution of each feature to the "defect rate anomaly". The feature with the highest contribution can be taken as the anomaly attribution result.

[0112] Therefore, by using machine learning models to calculate the feature contribution of analysis dimensions and related indicators, the actual impact weight of each feature on indicator anomalies can be quantified, overcoming the limitations and subjectivity of human experience judgment. By filtering features to obtain high-contribution target features, redundant interference factors can be eliminated, focusing on the core dimensions and key indicators that cause anomalies, making the anomaly attribution results more accurate, objective, and data-supported. At the same time, this method can adapt to multi-feature coupling situations in complex business scenarios, improving the depth and intelligence level of anomaly root cause localization, and effectively improving the efficiency of anomaly analysis and the reliability of decision-making.

[0113] It should be noted that the method used for anomaly attribution analysis of the first indicator to be processed (referred to as anomaly attribution analysis strategy in this disclosure) is merely exemplary. In practical applications, other methods may also be used, and this disclosure does not impose any restrictions on them.

[0114] Understandably, anomaly detection algorithms can be used to automatically detect anomalies based on the target value of the first indicator to be processed. This can quickly and accurately identify abnormal values ​​in the first indicator to be processed, so as to realize real-time monitoring of data quality and early warning of problems. When abnormal values ​​are found, anomaly attribution analysis can also be automatically performed to directly locate the root cause of the anomaly without the need for manual investigation. This improves the efficiency and accuracy of problem location and provides a reliable basis for subsequent data correction, process optimization and decision adjustment.

[0115] Step S143: Transform the target value of the first indicator to be processed to obtain indicator view data.

[0116] Using the example above, let's assume the first metrics to be processed include defect rate and code commits. The chart type is a scatter plot, and the time range is October, November, and December. We can set the X-axis to code commits and the Y-axis to defect rate. We can then segment and aggregate the X-axis by month, and separately count and calculate the code commits and defect rates of different projects within the same month to form the corresponding month's metric view data points, which will be used as the metric view data. Other information can use default data for subsequent scatter plot rendering.

[0117] Therefore, by matching the attribute information of the original data with that of the first indicator to be processed, efficient extraction and accurate filtering of the target original data can be achieved, effectively avoiding redundant data interference and improving data processing efficiency. Based on the target original data, the target value of the first indicator to be processed is determined, maintaining the data accuracy and reliability of the first indicator to be processed and providing a high-quality data foundation for subsequent analysis. Converting the target value into indicator view data helps to realize the visualization of the data, making it easier to understand and quickly interpret the indicator situation, reducing the threshold for data use, and improving the convenience of data application and decision-making efficiency.

[0118] Step S150: Render the page based on the indicator view data to display the visualization chart corresponding to the first indicator to be processed.

[0119] As an example, after obtaining the indicator view data, the front end can map the indicator view data into a data structure that can be recognized by the visualization component (such as ECharts). Then, it can call the visualization component to draw charts based on the data structure, generating visualization charts that include coordinate axes, data points, legends, tooltips, and interactive capabilities, thereby realizing the intuitive display and analysis of the indicator view data of the first indicator to be processed.

[0120] As one possible implementation, when an abnormal value exists in the first indicator to be processed, the target indicator view data corresponding to the abnormal value can be determined from the indicator view data. Anomaly annotations can be added to the target indicator view data corresponding to the abnormal value of the first indicator to be processed in the visualization chart. For example, the abnormal data points (referred to as target indicator view data in this disclosure) corresponding to the abnormal value of the first indicator to be processed can be color-coded in the visualization chart, such as highlighted in red; anomaly prompts can also be added to these abnormal data points, such as "Defect rate suddenly increased by 30%". Thus, by annotating abnormal values ​​in the visualization chart, an intuitive visual presentation of abnormal information is achieved, facilitating users to quickly locate the location and characteristics of anomalies without requiring manual line-by-line data verification. This improves the efficiency of anomaly identification and the ease of interpretation, while also enhancing the interactivity and practicality of data visualization, providing intuitive support for anomaly analysis and decision-making.

[0121] As another possible implementation, the anomaly attribution results can be displayed. For example, the anomaly attribution results can be annotated with text or displayed as a floating prompt. Thus, by displaying the anomaly attribution results, the reasons for the abnormal values ​​can be clearly and explicitly presented to the user, breaking down the information barrier between anomaly detection and cause localization. This allows users not only to know that an anomaly exists but also to quickly grasp its root cause; it eliminates the need for users to perform additional attribution investigation operations, reducing the manpower and time costs of anomaly analysis and facilitating subsequent data processing.

[0122] In another possible implementation of this disclosure, the value of the first indicator to be processed can be trend-predicted according to the target value of the first indicator to be processed, and multiple predicted values ​​can be obtained; then, the multiple predicted values ​​can be transformed to obtain the prediction indicator view data corresponding to the multiple predicted values, and the visualization chart can be supplemented based on the prediction indicator view data.

[0123] As an example, a prediction algorithm, such as linear regression or ARIMA (Autoregressive Integrated Moving Average Model), can be used to predict the future trend of the first indicator to be processed, using the target value of the first indicator to be processed as historical data, thereby obtaining predicted values ​​at N time points. Then, the multiple predicted values ​​can be converted into corresponding indicator view data (referred to as predicted indicator view data in this disclosure). That is, these predicted values ​​can be added to the data structure that the visualization component can recognize, and the indicator view data corresponding to these predicted values ​​can be plotted in the visualization chart. For example, the N predicted values ​​can be displayed in the form of lines in the visualization chart, thereby supplementing the data of the visualization chart.

[0124] Therefore, by predicting the trend of the value of the first indicator to be processed, multiple predicted values ​​are generated, enabling a scientific prediction of the future trend of the indicator and providing forward-looking data support for decision-making. Based on the predicted values, data supplementation to the visualization charts helps to intuitively present the historical, current and future trends of the indicator, enhance the information integrity and predictability of the charts, make it easier for users to intuitively grasp the development law of the indicator, formulate response strategies in advance, and improve the decision-making reference value of data visualization.

[0125] In another possible implementation of the present disclosure, when there are multiple first indicators to be processed, correlation analysis can be performed on the multiple first indicators to be processed to obtain correlation information and display the correlation information.

[0126] The correlation information can include, for example, positive or negative correlation, degree of association, correlation coefficient, and correlation confidence level. Positive or negative correlation indicates whether the corresponding first indicators to be processed are positively correlated, negatively correlated, or have no significant correlation; degree of association characterizes the strength of the correlation between the first indicators to be processed; correlation coefficient measures the degree of correlation between the first indicators to be processed; and correlation confidence level indicates the reliability of the calculated correlation results.

[0127] For example, when there are multiple first indicators to be processed, the Pearson correlation coefficient between any two first indicators to be processed can be calculated. Then, based on the Pearson correlation coefficient, the correlation information between the multiple first indicators to be processed can be determined, and the correlation information can also be displayed.

[0128] Therefore, when there are multiple indicators to be processed, correlation analysis can be performed on them to uncover the correlation patterns and mutual influence relationships between multiple indicators and obtain quantitative correlation information. Visualizing the correlation information can help users quickly identify the correlation between indicators, avoid isolated analysis of a single indicator, improve the comprehensiveness and depth of data insights, and provide key basis for multi-dimensional decision-making, indicator system optimization, and anomaly linkage investigation.

[0129] It is understandable that there may be situations where the target requirement information is incomplete. In such cases, users can supplement the target requirement information. Therefore, in one possible implementation of this disclosure embodiment, when supplementary requirement information is obtained to supplement the target requirement information, the process of generating and displaying the visualization chart can be re-executed based on the supplementary requirement information.

[0130] As an example, after obtaining supplementary requirement information to complement the target requirement information, the target requirement information and the supplementary requirement information can be concatenated to obtain concatenated requirement information. Then, the concatenated requirement information can be analyzed to obtain key information for the second indicator to be processed. Next, based on the key information and the indicator metadata of multiple preset indicators, the second indicator to be processed can be configured to obtain the target indicator configuration information for the second indicator to be processed. Then, based on the target indicator configuration information, indicator view data can be generated. Finally, page rendering can be performed based on the indicator view data to obtain the final visual chart. This process can be repeated, without further elaboration.

[0131] It should be noted that the first indicator to be processed can be the same as or different from the second indicator to be processed; this disclosure does not impose any restrictions on this.

[0132] Therefore, after obtaining supplementary requirements information, the entire process of generating visualization charts can be retried based on this information, enabling dynamic iteration and precise adaptation of chart content, dimensions, and display format; without the need for manual reconfiguration or rebuilding from scratch, it effectively improves the flexibility and efficiency of visualization charts in responding to changes in requirements, ensuring that the final displayed content is highly matched with user needs, and enhancing the practicality and interactivity of data visualization.

[0133] The information display method of this disclosure involves: acquiring target requirement information for instructing the execution of an information display task; analyzing the target requirement information to obtain key information of a first indicator to be processed; configuring the first indicator to be processed based on the key information and indicator metadata of multiple preset indicators to obtain target indicator configuration information for the first indicator to be processed; generating indicator view data based on the target indicator configuration information; and rendering the page based on the indicator view data to display a visual chart of the first indicator to be processed. This method can directly and accurately extract key information of the indicator to be processed based on actual display needs, avoiding interference from irrelevant information in the indicator processing process and ensuring the relevance of indicator analysis. By combining preset indicator metadata for information configuration, indicator definitions can be standardized, data standards unified, and repetitive configuration work and human configuration errors reduced, improving the standardization and efficiency of indicator configuration. Automatically generating indicator view data and completing page rendering based on the configuration information can quickly construct intuitive and clear visual charts, achieving data visualization and real-time display without requiring users to understand the underlying data table structure and field definitions, lowering the barrier to entry and technical cost of data visualization, improving the convenience and efficiency of information display, and maintaining consistency between the displayed content and the requirements.

[0134] To clearly illustrate the information display method disclosed herein, a detailed explanation is provided below with examples.

[0135] As an example, an information display method may include the following steps: 1. User Requirement Input Users can input their desired information in natural language on the front-end interface, such as "correlation between project defect rate and code commits in the past 3 months". The Vue front-end framework can then use Axios technology to send this user-inputted information to the AI ​​interface in the Spring Boot backend.

[0136] 2. Natural Language Parsing After receiving the target requirement information from the front end through the AI ​​interface, the backend can call the locally deployed large language model to parse the target requirement information based on the preset target words and extract keywords from the target requirement information, such as the time range "last 3 months", the indicator dimension "project", and the indicators to be processed "defect rate, code submission volume", etc.

[0137] 3. Indicator Configuration Generation The large language model, combined with pre-stored metric metadata, generates the final target metric configuration information. The Spring Boot backend automatically converts the target metric configuration information generated by the large language model into a parameter format that the original metric management module can recognize, and calls the corresponding generation interface to generate metric view data based on the converted parameters.

[0138] It should be noted that the following steps can be taken before generating the indicator view data: 1.1) Call the data analysis service based on Smile (Java machine learning library) to identify the cause of sudden changes in the indicator to be processed, such as identifying the cause of "a sudden increase of 30% in the defect rate of a certain project"; 1.2) Based on the historical values ​​of the indicator to be processed, perform trend prediction on the indicator to be processed to obtain multiple predicted values ​​for the future; supplement the multiple predicted values ​​into the target indicator configuration information; 1.3) If there are multiple indicators to be processed, perform correlation analysis on the multiple indicators to obtain correlation information; then supplement the target indicator configuration information with the correlation information.

[0139] 4. Visual presentation After obtaining the indicator view data, the front end can map the indicator view data into a data structure that can be recognized by the visualization component (such as ECharts). Then, the visualization component can be called to draw charts based on the data structure, generating visual charts that include coordinate axes, data points, legends, tooltips, and interactive capabilities, thus realizing the intuitive display and analysis of indicator data.

[0140] Optionally, the abnormal data points corresponding to the abnormal values ​​of the indicator to be processed can be color-coded in the visualization chart.

[0141] Optionally, multiple predicted values ​​can be displayed as lines in a visualization chart.

[0142] It should be noted that after obtaining the supplementary requirement information to supplement the target requirement information, the target requirement information and the supplementary requirement information can be combined to obtain the combined requirement information; then, the above process can be repeated based on the combined requirement information to obtain a visual chart.

[0143] The information display method disclosed herein has at least the following advantages: 1. During the indicator configuration process, users do not need to be proficient in the definition and relationship of relevant database tables. Users can automatically achieve unambiguous conversion from "natural language requirements" to "system-recognizable parameters" simply by inputting their requirements, thus lowering the barrier to entry.

[0144] 2. In related technologies, natural language processing (NLP) methods often rely solely on the semantic understanding capabilities of general-purpose large-scale language models. However, due to the lack of domain-specific expertise in general-purpose large-scale language models, parsing biases are prone to occur when facing the relevant requirements of a target domain (such as DevOps). For example, when parsing statements involving "defect rate," it may incorrectly associate them with "test case dataset" instead of the truly relevant data set, thus affecting the accuracy of subsequent processing results. The method disclosed in this paper provides LLM with domain-specific guidance and knowledge support by carefully designing domain-specific prompts and binding them to a dynamic terminology library. In this way, LLM can accurately understand the domain-specific terminology, effectively avoiding parsing errors caused by a lack of domain knowledge, and achieving an accurate and unambiguous conversion from "natural language requirements" to "system-recognizable parameters."

[0145] 3. In related technologies, visualization views have significant limitations in data processing and analysis. Their data value mining is relatively shallow, mostly remaining at the basic level of "data visualization display," lacking in-depth analysis and interpretation of the data. For example, they cannot automatically identify abnormal fluctuations in the data, nor can they make reasonable predictions about future data trends. In contrast, this disclosure can automatically call professional analysis services built on SMILE (a Java machine learning library), which can deeply analyze the data and accurately locate the causes of data anomalies.

[0146] In summary, the information display method disclosed herein supports the convenient display of various indicator data in relevant fields on a web page through AI dialogue, improving the efficiency of indicator configuration and providing more persuasive and intuitive visual data basis for subsequent data-driven decision-making.

[0147] To implement the above-described information display method, this disclosure also provides an information display device.

[0148] Figure 6 This is a schematic diagram of the structure of an information display device provided in an embodiment of the present disclosure.

[0149] like Figure 6 As shown, the information display device 600 includes: a first acquisition module 601, a first analysis module 602, a configuration module 603, a generation module 604, and a first processing module 605.

[0150] The acquisition module 601 is used to acquire target requirement information for instructing the execution of information display tasks.

[0151] The first analysis module 602 is used to analyze the target requirement information to obtain the key information of the first indicator to be processed.

[0152] The configuration module 603 is used to configure the information of the first indicator to be processed based on key information and indicator metadata of multiple preset indicators, so as to obtain the target indicator configuration information of the first indicator to be processed.

[0153] The generation module 604 is used to generate indicator view data based on the target indicator configuration information.

[0154] The first processing module 605 is used to render the page based on the indicator view data to display the visualization chart corresponding to the first indicator to be processed.

[0155] In one possible implementation of this disclosure, the configuration module 603 is configured to: for any preset indicator, determine the matching degree information between the first indicator to be processed and the preset indicator based on key information and the indicator metadata of the preset indicator; determine the target indicator metadata based on the matching degree information and the indicator metadata of multiple preset indicators; and determine the target indicator configuration information of the first indicator to be processed based on the target indicator metadata.

[0156] In one possible implementation of this disclosure, the configuration module 603 is configured to: extract target indicator information from the indicator metadata of any preset indicator; wherein the target indicator information is used to indicate the preset indicator; generate a first vector based on the target indicator information, and generate a second vector based on key information; perform similarity calculation on the first vector and the second vector to obtain the similarity between the first vector and the second vector; and determine the matching degree information between the first indicator to be processed and the preset indicator based on the similarity.

[0157] In one possible implementation of this disclosure, the first analysis module 602 is configured to: acquire indication information; wherein the indication information is used to indicate key information to be extracted; fill the indication information and target requirement information into the corresponding positions of the first prompt word template to obtain the first prompt word; input the first prompt word into the large language model, and obtain the key information in response to the output of the large language model.

[0158] In one possible implementation of this disclosure, the target indicator configuration information includes attribute information of a first indicator to be processed and a target dataset identifier. The target dataset indicated by the target dataset identifier is used to store raw data related to the first indicator to be processed. The generation module 604 is configured to: query the target dataset indicated by the target dataset identifier according to the attribute information of the first indicator to be processed, so as to extract target raw data matching the attribute information of the first indicator to be processed from the raw data of the target dataset; perform numerical calculation on the first indicator to be processed according to the target raw data to obtain the target value of the first indicator to be processed; and convert the target value of the first indicator to be processed to obtain indicator view data.

[0159] In one possible implementation of this disclosure, the target indicator configuration information further includes the calculation logic of the first indicator to be processed; the generation module 604 is used to: perform logical calculation on the first indicator to be processed based on the target original data according to the calculation logic of the first indicator to be processed, so as to obtain the target value of the first indicator to be processed.

[0160] In one possible implementation of this disclosure, the information display device 600 may further include: The detection module is used to perform anomaly detection on the target value of the first indicator to be processed using an anomaly detection algorithm, so as to determine whether there are any abnormal values ​​in the first indicator to be processed.

[0161] The second analysis module is used to respond to the existence of abnormal values ​​in the first indicator to be processed, and to perform anomaly attribution analysis on the first indicator to be processed to obtain the anomaly attribution results; wherein, the anomaly attribution analysis strategy includes at least one of the following: indicator correlation analysis method, dimensional hierarchical analysis method, time series analysis method, and feature importance analysis method.

[0162] In one possible implementation of this disclosure, the information display device 600 includes: The annotation module is used to respond to the existence of abnormal values ​​in the first indicator to be processed, determine the target indicator view data corresponding to the abnormal value from the indicator view data, and annotate the target indicator view data corresponding to the abnormal value in the visualization chart corresponding to the first indicator to be processed. And / or, The display module is used to show the anomaly attribution results.

[0163] In one possible implementation of this disclosure, the information display device 600 may further include at least one of the following: The second processing module is used to predict the trend of the value of the first indicator to be processed according to the target value of the first indicator to be processed, obtain multiple predicted values, transform the multiple predicted values ​​to obtain the predicted indicator view data corresponding to the multiple predicted values, and supplement the visualization chart based on the predicted indicator view data.

[0164] The third processing module is used to respond to the fact that there are multiple first indicators to be processed, to perform correlation analysis on the multiple first indicators to be processed, to obtain correlation information, and to display the correlation information.

[0165] In one possible implementation of this disclosure, the information display device 600 may further include: The second acquisition module is used to acquire supplementary requirement information to supplement the target requirement information.

[0166] The splicing module is used to splice the target requirement information and the supplementary requirement information to obtain the spliced ​​requirement information.

[0167] The execution module is used to re-execute the process of generating and displaying visual charts based on the splicing requirements information.

[0168] It should be noted that the information display device provided in this embodiment is capable of achieving the above-mentioned functions. Figures 1 to 5 All method steps implemented in the method embodiment can achieve the same technical effect. Therefore, the parts that are the same as those in the method embodiment and their beneficial effects will not be described in detail here.

[0169] To implement the above embodiments, this disclosure also proposes an electronic device, wherein the electronic device can be any device with computing capabilities, the electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the information display method proposed in the first aspect of the present disclosure.

[0170] As an example, Figure 7 This is a schematic diagram of the structure of an electronic device 700 as shown in an exemplary embodiment of this disclosure, as follows: Figure 7 As shown, the aforementioned electronic device 700 may further include: The system includes a memory 710 and a processor 720, and a bus 730 connecting different components (including the memory 710 and the processor 720). The memory 710 stores a computer program, and when the processor 720 executes the program, it implements the information display method described in this embodiment of the present disclosure.

[0171] Bus 730 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0172] Electronic device 700 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0173] Memory 710 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 740 and / or cache memory 750. Server 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 760 may be used to read and write non-removable, non-volatile magnetic media (… Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 730 via one or more data media interfaces. Memory 710 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0174] A program / utility 780 having a set (at least one) of program modules 770 may be stored, for example, in memory 710. Such program modules 770 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 770 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0175] Electronic device 700 can also communicate with one or more external devices 790 (e.g., keyboard, pointing device, display 791, etc.), and with one or more devices that enable a user to interact with electronic device 700, and / or with any device that enables electronic device 700 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 792. Furthermore, electronic device 700 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 793. As shown, network adapter 793 communicates with other modules of electronic device 700 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0176] The processor 720 executes various functional applications and data processing by running programs stored in the memory 710.

[0177] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the information display method of this disclosure embodiment, and will not be repeated here.

[0178] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the information display method as proposed in any of the foregoing embodiments of this disclosure.

[0179] To implement the above embodiments, this disclosure also proposes a computer program product that, when the instructions in the computer program product are executed by a processor, performs the information display method as proposed in any of the foregoing embodiments of this disclosure.

[0180] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0181] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0182] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0183] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0184] It should be understood that various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0185] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0186] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0187] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. An information display method, characterized in that, The method includes: Obtain the target requirement information used to instruct the execution of the information display task; The target demand information is analyzed to obtain the key information of the first indicator to be processed; Based on the key information and the metadata of multiple preset indicators, the first indicator to be processed is configured to obtain the target indicator configuration information of the first indicator to be processed. Generate indicator view data based on the target indicator configuration information; The page is rendered based on the indicator view data to display a visual chart corresponding to the first indicator to be processed.

2. The method according to claim 1, characterized in that, The step of configuring the first indicator to be processed based on the key information and the indicator metadata of multiple preset indicators to obtain the target indicator configuration information of the first indicator to be processed includes: For any of the preset indicators, the matching degree information between the first indicator to be processed and the preset indicator is determined based on the key information and the indicator metadata of the preset indicator. Based on the matching degree information and the metadata of the multiple preset indicators, the target indicator metadata is determined; Based on the target indicator metadata, the target indicator configuration information of the first indicator to be processed is determined.

3. The method according to claim 2, characterized in that, For any of the preset indicators, determining the matching degree information between the first indicator to be processed and the preset indicator based on the key information and the indicator metadata of the preset indicator includes: For any of the preset indicators, target indicator information is extracted from the indicator metadata of the preset indicator; wherein, the target indicator information is used to indicate the preset indicator; Based on the target indicator information, a first vector is generated, and based on the key information, a second vector is generated; Calculate the similarity between the first vector and the second vector to obtain the similarity between the first vector and the second vector. Based on the similarity, the matching degree information between the first indicator to be processed and the preset indicator is determined.

4. The method according to claim 1, characterized in that, The target indicator configuration information includes the attribute information of the first indicator to be processed and the target dataset identifier. The target dataset identifier indicates that the target dataset is used to store the original data related to the first indicator to be processed. The step of generating indicator view data based on the target indicator configuration information includes: Based on the attribute information of the first indicator to be processed, query the target dataset indicated by the target dataset identifier to extract the target original data that matches the attribute information of the first indicator to be processed from the original data of the target dataset. Based on the target raw data, numerical calculations are performed on the first indicator to be processed to obtain the target value of the first indicator to be processed. The target value of the first indicator to be processed is transformed to obtain the indicator view data.

5. The method according to claim 4, characterized in that, The target indicator configuration information also includes the calculation logic for the first indicator to be processed; The step of performing numerical calculations on the first indicator to be processed based on the target original data to obtain the target value of the first indicator to be processed includes: According to the calculation logic of the first indicator to be processed, based on the target original data, the first indicator to be processed is logically calculated to obtain the target value of the first indicator to be processed.

6. The method according to claim 4, characterized in that, The method includes: An anomaly detection algorithm is used to detect anomalies in the target value of the first indicator to be processed, so as to determine whether there are any abnormal values ​​in the first indicator to be processed. In response to the existence of abnormal values ​​in the first indicator to be processed, an anomaly attribution analysis strategy is adopted to perform anomaly attribution analysis on the first indicator to be processed, and anomaly attribution results are obtained.

7. The method according to claim 6, characterized in that, The anomaly attribution analysis strategy includes: Obtain multiple analytical dimensions for statistical analysis of the first indicator to be processed; For any of the aforementioned analysis dimensions, based on the target raw data, the first indicator to be processed is grouped and statistically analyzed in the analysis dimension to obtain the indicator value of at least one group of the first indicator to be processed in the analysis dimension. Based on the index value of at least one group of the first index to be processed under the analysis dimension, identify abnormal groups in the at least one group; Based on the anomaly grouping of the first indicator to be processed under each of the analysis dimensions, the anomaly attribution result is determined.

8. The method according to claim 6, characterized in that, The target values ​​are multiple, and each target value has a corresponding timestamp. The anomaly attribution analysis strategy includes: Based on the timestamps corresponding to the multiple target values, the multiple target values ​​are sorted in chronological order to obtain a time sorting sequence; Based on the time sorting sequence, identify the time node when the value of the first indicator to be processed is an abnormal value; Obtain at least one indicator-related event within a preset time period that matches the time node and is related to the first indicator to be processed; The anomaly attribution result is generated based on at least one indicator association event related to the first indicator to be processed.

9. The method according to claim 4, characterized in that, The method further includes at least one of the following: Based on the target value of the first indicator to be processed, the value of the first indicator to be processed is trend-predicted to obtain multiple predicted values. The multiple predicted values ​​are then transformed to obtain the predicted indicator view data corresponding to the multiple predicted values. Based on the predicted indicator view data, the visualization chart is supplemented with data. In response to the fact that there are multiple first indicators to be processed, a correlation analysis is performed on the multiple first indicators to be processed to obtain correlation information, and the correlation information is displayed.

10. The method according to any one of claims 1-9, characterized in that, The method further includes: Obtain supplementary requirement information to supplement the target requirement information; The target requirement information and the supplementary requirement information are concatenated to obtain the concatenated requirement information; Based on the splicing requirements, the process of generating and displaying the visualization charts is re-executed.

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