AI-based financial text data auxiliary processing system

Abstract

The invention relates to the technical field of financial data processing, and particularly discloses an AI-based financial text data auxiliary processing system, which comprises a data acquisition module, a data preprocessing module, an AI intelligent analysis module, a data verification module, an intelligent decision-making auxiliary module, a data storage and management module and a safety management and control module. The system can automatically collect multi-format and multi-source financial text data in batches, after the data quality is optimized through preprocessing, intelligent text classification, key information extraction and structured conversion are achieved through an improved deep learning algorithm, the accuracy and compliance of the data are guaranteed in combination with multi-dimensional verification, then decision support such as financial analysis and risk early warning is provided, and the accuracy and the reliability of the system are improved. And data management and security are guaranteed through distributed storage and an omnibearing security mechanism. According to the method, full-process automation of financial text processing is realized, the processing efficiency and data accuracy are improved, the problem of data dispersion of an existing system is solved, the intelligent level of financial decision is improved, and compliance requirements are met.

Description

Technical Field

[0001] This invention relates to the field of financial data processing technology, specifically to an AI-based financial text data auxiliary processing system. Background Technology

[0002] In current business operations and management, financial text data is a core basis for reflecting the company's operating status and supporting financial decisions. Its types include various forms such as paper / electronic financial statements, bank statements, invoices, purchase and sales contracts, audit reports, and tax returns. Existing methods for processing financial text data largely rely on manual operation or only employ semi-automated methods combining simple optical character recognition (OCR) with keyword matching, which have the following significant drawbacks:

[0003] Low processing efficiency: Manual processing requires financial staff to read, enter, and verify text data one by one. For large enterprises or massive financial texts in multi-business scenarios, long processing cycles and serious backlogs are likely to occur, which cannot meet the needs of real-time processing of financial data.

[0004] Insufficient data accuracy: During manual processing, data entry errors and omissions of key information are easily caused by factors such as fatigue and subjective judgment bias. The simple OCR + keyword matching method cannot deal with problems such as inconsistent text format, illegible handwriting, and ambiguous professional terms, which further reduces the accuracy of data processing.

[0005] Poor multi-source data fusion capability: Financial text data comes from scattered sources, and the text formats and data standards of different sources vary greatly. Existing processing methods are unable to achieve efficient fusion and unified management of multi-source data.

[0006] Lack of intelligent decision support: Existing systems mostly remain at the basic level of data processing and cannot conduct in-depth analysis based on the processed financial text data, making it difficult to provide effective support for decisions such as financial auditing, risk warning, and cost control.

[0007] Insufficient security and traceability: Financial text data contains core confidential information of enterprises. Existing processing methods are prone to information leakage risks during data transmission, storage and operation. Furthermore, the lack of complete operation logs and data traceability mechanisms fails to meet financial compliance requirements.

[0008] To address the aforementioned issues, current technologies lack an integrated system capable of fully automating and accurately processing financial text data throughout the entire process, while also possessing multi-source data fusion, intelligent decision support, and secure traceability capabilities. Therefore, designing an AI-based financial text data auxiliary processing system has become an urgent need in the field of financial data processing. Summary of the Invention

[0009] The technical problem to be solved by this invention is to provide an AI-based financial text data auxiliary processing system that automates the entire process of financial text data processing, from collection, cleaning, parsing to verification, analysis and decision support, improves processing efficiency and accuracy, strengthens the ability to integrate multi-source data, provides intelligent support for financial decision-making, and ensures the security and traceability of the data processing process.

[0010] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: an AI-based financial text data auxiliary processing system, comprising a data acquisition module, a data preprocessing module, an AI intelligent parsing module, a data verification module, an intelligent decision support module, a data storage and management module, and a security control module connected in sequence.

[0011] The data acquisition module automates the collection of financial text data; the data preprocessing module cleans and standardizes the collected data; the AI ​​intelligent parsing module uses deep learning algorithms to intelligently classify financial text, extract key information, and perform structured transformation; the data verification module ensures the accuracy and compliance of structured data; the intelligent decision support module provides financial analysis and decision support based on effective data; the data storage and management module enables secure storage and efficient management of data throughout the entire process; and the security control module ensures the security of system data transmission, storage, and operation.

[0012] Furthermore, the data acquisition module includes a multi-format adaptation unit, a batch acquisition unit, and an acquisition task management unit. The multi-format adaptation unit has a built-in format recognition engine, which is used to convert paper text into electronic images through high-definition scanning. It can directly read the metadata and content of PDF, Word, Excel, and TXT format electronic texts, and connect to ERP systems, banking systems, and tax systems through RESTful and SOAP interface protocols to obtain interface data. The batch acquisition unit supports batch uploading, batch scanning, and batch interface calls, and can resume interrupted uploads. The acquisition task management unit supports the creation, editing, deletion, and automated scheduling of acquisition tasks, and generates acquisition logs that record acquisition time, quantity, and status.

[0013] Furthermore, the data preprocessing module includes an image preprocessing unit, a text cleaning unit, a data splitting and merging unit, and a preprocessing quality detection unit. The image preprocessing unit performs noise reduction, tilt correction, resolution optimization, and edge enhancement on the electronic images converted from paper. The text cleaning unit uses a misspelling recognition algorithm based on a financial professional dictionary to remove redundant information, filter special characters, unify date / amount / encoding formats, and correct financial misspellings. The data splitting and merging unit automatically splits multi-page text or merges related text according to financial text type and assigns a unique identifier. The preprocessing quality detection unit evaluates data quality based on accuracy, completeness, and consistency indicators; if the data does not meet the threshold, it is returned for reprocessing.

[0014] Furthermore, the AI ​​intelligent parsing module includes a text classification unit, a key information extraction unit, a data structuring unit, and a model optimization unit; the text classification unit uses a FinBERT-V1 model pre-trained on a financial domain corpus to classify and recognize financial statements, invoices, bank statements, contracts, audit reports, and tax returns.

[0015] Furthermore, the key information extraction unit employs differentiated algorithms for different text types: for structured text in Excel format, a rule engine combined with a table recognition algorithm is used to extract key financial information; for semi-structured text in PDF format, the YOLOv8 object detection algorithm is used to locate key areas and then combined with OCR to extract information; for unstructured text of contract text, a BiLSTM-CRF model based on an attention mechanism is used to extract key information.

[0016] Furthermore, the data structuring unit transforms the extracted key information into structured data in JSON or XML format, and establishes information associations between invoices and contracts, bank statements and financial statements; the model optimization unit fine-tunes the FinBERT-V1, YOLOv8 and BiLSTM-CRF models by collecting parsing error cases reported by users.

[0017] Furthermore, the data verification module includes a rule verification unit, an AI intelligent verification unit, a manual review unit, and a verification report generation unit. The rule verification unit has a built-in financial rule library containing logical verification, compliance verification, and consistency verification, and supports user-defined rules. The AI ​​intelligent verification unit uses an Isolation Forest-LSTM hybrid anomaly detection model to analyze suspected erroneous data types and provide correction suggestions. The manual review unit pushes high-risk or uncertain erroneous data for manual review, and the review results are fed back to the model optimization unit.

[0018] Furthermore, the intelligent decision support module includes a financial indicator analysis unit, a risk warning unit, a cost control support unit, and a decision report generation unit. The financial indicator analysis unit automatically calculates indicators such as the debt-to-asset ratio and gross profit margin, and generates analysis reports through trend analysis and comparison with historical / industry data. The risk warning unit identifies credit, liquidity, and tax risks based on machine learning models and pushes tiered warnings and response suggestions. The cost control support unit analyzes various cost data, predicts cost trends, and provides optimization suggestions.

[0019] Furthermore, the data storage and management module adopts a distributed storage architecture: it stores raw text, preprocessed text, and various reports through HDFS; it stores structured financial data, user information, and verification rules through MySQL; and it stores processing logs through InfluxDB.

[0020] Furthermore, the security management module includes an identity authentication and authorization unit, a data encryption unit, a security audit unit, and an emergency response unit. The identity authentication and authorization unit adopts multi-factor authentication using account password + dynamic verification code + biometrics, and allocates operation permissions based on the RBAC model. The data encryption unit uses SSL / TLS protocol to encrypt transmitted data and AES-256 algorithm to encrypt stored data. The emergency response unit supports full / incremental backup and off-site storage, and provides emergency handling and data recovery solutions for sudden events.

[0021] The advantages of this invention compared to the prior art are:

[0022] This invention automates the entire process of financial text data processing, from collection to decision support. The batch collection and AI intelligent analysis functions greatly reduce manual operations and improve processing efficiency compared to traditional manual methods, which can meet the real-time processing needs of massive amounts of financial text data.

[0023] This invention optimizes data quality through image preprocessing and text cleaning, and achieves accurate extraction of key information by combining improved FinBERT-V1, YOLOv8 and BiLSTM-CRF models. A multi-dimensional data verification mechanism further ensures data accuracy.

[0024] This invention supports the collection and processing of multi-type and multi-source financial text data. Through data structuring and association mechanisms, it achieves unified management and fusion analysis of multi-source data, solving the problems of data dispersion and difficulty in integration in existing systems.

[0025] This invention provides scientific basis for decision-making in financial auditing, risk prevention and control, and cost control based on deep learning, thereby improving the intelligence level of corporate financial decision-making.

[0026] This invention comprehensively protects the security of financial data transmission, storage, and operation through multi-factor authentication, data encryption, security auditing, and emergency response mechanisms; and meets financial compliance requirements through full-process operation logs and data traceability mechanisms. Attached Figure Description

[0027] Figure 1 This is a system block diagram of an AI-based financial text data auxiliary processing system according to the present invention. Detailed Implementation

[0028] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0029] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0030] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0031] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0032] The following is a detailed description of an AI-based financial text data auxiliary processing system according to the present invention, with reference to the accompanying drawings.

[0033] Combined with appendix Figure 1 This invention will be described in detail below.

[0034] An AI-based financial text data processing system includes a data acquisition module, a data preprocessing module, an AI intelligent parsing module, a data verification module, an intelligent decision support module, a data storage and management module, and a security control module. These modules are connected sequentially to form a complete financial text data processing chain.

[0035] The data acquisition module is used to automatically collect various types and sources of financial text data, including but not limited to paper texts, electronic texts (PDF, Word, Excel, TXT, etc.), and system interface data; specifically including:

[0036] Multi-format adaptation unit: Built-in format recognition engine supports automatic recognition and adaptation of financial texts in different formats. For paper texts, it converts them into electronic images through high-definition scanning equipment; for electronic texts, it directly reads file metadata and content; for system interface data, it supports multiple interface protocols such as RESTful and SOAP to achieve interface with external systems such as ERP systems, banking systems, and tax systems.

[0037] Batch Acquisition Unit: Supports batch uploading, batch scanning, and batch API calls, enabling the one-time acquisition of massive amounts of financial text data. It also features breakpoint resume functionality to prevent data acquisition failures due to network interruptions or device malfunctions.

[0038] Data Acquisition Task Management Unit: Supports users in creating, editing, and deleting data acquisition tasks, setting parameters such as acquisition frequency and acquisition range, realizing automated scheduling and management of data acquisition tasks, and generating acquisition logs to record information such as acquisition time, acquisition quantity, and acquisition status.

[0039] The data preprocessing module is used to clean and standardize the collected raw financial text data, providing high-quality data input for subsequent AI analysis; specifically, it includes:

[0040] Image preprocessing unit: For electronic images converted from paper text, it performs denoising, tilt correction, resolution optimization, edge enhancement and other processing to remove interference information in the image and improve the accuracy of subsequent OCR recognition;

[0041] Text cleaning unit: For electronic text and text data after OCR recognition, redundant information is removed, special characters are filtered, format is standardized and typos are corrected. It adopts a typo recognition algorithm based on a financial professional dictionary to accurately correct common typos in the financial field.

[0042] Data splitting and merging unit: Based on the type of financial text (such as financial statements, invoices, contracts), automatically splits multiple pages of text or merges related text, and assigns a unique identifier to the split / merged text to ensure the relevance and integrity of the data;

[0043] Preprocessing quality inspection unit: The preprocessed text data is evaluated using three dimensions of evaluation indicators: accuracy, completeness, and consistency. If the preset threshold is not met, the data is returned to the previous processing stage for reprocessing until the quality requirements are met.

[0044] The AI-powered intelligent analysis module, as the core module of the system, is used to intelligently analyze preprocessed financial text data based on deep learning algorithms, extract key financial information, and perform structured processing; specifically, it includes:

[0045] Text classification unit: An improved BERT model is adopted. Based on the standard BERT model, this model is pre-trained with a financial domain corpus to achieve accurate classification of financial texts. The classification categories include financial statements, invoices, bank statements, contracts, audit reports, tax returns, etc.

[0046] Key Information Extraction Unit: Targeted extraction algorithms are employed for different types of financial text: For structured text (such as Excel-format financial statements), a rule engine combined with table recognition algorithms is used to extract key information (such as total assets, total liabilities, and owner's equity in the balance sheet); for semi-structured text (such as PDF-format invoices), the YOLOv8 object detection algorithm is used to locate key areas (such as invoice code, invoice number, amount, and invoice date), and then combined with OCR recognition to extract information; for unstructured text (such as contract text and audit opinions), a BiLSTMCRF model based on an attention mechanism is used to extract key information (such as contract amount, payment method, audit conclusion, and risk warnings).

[0047] Data structuring unit: Transforms the extracted key information into a unified structured data format (such as JSON, XML) and establishes information relationships;

[0048] Model optimization unit: It has a built-in model self-learning mechanism that regularly collects user feedback on parsing error cases and fine-tunes the FinBERTV1, YOLOv8 and BiLSTMCRF models to continuously improve parsing accuracy.

[0049] The data validation module performs multi-dimensional validation on structured financial data to ensure its accuracy and compliance; specifically, it includes:

[0050] Rule Validation Unit: Built-in financial domain validation rule library, including logic validation rules, compliance validation rules, and consistency validation rules, supporting users to add and modify validation rules;

[0051] AI Intelligent Verification Unit: Employs a deep learning-based anomaly detection model (Isolation Forest LSTM hybrid model) to further analyze suspected erroneous data discovered during the verification process, determine the error type, and provide preliminary correction suggestions;

[0052] Manual review unit: For erroneous or high-risk data that AI intelligent verification cannot determine, it is automatically pushed to the manual review interface, where financial personnel will manually review and correct it. The review results are simultaneously fed back to the model optimization unit for model fine-tuning.

[0053] Verification report generation unit: Generates detailed verification reports, recording verification time, verification scope, number of passes, number of errors, error types and handling status, and supports export and printing.

[0054] The intelligent decision support module is used to perform in-depth analysis and provide intelligent decision support based on the validated structured financial data; specifically, it includes:

[0055] Financial Indicator Analysis Unit: Automatically calculates key financial indicators and generates financial indicator analysis reports using methods such as trend analysis and comparative analysis;

[0056] Risk warning unit: It adopts a risk assessment model based on machine learning, combined with abnormal information in financial data, to identify financial risk points, and pushes early warning information according to the risk level, and provides risk response suggestions;

[0057] Cost control support unit: Classifies, statistically analyzes, and interprets various cost data of the enterprise, identifies weak links in cost control, and predicts cost change trends by combining historical data and market trends, providing cost optimization suggestions;

[0058] Decision Report Generation Unit: Supports user-defined decision report templates, automatically integrates financial indicator analysis results, risk warning information, cost control suggestions, etc., to generate personalized decision support reports, which can be exported, printed, and shared online.

[0059] The data storage and management module is used to achieve secure storage and efficient management of information such as financial text data, structured data, and processing logs; specifically, it includes:

[0060] Distributed storage unit: The distributed file system (HDFS) is used to store raw financial text data, preprocessed text data and various report files. The relational database (MySQL) is used to store structured financial data, user information, verification rules, etc., and the time-series database (InfluxDB) is used to store processed log data, ensuring the security and scalability of data storage.

[0061] Data indexing unit: Establishes multi-dimensional data indexes to support users in quickly retrieving the data they need;

[0062] Data lifecycle management unit: Supports setting data storage cycle, automatically archives or deletes expired data, and retains backups of core data to ensure data compliance and rational use of storage resources;

[0063] Log Management Unit: Records the entire process of system operation logs, including data acquisition logs, preprocessing logs, parsing logs, verification logs, decision support logs, user operation logs, etc. Log information includes the operator, operation time, operation content, operation result, etc., and supports log querying, exporting and auditing.

[0064] The security control module is used to ensure the security of system data transmission, storage, and operation processes, meeting the confidentiality requirements of financial data; specifically, it includes:

[0065] Identity authentication and authorization unit: Employs a multi-factor authentication mechanism (account password + dynamic verification code + biometric identification) to ensure the authenticity of user identity; Role-based access control (RBAC) model assigns different system operation permissions to users with different roles;

[0066] Data encryption unit: The data during transmission is encrypted using the SSL / TLS encryption protocol, and the stored data is encrypted using the AES256 encryption algorithm to ensure that the data is not leaked or tampered with during transmission and storage;

[0067] Security Audit Unit: Regularly audits system operation logs and data access logs to identify abnormal operations, promptly pushes security alerts, and generates security audit reports;

[0068] Emergency Response Unit: Establishes a data backup and recovery mechanism, supporting full backup and incremental backup, with backup data stored on a remote server; provides emergency handling procedures and data recovery solutions for emergencies such as data leaks and system failures, ensuring the system can quickly return to normal operation.

[0069] The specific implementation process of the AI-based financial text data auxiliary processing system of the present invention is as follows:

[0070] Data acquisition phase: The data acquisition module, through multi-format adaptation unit, batch acquisition unit and acquisition task management unit, realizes the automated acquisition of financial text data of multiple types and multiple sources, and generates acquisition logs;

[0071] Data preprocessing stage: The data preprocessing module performs image preprocessing, text cleaning, data splitting and merging on the collected raw data. After passing the preprocessing quality detection unit, high-quality text data is output.

[0072] AI Intelligent Analysis Stage: The AI ​​Intelligent Analysis module classifies the preprocessed text through the text classification unit, then extracts key information from different types of text through the key information extraction unit, and transforms it into unified structured data through the data structuring unit. The model optimization unit fine-tunes the model periodically based on user feedback.

[0073] Data verification phase: The data verification module performs multi-dimensional verification of structured data through the rule verification unit and the AI ​​intelligent verification unit. Suspected errors or high-risk data are pushed to the manual review unit. After manual review and correction, a verification report is generated.

[0074] Intelligent decision support stage: Based on the validated structured data, the intelligent decision support module performs financial indicator analysis, risk warning and cost control analysis, and generates personalized decision support reports;

[0075] Data storage and security control phase: The data storage and management module performs distributed storage and index management of various types of data and records the entire process operation log; the security control module ensures the security and traceability of the entire data processing process through identity authentication, data encryption, security auditing and emergency response.

[0076] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An AI-based financial text data auxiliary processing system, characterized in that: It includes a data acquisition module, a data preprocessing module, an AI intelligent analysis module, a data verification module, an intelligent decision support module, a data storage and management module, and a security control module that are connected in sequence. The data acquisition module automates the collection of financial text data; the data preprocessing module cleans and standardizes the collected data; the AI ​​intelligent parsing module uses deep learning algorithms to intelligently classify financial text, extract key information, and perform structured transformation; the data verification module ensures the accuracy and compliance of structured data; the intelligent decision support module provides financial analysis and decision support based on effective data; the data storage and management module enables secure storage and efficient management of data throughout the entire process; and the security control module ensures the security of system data transmission, storage, and operation.

2. The AI-based financial text data auxiliary processing system according to claim 1, characterized in that: The data acquisition module includes a multi-format adaptation unit, a batch acquisition unit, and an acquisition task management unit. The multi-format adaptation unit has a built-in format recognition engine, which is used to convert paper text into electronic images through high-definition scanning. It can directly read the metadata and content of PDF, Word, Excel, and TXT format electronic texts, and connect to ERP systems, banking systems, and tax systems through RESTful and SOAP interface protocols to obtain interface data. The batch acquisition unit supports batch uploading, batch scanning, and batch interface calls, and can resume interrupted uploads. The acquisition task management unit supports the creation, editing, deletion, and automated scheduling of acquisition tasks, and generates acquisition logs that record acquisition time, quantity, and status.

3. The AI-based financial text data auxiliary processing system according to claim 2, characterized in that: The data preprocessing module includes an image preprocessing unit, a text cleaning unit, a data splitting and merging unit, and a preprocessing quality detection unit. The image preprocessing unit performs noise reduction, tilt correction, resolution optimization, and edge enhancement on the electronic images converted from paper. The text cleaning unit uses a misspelling recognition algorithm based on a financial professional dictionary to remove redundant information, filter special characters, unify date / amount / encoding formats, and correct financial misspellings. The data splitting and merging unit automatically splits multi-page text or merges related text according to financial text type and assigns a unique identifier. The preprocessing quality detection unit evaluates data quality based on accuracy, completeness, and consistency indicators; if the data does not meet the threshold, it is returned for reprocessing.

4. The AI-based financial text data auxiliary processing system according to claim 3, characterized in that: The AI ​​intelligent analysis module includes a text classification unit, a key information extraction unit, a data structuring unit, and a model optimization unit. The text classification unit uses a FinBERT-V1 model pre-trained on a financial domain corpus to classify and recognize financial statements, invoices, bank statements, contracts, audit reports, and tax returns.

5. The AI-based financial text data auxiliary processing system according to claim 4, characterized in that: The key information extraction unit employs differentiated algorithms for different text types: for structured text in Excel format, a rule engine combined with a table recognition algorithm is used to extract key financial information; for semi-structured text in PDF format, the YOLOv8 object detection algorithm is used to locate key areas and then combined with OCR to extract information; for unstructured text of contract text, a BiLSTM-CRF model based on an attention mechanism is used to extract key information.

6. The AI-based financial text data auxiliary processing system according to claim 5, characterized in that: The data structuring unit converts the extracted key information into structured data in JSON or XML format and establishes information associations between invoices and contracts, bank statements and financial statements; the model optimization unit fine-tunes the FinBERT-V1, YOLOv8 and BiLSTM-CRF models by collecting parsing error cases reported by users.

7. The AI-based financial text data auxiliary processing system according to claim 6, characterized in that: The data verification module includes a rule verification unit, an AI intelligent verification unit, a manual review unit, and a verification report generation unit. The rule verification unit has a built-in financial rule library containing logical verification, compliance verification, and consistency verification, and supports user-defined rules. The AI ​​intelligent verification unit uses an Isolation Forest-LSTM hybrid anomaly detection model to analyze suspected erroneous data types and provide correction suggestions. The manual review unit pushes high-risk or undetermined error data to manual review, and the review results are fed back to the model optimization unit.

8. The AI-based financial text data auxiliary processing system according to claim 7, characterized in that: The intelligent decision support module includes a financial indicator analysis unit, a risk warning unit, a cost control support unit, and a decision report generation unit. The financial indicator analysis unit automatically calculates indicators such as the asset-liability ratio and gross profit margin, and generates analysis reports through trend analysis and comparison with historical / industry data. The risk warning unit identifies credit, liquidity, and tax risks based on machine learning models and pushes tiered warnings and response suggestions. The cost control auxiliary unit analyzes various cost data, predicts cost trends, and provides optimization suggestions.

9. The AI-based financial text data auxiliary processing system according to claim 8, characterized in that: The data storage and management module adopts a distributed storage architecture: HDFS stores raw text, preprocessed text, and various reports; MySQL stores structured financial data, user information, and verification rules; and InfluxDB stores processing logs.

10. The AI-based financial text data auxiliary processing system according to claim 9, characterized in that: The security management module includes an identity authentication and authorization unit, a data encryption unit, a security audit unit, and an emergency response unit. The identity authentication and authorization unit uses multi-factor authentication with account password, dynamic verification code, and biometric identification, and allocates operation permissions based on the RBAC model. The data encryption unit uses SSL / TLS protocol to encrypt transmitted data and AES-256 algorithm to encrypt stored data. The emergency response unit supports full / incremental backup and off-site storage, and provides emergency handling and data recovery solutions for sudden events.

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