Python-based efficient method for batch extraction of PDF data elements

Abstract

The invention relates to the field of judicial PDF material data extraction and processing, and discloses a Python-based efficient method for extracting PDF data elements in batches, which comprises the following steps: optimizing PDF analysis efficiency by deeply integrating PyPDF2, PDFMiner and PyMuPDF and combining multithreading, asynchronous processing, parallel computing and OCR (optical character recognition) technologies; a machine learning model is introduced to carry out PDF layout analysis, and document elements are identified and structured by using natural language processing and computer vision technologies; designing a rule-based engine framework, combining a graphical interface and a configuration file, and supporting a user to flexibly define an extraction rule through a visual interface; an extraction result is stored in a standardized table format, and the extraction result is directly imported into a relational database or an NoSQL database through support of an SQLAlchemy and a Pandaas database; according to the method, a simple and visual graphical user interface based on React or Vue.js is designed, and a configuration guide and real-time preview function based on JavaScript is provided in combination with an Ant Design or Material-UI component library. The method has the advantage that the key information can be quickly and accurately extracted from the PDF documents in various formats.

Description

Technical Field

[0001] The present invention relates to the field of judicial PDF material data extraction and processing, and specifically to an efficient method for batch extracting PDF data elementization based on Python. Background Art

[0002] Most of the existing PDF data extraction technologies rely on OCR (Optical Character Recognition) technology and specific PDF parsing libraries. These technologies identify and convert the content to an editable format by scanning the image or text layer in the PDF document. However, these methods are inefficient when processing a large number of documents, and for PDF documents with complex formats and variable layouts, their accuracy and stability are difficult to guarantee. When processing a large number of documents, the speed is slow and it is not suitable for large-scale data processing scenarios. For PDF documents with complex layouts, the recognition error rate is high, affecting the data quality. It is difficult to adapt to PDF files in different industries and different formats, and parameters and rules need to be frequently adjusted. It is difficult for users to customize the extraction logic and output format, restricting the application scope. Based on the above disadvantages, the present invention aims to provide an efficient method for batch extracting PDF data elementization based on Python, which can quickly and accurately extract key information from various formats of PDF documents, store the results in a standardized table form, and at the same time provide a user-friendly configuration interface to achieve a highly automated and general-purpose data extraction process. Summary of the Invention

[0003] (1) Technical Problems to be Solved

[0004] In view of the deficiencies of the prior art, the present invention provides an efficient method for batch extracting PDF data elementization based on Python, which has the advantages of being able to quickly and accurately extract key information from various formats of PDF documents, storing the results in a standardized table form, and at the same time providing a user-friendly configuration interface to achieve a highly automated and general-purpose data extraction process, and solves the problems in the above background art.

[0005] (2) Technical Solutions

[0006] To achieve the purpose of being able to quickly and accurately extract key information from various formats of PDF documents, store the results in a standardized table form, and at the same time provide a user-friendly configuration interface to achieve a highly automated and general-purpose data extraction process, the present invention provides the following technical solutions: An efficient method for batch extracting PDF data elementization based on Python, including the following steps:

[0007] S1: Optimize the PDF parsing efficiency by deeply integrating PyPDF2, PDFMiner, PyMuPDF, and combining multi-threading, asynchronous processing, parallel computing, and OCR technology.

[0008] The above-mentioned S1 further includes optimizing the parsing efficiency by deeply integrating PyPDF2, PDFMiner, and PyMuPDF, combining multi-threading, asynchronous processing, and parallel computing, introducing OCR technology to process scanned documents, and using machine learning or rule engines to intelligently analyze the PDF layout to automatically identify tables, paragraphs, and images in the document.

[0009] S2: Introduce a machine learning model for PDF layout analysis, use natural language processing and computer vision technologies to identify and structure document elements, and dynamically adjust the parsing strategy based on reinforcement learning.

[0010] The above-mentioned S2 further includes combining natural language processing and computer vision technologies, automatically identifying and parsing different elements in the PDF through a deep learning model, and dynamically optimizing the parsing strategy through reinforcement learning to automatically evaluate and adjust the processing methods for different document layouts.

[0011] The automatic identification and parsing of different elements in the PDF through the deep learning model includes extracting image features through a convolutional neural network, using an RNN model to process the text sequence in the PDF to extract text features, classifying the extracted features through a fully connected layer to determine the element type in the document, and converting the classification result into a probability output through the softmax function to obtain the category probability of each element. The formula is:

[0012]

[0013] In the formula, is the category prediction output by the model, representing the classification of elements in the PDF; I is the image or text content of the input PDF document; CNN(I) is the feature extraction of the PDF image through a convolutional neural network. For text data, it can be replaced by an RNN-based text encoding; W is the weight matrix of the classification layer, responsible for mapping the extracted features to the category space; b is the bias term of the classification layer.

[0014] S3: Design a rule engine framework, combined with a graphical interface and configuration files, to support users in flexibly defining extraction rules through a visual interface.

[0015] The above-mentioned S3 further includes supporting users in flexibly defining and managing data extraction rules through a graphical user interface, supporting complex matching algorithms such as regular expressions, keyword matching, and pattern recognition. Users set rules through dragging, selecting, and input methods, and the configuration files persistently store the rules, supporting cross-platform sharing and updating. The system provides a real-time preview function, allowing users to debug and optimize the rules, and dynamically display the extraction effect and error information.

[0016] The user sets rules through dragging, selecting, and inputting. The persistent storage rule of the configuration file includes constructing a comprehensive formula to represent the entire process of rule definition, rule application, matching algorithm, real-time preview, and error debugging in the graphical user interface. The formula is:

[0017]

[0018] In the formula, t is the text of the input PDF document, R = {r 1 , r 2 ,..., r n} is the rule set defined by the user. Each rule, where each rule r i is composed of a regular expression, keyword matching, or pattern recognition algorithm. f preview (t, r i ) is the real-time preview function that shows the matching effect of the matching rule r i on the text t. s(t, r i ) is the scoring function, and e(t, r i ) is the error feedback when showing rule matching. C is the configuration file that persistently stores the rule set R;

[0019] The matching quality of the rule on the text t is quantified by S(t, r i ). Through f preview (t, r i ), the user can view the effect after rule application in real time. If the rule matches, it returns 1, indicating the preview effect. If there is no match, it returns 0. e(t, r i ) is used to capture error information in rule application. If the rule fails to match, it returns 1 indicating an error, otherwise it returns 0. By maximizing the scoring function S(t, r i ), and combining real-time preview and error feedback, the optimal rule is selected.

[0020] S4: Save the extraction result in a standardized table format, support direct import into relational databases or NoSQL databases through SQLAlchemy and Pandas libraries, and integrate AWS S3 and Google Cloud Storage APIs to implement data upload to cloud storage services.

[0021] The above S4 further includes saving the extraction result in a standardized table format, and providing data cleaning, formatting, and custom setting functions, supporting field mapping and data type conversion. Through SQLAlchemy and Pandas libraries, the system is seamlessly docked with relational databases and NoSQL databases. By integrating AWS S3 and Google Cloud Storage, the system supports automated data upload, synchronization, and backup, and supports fine-grained permission control and data encryption.

[0022] S5: Design a simple and intuitive graphical user interface based on React or Vue.js, combined with the Ant Design or Material-UI component library, and provide a JavaScript-based configuration wizard and real-time preview function.

[0023] The above S5 further includes adopting a React or Vue.js front-end development framework, providing component-based development and virtual DOM technology, optimizing page rendering and data binding performance, integrating Ant Design or Material-UI, providing pre-designed controls, a configuration wizard and real-time preview mechanism implemented by JavaScript. The interface adopts a responsive design, automatically adapts to different devices and screen sizes, and also supports cross-platform compatibility.

[0024] (III) Beneficial effects

[0025] Compared with the prior art, the present invention provides an efficient method for batch extracting PDF data elements based on Python, having the following beneficial effects:

[0026] Through the combination of intelligent layout analysis and a rule engine, the present invention greatly reduces manual intervention and improves the automation level of data extraction. It supports multiple PDF formats and data types, and is applicable to multi-industry application scenarios such as finance, healthcare, and law. The friendly user interface design enables non-technical personnel to easily customize the data extraction process. It provides data preview and chart display functions, facilitating users to immediately verify the data quality and integrity.

[0027] 1. The batch processing ability significantly shortens the data preparation cycle and accelerates the data analysis and decision-making process.

[0028] 2. Accurate data extraction reduces subsequent data cleaning work and improves the overall data quality.

[0029] 3. The automated process reduces the dependence on professionals and lowers the labor cost.

[0030] 4. The highly customizable data extraction solution can quickly respond to business changes and enhance the competitiveness of enterprises. Description of the drawings

[0031] Figure 1 It is a schematic diagram of the method of the present invention. Detailed implementation manners

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] The present invention provides a technical solution: an efficient method for batch extracting PDF data elements based on Python, including the following steps:

[0034] S1: By deeply integrating PyPDF2, PDFMiner, and PyMuPDF, and combining multi-threading, asynchronous processing, parallel computing, and OCR technology, optimize the PDF parsing efficiency.

[0035] Deeply integrate various PDF parsing libraries such as PyPDF2, PDFMiner, and PyMuPDF, give full play to the advantages of each library, and provide the most suitable parsing solutions for different formats of PDF documents. PyPDF2 is used for basic PDF text extraction, PDFMiner provides more refined parsing capabilities when dealing with complex documents, and PyMuPDF can efficiently process various PDF elements such as images, texts, and links to achieve comprehensive content extraction. Use multi-threading and asynchronous processing technologies to achieve concurrent data parsing when processing a large number of PDF files, improving the processing capacity and efficiency of the system. Through task segmentation and parallel processing, assign the parsing tasks of each file to multiple threads or asynchronous processes to avoid single-thread blocking and improve the speed of large-scale data processing. Adopt parallel computing technology. For resource-intensive PDF parsing tasks such as image recognition and OCR, through distributed computing frameworks such as Dask or Ray, distribute the tasks to multiple computing nodes for parallel processing, greatly improving the data processing speed during the parsing process, especially suitable for processing a large number of PDF documents. When processing scanned PDF or image-based PDF documents, introduce OCR technology to extract the text in the images and convert it into editable text. Combine deep learning OCR models such as Tesseract or OCR models based on convolutional neural networks to improve the accuracy of text recognition, especially in low-quality or noisy scanned documents. Combine machine learning or rule engines to automatically analyze the page layout of PDF documents, optimize the parsing process, automatically judge the structures such as tables, paragraphs, and images in the documents, and ensure the accuracy of the extracted content. In complex layouts such as multi-column and nested tables, the algorithm can intelligently adapt and optimize the parsing strategy to reduce the misrecognition rate.

[0036] By deeply integrating multiple PDF parsing libraries, it is possible to process different types of PDF files more efficiently. Especially when faced with a large number of documents, through multi-threading, asynchronous processing, and parallel computing, the processing speed has been significantly improved. This enables the system to quickly complete large-scale data extraction tasks and shorten the overall processing time. The combination of multi-threading and parallel computing gives the system good scalability and enables it to handle document parsing tasks of different scales. Whether it is a small number of documents or large-scale documents, the system can automatically adjust the degree of parallelism or the number of threads to ensure the reasonable use of resources and maintain high performance. The introduction of OCR technology has greatly improved the ability to extract non-text information from scanned PDF documents. Combined with deep learning models, the recognition accuracy has been greatly improved, especially for the processing of handwritten text, low-resolution images, or graphic and text data with complex backgrounds. Intelligent layout analysis ensures the correct extraction and organization of data under complex formats and diverse structures. Through parallel computing and optimized PDF parsing processes, the system can quickly return processing results, significantly reducing the waiting time. Users can obtain and analyze the required PDF data more efficiently, enhancing the fluency and response speed of operations. Especially when processing a large number of documents, the system response remains rapid. The integration of multiple technologies enables the system to support different formats and types of PDF documents. Whether it is plain text, scanned images, or multimedia PDFs containing complex tables and images, the system can handle them flexibly. This broad adaptability improves the application effect of the system in different business scenarios and meets the needs of users in different fields.

[0037] S2: Introduce a machine learning model for PDF layout analysis, utilize natural language processing and computer vision technologies to identify and structure document elements, and dynamically adjust the parsing strategy based on reinforcement learning.

[0038] Combined with natural language processing technology, the model can automatically identify different document elements in the PDF, such as titles, paragraphs, tables, images, etc., and automatically annotate and classify them according to their semantics and structure. Computer vision technology is responsible for processing image data, especially graphics, images, and non-standard format data in scanned PDF documents. By using deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or transformers (Transformers), through the training of a large number of PDF documents, the model is equipped with powerful pattern recognition capabilities and can accurately parse complex document structures. Through the reinforcement learning model, the system can automatically evaluate and select the optimal parsing strategy when processing different types of PDF documents. The model optimizes the parsing path through continuous feedback learning, adjusts the response methods for different document layouts, and thus improves the parsing accuracy. Based on the automated rule generation of machine learning, the system can automatically generate parsing rules for different document types, reducing manual intervention. As the parsing process progresses, the system can adjust the parsing rules according to the feedback to adapt to document changes and new formats.

[0039] By integrating NLP and CV technologies, the system can deeply understand the structure and content of documents, automatically identify various elements and perform structured output, greatly reducing the need for manual configuration and adjustment of rules. Especially when facing complex layouts, it can maintain high accuracy and consistency. The introduction of reinforcement learning enables the parsing strategy to be dynamically adjusted according to different document contents and structures. It can not only process standard-format PDFs but also effectively handle various non-standard and complex-format PDFs. The system can adaptively optimize the parsing scheme when dealing with different scenarios, such as financial statements, contract documents, product catalogs, etc. Through the combined use of deep learning and reinforcement learning, the system can gradually improve the parsing speed and accuracy for documents with different layouts and styles during continuous training and adjustment processes, significantly enhancing the overall parsing efficiency, especially when dealing with large-scale document batch processing. Traditional PDF parsing relies on manually defining parsing rules and templates, while through automated learning, the system can reduce manual intervention, automatically identify and adapt to document content, greatly improving the degree of automation in processing, reducing labor costs, and at the same time improving the accuracy and consistency of the results.

[0040] S3: Design a rule engine framework that combines a graphical interface and configuration files to support users in flexibly defining extraction rules through a visual interface.

[0041] Based on rule engine technology, the system allows users to flexibly define and manage data extraction rules through a graphical interface. The rule engine can support complex logical conditions, data filtering, and matching algorithms, such as regular expressions, keyword matching, pattern recognition, etc., and can quickly generate extraction rules applicable to different documents through a preset rule library. The system provides a graphical user interface where users can define rules by dragging, selecting, inputting, etc. without writing code. The interface includes modules such as rule selection, condition setting, field mapping, and data output format to ensure that users can complete complex rule definition and adjustment in a short time. The system persists the rules defined by users through configuration files and can load, modify, and update these configuration files at any time for cross-platform sharing and reuse. Through the real-time preview function, users can view the immediate effects after rule application and debug during the rule adjustment process. The interface can dynamically display the extracted fields, the accuracy of data matching, and error messages to help users quickly optimize the rules.

[0042] Users can quickly design and adjust data extraction rules according to actual needs without relying on the coding capabilities of technical personnel. The introduction of the rule engine makes the setting of complex rules simple and intuitive, and can effectively process PDF documents in different formats and types, meeting the needs of multiple industries and scenarios. With the help of the graphical interface and configuration files, the management of rules becomes more systematic and modular. Users can flexibly modify, store, and load rules according to their needs without having to redefine them from scratch, reducing maintenance costs and improving the scalability of rule management. The real-time debugging and feedback mechanism enables users to quickly see the effects of rules and optimize them, avoiding situations of rule errors or mismatches in traditional methods, reducing the time and effort of manual debugging, and improving the accuracy and efficiency of data extraction. Through the rule engine, the system can process various complex document types and data structures. Especially when dealing with highly customized extraction requirements, the system can flexibly adapt to different rule settings and support specific requirements in industries such as finance, healthcare, and law. Users do not need to write complex code or have in-depth knowledge of technical details. The simple and intuitive graphical configuration interface lowers the technical threshold, enabling more non-technical personnel to participate in the definition and management of data extraction rules, greatly improving the convenience of use and user experience.

[0043] S4: Save the extraction results in a standardized table format, support direct import into relational databases or NoSQL databases through SQLAlchemy and Pandas libraries, and integrate AWS S3 and Google Cloud Storage APIs to upload data to cloud storage services.

[0044] The system saves the extracted results in a standardized table format, such as CSV or Excel, and provides data cleaning and formatting functions to ensure that the exported data meets the unified standard, facilitating further data analysis and processing. The table format supports settings such as custom fields, column name mapping, and data type conversion to meet different application requirements. The system supports seamless docking with relational databases, such as MySQL and PostgreSQL, and NoSQL databases, such as MongoDB and Cassandra, through libraries such as SQLAlchemy and Pandas. SQLAlchemy is responsible for database connection, data insertion, and transaction management, while Pandas is used for data processing and cleaning to ensure that data can be efficiently imported and stored in the target database. Through cloud storage APIs such as AWS S3 and Google Cloud Storage, the system can directly upload the extraction results to the cloud platform, supporting file storage, data backup, and remote access. Users can select different cloud storage services according to their needs and perform automated data upload and synchronization through the API to ensure data security and accessibility. The system supports real-time data synchronization to the target database and cloud storage service to ensure data consistency and high availability in a distributed environment. Through a regular backup mechanism, redundant storage of data can be achieved to avoid data loss and ensure that data can be restored at any time. Through integration with cloud storage services, the system can achieve fine-grained permission control and data encryption. Users can set different data access permissions, such as read and write permissions, sharing settings, etc., to ensure data security and avoid unauthorized access.

[0045] By saving data in a standardized table format, the system can ensure the standardization and consistency of data, facilitating subsequent analysis, sharing, and utilization. The combination of SQLAlchemy and Pandas makes the data processing process smoother and more efficient, reducing the complexity during data migration. The system supports integration with multiple databases and cloud storage services, allowing users to flexibly choose data storage solutions according to their needs. By integrating SQLAlchemy and Pandas, database operations become more efficient, enabling users to easily import data into relational or NoSQL databases for further processing and querying. By integrating AWS S3 and Google Cloud Storage, the system can automatically upload data to the cloud platform, simplifying the manual upload and management process and enhancing the automation level of data storage. Users can access the data stored in the cloud at any time, facilitating data sharing and remote management. Through the dual integration of cloud storage and databases, the system can provide users with higher data security, avoiding data loss caused by hardware failures or other reasons. The data encryption, backup, and permission management functions ensure the security during data storage, meeting enterprise-level data management requirements. Through the integration of cloud storage services, data can be efficiently shared between different platforms and devices. Users can access the data stored in the cloud via the Internet anytime and anywhere, simplifying cross-platform collaboration and multi-department sharing of data and improving work efficiency and collaboration capabilities.

[0046] S5: Design a simple and intuitive graphical user interface based on React or Vue.js, combined with the Ant Design or Material-UI component library, and provide a JavaScript-based configuration wizard and real-time preview function.

[0047] Adopt React or Vue.js as the front-end development framework. These frameworks can provide an efficient component-based development model, support rapid response to user interactions, optimize rendering performance, and enhance the efficiency of page updates and data binding. Through the virtual DOM technology, the framework can achieve efficient rendering and state management of the page. Integrate modern UI component libraries such as Ant Design or Material-UI, which provide a rich collection of pre-designed controls, such as form components, buttons, input boxes, tables, etc., to help developers quickly build responsive, user-friendly, and consistent user interfaces. The use of these libraries improves development efficiency while ensuring the consistency and aesthetics of the interface. Implement a configuration wizard through JavaScript to help users easily complete the configuration process. The wizard function guides users to set data extraction rules, select templates, adjust layouts, etc. in a step-by-step manner, ensuring that users can quickly get started regardless of their technical level. Integrate a real-time preview mechanism, allowing users to immediately view the effects of the changes made during the setting process, ensuring that the data extraction rules, display formats, and output styles are consistent with expectations. The real-time preview function can effectively reduce configuration errors and enhance the user's operation experience. The user interface adopts a responsive design, which can automatically adapt to different sizes of device screens, including desktops, tablets, and mobile devices, ensuring the usability and aesthetics of the interface on various devices. In addition, through a cross-platform development model, ensure compatibility across different operating systems and browsers.

[0048] The integration of React or Vue.js frameworks with Ant Design or Material-UI component libraries makes the interface development process more modular, maintainable, and significantly improves development efficiency. The component-based design makes the interface logic clear and the UI design unified, reducing repetitive development and debugging work. Through a simple and intuitive graphical interface and configuration wizard, users can complete complex configuration tasks without programming knowledge, lowering the usage threshold and enhancing the usability of the system. At the same time, the real-time preview function allows users to instantly verify the setting effects, avoiding incorrect configurations caused by operational mistakes and enhancing the operability of the system. Using the front-end architecture of React or Vue.js helps with the organization and reuse of system code, facilitating future feature expansion and maintenance. The component-based approach decouples the interface part from the business logic, making development and maintenance work more efficient. The responsive design ensures that the system can adapt to various device screens, greatly improving the accessibility of the system. Regardless of the device used by the user, they can obtain a consistent operation experience, enhancing the popularity and applicability of the system. The combination of the configuration wizard and the real-time preview function enables users to quickly and accurately set data extraction rules, view the configuration effects in real time, reduce the risk of repetitive operations and incorrect configurations, and enhance the accuracy and efficiency of operations.

[0049] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0050] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient method for batch extraction of PDF data elements based on Python, characterized by: The following steps are involved: S1: Optimize PDF parsing efficiency by deeply integrating PyPDF2, PDFMiner, and PyMuPDF, combining multi-threading, asynchronous processing, parallel computing, and OCR technology; S2: Introducing machine learning models for PDF layout analysis, using natural language processing and computer vision technology to identify and structure document elements, and dynamically adjusting parsing strategies based on reinforcement learning; S3: The design is based on the rule engine framework, combined with a graphical interface and configuration files, which supports users to flexibly define extraction rules through a visual interface; S4: Save the extracted results in a standardized table format, support direct import into relational databases or NoSQL databases through SQLAlchemy and Pandas libraries, and integrate AWS S3 and Google Cloud Storage APIs to upload data to cloud storage services; S5: Design a simple and intuitive graphical user interface based on React or Vue.js, combined with Ant Design or Material-UI component library, and provide JavaScript-based configuration wizard and real-time preview function.

2. According to the efficient method for batch extraction of PDF data elements based on Python in claim 1, it is characterized in that: The S1 further includes optimizing parsing efficiency by deeply integrating PyPDF2, PDFMiner and PyMuPDF, combining multi-threading, asynchronous processing and parallel computing, and introducing OCR technology to process scanned documents, while using machine learning or rule engines to intelligently analyze PDF layout and automatically identify tables, paragraphs and images in documents.

3. According to the efficient method for batch extraction of PDF data elements based on Python in claim 1, it is characterized in that: The S2 further includes combining natural language processing and computer vision technology to automatically identify and parse different elements in the PDF through a deep learning model, and dynamically optimize the parsing strategy through reinforcement learning to automatically evaluate and adjust the processing methods for different document layouts.

4. According to the efficient method for extracting PDF data elements in batches based on Python in claim 3, it is characterized in that: The automatic identification and parsing of different elements in PDF by deep learning model includes extracting image features by convolutional neural network, processing text sequences in PDF by RNN model, extracting text features, classifying the extracted features by fully connected layer, judging the element type in the document, converting the classification results into probability output by softmax function, and obtaining the category probability of each element. The formula is: In the formula, is the category prediction output by the model, indicating the classification of elements in the PDF; I is the image or text content of the input PDF document; CNN(I) extracts the features of the PDF image through a convolutional neural network. For text data, it can be replaced by RNN-based text encoding; W is the weight matrix of the classification layer, which is responsible for mapping the extracted features to the category space; b is the bias term of the classification layer.

5. According to the efficient method for extracting PDF data elements in batches based on Python in claim 1, it is characterized in that: The S3 further includes supporting users to flexibly define and manage data extraction rules through a graphical user interface, supporting regular expressions, keyword matching, and pattern recognition complex matching algorithms. Users set rules by dragging, selecting, and entering, and configure files to persistently store rules. It supports cross-platform sharing and updating. The system provides a real-time preview function, allowing users to debug and optimize rules, and dynamically display extraction effects and error messages.

6. The efficient method for extracting PDF data elements in batches based on Python according to claim 5 is characterized in that: The user sets the rules by dragging, selecting and inputting. The configuration file persistent storage rules include constructing a comprehensive formula to represent the entire process of rule definition, rule application, matching algorithm, real-time preview and error debugging in the graphical user interface. The formula is: Where t is the input PDF document text, R = {r1, r2, ..., r n } is a user-defined rule set, where each rule, where each rule r i It is composed of regular expressions, keyword matching or pattern recognition algorithms. preview (t,r i ) is the real-time preview function, showing the matching rules i The matching effect on text t, S(t, r i ) is the scoring function, e(t, r i ) is the error feedback when the rule is matched, C is the configuration file, and the rule set R is persistently stored; The matching quality of a rule on text t is expressed by S(t,r i ) quantized by f preview (t,r i ) The user can view the effect of the rule in real time; if the rule matches, 1 is returned, indicating the preview effect; if there is no match, 0 is returned; e(t, r i ) is used to capture error information in rule application. If the rule fails to match, it returns 1 to indicate an error, otherwise it returns 0. By maximizing the scoring function S(t,r i ), and select the best rule by combining real-time preview and error feedback.

7. The efficient method for batch extraction of PDF data elements based on Python according to claim 1 is characterized in that: The S4 further includes saving the extraction results in a standardized table format, and provides data cleaning, formatting and custom setting functions, supports field mapping and data type conversion, and through the SQLAIchemy and Pandas libraries, the system seamlessly connects with relational databases and NoSQL databases. By integrating AWS S3 and Google Cloud Storage, the system supports automated data upload, synchronization and backup, and supports fine-grained permission control and data encryption.

8. The efficient method for batch extraction of PDF data elements based on Python according to claim 1 is characterized in that: The S5 further includes the use of React or Vue.js front-end development framework, providing component-based development and virtual DOM technology, optimizing page rendering and data binding performance, integrating Ant Design or Material-UI, providing pre-designed controls, and a configuration wizard and real-time preview mechanism implemented through JavaScript. The interface adopts a responsive design, automatically adapts to different devices and screen sizes, and supports cross-platform compatibility.

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