Logistics data processing method, device and equipment

Abstract

The application discloses a logistics data processing method, device and equipment, the method is applied to the technical field of logistics, the method comprises the following steps: in the case that table data in a target page is loaded, the DOM structure of the table is traversed based on a style collection layer, a table header area, a table body area and a row and column area are identified, and a style mapping table of the table is constructed; corresponding business data is read from a database, an interface and a report platform based on a data acquisition layer; and a data row and column structure is obtained; the data row and column structure and the style mapping table are one-to-one bound according to a cell position, a data style intermediate result is obtained and is cached; if a data export instruction of a user is received, conditional style mapping is re-executed on a part of the data style intermediate result in which data changes, and an export file is generated. The application automatically completes style collection, data reading, format writing and file generation, and the automation degree of the export file is improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus and equipment for processing logistics data. Background Technology

[0002] In enterprise data management, report verification, and operation monitoring scenarios, users often need to export the data displayed on the front-end interface as an Excel file for verification, archiving, and reporting.

[0003] The relevant technologies all export pure data, which cannot restore the rules for distinguishing table header colors, background colors of important data, and font colors. Users need to reformat and recolor the data after exporting, which is time-consuming and laborious. When the data volume is large, the lack of color differentiation makes it easy to miss or misread the data, affecting the accuracy of verification. There is a lack of a systematic solution that deeply binds the interface display style with the data export. Summary of the Invention

[0004] This invention provides a logistics data processing method, apparatus, and equipment that can automatically extract the style attributes and visual attributes corresponding to cells, and automatically complete style acquisition, data reading, format writing, and file generation, thereby improving the automation level of file export.

[0005] On one hand, the present invention provides a logistics data processing method, the method comprising: Once the table data on the target page has been loaded, the DOM structure of the table is traversed based on the style collection layer to identify the header area, body area, and row and column areas; the table data is logistics data. Based on the cells corresponding to the header area, body area, and row and column areas, extract the style attributes and visual attributes corresponding to each cell; A style mapping table for the table is constructed based on the style and visual attributes corresponding to each cell; the style mapping table includes attribute information for different row and column positions in the table; The data acquisition layer reads corresponding business data from databases, interfaces, and reporting platforms; and performs validation, formatting, null value handling, and field mapping on the business data to ensure that the resulting data row and column structure is consistent with the front-end interface. Based on the style fusion layer, the data row and column structure is bound to the style mapping table one by one according to the cell position to obtain the intermediate data style results and cache them. If a user's data export instruction is received, the conditional style mapping is re-executed for the parts of the intermediate results of the data style that have changed, and the cached results are directly reused for the remaining parts to generate an export file.

[0006] On the other hand, a logistics data processing device is provided, the device comprising: The region recognition module is used to identify the header region, body region, and row and column regions of the table based on the style collection layer after the table data on the target page has been loaded; the table data is logistics data. The attribute extraction module is used to extract the style attributes and visual attributes of each cell based on the cells corresponding to the header area, body area and row and column areas. The mapping table construction module is used to construct a style mapping table for the table based on the style attributes and visual attributes corresponding to each cell; the style mapping table includes attribute information for different row and column positions in the table; The data processing module is used to read corresponding business data from the database, interface, and reporting platform based on the data acquisition layer; and to perform validation, formatting, null value handling, and field mapping on the business data to ensure that the resulting data row and column structure is consistent with the front-end interface. The result determination module is used to bind the data row and column structure to the style mapping table according to the cell position based on the style fusion layer, obtain the intermediate data style result and cache it. The file export module is used to, upon receiving a user's data export instruction, re-execute the conditional style mapping on the parts of the intermediate results of the data style that have changed according to the data export instruction, and directly reuse the cached results for the remaining parts to generate an export file.

[0007] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the logistics data processing method as described above.

[0008] On the other hand, a computer storage medium is provided that stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the logistics data processing method described above.

[0009] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the logistics data processing method as described above.

[0010] The logistics data processing method, apparatus, and equipment provided by this invention have the following technical effects: This invention, upon completion of table data loading on the target page, traverses the DOM structure of the table using a style acquisition layer to identify the header area, body area, and row / column areas; the table data is logistics data; based on the cells corresponding to the header area, body area, and row / column areas, it extracts the style attributes and visual attributes corresponding to each cell; based on the style attributes and visual attributes corresponding to each cell, it constructs a style mapping table for the table; the style mapping table includes attribute information for different row and column positions in the table; based on a data acquisition layer, it reads corresponding business data from a database, interface, and reporting platform; and performs validation, formatting, null value handling, and field mapping on the business data to ensure that the obtained data row / column structure is consistent with the front-end interface; based on a style fusion layer, it binds the data row / column structure to the style mapping table one by one according to cell position to obtain intermediate data style results and caches them; if a user's data export instruction is received, according to the data export instruction, it re-executes conditional style mapping on the parts of the intermediate data style results where the data has changed, and directly reuses the cached results for the remaining parts to generate an export file. This invention automatically captures the background color, font color, and font style of the front-end table, eliminating the need for manual settings by the user. Furthermore, it binds the style information to the data structure and synchronously writes it to Excel cells during export, achieving a WYSIWYG (What You See Is What You Get) experience. The system automatically completes style collection, data reading, format writing, and file generation, improving the automation level of file export. Attached Figure Description

[0011] To more clearly illustrate the technical solutions and advantages in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of a logistics data processing system provided in the embodiments of this specification; Figure 2 This is a flowchart illustrating a logistics data processing method provided in an embodiment of this specification; Figure 3 This is a flowchart illustrating a method for extracting style attributes and visual attributes corresponding to each cell, as provided in the embodiments of this specification. Figure 4 This is a flowchart illustrating a method for traversing the DOM structure of a table based on a style collection layer, as provided in an embodiment of this specification. Figure 5 This is a flowchart illustrating a method for determining the comprehensive attributes of a target style in a target cell, as provided in an embodiment of this specification. Figure 6 This is a schematic diagram of the structure of a logistics data processing device provided in the embodiments of this specification; Figure 7 This is a schematic diagram of the structure of a server provided in the embodiments of this specification. Detailed Implementation

[0013] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Please see Figure 1 , Figure 1 This is a schematic diagram of a logistics data processing system provided in the embodiments of this specification, such as... Figure 1 As shown, the logistics data processing system may include at least server 01 and client 02.

[0016] Specifically, in the embodiments of this specification, server 01 may include a standalone server, a distributed server, or a server cluster composed of multiple servers. It may also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Server 01 may include a network communication unit, a processor, and memory, etc. Specifically, server 01 can be used to construct the style mapping table of the table.

[0017] Specifically, in this embodiment of the specification, the client 02 may include physical devices such as smartphones, desktop computers, tablets, laptops, digital assistants, smart wearable devices, smart speakers, in-vehicle terminals, and smart TVs. It may also include software running on these physical devices, such as web pages provided to users by service providers, or applications provided to users by such service providers. Specifically, the client 02 can be used to generate an export file based on the data row and column structure of the business data and the style mapping table.

[0018] The following describes a logistics data processing method according to the present invention. Figure 2 This is a flowchart illustrating a logistics data processing method provided in an embodiment of this specification. This specification provides the operational steps of the method described in the embodiment or flowchart, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiment is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the method can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) as shown in the embodiment or drawings. Specifically, as... Figure 2 As shown, the method may include: S201: Once the table data on the target page has been loaded, the DOM structure of the table is traversed based on the style collection layer to identify the header area, body area, and row and column areas; the table data is logistics data.

[0019] In the embodiments described in this specification, after the front-end interface is loaded, the style collection layer automatically traverses the DOM structure of the table, header, and data rows.

[0020] S203: Extract the style attributes and visual attributes of each cell based on the cells corresponding to the header area, body area, and row and column areas.

[0021] This embodiment collects style information for each cell: background color (RGB / hexadecimal), font color, font weight, and alignment; it stores the style information in a structured manner according to row and column positions to form a style mapping table; it supports user-defined enabling / disabling of style export and supports default style rule configuration.

[0022] S205: Construct a style mapping table for the table based on the style attributes and visual attributes corresponding to each cell; the style mapping table includes attribute information for different row and column positions in the table.

[0023] In the embodiments described in this specification, the entire DOM tree structure of the table is traversed to identify different parts such as the header area, body area, total rows, or fixed columns. For each cell, the system reads its inline styles, CSS class names, and the calculated final style (i.e., the actual value rendered by the browser), and extracts visual attributes such as background color, font color, font weight, font size, and alignment. The collected results are recorded according to row and column positions (e.g., row 3, column 2), initially forming a style mapping table.

[0024] S207: Based on the data acquisition layer, read the corresponding business data from the database, interface, and reporting platform; and perform validation, formatting, null value handling, and field mapping on the business data to ensure that the obtained data row and column structure is consistent with the front-end interface.

[0025] In the embodiments described in this specification, the data acquisition layer reads corresponding business data from the database, interface, and reporting platform; performs data validation, formatting, null value handling, and field mapping; and maintains the data row and column structure completely consistent with the front-end interface.

[0026] S209: Based on the style fusion layer, the data row and column structure is bound to the style mapping table one by one according to the cell position to obtain the intermediate data style result and cache it.

[0027] In the embodiments of this specification, the style fusion layer binds the data structure and style mapping table one by one according to the cell position; the table header automatically matches the table header style, and the data row automatically matches the corresponding row / column style; it supports automatic coloring of important data and abnormal data according to the interface rules.

[0028] S2011: If a user's data export instruction is received, according to the data export instruction, the conditional style mapping is re-executed on the part of the data in the intermediate result of the data style that has changed, and the cached result is directly reused for the rest to generate an export file.

[0029] In this embodiment, after the table data on the target page has been loaded, the DOM structure of the table is traversed based on the style acquisition layer to identify the header area, body area, and row / column areas. The table data is logistics data. Style attributes and visual attributes corresponding to each cell in the header area, body area, and row / column areas are extracted. A style mapping table for the table is constructed based on the style attributes and visual attributes of each cell. The style mapping table includes attribute information for different row and column positions in the table. Corresponding business data is read from the database, interface, and reporting platform based on the data acquisition layer. The business data is validated, formatted, null values ​​are handled, and fields are mapped to ensure that the resulting data row / column structure is consistent with the front-end interface. The data row / column structure is bound to the style mapping table according to cell position using the style fusion layer to obtain intermediate data style results and cache them. If a user's data export command is received, the conditional style mapping is re-executed for the changed parts of the intermediate data style results according to the data export command, and the cached results are directly reused for the remaining parts to generate an export file. This invention automatically captures the background color, font color, and font style of the front-end table, eliminating the need for manual settings by the user. Furthermore, it binds the style information to the data structure and synchronously writes it to Excel cells during export, achieving a WYSIWYG (What You See Is What You Get) experience. The system automatically completes style collection, data reading, format writing, and file generation, improving the automation level of file export.

[0030] In some embodiments, such as Figure 3 As shown, the step of extracting the style and visual attributes of each cell based on the corresponding cells in the header area, body area, and row and column areas includes: S301: Obtain the cells corresponding to the header area, body area, and row and column areas respectively; S303: Read the inline styles, CSS class names, and rendered values ​​of each cell to obtain the style properties corresponding to each cell; S305: Extract the background color, font color, font weight, font size, and alignment of each cell to obtain the visual attributes corresponding to each cell.

[0031] In this embodiment, for each cell, the system reads its inline style, CSS class name, and the calculated final style (i.e., the actual value rendered by the browser), and extracts visual attributes such as background color, font color, font weight, font size, and alignment.

[0032] In some embodiments, such as Figure 4 As shown, when the table data on the target page has been loaded, traversing the DOM structure of the table based on the style collection layer includes: S401: During the process of loading the table on the target page, monitor the rendering events of the table in real time; S403: If the rendering event indicates that the cell style of the table is unstable, perform a polling wait operation; S405: Repeat the real-time monitoring of the table's rendering events until the rendering events indicate that the table's cell styles are unstable, then perform the polling and waiting operation until all cell styles of the table have been determined; S407: Perform a DOM structure traversal of the table based on the style capture layer.

[0033] In this embodiment, in business scenarios such as multi-level logistics outlets, tables are often dynamically generated by front-end frameworks (such as Vue and React), and the DOM structure may change with data changes. Some styles may even be added asynchronously by JavaScript at runtime. To address this, the system employs an "asynchronous wait + retry mechanism": During the page lifecycle, it listens for the table rendering completion event. If, due to network latency or data pagination loading, the styles of some cells are not yet stable, the system will briefly poll and wait until the styles of all visible cells are determined before starting data collection. Simultaneously, the system can identify dynamic rows and columns generated by virtual scrolling technology and automatically expand the collection range.

[0034] In some embodiments, the method further includes: If network latency is detected during the rendering of the table, it is determined that the cell style of the table is unstable. If pagination of the table is detected during the rendering process, it is determined that the cell style of the table is unstable.

[0035] In this embodiment, if an anomaly occurs in the style acquisition layer, the style acquisition results may be abnormal. In this case, the style recognition model can be called to identify the comprehensive style attributes. The abnormal situations include: the acquired cell is not fully loaded due to front-end framework abnormality or asynchronous style sheet, and the style of the acquired cell is empty or default value; the table uses non-standard components and cannot directly read DOM styles; the user changes some styles through browser plugins or special operations, causing the acquisition results to be inconsistent with the user's vision.

[0036] In some embodiments, such as Figure 5 As shown, the method further includes: S501: Obtain the comprehensive style attribute of each cell in the table collected by the style collection layer; the comprehensive sample attribute includes the style attribute and the visual attribute; S503: Determine the style confidence level of the overall style attribute of each cell based on the source of the overall style attribute of each cell; S505: If the style confidence level corresponding to any target cell is less than a preset threshold, obtain the first pixel region of the target cell and the second pixel region located around the target cell; S507: Input the first pixel region and the second pixel region into the style recognition model to perform style prediction, and output the comprehensive attribute of style prediction and the confidence level of style prediction; S509: Determine the target style comprehensive attribute of the target cell based on the style prediction comprehensive attribute of the target cell and the style comprehensive attribute of the target cell collected by the style acquisition layer.

[0037] In this embodiment, the traditional DOM scraping method may fail when the following abnormal conditions are encountered: Some cells have empty or default styles due to bugs in the front-end framework or incomplete loading of asynchronous style sheets; the table uses non-standard components (such as tables rendered by Canvas), making it impossible to directly read DOM styles; users have changed some styles through browser plugins or special operations, resulting in inconsistencies between the collected results and the user's visual perception.

[0038] To this end, the system comes pre-loaded with a lightweight convolutional neural network model (called the "style recognition model"). This model is trained using a large number of screenshots from backend reports, data platforms, and logistics management systems. It can directly infer the background color, font color, bolding, and other style attributes of a cell based on the visual image (pixel blocks) of the cell area on the screen.

[0039] Once the DOM is fully crawled, the system scores the style confidence of each cell. If a cell's style originates from an uncertain source (e.g., a default value or multiple asynchronous modifications) and its confidence score is below the threshold, a fallback model is triggered.

[0040] The system extracts the cell and a small surrounding pixel region, inputs them into the style recognition model, and the model outputs the predicted comprehensive attribute of the style and the style prediction confidence score. The second pixel region surrounding the target cell can be a region located at a distance less than a preset distance from the target cell.

[0041] For example, training methods for style recognition models include: Obtain the first sample pixel region of the sample cell and the second sample pixel region located in the surrounding area of ​​the sample cell; the sample cell is labeled with a comprehensive sample style attribute label and a sample style confidence label; Input the first sample pixel region and the second sample pixel region into the preset model, extract the background color, font color, and bold style attributes of the sample cell, and output the comprehensive attribute result of the sample style and the confidence result of the sample style. Based on the differences between the comprehensive attribute results of the sample styles and the comprehensive attribute labels of the sample styles, and the differences between the confidence results of the sample styles and the confidence labels of the sample styles, the preset model is trained to obtain a style recognition model.

[0042] In this embodiment, a first loss data is obtained based on the difference between the comprehensive attribute result of the sample style and the comprehensive attribute label of the sample style. A second loss data is obtained based on the difference between the confidence result of the sample style and the confidence label of the sample style. Then, the sum of the first loss data and the second loss data is calculated to obtain the target loss data. Finally, the model parameters of the preset model are adjusted according to the target loss data until the training termination condition is met, thus obtaining the style recognition model.

[0043] For example, determining the style confidence level of the overall style attribute of each cell based on the source of the overall style attribute of each cell includes: If the style value of the cell is the default value or the value is modified asynchronously multiple times, it is determined that the style confidence of the overall style attribute of the cell is less than a preset threshold. If the style value of the cell is not the default value and the value is modified asynchronously multiple times, the style confidence of the cell's overall style attribute is determined to be greater than or equal to a preset threshold.

[0044] The preset threshold can be set according to actual conditions. Based on the preset threshold, the style confidence of the cell's style comprehensive attribute can be divided into two types, thereby determining whether to trigger style attribute prediction based on the style recognition model.

[0045] In some embodiments, determining the target style comprehensive attribute of the target cell based on the style prediction comprehensive attribute of the target cell and the style comprehensive attribute of the target cell collected by the style acquisition layer includes: If the confidence level of the style prediction is greater than the preset confidence threshold, the comprehensive attribute of the style prediction will be determined as the comprehensive attribute of the target style of the target cell. If the confidence level of the style prediction is less than or equal to the preset confidence threshold, the comprehensive style attribute of the target cell collected by the style acquisition layer is determined as the comprehensive style attribute of the target cell.

[0046] This embodiment can fuse model prediction results with DOM collection results: if the model confidence is higher than the preset value, the model result is used to cover the original style; otherwise, the DOM collection results are retained.

[0047] The model also has adaptive capabilities. When the system detects that a certain type of table structure frequently triggers fallback, it will automatically add the corresponding screenshot to the model's incremental training set and fine-tune the model parameters periodically so that the model can gradually adapt to the interface style of a specific business system.

[0048] In some embodiments, the method further includes: If the source of the style comprehensive attribute of any cell includes at least two of the following: inline style, CSS class, parent inherited style, browser default style, and style prediction comprehensive attribute output by the style recognition model, the target source of the cell is determined according to the priority of each source. The comprehensive style attribute corresponding to the target source is determined as the comprehensive style attribute of the cell; the priority of the inline style, the CSS class, the predicted comprehensive style attribute, the parent inherited style, and the browser default style decreases in that order. The step of constructing the style mapping table of the table based on the style attributes and visual attributes corresponding to each cell includes: constructing the style mapping table of the table according to the comprehensive attribute of the target style corresponding to each cell, and according to the three-level mapping relationship of worksheet name, row index, and column index.

[0049] In this embodiment, the style of a cell may originate from multiple sources: inline styles, CSS classes, inherited styles from the parent element, browser default styles, and the results of the aforementioned intelligent recognition model. To avoid conflicts, the system has a built-in "style priority arbitration algorithm." This algorithm selects the final style according to the following priority order (from high to low): style rules "forced" by the user through the system configuration interface (e.g., "bold all amount columns"); inline styles (style attributes directly written on the tag); styles defined through CSS classes, where the class name matches the business-preset key styles (e.g., "error-cell" "warning-bg"); prediction results from the intelligent recognition model (only enabled when DOM collection is unreliable); calculated inherited styles (passed from the parent element); and browser default styles.

[0050] The arbitration algorithm records the source tag for each style attribute and outputs it to the system log for easy auditing and debugging later. For merged cells (rowspan / colspan), the arbitration algorithm copies its style to all merged logical cells, ensuring that each cell has an independent style definition during export.

[0051] After the style information is collected and arbitrated, it is stored as a style mapping table according to a three-level mapping relationship of "worksheet name → row index → ​​column index". This mapping table not only contains the complete style attributes of each cell, but also records the "hash fingerprint" of the style rules, which is used for quick comparison to see if the style has changed.

[0052] Description of the completed technical solution: This technical solution provides a multi-source logistics data processing method based on page synchronization, which reads corresponding business data from databases, interfaces, and reporting platforms, and then embeds a data quality assessment model (DQ) on this basis. Ensemble: Integrates Isolation Forest, Variational Autoencoder (VAE), and statistical rule-based multimodal anomaly detection algorithms to identify missing values, outliers, duplicate records, and format errors; outputs a quality score for each data entry and automatically marks high-risk data; for scenarios with multiple data sources, it introduces a multi-source data alignment algorithm based on hash and semantic similarity: automatically matching corresponding columns in different sources using field names, data types, and business primary keys; significantly improving the convenience and efficiency of data verification.

[0053] For example, the main technical solution of this application includes the following steps: Step S1: Style Collection and Configuration After the front-end interface is loaded, the style collection layer automatically traverses the DOM structure of the table, header, and data rows; Collect style information for each cell: background color (RGB / hexadecimal), font color, font weight, and alignment. Style information is stored in a structured manner according to row and column positions to form a style mapping table; it supports user-defined enabling / disabling of style export and supports default style rule configuration.

[0054] The system first automatically collects and structures the table styles for the front-end interface. Once the user opens the target page and the table data is loaded, the style collection layer begins its work. This includes the following detailed processes: 1. DOM Traversal and Basic Style Collection The system traverses the entire table's DOM tree structure, identifying different parts such as the header area, body area, total rows, and fixed columns. For each cell, the system reads its inline styles, CSS class names, and the calculated final style (i.e., the actual value rendered by the browser), extracting visual attributes such as background color, font color, font weight, font size, and alignment. The collected results are recorded by row and column position (e.g., row 3, column 2), initially forming a style mapping table.

[0055] 2. Enhanced data collection for dynamically rendered tables. In business scenarios such as multi-level logistics outlets, tables are often dynamically generated by front-end frameworks (such as Vue and React), and the DOM structure may change as the data changes. Some styles may even be added asynchronously by JavaScript at runtime. To address this, the system employs an "asynchronous wait + retry mechanism": During the page lifecycle, it listens for the table rendering completion event. If, due to network latency or data pagination loading, the styles of some cells are not yet stable, the system will briefly poll and wait until the styles of all visible cells are finalized before resuming data collection. Simultaneously, the system can recognize dynamic rows and columns generated by virtual scrolling technology and automatically expand the collection range.

[0056] 3. Intelligent style recognition model as a backup Traditional DOM scraping methods may fail under the following abnormal conditions: Some cells have empty or default styles after calculation due to bugs in the front-end framework or incomplete loading of asynchronous style sheets; The table uses non-standard components (such as tables rendered by Canvas), and DOM styles cannot be read directly; Users may have altered some styles through browser plugins or special operations, resulting in the collected results not matching the user's visual perception.

[0057] To address this, the system pre-installs a lightweight convolutional neural network model (called the "style recognition model"). This model is trained using numerous screenshots from backend reports, data platforms, and logistics management systems. It can directly infer style attributes such as background color, font color, and whether a cell is bold based on the visual image (pixel blocks) of the cell area on the screen. The specific workflow is as follows: Once the DOM is fully crawled, the system scores the style confidence of each cell. If a cell's style originates from an uncertain source (e.g., a default value or multiple asynchronous modifications) and its confidence score is below the threshold, a fallback model is triggered.

[0058] The system extracts the cell and a small area of ​​surrounding pixels, inputs it into the style recognition model, and the model outputs the predicted style label and confidence score.

[0059] The model prediction results are merged with the DOM collection results: if the model confidence is higher than the preset value, the model results are used to override the original styles; otherwise, the DOM collection results are retained.

[0060] The model also has adaptive capabilities. When the system detects that a certain type of table structure frequently triggers fallback, it will automatically add the corresponding screenshot to the model's incremental training set and fine-tune the model parameters periodically so that the model can gradually adapt to the interface style of a specific business system.

[0061] 4. Style Priority Arbitration Algorithm Because a cell's style may originate from multiple sources: inline styles, CSS classes, inherited styles from parent elements, browser default styles, and the results of the aforementioned intelligent recognition model, a built-in "style priority arbitration algorithm" is implemented to avoid conflicts. This algorithm selects the final style according to the following priority order (from highest to lowest): style rules "forced" by the user through the system configuration interface (e.g., "bold all amount columns"); inline styles (style attributes directly written on the tag); styles defined through CSS classes, where the class name matches the business-preset key styles (e.g., "error-cell" and "warning-bg"); prediction results from the intelligent recognition model (only enabled when DOM scraping is unreliable); calculated inherited styles (passed from the parent element); and browser default styles. The arbitration algorithm records the source tag for each style attribute and outputs it in the system log for later auditing and debugging. For merged cells (rowspan / colspan), the arbitration algorithm copies its style to all merged logical cells, ensuring that each cell has an independent style definition during export.

[0062] 5. Structured Storage and User Configuration After the style information is collected and arbitrated, it is stored as a style mapping table according to a three-level mapping relationship of "worksheet name → row index → ​​column index". This mapping table not only contains the complete style attributes of each cell, but also records the "hash fingerprint" of the style rules, which is used for quick comparison to see if the style has changed.

[0063] Users can customize whether to enable style export, whether to override certain styles (such as unifying the background color of all table headers to dark blue), and set default style rules in the system configuration interface (such as "cells with missing data are automatically displayed in gray italics"). These user configurations will participate in style priority arbitration to ensure that the final export result retains the original style of the interface while respecting the user's personalized needs.

[0064] Step S2: Business Data Acquisition The data acquisition layer reads corresponding business data from databases, interfaces, and reporting platforms; performs data validation, formatting, null value handling, and field mapping; and maintains the data row and column structure completely consistent with the front-end interface.

[0065] Step S3: Style and Data Integration The style fusion layer binds the data structure to the style mapping table one by one according to the cell position; the table header automatically matches the table header style, and the data rows automatically match the corresponding row / column style; it supports automatic coloring of important data and abnormal data according to the interface rules.

[0066] The style fusion layer is responsible for binding the style mapping table generated in step S1 with the business data obtained in step S2 according to the cell position. At the same time, it dynamically adjusts or supplements the style according to the specific value of the data or the business status to achieve the intelligent effect of "data-driven style".

[0067] 1. Basic binding: Position alignment and row / column matching The style blending layer first aligns the data structure (usually a two-dimensional array or a list of row records) with the style mapping table. Because the front-end interface may contain hidden columns, dynamic columns, or user-defined column order, while the original data source often only contains field names rather than absolute column positions, the system performs a "column name location" process: Extract the text content of the header row from the style map table (i.e., the field name displayed in each header cell). Perform fuzzy matching between the field names of the data source and these header texts (supports synonyms, aliases, and case insensitivity); After a successful match, a correspondence is established between "field name → column index" to determine which column's style should be applied to each column of data.

[0068] For each data row, the system maps the current row to its position in the style mapping table (usually starting from the second row) based on the absolute order of the current row in the dataset. If the number of data rows exceeds the number of rows recorded during data collection (e.g., new data added after pagination), the system will copy the last valid style as the default style for the new row, or perform interpolation smoothing based on the styles of adjacent rows.

[0069] 2. Conditional style mapping model: Data determines style In practical business operations, users often want exported files to automatically highlight abnormal data (such as shipments falling below warning levels), trend data (such as a month-on-month decrease exceeding 20%), or records that meet specific conditions. Traditionally, users need to manually set conditional formatting in Excel after exporting, which is inefficient. This system automates this function through a "conditional style mapping model."

[0070] At the core of this model is a rule engine that supports two types of rules: Explicit rules: Logical expressions configured by system administrators or users through a visual interface. For example: "If (actual quantity < predicted quantity * 0.8), then set the background color to light red and the font to bold." Rules can be combined (with / or conditions) and can reference multiple fields for calculation.

[0071] Implicit rules: The model automatically learns potential style association patterns by analyzing users' manual modifications to exported files in historical data (e.g., users changing certain cells to yellow). Specifically, the system records each secondary editing operation (color change, bolding, etc.) performed by the user after exporting the Excel file and combines these operations with the data characteristics at that time (numerical range, text keywords, network level, etc.) to form training samples. The conditional style mapping model uses a lightweight decision tree ensemble method to summarize common data-style correspondences from the samples and generate interpretable rules to recommend to the user. After user confirmation, implicit rules are converted into explicit rules, achieving "increasing intelligence with use."

[0072] When data fusion reaches each cell, the model evaluates all relevant rules in the row / column to which that cell belongs. If multiple rules are triggered simultaneously, the final style is determined according to rule priority (user-defined order or rule specificity), and a "style overlay" strategy is adopted: for example, if rule A requires the background color to be red and rule B requires the font to be bold, they will both take effect if they do not conflict; if the background colors conflict, the rule with higher priority will prevail.

[0073] 3. Style Gradient Mapping For continuous numerical data (such as completion rate, on-time rate, and cost deviation rate), simple threshold conditions cannot accurately reflect the data's changing trends. Therefore, the system provides a "style gradient mapping" function: Users select a numeric field and set a numeric range (e.g., 0% ~ 100%), then specify a start color and an end color (e.g., green to red).

[0074] The model automatically maps numerical values ​​linearly to their positions in the color space, calculating the background or font color that lies between the start and end colors.

[0075] For example, when the completion rate is 50%, the background color is yellow-orange; when the completion rate is 80%, the background color is light green.

[0076] Gradient mapping also supports non-uniform distributions (such as logarithmic mapping) to accommodate long-tailed data.

[0077] This feature is entirely integrated within the style blending layer, eliminating the need for any predefined colors on the front-end interface. This significantly reduces the workload of front-end development and ensures the readability of the exported data.

[0078] 4. Style Inheritance and Overriding Algorithm Tables often contain complex structures such as merged cells, spanning multiple rows and columns, and grouped headers. When merging styles, the system needs to correctly handle style inheritance relationships. Merged Cells: The style map only records the style of the top-left cell of the merged area. After the style blending layer recognizes the merge mark, it copies the style of the top-left cell to all logical cells covered by the merged area, ensuring that each sub-cell of the merged area has a uniform background and font effect after export.

[0079] Row / column style inheritance: If a row does not have a style explicitly recorded in the style map (e.g., a dynamically added data row), the system will look up the row style template (alternating row color rules, default table body background, etc.) for the table area containing that row and apply that template. Similarly, if a column does not have a style recorded, the font color or alignment of the column header cell will be inherited.

[0080] User override priority: Styles generated by the conditional style mapping model have higher priority than inherited styles, while styles "forced" by the user through the configuration interface have higher priority than conditional styles. This design ensures that the original visual style of the interface is preserved while giving users and business rules the final say.

[0081] 5. Enhanced style of abnormal data In step S2, the abnormal data marked by the data quality assessment model (such as missing values, values ​​outside the reasonable range, and duplicate records) will be automatically given an "abnormal style package" in the style fusion layer: a light red background, bold red font, and an added annotation (the content of which is the anomalous type, such as "missing value" or "exceeds threshold"). This way, when users open the exported file, they can immediately locate all the data points of interest without any additional filtering, significantly improving verification efficiency.

[0082] 6. Fusion result caching and incremental updates For export tasks involving large amounts of data or multiple tabs, the style fusion layer caches the intermediate "data + style" results that have already been bound. When a user modifies the filter conditions or re-requests to export the same data, the system only re-executes the conditional style mapping for the parts of the data that have changed, while directly reusing the cached results for the rest, thus speeding up the export process. Simultaneously, the cache employs a version-based expiration mechanism: as long as the front-end interface style remains unchanged (determined by the hash fingerprint of the style mapping table), the cache can continue to be used; once the style changes, the system clears the cache and triggers step S1 to re-collect data.

[0083] Step S4: File Generation and Export The file export layer calls the Excel export engine to write the merged data and styles into the file; the cell formatting completely replicates the interface: background fill, font color, font size, and bolding are applied simultaneously; the output is an Excel file that can be directly downloaded, supporting large data volume streaming export without lag or overflow.

[0084] Step S5: Data verification and optimization Exported files are visually consistent with the interface, allowing users to directly distinguish data types by color; color-coded filtering and sorting are supported to quickly locate abnormal or key data; manual secondary processing is reduced, significantly improving verification speed and accuracy.

[0085] As can be seen from the technical solutions provided in the embodiments of this specification above, this embodiment achieves: 1. Real-time capture of interface style: Automatically captures the background color, font color, and font style of the front-end table, eliminating the need for manual settings by the user.

[0086] 2. Style-Data Binding Export: Binds style information to data structure and writes it to Excel cells simultaneously during export, achieving "what you see is what you get".

[0087] 3. One-click export, fully automated: When the user clicks to export, the system automatically completes style collection, data reading, format writing, and file generation.

[0088] 4. Multi-dimensional style support: Supports simultaneous export of header background color, data row background color, font color, and font bolding.

[0089] 5. Structured architecture design: layered and modular, including style collection layer, data acquisition layer, style fusion layer, and file export layer, which is stable and easy to extend.

[0090] 6. Improve verification efficiency: The exported file has the same visual consistency with the original interface, and users can directly and quickly verify, filter, and locate data by color.

[0091] This embodiment has the following effects: 1. Significantly improved verification efficiency: The exported file is completely consistent with the interface style, and users do not need to re-format it, allowing for quick and direct verification.

[0092] 2. Fully automated: One-click export, no need for manual formatting, reducing operating costs and error rates.

[0093] 3. Strong visual consistency: Background color and font color are fully preserved, and important and abnormal data are clear at a glance.

[0094] 4. Stable and easily expandable system: The layered architecture supports access to multiple data sources and multiple export formats, and its functions can be quickly expanded in the future.

[0095] 5. High versatility: Applicable to various business scenarios such as data platforms, management back-ends, reporting systems, and verification systems.

[0096] This specification also provides a logistics data processing device, such as... Figure 6As shown, the device includes: The region identification module 610 is used to identify the header region, body region, and row and column regions by traversing the DOM structure of the table based on the style collection layer after the table data in the target page has been loaded; the table data is logistics data. The attribute extraction module 620 is used to extract the style attributes and visual attributes corresponding to each cell based on the cells corresponding to the header area, body area and row and column areas. The mapping table construction module 630 is used to construct a style mapping table of the table based on the style attributes and visual attributes corresponding to each cell; the style mapping table includes attribute information of different row and column positions in the table; The data processing module 640 is used to read corresponding business data from the database, interface and reporting platform based on the data acquisition layer; and to perform validation, formatting, null value handling and field mapping on the business data to make the obtained data row and column structure consistent with the front-end interface. The result determination module 650 is used to bind the data row and column structure to the style mapping table according to the cell position based on the style fusion layer, obtain the intermediate data style result and cache it. The file export module 660 is used to, upon receiving a user's data export instruction, re-execute the conditional style mapping on the part of the intermediate result of the data style where the data has changed, and directly reuse the cached result for the remaining part to generate an export file.

[0097] In some embodiments, the attribute extraction module is further configured to obtain the cells corresponding to the header area, body area, and row and column areas respectively; read the inline style, CSS class name, and rendered value of each cell to obtain the style attributes corresponding to each cell; and extract the background color, font color, font weight, font size, and alignment of each cell to obtain the visual attributes corresponding to each cell.

[0098] In some embodiments, the region identification module is further configured to: monitor the rendering events of the table in real time during the loading of the table on the target page; perform a polling and waiting operation when the rendering events indicate that the cell styles of the table are unstable; repeat the step of monitoring the rendering events of the table in real time until the rendering events indicate that the cell styles of the table are unstable, until all cell styles of the table are determined; and perform traversal of the DOM structure of the table based on the style collection layer.

[0099] In some embodiments, the apparatus further includes: The first determining module is used to determine that the cell style of the table is unstable if network latency is detected during the rendering process of the table. The second determining module is used to determine that the cell style of the table is unstable if data pagination loading of the table is detected during the rendering process of the table.

[0100] In some embodiments, the apparatus further includes: The attribute acquisition module is used to acquire the comprehensive style attributes of each cell in the table acquired by the style acquisition layer; the comprehensive sample attributes include the style attributes and the visual attributes. The confidence level determination module is used to determine the style confidence level of the overall style attribute of each cell based on the source of the overall style attribute of each cell; The pixel region acquisition module is used to acquire the first pixel region of the target cell and the second pixel region surrounding the target cell if the style confidence of any target cell is less than a preset threshold. The confidence prediction module is used to input the first pixel region and the second pixel region into the style recognition model to perform style prediction, and output the comprehensive attribute of style prediction and the confidence of style prediction. The target attribute determination module is used to determine the target style comprehensive attribute of the target cell based on the style prediction comprehensive attribute of the target cell and the style comprehensive attribute of the target cell collected by the style acquisition layer.

[0101] In some embodiments, the target attribute determination module is further configured to: if the style prediction confidence is greater than a preset confidence threshold, determine the style prediction comprehensive attribute as the target style comprehensive attribute of the target cell; if the style prediction confidence is less than or equal to the preset confidence threshold, determine the style comprehensive attribute of the target cell collected by the style acquisition layer as the target style comprehensive attribute of the target cell.

[0102] In some embodiments, the apparatus further includes: The target source determination module is used to determine the target source of a cell if the source of the comprehensive style attribute of any cell includes at least two of the following: inline styles, CSS classes, parent inherited styles, browser default styles, and the comprehensive style prediction attribute output by the style recognition model. The target source of the cell is determined according to the priority of each source. The target attribute determination module is used to determine the comprehensive style attribute corresponding to the target source as the comprehensive style attribute of the cell; the priority of the inline style, the CSS class, the predicted comprehensive style attribute, the parent inherited style, and the browser default style decreases in that order. The mapping table construction module is also used to construct the style mapping table of the table according to the comprehensive attribute of the target style corresponding to each cell, and according to the three-level mapping relationship of worksheet name, row index, and column index.

[0103] The apparatus and method embodiments described herein are based on the same inventive concept.

[0104] This specification provides an electronic device including a processor and a memory. The memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement the logistics data processing method provided in the above method embodiments.

[0105] Embodiments of the present invention also provide a computer storage medium, which can be disposed in a terminal to store at least one instruction or at least one program related to implementing a logistics data processing method in the method embodiments. The at least one instruction or at least one program is loaded and executed by the processor to implement the logistics data processing method provided in the above method embodiments.

[0106] Embodiments of the present invention also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the logistics data processing method provided in the above-described method embodiments.

[0107] Optionally, in the embodiments of this specification, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0108] The memory described in the embodiments of this specification can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for the functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0109] The logistics data processing methods provided in the embodiments of this specification can be executed on mobile terminals, computer terminals, servers, or similar computing devices. Taking running on a server as an example... Figure 7 This is a hardware structure block diagram of a server for a logistics data processing method provided in the embodiments of this specification. Figure 7 As shown, the server 700 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 710 (CPUs 710 may include, but are not limited to, microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 730 for storing data, and one or more storage media 720 (e.g., one or more mass storage devices) for storing application programs 723 or data 722. The memory 730 and storage media 720 may be temporary or persistent storage. The program stored in the storage media 720 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 710 may be configured to communicate with the storage media 720 and execute the series of instruction operations stored in the storage media 720 on the server 700. Server 700 may also include one or more power supplies 760, one or more wired or wireless network interfaces 750, one or more input / output interfaces 740, and / or one or more operating systems 721, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0110] The input / output interface 740 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 700. In one example, the input / output interface 740 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 740 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0111] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 700 may also include... Figure 7 The more or fewer components shown, or having the same Figure 7 The different configurations shown.

[0112] As can be seen from the embodiments of the logistics data processing method, apparatus, electronic device, or storage medium provided by the present invention, when the table data on the target page is loaded, the present invention traverses the DOM structure of the table based on the style acquisition layer to identify the header area, body area, and row and column areas; the table data is logistics data; based on the cells corresponding to the header area, body area, and row and column areas, the style attributes and visual attributes corresponding to each cell are extracted; based on the style attributes and visual attributes corresponding to each cell, a style mapping table of the table is constructed; the style mapping table includes attribute information of different row and column positions in the table; based on the data acquisition layer, corresponding business data is read from the database, interface, and reporting platform; and the business data is validated, formatted, null value processed, and field mapped to make the obtained data row and column structure consistent with the front-end interface; based on the style fusion layer, the data row and column structure is bound to the style mapping table one by one according to the cell position to obtain the intermediate data style result and cache it; if a user's data export instruction is received, according to the data export instruction, the conditional style mapping is re-executed on the part of the data that has changed in the intermediate data style result, and the cached result is directly reused for the rest to generate an export file. This invention automatically captures the background color, font color, and font style of the front-end table, eliminating the need for manual settings by the user. Furthermore, it binds the style information to the data structure and synchronously writes it to Excel cells during export, achieving a WYSIWYG (What You See Is What You Get) experience. The system automatically completes style collection, data reading, format writing, and file generation, improving the automation level of file export.

[0113] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0114] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0115] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer storage medium, such as a read-only memory, a disk, or an optical disk.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A logistics data processing method, characterized in that, The method includes: Once the table data on the target page has been loaded, the DOM structure of the table is traversed based on the style collection layer to identify the header area, body area, and row and column areas; the table data is logistics data. Based on the cells corresponding to the header area, body area, and row and column areas, extract the style attributes and visual attributes corresponding to each cell; A style mapping table for the table is constructed based on the style and visual attributes corresponding to each cell; the style mapping table includes attribute information for different row and column positions in the table; The data acquisition layer reads corresponding business data from databases, interfaces, and reporting platforms; and performs validation, formatting, null value handling, and field mapping on the business data to ensure that the resulting data row and column structure is consistent with the front-end interface. Based on the style fusion layer, the data row and column structure is bound to the style mapping table one by one according to the cell position to obtain the intermediate data style results and cache them. If a user's data export instruction is received, the conditional style mapping is re-executed for the parts of the intermediate results of the data style that have changed, and the cached results are directly reused for the remaining parts to generate an export file.

2. The method according to claim 1, characterized in that, The step of extracting the style and visual attributes of each cell based on the corresponding cells in the header area, body area, and row and column areas includes: Obtain the cells corresponding to the header area, body area, and row and column areas respectively; Read the inline styles, CSS class names, and rendered values ​​of each cell to obtain the style properties corresponding to each cell; Extract the background color, font color, font weight, font size, and alignment of each cell to obtain the visual attributes corresponding to each cell.

3. The method according to claim 1, characterized in that, Once the table data on the target page has been loaded, the DOM structure of the table is traversed based on the style collection layer, including: During the loading of the table on the target page, the rendering events of the table are monitored in real time. If the rendering event indicates that the cell style of the table is unstable, a polling wait operation is performed; Repeat the real-time monitoring of the table's rendering events until the rendering events indicate that the table's cell styles are unstable, then perform a polling and waiting operation until all cell styles of the table have been determined; Perform a style-based traversal of the DOM structure of the table.

4. The method according to claim 3, characterized in that, The method further includes: If network latency is detected during the rendering of the table, it is determined that the cell style of the table is unstable. If pagination of the table is detected during the rendering process, it is determined that the cell style of the table is unstable.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Obtain the comprehensive style attributes of each cell in the table collected by the style collection layer; the comprehensive sample attributes include the style attributes and the visual attributes; Based on the source of the overall style attribute of each cell, determine the style confidence level of the overall style attribute of each cell; If the style confidence score corresponding to any target cell is less than a preset threshold, obtain the first pixel region of the target cell and the second pixel region surrounding the target cell; Input the first pixel region and the second pixel region into the style recognition model for style prediction, and output the comprehensive attribute of style prediction and the confidence level of style prediction. The target style comprehensive attribute of the target cell is determined based on the predicted comprehensive attribute of the target cell and the comprehensive attribute of the target cell collected by the style acquisition layer.

6. The method according to claim 5, characterized in that, The step of determining the target style comprehensive attribute of the target cell based on the style prediction comprehensive attribute of the target cell and the style comprehensive attribute of the target cell collected by the style acquisition layer includes: If the confidence level of the style prediction is greater than the preset confidence threshold, the comprehensive attribute of the style prediction will be determined as the comprehensive attribute of the target style of the target cell. If the confidence level of the style prediction is less than or equal to the preset confidence threshold, the comprehensive style attribute of the target cell collected by the style acquisition layer is determined as the comprehensive style attribute of the target cell.

7. The method according to claim 5, characterized in that, The method further includes: If the source of the style comprehensive attribute of any cell includes at least two of the following: inline style, CSS class, parent inherited style, browser default style, and style prediction comprehensive attribute output by the style recognition model, the target source of the cell is determined according to the priority of each source. The comprehensive style attribute corresponding to the target source is determined as the comprehensive style attribute of the cell; the priority of the inline style, the CSS class, the predicted comprehensive style attribute, the parent inherited style, and the browser default style decreases in that order. The step of constructing the style mapping table of the table based on the style attributes and visual attributes corresponding to each cell includes: constructing the style mapping table of the table according to the comprehensive attribute of the target style corresponding to each cell, and according to the three-level mapping relationship of worksheet name, row index, and column index.

8. A logistics data processing device, characterized in that, The device includes: The region recognition module is used to identify the header region, body region, and row and column regions of the table based on the style collection layer after the table data on the target page has been loaded; the table data is logistics data. The attribute extraction module is used to extract the style attributes and visual attributes of each cell based on the cells corresponding to the header area, body area and row and column areas. The mapping table construction module is used to construct a style mapping table for the table based on the style attributes and visual attributes corresponding to each cell; the style mapping table includes attribute information for different row and column positions in the table; The data processing module is used to read corresponding business data from the database, interface, and reporting platform based on the data acquisition layer; and to perform validation, formatting, null value handling, and field mapping on the business data to ensure that the resulting data row and column structure is consistent with the front-end interface. The result determination module is used to bind the data row and column structure to the style mapping table according to the cell position based on the style fusion layer, obtain the intermediate data style result and cache it. The file export module is used to, upon receiving a user's data export instruction, re-execute the conditional style mapping on the parts of the intermediate results of the data style that have changed according to the data export instruction, and directly reuse the cached results for the remaining parts to generate an export file.

9. An electronic device, characterized in that, The device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the logistics data processing method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the logistics data processing method as described in any one of claims 1-7.

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