ERP system-based cost item reserve matter proposal intelligent comparison checking system

Abstract

The application discloses an ERP system-based cost item reserve matter proposal intelligent comparison and verification system and relates to the technical field of enterprise digital management and data processing.The system is independent of the operation of an existing ERP system, realizes data interaction through a standardized interface, integrates OCR image recognition, a rule engine and workflow management technology, and solves the problems of low efficiency, incomplete coverage and weak risk prevention and control capability of traditional manual verification.The system adopts a layered and micro-service combined architecture and is divided into a presentation layer, a business logic layer and a data access layer;functionally, the system includes a data interaction module, an OCR image recognition module, an intelligent comparison module and a closed-loop management module and the like.The application greatly improves the efficiency and accuracy of cost item reserve compliance verification, reduces system coupling degree and operation and maintenance cost, and provides technical support for enterprise digital transformation.

Description

Technical Field

[0001] This invention belongs to the field of enterprise digital management and data processing technology, specifically relating to an intelligent comparison and verification system for cost item reserve proposals based on an ERP system. Background Technology

[0002] In the context of deepening digital transformation in enterprises, reserve project management, as a key component of cost control and strategic planning, is becoming increasingly important. However, the compliance verification of traditional cost project reserves relies mainly on manual operations, which is not only inefficient but also fails to achieve comprehensive and accurate coverage, making it difficult to detect and effectively control potential risks in a timely manner. With the continuous expansion of enterprise business scale and the growth of data volume, the existing verification model can no longer meet the needs of efficient and intelligent management, becoming a significant bottleneck restricting the improvement of the digitalization level of reserve project management.

[0003] Traditional compliance verification of cost item reserves relies entirely on manual operations. Verifiers must compare structured reserve information in the ERP system with paper / scanned documents, resulting in low efficiency. Manual verification consumes significant time and effort from professionals, has long cycles, and struggles to handle the massive data processing demands of expanding business scale. Furthermore, it suffers from incomplete coverage and insufficient accuracy; manual comparisons are susceptible to fatigue and subjective judgment, leading to a high probability of missed or incorrect assessments. Risk control is also lagging: after issues are discovered through manual verification, the lack of standardized follow-up and rectification processes hinders closed-loop management, causing problems to recur. Therefore, developing an independent, intelligent, specialized verification system that efficiently interacts with the ERP system is an urgent need to address these pain points. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide an intelligent comparison and verification system for cost project reserve proposals based on an ERP system. This system integrates cutting-edge technologies such as OCR image recognition, rule engine, and workflow management to achieve automated comparison, anomaly warning, and closed-loop management of ERP reserve information and proposals, thereby overcoming the pain points of manual verification and promoting the transformation and upgrading of reserve project management towards digitalization and intelligence.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart comparison and verification system for cost item reserve proposals based on an ERP system is proposed. The system adopts a design approach that combines layered architecture and microservice architecture. It is divided into a presentation layer, a business logic layer, and a data access layer. Each core function is broken down into an independent microservice and deployed and run independently. The functional modules include: a data interaction module, an OCR image recognition module, an intelligent comparison module, and a closed-loop management module. The data interaction module is used to perform bidirectional data interaction with the independently operating ERP system through a standardized interface to obtain structured information on cost item reserves in the ERP system. The OCR image recognition module is used to preprocess, binarize, and perform optical character recognition on the text image of the cost project reserve proposal, converting unstructured image data into structured text data. The intelligent comparison module has a built-in rule engine, which is used to automatically and deeply compare the structured proposal text output by the OCR module with the ERP reserve item information obtained by the data interaction module according to preset verification rules, identify differences and generate anomaly warnings. The closed-loop management module, based on workflow technology, is used to track and process the abnormal early warning information generated by the intelligent comparison module throughout the entire process, record the rectification and review of problems, and form a complete verification closed loop.

[0006] Furthermore, the presentation layer is responsible for interacting with the user and providing an intuitive and user-friendly interface. It adopts a front-end and back-end separation technology. The front-end is built on the Vue.js framework to create responsive web pages that support PC and mobile access. The back-end provides RESTful API interfaces for secure and efficient data interaction with the front-end.

[0007] Furthermore, the business logic layer receives requests from the presentation layer, calls the data access layer to obtain data, processes it according to preset business rules, and finally returns the results to the presentation layer. The business logic layer includes a reserve project management microservice, a comparison rule management microservice, an OCR recognition microservice, and a closed-loop management microservice. Each microservice is deployed and developed independently, and they can call each other and work together through the Eureka service registration and discovery mechanism.

[0008] Furthermore, the data access layer is responsible for interacting with the database to perform data storage, query, update, and deletion operations. MyBatis is used as the persistence layer framework, supporting multiple databases such as MySQL and Oracle. SQL statements are defined through XML mapping files or annotations. At the same time, a Redis caching mechanism is introduced to cache frequently accessed data and reduce database access pressure.

[0009] Furthermore, the preprocessing operations of the OCR image recognition module specifically include: performing grayscale processing on the input text image in sequence to simplify image information, noise reduction processing to eliminate interference factors, and tilt correction processing to ensure that the image is displayed in the correct orientation.

[0010] Furthermore, the OCR image recognition module uses an improved binarization algorithm to perform black-and-white binarization on the preprocessed image, and performs character recognition based on the improved TesseractOCR engine, which integrates a sequence recognition module based on an LSTM neural network.

[0011] Furthermore, the character recognition process of the TesseractOCR engine includes: Page layout analysis: Perform text region detection on the input binarized image to determine the distribution and arrangement of the text; Line and character segmentation: Vertical projection of text lines is used to achieve line segmentation. The baseline of each line character is detected, and character segmentation is performed based on character spacing and connectivity between adjacent characters. A segmentation algorithm based on a statistical model is used to adaptively adjust the segmentation parameters. First identification and segmentation error correction: The segmented characters are first identified, and character blocks with segmentation errors such as character breakage, misjudgment of gaps, and character adhesion are finely segmented and corrected; Feature extraction: A convolutional neural network is used to extract shape, color, and texture features from a single character and generate a numerical feature vector; Feature matching: Matching feature vectors with a pre-trained character library using a static character classifier and an adaptive character classifier; Match evaluation: The matching results are evaluated in terms of shape, size, and color to determine whether the character has been correctly recognized; LSTM sequence recognition: The feature vector output by the convolutional neural network is used as the input of the LSTM neural network to model and recognize character sequences; Secondary recognition optimization: The recognition accuracy is analyzed. If the accuracy is lower than the preset threshold, the accurately recognized characters are used as training data to update the adaptive classifier. The page is traversed again for a second recognition, and the parameters are adjusted to correct the recognition error. Finally, structured text is output.

[0012] Furthermore, the rule engine of the intelligent comparison module supports custom configuration of multi-dimensional verification rules, which can accurately compare key fields such as project name, budget amount, implementation content, responsible entity, and time node in the ERP reserve information and the proposal text to uncover potential compliance risks.

[0013] Furthermore, the workflow of the closed-loop management module includes five stages in sequence: abnormal warning push, problem identification, rectification submission, review and verification, and closed-loop archiving. It records the processing nodes and operator information throughout the process, automatically generates verification reports, and supports dynamic optimization of verification rules and workflow processes based on historical verification data.

[0014] The beneficial effects of this invention are as follows: 1. This invention, through its automated comparison function, can quickly and accurately identify the differences between ERP reserve item information and item proposals, significantly shortening the verification cycle and reducing labor costs; the anomaly early warning mechanism can promptly capture potential risks, providing decision support for managers and enhancing the enterprise's risk control capabilities; and closed-loop management ensures that problems are tracked and resolved, improving the overall quality and efficiency of reserve project management.

[0015] 2. This invention integrates core technologies such as OCR image recognition, rule engine, and workflow management, promoting the innovative application and deep integration of these technologies in the field of enterprise compliance verification. Research on system performance optimization and cross-platform data compatibility not only improves the system's stability and adaptability but also provides valuable experience and technical references for the development of similar systems in the future, contributing to the improvement and development of the technology ecosystem during enterprise digital transformation.

[0016] 3. This invention breaks through the limitations of traditional simple data comparison by integrating OCR and a rule engine. OCR accurately identifies and transforms unstructured data, while the rule engine performs in-depth comparative analysis based on complex rules to uncover potential risks and improve the overall accuracy of verification.

[0017] 4. This invention is independent of the ERP system, reducing development difficulty and operational risks. It provides standardized interfaces to achieve efficient data interaction, balancing its own stability and flexibility with data utilization, seamlessly connecting verification and business processes, and improving overall efficiency.

[0018] 5. This invention constructs a closed-loop mechanism to track and handle verification issues throughout the entire process. It utilizes standardized workflows at each stage to ensure timely resolution of problems. Furthermore, it can dynamically optimize rules and processes, forming a self-improving intelligent verification system. Attached Figure Description

[0019] Figure 1 This is a technical roadmap of the present invention; Figure 2 This is a basic OCR processing flowchart of the present invention; Figure 3 This is a basic flowchart of character recognition according to the present invention; Figure 4 This is a diagram of the internal structure of the LSTM of the present invention. Detailed Implementation

[0020] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention.

[0021] like Figure 1-4As shown, this invention proposes an intelligent comparison and verification system for cost item reserve proposals based on an ERP system. The main body of the system adopts a design approach that combines layered architecture and microservice architecture. The system is divided into a presentation layer, a business logic layer, and a data access layer. Each core function is broken down into an independent microservice and deployed and run independently. Presentation Layer: Responsible for user interaction, providing an intuitive and user-friendly interface. It employs a front-end / back-end separation architecture. The front-end uses the Vue.js framework to build responsive web pages, supporting multi-terminal access, including PC and mobile devices (achieved through adaptation). The back-end provides RESTful API interfaces for data interaction with the front-end, ensuring efficient and secure data transmission.

[0022] The business logic layer is the core processing layer of the system, containing multiple microservice modules, such as the reserve project management microservice and the comparison rule management microservice. Each microservice is deployed and developed independently, and they communicate and collaborate with each other through a service registration and discovery mechanism (such as Eureka). The business logic layer receives requests from the presentation layer, calls the data access layer to obtain data, processes it according to preset business rules, and finally returns the results to the presentation layer.

[0023] Data Access Layer: Responsible for interacting with the database, implementing operations such as data storage, querying, updating, and deleting. It uses the MyBatis framework as the persistence layer framework, supporting multiple databases (such as MySQL and Oracle). SQL statements are defined through XML mapping files or annotations, improving the flexibility and maintainability of data access. Simultaneously, a caching mechanism (such as Redis) is introduced to cache frequently used data, reducing database access pressure and improving system performance.

[0024] like Figure 1 As shown, the core technical approach of this invention is as follows: In the process of processing text images to extract text content, the text image is first input. Next, a series of preprocessing operations are performed on the image, including grayscale conversion to simplify image information, noise reduction to eliminate interference factors, and tilt correction to ensure the image is displayed correctly. After preprocessing, an improved binarization algorithm is used to further process the image, making it present a clearer black and white effect, facilitating subsequent recognition. Finally, improved OCR technology is used to recognize the processed image, thereby accurately outputting the text content.

[0025] This system leverages advanced intelligent technology to complete verification quickly and accurately, significantly improving efficiency and accuracy, thus meeting enterprises' needs for cost reduction and efficiency improvement. Simultaneously, increasingly stringent regulations are putting greater pressure on enterprises to comply with laws and regulations, helping them to strictly adhere to regulations and mitigate risks. With the advancement of digitalization, enterprises are increasingly accepting intelligent management tools, and as a powerful tool for digital transformation, it is bound to gain widespread recognition and application in the market.

[0026] The functional modules of this invention specifically include: a data interaction module, an OCR image recognition module, an intelligent comparison module, and a closed-loop management module; The data interaction module is used to conduct bidirectional data interaction with the independently operating ERP system through a standardized interface to obtain structured information on cost item reserves in the ERP system.

[0027] The OCR image recognition module is used to preprocess, binarize, and perform optical character recognition on the text images of the cost project reserve proposal, converting unstructured image data into structured text data.

[0028] OCR is an open-source optical character recognition engine. Currently, it supports the recognition of over 100 languages ​​with extremely high accuracy. Its most notable feature is its ability to support user-trained character recognition models. It is very powerful, and since version 4.0, it has added a recognition engine based on an LSTM neural network.

[0029] The preprocessing operations of the OCR image recognition module specifically include: performing grayscale processing on the input text image in sequence to simplify image information, noise reduction processing to eliminate interference factors, and tilt correction processing to ensure that the image is displayed in the correct orientation.

[0030] The OCR image recognition module uses an improved binarization algorithm to perform black-and-white binarization on the preprocessed image, and performs character recognition based on the improved TesseractOCR engine, which integrates a sequence recognition module based on an LSTM neural network.

[0031] The character recognition process of the TesseractOCR engine includes: Page layout analysis: Text region detection is performed on the input binarized image to determine the distribution and arrangement of the text; LSTM neural network is used to recognize the text regions in the image to obtain character-level recognition results.

[0032] Line and character segmentation: Vertical projection of text lines is used to achieve line segmentation. The baseline of each line character is detected, and character segmentation is performed based on character spacing and connectivity between adjacent characters. A segmentation algorithm based on a statistical model is used to adaptively adjust the segmentation parameters. First identification and segmentation error correction: The segmented characters are first identified, and character blocks with segmentation errors such as character breakage, misjudgment of gaps, and character adhesion are finely segmented and corrected; Feature Extraction: Building upon character segmentation, the Tesseract-OCR engine employs a Convolutional Neural Network (CNN) from deep learning methods to extract features from each character. Feature extraction refers to the process of converting each character into a numerical feature vector. In this process, the Tesseract-OCR engine feeds each character block into the CNN to extract important features such as the shape, color, and texture of each character, and then converts these features into numerical vectors.

[0033] Feature matching: After feature extraction, characters are classified based on their features. The Tesseract-OCR engine has a static character classifier and an adaptive character classifier, which match the feature vector of each character with a pre-trained character library. The character library consists of a large number of known character samples, each with its corresponding feature vector. During matching, the Tesseract-OCR engine compares the feature vector of the current character with the feature vectors of samples in the character library to find the best matching character.

[0034] Match Evaluation: After finding the best matching character, the Tesseract-OCR engine evaluates the match result to ensure its accuracy. The evaluation process includes comparing and analyzing aspects such as the character's shape, size, and color to determine if the character was correctly recognized.

[0035] LSTM Sequence Recognition: After obtaining feature vectors, the Tesseract-OCR engine has added an LSTM neural network in its latest version. The output of the CNN is used as the input to the LSTM to model and recognize character sequences. Within the LSTM neural network, LSTM units are used to model the sequence.

[0036] Secondary recognition optimization: The recognition accuracy is analyzed. If the accuracy is lower than the preset threshold, the accurately recognized characters are used as training data to update the adaptive classifier. The page is traversed again for a second recognition, and the parameters are adjusted to correct the recognition error. Finally, structured text is output.

[0037] like Figure 3As shown, character recognition targets individual characters, so the Tesseract-OCR engine segments the characters before recognition. First, the Tesseract-OCR engine performs a vertical projection operation on the text lines. This projection creates gaps between lines, which are then used for simple line segmentation. Within each separated line, the Tesseract-OCR engine detects the text lines and attempts to find the baseline of each character—the horizontal line at the bottom of the character. The Tesseract-OCR engine subsequently identifies the position of each character based on this baseline detection. Character segmentation, in essence, separates the characters from each line.

[0038] During character segmentation, the Tesseract-OCR engine bases its segmentation on the spacing between each character and the connectivity of adjacent characters. The Tesseract-OCR engine employs a statistical model-based segmentation algorithm that adaptively adjusts segmentation parameters to better suit different character shapes and sizes. After segmenting the characters, an initial recognition process is performed to correct some segmentation errors. In this initial recognition, the Tesseract-OCR engine may encounter some character segmentation errors, such as a character being incorrectly split in half, gaps between characters being identified as part of a character, or characters appearing to be joined together. To correct these errors, the Tesseract-OCR engine checks and corrects each segmented character as thoroughly as possible, performing a further fine-tuning operation on problematic parts to ensure the final segmentation result is accurate.

[0039] Therefore, line segmentation and character segmentation are crucial steps in the character recognition process of the Tesseract-OCR engine. Only by effectively segmenting the characters in a text image through these two steps can more accurate input data be provided for subsequent feature extraction and classification.

[0040] After obtaining the segmented characters, the Tesseract-OCR engine can recognize them. Character recognition is the core function, and its basic process is as follows: Figure 2 As shown.

[0041] The intelligent comparison module, with a built-in rule engine, is used to automatically and deeply compare the structured proposal text output by the OCR module with the ERP reserve item information obtained by the data interaction module according to preset verification rules, identify differences and generate anomaly warnings. The intelligent comparison module's rule engine supports custom configuration of multi-dimensional verification rules, which can accurately compare key fields such as project name, budget amount, implementation content, responsible entity, and time node in ERP reserve information and proposal text to uncover potential compliance risks.

[0042] The closed-loop management module, based on workflow technology, is used to track and process the abnormal early warning information generated by the intelligent comparison module throughout the entire process, record the rectification and review of problems, and form a complete verification closed loop.

[0043] The workflow of the closed-loop management module includes five stages: abnormal warning push, problem identification, rectification submission, review and verification, and closed-loop archiving. It records the processing nodes and operator information throughout the process, automatically generates verification reports, and supports dynamic optimization of verification rules and workflow processes based on historical verification data.

[0044] The intelligent special verification system proposed in this invention has significant economic benefits. From a direct cost-saving perspective, the system automates the comparison between ERP reserve item information and item proposals, greatly reducing the workload of manual verification and lowering the company's labor costs. Verification tasks that previously required a large number of professionals and significant time investment can now be completed quickly and efficiently with the help of the system, allowing companies to reallocate human resources to more valuable tasks. Simultaneously, the system operates independently and possesses efficient interactive capabilities, avoiding coupling with the complex business logic of existing ERP systems, reducing system development and maintenance costs, lowering the risk of business interruption due to system failures, ensuring the continuity and stability of the company's business, and indirectly creating economic benefits for the company. Furthermore, the system's closed-loop management and dynamic optimization functions continuously improve the accuracy and efficiency of verification, helping companies to promptly identify problems in reserve project management, optimize resource allocation, avoid resource waste, and further improve the company's economic efficiency.

[0045] In terms of social benefits, the system has a wide range of positive impacts: First, its application has improved the overall digital management level of the industry. By promoting intelligent verification technology, it provides a model for other companies in the industry, driving the industry's transformation and upgrading towards digitalization and intelligence, and enhancing the overall competitiveness of the industry. Second, the system's high degree of automation and accuracy helps improve the quality and transparency of reserve project management, ensures project compliance, reduces violations, maintains market order, and creates a fair competitive market environment. Third, while improving work efficiency, the system reduces the workload of staff, improves the working environment, reflects care for workers' rights, helps improve employee job satisfaction and well-being, and promotes harmonious and stable social development. Finally, the system's dynamic optimization capabilities can be continuously adjusted according to business development needs, possessing a certain degree of foresight and adaptability, helping enterprises better cope with market changes and providing strong support for the stable development of the social economy.

[0046] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. An intelligent comparison and verification system for cost item reserve proposals based on an ERP system, characterized by: The system adopts a design approach that combines layered architecture and microservice architecture. It is divided into a presentation layer, a business logic layer, and a data access layer. Each core function is broken down into an independent microservice and deployed and run independently. The functional modules include: a data interaction module, an OCR image recognition module, an intelligent comparison module, and a closed-loop management module. The data interaction module is used to perform bidirectional data interaction with the independently operating ERP system through a standardized interface to obtain structured information on cost item reserves in the ERP system. The OCR image recognition module is used to preprocess, binarize, and perform optical character recognition on the text image of the cost project reserve proposal, converting unstructured image data into structured text data. The intelligent comparison module has a built-in rule engine, which is used to automatically and deeply compare the structured proposal text output by the OCR module with the ERP reserve item information obtained by the data interaction module according to preset verification rules, identify differences and generate anomaly warnings. The closed-loop management module, based on workflow technology, is used to track and process the abnormal early warning information generated by the intelligent comparison module throughout the entire process, record the rectification and review of problems, and form a complete verification closed loop.

2. The intelligent comparison and verification system for cost project reserve proposals according to claim 1, characterized in that: The presentation layer is responsible for interacting with the user and providing an intuitive and user-friendly interface. It adopts a front-end and back-end separation technology. The front-end is built on the Vue.js framework to create responsive web pages that support PC and mobile access. The back-end provides RESTful API interfaces for secure and efficient data interaction with the front-end.

3. The intelligent comparison and verification system for cost project reserve proposals according to claim 1, characterized in that: The business logic layer receives requests from the presentation layer, calls the data access layer to obtain data, processes it according to preset business rules, and finally returns the results to the presentation layer. The business logic layer includes a reserve project management microservice, a comparison rule management microservice, an OCR recognition microservice, and a closed-loop management microservice. Each microservice is deployed and developed independently, and they can call each other and work together through the Eureka service registration and discovery mechanism.

4. The intelligent comparison and verification system for cost project reserve proposals according to claim 1, characterized in that: The data access layer is responsible for interacting with the database to perform data storage, query, update, and deletion operations. MyBatis is used as the persistence layer framework, supporting multiple databases such as MySQL and Oracle. SQL statements are defined through XML mapping files or annotations. At the same time, a Redis caching mechanism is introduced to cache frequently accessed data and reduce database access pressure.

5. The intelligent comparison and verification system for cost project reserve proposals according to claim 1, characterized in that: The preprocessing operations of the OCR image recognition module specifically include: performing grayscale processing on the input text image in sequence to simplify image information, noise reduction processing to eliminate interference factors, and tilt correction processing to ensure that the image is displayed in the correct orientation.

6. The intelligent comparison and verification system for cost item reserve proposals according to claim 5, characterized in that: The OCR image recognition module uses an improved binarization algorithm to perform black-and-white binarization on the preprocessed image, and performs character recognition based on the improved TesseractOCR engine, which integrates a sequence recognition module based on an LSTM neural network.

7. The intelligent comparison and verification system for cost item reserve proposals according to claim 6, characterized in that: The character recognition process of the TesseractOCR engine includes: Page layout analysis: Perform text region detection on the input binarized image to determine the distribution and arrangement of the text; Line and character segmentation: Vertical projection of text lines is used to achieve line segmentation. The baseline of each line character is detected, and character segmentation is performed based on character spacing and connectivity between adjacent characters. A segmentation algorithm based on a statistical model is used to adaptively adjust the segmentation parameters. First identification and segmentation error correction: The segmented characters are first identified, and character blocks with segmentation errors such as character breakage, misjudgment of gaps, and character adhesion are finely segmented and corrected; Feature extraction: A convolutional neural network is used to extract shape, color, and texture features from a single character and generate a numerical feature vector; Feature matching: Matching feature vectors with a pre-trained character library using a static character classifier and an adaptive character classifier; Match evaluation: The matching results are evaluated in terms of shape, size, and color to determine whether the character has been correctly recognized; LSTM sequence recognition: The feature vector output by the convolutional neural network is used as the input of the LSTM neural network to model and recognize character sequences; Secondary recognition optimization: The recognition accuracy is analyzed. If the accuracy is lower than the preset threshold, the accurately recognized characters are used as training data to update the adaptive classifier. The page is traversed again for a second recognition, and the parameters are adjusted to correct the recognition error. Finally, structured text is output.

8. The intelligent comparison and verification system for cost project reserve proposals according to claim 1, characterized in that: The rule engine of the intelligent comparison module supports custom configuration of multi-dimensional verification rules, which can accurately compare key fields such as project name, budget amount, implementation content, responsible entity, and time node in ERP reserve information and proposal text to uncover potential compliance risks.

9. The intelligent comparison and verification system for cost project reserve proposals according to claim 1, characterized in that: The workflow of the closed-loop management module includes five stages: abnormal warning push, problem identification, rectification submission, review and verification, and closed-loop archiving. It records the processing nodes and operator information throughout the process, automatically generates verification reports, and supports dynamic optimization of verification rules and workflow processes based on historical verification data.

Images