A method and system for converting excel row data into visual blue print view

Abstract

The application belongs to the technical field of data processing, and specifically discloses a method and system for converting Excel row data into a visual blueprint view. Based on the row data structure of a worksheet, a row data metadata model is constructed, thereby not only automatically extracting the data structure, but also accurately mapping the data and the elements of the visual blueprint. Through a preset view element type library, data mapping rules, view style rules and layout rules, the complex data of the Excel file can be automatically converted into a blueprint view with clear structure, intuitive and easy to understand, avoiding errors caused by manual operation, and improving the accuracy and consistency of data visualization. Through the introduction of the automatic difference marking function of the benchmark version and the comparison version, the changes between different versions can be accurately presented, which is convenient for users to quickly identify and analyze data differences, thereby not only improving the efficiency of multiple version comparison, but also providing convenience for complex operations such as data merging and version management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a method and system for converting Excel row data into visual blueprint views. BACKGROUND

[0002] In daily data management, Excel is one of the commonly used tools. When dealing with complex data, especially when multiple versions of Excel data need to be compared or data needs to be merged, the traditional method usually relies on manual operation, which is inefficient and prone to errors. There is a lack of an effective tool in the prior art that can automatically convert Excel data into intuitive blueprint views and support data comparison and merging. SUMMARY

[0003] The main purpose of the present application is to provide a method for converting Excel row data into visual blueprint views, aiming to solve the technical problems in the prior art.

[0004] The present application provides a method for converting Excel row data into visual blueprint views, comprising:

[0005] Obtaining a plurality of worksheets of a to-be-processed Excel file and a row data structure of each worksheet, and constructing a row data metadata model according to the row data structure of each worksheet;

[0006] Obtaining a view element type library of a preset visual blueprint view, and configuring a conversion relationship between row data and the preset visual blueprint view according to the row data metadata model and the view element type library, wherein the conversion relationship includes data mapping rules, view element style rules, and view layout rules;

[0007] Obtaining a plurality of to-be-converted Excel files, and converting row data of each to-be-converted Excel file into an initial visual blueprint view according to the data mapping rules, the view element style rules, and the view layout rules, wherein the to-be-converted Excel files include a baseline version Excel file and a plurality of comparison version Excel files;

[0008] Analyzing the baseline version Excel file and each comparison version Excel file to obtain corresponding data difference information;

[0009] According to each of the data difference information, the corresponding initial visual blueprint view is annotated to obtain a corresponding annotated visual blueprint view.

[0010] As a preferred embodiment, the step of constructing a row data metadata model according to the row data structure of each worksheet comprises:

[0011] obtaining a row data structure, wherein the row data structure comprises a plurality of column names;

[0012] obtaining a cell format attribute, a cell format string and all non-empty cells of each column corresponding to a column name, and obtaining a data type and a data format of the column name according to each of the cell format attribute and the cell format string;

[0013] obtaining non-empty data of each non-empty cell, and obtaining a data length of the column name according to a plurality of the non-empty data;

[0014] obtaining column data sets of any two column names, and determining a data association relationship according to two column data sets, wherein the column data set comprises all non-empty data of the column name;

[0015] taking each column name as a core index, and associating a data type, a data format and a data length of each column name with the corresponding core index to obtain column-level metadata corresponding to the column name;

[0016] obtaining all data association relationships of each column name, and constructing an association relationship mapping between each column name and other column names with the corresponding core index as the center;

[0017] integrating a plurality of the column-level metadata and the association relationship mapping to form a row data metadata model.

[0018] As a preferred, the step of configuring a conversion relationship between the row data and a preset visual blueprint view according to the row data metadata model and a view element type library comprises:

[0019] obtaining a view element type library of the preset visual blueprint view, and matching a corresponding view element type from the view element type library according to a data type of each column name in the row data metadata model;

[0020] configuring a data mapping rule according to a data format and a data length of each column name in the row data metadata model and the matched view element type;

[0021] determining a color attribute configuration, a border style and a font attribute configuration of a view element according to a data association relationship and a data format of each column name in the row data metadata model, wherein the font attribute configuration comprises a font type and size, and a font color and effect;

[0022] determining a border width of the view element according to a data association relationship and a data length of each column name in the row data metadata model, and obtaining a border attribute configuration according to the border width and the border style;

[0023] obtaining the number of non-empty data rows and the total number of rows of each column name in the row data metadata model, and obtaining the corresponding data non-empty proportion according to each of the number of non-empty data rows and the total number of rows;

[0024] determining the transparency attribute configuration of the view element according to each of the data non-empty proportions, and obtaining the view element style rule according to the transparency attribute configuration, the border attribute configuration, the color attribute configuration and the font attribute configuration;

[0025] obtaining the width size and the height size of the preset visual blueprint view, and determining the horizontal basic parameter and the vertical basic parameter according to the width size and the height size and the column number feature and the row number feature in the row data metadata model respectively;

[0026] determining the coordinate position of each view element in the preset visual blueprint view according to the horizontal basic parameter and the vertical basic parameter, and obtaining the view layout rule.

[0027] As a preferred, the step of converting the row data of each to-be-converted Excel file into the initial visual blueprint view according to the data mapping rule, the view element style rule and the view layout rule comprises:

[0028] obtaining a plurality of worksheets of the to-be-converted Excel file, and associating and integrating the row data of the plurality of worksheets according to the data association relationship in the row data metadata model to obtain associated and integrated row data;

[0029] obtaining the data type of each row data in the associated and integrated row data, and obtaining the corresponding view element type according to the data mapping rule and the data type of each row data;

[0030] determining the view element attribute parameter required to be configured for each view element type according to the data mapping rule;

[0031] determining the style attribute parameter required to be configured for each view element type according to the view element style rule;

[0032] determining the coordinate position parameter of each view element in the blueprint view according to the style attribute parameter and the view element attribute parameter and based on the view layout rule;

[0033] drawing the corresponding each view element on the preset visual blueprint view according to the view element type, the core attribute parameter, the style attribute parameter and the coordinate position parameter, and arranging and combining all view elements according to the coordinate position parameter to generate the initial visual blueprint view.

[0034] As a preferred, the step of analyzing the baseline version Excel file and each comparison version Excel file to obtain the corresponding data difference information comprises:

[0035] obtaining a row identification field according to the row data metadata model of the baseline version Excel file and each comparison version Excel file;

[0036] extracting a baseline row identification set of the baseline version Excel file and a comparison row identification set of the row data of each comparison version Excel file respectively, and obtaining an identification similarity between each baseline row identification in the baseline row identification set and each comparison row identification in each comparison row identification set;

[0037] determining a corresponding row matching relationship according to each identification similarity, and obtaining a row-level difference type according to the row matching relationship;

[0038] obtaining a cell data type of a matching row in the row-level difference type, obtaining a corresponding cell difference degree according to each cell data type, and determining a cell difference type according to each cell difference degree;

[0039] obtaining a baseline data association relationship set of the baseline version Excel file and a comparison data association relationship set of each comparison version Excel file, and determining an association relationship type according to the baseline data association relationship set and each comparison data association relationship set;

[0040] integrating the association relationship type, the cell difference type and the row-level difference type to obtain data difference information.

[0041] As a preferred, a step of annotating a corresponding initial visual blueprint view according to each data difference information to obtain a corresponding annotated visual blueprint view, comprising:

[0042] establishing a mapping relationship between data difference information and a view element according to a row identification in each data difference information and a row association relationship between the view element in the initial visual blueprint view;

[0043] extracting corresponding new data row information, deleted data row information, modified data cell information and data association relationship change information from the data difference information according to each mapping relationship;

[0044] obtaining a corresponding new annotation attribute according to each new data row information, and generating a corresponding new annotation view element according to each new annotation attribute;

[0045] obtaining a corresponding deletion annotation attribute according to each deleted data row information, and generating a corresponding deletion annotation view element according to each deletion annotation attribute;

[0046] According to each modification data cell information, a corresponding modification annotation attribute is obtained, and according to each modification annotation attribute, a corresponding modification annotation view element is generated;

[0047] According to each data association relationship change information, a corresponding association annotation attribute is obtained, and according to each association annotation attribute, a corresponding association annotation view element is generated;

[0048] The plurality of association annotation view elements, modification annotation view elements, deletion annotation view elements and new annotation view elements are integrated with the unannotated view elements in the initial visual blueprint view, to obtain a corresponding annotated visual blueprint view.

[0049] The application also provides a system for converting Excel row data into a visual blueprint view, comprising:

[0050] A construction module is configured to obtain a plurality of worksheets of a to-be-processed Excel file and a row data structure of each worksheet, and construct a row data metadata model according to the row data structure of each worksheet;

[0051] A configuration module is configured to obtain a view element type library of a preset visual blueprint view, and configure a conversion relationship between row data and the preset visual blueprint view according to the row data metadata model and the view element type library, wherein the conversion relationship comprises data mapping rules, view element style rules and view layout rules;

[0052] A conversion module is configured to obtain a plurality of to-be-converted Excel files, and convert row data of each to-be-converted Excel file into an initial visual blueprint view according to the data mapping rules, the view element style rules and the view layout rules, wherein the to-be-converted Excel files comprise a baseline version Excel file and a plurality of comparison version Excel files;

[0053] A parsing module is configured to parse and analyze the baseline version Excel file and each comparison version Excel file, to obtain corresponding data difference information;

[0054] An annotation module is configured to annotate the corresponding initial visual blueprint view according to each data difference information, to obtain a corresponding annotated visual blueprint view.

[0055] Preferably, the construction module comprises:

[0056] A first obtaining unit is configured to obtain a row data structure, wherein the row data structure comprises a plurality of column names;

[0057] The second acquisition unit is configured to acquire the cell format attribute, the cell format string and all non-empty cells of each column name corresponding column, and acquire the data type and the data format of the corresponding column name according to each cell format attribute and cell format string respectively;

[0058] The third acquisition unit is configured to acquire non-empty data of each non-empty cell, and acquire the data length of the corresponding column name according to a plurality of non-empty data;

[0059] The determination unit is configured to acquire column data sets of any two column names, and determine the data association relationship according to the two column data sets, wherein the column data set includes all non-empty data of the column name;

[0060] The association unit is configured to take each column name as a core index, and associate the data type, the data format and the data length of each column name with the corresponding core index to obtain the corresponding column-level metadata;

[0061] The construction unit is configured to acquire all data association relationships of each column name, and construct the association relationship mapping of each column name and other column names with the corresponding core index as the center;

[0062] The integration unit is configured to integrate a plurality of column-level metadata and association relationship mappings to form a row data metadata model.

[0063] The application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method for converting Excel row data into a visual blueprint view when executing the computer program.

[0064] The application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method for converting Excel row data into a visual blueprint view when executed by a processor.

[0065] The beneficial effects of the present application are: the present application constructs a row data metadata model based on the row data structure of the worksheet, thereby not only automating the extraction of the data structure, but also accurately mapping the data and the elements of the visualization blueprint, so that the generated blueprint view is more in line with the actual content and display requirements of the data, and the complex data of the Excel file can be automatically converted into a blueprint view with clear structure, intuitive and easy to understand, through the preset view element type library, data mapping rules, view style rules and layout rules, avoiding errors caused by manual operation, and improving the accuracy and consistency of data visualization, through the introduction of the automatic difference marking function of the benchmark version and the comparison version, the changes between different versions can be accurately presented, which is convenient for users to quickly identify and analyze the data difference, thereby not only improving the efficiency of multiple version comparison, but also providing convenience for complex operations such as data merging and version management, greatly reducing the manual processing time. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 The method flowchart of an embodiment of the present application.

[0067] Figure 2 The system structure schematic diagram of an embodiment of the present application.

[0068] Figure 3 The internal structure schematic diagram of the computer device of an embodiment of the present application.

[0069] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are merely intended to explain the present application, and are not intended to limit the present application.

[0071] As shown in the drawings, the present application provides a method for converting Excel row data into a visualization blueprint view, comprising: Figure 1 S1, obtaining a plurality of worksheets of a to-be-processed Excel file and a row data structure of each worksheet, and constructing a row data metadata model according to the row data structure of each worksheet;

[0072] S2, obtaining a view element type library of a preset visualization blueprint view, and configuring a conversion relationship between the row data and the preset visualization blueprint view according to the row data metadata model and the view element type library, wherein the conversion relationship comprises data mapping rules, view element style rules and view layout rules;

[0073]

[0074] ​S3, acquire a plurality of to-be-converted Excel files, and convert row data of each to-be-converted Excel file into an initial visual blueprint view according to the data mapping rule, the view element style rule and the view layout rule, wherein the to-be-converted Excel files include a baseline version Excel file and a plurality of comparison version Excel files;

[0075] S4, performing parsing analysis on the baseline version Excel file and each comparison version Excel file to obtain corresponding data difference information;

[0076] S5, differentially marking the corresponding initial visual blueprint view according to each data difference information to obtain a corresponding marked visual blueprint view.

[0077] As described in the above steps S1-S5, wherein the to-be-processed Excel file refers to a basic Excel file used for constructing row data metadata model and configuring conversion relationship, which is a core input data carrier in the rule making stage of the entire technical solution, and its core role is to provide a row data structure benchmark for extracting common features of data (such as column name, data type, format, association relationship, etc.), and its essence is a rule source file. By analyzing the row data structure of the file, a general standard for converting all subsequent Excel files into a blueprint view is established, avoiding repeated conversion logic for each version. The to-be-converted Excel file refers to a set of Excel files that actually perform initial visual blueprint view conversion based on the configured conversion relationship, which is a core input data carrier in the rule application stage of the technical solution, and its core role is to serve as a conversion object to apply the general conversion rules extracted from the to-be-processed Excel file, generating visual blueprint views that can be used for difference comparison and data merging. The essence is a rule application object file, which is the main data body that needs to be presented and analyzed through the blueprint view. The to-be-processed Excel file and the to-be-converted Excel file have three types of associations: structural consistency association, rule dependency association and target consistency association. The structural consistency association means that the structure of the to-be-converted file must match the metadata model extracted from the to-be-processed file. The rule dependency association means that the blueprint view conversion of the to-be-converted file completely depends on the conversion relationship configured by the to-be-processed file. The target consistency association means that both serve the core target of multi-version data visualization comparison and merging.

[0078] The application obtains a plurality of worksheets of an Excel file to be processed and a row data structure of each worksheet, constructs a row data metadata model according to the row data structure of each worksheet, and effectively provides a basic framework for subsequent data processing by obtaining the worksheet and the row data structure of the Excel file. Unlike the manual import of Excel and the viewing one by one in the prior art, the application ensures the accuracy of data acquisition by automatically identifying the file structure and the row data, and reduces the workload of manual checking and inputting by automatically extracting the row data structure of each worksheet, thereby improving the data processing efficiency, avoiding data omission, errors and inconsistent structures caused by manual operation, and abstractly representing the data structure of each worksheet in the Excel file by constructing the row data metadata model. The construction of the metadata model enables the system to clearly understand the data structure of each worksheet and the relationship therebetween, thereby avoiding errors and redundant operations when subsequent mapping and conversion are performed. The establishment of the metadata model enables the system to flexibly adapt to the data structure of different Excel files and efficiently process cross-table data association problems, avoids the tedious processing of data structure by manual operation, acquires a view element type library of a preset visual blueprint view, configures a conversion relationship between the row data and the preset visual blueprint view according to the row data metadata model and the view element type library, and makes the subsequent view generation process customizable and standardized. Compared with the manual selection and adjustment of chart elements in the traditional method, the application provides a standardized element library, reduces human intervention, and ensures the consistency and standardization of the visual view. Different versions of Excel data can be uniformly displayed according to the standard view element types in the library, improving the readability, accuracy and overall consistency of the view. The use of the view element type library also improves the flexibility of visual design, facilitating subsequent view style updating and expansion. By associating the row data metadata model with the view element type library, a mapping rule of data to view is generated, ensuring that the conversion between data and visual elements not only conforms to the rules, but also has clear logic and reasonable structure. Compared with the traditional manual adjustment of the relationship between data and graphics, this automatic configuration reduces human errors, so that the data can automatically follow the established rules when mapped to the view elements, reducing the complexity of operation and the possibility of errors. Automatic mapping not only improves the efficiency, but also ensures the consistency of data and view, so that omission and error conversion can be avoided when processing complex data.

[0079] By acquiring a plurality of to-be-converted Excel files, and converting row data of each to-be-converted Excel file into an initial visual blueprint view according to data mapping rules, view element style rules and view layout rules, the implementation of the present application relies on the cooperation of the data mapping rules, the view element style rules and the view layout rules, automatically acquires data from a plurality of to-be-converted Excel files and uniformly visualizes the data, compared with the prior art of manually comparing a plurality of Excel versions and manually processing data mapping, the present application automatically completes the conversion of data to view through preset rules, greatly improves the processing speed and accuracy, especially when facing a large amount of data or complex structure data, manual operation of the traditional method is very prone to error, and the automatic conversion can guarantee efficient and accurate data display, reduces the need for manual intervention, significantly improves work efficiency and accuracy, by analyzing the reference version Excel file and each comparison version Excel file, corresponding data difference information is obtained, by analyzing the differences between the reference version Excel and the plurality of comparison version Excel files, accurate comparison between the plurality of version Excel files can be performed, which not only helps users to quickly find the differences between versions, but also provides a basis for subsequent decision-making and analysis, manual comparison of a plurality of versions of Excel files in the prior art may miss some details, and even lead to misunderstanding of the comparison result, and automatic difference analysis not only saves a lot of manual comparison time, but also guarantees accurate detection of the difference, the result of the difference analysis provides a basis for subsequent difference marking, and ensures that the changes between each version can be clearly reflected in the visual view, each data difference information is marked on the corresponding initial visual blueprint view to obtain a corresponding marked visual blueprint view, the difference marking can intuitively present the difference between the reference version and the comparison version in the visual blueprint view, compared with manual marking or simple difference comparison in the prior art, the present application reflects the difference information clearly and intelligibly in the final visual view through the automatic marking process, which not only improves the accuracy of difference analysis, but also greatly reduces the risk of manual operation, through the graphical marking, the user can intuitively understand the specific difference between each version, and further perform data analysis or decision-making according to the marking information, thereby improving the efficiency and accuracy of information processing.

[0080] In one embodiment, the step S1 of constructing the row data metadata model according to the row data structure of each worksheet comprises:

[0081] S11, acquiring a row data structure, wherein the row data structure comprises a plurality of column names;

[0082] S12, acquire the cell format attribute, cell format string and all non-empty cells of each column name corresponding column, and acquire the data type and data format of the corresponding column name according to each cell format attribute and cell format string respectively;

[0083] S13, acquire the non-empty data of each non-empty cell, and acquire the data length of the corresponding column name according to a plurality of non-empty data;

[0084] S14, acquire the column data set of any two column names, and determine the data association relationship according to two column data sets, wherein the column data set includes all non-empty data of the column name;

[0085] S15, take each column name as a core index, and associate the data type, data format and data length of each column name with the corresponding core index to obtain the corresponding column-level metadata;

[0086] S16, acquire all data association relationships of each column name, and construct the association relationship mapping of each column name and other column names with the corresponding core index as the center;

[0087] S17, integrate a plurality of column-level metadata and association relationship mappings to form a row data metadata model.

[0088] As described in steps S11-S17, wherein the non-empty cell describes the physical carrier in the Excel table, and the essence is to locate the cell position filled with content in the column, and the core function is to locate the physical range of data source. The non-empty data describes the specific information carried in the non-empty cell, and the essence is the content extracted from the non-empty cell itself, and the core function is to serve as the core object of analysis and calculation. According to the cell format attribute, the data type of the column name is determined, including: if the number format in the cell format attribute is regular and the data in the column can be converted into a floating point number, then according to the cell format attribute and the data conversion result, the data type is determined to be numerical; if the number format in the cell format attribute is text, or the data in the column contains non-numeric characters and cannot be converted into a floating point number, then according to the cell format attribute and the data characteristics, the data type is determined to be text; if the number format in the cell format attribute is date or time, then according to the cell format attribute, the data type is determined to be date; if the data in the column only contains TRUE (true) or FALSE (false) two values, then according to the data value characteristics, the data type is determined to be Boolean.

[0089] The data format of the column name is determined according to the cell format string, including: if the cell format string is "0.00", the data format is numerical data with 2 decimal places according to the parsing result of the format string; if the cell format string is "yyyy-mm-dd", the data format is date data in the year-month-day format according to the parsing result of the format string; and if the cell format string is text, the data format is text data in the original character according to the parsing result of the format string.

[0090] The data length of the column name is determined according to the plurality of non-empty data, including: for text data, the maximum value of the character number of all non-empty text data in the column is taken as the length of the data corresponding to the column name by calculating the character number of each non-empty cell data; for numerical data, the maximum value of the sum of the integer part and the decimal part of all non-empty numerical data in the column is taken as the length of the data corresponding to the column name by calculating the sum of the integer part and the decimal part of each non-empty cell data; for date data, the length of the data corresponding to the column name is directly determined to be 8 bits according to the format characteristics; and for Boolean data, the maximum value of the character number of TRUE and FALSE is taken as the length of the data corresponding to the column name since the Boolean data only contains two fixed-length values TRUE and FALSE, and the character number of TRUE is 4 and the character number of FALSE is 5.

[0091] The data association relationship is determined according to the two column data sets, including: for any two column names A and B, it is judged whether all non-empty data in the column data set of column name A are contained in the column data set of column name B, if yes, the data association relationship between the column names is determined to be a primary-foreign key association according to the data containing relationship; if there is data in the column data set of column name C that satisfies the sum of the jth row data of column name A and the jth row data of column name B equal to the jth row data of column name C, the data association relationship between column name A and C, and column name C and B is determined to be a logical dependency association according to the data logical operation relationship; and if the column data sets corresponding to any two column names do not satisfy the containing relationship of the primary-foreign key association or the operation relationship of the logical dependency association, the data association relationship between the two column names is determined to be no association.

[0092] The row data metadata model is a structured metadata system formed by taking column names as core indexes and storing attribute information corresponding to each column name in association, and an independent attribute storage unit is created for each column name to store multi-dimensional attribute information obtained subsequently, so that attribute data of each column name can be individually retrieved and updated, and the association relationship mapping of each column name and other column names can be constructed around the core index, thereby forming a column association network with each column name core index as a node and the association relationship as an edge, so that the mutual dependence or association state of all column names can be completely reflected, and the column-level metadata is a core component of the data metadata, which takes column names as core indexes, and the system stores basic descriptive information of the column data, including data type, data format, data length and associated column data association relationship, and is a key basic information carrier for subsequent data conversion, data verification, data visualization configuration and other operations.

[0093] By obtaining multiple column names of the row data structure, obtaining the cell format attribute, cell format string and all non-empty cells of each column corresponding to the column name, and obtaining the data type and data format of the corresponding column name according to each cell format attribute and cell format string, the system can clearly determine the variables in the data set, form a global view, and ensure that a unified reference is used in subsequent steps, and the format attribute and format string of the cell are helpful for in-depth analysis of the structure and properties of the data, and the accuracy of the data is ensured, and by clearly determining the data type and format of each column, the automatic classification and processing of the data can be realized, so that the subsequent data operation (such as merging, comparison, etc.) is more efficient and standardized, compared with the manual type specification in the traditional method, the present application can automatically adapt to various data forms, greatly reducing the workload of manual discrimination and improving the work efficiency, by obtaining the non-empty data of each non-empty cell and obtaining the data length of the corresponding column name according to multiple non-empty data, by obtaining the non-empty data, the system can effectively filter invalid information and process useful data, so as to not only improve the data quality, but also help to analyze the distribution and effectiveness of the data, and the traditional method is often affected by the null value when processing data, resulting in inaccurate data analysis, but the present application can make the subsequent data analysis more reliable.

[0094] The prior art generally cannot systematically identify the connection between data by acquiring column data sets of any two column names and determining the data association relationship according to the two column data sets, which is easy to lead to one-sidedness of data analysis, and the present application determines the association relationship between data, which is the key to understanding the data set. This step not only helps to discover potential correlations and relationship patterns between data, but also lays the foundation for interactive analysis of complex data sets. By taking each column name as a core index and associating the data type, data format, and data length of each column name with the corresponding core index, the corresponding column-level metadata is obtained. By taking the column name as the core index, a clear and structured metadata management framework is provided, enabling users to quickly access and analyze the data properties of a specific column. This structured information presentation method provides higher logicality and visual convenience compared to traditional flat data processing methods. By acquiring all data association relationships of each column name and constructing the association relationship mapping of each column name with other column names centered on the corresponding core index, the row data metadata model is formed by integrating multiple column-level metadata and association relationship mappings. The association relationship mapping of each column name provides an intuitive view, helping users understand the complex relationships between columns and supporting more in-depth data analysis, such as data exploration and aggregation analysis. Compared to traditional methods that lack such clear relationship mapping support, the efficiency of data mining is greatly improved. The final integration step can form a complete row data metadata model, providing a one-stop data view. This not only improves the systematicness and orderliness of data management, but also provides reliable support for subsequent decision-making. Compared to the prior art, the present application significantly improves the efficiency and accuracy of data processing through a systematic and automated method, reduces the risk of human error, and enhances the depth of data analysis, providing more accurate decision support for enterprises and users.

[0095] In one embodiment, the step S2 of configuring the conversion relationship of the row data with the preset visualization blueprint view according to the row data metadata model and the view element type library includes:

[0096] S21, acquiring the view element type library of the preset visualization blueprint view, and matching the corresponding view element type from the view element type library according to the data type of each column name in the row data metadata model;

[0097] S22, configuring the data mapping rule according to the data format and data length of each column name in the row data metadata model and the matched view element type;

[0098] S23, determining the color attribute configuration, border style, and font attribute configuration of the view element according to the data association relationship and data format of each column name in the row data metadata model, wherein the font attribute configuration includes font type and size, and font color and effect.

[0099] S24, determine the border width of the view element according to the data association relationship and data length of each column name in the row data metadata model, and obtain the border attribute configuration according to the border width and border style;

[0100] S25, obtain the number of non-empty data rows and the total number of rows of each column name in the row data metadata model, and obtain the corresponding data non-empty proportion according to the ratio of each non-empty data row number and total row number;

[0101] S26, determine the transparency attribute configuration of the view element according to each data non-empty proportion, and obtain the view element style rule according to the transparency attribute configuration, border attribute configuration, color attribute configuration and font attribute configuration;

[0102] S27, obtain the width size and height size of the preset visual blueprint view, and determine the horizontal basic parameter and vertical basic parameter according to the width size and height size and the column number feature and row number feature in the row data metadata model respectively;

[0103] S28, determine the coordinate position of each view element in the preset visual blueprint view according to the horizontal basic parameter and vertical basic parameter, and obtain the view layout rule.

[0104] As described above in steps S21-S28, wherein the data mapping rule is used to establish the correspondence between the row data and the view element type and attribute, the view element style rule is used to define the visual presentation features of the view element, and the view layout rule is used to plan the spatial position of the view element in the blueprint view. Determining the view element style rule requires covering four core dimensions of color, border, font and transparency, ensuring that the style attribute is accurately corresponding to the data association relationship and data format. The data format includes numerical data, text data, date data and Boolean data. The color configuration of the view element corresponding to the numerical data is determined according to the format and association relationship of the numerical data. The color configuration of the view element corresponding to the text data is determined according to the format and association relationship of the text data. The color configuration of the view element corresponding to the date data is determined according to the format and association relationship of the date data. The color configuration of the view element corresponding to the Boolean data is determined according to the format and association relationship of the Boolean data. The border style and font type and size of the view element are determined according to the data association relationship and data format type of each column name in the row data metadata model. The font color and effect of the view element are determined according to the data association relationship and data format precision requirement of each column name in the row data metadata model.

[0105] The steps for obtaining data mapping rules based on the data format and length of each column name in the row data metadata model and the matching view element type configuration include: For numeric data and bar chart elements, the mapping rule configuration is as follows: when the view element type is a bar chart (matching numeric data), the height, width, and data label precision of the bar chart are defined based on the decimal places and data length in the data format; for text data and text box elements, the mapping rule configuration is as follows: when the view element type is a text box (matching text data), the font format (such as regular, bold, italic) and data length are defined based on the data format. The mapping rules for defining the width, height, and font attributes of a text box are configured based on its length. For date data and timeline elements, when the view element type is timeline (matching date data), the mapping rules for defining the timeline node spacing, node style, and date label format are defined based on the date display format and data length in the data format. For boolean data and switch elements, when the view element type is switch element (matching boolean data), the mapping rules for defining the switch element's status, size, and displayed text are defined based on the boolean value display format and data length in the data format.

[0106] A grid layout algorithm, combined with the number of columns, rows, and data length in the row data metadata model, is used to determine the coordinate position of each view element in the blueprint view. The horizontal basic parameter (column spacing) is calculated by dividing the sum of the widths of all column view elements in the row data metadata model by the total number of columns. Similarly, the vertical basic parameter (row spacing) can be calculated from the height. The coordinate position of the view element in the preset visualization blueprint view is calculated using the following formula: ,in, Indicates the first Liede The x-coordinate of the top-left corner of a row view element. Indicates column index, Indicates row index, Indicates horizontal spacing. Indicates the first Column view element width; ,in, Indicates the first Liede The top-left y-coordinate of a row view element. Indicates column index, Indicates row index, Indicates vertical spacing. The average height of the view element is represented; finally, the verification and correction of the data mapping rule is performed by selecting part of the non-empty row data in the row data metadata model as sample data, generating corresponding view elements according to the mapping rule configured above, verifying the matching of the view element attribute and the data format and the data length, the layout rationality and the consistency of the view element and the data content respectively until all the view elements meet the accurate mapping, and ensuring the effectiveness of the data mapping rule.

[0107] The application provides a basis and a framework for further data visualization by acquiring the view element type library of the preset visual blueprint view and matching the corresponding view element type from the view element type library according to the data type of each column name in the row data metadata model, different types of view elements (such as columnar charts, pie charts, tables and the like) can be optimized according to different data types and analysis requirements, the preset library system can be flexibly configured according to the user demand, thereby improving the adaptability and accuracy of visualization, the data type is automatically matched with the view element, and the rationality and effectiveness of data display are ensured, for example, numerical data uses a columnar chart, and category data uses a pie chart, the intelligent matching significantly improves the accuracy of the visualization result, in the traditional technology, the user is often required to manually select the element type, which is easy to cause errors or inconsistency, and reduces the effectiveness of data interpretation, the data mapping rule is obtained by matching the data format and the data length of each column name in the row data metadata model with the view element type, the configuration of the data mapping rule can ensure that the data display is consistent with the nature of the data, avoids misleading display, and sets the rule by analyzing the data format (such as date, number) and the length (such as the number of characters), so that the user can more intuitively understand the data.

[0108] The color attribute configuration, border style, and font attribute configuration of the view element are determined by the data association relationship and data format of each column name in the row data metadata model, the visual properties of the view element are customized according to the data association relationship and format, which can provide more rich visual information and guidance for users, for example, different colors are used to represent different types of data trends, which can help users quickly identify key data points and changes, the traditional method usually relies on fixed style configuration, which lacks flexibility and cannot effectively convey the emotions and trends behind the data, the border width of the view element is determined by the data association relationship and data length of each column name in the row data metadata model, and the border attribute configuration is obtained according to the border width and border style, the border width of the view element can reflect the relevance and importance of the data, by setting different widths, users can identify which data is more prominent or needs attention at a glance, this dynamic adjustment capability is more effective than the traditional method of setting a static border in providing clear information, significantly improving the interactivity and readability of visualization, the number of non-empty data rows and the total number of rows of each column name in the row data metadata model are obtained, and the corresponding data non-empty proportion is obtained according to the ratio of each non-empty data row number and the total number of rows, by analyzing the non-empty proportion of data, the system can quantify the integrity and reliability of the data, this analysis helps users identify data quality problems, so as to carry out targeted data cleaning or supplement, and enhance the usability of data.

[0109] The transparency attribute configuration of the view element is determined according to the data non-empty proportion of each data, and the view element style rule is obtained according to the transparency attribute configuration, the border attribute configuration, the color attribute configuration and the font attribute configuration. The setting of the transparency can not only provide a more intuitive information level feeling, but also guide the user to pay attention to more important or attention-required data, improve data insight, provide flexibility compared to the hard setting in the traditional method, and make the visualization result more expressive and attractive. The width size and the height size of the preset visualization blueprint view are obtained, and the horizontal basic parameter and the vertical basic parameter are respectively determined according to the width size and the height size and the column number feature and the row number feature in the row data metadata model. Through comprehensive consideration of the view size, the system can reasonably arrange the view element, and ensure the effectiveness of information transmission. The reasonable arrangement reduces the crowded feeling of information, and is easy for the user to read and understand. The coordinate position of each view element in the preset visualization blueprint view is determined according to the horizontal basic parameter and the vertical basic parameter, the view layout rule is obtained, the coordinate position of the view element is determined, a scientific and reasonable layout rule is formed, the orderliness and the logic of information display are ensured, the effective layout design makes the data display more aesthetic and professional, compared with the traditional manual layout, the automatic positioning element can significantly improve the efficiency of the visualization design, and improve the neatness and the readability of the final presentation. The present application performs deep analysis and automatic configuration through multiple dimensions such as data type, attribute and association, thereby solving the problems of low manual operation efficiency and easy error in the prior art, making complex data processing more efficient and reliable, not only improving the performance of data visualization, but also providing strong support for user decision-making.

[0110] In one embodiment, the step S3 of converting the row data of each to-be-converted Excel file into an initial visualization blueprint view according to the data mapping rule, the view element style rule and the view layout rule comprises:

[0111] S31, a plurality of worksheets of the to-be-converted Excel file are obtained, and the row data of the plurality of worksheets are associated and integrated according to the data association relationship in the row data metadata model, to obtain associated and integrated row data;

[0112] S32, the data type of each row data in the associated and integrated row data is obtained, and the corresponding view element type is obtained according to the data mapping rule and the data type of each row data;

[0113] S33, the view element attribute parameter required to be configured for each view element type is determined according to the data mapping rule;

[0114] S34, the style attribute parameter required to be configured for each view element type is determined according to the view element style rule;

[0115] S35, determining coordinate position parameters of each view element in the blueprint view according to the style attribute parameters and the view element attribute parameters and based on view layout rules;

[0116] S36, drawing each view element on a preset visual blueprint view according to the view element type, the core attribute parameters, the style attribute parameters and the coordinate position parameters, and arranging and combining all view elements according to the coordinate position parameters to generate an initial visual blueprint view.

[0117] As described in the above steps S31-S36, wherein the view element type includes a column chart, a text box, a time axis and a switch element, the view element attribute parameters include a height reference value and a proportion coefficient of the column chart, a character width reference value and a single line height reference value of the text box, a time span coefficient of the time axis and a color threshold value of the switch element, the style attribute parameters include a color value, a font attribute, a border attribute and a transparency, wherein the color value is calculated by a data value range in the view element attribute parameters, the font attribute is determined according to a column type in the column name association relationship, the border attribute is set according to a display priority of the view element type, and the transparency is calculated according to an importance weight of the data, and the coordinate position parameters include a left upper corner horizontal coordinate, a left upper corner vertical coordinate, a view element width and a view element height, wherein the horizontal coordinate is calculated by a total width of the blueprint view, a column number and a horizontal spacing, the vertical coordinate is calculated by a total height of the blueprint view, a row number and a vertical spacing, and the view element width and height are calculated by the reference values in the view element attribute parameters and data features (data length, data maximum value).

[0118] The application obtains a plurality of worksheets of the Excel file to be converted, and associates and integrates the row data of the plurality of worksheets according to the data association relationship in the row data metadata model to obtain associated and integrated row data. By first obtaining a plurality of worksheets of the Excel file to be converted and then integrating based on the data association relationship in the row data metadata model, the traditional manual processing process can be avoided, and the manual errors in the manual comparison and association process are eliminated through automatic data association, thereby greatly improving the processing efficiency and reducing the risk of errors. Compared with the manual operation mode in the prior art, this automatic integration mode not only saves time, but also ensures the accuracy and consistency of the data. The data type of each row data in the associated and integrated row data is obtained, and the corresponding view element type is obtained according to the data mapping rule and the data type of each row data, thereby ensuring the structured processing of the data. The type of each row data determines the appropriate view element type according to the data mapping rule, so that each data point is directly bound to the corresponding visual display element, avoiding the problems of inconsistent, chaotic or unable to reflect the essence of data in the traditional mode. The data mapping rule system can intelligently determine the best presentation mode for each data type, thereby improving the accuracy and flexibility of data display compared with the prior art.

[0119] The view element attribute parameter required to be configured for each view element type is determined through the data mapping rule, the attribute parameter required by each view element is automatically generated according to the type of the view element, the work flow of the designer is simplified, the automatic configuration through the data mapping rule can ensure that each view element meets the predetermined standard in visual effect, and the standardization and consistency of the entire design process are improved, the style attribute parameter required to be configured for each view element type is determined through the view element style rule, the style of the view element in the prior art is usually manually configured by a designer, which is time-consuming and easy to produce an inconsistent design style, and the application of the automatic style rule can unify and optimize the style configuration of each view element, so that not only the artificial intervention is reduced and the efficiency is improved, but also the style of all generated view elements is consistent and meets the design specification, thereby improving the visual effect of the final product, the coordinate position parameter of each view element in the blueprint view is determined through the style attribute parameter and the view element attribute parameter and based on the view layout rule, the layout of the view element in the traditional technology is usually completed by artificial experience, and there is certain randomness and instability, and the application can ensure the order and standardization of the view layout by automatically calculating the coordinate position of each view element according to the view layout rule, which not only improves the layout efficiency, but also reduces human errors, so that the finally generated blueprint is more neat and has high readability, the initial visual blueprint view is generated by drawing each view element corresponding to the view element type, the core attribute parameter, the style attribute parameter and the coordinate position parameter on the preset visual blueprint view, and arranging and combining all view elements according to the coordinate position parameter, the drawing efficiency and accuracy of the blueprint are greatly improved through the automatic drawing function, the drawing of the blueprint in the prior art usually needs to be manually adjusted by a designer according to different requirements, which usually consumes a lot of time and is easy to produce misplacement or inconsistent elements, and the application can accurately draw each view element according to the previous parameters, ensure the efficient generation of the view, and avoid human errors, through the automatic arrangement and combination function, the accurate position and arrangement order of each element are ensured, the repeated labor in the artificial process is avoided, so that the efficiency is improved, and the generated blueprint view is more consistent and neat in vision, and the visual experience of the user is enhanced.

[0120] In one embodiment, the step S4 of obtaining corresponding data difference information by parsing and analyzing the benchmark version Excel file and each comparison version Excel file includes:

[0121] S41, obtaining a row identification field according to the row data metadata model of the benchmark version Excel file and each comparison version Excel file;

[0122] S42, respectively extract the reference row identifier set of the reference version Excel file and the comparison row identifier set of the row data of each comparison version Excel file, and obtain the identifier similarity between each reference row identifier in the reference row identifier set and each comparison row identifier in each comparison row identifier set;

[0123] S43, determine the corresponding row matching relationship according to each identifier similarity, and obtain the row-level difference type according to the row matching relationship;

[0124] S44, obtain the cell data type of the matching row in the row-level difference type, obtain the corresponding cell difference degree according to each cell data type, and determine the cell difference type according to each cell difference degree;

[0125] S45, obtain the reference data association relationship set of the reference version Excel file and the comparison data association relationship set of each comparison version Excel file, and determine the association relationship type according to the reference data association relationship set and each comparison data association relationship set;

[0126] S46, integrate the association relationship type, the cell difference type and the row-level difference type to obtain the data difference information.

[0127] As described in the above steps S41-S46, wherein the determining step of the row identification field is to check the column name association relationship of the row data metadata model of the reference version Excel file and each comparison version Excel file, if there is a primary foreign key association, the primary key column is extracted as the row identification field; if there is no primary foreign key association, the key column combination whose data uniqueness meets the preset threshold is screened from the column name set, and the data uniqueness is calculated by the repetition rate of each column data (the ratio of the number of repeated data to the total number of data); the row-level difference type includes new row, deleted row and matched row, if there is a row identification in the comparison version Excel file whose identification similarity with all row identifications in the reference version Excel file is less than the preset similarity threshold, it is determined as a new row; if there is a row identification in the reference version Excel file whose identification similarity with all row identifications in the comparison version Excel file is less than the preset similarity threshold, it is determined as a deleted row; if there is a row identification in the comparison version Excel file whose identification similarity with the row identification existing in the reference version Excel file is not less than the preset similarity threshold, it is determined as a matched row; the calculation formula principle of the identification similarity is 1 minus the difference degree, the calculation method of the cell difference degree is the proportion of the absolute difference value of two cell data and the maximum value of the absolute value of two cell data, the cell difference type includes modified cell and non-difference cell, if the cell difference degree is greater than the preset difference degree threshold, the cell is determined as a modified cell, if the cell difference degree is not greater than the preset difference degree threshold, the cell is determined as a non-difference cell.

[0128] The application obtains a row identification field according to a row data metadata model of a benchmark version Excel file and each comparison version Excel file, ensures unique identification and accurate positioning of each row data by obtaining the row data metadata model in the Excel file and extracting the row identification field, can provide a unique identifier for the row data of each version of the Excel file after extracting the row identification field, thereby effectively improving the data accuracy in the comparison process, can accurately compare each row by obtaining the row identification field, avoids the unclear or inconsistent row identification in the traditional method, extracts a benchmark row identification set of the benchmark version Excel file and a comparison row identification set of the row data of each comparison version Excel file, can divide the data set into clear comparison units by extracting the row identification set in the benchmark version and the comparison version Excel file, provides a basis for subsequent difference analysis, eliminates the data redundancy and confusion problems that may occur in the comparison process in the traditional method, can ensure consistent and accurate comparison of the row data between each version by the clear benchmark row identification and comparison row identification set, improves the quality and efficiency of data comparison, and obtains an identification similarity between each benchmark row identification in the benchmark row identification set and each comparison row identification in each comparison row identification set, the identification similarity calculation can accurately evaluate the matching degree between the benchmark row and the comparison row, is convenient for judging which rows in the two versions are similar, which rows are newly added or deleted, can greatly reduce the error of manual judgment, and improves the processing capacity of complex data sets, especially in the case of large data volume, can maintain high comparison efficiency.

[0129] The corresponding row matching relationship is determined through each identification similarity, the matching relationship of the corresponding row is further determined according to the identification similarity, the comparison between data can be orderly carried out, through the introduction of the identification similarity, more accurate matching relationship can be dynamically generated, for the comparison of multiple versions of data, the traditional method can not effectively judge the order change between rows, the influence of deleting or inserting data, and the present application can help the system better handle these situations through similarity analysis, greatly improving the automation and accuracy of data comparison, and obtaining a row-level difference type according to the row matching relationship, further analyzing the data difference of each row based on the row-level matching relationship, so that the data difference of each row can be classified, and it is clear which difference is at the row level and which difference is at the cell level, avoiding the confusion of the wrong level, improving the accuracy of the comparison, especially when comparing multiple versions, all changes can be accurately identified, greatly improving the work efficiency and accuracy, by obtaining the cell data type of the matching row in the row-level difference type, obtaining the corresponding cell difference degree according to each cell data type, obtaining each cell data type and judging the difference type according to the difference degree, which is a further refinement of the data difference processing, through the judgment of the cell difference degree, the present application can identify the data changes at the detail level, such as slight changes in cell content, format changes, etc., greatly improving the depth and delicacy of data comparison, and then being able to cope with complex Excel file formats and data types, avoiding the neglect of format or small range changes in the traditional method, ensuring that all differences can be identified and accurately classified in time, and determining the cell difference type according to each cell difference degree, further refining the cell difference type, which can identify the specific difference type of each cell, through the judgment of the difference of the cell data type, more specific difference information can be provided for the user, so that the final comparison result not only stays at the large-scale comparison level, but also goes deep into each detail.

[0130] By acquiring the reference data association relationship set of the reference version Excel file and the comparison data association relationship set of each comparison version Excel file, and determining the association relationship type according to the reference data association relationship set and each comparison data association relationship set, the data dependency between different versions can be better understood by analyzing the association relationship of the data, and in the prior art, manual comparison can only focus on the data of a single row or cell, and lacks in-depth analysis of the mutual correlation between the data, while the present application can help to identify the logic and relationship between the data in multiple versions, especially in the case of complex dependence between the data, the corresponding relationship between the data can be accurately judged, the ability to process complex data sets is significantly improved, the result of data merging or version comparison is more accurate and comprehensive, by integrating the association relationship type, the cell difference type and the row-level difference type, the data difference information is obtained, by integrating multiple difference types, a comprehensive difference report can be provided in the final result, this integration step eliminates the defect of fragmented processing of difference information in the traditional method, can provide complete difference information and clear report for the user, helps the user to quickly understand and make decisions, compared with the prior art, the present application can significantly improve the accuracy, efficiency and flexibility of data processing, eliminate the uncertainty caused by manual operation in an automated and intelligent manner, and further improve the quality of data comparison and merging.

[0131] In one embodiment, according to each of the data difference information, the corresponding initial visualization blueprint view is annotated to obtain a corresponding annotated visualization blueprint view in step S5.

[0132] S51, according to the row identification in each of the data difference information and the row association relationship between the view elements in the initial visualization blueprint view, a mapping relationship between the data difference information and the view elements is established;

[0133] S52, according to each of the mapping relationship, the corresponding new data row information, the deleted data row information, the modified data cell information and the data association relationship change information are extracted from the data difference information;

[0134] S53, according to the row identification of each new data row information, the mapped view element is searched and the current attribute of the corresponding view element is obtained, and according to the annotation rule of each new data row information, the current attribute of the view element is adjusted to obtain a corresponding new annotation attribute;

[0135] S54, according to each of the new annotation attribute, the mapped view element is annotated to generate a corresponding new annotated view element;

[0136] S55, find the mapped view element according to the row identifier of each delete data row information, and obtain the current transparency and current color of the corresponding view element, and adjust the current transparency and current color of the view element according to the marking rule of each delete data row information, to obtain the corresponding delete marking attribute;

[0137] S56, mark the mapped view element according to each of the delete marking attribute, to generate the corresponding delete marking view element;

[0138] S57, find the mapped view element according to the modification row identifier and the modification column identifier of each modification data cell information, and obtain the current border attribute of the corresponding view element, and adjust the current border attribute of the view element according to the marking rule of each modification data cell information, to obtain the corresponding modification marking attribute;

[0139] S58, mark the mapped view element according to each of the modification marking attribute, to generate the corresponding modification marking view element;

[0140] S59, find two mapped view elements according to the associated column identifier of each data association relationship change information, and obtain the current position coordinates of the corresponding two view elements, and adjust the current position coordinates of the two view elements according to the marking rule of each data association relationship change information, to obtain the corresponding association marking attribute;

[0141] S510, mark the two mapped view elements according to each of the association marking attribute, to generate the corresponding association marking view element;

[0142] S511, integrate the plurality of association marking view elements, modification marking view elements, delete marking view elements and new marking view elements with the unmarked view elements in the initial visual blueprint view, to obtain the corresponding marked visual blueprint view.

[0143] As described in steps S51-S511 above, the application establishes a mapping relationship between data difference information and view elements by the row identifier in each data difference information and the row association relationship of the view elements in the initial visualization blueprint view. The mapping of data difference information and view elements through the row identifier and the row association relationship of the view elements means that the data difference of each row has an accurate visualization mapping relationship. Compared with the traditional technology, which often relies on manual comparison and manual adjustment of the correspondence between rows and view elements, the error rate of manual intervention is high, and the efficiency is low. Through this mapping relationship, manual operation can be reduced, and the difference between data and view can be automatically and efficiently mapped, ensuring accuracy and avoiding the use of fuzzy matching or rough data comparison. This ensures that each row of difference information and its corresponding view element has consistency, reduces visualization display errors caused by mis-matching, and extracts corresponding new data row information, deleted data row information, modified data cell information, and data association relationship change information from each mapping relationship. By extracting new, deleted, modified, and data association change information from each mapping relationship, the view elements can be adjusted according to different types of data changes (such as new, delete, modify). This can accurately and systematically extract different categories of data difference information, avoid missing some details, reduce errors in manual operation, and improve efficiency through the differentiation and processing of different types of difference information. This simplifies the process of comparing multiple versions of data and greatly reduces the risk and errors introduced by manual processing.

[0144] The current attribute of the mapped view element is obtained through the row identifier of each new data row information, and the current attribute of the view element is adjusted according to the labeling rule of each new data row information to obtain the corresponding new labeling attribute. After the new data row is mapped to a specific view element, the view attribute can be automatically updated according to the new data, such as color, border, label, etc. This is more accurate than the traditional manual update of the view element, and ensures that each view update is completely consistent with the data change. The labeling rule is used to adjust the attribute of the view element, so that the display mode of the new data can automatically adapt to the change of the data content without manual intervention. Not only does it improve the automation level, but it also maintains a high consistency between the data and the visualization blueprint. Each new labeling attribute is used to label the mapped view element to generate the corresponding new labeled view element. The current transparency and current color of the mapped view element are obtained through the row identifier of each deleted data row information, and the current transparency and current color of the view element are adjusted according to the labeling rule of each deleted data row information to obtain the corresponding deletion labeling attribute. When deleting the data row, the transparency and color of the view element are adjusted to reflect the deletion of the data, which can directly show the operation of deleting the data and avoid the blank or error state left after deleting in the traditional method. The change of transparency and color not only makes the user understand at a glance, but also provides a more flexible view update method. Compared with the traditional method, the present application can better reflect the actual impact of data deletion by automatically adjusting the view attribute using rules, without the need for manual modification of the chart, reducing the chance of error.

[0145] The view element mapped by each deletion annotation attribute is annotated and processed to generate a corresponding deletion annotated view element, the view element mapped by each modified data cell information is found through the modification row identifier and the modification column identifier of each modified data cell information, and the current border attribute of the corresponding view element is obtained, and the current border attribute of the view element is adjusted according to the annotation rule of each modified data cell information to obtain a corresponding modified annotation attribute. When modifying the data cell, the border attribute of the view element is automatically adjusted, which can help the user quickly identify which data has changed. Through the regularized border adjustment mechanism, it can effectively ensure that each data change is clearly and accurately presented. Through the adjustment of the border attribute, the readability and interactivity of the view element can be enhanced, and the user can quickly identify the data modification part when viewing, thereby improving the efficiency of data comparison and contrast. The two view elements mapped by each modified annotation attribute are annotated and processed to generate a corresponding modified annotated view element, the current position coordinates of the two view elements are obtained through the association column identifier of each data association relationship change information, and the current position coordinates of the two view elements are adjusted according to the annotation rule of each data association relationship change information to obtain a corresponding association annotation attribute. Through each association annotation attribute, the two view elements mapped are annotated and processed to generate a corresponding association annotated view element, thereby solving the change of the association relationship between multiple columns or multiple rows of data. The logical consistency of the data and the synchronization of the view element can be maintained through accurate coordinate adjustment. Unlike the position disorder or misidentification that may occur in traditional methods, this method can ensure that the position relationship of the view after updating still meets the expectation after the data relationship changes. Through the use of data association changes to accurately adjust the position of the view element, the consistency and integrity of data visualization are ensured, so that the entire view can still maintain logicality and intuitiveness when updating data. By integrating the multiple association annotated view elements, the modified annotated view elements, the deletion annotated view elements and the newly added annotated view elements with the unannotated view elements in the initial visual blueprint view, a corresponding annotated visual blueprint view is obtained. By recombining all the annotation elements and integrating them with the unannotated view elements, a final visual blueprint is formed. Compared with the traditional manual step-by-step annotation addition, the automated integration process can greatly improve the efficiency and ensure the logical coherence and data consistency of the final view. Through automatic integration, annotation omissions or errors caused by human operation errors are reduced, the efficient display and high accuracy of the final view element are ensured, the conversion and update between data and the visual blueprint are more automated and intelligent, thereby improving the processing efficiency, reducing the risk of human intervention, and improving the accuracy and visualization effect.

[0146] As Figure 2 shown, the application also provides a system for converting Excel row data into a visual blueprint view, comprising:

[0147] a constructing module configured to acquire a plurality of worksheets of an Excel file to be processed and a row data structure of each worksheet, and construct a row data metadata model according to the row data structure of each worksheet;

[0148] a configuring module configured to acquire a view element type library of a preset visual blueprint view, and configure a conversion relationship between row data and the preset visual blueprint view according to the row data metadata model and the view element type library, wherein the conversion relationship comprises data mapping rules, view element style rules, and view layout rules;

[0149] a converting module configured to acquire a plurality of Excel files to be converted, and convert row data of each Excel file to be converted into an initial visual blueprint view according to the data mapping rules, the view element style rules, and the view layout rules, wherein the Excel files to be converted comprise a baseline version Excel file and a plurality of comparison version Excel files;

[0150] a parsing module configured to perform parsing analysis on the baseline version Excel file and each comparison version Excel file to obtain corresponding data difference information;

[0151] a marking module configured to mark differences in the corresponding initial visual blueprint view according to each of the data difference information to obtain a corresponding marked visual blueprint view.

[0152] In one embodiment, the constructing module comprises:

[0153] a first acquiring unit configured to acquire a row data structure, wherein the row data structure comprises a plurality of column names;

[0154] a second acquiring unit configured to acquire a cell format attribute, a cell format string, and all non-empty cells of a column corresponding to each column name, and acquire a data type and a data format of the corresponding column name according to each of the cell format attribute and the cell format string;

[0155] a third acquiring unit configured to acquire non-empty data of each non-empty cell, and acquire a data length of the corresponding column name according to a plurality of the non-empty data;

[0156] a determining unit configured to acquire column data sets of any two column names, and determine a data association relationship according to two column data sets, wherein a column data set comprises all non-empty data of a column name;

[0157] an associating unit configured to take each column name as a core index, and associate a data type, a data format, and a data length of each column name with the corresponding core index to obtain corresponding column-level metadata;

[0158] The constructing unit is configured to obtain all data association relationships of each column name, and construct an association relationship mapping of each column name and other column names with the corresponding core index as the center;

[0159] The integrating unit is configured to integrate the plurality of column-level metadata and association relationship mappings to form a row data metadata model.

[0160] It should be noted that each module and unit in the system for converting Excel row data into a visual blueprint view corresponds to a step in the method for converting Excel row data into a visual blueprint view.

[0161] As shown in Figure 3 The present application also provides a computer device, which can be a server, and the internal structure thereof can be as shown in Figure 3 The computer device comprises a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store all data required in the process of the method for converting Excel row data into a visual blueprint view. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the method for converting Excel row data into a visual blueprint view.

[0162] Those skilled in the art can understand, Figure 3 that the structure shown in

[0163] The present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by the processor to implement any one of the above methods for converting Excel row data into a visual blueprint view.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, databases, or other media in this application and in examples used herein, unless specifically stated otherwise, can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0165] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, device, article, or method that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, device, article, or method. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, device, article, or method that includes the element.

[0166] The above description is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, based on the content of the present application specification and drawings, is also included in the patent protection scope of the present application.

Claims

1. A method for converting Excel row data into a visual blueprint view, characterized in that, include: Obtain multiple worksheets from the Excel file to be processed and the row data structure of each worksheet, and construct a row data metadata model based on the row data structure of each worksheet; Obtain the view element type library of the preset visualization blueprint view, and configure the conversion relationship between row data and the preset visualization blueprint view according to the row data metadata model and the view element type library. The conversion relationship includes data mapping rules, view element style rules and view layout rules. Multiple Excel files to be converted are obtained, and the row data of each Excel file to be converted is converted into an initial visualization blueprint view according to the data mapping rules, view element style rules and view layout rules. The Excel files to be converted include a baseline version Excel file and multiple comparison version Excel files. The baseline Excel file and each comparison Excel file are parsed and analyzed to obtain the corresponding data difference information; Based on each of the data difference information, the corresponding initial visualization blueprint view is annotated to obtain the corresponding annotated visualization blueprint view; The steps for building a row data metadata model based on the row data structure of each worksheet include: Obtain the row data structure, wherein the row data structure includes multiple column names; Get the cell format attributes, cell format strings, and all non-empty cells for each column name, and get the data type and data format of the corresponding column name based on each cell format attribute and cell format string; Obtain the non-empty data of each non-empty cell, and obtain the data length of the corresponding column name based on the multiple non-empty data; Get the column data sets of any two column names, and determine the data relationship based on the two column data sets, where the column data sets include all non-empty data of the column names; Each column name is used as a core index, and the data type, data format, and data length of each column name are associated with the corresponding core index to obtain the corresponding column-level metadata; Obtain all data relationships for each column name, and construct a mapping of relationships between each column name and other column names centered on the corresponding core index; The multiple column-level metadata and relationship mappings are integrated to form a row data metadata model; The steps of parsing and analyzing the baseline Excel file and each comparison Excel file to obtain corresponding data difference information include: The row identifier field is obtained based on the row data metadata model of the baseline version Excel file and each comparison version Excel file; Extract the baseline row identifier set of the baseline version Excel file and the comparison row identifier set of the row data of each comparison version Excel file respectively, and obtain the identifier similarity between each baseline row identifier in the baseline row identifier set and each comparison row identifier in each comparison row identifier set; The corresponding row matching relationship is determined based on the similarity of each identifier, and the row-level difference type is obtained based on the row matching relationship; Obtain the cell data type of the matching row in the row-level difference type, obtain the corresponding cell difference degree according to each cell data type, and determine the cell difference type according to each cell difference degree; Obtain the baseline data association set of the baseline version Excel file and the comparison data association set of each comparison version Excel file, and determine the association type based on the baseline data association set and each comparison data association set; By integrating the aforementioned relationship types, cell difference types, and row-level difference types, data difference information is obtained.

2. The method for converting Excel row data into a visual blueprint view according to claim 1, characterized in that, The steps for configuring the conversion relationship between row data and preset visualization blueprint views based on the row data metadata model and view element type library include: And match the corresponding view element type from the view element type library according to the data type of each column name in the row data metadata model; Data mapping rules are obtained by configuring the data format and data length of each column name in the row data metadata model with the matching view element type. The color attribute configuration, border style, and font attribute configuration of the view element are determined based on the data association and data format of each column name in the row data metadata model. The font attribute configuration includes font type and size, and font color and effect. The border width of the view element is determined based on the data association and data length of each column name in the row data metadata model, and the border attribute configuration is obtained based on the border width and border style. Obtain the number of non-empty data rows and the total number of rows for each column name in the row data metadata model, and obtain the corresponding non-empty data percentage based on each of the non-empty data rows and the total number of rows; The transparency attribute configuration of the view element is determined based on the proportion of non-empty data for each of the aforementioned data, and the style rules of the view element are obtained based on the transparency attribute configuration, border attribute configuration, color attribute configuration, and font attribute configuration. Obtain the width and height dimensions of the preset visualization blueprint view, and determine the horizontal and vertical basic parameters based on the width and height dimensions and the column and row number characteristics in the row data metadata model, respectively. Based on the horizontal and vertical basic parameters, the coordinate position of each view element in the preset visualization blueprint view is determined, and the view layout rules are obtained.

3. The method for converting Excel row data into a visual blueprint view according to claim 1, characterized in that, The steps for converting row data of each Excel file to be converted into an initial visual blueprint view according to the data mapping rules, view element style rules, and view layout rules include: Obtain multiple worksheets from the Excel file to be converted, and integrate the row data of the multiple worksheets according to the data relationship in the row data metadata model to obtain the integrated row data; Obtain the data type of each row in the associated and integrated row data, and obtain the corresponding view element type according to the data mapping rules and the data type of each row; The required view element attribute parameters for each view element type are determined based on the data mapping rules. The style attribute parameters required for each view element type are determined based on the view element style rules. Based on the style attribute parameters and view element attribute parameters, and according to the view layout rules, determine the coordinate position parameters of each view element in the blueprint view; Based on the view element type, core attribute parameters, style attribute parameters, and coordinate position parameters, draw each corresponding view element on the preset visualization blueprint view, and arrange and combine all view elements according to the coordinate position parameters to generate the initial visualization blueprint view.

4. The method for converting Excel row data into a visual blueprint view according to claim 1, characterized in that, The steps of annotating the corresponding initial visualization blueprint view based on each of the data difference information to obtain the corresponding annotated visualization blueprint view include: A mapping relationship between data difference information and view elements is established based on the row identifier in each of the data difference information and the row association relationship of view elements in the initial visualization blueprint view; Based on each of the mapping relationships, extract the corresponding information on newly added data rows, deleted data rows, modified data cells, and changes in data relationships from the data difference information; Obtain the corresponding new annotation attribute based on each new data row information, and generate the corresponding new annotation view element based on each new annotation attribute; Obtain the corresponding deletion label attribute based on each deleted data row information, and generate a corresponding deletion label view element based on each deletion label attribute; Obtain the corresponding modified annotation attribute based on the information of each modified data cell, and generate the corresponding modified annotation view element based on each of the modified annotation attributes; Obtain the corresponding associated annotation attributes based on the change information of each data relationship, and generate the corresponding associated annotation view element based on each of the associated annotation attributes; By integrating multiple associated annotation view elements, modified annotation view elements, deleted annotation view elements, and added annotation view elements with the unannotated view elements in the initial visualization blueprint view, a corresponding annotation visualization blueprint view is obtained.

5. A system for converting Excel row data into a visual blueprint view, used to implement the method according to any one of claims 1 to 4, characterized in that, include: The module is used to obtain multiple worksheets of the Excel file to be processed and the row data structure of each worksheet, and to build a row data metadata model based on the row data structure of each worksheet. The configuration module is used to obtain the view element type library of the preset visualization blueprint view, and configure the conversion relationship between row data and the preset visualization blueprint view according to the row data metadata model and the view element type library. The conversion relationship includes data mapping rules, view element style rules and view layout rules. The conversion module is used to acquire multiple Excel files to be converted, and convert the row data of each Excel file to be converted into an initial visualization blueprint view according to the data mapping rules, view element style rules and view layout rules. The Excel files to be converted include a baseline version Excel file and multiple comparison version Excel files. The parsing module is used to parse and analyze the baseline version Excel file and each comparison version Excel file to obtain the corresponding data difference information; The annotation module is used to annotate the corresponding initial visualization blueprint view according to each of the data difference information, so as to obtain the corresponding annotated visualization blueprint view.

6. The system for converting Excel row data into a visual blueprint view according to claim 5, characterized in that, The building module includes: The first acquisition unit is used to acquire a row data structure, wherein the row data structure includes multiple column names; The second acquisition unit is used to acquire the cell format attribute, cell format string and all non-empty cells of the column corresponding to each column name, and to acquire the data type and data format of the corresponding column name according to each cell format attribute and cell format string; The third acquisition unit is used to acquire the non-empty data of each non-empty cell and to acquire the data length of the corresponding column name based on the multiple non-empty data. The determination unit is used to obtain the column data sets of any two column names and determine the data relationship based on the two column data sets, wherein the column data sets include all non-empty data of the column names; The association unit is used to treat each column name as a core index and associate the data type, data format, and data length of each column name with the corresponding core index to obtain the corresponding column-level metadata; The building unit is used to obtain all data relationships for each column name and to build a mapping of relationships between each column name and other column names centered on the corresponding core index; The integration unit is used to integrate multiple column-level metadata and relationship mappings to form a row data metadata model.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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