A general data presentation and interactive processing method, product and device

Abstract

The present invention discloses a general data presentation and interactive processing method, product and device, which belongs to the field of data processing technology. The method takes low-code as the core, and realizes the full-process processing of data sets from input to presentation, interaction and analysis through standardized interfaces and automated processing; including: data presentation and page construction: first load parameters, obtain data from the data source, generate pages and add interactive functions, and personalize the pages; data set processing and page customization: obtain data sets, divide them into two categories: files and interfaces, render them, and then combine them to generate complete pages; use low-code designers to process pages to realize user personalization; data screening: determine the screening columns and conditions, generate screening logic according to whether there is configuration processing logic, and return the screened data set to re-render the page. The present invention can realize the full-process processing of data sets from input to presentation, interaction and analysis, improve efficiency and data quality, and optimize user experience.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a general data presentation and interactive processing method, product and device. Background Art

[0002] Traditional methods usually require a lot of code writing to implement data presentation and interactive functions, with long development cycles and high costs. It is also difficult to quickly adjust according to user needs, lacking flexibility and efficiency. Interactive operations such as data filtering and sorting often require users to have certain technical knowledge. The operation steps are cumbersome and prone to errors, and there is a lack of consistency and convenience between different functions. During the data import and processing process, there is a lack of effective automation means for operations such as data matching and missing data supplementation. Manual processing is relied upon, which is inefficient and prone to errors. In addition, when faced with large amounts of data, the system is prone to problems such as freezing and slow response, which affects the user experience. In terms of cross-system data synchronization, the development cost is high, the cycle is long, and there is a lack of universal solutions.

[0003] Therefore, it is particularly important to focus on data set processing, strive to solve a series of complex technical problems currently faced in the process of data presentation, interaction, and analysis, and be able to reuse it in various functional pages of the application system to ensure consistent user experience. Summary of the Invention

[0004] The present invention aims to solve one of the technical problems in the above-mentioned related art at least to a certain extent.

[0005] To this end, the purpose of the present invention is to provide a universal data presentation and interactive processing method, product and device, which can realize the full process processing of data sets from input to presentation, interaction and analysis, without the need for developers to repeatedly develop corresponding functions for different pages, thereby improving work efficiency and data quality and optimizing user experience.

[0006] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0007] The present invention provides a general data presentation and interactive processing method. The method is based on low-code and uses standardized interfaces and automated processing to achieve the full process of data set processing from input to presentation, interaction, and analysis. Users only need simple training, and each functional page in the application system can be uniformly operated and set up to meet their needs, taking into account user experience, versatility, and personalization. The content of the method includes:

[0008] Data presentation and page construction: First, load and set relevant parameters, obtain data from the data source, then generate the page and interactive function buttons, and finally, users can personalize the page according to their needs;

[0009] Dataset processing and page customization: Datasets are acquired and divided into two categories: files and interfaces. Each node in the dataset is identified as a one-dimensional or two-dimensional dataset, rendered into blocks or spreadsheets, and then combined to generate a complete page. In practice, a single table can be a page, or multiple blocks can constitute a page. The page is then processed using a low-code designer to achieve user customization and store the customization results.

[0010] Data filtering: After the user selects a filter column and enters conditions, the data is submitted to the backend. The backend then determines whether there is configured processing logic. If so, it uses the configured processing logic. If not, it automatically generates filtering logic based on the data source. The filtering operation is then performed, and the filtered data set is returned. The frontend then re-renders the page based on this data set, improving the accuracy and efficiency of the filtering.

[0011] Data behavior and interaction: Generate a dynamic form by selecting data fields and corresponding configurations to add and modify data. If a form configuration exists, a new header button and a modified row button will be generated. Generate a delete button by setting a delete action, and perform single row deletion or batch deletion based on whether a delete operation exists.

[0012] Setting parameters is divided into administrator unified settings and user personalized settings. If there is no user personalized settings, the parameters set by the administrator for all users are loaded for rendering. Settings between different users do not interfere with each other. The administrator's unified setting capabilities are greater than the user's personalized settings capabilities. For example: settings that require permissions cannot be set by unauthorized users.

[0013] During data filtering, when data is presented, the user configuration is read. If there is a corresponding configuration, filtering is performed according to the user configuration. The user can control the display, hiding, order, default value, etc. of the filtering conditions. The filtering conditions can be sent to the backend based on the user's operation on the data column or the operation at a fixed position. The backend uses the configured processing logic for filtering if there is a configuration. If there is no configuration, the filtering logic is automatically generated and the filtering operation is performed, returning the filtered data set. The frontend re-renders the page accordingly to improve the accuracy and efficiency of the filtering. If the filtering logic is not configured, the backend automatically generates the filtering conditions that can be executed by the corresponding data source through the operation selected by the user, such as the database generates: column = 'condition'. If no relevant filtering is provided when calling a third-party API, all data is loaded into memory, and then memory filtering is performed. The aforementioned default value refers to the default value set for a text box or drop-down box when it is presented. For example, when searching for a region, the default value = Beijing.

[0014] In addition, the general data presentation and interactive processing method according to the present invention may also have the following additional technical features:

[0015] In some embodiments, the method further comprises:

[0016] Data sorting: Load system default and user-defined setting data, determine the sorting rules to be used based on whether the user has set them, submit the sorting conditions to the backend for processing, and finally return the processed data to meet the sorting needs of different users.

[0017] In some embodiments, the method further comprises:

[0018] Data import: After the user triggers the import operation, they upload the file. Based on the file size, the file is parsed on the client or backend. After obtaining the input parameters from the backend storage interface, data matching is performed. Matching is completed automatically or manually, and missing data is supplemented. After saving the import settings, the backend is submitted for import. The backend uses optimization technology to process large amounts of data to ensure accurate and efficient data import.

[0019] Data export: After the user clicks the export button, the configuration is loaded, columns are removed or the export column names are modified based on whether there are settings, and then the data is exported to meet the user's different export requirements.

[0020] In some embodiments, the method further comprises:

[0021] User select all operation: After the user selects the data, a select all judgment is made. If all has been selected, a batch operation button will pop up in the position of the select all button, allowing users to quickly process batch data and improve the operation experience. When selecting all, the total quantity is compared with the selected quantity. If the quantity is inconsistent, it means that some data has not been selected, and the user will be reminded that not all data has been selected.

[0022] Data subscription operation: Users can select subscription objects from multiple datasets. The subscription method is divided into two types: row-based and cell-based. The system responds to subscriptions based on the selected method, realizing the linkage of operations on multiple datasets. For example: Selecting a row or cell in dataset A will change dataset B.

[0023] Data analysis includes routine analysis and data insights. Routine analysis is based on loaded data or background data. Data insights create new datasets by copying views, adjusting filtering parameters, and aggregating multiple datasets. New blocks are presented based on settings, enabling year-on-year and month-on-month analysis. Finally, analytical views are stored to meet data analysis needs at different levels.

[0024] In some embodiments, the method further comprises:

[0025] View conversion and generation: Select a view conversion method to automatically detect relational data in the dataset and generate a view if conditions are met. Otherwise, manually select data columns. After the system learns the features, users can customize the rendered view to provide a variety of view displays.

[0026] View replication and aggregation: The main view is replicated to generate multiple replicated views. Data in the replicated views is aggregated and then displayed in different view formats to facilitate user comparison and analysis of data. When comparing data, the difference comparison algorithm is used to highlight data differences.

[0027] In some embodiments, the method further comprises:

[0028] Data loading and rendering optimization: Determine the amount of display data. If it is greater than the specified number, enable rendering optimization, render part of the data and store the excess data, add response events, and determine whether to load excess data to render the view based on user scrolling operations and viewport position; if it is less than or equal to the specified number, no optimization is required, thereby effectively avoiding UI node rendering jams caused by excessive data.

[0029] In some embodiments, the method further comprises:

[0030] Data supplementation and loading: Multiple data supplementation interfaces can be configured to ensure data integrity and accuracy. Cell data supplementation interfaces can be configured through the low-code platform to automatically supplement missing data.

[0031] In some embodiments, the method further comprises:

[0032] Data verification and cross-system data synchronization: Configure verification rules for data accuracy issues, display problematic data differently, and remind users to revise them. When synchronizing data across systems, verify the data after obtaining and reporting the configuration. If it passes, report it. If login and plug-ins are involved, perform corresponding processing, and then convert the reporting logic into a scheduled task for execution.

[0033] An embodiment of the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the content of the general data presentation and interactive processing method as described in any one of the above items.

[0034] An embodiment of the present invention also provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the content of the general data presentation and interactive processing method as described in any one of the above items.

[0035] Compared with the prior art, the present invention has at least the following beneficial effects:

[0036] In the embodiments of the present invention, the general data presentation and interactive processing method provided can improve work efficiency, automate data processing processes and facilitate interactive operations, reduce manual intervention and repetitive work, and eliminate the need for developers to repeatedly develop corresponding functions for different pages, thereby greatly improving the efficiency of data processing and analysis and saving time and labor costs.

[0037] In the embodiments of the present invention, the general data presentation and interactive processing method provided can improve data quality: ensure the integrity and accuracy of data, provide a reliable basis for subsequent data analysis and decision-making, and avoid decision-making errors caused by data errors;

[0038] In the embodiments of the present invention, the general data presentation and interaction processing method provided can optimize the user experience: meet the personalized needs of users, provide diversified view display and interaction methods, enable users to more conveniently process and analyze data, and improve user satisfaction;

[0039] In the embodiments of the present invention, the general data presentation and interactive processing method provided can adapt to business development: in the case of growing data volume and increasingly complex business needs, it optimizes system performance and compatibility, ensures stable system operation, adapts to business scenarios of different scales and types, and provides strong support for the development of enterprises and organizations.

[0040] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of data presentation and page construction disclosed in one embodiment of the present invention;

[0042] Figure 2 A flowchart of data set processing and page customization disclosed in one embodiment of the present invention;

[0043] Figure 3 A flowchart of a data screening operation disclosed in one embodiment of the present invention;

[0044] Figure 4 A flowchart of a data sorting operation disclosed in one embodiment of the present invention;

[0045] Figure 5 A flowchart of data import operation disclosed in one embodiment of the present invention;

[0046] Figure 6 A flowchart of a data export operation disclosed in one embodiment of the present invention;

[0047] Figure 7 A flowchart of a user selection operation disclosed in one embodiment of the present invention;

[0048] Figure 8 A flowchart of a data subscription operation disclosed in one embodiment of the present invention;

[0049] Figure 9 A flowchart of view conversion and generation disclosed in one embodiment of the present invention;

[0050] Figure 10 A flowchart of view replication and aggregation disclosed in one embodiment of the present invention;

[0051] Figure 11 A flowchart of data loading and rendering optimization disclosed in one embodiment of the present invention;

[0052] Figure 12 A flowchart of data supplementation and loading disclosed in one embodiment of the present invention;

[0053] Figure 13 A flowchart of a data reporting operation disclosed in one embodiment of the present invention;

[0054] Figure 14 A flowchart of matching reported page data disclosed in one embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] The embodiments of the present invention are described in detail below through specific embodiments and application scenarios with reference to the accompanying drawings.

[0057] See also Figure 1 As shown, some embodiments of the present invention provide a universal data presentation and interactive processing method. This method, centered around low-code, implements an end-to-end process from dataset input to presentation, interaction, and analysis through standardized interfaces and automated processing. Based on user-provided datasets, the system dynamically generates a configurable spreadsheet UI. Modular functionality (such as filtering, sorting, importing, and exporting) enhances versatility, while supporting personalized settings and multi-view linkage, ultimately reducing development costs and optimizing the user experience.

[0058] In some embodiments of the present invention, the general data presentation and interaction processing method includes the following (the following steps are ordered by function, not by implementation):

[0059] Step 1: Data presentation and page construction, such as Figure 1 shown.

[0060] Step 1.1: Load settings. This step is the basis of the entire process and is used to set relevant parameters and configuration information.

[0061] Step 1.2: Load data. Based on the parameters set in step 1.1, obtain the data to be processed from the data source and proceed to the next step.

[0062] Step 1.3: Generate a page. After obtaining the data in step 1.2, present the data in a specific page format.

[0063] Step 1.4: Generate functional operations. After the page is presented in step 1.3, in order to make the page interactive, automatically generate functional operations such as adding, modifying, deleting, filtering, sorting, importing, exporting, and data analysis for each block-level unit on the page.

[0064] Step 1.5: Customize the page based on your needs. Customize the generated page based on your needs, such as displaying table stripes, hiding columns, changing column names, alignment, column sorting rules, whether columns need to be exported, data presentation conversion (e.g., 1 = male, 2 = female), adjusting row height, enabling column editing, fixing columns, cell display (e.g., using labels, images, different colors), and switching from a table to a chart (pie chart, bar chart, line chart, mixed chart, etc.) with one click.

[0065] Step 2: Dataset processing and page customization, such as Figure 2 shown.

[0066] Step 2.1: Get the dataset and divide it into two categories: files and interfaces.

[0067] Step 2.2: Identify each dataset, whether it is a one-dimensional dataset or a two-dimensional dataset. The styles generated by different datasets are inconsistent.

[0068] Step 2.3: Dataset rendering. Render the one-dimensional dataset into blocks and the two-dimensional dataset into a spreadsheet. Repeat this process until all datasets on the entire page have been rendered.

[0069] Step 2.4: Combine the rendered data to generate a complete displayable page.

[0070] Step 2.5: For the generated complete page, the generated page provides users with setting functions. Users can set it themselves when needed. During the setting, users are provided with a low-code designer for processing. Combined with the customization capabilities provided by tables and blocks, page generation can be quickly realized to achieve user personalized customization.

[0071] Step 2.6: Store the personalized customization results.

[0072] In some embodiments of the present invention, the aforementioned two-dimensional data sets are uniformly rendered into a spreadsheet. By injecting a backend interactive interface into the spreadsheet component, the spreadsheet internally calls the backend interface to obtain the data required by the spreadsheet (including unified settings, personalized settings, data, etc.), which is then rendered into a designated area within the spreadsheet. The spreadsheet internally provides users with unified operations such as adding, deleting, modifying, filtering, sorting, setting, importing, exporting, and displaying. This achieves an integrated, versatile, high-performance, easy-to-operate, rapidly integrated, personalized, and low-code two-dimensional data table (spreadsheet), as follows:

[0073] 1) Inject interactive interfaces into the spreadsheet component, including: save or modify (including batch), delete (including batch), user settings storage interface, data acquisition interface, table settings acquisition interface, save settings and other required interfaces;

[0074] 2) Render to the designated area and automatically generate corresponding operation functions, including: add, delete, modify, filter, personalize, sort, import, export, data operation, view switching, etc.

[0075] 3) New and modified settings are controlled based on permissions, for example, only available to administrators with professional knowledge. Dynamic forms are generated by setting their execution statements and input parameter field configurations, such as whether they are required and the type of input box.

[0076] 4) The deletion settings are also controlled based on permissions. For example, they are only open to administrators with professional knowledge. By setting the execution statement, a corresponding delete button can be generated. Deletion is divided into row deletion and batch deletion. The corresponding button is registered by judging the execution statement.

[0077] 5) There is a filter button in each column of the table, and users can use the filter to quickly query the corresponding data rows in the current page.

[0078] 6) Users can set a sorting method for the corresponding column in the column settings. The sorting methods are divided into ascending and descending. After saving and taking effect, the query interface will default to the corresponding sorting conditions to implement the sorting function.

[0079] 7) There are 8 categories of settings, including view settings, table settings, column settings, export settings, search settings, chart settings, other settings and pop-up settings. These can be expanded and increased according to user needs in the future.

[0080] 8) Administrators can generate an import button by setting a new execution statement for the table. If there is a new execution operation, the import button will be registered and displayed. The import is completed step by step. Step 1: Upload the file (multiple file uploads are supported, and the consistency of their headers must be confirmed) and define the row number of the header; Step 2: Parse the file to obtain its data set, match the input parameter settings with the header item (column header) name in the data set, and supplement the data; Step 3: Execute the background interface of the import, display the import progress in real time, change the status when the import is completed, and provide buttons to close and save the matching results.

[0081] 9) Users can export all data under the current conditions, sort them according to the set export settings, or export the currently displayed data (default and according to export settings).

[0082] 10) Users can switch views as needed, such as chart, board, Gantt chart, block, list, tree, calendar, flowchart, organization chart, mind map, etc. Charts can set their chart type, set their X-axis data columns and Y-axis data columns, and render their charts.

[0083] In the present invention, each user can personalize the table to display data according to personal usage habits, and the administrator can control the functions open to users based on permissions or settings, and can adjust them at any time.

[0084] Step 3: Data screening

[0085] Top filtering, users can control common filtering functions, and can control filtering conditions such as order, display, hiding, default values, etc., fixed to the specified position of the table, without having to look for columns to filter everywhere, which is convenient for user operation habits. Column filtering: Filter based on loaded data, and the batch operation function will automatically pop up after filtering to improve the convenience of operation. Column backend filtering: Users select operators (contains, does not contain, equal to, not equal to, is empty, not empty) and values ​​(single or multiple values) based on the column based on the backend filtering condition settings. If there is no setting, the corresponding filtering conditions will be automatically generated according to the data source and organized into data query statements to solve the problem of insufficient consideration in the development stage.

[0086] Data filtering is a common interactive operation, such as Figure 3 As shown, the steps include:

[0087] Step 3.1: Used to select the column to filter.

[0088] Step 3.2: The filter processing box pops up.

[0089] Step 3.3: Enter the filtering conditions, that is, clarify the filtering rules.

[0090] Step 3.4: Submit the filtering operation to the backend for processing.

[0091] Step 3.5: The backend determines whether the processing logic is configured. If so, the configured processing logic is used. If not, the filtering logic is generated based on the input.

[0092] Step 3.6: Perform data filtering: The data set will be detected. If the data contains mixed data of both strings and numbers, and if the filtering logic uses numerical operations such as greater than or less than, the strings will be ignored.

[0093] Step 3.7: Return the filtered dataset.

[0094] Step 3.8: Re-render based on the new dataset.

[0095] The entire screening process is closely linked, from user input to back-end processing to front-end page updates, and each step is indispensable.

[0096] Step 4: Sorting the data

[0097] The backend can set the default sorting and set the default sorting rules for all users; users can customize the default sorting as needed, and the sorting rules set by users will replace the default sorting rules.

[0098] like Figure 4 As shown, the steps of data sorting include:

[0099] Step 4.1: Load the unified setting data and obtain the system default unified setting parameters.

[0100] Step 4.2: Load user setting data and read user-defined setting information.

[0101] Step 4.3: Determine whether the user has set it. If yes, use the user setting. If not, use the unified setting.

[0102] Step 4.4: Organize the sorting conditions; there may be multiple columns to be sorted at the same time, so organize the data sorting rules, such as: first sort in ascending order by column A, then sort in descending order by column B, and then sort in ascending order by column N.

[0103] Step 4.5: Organize the sorting conditions and submit them to the backend for processing.

[0104] Step 4.6: Return the processed data.

[0105] Here, sorting conditions are organized and back-end processing is performed according to different user and system settings, reflecting the flexibility and targetedness of the operation.

[0106] Step 5: Data import

[0107] Import (on-demand loading): can dynamically coordinate the import data column and the storage data column, without repeated development; dynamically call the interface to supplement the data, solve the necessary data that does not exist in the imported file data, reduce development and maintenance costs, and improve versatility. Figure 5 As shown, the steps of data import include:

[0108] Step 5.1: User triggers the import operation.

[0109] Step 5.2: Upload the file to be imported.

[0110] Step 5.3: Parse the file and let the user specify the row where the data is located.

[0111] Small files are parsed directly on the client, while large files are uploaded to the backend for processing. Files that do not exceed the set threshold are considered small, while those that do exceed the threshold are considered large.

[0112] Step 5.4: Get all the input parameters of the spreadsheet backend storage interface. Input parameters refer to the parameters submitted by the user to the server and allowed to be stored by the server.

[0113] Step 5.5: Automatically match the parsed data with the acquired input parameters. If an automatic match fails, perform a manual match. Automatic matching automatically matches columns with identical or similar meanings. For unmatched columns, the user specifies a pairing and the number of entries submitted to the backend for processing.

[0114] Step 5.6: Specify the data supplement interface to supplement the missing data.

[0115] Since there may be missing data in external files, these data need to be supplemented during import. If the data volume is large, manual processing will take a lot of time. The administrator opens optional interfaces based on the business for users to select interfaces for data supplementation. Data supplementation occurs before each row is persisted. Data is obtained through the supplement interface and supplemented to each row. For example, the deduction list provided by the Medical Insurance Bureau to the hospital may be missing department codes, doctor codes, etc. When the data is imported into the hospital for internal processing, these data need to be supplemented.

[0116] Step 5.7: Save the user import settings for easy reuse later. When you use it again, directly load the saved settings to achieve automatic settings.

[0117] Step 5.8: Submit the backend import.

[0118] Importing can be achieved by directly using the batch saving interface provided by the backend. After receiving the data, the backend processes the data according to the settings and uses concurrency, caching and other technologies to optimize performance when the data volume is large.

[0119] This process takes into account various factors such as file size and data matching to ensure accurate data import.

[0120] Step 6: Data export (on-demand loading)

[0121] The present invention provides users with a function of exporting all data; provides users with a function of exporting according to display effects, such as merging multiple rows of data; and users can also set the data to be exported as needed.

[0122] like Figure 6 As shown, the steps of data export include:

[0123] Step 6.1: The user clicks the export button to issue an export instruction;

[0124] Step 6.2: Load user configuration;

[0125] Step 6.3: Determine whether there are settings. If there are settings, remove columns or convert data (for example, 1=male, 2=female) according to the settings. If there are no settings, directly export the data.

[0126] The data is pre-processed and exported according to user configuration to meet different export requirements of users.

[0127] Step 7: User selects all

[0128] The solution of the present invention supports multiple selection-batch operation and realizes batch data processing.

[0129] like Figure 7 As shown, the steps of the user's select all operation include:

[0130] Step 7.1: User selects data.

[0131] Step 7.2: Determine the selected data. If not all data are selected, remind the user that there is unselected data and confirm whether it meets the requirements. If all data are selected, proceed to the next step.

[0132] Because the user-side processing logic loads data through paging, the data that can be selected may be larger than the data presented.

[0133] Step 7.3: A pop-up next action button will appear in the Select All button's location. Common operations include batch deletion, batch modification, and data copying. This pop-up button can help users avoid searching for action buttons on the page and speed up processing, improving the user experience.

[0134] Step 7.4: Perform user selection operation.

[0135] In the user's select all operation, data that has been filtered and selected multiple times can be remembered; when pasting data in the filter input box, spaces are automatically removed and the last search conditions are cleared; paging can be done using buttons or sliding loading.

[0136] In some embodiments of the present invention, data imported from a third party or a file may be optimized to improve the performance of user data processing. The optimization methods include:

[0137] 1) Index optimization - bitmap;

[0138] 2) Concurrent loading;

[0139] 3) Automatically create in-memory indexes asynchronously based on configured columns or search conditions during loading.

[0140] When there are multiple data sets to be processed on a page, multi-table processing is supported:

[0141] 1) Linkage: Multiple views on the same page or multiple pages can subscribe to row or column cell click events of a specified view and generate data linkage. During processing, the row data is automatically matched with the filter conditions of the subscribed view, and a load event is triggered to complete the data update and loading;

[0142] 2) Request merging: When multiple views of the same interface are used in the same page, requests are automatically merged;

[0143] 3) Automatically implement relationship subscription based on the blood relationship of multi-table data (such as the same column values ​​in multiple tables and the primary and foreign key relationships returned by the backend).

[0144] Step 8: Data subscription operation

[0145] like Figure 8 As shown, for data subscription operations, users can "Set Subscriptions" for "Dataset A," "Dataset B," and "Dataset C," then determine the "Subscription Method," which can be "By Row" or "By Cell," and then "Respond to Subscriptions" based on the selected method. This process implements user subscription settings for different datasets and the system's response mechanism.

[0146] Some embodiments of the present invention also feature multi-view display capabilities, such as tables, forms, charts, dashboards, Gantt charts, blocks, lists, trees, calendars, flowcharts, organizational charts, and mind maps. Content is automatically extracted and generated based on data features. Long text is optimized to display as tips to ensure display integrity and a neat page layout. Operation memory functions are also provided, such as the number of items displayed per page, column width (adjustable by the user), and aliases. The display is automatically optimized based on screen resolution, and different display information is loaded according to the user and different screen information.

[0147] Step 9: View conversion and generation

[0148] like Figure 9 As shown, the process for view conversion and generation is as follows: First, perform the "view conversion operation," with options for various formats, including flowcharts, mind maps, organizational charts, tree columns, and tables. Then, perform "automatic detection," specifically detecting whether the dataset contains various relational data types, such as ID, code, parent ID, Parent ID, PID, code, Parent Code, parent code, sub, child, child, subordinate, data grouping, and similar relationships. If the conditions are met, "Generate View" is performed; if not, "Manually Select Data Columns" is performed. The system then learns and identifies features for use in the next view generation. View generation is performed based on data relationships or manual settings. After the view is generated, "Users can customize the rendered view as needed." This process automatically or manually generates views based on dataset characteristics, and supports user customization.

[0149] Step 10: Data analysis, divided into regular analysis and data insights.

[0150] Conventional analysis: Customize data analysis functions based on all loaded or background data, such as calculating average, percentage, maximum, minimum, sum, etc. by row or column.

[0151] Data Insight: By copying a view, the copied table inherits all the functions and personalized settings of the original table. Users can adjust the filtering parameters, aggregate data from multiple tables, and form new analytical reports, enabling comparative analysis and month-on-month analysis. Users can then use the inherited functions to hide or switch to various analytical views, ultimately forming and storing the analytical view.

[0152] Step 11: View replication and aggregation

[0153] like Figure 10 As shown in the figure, the process of view replication and aggregation includes: the main view can be replicated to generate a "replicated view" Figure 1 ", "Copy View Figure 2 ” and other copied views. Then perform “data aggregation” on the copied views, and then use “view selection” to select different view formats such as tables and charts for subsequent display. Here, through view copying and aggregation, users are provided with a variety of view options to meet different data display needs.

[0154] In some embodiments of the present invention, multiple versions of data are copied into multiple views through the view copy function, and the views are tiled in the UI. The user adjusts the filter conditions and combines the built-in difference comparison algorithm (such as the Myers difference algorithm) to highlight the data differences. When the view scrolls, it scrolls synchronously to quickly compare and discover the differences in data changes in multiple versions.

[0155] In some embodiments of the present invention, for the same data, it is necessary to discover the data situation under different conditions. The view copy function can be used to copy multiple views, adjust the filtering conditions, and distill out the data difference view required by the user; the view, view settings, filtering conditions, etc. are all copied to the new view, and the user then adjusts the required functions based on the copied view; it can also be integrated with a third-party system to notify or push content changes to the third-party system. Specifically: first capture the changes (deletion, modification, addition, including changes in the front and back ends) and generate code to call the third interface through the low-code platform to realize notification or push of changes.

[0156] Step 12: Data loading and rendering optimization

[0157] When a large amount of data is loaded, the UI may freeze on machines with different configurations, and optimization is required for specific scenarios. Figure 11 As shown, when the data is loaded, the "display data volume" is judged, specifically the number of items displayed per page. When it is greater than the default or specified number, performance optimization is enabled. If the judged data volume is greater than 200, the "automatically enable rendering optimization" function is used to render 200 items of data and store the excess data in memory. Then, a response event is added to the rendering view. After the user operates the rendering view with the response event added, the "view is re-rendered", and the optimization is completed. Responding to user operations includes: responding to user actions such as sliding, single selection, multiple selection, full selection, editing, and deletion. The data in the memory is manipulated, and the rendered part responds to the rendering action. When sliding up and down, the number of items displayed is calculated based on the screen. When the upper and lower values ​​are less than a certain threshold, the continuous rendering data is appended or inserted. If the judged data volume is less than 200, "no optimization is required". This process performs different processing based on the size of the data to optimize rendering performance and avoid UI freezes.

[0158] Step 13: Data supplementation and loading

[0159] like Figure 12 As shown in the figure, data supplementation and loading: "Configure data supplementation interfaces" to supplement data from various sources such as WebService, RESTful, database (SQL), dynamic library, cache, socket, and others, and finally "load data". Supplementing data through multiple interfaces ensures data integrity and accuracy.

[0160] Automatic supplementation of missing data: When processing data, the data of the initial query is often missing or abnormal due to various factors, and the data needs to be automatically supplemented or revised. The low-code platform configures the data supplement interface for the cell to achieve automatic compensation, and the configured batch modification interface can be called according to user operations to achieve persistent storage of data.

[0161] In some embodiments of the present invention, data verification: when processing the obtained data, it is often necessary to perform a series of processing on the data before the next operation (such as data reporting) can be performed, but the obtained data often has accuracy problems (missing data, third-party sources, etc.), such as: the existence of empty data, illegal format (such as email, phone, amount, date, etc.), incorrect logical relationship between the front and back (such as inconsistency between date of birth and age, inconsistency between diagnosis name and diagnosis code, inconsistency between the patient's project and gender, age or scenario restrictions on the use of projects, creation time greater than reporting time, etc.), etc. At this time, verification rules can be configured for cells or front and back data logic to achieve data accuracy verification. For data with verification problems, the user can be reminded of the problems with the data through differentiated display. When operating the corresponding cell, detailed verification content is displayed in the form of Tips, and before the next operation, it is mandatory to revise the data.

[0162] In some embodiments of the present invention, cross-system data synchronization: When processing data, there is usually a scenario where institution A generates data and needs to report it to institution B. In the past, it was necessary to develop corresponding interfaces to implement the data reporting function, and the development cycle and cost were very high. The use of this technology can quickly realize the scenario of cross-institutional data reporting or synchronization. The specific implementation process is as follows: Figure 13 When reporting data, first "get data", then "get reporting configuration", then "check data", and then "execute report". If login and other operations are involved, the user will "open the report page" after logging in, fill in the data and "submit the filled data". At the same time, it is necessary to determine whether the browser plug-in is installed and handle it accordingly. This process ensures the accuracy and security of data reporting and handles issues related to login, plug-ins, etc.

[0163] The above reporting logic customization process is similar to the matching logic of data import, and the missing data can be supplemented through the interface, such as Figure 14 As shown below: After opening the "target reporting page," "Extract the page's input components," perform "Automatic Data Lineage Matching," and if automatic matching fails, "Manually Configure," and finally, "Save Matching Results." This step ensures accurate data matching and configuration on the reporting page. For repetitive or periodic data reporting, this method can also convert customized reporting logic into scheduled tasks, supporting scheduled reporting tasks on both the front-end and back-end.

[0164] Parts of the present invention that are not described in detail may refer to the prior art or are well-known technologies to those skilled in the art, and this embodiment does not limit this and will not be described in detail here.

[0165] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A general data presentation and interactive processing method, characterized in that: The method is based on low-code and uses standardized interfaces and automated processing to achieve the full process of data set processing from input to presentation, interaction and analysis; The method includes: Data presentation and page construction: First, load and set relevant parameters, obtain data from the data source, then generate the page and interactive function buttons, and finally, users can personalize the page according to their needs; Dataset processing and page customization: Obtain data sets, categorized as files and interfaces, identify each node in the data set as a one-dimensional or two-dimensional data set, render them into blocks or spreadsheets, and then combine them to generate a complete page; use a low-code designer to process the page, implement user customization, and store the customization results; Data filtering: After the user selects a filter column and enters conditions, the data is submitted to the backend. The backend then determines whether there is configured processing logic. If so, it uses the configured processing logic. If not, it automatically generates filtering logic based on the data source. The filtering operation is then performed, and the filtered data set is returned. The frontend then re-renders the page based on this data set, improving the accuracy and efficiency of the filtering. Data behavior and interaction: Generate dynamic forms by selecting data fields and corresponding configurations to add and modify data. If form configurations exist, generate header buttons for adding new rows and buttons for modifying rows. Generate delete buttons by setting delete actions, and perform single row deletion or batch deletion based on whether a delete operation exists. View conversion and generation: Select a view conversion method to automatically detect relational data in the dataset and generate a view if conditions are met. Otherwise, manually select data columns. After the system learns the features, users can customize the rendered view to provide a variety of view displays. View replication and aggregation: The main view is replicated to generate multiple replicated views. Data in the replicated views is aggregated and then displayed in different view formats to facilitate user comparison and analysis of data. During data comparison, the difference comparison algorithm is used to highlight data differences. Data loading and rendering optimization: Determine the amount of display data. If it is greater than the specified number, enable rendering optimization, render part of the data and store the excess data, add response events, and determine whether to load excess data to render the view based on user scrolling operations and viewport position. If it is less than or equal to the specified number, no optimization is required, thus effectively avoiding UI node rendering jams caused by excessive data. Data supplementation and loading: Configure multiple data supplementation interfaces to ensure data integrity and accuracy. Configure cell data supplementation interfaces through the low-code platform to automatically supplement missing data. Data verification and cross-system data synchronization: Configure verification rules for data accuracy issues, display problematic data differently, and remind users to revise them. When synchronizing data across systems, verify the data after obtaining and reporting the configuration. If it passes, report it. If login and plug-ins are involved, perform corresponding processing, and then convert the reporting logic into a scheduled task for execution.

2. The general data presentation and interactive processing method according to claim 1, characterized in that: The method also includes: Data sorting: Load system default and user-defined setting data, determine the sorting rules to be used based on whether the user has set them, submit the sorting conditions to the backend for processing, and finally return the processed data to meet the sorting needs of different users.

3. The general data presentation and interactive processing method according to claim 1, characterized in that: The method also includes: Data import: After the user triggers the import operation, they upload the file. Multiple file uploads are supported. File parsing is performed on the client or backend based on the file size. After obtaining the input parameters from the backend storage interface, data matching is performed automatically or manually. After saving the import settings, the backend import is submitted. The backend uses optimization technology to process large data volumes to ensure accurate and efficient data import. Data export: After the user clicks the export button, the configuration is loaded, and the data of the column is removed or the column name is modified according to whether there is a setting, and then the data is exported to meet the user's different export needs.

4. The general data presentation and interactive processing method according to claim 1, characterized in that: The method also includes: User select all operation: After the user selects the data, they can make a selection. If all data has been selected, a batch operation button will pop up at the position of the select all button, which is convenient for users to process batch data and improve the operation experience. Data subscription operation: Users can select subscription objects from multiple data sets. The subscription method is divided into two types: row-by-row and cell-by-cell. The system responds to subscriptions based on the selected method, enabling flexible monitoring of different data sets. Data analysis includes routine analysis and data insights. Routine analysis is based on loaded or background data. Data insights create and present new data sets by copying views, adjusting filtering parameters, and aggregating data from multiple tables. This allows for year-on-year and month-on-month analysis, and finally stores analytical views to meet data analysis needs at different levels.

5. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the content of the general data presentation and interactive processing method according to any one of claims 1 to 4 is realized.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the content of the general data presentation and interactive processing method according to any one of claims 1 to 4.

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