General data presentation and interactive processing method, product and equipment

Abstract

The invention discloses a general data presentation and interactive processing method, a product and equipment, and belongs to the technical field of data processing. According to the method, a low code is taken as a core, and full-process processing of a data set from input to presentation, interaction and analysis is realized through a standardized interface and automatic processing; comprising the following steps: data presentation and page construction: firstly loading parameters, obtaining data from a data source, generating a page, adding an interaction function, and carrying out personalized adjustment on the page; data set processing and page customization: acquiring a data set, dividing the data set into files and interfaces, rendering the data set, and combining the data set to generate a complete page; a low-code designer is used for processing the page, and personalized customization of the user is achieved; and data screening: determining a screening column and conditions, generating screening logic according to whether configuration processing logic exists or not, and returning the screened data set to render the page again. According to the method, full-process processing of the data set from input to presentation, interaction and analysis can be realized, the efficiency and the data quality are improved, and the user experience is optimized.

Description

Technical Field

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

[0002] Traditional methods usually require a large amount of code writing to implement data presentation and interaction functions, with a long development cycle, high cost, and difficulty in quickly adjusting according to user needs, lacking flexibility and efficiency. Interactive operations such as data filtering and sorting often require users to have certain technical knowledge, with cumbersome operation steps, easy to make mistakes, and lack of coherence and convenience between different functions. In the process of data import and processing, there are no effective automated means for operations such as data matching and missing data supplementation, relying on manual processing, with low efficiency and easy to make mistakes. In addition, in the face of a large amount of data, the system is prone to problems such as freezing and slow response, affecting 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 a general solution.

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

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

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

[0006] In order to solve the above technical problems, the present invention is implemented as follows: The embodiment of the present invention provides a general data presentation and interaction processing method. The method takes low code as the core and realizes the full-process processing of the dataset from input to presentation, interaction and analysis through standardized interfaces and automated processing. Users only need simple training, and various functional pages in the application system can be uniformly operated and set to meet the requirements, taking into account user experience, generality, personalization and other requirements. The content of the method includes: Data presentation and page construction: First, load and set relevant parameters, obtain data from the data source accordingly, then generate pages and interactive function buttons, and finally, users can make personalized adjustments to the pages according to their own needs. Dataset Processing and Page Customization: Obtain datasets, which are divided into two categories: files and interfaces. Each node of the dataset is respectively identified as a one-dimensional or two-dimensional dataset, and is correspondingly rendered as a block or a spreadsheet, and then combined to generate a complete page. In practice, a single table can be a page, or multiple blocks can form a page; then use a low-code designer to process the page to achieve user personalization and store the customization results; Data Filtering: After the user selects the filtering columns and enters the conditions and submits them to the backend, the backend determines whether there is a configured processing logic; if so, use the configured processing logic, if not, automatically generate a filtering logic based on the data source; then perform the filtering operation and return the filtered dataset, and the frontend re-renders the page accordingly to improve the accuracy and efficiency of filtering; Data Behaviors and Interactions: Generate dynamic forms by selecting data fields and corresponding configurations to perform data addition and modification. If there is a form configuration, generate an additional header button and a row button for modification; generate a delete button through the set delete action, and perform single-row deletion or batch deletion according to whether there is a delete operation.

[0007] Parameter settings are divided into administrator unified settings and user personal settings. When there is no user personal setting, load the parameters uniformly set by the administrator for all users for rendering. The settings between different users do not interfere with each other, and the administrator's unified setting ability is greater than the user's personal setting ability. Example: Settings that require permissions cannot be set by users without permissions; In data filtering, when presenting data, read the user configuration. If there is a corresponding configuration, perform filtering according to the user configuration. The user can control the display, hiding, order, default value, etc. of the filtering conditions. After the user's operation on the data column or the operation at a fixed position, send the filtering conditions to the backend. If there is a configured processing logic, the backend uses the configured logic for filtering. If there is no configuration, it automatically generates a filtering logic and performs the filtering operation, returning the filtered dataset, and the frontend re-renders the page accordingly to improve the accuracy and efficiency of filtering. When there is no configured filtering logic, the backend automatically generates the filtering conditions that can be executed by the corresponding data source based on the user's selection operation. For example, in a database, it generates: column = 'condition'. When calling a third-party API and no relevant filtering is provided, load all data into memory and then perform in-memory filtering. The aforementioned default value refers to the default value set when a certain text box or dropdown box is presented. For example, when searching for regions, the default value = Beijing.

[0008] In addition, according to the general data presentation and interaction processing method of the present invention, it may also have the following additional technical features: In some of the embodiments, the content of the method further includes: Data Sorting: Load the system default and user-defined setting data, determine the sorting rules to be used according to whether the user has set them, organize the sorting conditions and then submit them to the backend for processing, and finally return the processed data to meet the sorting requirements of different users.

[0009] In some of the embodiments, the content of the method further includes: Data Import: After the user triggers the import operation, upload the file, determine whether to parse the file on the client or in the background according to the file size, obtain the input parameters of the backend storage interface and then perform data matching, automatically or manually complete the matching and supplement the missing data, save the import settings and then submit them to the backend for import. The backend uses optimization techniques to process large amounts of data to ensure accurate and efficient data import; Data Export: After the user clicks the export button, load the configuration, determine whether to exclude columns or modify the export column names according to the settings, and then export the data to meet the different export requirements of the user.

[0010] In some of the embodiments, the content of the method further includes: User Select-All Operation: After the user selects the data, perform a select-all judgment. If all data has been selected, batch operation buttons will pop up at the select-all button position to facilitate the user to quickly perform batch data processing and improve the operation experience; when performing the select-all judgment, compare the total quantity with the selected quantity. If the quantities are inconsistent, it means that there is still data not selected, and the user will be reminded that not all data has been selected.

[0011] Data Subscription Operation: The user can select subscription objects from multiple data sets. The subscription methods are divided into two types: by row and by cell. The system responds to the subscription according to the selected method to achieve the operation linkage of multiple data sets. Example: When a certain row or cell is selected in Data Set A, Data Set B changes; Data Analysis, including regular analysis and data insight: Regular analysis is based on the loaded data or backend data; Data insight forms a new data set by copying views, adjusting filtering parameters, and aggregating multiple data sets, and presents new blocks according to the settings to achieve year-on-year and month-on-month analysis. Finally, store the analysis views to meet the data analysis needs at different levels.

[0012] In some of the embodiments, the content of the method further includes: View Conversion and Generation: Select the view conversion form, automatically detect the relational data in the data set. If the conditions are met, generate the view; otherwise, manually select the data columns. After the system learns the features, the user can adjust the rendered view personalized to provide diverse view displays; View Copying and Aggregation: The main view is copied to generate multiple copied views, the data of the copied views is aggregated, and then different view forms are selected for display to facilitate the user to compare and analyze the data. When comparing the data, use the difference comparison algorithm to highlight the data differences.

[0013] In some of these embodiments, the content of the method further includes: Data loading and rendering optimization: Determine the amount of display data. If it is greater than the specified number of items, enable rendering optimization, render some data, store the excess data, add a response event, and determine whether it is necessary to load the excess data for rendering the view based on the user's scrolling operation and the viewport position; if it is less than or equal to the specified number of items, no optimization is required, thus effectively avoiding UI node rendering jams caused by excessive data.

[0014] In some of these embodiments, the content of the method further includes: Data supplementation and loading: Multiple data supplementation interfaces can be configured to ensure data integrity and accuracy. Configure the cell data supplementation interface through a low-code platform to automatically supplement missing data.

[0015] In some of these embodiments, the content of the method further includes: Data verification and cross-system data synchronization: Configure verification rules for data accuracy issues, differentially display the problematic data, and prompt the user to revise it; during cross-system data synchronization, after obtaining the data and reporting configuration, perform verification, and execute the reporting after passing. When it involves login and plugins, perform corresponding processing, and then convert the reporting logic into a scheduled task for execution.

[0016] An embodiment of the present invention also provides a computer program product, including a computer program, which when executed by a processor, implements the content of the general data presentation and interaction processing method described in any one of the above.

[0017] An embodiment of the present invention also provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the content of the general data presentation and interaction processing method described in any one of the above.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: In the embodiment of the present invention, the provided general data presentation and interaction processing method can improve work efficiency. The automated data processing process and convenient interaction operations reduce manual intervention and repetitive labor. There is no need for developers to repeatedly develop corresponding functions for different pages, greatly improving the efficiency of data processing and analysis, and saving time and labor costs; In the embodiment of the present invention, the provided general data presentation and interaction processing method can improve data quality: Ensure data integrity and accuracy, provide a reliable basis for subsequent data analysis and decision-making, and avoid decision-making mistakes caused by data errors; In the embodiments of the present invention, the provided general data presentation and interaction processing method can optimize the user experience: meet the personalized needs of users, provide diverse view displays and interaction methods, enable users to process and analyze data more conveniently, and improve user satisfaction; In the embodiments of the present invention, the provided general data presentation and interaction processing method can adapt to business development: in the case of continuous growth of data volume and increasingly complex business requirements, optimize system performance and compatibility, ensure stable operation of the system, adapt to different scales and types of business scenarios, and provide strong support for the development of enterprises and organizations.

[0019] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. Description of the Drawings

[0020] Figure 1 It is a flowchart of data presentation and page construction disclosed in an embodiment of the present invention; Figure 2 It is a flowchart of dataset processing and page customization disclosed in an embodiment of the present invention; Figure 3 It is a flowchart of data screening operation disclosed in an embodiment of the present invention; Figure 4 It is a flowchart of data sorting operation disclosed in an embodiment of the present invention; Figure 5 It is a flowchart of data import operation disclosed in an embodiment of the present invention; Figure 6 It is a flowchart of data export operation disclosed in an embodiment of the present invention; Figure 7 It is a flowchart of user full selection operation disclosed in an embodiment of the present invention; Figure 8 It is a flowchart of data subscription operation disclosed in an embodiment of the present invention; Figure 9 It is a flowchart of view conversion and generation disclosed in an embodiment of the present invention; Figure 10 It is a flowchart of view copying and aggregation disclosed in an embodiment of the present invention; Figure 11 It is a flowchart of data loading and rendering optimization disclosed in an embodiment of the present invention; Figure 12 It is a flowchart of data supplement and loading disclosed in an embodiment of the present invention; Figure 13 It is a flowchart of data reporting operation disclosed in an embodiment of the present invention; Figure 14 It is a flowchart of matching reported page data disclosed in an embodiment of the present invention. Detailed Implementation Manner

[0021] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Next, in combination with the accompanying drawings, the embodiments of the present invention will be described in detail through specific embodiments and their application scenarios.

[0023] Please refer to Figure 1 As shown, in some embodiments of the present invention, a general data presentation and interaction processing method is provided. With low code as the core, through standardized interfaces and automated processing, an end-to-end process from dataset input to presentation, interaction, and analysis is realized. The system dynamically generates a configurable spreadsheet UI based on the dataset provided by the user, and improves versatility through modular functions (such as filtering, sorting, importing, exporting, etc.). At the same time, it supports personalized settings and multi-view linkage, ultimately reducing development costs and optimizing the user experience.

[0024] In some embodiments of the present invention, the content of the general data presentation and interaction processing method includes (the following step order is the function sorting, rather than the order limit in implementation): Step 1: Data presentation and page construction, as Figure 1 shown.

[0025] Step 1.1: Loading settings. This step is the basis of the entire process and is used to set relevant parameters and configuration information.

[0026] Step 1.2: Loading data. According to the parameters set in the previous step 1.1, obtain the data to be processed from the data source, and then enter the next step.

[0027] Step 1.3: Generating a page. After obtaining the data through step 1.2, present the data in a specific page form.

[0028] Step 1.4: Generating function operations. After presenting the page in step 1.3, in order to make the page interactive, automatically generate operation functions such as adding, modifying, deleting, filtering, sorting, importing, exporting, and data analysis for each block-level unit of the page.

[0029] Step 1.5: The user customizes the page according to requirements. Make personalized adjustments to the generated page according to actual needs, such as table stripe display, hiding columns, modifying column names, alignment, column sorting rules, whether columns need to be exported, data presentation conversion (such as 1 = male, 2 = female), adjusting row height, enabling column editing, fixing columns, cell display methods (such as using labels, pictures, different colors), and switching the table to a chart (pie chart, bar chart, line chart, mixed chart, etc.) with one key and other personalized settings.

[0030] Step 2: Dataset processing and page customization, such as Figure 2 as shown.

[0031] Step 2.1: Obtain the dataset and divide it into two categories: files and interfaces.

[0032] Step 2.2: Identify each dataset to determine whether it is a one-dimensional dataset or a two-dimensional dataset. The generated styles for different datasets are inconsistent.

[0033] Step 2.3: Dataset rendering. Render the one-dimensional dataset into blocks and the two-dimensional dataset into spreadsheets. Loop through this process until all datasets on the entire page are rendered.

[0034] Step 2.4: Combine the rendered data to generate a complete and displayable page.

[0035] Step 2.5: For the generated complete page, the generated page provides a setting function for the user. The user can set it by themselves when needed. When setting, a low-code designer is provided for the user to process. Combining the customization capabilities provided by the table and blocks, quickly realize page generation and achieve user personalization customization.

[0036] Step 2.6: Store the results of personalization customization.

[0037] In some embodiments of the present invention, the above two-dimensional datasets are uniformly rendered into spreadsheets. By injecting a backend interaction interface into the spreadsheet component, the spreadsheet internally calls the backend interface to obtain the data required for the spreadsheet (including unified settings, personalized settings, data, etc.), renders it internally to the specified area, and provides the user with unified operations such as adding, deleting, modifying, filtering, sorting, setting, importing, exporting, and displaying. To achieve an integrated, highly versatile, high-performance, easy-to-operate, quickly integrated, personalized, and low-code two-dimensional data table (spreadsheet), specifically as follows: 1) Inject an interaction interface into the spreadsheet component, including: save or modify (including batch), delete (including batch), user setting storage interface, data acquisition interface, table setting acquisition interface, save settings, and other required interfaces; 2) Render to the specified area and automatically generate corresponding operation functions, including: add, delete, modify, filter, personalized settings, sorting, import, export, data operation functions, view switching functions, etc.

[0038] 3) The settings for addition and modification are controlled based on permissions. For example, it is only open to the administrator role with professional knowledge. By setting its execution statement and the configuration of input parameter fields, such as whether it is required and the type of input box, a dynamic form can be generated.

[0039] 4) The settings for deletion are also controlled based on permissions. For example, it is only open to the administrator role with professional knowledge. By setting its execution statement, corresponding delete buttons can be generated. Deletion is divided into row deletion and batch deletion, and the corresponding buttons are registered by judging the execution statement.

[0040] 5) There is a filter button in each column of the table, and users can quickly query the corresponding data rows on the current page through filtering.

[0041] 6) Users can set a sorting method for the corresponding column in the column settings. The sorting methods are ascending and descending. The saved and effective query interfaces will default to bring in the corresponding sorting conditions to achieve the sorting function.

[0042] 7) The settings have 8 categories: view settings, table settings, column settings, export settings, search settings, chart settings, other settings, and pop-up window settings, which can be expanded and added according to user needs in the future.

[0043] 8) The administrator can generate an import button by setting the execution statement for adding to the table. If there is an execution operation for addition, the import button will be registered and displayed. The import is completed step by step. Step 1: Upload files (supporting multi-file upload, and it is necessary to confirm whether their headers are the same), and define the row number where the header is located; Step 2: Parse the file to obtain its dataset, pair the input parameter settings with the header item (column header) names in the dataset and data supplementation; Step 3: Execute the background interface for import, and display the import progress in real time. When the import is completed, change the status and provide buttons to close and save the paired results.

[0044] 9) Users can export all data under the current conditions, can also sort according to the set export settings, or export the currently displayed data (default and according to the export settings).

[0045] 10) Users can switch views as needed, such as charts, dashboards, Gantt charts, blocks, lists, trees, calendars, flowcharts, organizational structure charts, mind maps, etc. For charts, the chart type can be set, the data columns for the X-axis and Y-axis can be set, and the chart can be rendered.

[0046] In the present invention, each user can customize the table according to their personal usage habits to display data. The administrator can control the functions open to users for operation according to permissions or settings and can adjust them at any time.

[0047] Step Three: Data Screening Top screening: The user can control common screening functions, and can control screening conditions such as order, display, hiding, default value, etc., and fix them to a specified position in the table, eliminating the need to search everywhere for columns to screen, which is convenient for the user's operation habits. Column screening: Based on the loaded data for screening, a batch operation function will automatically pop up after screening, improving the operation convenience. Column back-end screening: The user sets screening conditions based on column selection operators (including, not including, equal to, not equal to, is empty, is not empty), values (single or multiple values) based on back-end screening conditions. If no settings are made, corresponding screening conditions will be automatically generated according to the data source and organized into a data query statement to solve the problem of insufficient consideration in the development stage.

[0048] Data screening is a common interactive operation, such as Figure 3 as shown, and its steps include: Step 3.1: Used to select the columns to be screened.

[0049] Step 3.2: Pop up a screening processing box.

[0050] Step 3.3: Enter screening conditions, that is, clarify the screening rules.

[0051] Step 3.4: Submit the screening operation to the back-end for processing.

[0052] Step 3.5: The back-end judges "whether to configure the processing logic". If there is a configuration, "adopt the configured processing logic"; if not, "generate screening logic according to the input".

[0053] Step 3.6: Execute data screening: For the detection data set, such as a mixed data set with both strings and numerical values in the data, if the screening logic uses numerical operations such as greater than or less than, the strings will be ignored.

[0054] Step 3.7: Return the screened data set.

[0055] Step 3.8: Re-render according to the new data set.

[0056] The entire screening process is closely linked. From user input to back-end processing and then to the update of the front-end page, each step is indispensable.

[0057] Step Four: Data Sorting The back-end can set the default sorting to set the default sorting rules for all users; the user can customize the default sorting according to needs, and the sorting rules set by the user will replace the default sorting rules.

[0058] As Figure 4 shown, the steps of data sorting include: Step 4.1: Load the unified setting data and obtain the system default unified setting parameters.

[0059] Step 4.2: Load the user setting data and read the user-defined setting information.

[0060] Step 4.3: Judge whether the user has made settings. If there are settings, "use user settings"; if not, "use unified settings".

[0061] Step 4.4: Organize the sorting conditions; there may be cases where multiple columns are sorted simultaneously. Sort out the rules for data sorting, such as: sort in ascending order by column A first, then in descending order by column B, and then in ascending order by column N.

[0062] Step 4.5: Organize the sorting conditions and submit them to the backend for processing.

[0063] Step 4.6: Return the processed data.

[0064] Here, according to different situations of user and system settings, the sorting conditions are organized and backend processing is carried out, which reflects the flexibility and pertinence of the operation.

[0065] Step Five: Data Import Import (load on demand): It can dynamically cooperate with the imported data columns and stored data columns without repeated development; dynamically call the interface to supplement data, solve the necessary data that does not exist in the imported file data, reduce the development and maintenance costs, and improve the generality. As Figure 5 shown, the steps of data import include: Step 5.1: The user triggers the import operation.

[0066] Step 5.2: Upload the file to be imported.

[0067] Step 5.3: Parse the file and specify the row where the data is located by the user.

[0068] Small files are directly parsed on the client side, and large files are uploaded to the backend for processing. When the size of the file does not exceed the set threshold, it is determined as a small file; when it exceeds the set threshold, it is determined as a large file.

[0069] Step 5.4: Obtain all the input parameters of the spreadsheet backend's storable interface. The meaning of the input parameters is: the parameters submitted from the user side to the server side and allowed to be stored by the server side.

[0070] Step 5.5: Automatically match the parsed data with the obtained input parameters, and for those that cannot be automatically matched, manual matching by the user is required. During automatic matching, those with the same or similar column names are automatically matched; for those that cannot be matched, the user specifies the pairing and the number of records submitted to the backend for processing each time.

[0071] Step 5.6: Specify a data supplementation interface to supplement missing data.

[0072] Since there may be missing data in the external file, these data need to be supplemented during import. If the amount of data is large, it will take a lot of time to process manually. The administrator provides optional interfaces according to the business for users to select for data supplementation. Data supplementation occurs before each row is persistently saved. The data is obtained through the supplementation interface and supplemented into each row; 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 processing, these data need to be supplemented.

[0073] Step 5.7: Save the user's import settings for convenient reuse later. When using again, directly load the saved settings to achieve automatic settings.

[0074] Step 5.8: Submit for backend import.

[0075] Directly use the batch save interface provided by the backend to achieve import. After receiving the data, the backend processes the data according to the set situation. When dealing with a large amount of data, technologies such as concurrency and caching are used for performance optimization.

[0076] This process takes into account various situations such as file size and data matching to ensure accurate data import.

[0077] Step Six: Data Export (On-demand Loading) The present invention provides users with a function to export all data; provides a function to export according to the display effect, such as when multiple rows of data are merged; users can also set the data to be exported as needed.

[0078] Such as Figure 6 shown, the steps of data export include: Step 6.1: The user clicks the export button to send an export instruction; Step 6.2: Load the user configuration; Step 6.3: Judge "whether there are settings". If there are settings, "exclude columns or convert (example: 1 = male, 2 = female) data according to the settings", and if there are no settings, directly "export data".

[0079] Preprocess the data according to the user configuration and then export it to meet the different export requirements of users.

[0080] Step Seven: User Select All Operation The solution of the present invention supports multi-selection - batch operation to achieve the processing of batch data.

[0081] As Figure 7 shown, the steps of the user Select All operation include: Step 7.1: The user selects data.

[0082] Step 7.2: Judge the selected data. If it is determined that not all data is selected, remind the user that there is unselected data and confirm whether it meets the requirements; if it is determined that all data is selected, proceed to the next step.

[0083] Since the logic of the user side for data processing is to load data through paging, the data that can be selected may be greater than the presented data.

[0084] Step 7.3: Pop up the next operation button at the position of the Select All button. Common operations include: batch deletion, batch modification, data copying, etc. Popping up the Select All box can reduce the user's search for operation buttons on the page and speed up the processing speed, improving the user experience.

[0085] Step 7.4: Perform the user selection operation.

[0086] In the user Select All operation, the data selected after multiple screenings can be remembered; when pasting data into the screening input box, spaces are automatically removed and the previous search condition is cleared; the paging method can use buttons or sliding loading, etc.

[0087] In some embodiments of the present invention, it is also possible to optimize the data imported from a third party or a file to improve the user's data processing performance. The optimization means include: 1) Index optimization - bitmap; 2) Concurrent loading; 3) Asynchronous automatic creation of in-memory indexes based on configured columns or search conditions during loading.

[0088] When there are multiple data sets to be processed on a page, multi-table processing is supported: 1) Linkage. For multiple views on the same page or multiple pages, it is possible to subscribe to the click events of the rows or column cells of the specified view and generate data linkage. When processing, the row data is automatically matched with the screening conditions of the subscribed view, and the loading event is triggered to complete the data update and loading; 2) Request merging. When using multiple views with the same interface on the same page, the requests are automatically merged; 3) Automatically implement relationship subscription based on the data blood relationship of multiple tables (such as the similarity of a certain column value in multiple tables, the master - foreign key relationship returned by the backend).

[0089] Step Eight: Data Subscription Operation AsFigure 8 As shown, for data subscription operations: "Set subscriptions" can be made from "Dataset A", "Dataset B", and "Dataset C", and then the "subscription method" is judged, which is divided into two types: "by row" and "by cell", and "respond to the subscription" according to the selected method. This process realizes the subscription settings of different datasets by users and the response mechanism of the system.

[0090] In some embodiments of the present invention, it also has a multi-view display function, such as tables, forms, charts, dashboards, Gantt charts, blocks, lists, trees, calendars, flowcharts, organizational charts, mind maps, etc., and automatically extracts and generates content based on data characteristics; for long texts, it is optimized into tips display to ensure the integrity of the display and the neatness of the page layout. It has an operation memory function, such as the number of items displayed per page, column width (after being adjusted by the user), aliases, etc. It automatically optimizes the display based on the screen resolution; according to the user and different screen information, different display information is loaded.

[0091] Step Nine: View Conversion and Generation As Figure 9 shown, the process of view conversion and generation is as follows: First, perform "view conversion operations", and various forms such as flowcharts, mind maps, organizational frameworks, tree columns, and tables can be selected. Then, perform "automatic detection", specifically detecting whether there are various relationship data such as ID, encoding, parent ID, ParentID, PID, Code, ParentCode, superior encoding, Sub, child, sub-level, lower-level, data grouping, and similar relationships in the dataset; if the conditions are met, "generate a view", if not, "manually select data columns", and then the system learns and identifies features for use in generating views next time. When generating a view, a view is generated according to the data relationship or manual settings. After generating the view, "the user can customize and adjust the rendered view according to needs". This process automatically or manually generates views according to the dataset characteristics and supports user customization.

[0092] Step Ten: Data Analysis, which is divided into conventional analysis and data insight.

[0093] Conventional analysis: Customize data analysis functions based on the loaded or background data, such as calculating the average, percentage, maximum, minimum, sum, etc. by row or by column.

[0094] Data insight: By copying the view, the copied table inherits all the functions and personalized settings of the original table. The user adjusts the screening parameters, aggregates multi-table data, forms a new analysis report, realizes year-on-year analysis and month-on-month analysis, and the user can hide or switch to various analysis views through the inherited functions, and finally forms an analysis view, and finally stores the analysis view.

[0095] Step Eleven: View Copying and Aggregation As shown Figure 10 in the figure, the processes of view replication and aggregation include: the main view can be replicated to generate multiple replicated views such as "Replicated View Figure 1 ", "Replicated View Figure 2 ". After that, "data aggregation" is performed on the replicated views, and then different view forms such as tables and charts are selected through "view selection" for subsequent display. Here, through view replication and aggregation, multiple view selections are provided for users to meet different data display requirements.

[0096] In some embodiments of the present invention, for multiple versions of data, through the view replication function, multiple views are replicated, the views are tiled in the UI, the user adjusts the filtering conditions, and combined with the built-in difference comparison algorithm (such as the Myers difference algorithm), the data differences are highlighted. When the view scrolls, it scrolls synchronously, quickly comparing and discovering the data change differences between multiple versions.

[0097] In some embodiments of the present invention, for the same data, if it is necessary to discover the data situation under different conditions, the view replication function can be used to replicate multiple views, adjust the filtering conditions, and distill the data difference views required by the user; all the views, view settings, filtering conditions, etc. are copied to the new view, and the user then adjusts the required functions based on the replicated views; it can also be integrated with a third-party system to notify or push the content change situation to the third-party system. Specifically: first capture the changes (deletion, modification, addition, including front-end and back-end changes) and generate code for calling the third interface through a low-code platform to implement the notification or push of the change situation.

[0098] Step Twelve: Data Loading and Rendering Optimization When a large amount of data is loaded, the UI may freeze on machines with different configurations, and optimization needs to be carried out for the scenario. As Figure 11 shown in the figure, when loading data, judge the "displayed data volume", specifically judge the number of items displayed per page, and when it is greater than the default or specified number of items, start performance optimization. If the judged data volume is greater than 200 items, the "automatic rendering optimization" function is enabled, 200 items of data are rendered and the excess data is stored in memory, and then a response event is added to the rendered view. After the user operates on the rendered view with the response event added, "re-render the view", and the optimization ends. Responding to user operations includes: responding to the user's sliding, single selection, multiple selection, full selection, editing, deletion and other actions, operating on the data in memory, and the rendered part responds to the rendering action; when sliding up and down, calculate the number of items displayed according to the screen, and when the up and down are less than a certain threshold, append or insert continuous rendered data. If the judged data volume is less than 200 items, then "no optimization is required". This process performs different treatments according to the data volume size, optimizes the rendering performance, and avoids UI lags.

[0099] Step Thirteen: Data Supplement and Loading As shown Figure 12 in the figure, data supplementation and loading: "Configure the data supplementation interface", which can supplement data from multiple sources such as WebService, RESTful, database (SQL), dynamic library, cache, Socket, etc., and finally "load the data". Supplement data through multiple interfaces to ensure the integrity and accuracy of the data.

[0100] Automatic supplementation of missing data: When processing data, the initially queried data often lacks or has anomalies in some data due to various factors, and it is necessary to automatically supplement or revise the data. Configure the data supplementation interface for cells through a low-code platform to achieve automatic compensation, and according to user operations, call the configured batch modification interface to achieve persistent storage of the data.

[0101] In some embodiments of the present invention, data verification: When processing the obtained data, a series of processing of the data is often required before the next operation (such as data reporting) can be carried out. However, the obtained data often has accuracy problems (such as missing filling, third-party sources, etc.), such as: there are empty data, illegal formats (such as email, phone number, amount, date, etc.), incorrect logical relationships before and after (such as inconsistent date of birth and age, inconsistent diagnosis name and diagnosis code, the items done by the patient do not match the gender, age or scenario restrictions for the use of the item, creation time is greater than the filling time, etc.). At this time, verification rules can be configured for cells or the data logic before and after to achieve accurate verification of the data. For the data with problems in verification, display the problems in a differentiated manner to remind the user of the problems with the data. When operating on the corresponding cell, display the detailed verification content in the form of Tips, and force the revision of the data before the next operation.

[0102] In some embodiments of the present invention, cross-system data synchronization: When processing data, there is usually a scenario where data generated by organization A needs to be reported to organization B. In the past, corresponding interfaces needed to be developed to implement the data reporting function, and the development cycle and cost were very high. Using this technology, the scenario of cross-organization data reporting or synchronization can be achieved quickly. The specific implementation process is as Figure 13 shown in the figure. When reporting data, first "obtain the data", then "obtain the reporting configuration", and then perform "data verification". If the verification passes, "execute the reporting". If operations such as login are involved, after the user logs in, "open the reporting page", fill in the data and "submit the filled data". At the same time, it is necessary to judge whether the browser plugin is installed and handle it according to the situation. This process ensures the accuracy and security of data reporting and handles related issues such as login and plugins. The above reporting logic customization process is the same as and different from the pairing logic of data import. The missing data can also be supplemented through the interface, such as Figure 14As shown: After opening the "Target Report Page", "extract the input components of the page", perform "automatic data lineage matching", and perform "manual configuration" for those that cannot be automatically matched. Finally, "save the pairing result". This step ensures the accurate matching and configuration of the data on the report page. For data reporting, which is often repetitive or needs to be reported at fixed intervals, this method can also convert the customized reporting logic into a scheduled task for execution, supporting the front-end and back-end to execute reporting tasks at scheduled times.

[0103] For the parts not detailed in the present invention, reference can be made to the prior art or the well-known technology to those skilled in the art. This embodiment does not make any limitations in this regard and will not be described in detail herein.

[0104] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention, and all of them fall within the protection scope of the present invention.

Claims

1. A general data presentation and interaction processing method, characterized in that The method takes low-code as the core and realizes the full-process processing of the dataset from input to presentation, interaction, and analysis through standardized interfaces and automated processing; The content of the method includes: Data presentation and page construction: First, load and set relevant parameters, obtain data from the data source accordingly, then generate the page and interactive function buttons, and finally, the user can customize the page according to their own needs; Dataset processing and page customization: Obtain the dataset, which is divided into two categories: files and interfaces. Identify each node in the dataset as a one-dimensional or two-dimensional dataset, render it into a block or a spreadsheet accordingly, and then combine them to generate a complete page; Use the low-code designer to process the page, realize user personalization customization and store the customization results; Data screening: After the user selects the screening column and enters the conditions and submits them to the backend, the backend determines whether there is a configured processing logic; If so, use the configured processing logic. If not, automatically generate the screening logic according to the data source; Then perform the screening operation and return the screened dataset. The front end re-renders the page accordingly to improve the accuracy and efficiency of screening; Data behavior and interaction: Generate a dynamic form by selecting data fields and corresponding configurations to add and modify data. If there is a form configuration, generate a new header button and a modified row button; Generate a delete button through the set delete action, and perform single-row deletion or batch deletion according to whether there is a delete operation.

2. The general data presentation and interaction processing method according to claim 1, wherein The content of the method also includes: Data sorting: Load the system default and user-defined setting data, determine the sorting rule to be used according to whether the user has set it, organize the sorting conditions and submit them 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 interaction processing method according to claim 1, wherein The content of the method also includes: Data import: After the user triggers the import operation, upload files, support multi-file upload, determine whether to parse the files on the client or in the background according to the file size, obtain the input parameters of the backend storage interface and perform data matching, complete the matching automatically or manually, save the import settings and submit them to the backend for import. The backend uses optimization technology to process large amounts of data to ensure accurate and efficient data import; Data export: After the user clicks the export button, load the configuration, and export the data according to whether there are columns to be excluded or column names to be modified set, to meet the different export needs of users.

4. The general data presentation and interaction processing method according to claim 1, characterized in that The content of the method also includes: User select-all operation: After the user selects data, perform a select-all judgment. If all data has been selected, a batch operation button will pop up at the select-all button position, which is convenient for the user to perform batch data processing and improve the operation experience; Data subscription operation: The user can select subscription objects from multiple datasets. The subscription methods are divided into two types: by row and by cell. The system responds to the subscription according to the selected method to achieve flexible monitoring of different datasets; Data analysis, including regular analysis and data insight: Regular analysis is based on the loaded or background data; Data insight is achieved by copying views, adjusting screening parameters, aggregating multi-table data to form a new dataset and presenting it, realizing year-on-year and month-on-month analysis, and finally storing the analysis view to meet the data analysis needs at different levels.

5. The general data presentation and interaction processing method according to claim 1, characterized in that The content of the method also includes: View Transformation and Generation: Select the view transformation form, automatically detect the relational data in the dataset, generate views if the conditions are met, otherwise manually select data columns. After the system learns the features, the user can adjust the rendered view personalized to provide diverse view displays; View Copying and Aggregation: The main view is copied to generate multiple copied views, the data of the copied views is aggregated, and then different view forms are selected for display to facilitate users to compare and analyze data. When comparing data, the difference comparison algorithm is used to highlight the data differences.

6. The general data presentation and interaction processing method according to claim 1, characterized in that The content of the method further includes: Data Loading and Rendering Optimization: Determine the amount of displayed data. If it is greater than the specified number of items, start rendering optimization, render part of the data and store the redundant data, add a response event, and determine whether to load the redundant data for rendering the view according to the user's scroll operation and the viewport position; if it is less than or equal to the specified number of items, no optimization is required, thus effectively avoiding the rendering lag of UI nodes caused by excessive data.

7. The general data presentation and interaction processing method according to claim 1, wherein The content of the method further includes: Data Supplement and Loading: Configure multiple data supplement interfaces to ensure data integrity and accuracy, and configure the cell data supplement interface through a low-code platform to automatically supplement the missing data.

8. The general data presentation and interaction processing method according to claim 1, characterized in that The content of the method further includes: Data Verification and Cross-System Data Synchronization: Configure verification rules for data accuracy issues, display the problematic data differently and remind the user to revise it; when performing cross-system data synchronization, verify after obtaining the data and reporting configuration, and execute the reporting after passing. When logging in and plugins are involved, corresponding processing is performed, and then the reporting logic is converted into a scheduled task for execution.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the content of the general data presentation and interaction processing method described in any one of claims 1-8.

10. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the content of the general data presentation and interaction processing method described in any one of claims 1-8.

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