Table data batch screening method based on data intelligent classification

Abstract

The invention relates to the technical field of data processing, and provides a table data batch screening method based on data intelligent classification, which comprises the following steps: providing an integrated shuttle box interaction interface which simultaneously comprises a column item screening panel, a content screening panel and a selected data management panel and is used for centrally managing the screening process of table data; in the column item screening panel, in response to batch selection and search operation of a user on table column items, determining a target column set; for each column in the target column set, conventional screening of downlink data content is provided in a content screening panel, a service classification label is generated and recommended at the same time, and a target content set is determined in response to selection of a user on content items or classification labels; and generating screening conditions based on the target column set and the target content set, applying the screening conditions to the table, and performing unified visual display and management on all the effective screening conditions in the selected data management panel. According to the invention, the centralization and interaction efficiency of screening operation can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for batch filtering of tabular data based on intelligent data classification. Background Technology

[0002] In many fields such as data analysis, business intelligence, report processing, and daily office work, spreadsheet software (such as Microsoft Excel, WPS Spreadsheet, and Google Sheets) has become an indispensable core tool. Its powerful data organization, calculation, and visualization capabilities support the entire process from basic data recording to complex business analysis. In the process of processing spreadsheet data, "filtering" is a high-frequency and crucial operation. It enables users to quickly focus on subsets of interest from massive amounts of data, serving as a fundamental step for data insight, trend analysis, and decision support.

[0003] Currently, most mainstream spreadsheet software has built-in data filtering functions, and the technology is relatively mature. These functions typically allow users to filter row data by setting conditions for a single column (field) based on the specific data type (such as text, number, date). Common filtering modes include: fuzzy matching or exact value selection for text, setting ranges for numbers, and selecting periods for dates. Furthermore, advanced filtering functions support users writing complex formulas to achieve logical combinations of filtering across columns. In terms of interaction, existing technologies mainly employ two modes: one is to activate a drop-down menu in the column header, where specific values ​​or conditions are selected; the other is through a separate "Advanced Filter" dialog box, defining multiple condition combinations in a form format. These technical solutions constitute the mainstream paradigm for filtering operations in current spreadsheet data processing.

[0004] However, with the surge in data volume and the increasing complexity of analytical dimensions, existing screening technologies are gradually revealing the following limitations and shortcomings in terms of interaction efficiency and business adaptability:

[0005] Firstly, existing workflows for multi-column collaborative filtering and data view management are cumbersome, fragmented, and inefficient. When users need to filter multiple non-contiguous columns or compare displayed and hidden data after filtering, current technologies lack centralized, batch operation support. For example, if a user wants to filter columns A, C, and F simultaneously, they must operate separately in three different column header drop-down menus, resulting in fragmented steps. For the requirement to "show only specific columns," existing tools generally do not provide direct selection functionality but require users to hide all other columns first. This reverse operation logic is not intuitive, and when there are many columns, the experience of selecting columns one by one by holding down the Ctrl key or dragging is cumbersome and error-prone. More importantly, after applying multiple filter conditions, users lack a global view to intuitively review and manage all effective filter conditions, making it easy to get lost in complex filtering states and difficult to quickly adjust or reset specific conditions.

[0006] Secondly, existing technologies have significant shortcomings in terms of the intelligence and business semantic understanding of filtering. The filtering logic of current tools essentially remains at the level of matching and comparing data at the table level (such as characters and numbers), failing to understand the business meaning behind the data. For example, for a column of data named "Cities," the system can list all city names for the user to select, but it cannot automatically identify which cities belong to "first-tier cities," "Yangtze River Delta region," or "provincial capital cities." If users want to filter based on such business concepts, they must add category label columns to the data source in advance, or manually memorize and select all cities belonging to that category. This requires users to have deep business knowledge and undertake tedious data preprocessing or filtering settings, making data analysis unable to quickly respond to flexible and ever-changing business inquiries.

[0007] Chinese patent CN103678585A discloses a method and system for filtering user data. It achieves non-programming, composable, multi-step data filtering by predefining and reusing "filtering templates," solving the flexibility problem of moving from fixed code to configurable templates. However, its interaction method is still cumbersome, and its technical essence has not yet deviated from the scope of "explicit rule matching based on fields and values." It has inherent limitations in terms of the centralized efficiency of interaction, the intelligent semanticization of filtering, and the intuitiveness of state management.

[0008] Therefore, how to provide a method that can integrate batch filtering operations with multiple conditions and enable the system to understand the connotation of data business, thereby improving the efficiency and intelligence level of tabular data processing, has become an urgent technical problem to be solved. Summary of the Invention

[0009] In view of this, in order to overcome the shortcomings of the prior art, the present invention aims to provide a method for batch filtering of tabular data based on intelligent data classification.

[0010] According to a first aspect of the present invention, a method for batch filtering of tabular data based on intelligent data classification is provided, the method comprising:

[0011] Step S1: Provide an integrated shuttle box interactive interface, which includes a column filter panel, a content filter panel, and a selected data management panel for centralized management of the table data filtering process.

[0012] Step S2: In the column filtering panel, in response to the user's batch selection and search operations on table columns, determine the target column set;

[0013] Step S3: For each column in the target column set, provide regular filtering of its downstream data content in the content filtering panel, and generate and recommend business category tags in response to the user's selection of content items or category tags to determine the target content set;

[0014] Step S4: Generate filter conditions based on the target column set and target content set and apply them to the table. In the selected data management panel, perform unified visualization and management of all effective filter conditions.

[0015] Optionally, in the table data batch filtering method based on intelligent data classification of the present invention, the shuttle box interactive interface in step S1 is embedded in the table software interface in the form of a floating panel or sidebar; the column filtering panel displays all columns of the table in the form of a multi-selectable list and provides functions for selecting all, deselecting, and fuzzy search; the content filtering panel displays content linked to the user's selection in the column filtering panel, and is used to display and filter data content items under specific columns; the selected data management panel summarizes and displays all currently selected columns and their corresponding content filtering conditions in real time in the form of a structured list or table.

[0016] Optionally, in the table data batch filtering method based on intelligent data classification of the present invention, in step S2, the fuzzy search operation performed in the column filtering panel supports multiple pattern matching for column names, including full name, keywords, pinyin and pinyin abbreviation; the search box provides a drop-down suggestion list in real time according to the user input, and automatically locates and highlights the corresponding column in the column list after the user selects the target from the suggestion list.

[0017] Optionally, in the batch filtering method for tabular data based on intelligent data classification of the present invention, in step S3, business classification tags are generated and recommended in the following manner:

[0018] Analyze the data items contained in the currently active column of the content filtering panel;

[0019] By matching preset business classification rules with a pre-built rule engine, and / or by using a pre-trained model pre-trained with business corpus for semantic recognition, data content items are divided into different business categories.

[0020] The identified business categories are displayed in the content filtering panel as clickable multi-select tag groups, serving as a quick filtering entry point based on business semantics.

[0021] Optionally, in the batch filtering method for tabular data based on intelligent data classification of the present invention, in step S3, the business category tag group is displayed above the list of data content items in the content filtering panel. When the user clicks to select any business category tag, the data items belonging to that category in the corresponding list of data content items below the content filtering panel will be automatically filtered, and their selection status will be updated synchronously according to the user's operation.

[0022] Optionally, in the batch filtering method for tabular data based on intelligent data classification of the present invention, when classifying data content by combining a rule engine and a pre-trained model in step S3, the rule engine is used first for matching and classification. For data content items that the rule engine cannot cover or classify, the pre-trained model is then called for semantic analysis and classification prediction.

[0023] Optionally, in the batch filtering method for tabular data based on intelligent data classification of the present invention, in step S4, all effective filtering conditions are uniformly visualized and managed in the selected data management panel in the following manner:

[0024] In the selected data management panel, the content filtering conditions applied to each column are displayed in the form of items, and the total amount of currently selected data is also displayed.

[0025] Provide a separate entry point for editing and resetting each filter criterion;

[0026] At the bottom of the selected data management panel, there is a one-click reset button to clear all filter conditions at once, and a show / hide selection toggle button to control the display status of the selected data in the table.

[0027] Optionally, in response to a user's editing operation on a filter condition in the selected data management panel, the shuttle box interaction interface of the present invention is linked in the following way: the column filter panel automatically locates and highlights the column corresponding to the filter condition; the content filter panel simultaneously switches to the column and displays all its content items and business category labels, while restoring the previously set selection state on the column for the user to modify.

[0028] Optionally, the table data batch filtering method based on intelligent data classification of the present invention uses a show / hide selection switch button to control the following two view modes: in the show selection mode, only data rows that meet all current filtering conditions are displayed in the table; in the hide selection mode, data rows that do not meet the current filtering conditions are displayed in the table.

[0029] Optionally, the table data batch filtering method based on intelligent data classification of the present invention is applied to a plug-in integrated with table software. The plug-in performs data filtering and view update operations by calling the application programming interface provided by the table software.

[0030] The batch filtering method for tabular data based on intelligent data classification of the present invention has the following beneficial technical effects:

[0031] I. Centralizing the filtering process and significantly improving interaction efficiency.

[0032] By providing an integrated shuttle interface, the core functional modules of "column filtering," "row (content) filtering," and "filter result management" are integrated into a unified view. Users no longer need to switch back and forth between the table interface and multiple pop-up windows; they can complete the entire process from selecting the target column, setting content conditions, to previewing and managing results in one panel. This centralized operation mode transforms the originally discrete multi-step task into a coherent linear process, greatly simplifying the interaction path and reducing the user's cognitive load and operation time. Especially when dealing with complex filtering tasks involving multiple columns, the interaction efficiency is fundamentally improved.

[0033] Second, introduce intelligent filtering capabilities based on business semantics to overcome the limitations of traditional matching-based filtering.

[0034] By integrating a rule engine and / or pre-trained models, the plugin is empowered with the ability to intelligently recognize semantics and categorize business information in column data. It can automatically divide a column of data into meaningful categories based on its business attributes and recommend these categories to users as selectable tags. Users can directly filter based on high-level business concepts without needing to know or manually select all specific values ​​under a category beforehand. This frees users from tedious manual categorization and memorization, enabling data analysis to respond more quickly and directly to flexible business query needs, achieving an intelligent leap from "formal matching" to "semantic understanding."

[0035] III. Enhance the visualization and global controllability of complex screening states

[0036] By featuring a dedicated "Selected Data Management Panel" within the shuttle frame, all effective filter conditions (including columns and specific content items) are displayed in real-time and in a structured manner, clearly indicating the amount of data in the current filter results. A global view of the filter status is provided. Users can directly "edit" or "reset" any single condition in this panel, or use the "one-click reset" function to quickly clear all filters, or flexibly switch data views for comparative analysis using the "show / hide selected" button. This gives users global control over complex filter statuses and the freedom to adjust them flexibly, effectively solving the pain points of opaque filter statuses and difficult management in traditional methods.

[0037] IV. Ensure seamless compatibility and low learning cost with the native work environment.

[0038] Implemented as a plugin, the user interface operates as an independent panel. All filtering logic ultimately updates the data view by calling the underlying spreadsheet software's native interfaces. This ensures that the plugin's advanced batch filtering and intelligent categorization functions do not conflict with the spreadsheet software's core functions such as basic filtering, sorting, and formulas. Users can seamlessly combine the plugin's efficiency tools with the software's built-in basic operations without altering their existing operating habits. This non-intrusive design philosophy allows users to gain powerful functional benefits without incurring additional learning costs, guaranteeing the technology's usability and acceptability, and facilitating its rapid popularization and application. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating a method for batch filtering of tabular data based on intelligent data classification according to an exemplary embodiment of the present invention.

[0041] Figure 2 This is a first application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0042] Figure 3 This is a second application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0043] Figure 4 This is a third application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0044] Figure 5 This is a fourth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0045] Figure 6 This is a fifth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0046] Figure 7 This is a sixth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0047] Figure 8 This is a seventh application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0048] Figure 9 This is an eighth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0049] Figure 10 This is a ninth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0050] Figure 11 This is a tenth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0051] Figure 12 This is an eleventh application example of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0052] Figure 13 This is a twelfth application example of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0053] Figure 14 This is a thirteenth application example of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0054] Figure 15 This is a fourteenth application example of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention;

[0055] Figure 16 This is a fifteenth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Detailed Implementation

[0056] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0057] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0058] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0059] An exemplary embodiment of the present invention provides a method for batch filtering of tabular data based on intelligent data classification. Figure 1 This is a flowchart illustrating a method for batch filtering of tabular data based on intelligent data classification according to an exemplary embodiment of the present invention, as follows: Figure 1 As shown, the method in this embodiment is implemented in the following manner:

[0060] Step S1: Provide an integrated shuttle box interface, which includes a column filter panel, a content filter panel, and a selected data management panel for centralized management of the table data filtering process.

[0061] In this embodiment, the shuttle box interaction interface is embedded in the spreadsheet software interface as a floating panel or sidebar; the column filter panel displays all columns of the table in a multi-selectable list format, and provides functions for selecting all, deselecting, and fuzzy search; the content filter panel displays content linked to the user's selection in the column filter panel, and is used to display and filter data content items under specific columns; the selected data management panel summarizes and displays all currently selected columns and their corresponding content filtering conditions in real time in the form of a structured list or table.

[0062] Step S2: In the column filter panel, in response to the user's batch selection and search operations on table columns, determine the target column set.

[0063] In this embodiment, the fuzzy search operation performed in the column filtering panel supports multiple pattern matching for column names, including full name, keywords, pinyin and pinyin abbreviation; the search box provides a drop-down suggestion list in real time according to the user's input, and automatically locates and highlights the corresponding column in the column list after the user selects a target from the suggestion list.

[0064] Step S3: For each column in the target column set, provide regular filtering of its downstream data content in the content filtering panel, and generate and recommend business category tags in response to the user's selection of content items or category tags to determine the target content set.

[0065] In this embodiment, business category tags are generated and recommended in the following manner: For the currently active column in the content filtering panel, the data content items contained therein are analyzed; the data content items are divided into different business categories by matching the preset business classification rules with the preset rule engine and / or by using a pre-trained model pre-trained with business corpus for semantic recognition; and the identified business categories are displayed in the content filtering panel as clickable multi-select tag groups as a quick filtering entry point based on business semantics.

[0066] As an optional example, in this embodiment, the business category tag group is displayed above the list of data content items in the content filtering panel. When the user clicks to select any business category tag, the data items belonging to that category in the corresponding list of data content items below the content filtering panel will be automatically filtered, and their selection status will be updated synchronously according to the user's operation.

[0067] It should be noted that in this embodiment, when classifying data content using a combination of rule engine and pre-trained model, rule engine is used first for matching and classification. For data content items that the rule engine cannot cover or classify, the pre-trained model is then called for semantic analysis and classification prediction.

[0068] Step S4: Generate filter conditions based on the target column set and target content set and apply them to the table. In the selected data management panel, perform unified visualization and management of all effective filter conditions.

[0069] In this embodiment, the selected data management panel displays the content filtering conditions applied to each column in the form of items, and shows the total amount of currently selected data; it provides an independent editing and reset operation entry for each filtering condition; at the bottom of the selected data management panel, it provides a one-click reset button to clear all filtering conditions at once, and a show / hide selection toggle button to control the display status of selected data in the table.

[0070] In response to a user's editing of a filter condition in the selected data management panel, the shuttle interface works in the following way: the column filter panel automatically locates and highlights the column corresponding to the filter condition; the content filter panel switches to that column simultaneously and displays all its content items and business category tags, while restoring the previously set selection state of the column for the user to modify.

[0071] In this embodiment, the show / hide selection toggle button controls the following two view modes: in show selection mode, the table only displays data rows that meet all current filter conditions; in hide selection mode, the table displays data rows that do not meet the current filter conditions.

[0072] The method in this embodiment is applied to a plugin integrated with spreadsheet software. The plugin performs data filtering and view update operations by calling the application programming interface provided by the spreadsheet software.

[0073] The method of this embodiment will be further described in detail below in a specific scenario.

[0074] Figure 2 This is a first application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown in the figure. Figure 2 As shown, in this scenario, the shuttle box interface is divided into three parts: columns, content, and selected data, which respectively display the filtering of columns, the filtering of rows, and the management of selected data.

[0075] Figure 3 This is a second application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 4 This is a third application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown in the figure. Figure 3 and Figure 4 As shown, in this scenario, all columns of the table are displayed as a multi-option list, with all selected by default. Users can check or decheck each column, or select all, deselect all (by clicking the select all checkbox), or invert the selection at the bottom.

[0076] Figure 5 This is a fourth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 6 This is a fifth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 7 This is a sixth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 8 This is a seventh application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 9 This is an eighth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown in the figure. Figures 5 to 9 As shown, in this scenario, users can use the input boxes at the top of the panel to query column items. Searches support fuzzy searches by full name, keyword, pinyin, and pinyin abbreviation. Search results are displayed synchronously in the drop-down box; if no results are found, a suggestion is displayed in the drop-down list. After the user selects a search result from the drop-down box, the system searches for the target column name and highlights it.

[0077] Figure 10 This is a ninth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 11 This is a tenth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown in the figure. Figure 10 and Figure 11 As shown, in this scenario, after a user clicks on any column item, the content panel on the right displays all the filter options for that column. The displayed information and operations retain the filtering design of the table tool, including counting each data item, selecting all and deselecting data, selecting duplicate and unique items, and sorting by name and count. Users can enter keywords in the search box at the top to search for content; the interaction logic is consistent with the column item search.

[0078] Figure 12 This is an eleventh application example of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention. Figure 13 This is a twelfth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown in the figure. Figure 12 and Figure 13 As shown, the difference between this scenario and mainstream spreadsheet tools lies in the fact that data is categorized from multiple perspectives based on high-frequency business scenarios, and these categorizations are presented as clickable multi-select labels to allow users to quickly select data without needing to analyze or pre-classify each type of data. After a user clicks the corresponding category label, the selectable data column below automatically displays options that match the category. All category labels can be selected simultaneously. Precise classification based on rules and knowledge bases can be used, as well as semantic classification based on pre-trained models, or a combination of both: first using a rule engine, and then using a lightweight model to predict items that cannot be matched.

[0079] Figure 14 This is a thirteenth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown in the figure. Figure 14 As shown, in this scenario, the selected panel displays the columns and content selected by the user in a table format, while unselected columns are not displayed in the table. The upper right corner of the panel displays the current amount of selected data and the total amount of data in the table. The search box below allows users to perform fuzzy searches for specific search terms, and the interaction logic is consistent with the search functionality of the column and content panels.

[0080] Figure 15 This is a fourteenth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown below. Figure 15As shown, in this scenario, the table displays all columns and content by default. Therefore, when all column contents are displayed, users can only edit and cannot reset them. After clicking "Edit," the column panel on the left automatically slides to the corresponding column and displays the relevant content in the content panel.

[0081] Figure 16 This is a fifteenth application example diagram of the table data batch filtering method based on intelligent data classification according to an embodiment of the present invention, as shown below. Figure 16 As shown, in this scenario, after filtering column content, users can view the filter options in the content column and edit or reset them. Clicking "Reset" on a single column item or content will select all data in that column, while clicking "One-Click Reset" in the lower right corner will select all data in the column and content. The lower left corner of the selected panel is a toggle button to show or hide the selected data. By default, the plugin displays the data selected by the user. Users can also click the "Hide Selected" button to hide the selected data and display the unselected data in the table.

[0082] In practical applications, the batch filtering method for tabular data based on intelligent data classification of the present invention has the following beneficial technical effects:

[0083] I. Centralizing the filtering process and significantly improving interaction efficiency.

[0084] By providing an integrated shuttle interface, the core functional modules of "column filtering," "row (content) filtering," and "filter result management" are integrated into a unified view. Users no longer need to switch back and forth between the table interface and multiple pop-up windows; they can complete the entire process from selecting the target column, setting content conditions, to previewing and managing results in one panel. This centralized operation mode transforms the originally discrete multi-step task into a coherent linear process, greatly simplifying the interaction path and reducing the user's cognitive load and operation time. Especially when dealing with complex filtering tasks involving multiple columns, the interaction efficiency is fundamentally improved.

[0085] Second, introduce intelligent filtering capabilities based on business semantics to overcome the limitations of traditional matching-based filtering.

[0086] By integrating a rule engine and / or pre-trained models, the plugin is empowered with the ability to intelligently recognize semantics and categorize business information in column data. It can automatically divide a column of data into meaningful categories based on its business attributes and recommend these categories to users as selectable tags. Users can directly filter based on high-level business concepts without needing to know or manually select all specific values ​​under a category beforehand. This frees users from tedious manual categorization and memorization, enabling data analysis to respond more quickly and directly to flexible business query needs, achieving an intelligent leap from "formal matching" to "semantic understanding."

[0087] III. Enhance the visualization and global controllability of complex screening states

[0088] By featuring a dedicated "Selected Data Management Panel" within the shuttle frame, all effective filter conditions (including columns and specific content items) are displayed in real-time and in a structured manner, clearly indicating the amount of data in the current filter results. A global view of the filter status is provided. Users can directly "edit" or "reset" any single condition in this panel, or use the "one-click reset" function to quickly clear all filters, or flexibly switch data views for comparative analysis using the "show / hide selected" button. This gives users global control over complex filter statuses and the freedom to adjust them flexibly, effectively solving the pain points of opaque filter statuses and difficult management in traditional methods.

[0089] IV. Ensure seamless compatibility and low learning cost with the native work environment.

[0090] Implemented as a plugin, the user interface operates as an independent panel. All filtering logic ultimately updates the data view by calling the underlying spreadsheet software's native interfaces. This ensures that the plugin's advanced batch filtering and intelligent categorization functions do not conflict with the spreadsheet software's core functions such as basic filtering, sorting, and formulas. Users can seamlessly combine the plugin's efficiency tools with the software's built-in basic operations without altering their existing operating habits. This non-intrusive design philosophy allows users to gain powerful functional benefits without incurring additional learning costs, guaranteeing the technology's usability and acceptability, and facilitating its rapid popularization and application.

[0091] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for batch filtering of tabular data based on intelligent data classification, characterized in that, The method includes: Step S1: Provide an integrated shuttle box interactive interface, which includes a column filter panel, a content filter panel, and a selected data management panel for centralized management of the table data filtering process. Step S2: In the column filter panel, in response to the user's batch selection and search operations on table columns, determine the target column set; Step S3: For each column in the target column set, provide regular filtering of its downstream data content in the content filtering panel, and generate and recommend business category tags in response to the user's selection of content items or category tags to determine the target content set; Step S4: Generate filter conditions based on the target column set and target content set and apply them to the table. In the selected data management panel, perform unified visualization and management of all effective filter conditions.

2. The method for batch filtering of tabular data based on intelligent data classification according to claim 1, characterized in that, The shuttle box interactive interface in step S1 is embedded in the spreadsheet software interface as a floating panel or sidebar; the column filter panel displays all columns of the table in a multi-selectable list and provides functions for selecting all, deselecting, and fuzzy search; the content filter panel displays content that is linked to the user's selection in the column filter panel and is used to display and filter data content items under specific columns. The Selected Data Management panel summarizes and displays all currently selected columns and their corresponding content filtering conditions in real time in the form of a structured list or table.

3. The method for batch filtering of tabular data based on intelligent data classification according to claim 2, characterized in that, In step S2, the fuzzy search operation performed in the column filter panel supports multiple matching modes for column names, including full name, keywords, pinyin and pinyin abbreviation; the search box provides a drop-down suggestion list in real time according to the user's input, and automatically locates and highlights the corresponding column in the column list after the user selects the target from the suggestion list.

4. The method for batch filtering of tabular data based on intelligent data classification according to claim 1, characterized in that, In step S3, business category tags are generated and recommended as follows: Analyze the data items contained in the currently active column of the content filtering panel; By matching preset business classification rules with a pre-built rule engine, and / or by using a pre-trained model pre-trained with business corpus for semantic recognition, data content items are divided into different business categories. The identified business categories are displayed in the content filtering panel as clickable multi-select tag groups, serving as a quick filtering entry point based on business semantics.

5. The method for batch filtering of tabular data based on intelligent data classification according to claim 4, characterized in that, In step S3, the business category tag group is displayed above the list of data content items in the content filter panel. When the user clicks to select any business category tag, the data items belonging to that category in the corresponding list of data content items below the content filter panel will be automatically filtered, and their selection status will be updated synchronously according to the user's operation.

6. The method for batch filtering of tabular data based on intelligent data classification according to claim 4, characterized in that, In step S3, when classifying data content using a combination of rule engine and pre-trained model, rule engine is used first for matching and classification. For data content items that the rule engine cannot cover or classify, the pre-trained model is then called for semantic analysis and classification prediction.

7. The method for batch filtering of tabular data based on intelligent data classification according to claim 1, characterized in that, In step S4, all effective filter conditions are uniformly visualized and managed in the selected data management panel as follows: In the selected data management panel, the content filtering conditions applied to each column are displayed in the form of items, and the total amount of currently selected data is also displayed. Provide a separate entry point for editing and resetting each filter criterion; At the bottom of the selected data management panel, there is a one-click reset button to clear all filter conditions at once, and a show / hide selection toggle button to control the display status of selected data in the table.

8. The method for batch filtering of tabular data based on intelligent data classification according to claim 7, characterized in that, In response to a user's editing of a filter condition in the selected data management panel, the shuttle box interaction interface will be linked as follows: the column filter panel will automatically locate and highlight the column corresponding to the filter condition; the content filter panel will simultaneously switch to that column and display all its content items and business category tags, while restoring the previously set selection state on that column for the user to modify.

9. The method for batch filtering of tabular data based on intelligent data classification according to claim 7, characterized in that, The Show / Hide Selected toggle button controls the following two view modes: In Show Selected mode, the table only displays data rows that meet all current filter criteria; In the hidden selection mode, the table displays data rows that do not meet the current filter criteria.

10. The method for batch filtering of tabular data based on intelligent data classification according to claim 1, characterized in that, The method is applied to a plugin integrated with spreadsheet software, which performs data filtering and view update operations by calling the application programming interface provided by the spreadsheet software.

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