Visualization system and method for low-code index arrangement and chart reuse of linkage label system and medium

Abstract

The invention discloses a visualization system and method for low-code index arrangement and chart reuse of a linkage label system and a medium, and the method comprises the following steps: realizing accurate group screening through data definition and label integration. Collaborative data filtering of a data set and a label system is carried out to realize rapid group screening; based on a defined data set, a user can arrange a data applet or a data canvas through a self-service pattern component supporting and pulling mode, the complex index calculation requirement is met by means of flexible data operation support, the component reuse efficiency is improved by means of a data applet reuse mechanism, and the user experience is improved. A convenient canvas arrangement and output scheme is combined to adapt to diversified office scenes, meanwhile, non-homologous configuration of a data text component is innovated to break through the limitation of multi-source data integration, and a set of efficient, flexible and functional data analysis and visualization solution is integrally constructed.

Description

Technical Field

[0001] This invention belongs to the field of fully automated products and relates to a low-code indicator arrangement and chart reuse visualization system, method and medium for a linked label system. Background Technology

[0002] With the popularization of data analysis, although data visualization tools can achieve data connection and basic chart display, data visualization has the following disadvantages: Data definition lacks extensibility Most common visualization tools on the market perform analysis directly based on data definitions under existing data sources. They do not support the rapid definition and use of data definitions. When it is necessary to create new definitions and analyze them for a specific group, it is necessary to adjust the content under the data source through online implementation, which lacks scalability and flexibility.

[0003] Low-code computing power is limited. Common data visualization tools on the market only support simple statistical analysis. Complex data statistics require users to have certain technical skills. In complex business scenarios, it is difficult to implement flexible and complex multi-indicator data arrangement and calculation in low-code, as well as high-frequency use scenarios and advanced calculation needs such as multi-dimensional, year-on-year and month-on-month comparisons, and daily and monthly averages for various time frequencies in natural day and business date scenarios.

[0004] Chart components suffer from poor reusability. Most market tools are designed for single-use creation, failing to enable efficient component reuse across scenarios and users, leading to significant repetitive development work. Even when creating similar charts, developers still need to redesign and configure them, consuming considerable time and effort, and cannot modify existing similar charts to improve chart design quality. Furthermore, the lack of a unified component library often results in differences in chart styles and functionality between different projects, impacting user experience.

[0005] Insufficient canvas layout and output adaptability. The canvas size of commonly used tools on the market is difficult to automatically adapt to the size of commonly used document types in office scenarios such as PPT and WORD, and they do not include multi-page configuration functions, making it impossible to split pages according to content logic to achieve the effect of page-by-page division and collaboration. In addition, the output format has poor compatibility with office software, making it difficult to quickly generate materials that can be directly used for reporting, resulting in low efficiency from data analysis to results implementation.

[0006] Text components suffer from a common-origin limitation. Most tools on the market require text components to display multiple dynamically calculated metrics, meaning these metrics must originate from the same dataset (common-origin configuration). This prevents flexible integration of metrics from multiple datasets. To display multi-source metrics, one must manually merge datasets or create multiple independent text components, a cumbersome process that disrupts text flow. Summary of the Invention

[0007] To address the aforementioned problems, the technical solution adopted by this invention is: a low-code indicator arrangement and chart reuse visualization system for a linked tagging system, comprising: Dataset module: Used to store at least one dataset; The labeling system is associated with the dataset module and is used to assign calculated labels to data elements in the dataset based on the relationship between the dataset and the labeling system. Filtering module: Connects to the dataset module and the tagging system respectively, and is used to display and provide optional group filtering conditions and return a list of optional groups; The visualization orchestration module provides a graphical component library and a drag-and-drop interactive interface for dragging and dropping graphical components to generate data mini-programs or data canvases. The data processing engine module, embedded in the visualization orchestration module, is used to perform complex index calculations on the dataset; The heterogeneous data integration module includes a rich text component and a data text component, which are used to display multiple heterogeneous data sources in a text and realize the index calculation results of field-level data. Output module: Used to convert the data canvas into file and display formats compatible with various office scenarios.

[0008] Furthermore, the process for determining the association between the dataset and the labeling system is as follows: S21: Associate the dataset with different labeled subjects, and use the label results to perform group screening and data filtering. S22: Use the main members under different tags as the filtering query conditions, and link with other datasets associated with the tags to achieve switching the query conditions of different tag members, that is, switching to the corresponding tag group profile. S23: Connect the standardized API interface of the tag system with the data service type dataset.

[0009] Furthermore, the configuration of the data mini-program is as follows: Get 1 dataset; Determine the data dimensions of the dataset that the current chart component ultimately needs to summarize, statistically analyze, and display; Filter the time range in the time type field of the accessed dataset; Configure the dataset to achieve the business expectations based on data processing; Sort the configuration data by dimension and metric; For the X data entries returned after configuration, data updates and rendering are implemented based on linkage rules.

[0010] Furthermore: the implementation of complex metric calculations for the dataset includes: Configure the indicator calculation rules, specifically including the following: Based on the selected dimension, combine multiple fields to form an arithmetic operation expression for multiple field aggregation. On the accessed data, perform individual aggregation or non-aggregation statistics on Y fields (measures) according to X fields (dimensions). At the same time, set coefficients on the aggregated fields and perform arithmetic operations between the Y aggregated measure fields. The calculation rules include grouped statistical filtering conditions, statistical caliber definition, calculation method declaration, and internal percentage calculation method; The grouped statistical filtering conditions are set separately for different metric fields. Statistical scope: including conventional statistical scope: the results of statistical indicator calculations based on indicator calculations and grouped statistical filtering conditions; Daily average statistical caliber: Based on the conventional statistical caliber, divided by the number of trading days within the time range; Calculation methods include: Conventional calculation method: Calculation is performed according to the statistical results based on the statistical caliber; Month-on-month calculation method: Based on the conventional calculation method, it automatically divides the selected time period by the calculation result within the statistical time range of the previous period, according to the frequency of the selected time period (day, week, month, quarter, year). Year-on-year calculation method: Based on the conventional calculation method, it is automatically divided by the calculation result within the same sequential period of the previous year, according to the frequency of the selected time period (day, week, month, quarter, year). Percentage calculation method: Based on the conventional calculation method, divide by the index calculation result after the index calculation is summarized according to the percentage statistical filtering conditions. The dividend is a fixed value after summary statistics. Internal percentage calculation method: The denominator in the percentage calculation is also grouped and calculated according to the dimension, that is, the percentage is calculated under the same dimension.

[0011] Furthermore: when the data canvas is invoked, existing data mini-programs are directly reused through drag-and-drop using copy and reference modes; In the copy mode, the data mini-program exists as an independent component in the canvas, allowing users to perform secondary editing. In reference mode, the data mini-program is synchronized with the mini-program in the canvas. When the original mini-program changes, the data mini-program referenced in the canvas also changes accordingly.

[0012] Furthermore, the data canvas and data applet generate a unique link that can be used by third-party applications or system integrations.

[0013] Furthermore, the process of implementing non-same-origin configuration for the text component is as follows: A text component is associated with N data text components, and each data text component is independently associated with a different dataset, where N≥1.

[0014] Each data text component calculates and generates multiple metrics from its associated dataset, and then exposes the metric results to the text component as variables. When editing a text component, the user can insert indicator result variables at any text location; When data is updated, the text component automatically synchronizes the indicator calculation results of all related data text components, dynamically updates variable values, and ensures that the text content is updated in real time.

[0015] A low-code visualization method for arranging metrics and reusing charts in a linked tagging system includes the following steps: Get at least one dataset saved; Based on the association between the dataset and the labeling system, computed labels are assigned to data elements in the dataset. Submitting group filtering criteria via drag-and-drop operation returns a filtered subset of data. Generate data mini-programs or data canvases by dragging and dropping graphical components. Based on data processing, it enables the calculation of complex indicators in datasets; Bind at least two heterogeneous data sources through a configurable interface and achieve field-level data fusion; The data canvas is converted into a display format compatible with various office scenarios.

[0016] A readable storage medium that stores a program module, which, when executed in a processor, can implement any of the methods described herein.

[0017] This invention provides a low-code visualization system, method, and medium for linked tagging systems, enabling indicator orchestration and chart reuse. This system boasts multiple core advantages: precise group analysis is achieved through deep integration of data definition and tagging systems; flexible data processing capabilities meet the computational needs of complex indicators; component reuse efficiency is improved by relying on a data mini-program reuse mechanism; convenient canvas orchestration and diverse output schemes adapt to different office scenarios; innovative non-same-source configuration of data text components breaks through the limitations of multi-source data integration; and system performance is ensured through query and rendering optimizations. Ultimately, this system constructs an efficient, flexible, and fully functional data analysis and visualization solution.

[0018] The present invention has the following effects: Improve component reusability: Chart components can be reused across canvases, reducing redundant development, lowering the barrier to entry, and improving efficiency.

[0019] Enhanced data processing capabilities: Supports flexible and complex calculations, meets the needs of in-depth analysis, and improves scenario coverage.

[0020] Achieve precise group analysis: Integrate a tagging system to quickly locate and analyze specific groups, expanding the application scenarios.

[0021] Improve canvas layout and output efficiency: Low-code configuration allows new users to complete their first canvas in 10 minutes; adapted to office scenarios, with improved compatibility.

[0022] Break through the limitation of homogeneity of text components: Support the integration of indicators from multiple datasets without the need for merging or multiple components, improving production efficiency; dynamic variables ensure coherence and completeness, increasing information density. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is the overall architecture diagram of the system; Figure 2 This is a diagram illustrating the addition of a new dataset under a database data source; Figure 3 This is a diagram illustrating the configuration of associated tags under a database data source; Figure 4 This is a diagram illustrating the dimension selection for a data mini-program; Figure 5 This is a diagram illustrating the dynamic time range configuration (relative or absolute time against the background of the transaction date) for a data mini-program; Figure 6 This is a diagram illustrating data processing. Figure 7 This is a diagram illustrating tag filtering; Figure 8 This is a diagram illustrating the data processing and tag filtering configuration of the data mini-program; Figure 9 This includes the reuse of data mini-programs in the data canvas (charts in the mini-program bar on the left can be dragged directly to the canvas area in the middle without any configuration), and a diagram illustrating the switching of tag filters and group profiles. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. 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] A low-code visualization system for metric orchestration and chart reuse based on a linked tagging system includes: The dataset module is used to store at least one dataset. The data source in this application serves as the data source for data analysis and supports two data source types: database and data service. This application's method pre-configures an AnalyticDB database data source and also pre-configures a tag system API as the data service dataset.

[0028] You can create a dataset that meets your business expectations on the data source through table association configuration or custom SQL, and define the dataset information, including but not limited to: name, security level, category, field information (field names in English and Chinese, field types, etc.).

[0029] The labeling system is associated with the dataset module and is used to assign calculated labels to data elements in the dataset based on the relationship between the dataset and the labeling system. Filtering module: Connects to the dataset module and the tagging system respectively, and is used to display and provide optional group filtering conditions and return a list of optional groups; The visualization orchestration module provides a graphical component library and a drag-and-drop interactive interface for dragging and dropping graphical components to generate data mini-programs or data canvases. The data processing engine module, embedded in the visualization orchestration module, is used to perform complex index calculations on the dataset; The heterogeneous data integration module includes a rich text component and a data text component, which are used to display the indicator calculation results of multiple heterogeneous data sources in a single text. Output module: Used to convert the data canvas into file and display formats compatible with various office scenarios.

[0030] The process for determining the association between the dataset and the labeling system is as follows: S21: Associate the dataset with different labeled subjects, and use the label results to perform group screening and data filtering. S22: Use the main members under different tags as the filtering query conditions, and link with other datasets associated with the tags to achieve switching the query conditions of different tag members, that is, switching to the corresponding tag group profile. S23: Connect the standardized API interface of the tag system with the data service type dataset.

[0031] Furthermore, the configuration of the data mini-program is as follows: Get one dataset; select one from the defined datasets (supports database SQL and data service HTTP interface types) for access; Determine the data dimensions of the dataset that the current chart component ultimately needs to summarize and display; select one or more fields from the fields of the access dataset according to the chart type as the summary and display dimensions (e.g., set the X-axis of the bar chart to month and the Y-axis to profit and loss). The time range is filtered on the time type field of the accessed dataset; based on the time field of the dataset, the filtering supports absolute / relative time, and the relative time includes frequencies such as day, week, month, quarter, and year, and only for precise statistics on transaction dates; Configure the dataset to achieve business expectations based on data operations; data operations include indicator operations (aggregation of dimension / measure fields, setting coefficients, multi-field arithmetic operations), group statistical filtering (multi-condition combination filtering), statistical caliber (regular / daily average), calculation method (regular / month-on-month / year-on-year / percentage), unit conversion, data precision, and tag filtering (selective tag filtering of groups, drop-down list switching of multiple group profiles). Sort the configured data according to the sorting rules based on dimensions and measures; configure the data sorting rules based on dimensions and measures; data return: limit the return of only the first x data items; For the X data items returned after configuration, data updates and rendering are implemented based on linkage rules. The linkage rules between components are configured, and the linked components receive the linkage value as a filter / parameter value to re-query and update the data.

[0032] Furthermore: the implementation of complex metric calculations for the dataset includes: Configure the indicator calculation rules, specifically including the following: Based on the selected dimension, combine multiple fields to form an arithmetic operation expression for multiple field aggregation. On the accessed data, perform individual aggregation or non-aggregation statistics on Y fields (measures) according to X fields (dimensions). At the same time, set coefficients on the aggregated fields and perform arithmetic operations between the Y aggregated measure fields. The calculation rules include grouped statistical filtering conditions, statistical caliber definitions, calculation method declarations, and internal percentage calculation methods, as detailed below: Grouped statistical filtering conditions: Set separate grouped statistical filtering conditions on different measure fields; Statistical scope: including conventional statistical scope: the results of statistical indicator calculations based on indicator calculations and grouped statistical filtering conditions; Daily average statistical caliber: Based on the conventional statistical caliber, divided by the number of trading days within the time range; Calculation methods include: Conventional calculation method: Calculation is performed according to the statistical results based on the statistical caliber; Month-on-month calculation method: Based on the conventional calculation method, it automatically divides the selected time period by the calculation result within the statistical time range of the previous period, according to the frequency of the selected time period (day, week, month, quarter, year). Year-on-year calculation method: Based on the conventional calculation method, it is automatically divided by the calculation result within the same sequential period of the previous year, according to the frequency of the selected time period (day, week, month, quarter, year). Percentage calculation method: Based on the conventional calculation method, divide by the index calculation result after the index calculation is summarized according to the percentage statistical filtering conditions. The dividend is a fixed value after summary statistics. Internal percentage calculation method: The denominator in the percentage calculation is also grouped and calculated according to the dimension, that is, the percentage is calculated under the same dimension; Tag filtering: This involves filtering and statistically analyzing relevant data through data table association, thereby enabling group data statistics and analysis.

[0033] Furthermore, when the data canvas is invoked, it adopts copy mode and reference mode, and can directly reuse existing data mini-programs through drag and drop; In the copy mode, the data mini-program exists as an independent component in the canvas, allowing users to perform secondary editing. In reference mode, the data mini-program is synchronized with the mini-program referenced in the canvas. When the original mini-program changes, the data mini-program referenced in the canvas also changes accordingly.

[0034] Furthermore, the data canvas and data applet generate a unique link that can be used by third-party applications or system integrations.

[0035] Furthermore, the data mini-program or data canvas uses a visual drag-and-drop operation to select datasets, configure dimensions / measures, and adjust appearance styles, thereby enabling rapid chart generation.

[0036] Furthermore, the process of implementing non-same-origin configuration for the text component is as follows: A text component is associated with N data text components, and each data text component is independently associated with a different dataset, where N≥1.

[0037] Each data text component calculates and generates multiple metrics from its associated dataset, and then exposes the metric results to the text component as variables. When editing the text component, the user can insert the above variables at any text position; When data is updated, the text component automatically synchronizes the indicator calculation results of all related data text components, dynamically updates variable values, and ensures that the text content is updated in real time.

[0038] A low-code visualization method for arranging metrics and reusing charts in a linked tagging system includes the following steps: Get at least one dataset saved; Based on the association between the dataset and the labeling system, computed labels are assigned to data elements in the dataset. Submitting group filtering criteria via drag-and-drop operation returns a filtered subset of data. Generate data mini-programs or data canvases by dragging and dropping graphical components. Based on data processing, it enables the calculation of complex indicators in datasets; Bind at least two heterogeneous data sources through a configurable interface and achieve field-level data fusion; The data canvas is converted into a display format compatible with various office scenarios.

[0039] A readable storage medium that stores a program module, which, when executed in a processor, can implement the method as described in any one of the claims.

[0040] Example 1 A low-code visualization system for metric orchestration and chart reuse based on a linked tagging system includes: Dataset module: Used to store at least one dataset; The tagging system, associated with the dataset module, is used to assign calculated tags to data elements in the dataset based on the relationship between the dataset and the tagging system. The tagging system is a self-service tagging system that supports assigning various tags (including qualitative and quantitative tags) to members of various tag subjects (including warehouses, members, customers, varieties, contracts, etc.). The tag results mainly include: who (tag subject member) was tagged with what tag (tag value) at what time (data date). This content constitutes the overall tagging system of the tagging system.

[0041] Filtering module: Connects to the dataset module and the tagging system respectively, and is used to display and provide optional group filtering conditions and return a list of optional groups; The visualization orchestration module provides a graphical component library and a drag-and-drop interactive interface for dragging and dropping graphical components to generate data mini-programs or data canvases. The data processing engine module, embedded in the visualization orchestration module, is used to perform complex index calculations on the dataset; The heterogeneous data integration module includes a rich text component and a data text component, which are used to display the indicator calculation results of multiple heterogeneous data sources in a single text. Output module: Used to convert the data canvas into file and display formats compatible with various office scenarios.

[0042] Figure 1 This is the overall architecture diagram of the system. The data graph system adopts a layered architecture, including an application layer, service layer, interface layer, scheduling layer, execution layer, and storage layer.

[0043] The application layer includes modules such as data source, dataset, permissions, mini-program, canvas, and operations, providing user interaction interface and various business functions; The service layer encapsulates various functional services of the system; the interface layer provides general API capabilities to support system integration. The scheduling layer is responsible for the core scheduling optimization of the query engine; The execution layer contains execution components for the database and data services; The storage layer supports the storage resources of the entire system and query engine services.

[0044] Figure 2 This is a diagram illustrating the addition of a new dataset under a database data source; Figure 3 This is a diagram illustrating the configuration of associated tags under a database data source; This application deeply integrates the visualization system with the tagging system to enable the referencing and linkage of the tagging system within the system, facilitating rapid analysis of group profile data based on the tagging system. The integration primarily involves the following processes: S11: The linkage between dataset and tag body. When users create their own datasets, they can associate the datasets with different tag bodies, and the datasets can then use the tag results for group screening and data filtering. S12: Subject members under different tags can be used as filtering query conditions and linked with other datasets associated with tags to achieve switching of different tag subject member query conditions, that is, switching to the corresponding tag group profile. S13 pre-installs the standardized API interface of the tag system as a data service type dataset into this system. Users can directly use these datasets to call the tag results, enabling the rapid construction and flexible switching of targeted individual and group profiles.

[0045] Another example is the implementation of group data analysis: In customer profiling analysis, the "high-quality customer" tag is selected through the integrated tag center. The system obtains the set of customer IDs under this tag, associates them with the transaction data table, and quickly calculates data such as transaction amount and transaction frequency for this group, and generates analysis charts. The entire process takes an average of only 1 minute, while traditional manual screening and analysis takes more than 10 minutes.

[0046] The definition of a data mini-program is as follows: A data mini-program in this system is based on one or more native chart components. Each component can be configured with appearance attributes and data attributes. After users configure these attributes, a reusable data mini-program is formed. The system provides a default native chart component library. The types of components include, but are not limited to: charts, tables, text, controls, etc. Each type contains a rich variety of component styles, which can be customized according to data characteristics and user needs.

[0047] The data mini-program development process is as follows: Select one or more native chart components, and configure the data and appearance attributes of each component according to business needs. Then, save the components and attribute configurations together to form a data mini-program that carries business data, has a customizable style, and is reusable. The data attributes cover the configurations required for data operations, including the following configuration process: S21: Data Access; Select one from the defined datasets, supporting database SQL datasets and data service HTTP interface datasets; S22: Select Dimension; Select the data dimension that the current chart component will ultimately summarize and display. Select one or more fields from the data set you have accessed. The specific selection depends on the type of the current chart component. For example, for a bar chart, you can select the X-axis / dimension as commodity and the color grouping indicator / dimension as contract. Then, this chart will summarize and display data according to commodity and contract dimensions. Figure 4 It refers to the dimension selection for data mini-programs; S23: Time Range Selection. This feature filters the time range based on the time type field of the accessed dataset. It focuses on two key aspects: First, it supports selecting absolute or relative time ranges. Relative time ranges include various time frequencies and options, including but not limited to: day (previous trading day), week (this week, last week), month (this month, last month, last month, last three months), quarter (this quarter, last quarter), and year (this year, last year, last six months, last year). Second, the time range filtering only applies to transaction dates within the selected time's natural day range, enabling statistical filtering based on transaction dates. The system maintains transaction date data up to the current day in the database. Based on this data, the system can calculate the specific transaction date range to be calculated, as shown in Table 1. Table 1 Transaction Date Range

[0048] Figure 5 Dynamic time range configuration for data mini-programs (relative or absolute time against the background of transaction date). S24: Configure the data in the dataset to achieve the business expectations based on data operations; S25: Sort the configuration data by dimension and metric according to the sorting rules; S26: Limit the return of only the first x specified data items; S27: Data update and rendering are implemented based on linkage rules. The specific process is as follows: Configure the linkage rules with other components. You can add one or more linkage rules. Each rule can be configured to associate specific dimensions of the current component (which must be dimensions that the component has already queried) with specific fields (database type datasets) or parameters (data service type datasets) of other components. When a user triggers a component linkage operation, according to the linkage rules, the linked component receives the specific values ​​of certain dimensions passed from the linked component and uses them as filter conditions or parameter values ​​for its corresponding fields. After re-querying the data, the data is updated and rendered.

[0049] The data processing includes: Indicator Calculation: Based on the selected dimensions, multiple fields are combined to form arithmetic expressions for multiple field aggregation. Users can perform aggregation (or non-aggregation) statistics on Y fields (measures) according to X fields (dimensions) on the data they have entered. At the same time, coefficients can be set on the aggregated fields. Aggregation operations include but are not limited to: SUM, AVG, MAX, MIN, COUNT, COUNTDISTINCT. Arithmetic operations can be performed on the Y aggregated measure fields, such as data calculation logic like 0.5*SUM(transaction amount) / (100*MAX(transaction volume)+100*AVG(holding volume)). Grouped statistical filtering conditions: Set separate grouped statistical filtering conditions on different metric fields. For example, if the metric data is calculated as [0.5*SUM(volume) / SUM(open interest)], the independent statistical filter condition could be: [commodity = soybean oil AND customer type = corporate customer OR (commodity in (soybean meal, corn) AND customer type = general customer)]. This would count the [one-sided transaction and open interest ratio of corporate customers in soybean oil or general customers in soybean meal and corn]. Statistical definition: The statistical definition includes the conventional definition: the result of statistical indicator calculation based on indicator calculation and grouped statistical filtering conditions; Daily average: Based on the conventional statistical method, divided by the number of trading days within the time range; Calculation method: The calculation method includes: Standard calculation method: Based on the statistical results according to the statistical caliber, no other processing is performed; Month-on-month comparison (based on the standard calculation method, automatically divided by the calculation result within the statistical time range of the previous period according to the frequency of the selected time period (day, week, month, quarter, year); Year-on-year calculation method: Based on the conventional calculation method, it is automatically divided by the calculation result within the same sequential period of the previous year, according to the frequency of the selected time period (day, week, month, quarter, year). Percentage calculation method: Based on the conventional calculation method, the result of the index calculation is divided by the index calculation result after the percentage statistical filtering conditions are summarized. The dividend is a fixed value after the summary statistics. At the same time, it supports setting the percentage statistical filtering conditions separately, which are different from the filtering conditions of the numerator in the percentage calculation. For example, the proportion of the transaction volume of unit customers in soybean oil to the transaction volume of general customers and unit customers in agricultural products.

[0050] Simultaneously, the use of the internal percentage method is supported, meaning that the denominator in the percentage calculation is also grouped and calculated according to the dimension, that is, the percentage is calculated under the same dimension, such as: the proportion of a unit customer's transaction volume in soybean oil to the unit customer's transaction volume in agricultural products); for different statistical time ranges and calculation methods, the values ​​of the start and end times of the time range are shown in Table 2 below (the required start and end dates can be calculated based on the transaction date data): Table 2: Calculation Method

[0051] The conversion units include: thousands, ten thousands, hundreds of millions, trillions, percentages, etc. Data precision: up to 4 decimal places; Tag Filtering: If the dataset in the data access is configured with a tag body associated with a tag system, a qualitative tag under that tag body can be selected for filtering (group filtering). Data filtering and statistics are performed through data table association, thereby achieving group data statistics and analysis. For example, selecting the tag "High-Net-Worth Clients" reveals N clients tagged with this tag. The client IDs of these N clients (stored in the table) can be obtained, and the dataset can be associated with these client IDs to achieve filtering. It also supports adding a list of tag groups (such as "High-Net-Worth Clients," "Overseas Clients," "Iron Ore Industry Clients," etc.) to the dropdown component, and associating this dropdown with other graphic components that use tag filtering allows for one-click switching of the tag group in the dropdown. Other components simultaneously switch to statistical data indicators for the switched group, enabling one-click switching between multiple group profiles.

[0052] Figure 6 This is a diagram illustrating data processing. Figure 7 This is a diagram illustrating tag filtering; Figure 8 This is a diagram illustrating the data processing and tag filtering configuration of the data mini-program; An application based on complex data processing was developed to analyze the month-on-month growth of product sales in different regions. Utilizing the system's complex data processing capabilities, the "Sales Revenue" field was selected, grouping was set by "Region," the statistical caliber was set to "Regular," and the calculation method was set to "Month-on-Month." The system quickly completed the calculation and generated charts, accurately displaying the month-on-month changes in sales revenue for each region. The entire calculation process took an average of no more than 3 seconds, representing an efficiency improvement of approximately 50% compared to traditional tools.

[0053] Definition of a data canvas: A canvas can contain one or more mini-program or native chart components. The configurable properties of the canvas include appearance properties and data properties. After configuring these properties, users can design and arrange the data within the canvas to form a usable canvas.

[0054] Building a data mini-program reuse mechanism: Reuse in the Data Canvas. The Data Canvas allows for the reuse of data mini-programs, supporting both copy and reference modes. In copy mode, the mini-program exists as an independent component within the canvas, unrelated to the original mini-program, and users can perform secondary editing. In reference mode, the mini-program remains synchronized with the original mini-program; changes to the original mini-program are reflected in the referenced mini-program, and users cannot modify it within the canvas. The specific implementation involves the front-end page dynamically obtaining the overall configuration of the data mini-program via an API request, parsing it into component granularity, and then merging it into the canvas for rendering, thus enabling mini-program reuse within the canvas.

[0055] Reuse in third-party systems. The system will generate a unique link for each saved data canvas and data mini-program. This link can be integrated by third-party applications or systems. When integrating data canvases and data mini-programs, a micro-frontend framework solution can be used, such as MicroApp, to effectively reduce integration costs and coupling between projects, realize the reuse of data assets, and improve development efficiency.

[0056] Figure 9 This includes the reuse of data mini-programs in the data canvas (charts in the mini-program bar on the left can be dragged directly to the canvas area in the middle without any configuration), and a diagram illustrating the switching of tag filters and group profiles. Both the data canvas and the data mini-program are created using a visual drag-and-drop operation. Users do not need to write code. They only need to complete simple steps such as selecting the dataset, configuring the dimension / measure, and adjusting the appearance style to quickly generate charts, achieving low-code and fast configuration. The options for multi-size adaptation include the following: Data canvas size: Supports preset sizes commonly used in office scenarios, including standard PPT ratios (16:9, 4:3) and document sizes (A4, A5, B5), and also supports custom width and height in pixels to meet the needs of different reporting scenarios. Users can select the size when creating the data canvas or switch it at any time during editing, and the system will automatically adjust the component layout proportionally. Data Mini Program Size: In addition to supporting preset sizes commonly used in office scenarios, the data mini program also supports adaptive size. When dragged to the canvas, it scales proportionally and automatically adapts to the data canvas size to avoid layout errors.

[0057] Multi-page configuration: Supports freely adding and removing pages in the data canvas. Pages can be inserted or removed at any position using the "Add Page," "Delete Page," and "Copy Page" buttons, and the page order can be adjusted. Based on the data canvas size, each page can be paginated according to data mini-programs, that is, data mini-programs of the same size are included as one page in the data canvas.

[0058] Multi-format output: Supports one-click export of the canvas to PDF (split into multiple pages) or images (PNG / JPG format), maintaining the component layout and style during export. Exported files can be directly inserted into office software such as PPT and Word.

[0059] In a data analytics scenario within the financial industry, business personnel created a mini-program that displays a bar chart of monthly sales figures for a specific product. When this chart is needed in the annual sales analysis canvas, it can be dragged and dropped into the canvas using the reference mode. When the monthly sales figures in the mini-program are modified due to data updates, the mini-program in the annual sales analysis canvas is automatically updated, eliminating the need for manual reconfiguration and saving approximately 90% or more of the configuration time.

[0060] A business department needs to create a "Quarterly Business Review" report, which must include analytical charts for 5 core indicators and be compatible with PowerPoint (16:9) format.

[0061] Operation process: Select the five mini-programs you have created from the "Mini-Program Library": "Sales Trend", "Profit Margin Analysis", "Customer Growth", "Regional Distribution" and "Competitor Comparison". Select the "PPT16:9" size for each mini-program. When creating a new data canvas, select the "PPT16:9" size and click the "Add Page" button five times in a row to add the five data mini-programs to the corresponding pages of the data canvas. Fine-tune the title, background color and the first page of the data canvas, which takes about 5 minutes. Click "Export as PDF", and the system will split the data into five pages to generate a PDF file.

[0062] Results: The entire process, from data canvas creation to output completion, takes only 8 minutes. The exported PDF can be directly inserted into PowerPoint for presentations or played directly in PDF format. Compared to traditional tools (which require manual adjustment of size, pagination, and formatting, taking an average of 20 minutes), efficiency is improved by about 40%.

[0063] Furthermore, the data text component is defined as follows: a fundamental component focused on data querying and calculation. Each data text component is associated with a dataset, from which multiple metrics can be calculated (e.g., calculating "total sales revenue" and "average order value" from the "sales dataset"). It supports the complex calculations mentioned above (metric calculations, group filtering, year-on-year and month-on-month comparisons, etc.). The data text component is used only for backend calculations and is only visible during user editing, displaying the specific metric data values. It will be hidden when previewing the data canvas or mini-program and does not occupy frontend display space.

[0064] Text component definition: A container component used to integrate and display text content, supporting static text editing and dynamic data insertion, and can be associated with multiple data text components.

[0065] The process of implementing non-same-origin configuration for the text component is as follows: A text component can be associated with N (N≥1) data text components, and each data text component can be independently associated with a different dataset (supporting cross-dataset and cross-data source), realizing "one text component associated with multiple source data".

[0066] After each data text component calculates and generates multiple metrics from its associated dataset, it exposes the metric results to the text component in the form of variables (e.g., the metric variables of data text component 1 are {{metric1-1}} and {{metric1-2}}, and the metric variables of data text component 2 are {{metric2-1}}, etc.).

[0067] When editing the text component, users can insert the above variables at any text location. For example, in the static text "Total sales this quarter are {{Indicator 1-1}}, and the number of active users is {{Indicator 2-1}}", {{Indicator 1-1}} comes from the "Sales Dataset" and {{Indicator 2-1}} comes from the "User Dataset".

[0068] When data is updated, the text component automatically synchronizes the indicator calculation results of all related data text components, dynamically updates variable values, and ensures that the text content is updated in real time.

[0069] Implementation of non-same-origin text component configuration: A business department needs to create a "Monthly Business Summary" text, which needs to include the following dynamic indicators: Indicator A: "Total Sales (Month-on-Month Growth)" from the "Sales Dataset"; Indicator B: "Number of Active Users (Year-on-Year Growth)" from the "User Dataset"; Indicator C: "Average Order Amount" from the "Order Dataset".

[0070] Operation Flow: Create 3 data text components: Data Text Component 1: Connect to the "Sales Dataset" and configure indicator A (calculation rule: SUM (sales revenue), calculation method: month-on-month); Data Text Component 2: Connect to the "User Dataset" and configure indicator B (calculation rule: COUNT (user ID), calculation method: year-on-year); Data Text Component 3: Connect to the "Order Dataset" and configure indicator C (calculation rule: AVG (order amount)). Create a text component, associate it with the above 3 data text components, and obtain the variables {{Indicator A}}, {{Indicator B}}, and {{Indicator C}}. Edit the content in the text component: "Total sales revenue this month is {{Indicator A}}, active user count increased year-on-year by {{Indicator B}}, average order amount reached {{Indicator C}}, and overall business is on an upward trend." The system automatically calculates and populates the indicator values, generating complete text.

[0071] Results: The entire text creation process took approximately 3 minutes, requiring no technical personnel to merge the datasets, and the text remained coherent and complete. Compared to traditional tools (which require creating 3 text components or manually merging 3 datasets, averaging 10 minutes), this represents a 60% improvement in efficiency.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-code indicator arrangement and chart reuse visualization system with a linked tagging system, characterized in that: include: Dataset module: Used to store at least one dataset; The labeling system is associated with the dataset module and is used to assign calculated labels to data elements in the dataset based on the relationship between the dataset and the labeling system. Filtering module: Connects to the dataset module and the tagging system respectively, and is used to display and provide optional group filtering conditions and return a list of optional groups; The visualization orchestration module provides a graphical component library and a drag-and-drop interactive interface for dragging and dropping graphical components to generate data mini-programs or data canvases. The data processing engine module, embedded in the visualization orchestration module, is used to perform complex index calculations on the dataset; The heterogeneous data integration module includes a rich text component and a data text component, which are used to display the indicator calculation results of multiple heterogeneous data sources in a single text. Output module: Used to convert the data canvas into file and display formats compatible with various office scenarios.

2. The visualization system for low-code indicator arrangement and chart reuse of a linked tagging system according to claim 1, characterized in that: The process for determining the association between the dataset and the labeling system is as follows: S21: Associate the dataset with different labeled subjects, and use the label results to perform group screening and data filtering. S22: Use the main members under different tags as the filtering query conditions, and link with other datasets associated with the tags to achieve switching the query conditions of different tag members, that is, switching to the corresponding tag group profile. S23: Connect the standardized API interface of the tag system with the data service type dataset.

3. The visualization system for low-code indicator arrangement and chart reuse of a linked tagging system according to claim 1, characterized in that: The configuration of the data mini-program is as follows: Get 1 dataset; Determine the data dimensions of the dataset that the current chart component ultimately needs to summarize, statistically analyze, and display; Filter the time range in the time type field of the accessed dataset; Configure the dataset to achieve the business expectations based on data processing; Sort the configuration data by dimension and metric; For the X data entries returned after configuration, data updates and rendering are implemented based on linkage rules.

4. The visualization system for low-code indicator arrangement and chart reuse of a linked tagging system according to claim 3, characterized in that: The implementation of complex metric calculations for the dataset includes: Configure the indicator calculation rules, specifically including the following: Based on the selected dimension, combine multiple fields to form an arithmetic operation expression for multiple field aggregation. On the accessed data, perform individual aggregation or non-aggregation statistics on Y fields (measures) according to X fields (dimensions). At the same time, set coefficients on the aggregated fields and perform arithmetic operations between the Y aggregated measure fields. The calculation rules include grouped statistical filtering conditions, statistical caliber definition, calculation method declaration, and internal percentage calculation method; The grouped statistical filtering conditions are set separately for different metric fields. Statistical scope: including conventional statistical scope: the results of statistical indicator calculations based on indicator calculations and grouped statistical filtering conditions; Daily average statistical caliber: Based on the conventional statistical caliber, divided by the number of trading days within the time range; Calculation methods include: Conventional calculation method: Calculation is performed according to the statistical results based on the statistical caliber; Month-on-month calculation method: Based on the conventional calculation method, it automatically divides the selected time period by the calculation result within the statistical time range of the previous period, according to the frequency of the selected time period (day, week, month, quarter, year). Year-on-year calculation method: Based on the conventional calculation method, it is automatically divided by the calculation result within the same sequential period of the previous year, according to the frequency of the selected time period (day, week, month, quarter, year). Percentage calculation method: Based on the conventional calculation method, divide by the index calculation result after the index calculation is summarized according to the percentage statistical filtering conditions. The dividend is a fixed value after summary statistics. Internal percentage calculation method: The denominator in the percentage calculation is also grouped and calculated according to the dimension, that is, the percentage is calculated under the same dimension.

5. The visualization system for low-code indicator arrangement and chart reuse of a linked tagging system according to claim 1, characterized in that: When the data canvas is invoked, existing data mini-programs can be directly reused through drag and drop based on copy mode and reference mode. In the copy mode, the data mini-program exists as an independent component in the canvas, allowing users to perform secondary editing. In reference mode, the data mini-program is synchronized with the mini-program referenced in the canvas. When the original mini-program changes, the data mini-program referenced in the canvas also changes accordingly.

6. The visualization system for low-code indicator arrangement and chart reuse of a linked tagging system according to claim 1, characterized in that: The data canvas and data mini-program generate a unique link that can be used by third-party applications or system integration.

7. A low-code indicator arrangement and chart reuse visualization system for a linked tagging system according to claim 1, characterized in that: The process of implementing non-same-origin configuration for the text component is as follows: A text component is associated with N data text components, and each data text component is independently associated with a different dataset, where N≥1. Each data text component calculates and generates multiple metrics from its associated dataset, and then exposes the metric results to the text component as variables. When editing a text component, the user can insert indicator result variables at any text location; When data is updated, the text component automatically synchronizes the indicator calculation results of all related data text components, dynamically updates variable values, and ensures that the text content is updated in real time.

8. A low-code visualization method for arranging indicators and reusing charts in a linked tagging system, characterized in that: Includes the following steps: At least one dataset must be saved; Based on the association between the dataset and the labeling system, computed labels are assigned to data elements in the dataset. Submitting group filtering criteria via drag-and-drop operation returns a filtered subset of data. Data mini-programs or data canvases can be generated by dragging and dropping graphical components. Based on data processing, it enables the calculation of complex indicators of datasets; Bind at least two heterogeneous data sources through a configurable interface and achieve field-level data fusion; The data canvas is converted into a display format compatible with various office scenarios.

9. A readable storage medium storing a program module, characterized in that, The program module, when run in a processor, can implement the method as described in any one of claims 8.

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