Adaptive method and system for data analysis based on configuration and application evolution

Abstract

This invention discloses an adaptive data analysis method and system based on configuration and application evolution, belonging to the field of data visualization technology. It includes: Step 1, analyzing database table structure information, field business semantics, and inter-table relationships to construct a data source relationship graph; parsing natural language data access instructions to generate business indicator query results; Step 2, determining the display data structure and chart component types, generating an initial layout scheme, and coordinating adjustments; Step 3, adjusting the current display data structure and chart component types to achieve dynamic rendering during runtime; Step 4, identifying abnormal chart components, evaluating the priority of abnormal display, and performing core area linkage adjustments, early warning broadcasts, and overlapping coordinated rendering. This solves the problem of abnormal chart components competing for the core display area when multiple abnormalities are triggered concurrently, leading to component overlap and occlusion, lack of prominence of key abnormalities, and unstable large-screen layout rendering.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, specifically to an adaptive data analysis method and system based on configuration and application evolution. Background Technology

[0002] With the continuous development of enterprise digital operations, business monitoring, and intelligent decision-making scenarios, visual dashboards have been widely applied in fields such as urban governance, industrial production, park management, business analysis, and emergency command. They are used to centrally display multi-source business data, operational status, and changes in key indicators. This field involves technologies such as data visualization, database management, natural language processing, front-end rendering, business monitoring, and intelligent interaction, emphasizing the centralized presentation, real-time updates, and intuitive expression of complex business information.

[0003] For example, Chinese patent CN117270867A discloses a web front-end large-screen adaptation method, device, and its application, including S00: loading and reading the code of the main entry program and converting the string of the code into an abstract syntax tree; S10: traversing the abstract syntax tree and filtering the style file and the file path referenced by the import statement based on the ImportDeclaration node value in the abstract syntax tree; wherein, the style file is a CSS file; S20: obtaining the CSS string in the style file, calculating the CSS string according to the total width of the UI design draft, so that the value of the virtual pixel unit is converted into the value of the vw unit, and obtaining a new style file; S30: generating a new main entry file based on the new style file and inserting it into the HTML file. This application can adapt to various resolutions with great flexibility, regardless of the UI design draft and screen size, and scales proportionally without distortion of width and height.

[0004] For example, Chinese patent CN113253959A discloses a front-end projection method adapted to different large screen resolutions, belonging to the field of front-end large screen development. The technical problem it addresses is how to develop a 1:1 representation of the design mockup without considering different monitor resolutions, thus adapting to the ever-changing needs of large screen development. The technical solution is as follows: This method defines a configuration file and configures the resolution within it. After proportional scaling using the unified processing function handleInitAuto(), the image is projected onto the monitor for height restoration. The specific steps are as follows: S1, Load the predefined height and width from the configuration file; S2, Obtain the actual screen width and height using the unified processing function handleInitAuto(); S3, Calculate the ratio wScale (predefined width to actual width) and hScale (predefined height to actual height) using the unified processing function handleInitAuto(); S4, Dynamically scale the actual width and height proportionally using the unified processing function handleInitAuto(); S5, Project the scaled image onto the actual screen.

[0005] However, in existing multi-indicator monitoring dashboards, a common practice is to enlarge, highlight, prioritize, or pop up components that trigger anomalies to improve their visibility. When multiple anomalies are triggered concurrently within the same refresh cycle or short time window, if the system enlarges or prioritizes multiple components simultaneously according to a strategy of handling each anomaly independently, it can easily lead to overlapping occlusion, font compression, chart scaling distortion, and visual focus splitting within the limited core view area. Furthermore, the lack of dynamic importance assessment and occupancy arbitration among concurrent anomalies results in uncontrollable contention for anomaly components in core areas. This causes a decrease in the visible area and readability of critical anomaly components, an increase in the number of steps and eye jumps required for users to locate the most critical anomalies, and triggers frequent calculations and rendering jitter during layout reordering, affecting the availability and stability of the dashboard during high-concurrency alarm periods.

[0006] Therefore, in order to address the above issues, there is an urgent need for adaptive data analysis methods and systems based on configuration and application evolution. Summary of the Invention

[0007] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an adaptive data analysis method and system based on configuration and application evolution, which solves the problems of abnormal chart components competing for the core display area when multiple abnormalities are triggered concurrently, resulting in component overlap and occlusion, key abnormalities not being highlighted, and unstable large-screen layout rendering.

[0008] Technical solution

[0009] To achieve the above objectives, this invention employs the following technical solution: a data analysis adaptive method based on configuration and application evolution, comprising the following steps: Step 1, connecting to the business database, analyzing database table structure information, field business semantics, and inter-table relationships to construct a data source relationship graph; parsing natural language data access instructions and generating corresponding business indicator query results; Step 2, determining the display data structure and chart component types based on the business indicator query results, generating an initial layout scheme based on the chart component types and the parsing results of the natural language data access instructions, coordinating and adjusting the initial layout scheme to form a large-screen configuration scheme; Step 3, receiving updated business indicator data in real time, adjusting the current display data structure and chart component types, and combining animation transitions to achieve dynamic rendering during runtime; Step 4, determining whether the business indicators in the business indicator query results are abnormal, identifying abnormal chart components, assessing the degree of impact of the abnormality, obtaining the abnormal display priority, and performing core area linkage adjustment, early warning broadcasting, and overlapping coordinated rendering on the abnormal chart components according to the abnormal display priority to achieve dynamic collaborative display of abnormal information on the large screen.

[0010] Furthermore, the specific process of connecting to the business database, analyzing database table structure information, field business semantics, and inter-table relationships to construct a data source relationship graph is as follows: Connecting to the business database to read database table structure information, table comments, and field sample values ​​yields business data source metadata; the database table structure information includes table name, field name, field type, field length, and field constraints; inputting field names, table comments, field types, and field sample values ​​into a pre-trained large language model to identify the field business meaning and obtain field business semantic results; performing string matching on field names in different tables, identifying the corresponding field as a field name association field when two field names contain the same business keyword; reading the field sample values ​​corresponding to each field, and... The process involves counting the number of identical data values ​​in the sample value sets of two fields, dividing this number by the minimum of the total number of sample values ​​in both fields to obtain the sample overlap ratio. When the overlap ratio is greater than a threshold, the corresponding field is identified as a field value association field. Foreign key constraints are read from the field constraint relationships. When a foreign key relationship exists between two fields, the corresponding field is identified as a structural association field. When two fields are identified as structural association fields, or simultaneously as field name association fields and field value association fields, a field association relationship is established. Each data table is treated as a table node. Based on the field association relationships, an inter-table association relationship is determined between the two tables to which the nodes belong, and an inter-table association edge is established between the corresponding two table nodes, forming a data source association graph.

[0011] Further, the specific process of parsing natural language data access instructions and generating corresponding business indicator query results is as follows: The user-input natural language data access instruction is received; semantic parsing is performed on the instruction based on a pre-trained large language model to identify the business object, statistical indicators, time range, and display requirements, resulting in a semantic parsing result; based on the business object and statistical indicators in the semantic parsing result, the corresponding business object field and statistical indicator field are matched based on the field business semantic results; the data table to which the corresponding business object field and statistical indicator field belong, as well as the associated data table with which the data table belongs and the associated fields establishing the associated fields, are determined from the data source association graph; based on the business object field, statistical indicator field, data table, associated data table, associated fields, and time range, an SQL query statement is generated and executed to obtain the business indicator query results, which include: business object, statistical indicator, indicator value, and indicator timestamp; the business data source metadata, field business semantic results, data source association graph, natural language data access instruction, semantic parsing result, SQL query statement, and business indicator query results are associated and stored to form a data access result dataset.

[0012] Furthermore, the specific process of determining the display data structure and chart component type based on the business indicator query results is as follows: Based on the business objects, statistical indicators, indicator values, and indicator timestamps in the business indicator query results, determine the display data structure, which includes: time series data, object comparison data, and indicator overview data; determine the chart component type based on the display data structure and display requirements, generate a unique chart component number for the corresponding chart component, and establish a correspondence between the chart component number and the business indicator query results.

[0013] Furthermore, the specific process of generating an initial layout scheme based on the chart component type and the natural language data access instruction parsing results is as follows: Obtain the large-screen terminal configuration data, including screen width, screen height, and preset core display area coordinates; divide the large-screen display area into a core display area, adjacent display areas, and normal display areas according to the large-screen terminal configuration data, and record the width and height of the corresponding display areas; determine the display priority of chart components according to display requirements: when the display requirement is an overview display, the corresponding chart component is determined as a first-level display priority; when the display requirement is a trend display, the corresponding chart component is determined as a second-level display priority; when the display requirement is a comparison display, the corresponding chart component is determined as a third-level display priority. The system employs a three-tiered display priority system. First-tier priority chart components are placed in the core display area, second-tier priority components in adjacent display areas, and third-tier priority components in the normal display area. The display area is divided based on its width, height, and the number of chart components within the same area. The width and height of each chart component are determined, and their current coordinates are obtained. Chart components are ordered from highest to lowest priority based on their display priority and number. An initial layout scheme is generated based on the chart component type, number, width, height, current coordinates, and order.

[0014] Furthermore, the specific process of coordinating and adjusting the initial layout scheme to form the large-screen configuration scheme is as follows: Based on the initial layout scheme and the width, height, and current position coordinates of the chart components, determine the display area boundaries of each chart component in the large-screen display area; determine the chart-related component numbers based on business objects and statistical indicators: when two chart components correspond to the same business object or the same statistical indicator, it is determined that the two chart components have an associated display relationship, and the two chart component numbers are mutually marked as each other's chart-related component numbers; perform overlap judgment and overlap adjustment on the initial layout scheme: determine the display area of ​​any two chart components. Check if the domain boundaries overlap; when the display area boundaries of any two chart components overlap, adjust the current position coordinates of the chart component with the later hierarchy order according to the chart component hierarchy order of the two chart components until the display area boundaries of any two chart components no longer overlap; determine the initial layout scheme after the overlap adjustment as the large screen configuration scheme, which includes: chart component number, chart component type, chart component width, chart component height, current position coordinates of chart component, chart component display priority, chart component hierarchy order and chart associated component number, and associate the large screen configuration scheme and the data access result dataset for storage.

[0015] Furthermore, the specific process of receiving real-time updated business indicator data, adjusting the current displayed data structure and chart component types, and combining animation transitions to achieve dynamic rendering during runtime is as follows: Real-time reception of updated data corresponding to business indicator query results, including updated data for business objects, statistical indicators, indicator values, and indicator timestamps, and redetermining the current displayed data structure based on the updated data; when the current displayed data structure matches the corresponding chart component type, the corresponding chart component type remains unchanged; when the current displayed data structure does not match the corresponding chart component type, the chart component type is redetermined based on the current displayed data structure, and the chart component type of the corresponding chart component number is updated; rendering is performed on the corresponding chart component, with animation transitions executed during the rendering process, and the updated data and the updated chart component type are associated and stored to form a runtime rendering result dataset.

[0016] Further, the specific process for determining whether business metrics in the business metric query results are abnormal, identifying abnormal chart components, assessing the degree of impact of the abnormality, and obtaining the priority of abnormal display is as follows: Based on the real-time metric values ​​and the abnormal threshold of the corresponding statistical metric, determine whether the statistical metric is abnormal. If so, mark the chart component number corresponding to the statistical metric as an abnormal chart component. When the statistical metric changes from a non-abnormal state to an abnormal state, record the abnormal start time. For the abnormal chart component, calculate the abnormal deviation amplitude, abnormal duration, and number of chart-related components. Among them, the abnormal deviation amplitude is determined based on the difference between the metric value and the abnormal threshold, the abnormal duration is determined based on the difference between the current metric timestamp and the abnormal start time, and the number of chart-related components is determined based on the number of chart-related component numbers. After normalizing the abnormal deviation amplitude, abnormal duration, and number of chart-related components, a weighted fusion is performed to obtain the abnormal display priority value.

[0017] Furthermore, based on the priority of anomaly display, the specific process of dynamically and collaboratively displaying abnormal information on the large screen by performing core area linkage adjustment, early warning broadcasting, and overlapping coordinated rendering on abnormal chart components is as follows: When there are more than one abnormal chart component under the same indicator timestamp, all abnormal chart components are extracted and sorted from high to low according to their anomaly display priority value. The chart component hierarchy order of the abnormal chart components is determined according to the sorting result, and the non-abnormal chart components are arranged sequentially after the abnormal chart components according to their original chart component hierarchy order; the abnormal chart component with the highest anomaly display priority value is determined as the priority display chart component and adjusted to the core display area; the remaining abnormal chart components and non-abnormal chart components are arranged according to their chart component hierarchy order. The abnormal chart components are sequentially adjusted to adjacent display areas and normal display areas, and the abnormal chart components are highlighted. When the abnormal display priority value of the abnormal chart component is greater than the broadcast threshold, a warning broadcast content is generated and a voice broadcast is executed. The warning broadcast content includes the abnormal business object, statistical indicators, indicator values, indicator timestamps, abnormal start time, and abnormal duration. Based on the adjusted chart component width, chart component height, and current position coordinates of the chart component, the display area boundaries of each chart component are redefined, and overlap judgment and overlap adjustment are performed again to obtain the layout scheme after overlap adjustment and execute rendering. The abnormal chart component mark, abnormal display priority value, warning broadcast content, and overlap adjustment layout scheme are associated and stored to form a warning linkage result dataset.

[0018] The second aspect of this invention provides a data analysis adaptive system based on configuration and application evolution, comprising: a data source intelligent detection module, used to connect to a business database, analyze database table structure information, field business semantics, and inter-table relationships, and construct a data source relationship graph; parse natural language data access instructions and generate corresponding business indicator query results; a large screen automatic configuration module, used to determine the display data structure and chart component type based on the business indicator query results, generate an initial layout scheme based on the chart component type and combined with the natural language data access instruction parsing results, coordinate and adjust the initial layout scheme to form a large screen configuration scheme; a runtime adaptive rendering module, used to receive business indicator update data in real time, adjust the current display data structure and chart component type, and combine animation transitions to achieve runtime dynamic rendering; and an intelligent early warning identification module, used to determine whether the business indicators in the business indicator query results are abnormal, identify abnormal chart components, assess the degree of abnormal impact, obtain the abnormal display priority, and perform core area linkage adjustment, early warning broadcast, and overlapping coordinated rendering on abnormal chart components according to the abnormal display priority, so as to realize the dynamic collaborative display of abnormal information on the large screen.

[0019] Beneficial effects

[0020] The present invention has the following beneficial effects: (1) This invention can automatically determine the relationship between business objects, statistical indicators and data tables by analyzing database table structure, recognizing field business semantics, constructing data source association graphs and parsing natural language data access instructions, thereby reducing the workload of manually understanding database structure and writing SQL, and improving the efficiency of business indicator access and chart generation.

[0021] (2) This invention, by displaying data structure identification, chart component type determination, display priority division, core display area allocation and overlap determination adjustment, enables different chart components to be reasonably distributed according to business display needs, reduces the probability of component overlap, obstruction and hierarchical confusion, and improves the clarity and display stability of the initial configuration of the large screen.

[0022] (3) This invention receives real-time updated business indicator data, re-determines the current display data structure, and automatically updates the chart component type when the display data structure does not match the chart component type. At the same time, it combines animation transitions for dynamic rendering, so that the large screen can automatically adjust the display method according to changes in business data, thereby improving the accuracy and continuity of information expression.

[0023] (4) This invention, through abnormal chart component identification, abnormal display priority evaluation, abnormal chart sorting, core display area linkage adjustment, non-abnormal component hierarchical extension, early warning broadcast and overlapping coordinated rendering, enables key abnormal charts to enter the core area first and be highlighted, avoiding the problems of multiple abnormal charts occupying the core field of view at the same time, causing occlusion, visual focus dispersion and key abnormalities being marginalized.

[0024] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0025] Figure 1 Flowchart of an adaptive data analytics approach based on configuration and application evolution; Figure 2 An adaptive system architecture diagram for data analysis based on configuration and application evolution; Figure 3 This is a schematic diagram of the initial layout of the large screen based on configuration evolution; Figure 4 A flowchart of the adaptive data analytics approach based on configuration and application evolution. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. As those skilled in the art will understand, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figures 1-4 This invention provides a technical solution: an adaptive data analysis method based on configuration and application evolution, such as... Figure 1 As shown, the process includes the following steps: Step 1: Connect to the business database, analyze the database table structure information, field business semantics, and inter-table relationships to construct a data source relationship graph; parse natural language data access instructions and generate corresponding business indicator query results; Step 2: Determine the display data structure and chart component types based on the business indicator query results, generate an initial layout scheme based on the chart component types and the parsing results of the natural language data access instructions, and coordinate and adjust the initial layout scheme to form a large screen configuration scheme; Step 3: Receive updated business indicator data in real time, adjust the current display data structure and chart component types, and combine animation transitions to achieve dynamic rendering during runtime; Step 4: Determine whether the business indicators in the business indicator query results are abnormal, identify abnormal chart components, assess the degree of impact of the abnormality, obtain the abnormal display priority, and perform core area linkage adjustment, early warning broadcast, and overlapping coordinated rendering on abnormal chart components according to the abnormal display priority to achieve dynamic collaborative display of abnormal information on the large screen.

[0028] Specifically, the process of connecting to the business database, analyzing database table structure information, field business semantics, and inter-table relationships to construct a data source relationship graph is as follows: Connecting to the business database and reading database table structure information, table comments, and field sample values ​​yields business data source metadata. The database table structure information includes the table name, field name, field type, field length, and field constraints. Field sample values ​​are actual data values ​​extracted from the corresponding fields. When the data volume of a corresponding field is less than or equal to a preset full-read threshold, all actual data values ​​of the field are read as field sample values. When the data volume of a corresponding field exceeds the preset full-read threshold, the actual data values ​​of the field are read in paginated batches as field sample values. The preset full-read threshold is pre-set based on the single-field data volume of the business database, database query response time, and field semantic analysis time, preferably between 10,000 and 100,000 data records. Field sample values ​​are used to assist in determining the actual stored content of fields and the data correspondence between different fields. Field constraints include primary key constraints, foreign key constraints, unique constraints, and NOT NULL constraints. The field name, table comments, field type, and field sample values ​​are input into a pre-trained large language model to identify the business meaning of the fields and obtain the field business semantic results. The pre-trained large language model is a natural language semantic analysis model pre-trained based on a large-scale text corpus, preferably a large language model with text semantic understanding and field semantic mapping capabilities, and can be a natural language semantic parsing model based on the Transformer architecture. The field business semantic results include the business meaning corresponding to the field, the business object category corresponding to the field, and whether the field is used to represent statistical indicators, so as to match business object fields and statistical indicator fields according to natural language data access instructions. Business object categories include, for example, region, equipment, user, production line, workshop, or monitoring point. String matching is performed on the field names in different data tables. String matching includes uniform case processing of field names, delimiter splitting, and keyword extraction. Delimiters include underscores, hyphens, and camelCase naming boundaries. Business keywords are the terms in the field names used to represent business objects or business meanings. The business keywords are determined jointly by the field name segmentation results, the preset business dictionary, and the field business semantic results. The field name segmentation results are obtained by processing the field names with consistent capitalization, delimiter splitting, and camelCase naming. The preset business dictionary is pre-defined, preferably constructed based on an industry-standard business terminology library, historical database field naming records, and natural language corpus statistical results, and is used to record synonyms, abbreviations, and Chinese-English correspondences. The field business semantic results are used to supplement the business meanings that do not appear directly in the field names but are identified by the pre-trained large language model. When two field names contain the same business keyword, the corresponding field is identified as the field name association field.The same business keywords include keywords with completely identical field name segmentation results, and keywords belonging to the same business meaning after being merged by a preset business dictionary. The sample values ​​corresponding to each field are read, and the number of identical data values ​​in the two field sample value sets is counted. The minimum value between the two field sample value sets is used to obtain the sample overlap ratio. Duplicate values ​​are removed from the field sample values ​​of the two fields to form a first sample value set A and a second sample value set B, both of which are non-empty sets. The sample overlap ratio is determined according to R=|A∩B| / min(|A|,|B|). When either the first sample value set A or the second sample value set B is empty, the sample overlap ratio is not calculated, and the corresponding two fields are determined not to meet the field value association condition. By removing duplicate field sample values ​​before calculating the sample overlap ratio, the impact of repeated high-frequency data values ​​on the field association judgment results can be reduced, improving the accuracy of field value association identification. When the sample overlap ratio exceeds the overlap ratio threshold, the corresponding field is identified as a field value association field. The overlap ratio threshold is preset based on the field sample size in the business database and the allowable erroneous association ratio, with a preferred value range of 0.5 to 0.95. When the field values ​​in the business database are highly standardized, a larger overlap ratio threshold is preferred. When the business database contains historical dirty data, missing data, or heterogeneous data from multiple sources, a smaller overlap ratio threshold is preferred. Foreign key constraints in the field constraint relationships are read. When a foreign key relationship exists between two fields, the corresponding field is identified as a structural association field. Structural association fields represent fields where a table join relationship is clearly established at the database structure level. Field value association fields and field name association fields are used to assist in discovering candidate table join relationships when foreign key constraints are lacking. When two fields are identified as structurally related fields, or simultaneously as field name related fields and field value related fields, a field association relationship is established. That is, if a foreign key relationship exists, the field association relationship is established directly; if no foreign key relationship exists, both field name and field value relationships must be satisfied before establishing a field association relationship, to reduce the probability of false associations based solely on similar field names or overlapping sample values. If the same field simultaneously meets the conditions for establishing a field association relationship with multiple candidate fields, the field association relationships are established separately for each field, and the sample overlap ratio, association type, and corresponding data table of the corresponding field are recorded in the data source association graph. Each data table is treated as a table node. Based on the field association relationship, it is determined whether there is an inter-table association relationship between the two corresponding data tables, and an inter-table association edge is established between the two corresponding table nodes, forming the data source association graph.In the data source association graph, table nodes represent data tables, and inter-table association edges represent the relationships between two data tables. Inter-table association edges record the corresponding field relationships, the names of the associated fields, and the percentage of sample overlap. These are used to determine the associated data tables and the associated fields for establishing the inter-table relationships when generating SQL query statements.

[0029] In this implementation plan, by reading database table structure information, field sample values, and field constraint relationships, and combining this with a pre-trained large language model for field business semantic recognition and inter-table relationship analysis, a data source relationship graph can be automatically constructed, improving the accuracy of identifying business object fields and statistical indicator fields in complex business databases. Simultaneously, through a joint determination mechanism of field name association, field value association, and structural association, the probability of erroneous associations between different data tables is reduced, improving the accuracy and feasibility of subsequent SQL query generation and multi-source business data association queries.

[0030] Specifically, the process of parsing natural language data access instructions and generating corresponding business indicator query results is as follows: The user inputs a natural language data access instruction, which is a text-based data query and display requirement, such as a statement describing the query object, statistical content, time conditions, and display method. Based on a pre-trained large language model, the natural language data access instruction is semantically parsed to identify the business object, statistical indicator, time range, and display requirement, resulting in a semantic parsing result. Here, the business object represents the category of the queried and displayed object, the statistical indicator represents the indicator content of the corresponding object to be queried, the time range limits the data time interval corresponding to the query, and the display requirement represents the user's desired display format. After the pre-trained large language model performs semantic parsing on the natural language data access instruction, it outputs the semantic parsing result according to a preset structured format. The semantic parsing result includes at least four types of fields: business object, statistical indicator, time range, and display requirement, and outputs the parsing confidence score for each type of field. The parsed business object and statistical indicator are matched and validated with the field business semantic results, the parsed time range is converted into executable time filtering conditions, and the parsed display requirement is mapped to a preset display requirement type. When the confidence level of a business object or statistical indicator is lower than a preset confidence threshold, or when the corresponding field cannot be matched in the field's business semantic results, an SQL query statement is not generated directly. Instead, a confirmation prompt is generated to prompt the user to supplement the business object, statistical indicator, or time range. When the natural language data access instruction contains multiple business objects or multiple statistical indicators, the corresponding business objects and statistical indicators are identified separately, and the corresponding business object fields and statistical indicator fields are matched separately based on the field's business semantic results. Through the above-mentioned structured output, field mapping verification, and ambiguity fallback mechanism, field matching errors and SQL generation deviations caused by relying solely on the free text output of the large language model can be avoided, improving the reproducibility and feasibility of the natural language data access process.Based on the business objects and statistical indicators in the semantic parsing results, the corresponding business object fields and statistical indicator fields are matched according to the field business semantic results. The business object field is used to represent the identifier or name field of the business object in the business database, and the statistical indicator field is used to represent the numerical field or status field of the statistical indicator in the business database. For example, when the natural language data access instruction is "display the temperature change trend of each region", the business object field corresponding to the business object "region" can be region_name, and the statistical indicator field corresponding to the statistical indicator "temperature" can be temperature_value. When the natural language data access instruction is "display the ranking of device alarm counts", the business object field corresponding to the business object "device" can be device_id, and the statistical indicator field corresponding to the statistical indicator "alarm counts" can be alarm_count. The system identifies the data tables to which the corresponding business object fields and statistical indicator fields belong, as well as the associated data tables and fields that have inter-table relationships with the belonging data tables, from the data source association graph. The belonging data tables determine the data source of the business object fields and statistical indicator fields; the associated data tables provide the join query path when the business object fields and statistical indicator fields are not in the same data table; and the associated fields serve as the fields for performing join queries between different data tables. When the business object fields and statistical indicator fields are not in the same table, the system reads the associated data tables and corresponding associated fields that have inter-table relationships with the belonging data tables from the data source association graph, and generates inter-table join query relationships based on the associated fields. When multiple associated data tables exist, the associated data table with the fewest join hops is preferred as the join query path. Based on the business object field, statistical indicator field, parent data table, related data table, related fields, and time range, an SQL query statement is generated. The SQL query statement includes the query field, source data table, join conditions, and time filter conditions. The query field is determined by the business object field and statistical indicator field; the source data table is determined by the parent data table and related data table; the join conditions are determined by the related fields; and the time filter conditions are determined by the time range. Specifically, when the business object field and statistical indicator field are in the same data table, the SQL query statement is generated based on that data table. When the business object field and statistical indicator field are in different data tables, the SQL query statement generates an inter-table join query statement based on the related fields. When the business object field and statistical indicator field are in different data tables, the JOIN join path is determined based on the inter-table association edges recorded in the data source association graph, and the join conditions are generated based on the related fields corresponding to the inter-table association edges. When the business object field and statistical indicator field are in the same data table, no inter-table join conditions are generated.When the statistical indicator corresponding to the statistical indicator field is a summation indicator, count indicator, average indicator, or detail indicator, SUM, COUNT, AVG, or the original value of the field are used as the query expression, respectively. The time range is converted into the WHERE time filter condition in the SQL query statement, and the time filter condition is generated based on the field corresponding to the indicator timestamp. Aliases are set for the business object field and the statistical indicator field, preferably using the business object and statistical indicator names from the semantic parsing results. After the SQL query statement is generated, permission and keyword checks are performed. Permission checks determine whether the current user has permission to access the corresponding source data table and field, while keyword checks prohibit the execution of SQL keywords for deleting, updating, inserting, or modifying the table structure, ensuring that the SQL query statement is only used for data querying. The SQL query statement is then executed to obtain the business indicator query results, which include: business object, statistical indicator, indicator value, and indicator timestamp. The business object originates from the business object field, the statistical indicator originates from the indicator meaning corresponding to the statistical indicator field, the indicator value originates from the query results of the statistical indicator field, and the indicator timestamp originates from the data time field corresponding to the time range. The data access result dataset is formed by associating and storing business data source metadata, field business semantic results, data source relationship graph, natural language data access instructions, semantic parsing results, SQL query statements, and business indicator query results. This dataset records the correspondence between natural language data access instructions, SQL query statements, and business indicator query results. This allows the subsequent automatic configuration module of the large screen to determine the display data structure and chart component types based on business objects, statistical indicators, indicator values, and indicator timestamps. When business indicator query results are updated, the data access result dataset is updated based on the updated business objects, statistical indicators, indicator values, and indicator timestamps. Subsequent automatic configuration, runtime adaptive rendering, and intelligent early warning identification phases, based on the updated data access result dataset, perform adjustments to the display data structure, chart component types, runtime dynamic rendering, and anomaly linkage display, respectively.

[0031] In this implementation plan, by performing semantic parsing on natural language data access commands and automatically matching business object fields, statistical indicator fields, belonging data tables, related data tables, and related fields with field business semantic results and data source association graphs, the user's textual query requirements can be converted into executable SQL query statements, reducing the workload of manually understanding field meanings, finding relationships between tables, and writing SQL. At the same time, by forming a data access result dataset, an accurate and traceable data foundation is provided for subsequent determination of chart component types, identification of display data structures, and automatic configuration of large screens.

[0032] Specifically, the process of determining the display data structure and chart component type based on the business indicator query results is as follows: Based on the business objects, statistical indicators, indicator values, and indicator timestamps in the business indicator query results, the display data structure is determined. This display data structure represents the data organization form corresponding to the business indicator query results. The display data structure includes at least: time series data, object comparison data, and indicator overview data. In the business indicator query results, when the same statistical indicator corresponds to indicator values ​​under multiple indicator timestamps, it is preferable to determine the display data structure as time series data. When the same statistical indicator corresponds to indicator values ​​under multiple business objects, it is preferable to determine the display data structure as object comparison data. When a business indicator query result corresponds to a single indicator value at the current time or within a specified time range, the preferred data structure for display is indicator overview data. When a business indicator query result simultaneously meets multiple display data structure criteria, the final display data structure is determined based on the display requirements. For example, when a business indicator query result contains indicator values ​​corresponding to multiple business objects under multiple indicator timestamps, the query result simultaneously meets the criteria for time series data and object comparison data. In this case, if the display requirement is trend display, the final display data structure is determined to be time series data; if the display requirement is comparison display, the final display data structure is determined to be object comparison data. Time series data represents the trend relationship of indicator values ​​over time, object comparison data represents the indicator difference relationship between different business objects, and indicator overview data represents the overall status of the current business indicator or the results of key indicators. The chart component type is determined based on the data structure and display requirements. This type represents the visualization method of the corresponding business indicator query results on the large screen. When the data structure is time-series data and the display requirement is trend display, the chart component type is determined to be a line chart. When the data structure is object comparison data and the display requirement is comparison display, the chart component type is determined to be a bar chart or column chart. When the data structure is indicator overview data and the display requirement is overview display, the chart component type is determined to be an indicator card or dashboard. A unique chart component number is generated for each corresponding chart component. This number uniquely identifies each chart component corresponding to the current business indicator query result. The chart component number is preferably generated by combining the business object, statistical indicator, and generation order. Specifically, the business object distinguishes chart components corresponding to different business objects, the statistical indicator distinguishes chart components corresponding to different business indicators, and the generation order distinguishes multiple chart components generated under the same business object and the same statistical indicator. The chart component number is preferably generated using string concatenation to ensure the uniqueness and traceability of different chart component numbers.Chart component types are stored as updatable attributes associated with chart component numbers. When changes in the displayed data structure cause a switch in chart component types, the original chart component number remains unchanged, and the chart component type associated with that chart component number is updated. For example, when the business object is a region and the statistical indicator is temperature, a chart component number in the form of "Region_Temperature_001" can be generated; when the business object is a device and the statistical indicator is alarm count, a chart component number in the form of "Device_AlarmCount_001" can be generated. These chart component numbers are unique within the same large-screen configuration scheme. A correspondence is established between chart component numbers and business indicator query results to quickly locate the corresponding chart component and perform runtime dynamic rendering and anomaly-related adjustments based on updated business indicator data.

[0033] In this implementation plan, by identifying the data organization format of the business indicator query results and determining the corresponding chart component type in combination with the display requirements, different types of business data can be displayed in a more suitable chart format. At the same time, by generating a unique number for the chart component and establishing its correspondence with the business indicator query results, it is easier to quickly locate the target chart component when the business indicator is updated or an anomaly is triggered, thereby improving the accuracy and response efficiency of automatic configuration of the large screen, dynamic rendering during runtime, and abnormal linkage adjustment.

[0034] Specifically, the process of generating an initial layout scheme based on the chart component type and the parsing results of natural language data access instructions is as follows: Obtain large-screen terminal configuration data, including screen width, screen height, and preset core display area coordinates. This data originates from pre-registered terminal configuration records in the large-screen management platform. These records record the display size, resolution, and core display area configuration for different large-screen terminals. The screen width represents the horizontal display size, the screen height represents the vertical display size, and the preset core display area coordinates represent the target area position for prioritizing the display of key chart components. The preset core display area coordinates are represented using a two-dimensional pixel coordinate format consistent with the large-screen display area, preferably including the horizontal coordinate of the upper left corner, the vertical coordinate of the upper left corner, the width of the core display area, and the height of the core display area. The large-screen coordinate system uses the upper left corner of the large-screen display area as its origin, with the horizontal coordinate ranging from 0 to the screen width and the vertical coordinate ranging from 0 to the screen height. Based on the large-screen terminal configuration data, the large-screen display area is divided into a core display area, adjacent display areas, and normal display areas, and the width and height of each display area are recorded. The core display area is determined by a rectangular area corresponding to preset core display area coordinates. The adjacent display areas are the display areas adjacent to the boundary of the core display area. The normal display areas are the remaining display areas in the large-screen display area excluding the core and adjacent display areas. The width and height of each display area are used to determine the display size and placement of chart components. Specifically, the large-screen display area is set as a rectangular area with the upper left corner of the large screen as the origin, the screen width as the horizontal length, and the screen height as the vertical length. The upper left corner coordinates, width, and height of the core display area are determined based on the preset core display area coordinates. The adjacent display areas are the display areas within a preset distance outside the core display area, and their width and height are determined based on the remaining space between the boundary of the core display area and the boundary of the large-screen display area. The preset distance range is set proportionally to the screen width and screen height, preferably 5% to 20% of the smaller of the screen width and screen height. Adjacent display areas include at least one rectangular area located above, below, to the left, or to the right of the core display area and connected to the boundary of the core display area; the specific number is determined by the available space between the core display area and the boundary of the large screen display area. When the remaining space in a certain direction is insufficient to form an area that meets the preset minimum display area threshold, no adjacent display area is generated in that direction. The normal display area is the remaining rectangular area in the large screen display area excluding the core display area and adjacent display areas; its width and height are determined based on the remaining display space.The display priority of chart components is determined based on the display requirements: when the display requirement is an overview, the corresponding chart component is assigned a first-level display priority; when the display requirement is a trend display, the corresponding chart component is assigned a second-level display priority; and when the display requirement is a comparison display, the corresponding chart component is assigned a third-level display priority. The display priority determines the initial display area and hierarchical order of chart components on the large screen, with first-level priority exceeding second-level priority, and second-level priority exceeding third-level priority. First-level priority chart components are placed in the core display area, second-level priority chart components are placed in adjacent display areas, and third-level priority chart components are placed in ordinary display areas. When multiple chart components exist within the same display area, they are arranged according to the generation order of their chart component numbers. The preferred arrangement method is row-priority, which means placing chart components sequentially from left to right along the horizontal axis. When the remaining width of the current row is insufficient to place the next chart component, the next component is placed along the vertical axis. The display area is divided based on its width, height, and the number of chart components within the same display area. Optionally, the display area can be divided evenly to obtain multiple sub-display areas. The width and height of each sub-display area are determined as the width and height of the corresponding chart component, and the current position coordinates of the chart component are obtained. Furthermore, the chart component preferably has a set display area range, including a preset minimum display area threshold and a preset maximum display area threshold. The display area formed by the width and height of the chart component should be greater than or equal to the preset minimum display area threshold and less than or equal to the preset maximum display area threshold. The preset minimum display area threshold ensures the readability of the chart component content, while the preset maximum display area threshold prevents a single chart component from excessively occupying the display area. The preset minimum and maximum display area thresholds are preset based on the screen resolution, chart component type, font size, and chart content density. When the area of ​​the sub-display area after dividing the current display area according to the number of chart components is less than the preset minimum display area threshold, the chart components with a lower hierarchical order are moved to the empty sub-display area in other display areas; when the area of ​​the sub-display area is greater than the preset maximum display area threshold, the display area of ​​the corresponding chart component is limited to within the preset maximum display area threshold, and the remaining space is reserved as an empty sub-display area. The width and height of the chart component are determined by the size of the sub-display area after the corresponding display area is divided, and the current position coordinates of the chart component are the coordinates of the upper left corner of the chart component in the large screen coordinate system; the large screen coordinate system has the upper left corner of the large screen display area as the origin, the horizontal direction as the x-axis direction, and the vertical direction as the y-axis direction.The chart component hierarchy is determined by ranking the chart components according to their display priority and component number from highest to lowest. Chart components with higher display priority appear earlier in the hierarchy. When two chart components have the same display priority, the one whose component number was generated earlier appears earlier in the hierarchy. In other words, the chart component hierarchy is first sorted by display priority, with first-level priority chart components preceding second-level priority chart components, and second-level priority chart components preceding third-level priority chart components. When display priorities are the same, the order in which the component numbers were generated is then used to avoid ambiguity caused by directly relying on numerical values. An initial layout scheme is generated based on the chart component type, component number, width, height, current position coordinates, and hierarchy. This initial layout scheme records the initial display method, size, position, and hierarchy of each chart component on the large screen, and serves as the foundation for subsequent overlap detection, runtime adaptive rendering, and abnormal linkage adjustments.

[0035] In this implementation plan, by reading the configuration data of the large-screen terminal and clearly dividing the core display area, adjacent display area and ordinary display area, a definite layout position and size basis can be provided for chart components with different display priorities. At the same time, based on the display requirements, the generation order of chart component numbers and the display area size, the position, width and height and hierarchical order of chart components are determined, so that the initial layout scheme has an implementable, traceable and adjustable configuration basis, reducing the difficulty of random stacking of chart components, hierarchical chaos and subsequent overlapping adjustments.

[0036] Specifically, the process of coordinating and adjusting the initial layout scheme to form the large-screen configuration scheme is as follows: Based on the initial layout scheme and the width, height, and current position coordinates of the chart components, determine the display area boundary of each chart component in the large-screen display area; whereby the display area boundary represents the area occupied by the chart component in the large-screen coordinate system, preferably determined by the top-left corner coordinates corresponding to the current position coordinates of the chart component, the width, and the height of the chart component. Determine the chart-related component number based on business objects and statistical indicators: When two chart components correspond to the same business object or the same statistical indicator, it is determined that the two chart components have a related display relationship, and the two chart component numbers are mutually marked as each other's chart-related component numbers; the chart-related component number is used to record other chart components that have a related relationship with the current chart component in terms of business objects or statistical indicators, so as to synchronously determine the related display objects during subsequent runtime adaptive rendering and abnormal linkage adjustments. The initial layout scheme is subjected to overlap determination and adjustment: it is determined whether the display area boundaries of any two chart components overlap; the preferred determination method is: based on the display area boundaries of the two chart components, it is determined whether there is an intersection area between the two display areas; if there is an intersection area between the two display areas and the area of ​​the intersection area is greater than zero, then it is determined that the display area boundaries of the two chart components overlap; if there is no intersection area between the two display areas, or the area of ​​the intersection area is equal to zero, then it is determined that the display area boundaries of the two chart components do not overlap. When the display area boundaries of any two chart components overlap, the current position coordinates of the chart component with the later hierarchical order are adjusted according to the chart component hierarchy order until the display area boundaries of the two chart components no longer overlap. Specifically, the chart component with the earlier hierarchical order remains in its current position, while the chart component with the later hierarchical order determines its movement direction and distance based on the intersection area of ​​the two chart components. When the width of the intersection area is less than its height, the chart component with the later hierarchical order is moved horizontally by a distance equal to the width of the intersection area plus a preset interval distance. When the height of the intersection area is less than or equal to its width, the chart component with the later hierarchical order is moved vertically by a distance equal to the height of the intersection area plus a preset interval distance, ensuring that the display area boundaries of the two chart components no longer overlap. The preset interval distance is used to maintain a display spacing between the two chart components, preferably 5 to 20 pixels. After each adjustment of the current position coordinates of a chart component that is later in the hierarchy, it is re-evaluated whether there is an intersection between that chart component and the other chart components. If there is still an intersection, the adjustment continues according to the above movement rules until the display area boundaries of any two chart components no longer overlap, or the number of iterations reaches the preset adjustment limit.If overlap still exists after the preset adjustment limit is reached, the current position coordinates of the chart component with the higher hierarchy remain unchanged, and the chart component with the lower hierarchy is moved to other available sub-display areas. If no available sub-display areas exist, the chart component with the lower hierarchy is scaled down proportionally based on the intersection area, and the overlap determination is re-executed. The initial layout scheme after overlap adjustment is determined as the large screen configuration scheme. The large screen configuration scheme includes: chart component number, chart component type, chart component width, chart component height, current position coordinates of the chart component, chart component display priority, chart component hierarchy order, and chart associated component number. The large screen configuration scheme and the data access result dataset are stored together. By storing the large screen configuration scheme together with the data access result dataset, the correspondence between chart components, business indicator query results, displayed data structure, and layout position can be maintained, providing a configuration basis for subsequent business indicator updates, chart type switching, abnormal chart identification, and core area linkage adjustments.

[0037] In this embodiment, Table 1 is a data table for the initial layout configuration of the large screen. The table details the display requirements, display priority, display area, chart component width, chart component height, top-left X-coordinate, and top-left Y-coordinate for each of the five chart component numbers. Specifically, the display requirements for chart component number Region_Temperature_001 are overview display, display priority is level 1, display area is the core display area, chart component width is 520, chart component height is 300, top-left X-coordinate is 120, and top-left Y-coordinate is 80; the display requirements for chart component number Region_Temperature_002 are trend display, display priority is level 2, display area is the adjacent display area, chart component width is 360, chart component height is 220, top-left X-coordinate is 700, and top-left Y-coordinate is 80; the display requirements for chart component number Device_Alarm_001 are comparison display, display priority is level 3. The display area is the normal display area, the chart component width is 280, the chart component height is 180, the top-left X coordinate is 1120, and the top-left Y coordinate is 420; the display requirement corresponding to chart component number Energy_Trend_001 is trend display, the display priority is level two, the display area is the adjacent display area, the chart component width is 360, the chart component height is 220, the top-left X coordinate is 700, and the top-left Y coordinate is 340; the display requirement corresponding to chart component number Overview_Load_001 is overview display, the display priority is level one, the display area is the core display area, the chart component width is 520, the chart component height is 300, the top-left X coordinate is 120, and the top-left Y coordinate is 420.

[0038]

[0039] like Figure 3 The image shows a schematic diagram of the initial layout of a large screen based on configuration evolution. It illustrates the results of chart component area division and layout in the initial layout phase using a data analysis adaptive method based on configuration and application evolution. The entire large screen display area establishes a two-dimensional coordinate system with the top-left corner as the origin, where the horizontal width is the X-axis and the vertical height is the Y-axis. The large screen display area is divided into a core display area, adjacent display areas, and a normal display area based on display priority. Specifically: the core display area is located on the left and is used to display chart components with first-level display priority; the adjacent display area is located in the middle and is used to display chart components with second-level display priority; the normal display area is located on the right and is used to display chart components with third-level display priority. In the diagram: Region_Temperature_001 and Overview_Load_001 represent overview displays, corresponding to first-level display priority, and are therefore placed in the core display area; Region_Temperature_002 and Energy_Trend_001 represent trend displays, corresponding to second-level display priority, and are therefore placed in adjacent display areas; Device_Alarm_001 represents comparison displays, corresponding to third-level display priority, and is therefore placed in the normal display area. Each chart component determines its width, height, and current position coordinates based on the width and height of its corresponding display area and the number of components, and is arranged in row-major order. There is no overlap in display areas between different chart components, indicating that the system has completed the initial layout phase of area division and component placement.

[0040] In this implementation plan, by determining the display area boundaries of chart components, identifying related chart components, and performing overlap judgment and position adjustment on the initial layout scheme, the occlusion conflict between chart components can be eliminated before generating the large screen configuration scheme. At the same time, according to the hierarchical order of chart components, the positions of higher-level components are reserved first, and the layout coordination and readability are improved by moving, iteratively adjusting, and shrinking lower-level components when necessary. This provides a stable layout foundation for subsequent business indicator updates, abnormal chart identification, and core area linkage adjustments.

[0041] Specifically, the process of receiving real-time updated business indicator data, adjusting the current displayed data structure and chart component types, and combining this with animated transitions to achieve dynamic rendering during runtime is as follows: Real-time reception of updated data corresponding to business indicator query results, including updated data on business objects, statistical indicators, indicator values, and indicator timestamps. This updated data originates from new or changed records of business objects and statistical indicators corresponding to the generated chart components in the business database, representing the latest changes in the current business indicators during runtime. Based on the correspondence between chart component numbers and business indicator query results, the updated data is matched to the corresponding chart component. The current displayed data structure is then redefined based on the updated data. The method for redefining the current displayed data structure is consistent with the method used in the automatic configuration phase of the large screen, i.e., determining the current data organization form based on the business objects, statistical indicators, indicator values, and indicator timestamps in the updated data. Furthermore, the matching relationship between the current displayed data structure and the corresponding chart component type is preferably consistent with the mapping relationship between the displayed data structure and chart component type during the automatic configuration phase of the large screen. When the current displayed data structure is time series data and the current chart component type is a line chart component, it is determined that the current displayed data structure and the corresponding chart component type match. When the current displayed data structure is object comparison data and the current chart component type is a bar chart component or column chart component, it is determined that the current displayed data structure and the corresponding chart component type match. When the current displayed data structure is indicator overview data and the current chart component type is an indicator card component or dashboard component, it is determined that the current displayed data structure and the corresponding chart component type match. Otherwise, it is determined that the current displayed data structure and the corresponding chart component type do not match. When the current displayed data structure and the corresponding chart component type match, the corresponding chart component type remains unchanged. Here, "match" means that the current chart component type can still express the data organization form corresponding to the current displayed data structure. For example, time series data corresponds to trend chart components, object comparison data corresponds to comparison chart components, and indicator overview data corresponds to overview chart components. When the current displayed data structure does not match the corresponding chart component type, the chart component type is redefined based on the current displayed data structure, and the chart component type of the corresponding chart component number is updated. The method for redefined chart component type is consistent with the method used in the automatic configuration phase of the large screen to determine the chart component type based on the displayed data structure and display requirements, ensuring that the chart type switching rules during runtime remain consistent with the initial configuration rules. Rendering is performed on the corresponding chart component, with animation transitions during the rendering process. Rendering includes redrawing the chart content according to the updated chart component type and updating the indicator values ​​and timestamps from the updated data to the corresponding chart component. Animation transitions preferably use gradient, translation, or scaling transitions during chart component type switching, data refresh, and component redrawing to smoothly transition the chart component from its pre-adjustment display state to its post-adjustment display state.The updated data and the updated chart component types are then associated and stored to form a runtime rendering result dataset. This dataset records updated business metrics, chart component numbers, updated chart component types, and corresponding rendering times, enabling subsequent intelligent alerts to identify abnormal chart components based on real-time metric values.

[0042] In this implementation plan, by receiving real-time updated business metrics data and re-evaluating the current data structure, the system can automatically update chart component types and dynamically render them during runtime when changes in business data cause the original chart component types to become incompatible. At the same time, by using animation transitions, the system reduces visual jumps during chart switching and data refresh, thereby improving the continuity of information expression, display adaptability, and user viewing stability during large-screen operation.

[0043] Specifically, the process of determining whether business metrics in the query results are abnormal, identifying abnormal chart components, assessing the degree of impact of the abnormality, and determining the priority of abnormal display is as follows: Based on the real-time metric value and the corresponding statistical metric's abnormal threshold, determine whether the statistical metric is abnormal. If so, mark the chart component number corresponding to the statistical metric as an abnormal chart component. The abnormal threshold is preset based on the corresponding statistical metric. When the statistical metric is an upper limit constraint metric, if the real-time metric value is greater than the corresponding abnormal threshold, the statistical metric is determined to be abnormal. When the statistical metric is a lower limit constraint metric, if the real-time metric value is less than the corresponding abnormal threshold, the statistical metric is determined to be abnormal. The abnormal chart component is used to represent the chart component currently carrying the task of displaying abnormal statistical metrics. When a statistical metric changes from a non-abnormal state to an abnormal state, the abnormal start time is recorded. The abnormal start time is the timestamp of the metric when the statistical metric first meets the abnormality judgment condition. If the statistical metric is in an abnormal state for multiple consecutive metric timestamps, the abnormal start time remains unchanged to ensure that the duration of the abnormality reflects the continuous duration of the abnormality. For abnormal chart components, the abnormal deviation magnitude, abnormal duration, and number of associated chart components are calculated. The abnormal deviation magnitude is determined by the difference between the indicator value and the abnormal threshold, representing the degree to which the current indicator value deviates from the normal range. When the statistical indicator is an upper limit constraint indicator, the abnormal deviation magnitude is preferably the positive difference between the indicator value and the abnormal threshold; when the statistical indicator is a lower limit constraint indicator, the abnormal deviation magnitude is preferably the positive difference between the abnormal threshold and the indicator value. The abnormal duration is determined by the difference between the current indicator timestamp and the abnormal start time, representing the length of time the abnormal state has lasted. The number of associated chart components is determined by the number of associated chart component numbers, representing the scope of related display affected by the current abnormal chart component. After normalization, the abnormal deviation magnitude, abnormal duration, and number of associated chart components are weighted and merged to obtain an abnormal display priority value. The normalization process is used to convert the abnormal deviation magnitude, abnormal duration, and number of chart-related components to the same value range, preferably to the range of 0 to 1. The normalization method is preferably minimum-maximum normalization, with the maximum and minimum values ​​obtained based on the corresponding window statistics. The weights corresponding to the abnormal deviation magnitude, abnormal duration, and number of chart-related components are all preset according to the importance of the severity, stability, and scope of the abnormality in the business monitoring scenario, with a value range of 0 to 1, and the sum of the three weights is 1. The weight corresponding to the abnormal deviation magnitude is preferably 0.4 to 0.6, the weight corresponding to the abnormal duration is preferably 0.2 to 0.4, and the weight corresponding to the number of chart-related components is preferably 0.1 to 0.3.

[0044] In this implementation plan, abnormal chart components are identified by an anomaly threshold, and the priority value for anomaly display is evaluated by combining the magnitude of the anomaly deviation, the duration of the anomaly, and the number of associated chart components. This can simultaneously reflect the severity of the anomaly, its continuous stability, and the scope of its associated impact. When multiple anomalies occur concurrently, it can provide a clear basis for subsequent anomaly chart sorting, core area linkage display, and early warning broadcasting, avoiding low-impact anomalies from occupying key display areas and improving the accuracy of key anomaly identification and display.

[0045] Specifically, the process of dynamically and collaboratively displaying abnormal information on the large screen involves adjusting the core area of ​​abnormal chart components, issuing warnings, and coordinating overlapping rendering based on the priority of abnormal display. When more than one abnormal chart component exists under the same indicator timestamp, all abnormal chart components are extracted and sorted from highest to lowest abnormal display priority. A higher priority value indicates that the corresponding abnormal chart component needs to occupy core display resources more preferentially under the current indicator timestamp. The sorting result determines the display order among multiple abnormal chart components. The hierarchical order of abnormal chart components is determined based on the sorting result, with non-abnormal chart components arranged sequentially after the abnormal chart components according to their original hierarchical order. The hierarchical order of abnormal chart components is higher than that of non-abnormal chart components to ensure that abnormal chart components are displayed first in case of display conflicts. The abnormal chart component with the highest abnormal display priority value is designated as the priority display chart component and adjusted to the core display area. The core display area is a pre-defined key display area in the large screen configuration scheme. When the priority display chart component is adjusted to the core display area, the current position coordinates of the corresponding chart component are updated according to the coordinate range of the core display area. The remaining abnormal and non-abnormal chart components are adjusted sequentially to adjacent and normal display areas according to their chart component hierarchy, with abnormal chart components highlighted. Specifically, abnormal chart components with higher hierarchical order are prioritized for adjustment to adjacent display areas, while those with lower hierarchical order are adjusted to normal display areas. Highlighting includes at least one of the following: border highlighting, background color change, or flashing prompts, used to distinguish between abnormal and non-abnormal chart components. When the remaining available display area in either the adjacent or normal display area is insufficient to accommodate the chart component to be adjusted, it is first determined whether there is a spare sub-display area within the same display area that is larger than or equal to the display area of ​​the chart component to be adjusted. If so, the chart component to be adjusted is moved to that spare sub-display area; otherwise, according to the chart component hierarchy, the chart components with lower hierarchical order are moved to the spare sub-display area in the next display area, where the next display area after the adjacent display area is the normal display area. If no available sub-display area meets the preset minimum display area threshold in the normal display area, then chart components that are later in the hierarchy are scaled down proportionally. When the scaled-down chart component's display area is greater than or equal to the preset minimum display area threshold, the scaled-down chart component is placed in the remaining space in the normal display area. When the scaled-down chart component's display area is less than the preset minimum display area threshold, the original display area of ​​the chart component remains unchanged, and only a highlighting tooltip is applied. The preferred arrangement for multiple abnormal chart components in the same display area is to arrange them in row-major order according to their hierarchical order.When the priority value of an abnormal chart component exceeds the broadcast threshold, a warning broadcast is generated and a voice broadcast is executed. The broadcast threshold is pre-set based on the level of abnormality requiring proactive broadcasting in the business monitoring scenario, used to filter high-priority abnormal chart components that require voice alerts. The warning broadcast content includes the abnormal business object, statistical indicators, indicator values, indicator timestamps, abnormal start time, and abnormal duration. The voice broadcast converts the warning broadcast content into a voice signal through a speech synthesis module, ensuring that monitoring personnel can receive high-priority abnormal information even when not continuously watching the large screen. Based on the adjusted chart component width, height, and current position coordinates, the display area boundaries of each chart component are redefined, and overlap judgment and adjustment are performed again to obtain a layout scheme after overlap adjustment, which is then rendered. The overlap judgment and adjustment methods are consistent with those in the large screen automatic configuration phase to avoid new chart component occlusion after the core area is adjusted. Rendering includes updating the chart component positions, hierarchy order, and highlight status according to the layout scheme after overlap adjustment. The abnormal chart component tags, abnormal display priority values, early warning broadcast content, and overlapping adjustment layout schemes are associated and stored to form an early warning linkage result dataset. This dataset records the sorting results, core area adjustment results, voice broadcast results, and overlapping coordination results of abnormal chart components under the same metric timestamp, providing historical data for subsequent configuration evolution and abnormal display strategy optimization. When business metrics are updated, chart component types are switched, or abnormal chart component linkage adjustments trigger a new large-screen configuration scheme, the new scheme inherits the chart component number, associated component number, and current position coordinates of unadjusted chart components from the previous version. It also updates the changed chart component type, width, height, current position coordinates, hierarchy order, and display priority. A traceable configuration evolution record is formed through the configuration version number, configuration generation time, configuration trigger source, and inheritance relationship from the previous version.

[0046] like Figure 4The diagram illustrates the workflow of an adaptive data analysis method based on configuration and application evolution. It showcases the overall processing flow of this method, comprehensively describing the entire process from natural language data access, automatic layout generation, runtime dynamic rendering, to anomaly-related interactive display. At the start of the process, the user inputs a natural language data access command, entering the "Intelligent Data Source Exploration" stage. This stage connects to the business database, reads database table structure information, field sample values, and field constraint relationships, and combines this with a pre-trained large language model to identify the business semantics of the fields, constructing a data source relationship graph. Based on this, an SQL query statement is generated according to the natural language data access command and executed to obtain the business indicator query results. The next stage is the "Automatic Large Screen Configuration" stage. This stage determines the display data structure based on the business indicator query results, including time series data, object comparison data, and indicator overview data, and further determines the corresponding chart component types. The core display area, adjacent display areas, and ordinary display areas are divided according to display requirements, generating an initial layout scheme. Subsequently, overlap judgment and coordination adjustments identify display area conflicts between chart components, adjust the positions of the chart components, and finally form the large screen configuration scheme. During the runtime phase, the system enters the "Runtime Adaptive Rendering" phase. It receives real-time updates to business metrics and reassesses whether the currently displayed data structure has changed. If the current data structure matches the current chart component type, the original chart component type remains unchanged; only the metric values ​​are updated, and dynamic rendering is performed. If the data structure changes and does not match the current chart component type, the chart component type is redefined, and the chart component type associated with the corresponding chart component number is updated. Simultaneously, dynamic rendering is performed in conjunction with animation transitions, forming the runtime rendering result dataset. Next, the system enters the "Intelligent Early Warning Recognition" phase. It determines whether statistical metrics are abnormal based on real-time metric values ​​and anomaly thresholds. If no abnormal chart components exist, the current large-screen display state is maintained. If abnormal chart components exist, the abnormal deviation magnitude, duration of the abnormality, and the number of associated chart components are further calculated, and an abnormal display priority value is generated. Abnormal chart components are reordered according to their abnormal display priority value. The chart component with the highest abnormal display priority value is moved to the core display area, while other abnormal and non-abnormal chart components are moved to adjacent display areas and normal display areas. Abnormal chart components are then highlighted. When the priority value for an anomaly display exceeds the preset broadcast threshold, a corresponding warning broadcast is generated and a voice broadcast is executed. Afterwards, overlap detection and adjustment are performed again to prevent new chart components from obscuring each other after the anomaly linkage adjustment, ultimately forming the warning linkage result dataset.

[0047] In this implementation scheme, concurrent abnormal chart components are sorted by anomaly display priority, and the highest priority abnormal chart component is adjusted to the core display area in a linked manner. This can avoid multiple abnormal charts competing for key display space at the same time. At the same time, through highlighting, voice broadcasting, and overlapping coordinated rendering, high-priority anomalies can be presented first and promptly alerted, reducing the risk of abnormal charts being obscured, visual focus being scattered, and key anomalies being marginalized, thereby improving the information transmission efficiency and operational stability of the large screen in scenarios with multiple concurrent anomalies.

[0048] Reference Figure 2 As shown, the second aspect of this invention provides a data analysis adaptive system based on configuration and application evolution, applied to the aforementioned data analysis adaptive method based on configuration and application evolution, comprising: a data source intelligent exploration module, used to connect to the business database, analyze database table structure information, field business semantics, and inter-table relationships, and construct a data source relationship graph; parse natural language data access instructions and generate corresponding business indicator query results; a large screen automatic configuration module, used to determine the display data structure and chart component type based on the business indicator query results, generate an initial layout scheme based on the chart component type and combined with the natural language data access instruction parsing results, coordinate and adjust the initial layout scheme to form a large screen configuration scheme; a runtime adaptive rendering module, used to receive business indicator update data in real time, adjust the current display data structure and chart component type, and combine animation transitions to achieve runtime dynamic rendering; and an intelligent early warning identification module, used to determine whether the business indicators in the business indicator query results are abnormal, identify abnormal chart components, assess the degree of abnormal impact, obtain the abnormal display priority, and perform core area linkage adjustment, early warning broadcast, and overlapping coordinated rendering on abnormal chart components according to the abnormal display priority, to achieve dynamic collaborative display of abnormal information on the large screen.

[0049] This implementation plan achieves closed-loop management from business data access, chart component generation, layout configuration to abnormal linkage display through the coordinated efforts of intelligent data source exploration, automatic large screen configuration, runtime adaptive rendering, and intelligent early warning identification. It can reduce the reliance on manual modeling and adjustment for large screen configuration, improve the adaptability of chart display to changes in business indicators, and coordinate core areas, issue early warnings, and coordinate overlaps based on the priority of abnormal display when multiple abnormalities occur concurrently, thereby improving the timeliness, readability, and stability of the large screen operation.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. As those skilled in the art will understand, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An adaptive data analysis method based on configuration and application evolution, characterized in that, Includes the following steps: Step 1: Connect to the business database, analyze the database table structure information, field business semantics, and inter-table relationships, and construct a data source relationship graph; Parse natural language data access commands and generate corresponding business indicator query results; Step 2: Determine the data structure and chart component types to be displayed based on the business indicator query results. Based on the chart component types and the natural language data access command parsing results, generate an initial layout scheme. Coordinate and adjust the initial layout scheme to form a large screen configuration scheme. Step 3: Receive real-time updates of business metrics, adjust the current data structure and chart component types, and combine animation transitions to achieve dynamic rendering during runtime. Step four: Determine whether the business metrics in the business metric query results are abnormal, identify abnormal chart components, assess the degree of impact of the abnormality, obtain the priority of abnormal display, and perform core area linkage adjustment, early warning broadcast and overlapping coordinated rendering on the abnormal chart components according to the priority of abnormal display, so as to realize the dynamic and collaborative display of abnormal information on the large screen.

2. The adaptive data analysis method based on configuration and application evolution according to claim 1, characterized in that, The specific process of connecting to the business database, analyzing database table structure information, field business semantics, and inter-table relationships, and constructing a data source relationship graph is as follows: The system connects to the business database to read the database table structure information, table comments, and field sample values ​​to obtain the business data source metadata. The database table structure information includes the table name, field name, field type, field length, and field constraint relationships. The field name, table comments, field type, and field sample values ​​are then input into a pre-trained large language model to identify the business meaning of the fields and obtain the business semantic results of the fields. String matching is performed on field names in different data tables. When two field names contain the same business keyword, the corresponding field is identified as a field name association field. Sample values ​​for each field are read, and the number of identical data values ​​in the sample value sets of two fields is counted. The number of identical data values ​​is divided by the minimum of the total number of sample values ​​in both field sets to obtain the sample overlap ratio. When the sample overlap ratio is greater than the overlap ratio threshold, the corresponding field is identified as a field value association field. Foreign key constraints in field constraints are read. When a foreign key relationship exists between two fields, the corresponding field is identified as a structure association field. When two fields are identified as structural association fields, or simultaneously identified as field name association fields and field value association fields, a field association relationship is established; each data table is treated as a table node, and an inter-table association relationship is determined based on the field association relationship between the two data tables, and an inter-table association edge is established between the corresponding two table nodes to form a data source association graph.

3. The adaptive data analysis method based on configuration and application evolution according to claim 2, characterized in that, The specific process of parsing natural language data access instructions and generating corresponding business indicator query results is as follows: Receive natural language data access instructions from users, perform semantic parsing on the natural language data access instructions based on a pre-trained large language model, identify the business objects, statistical indicators, time ranges and display requirements, and obtain semantic parsing results; Based on the business objects and statistical indicators in the semantic parsing results, the corresponding business object fields and statistical indicator fields are matched based on the field business semantic results. The data tables to which the corresponding business object fields and statistical indicator fields belong, as well as the associated data tables that have inter-table relationships with the data tables to which they belong and the associated fields that establish inter-table relationships, are determined from the data source association graph. Based on the business object field, statistical indicator field, the data table to which it belongs, the related data table, the related field, and the time range, generate an SQL query statement and execute the SQL query statement to obtain the business indicator query results. The business indicator query results include: business object, statistical indicator, indicator value, and indicator timestamp. The data access result dataset is formed by associating and storing business data source metadata, field business semantic results, data source association graph, natural language data access instructions, semantic parsing results, SQL query statements, and business indicator query results.

4. The adaptive data analysis method based on configuration and application evolution according to claim 3, characterized in that, The specific process for determining the data structure and chart component type based on the business indicator query results is as follows: Based on the business objects, statistical indicators, indicator values, and indicator timestamps in the business indicator query results, determine the data structure to be displayed. The data structure to be displayed includes: time series data, object comparison data, and indicator overview data. The chart component types are determined based on the data structure and display requirements, and a unique chart component number is generated for each chart component. A correspondence is then established between the chart component number and the business indicator query results.

5. The adaptive data analysis method based on configuration and application evolution according to claim 1, characterized in that, The specific process of generating an initial layout scheme based on the chart component type and the parsing results of natural language data access instructions is as follows: Obtain large-screen terminal configuration data, including: screen width, screen height, and preset core display area coordinates; divide the large-screen display area into core display area, adjacent display area, and normal display area according to the large-screen terminal configuration data, and record the width and height of the corresponding display area; The display priority of chart components is determined based on the display requirements: when the display requirement is an overview, the corresponding chart component is set to the first-level display priority; when the display requirement is a trend display, the corresponding chart component is set to the second-level display priority; when the display requirement is a comparison display, the corresponding chart component is set to the third-level display priority. First-priority chart components are placed in the core display area, second-priority chart components are placed in adjacent display areas, and third-priority chart components are placed in the normal display area. The display area is divided according to the width and height of the corresponding display area and the number of chart components in the same display area to determine the width and height of the chart components and obtain the current position coordinates of the chart components. The chart component hierarchy is determined according to the display priority and chart component number from high to low. Generate an initial layout scheme based on the chart component type, chart component number, chart component width, chart component height, current position coordinates of the chart component, and chart component hierarchy order.

6. The adaptive data analysis method based on configuration and application evolution according to claim 5, characterized in that, The specific process of coordinating and adjusting the initial layout scheme to form a large-screen configuration scheme is as follows: Based on the initial layout plan, and using the chart component width, chart component height, and the current position coordinates of the chart component, determine the display area boundary of each chart component in the large screen display area; Determine the chart association component number based on business objects and statistical indicators: When two chart components correspond to the same business object or the same statistical indicator, it is determined that the two chart components have an associated display relationship, and the two chart component numbers are marked as each other's chart association component numbers. Perform overlap detection and adjustment on the initial layout scheme: determine whether the display area boundaries of any two chart components overlap; when the display area boundaries of any two chart components overlap, adjust the current position coordinates of the chart component with the later hierarchy according to the chart component hierarchy order of the two chart components, until the display area boundaries of any two chart components no longer overlap. The initial layout scheme, after overlapping adjustments, is determined as the large screen configuration scheme. The large screen configuration scheme includes: chart component number, chart component type, chart component width, chart component height, current position coordinates of chart component, chart component display priority, chart component hierarchy order, and chart associated component number. The large screen configuration scheme and the data access result dataset are then linked and stored.

7. The adaptive data analysis method based on configuration and application evolution according to claim 1, characterized in that, The specific process of receiving real-time updated business metrics data, adjusting the currently displayed data structure and chart component types, and combining this with animated transitions to achieve dynamic rendering during runtime is as follows: It receives updated data corresponding to the query results of business indicators in real time, including updated data of business objects, statistical indicators, indicator values ​​and indicator timestamps, and redetermines the current data structure based on the updated data; When the current data structure matches the corresponding chart component type, the corresponding chart component type remains unchanged; when the current data structure does not match the corresponding chart component type, the chart component type is redefined based on the current data structure, and the chart component type of the corresponding chart component number is updated. Render the corresponding chart component, perform animation transitions during the rendering process, and store the updated data and the updated chart component type together to form a runtime rendering result dataset.

8. The adaptive data analysis method based on configuration and application evolution according to claim 1, characterized in that, The specific process for determining whether the business metrics in the business metric query results are abnormal, identifying abnormal chart components, assessing the degree of impact of the abnormality, and obtaining the priority of abnormal display is as follows: Based on the real-time indicator values ​​and the corresponding abnormal thresholds of the statistical indicators, determine whether the statistical indicators are abnormal. If so, mark the chart component number corresponding to the statistical indicator as an abnormal chart component. When the statistical indicator changes from a non-abnormal state to an abnormal state, record the start time of the abnormality. For abnormal chart components, the abnormal deviation magnitude, abnormal duration, and number of associated chart components are calculated. The abnormal deviation magnitude is determined by the difference between the indicator value and the abnormal threshold, the abnormal duration is determined by the difference between the current indicator timestamp and the abnormal start time, and the number of associated chart components is determined by the number of associated chart component numbers. After normalizing the abnormal deviation magnitude, abnormal duration, and number of associated chart components, a weighted fusion is performed to obtain the abnormal display priority value.

9. The adaptive data analysis method based on configuration and application evolution according to claim 8, characterized in that, The specific process of dynamically and collaboratively displaying abnormal information on the large screen by performing core area linkage adjustment, early warning broadcasting, and overlapping coordinated rendering on the abnormal chart component according to the priority of abnormal display is as follows: When there are more than one abnormal chart component under the same indicator timestamp, all abnormal chart components are extracted and sorted from high to low according to the abnormal display priority value. The chart component hierarchy order of abnormal chart components is determined according to the sorting result, and non-abnormal chart components are arranged after the abnormal chart components according to the original chart component hierarchy order. The abnormal chart component with the highest abnormal display priority value is identified as the priority display chart component and moved to the core display area; the remaining abnormal chart components and non-abnormal chart components are moved to the adjacent display area and normal display area in order of chart component hierarchy, and the abnormal chart components are highlighted. When the priority value of the abnormal display of the abnormal chart component is greater than the broadcast threshold, an early warning broadcast is generated and a voice broadcast is executed; the early warning broadcast content includes the abnormal business object, statistical indicators, indicator values, indicator timestamps, abnormal start time and abnormal duration; Based on the adjusted width, height, and current position coordinates of the chart components, the display area boundaries of each chart component are redefined, and overlap judgment and adjustment are performed again to obtain the layout scheme after overlap adjustment and execute rendering; the abnormal chart component mark, abnormal display priority value, warning broadcast content, and the layout scheme after overlap adjustment are associated and stored to form a warning linkage result dataset.

10. A data analysis adaptive system based on configuration and application evolution, characterized in that, include: The intelligent data source exploration module is used to connect to the business database, analyze the database table structure information, field business semantics and inter-table relationships, and build a data source relationship graph. Parse natural language data access commands and generate corresponding business indicator query results; The large screen automatic configuration module is used to determine the data structure and chart component type to be displayed based on the business indicator query results. Based on the chart component type and combined with the natural language data access command parsing results, it generates an initial layout scheme and coordinates and adjusts the initial layout scheme to form a large screen configuration scheme. The runtime adaptive rendering module is used to receive real-time updates of business metrics, adjust the current data structure and chart component types, and combine them with animation transitions to achieve dynamic rendering during runtime. The intelligent early warning and identification module is used to determine whether the business indicators in the business indicator query results are abnormal, identify abnormal chart components, assess the degree of impact of the abnormality, obtain the priority of abnormal display, and perform core area linkage adjustment, early warning broadcast and overlapping coordinated rendering on the abnormal chart components according to the priority of abnormal display, so as to realize the dynamic and collaborative display of abnormal information on the large screen.

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