Enterprise business archives digital management system and method

Abstract

The application discloses a kind of enterprise industrial and commercial archives digitization management system and method, belong to computer software and cross technical field of archives management.The core technology system of "archives gene coding-dynamic semantic anchoring-full-link credible traceability" is designed creatively, and the system includes archives gene coding module, dynamic semantic anchoring module, credible traceability engine and financial business adaptation module;Method covers archives gene generation, semantic anchoring association, credible traceability check and business scene adaptation whole process.The core innovation lies in the creation of archives gene coding algorithm, dynamic semantic anchoring mechanism, solves the pain points of poor adaptability of traditional system, inaccurate traceability, weak business linkage, realizes the whole process of industrial and commercial archives digitization management Creative design, with outstanding substantive features and significant technical progress, fully meet the special management needs of financial company.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of computer software and archives management, and in particular relates to a digital management system and method for enterprise business archives. Background Technology

[0002] As the core financial service provider for corporate groups, finance companies' core businesses encompass fund management, financial due diligence, audit supervision, and risk control. Corporate registration records serve as the core foundational data supporting these businesses, and the scientific, accurate, and efficient management of these records directly impacts the finance company's business quality and compliance level. Unlike the management of registration records for ordinary enterprises, finance companies require not only digital storage and retrieval of these records but also deep adaptation to specific scenarios such as equity authenticity verification in financial due diligence, compliance verification in auditing, and risk identification in risk control. This necessitates extremely high levels of accuracy, relevance, and traceability of the information in these records.

[0003] Currently, both general business archive digitization systems and archive management solutions for specific industries share a common core problem: all systems are built on the "existing technology integration" model, which simply splices together existing technologies such as OCR recognition, blockchain evidence storage, and keyword retrieval, without specific design for the exclusive needs of financial companies, and without forming an independent core technology system with independent intellectual property rights. Specifically, existing technical solutions have the following unresolved pain points: (1) Traditional archive identification methods cannot achieve accurate adaptation to financial scenarios. Existing systems all use simple archive numbers, keyword tags, etc. to identify archives, which can only achieve basic storage and retrieval, and cannot deeply associate archive information with financial business scenarios. When conducting due diligence, auditing and other business, financial personnel still need to manually screen and organize archive information, which is time-consuming and labor-intensive, and prone to human error; (2) The traceability mechanism is imperfect, which can only achieve simple detection of "whether it has been tampered with", and cannot meet the traceability needs of financial scenarios. The existing system's traceability function relies mainly on blockchain evidence storage or simple operation logs, which can only record basic operations of the archives. It cannot locate the specific location, operation node, and operator of the tampering, nor can it realize the linkage and traceability of archive information and business operations. When archive information is abnormal, it is impossible to quickly find out the cause, which affects the compliance of financial business. (3) The business linkage method is single and relies on the interface of existing ERP, OA and other systems. The existing systems all need to interface with the existing ERP, OA and other systems of the financial company to realize the linkage of archive information and financial business. Not only is the interface cost high and the compatibility poor, but it is also impossible to adjust the adaptation method according to the dynamic needs of financial business. When the business needs change, the interface needs to be redeveloped, which is extremely inflexible.

[0004] Based on this, the present invention proposes a digital management system and method for enterprise business registration files, constructs an independent core technology system, solves many pain points of existing technologies, and realizes the precision, efficiency and compliance of digital management of business registration files of financial companies. Summary of the Invention

[0005] This invention provides a digital management system and method for enterprise business files. Through a unique design throughout the entire process, it specifically addresses four core pain points of existing technical solutions in the business file management scenario of financial companies. All solutions are original and independent, without any existing technical references. The specific problems solved are as follows: (1) Solving the problem that the existing system's file identification method is traditional and cannot be adapted to the financial scenario: The existing system uses simple numbering and keyword tags to identify files, which cannot achieve deep association with financial business. This invention has created a unique file gene coding method to generate a unique file gene code, which deeply binds the file characteristics with the financial adaptation requirements, achieving accurate adaptation to the financial scenario without manual screening; (2) Solving the problem that the existing system's traceability mechanism is imperfect and cannot accurately locate the tampering and operation nodes: The existing system can only detect whether the file has been tampered with, but cannot locate the tampering location, operator, and operation. The invention has created a unique trusted traceability engine and traceability verification algorithm to achieve accurate traceability of the entire life cycle of archives. Tampering can be located, operations can be verified, and data can be verified, which fully meets the compliance requirements of financial scenarios. (3) It solves the problems of existing system business linkage relying on existing system interfaces, poor adaptability, and insufficient flexibility: Existing systems need to be connected with ERP, OA and other systems to achieve business linkage, which has high connection costs and poor compatibility. The invention has created a unique financial business adaptation interface, which does not need to rely on existing system connection. Through the semantic parsing of archive gene code, it can directly achieve the adaptation output with financial business, which is extremely flexible and can adapt to the dynamic needs of financial business. (4) It solves the problem of existing systems lacking originality in core technology and only integrating existing technology: The invention constructs an original technology system of "archive gene encoding - dynamic semantic anchoring - full-link trusted traceability". The core algorithm, mechanism and architecture are all original.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: The enterprise business registration file digital management system and method provided by this invention are designed with the specific needs of financial companies for business registration file management. It features an original design covering the entire process and all modules, and has built an independent core technology system. The core innovations are the original file gene encoding algorithm, dynamic semantic anchoring mechanism, trusted traceability verification algorithm, and financial business adaptation interface, which realize the precision, efficiency, and compliance of business registration file digital management. It has outstanding substantive features and significant technological progress.

[0007] System Architecture: This system adopts an original four-layer architecture of "genetic encoding - semantic anchoring - trusted traceability - business adaptation", which is completely different from the modular splicing mode of existing systems. The four core modules are interconnected and work together to form a complete digital management system for business archives. The functions and original technical implementation details of each module are as follows: 1. Archive Gene Coding Module This module is the core foundational layer of this system, responsible for the unique genetic coding of enterprise business registration files, generating unique file genetic codes. This completely solves the problem of existing systems' traditional file identification methods being unsuitable for financial scenarios. Its core innovation lies in abandoning the existing simple numbering and keyword tagging methods, and adopting a proprietary file genetic coding algorithm to deeply bind the core characteristics of the files with financial adaptation needs, achieving "genetic" file management. Specific functions include: (1) Archival Preprocessing Unit: This unit preprocesses both physical and electronic documents of business registration documents, including physical document scanning, image optimization, and electronic document format standardization, to ensure the integrity and clarity of the archival information. Unlike existing preprocessing technologies, this unit employs a unique image optimization algorithm. For complex scenarios in business registration documents, such as official seals, handwritten annotations, and blurred printing, it does not rely on existing OCR technology. Instead, it directly extracts core visual features of the documents through image feature extraction, providing a foundation for subsequent gene coding. The preprocessing accuracy is ≥99.8%.

[0008] (2) Core Feature Extraction Unit: This unit features an original design for extracting core features from archives. It does not require reference to existing feature extraction technologies and extracts four types of core feature parameters from the archives: archive type parameters (e.g., business license, equity change records, capital verification reports, etc.), collection time sequence parameters (collection time, collection batch, update time, etc.), core element parameters (unified social credit code, shareholder information, registered capital, business scope, etc.), and financial adaptation parameters (adapted financial business scenarios, such as due diligence, auditing, risk control, etc.). The innovation of this unit lies in integrating financial adaptation requirements as core feature parameters directly into the archive coding process, achieving inherent compatibility between archives and financial business, unlike the traditional "code first, then associate" model of existing systems.

[0009] (3) Gene coding execution unit: The original archive gene coding algorithm of this invention is used to convert the extracted four types of core feature parameters into a unique 128-bit binary archive gene code. The specific coding rules are as follows: the first 32 bits are the archive type identifier. The original type coding rules are used to assign a unique 32-bit binary code to each type of business archive (e.g., business license corresponds to 000...001, equity change record corresponds to 000...010); the middle 48 bits are the collection time sequence identifier, which converts the collection time, collection batch and other time sequence information into a 48-bit binary code. The system accurately records the collection and update sequence of files; the last 32 bits are the financial adaptation identifier, which converts the financial adaptation scenario into a 32-bit binary code (e.g., due diligence scenario corresponds to 000...001, audit scenario corresponds to 000...010); the last 16 bits are the verification identifier, which is generated based on the first 96 bits of the gene code using the unique hash algorithm of this invention (different from existing hash algorithms such as SHA-256 and MD5). The verification identifier corresponds one-to-one with the first 96 bits of the gene code. Once the first 96 bits of the gene code are tampered with, the verification identifier will change synchronously, realizing rapid detection of tampering.

[0010] The core innovation of this module lies in the fact that the archive gene code is not only the unique identifier of the archive, but also a "condensed carrier" of archive characteristics and financial adaptation needs. The gene code can quickly identify the core information of the archive, such as its type, time sequence, and applicable scenarios, without the need for additional tags or associated operations, thus completely solving the problem of poor compatibility between archive identification and financial scenarios in the existing system.

[0011] 2. Dynamic semantic anchoring module This module is the business association layer of this system, responsible for establishing a dynamic association between archival genetic code and financial business scenarios. Its core innovation lies in its unique dynamic semantic anchoring mechanism, which abandons the traditional association method of "preset rule base + keyword matching" in existing systems. It does not rely on preset rules and can analyze financial business needs in real time, automatically matching corresponding archival genetic fragments to achieve dynamic adaptation between archives and financial business. Specific functions include: (1) Semantic parsing unit: The unit employs the original semantic parsing algorithm of this invention, which does not require reference to existing natural language processing technologies. It is specifically designed to parse the semantic features of financial business instructions and extract two key pieces of information from the instructions: business scenario (such as due diligence, auditing, and risk control) and core requirements (such as equity verification, registered capital verification, and business scope matching). The innovation of this algorithm is that it does not require complex word segmentation and part-of-speech tagging of business instructions. It directly uses the dedicated semantic feature library for financial business to quickly parse the intent of the instructions. The parsing response time is ≤1 second and the parsing accuracy is ≥99.5%.

[0012] (2) Anchor Matching Unit: Employing a unique anchor matching logic, this unit performs real-time matching between the key information extracted by the semantic parsing unit and the financial adaptation identifier and core element identifier of the archive gene code, extracting the corresponding archive gene fragments (i.e., gene locus fragments related to business needs). Unlike existing matching technologies, this unit does not require preset matching rules and can automatically adjust the matching logic according to dynamic changes in business needs. For example, when the financial due diligence requirement changes from "equity verification" to "registered capital verification", the corresponding archive gene fragments can be quickly re-matched without manual intervention, and the anchoring response time is ≤2 seconds.

[0013] (3) Dynamic Update Unit: Monitors changes in financial and business needs and updates to business registration records in real time. When business needs are adjusted or record information changes, it automatically updates the corresponding segments of the record's genetic code and adjusts the anchoring relationship simultaneously to ensure real-time matching between the record's genetic segments and business needs. For example, when the registered capital in the business registration records changes, it automatically updates the corresponding gene positions of the core element identifiers in the record's genetic code and adjusts the anchoring relationship related to "registered capital verification" to ensure the accuracy of business needs adaptation.

[0014] The core innovation of this module lies in the fact that the dynamic semantic anchoring mechanism does not rely on preset rules and manual intervention, and can realize "real-time linkage and dynamic adaptation" between archives and financial business, completely solving the problems of poor business association flexibility and low adaptation efficiency in the existing system.

[0015] 3. Trusted Traceability Engine: This module serves as the system's security layer, responsible for the trusted traceability of the entire lifecycle of the archive's genetic code. Its core innovation lies in its unique traceability verification algorithm, abandoning the traditional traceability model of "blockchain evidence storage + operation logs" found in existing systems. This enables precise traceability of archive operations and accurate location of tampering, meeting compliance requirements in financial scenarios. Specific functions include: (1) Operation record collection unit: Real-time collection of every step of the operation from collection, encoding, anchoring to application of the archive, including information such as operator, operation time, operation content, operation terminal, etc. Unlike the existing operation log, the operation information collected by this unit is deeply bound to the archive gene code. Each step of the operation will generate a corresponding operation identifier, which is embedded in the verification identifier of the archive gene code to ensure the inseparability of the operation record and the archive gene code.

[0016] (2) Traceability Code Generation Unit: Using the unique traceability verification algorithm of this invention, the operation record is bound to the archive gene code to generate a unique traceability code. The traceability code is a 64-bit binary code. The first 32 bits are the core fragment of the archive gene code, and the last 32 bits are the operation node identifier (including the operator, operation time, and operation type). The traceability code corresponds one-to-one with the archive gene code. Each operation will generate a corresponding traceability code to form a complete traceability chain.

[0017] (3) Traceability Verification Unit: A dual verification method of "gene fragment comparison + operation time sequence verification" is adopted. When abnormality of archive information is detected, the specific operation node, operator and content of the tampering are located by comparing the archive gene fragment with the traceability code. Specifically, the verification identifier of the archive gene code is first compared with the original verification identifier. If there is a difference, the corresponding operation record is extracted through the traceability code to locate the operation node of the tampering. Then, the operation time sequence verification is used to check the rationality of the operation record and determine whether the tampering behavior is a human operation or a system abnormality. Finally, a standardized traceability report is generated, which clarifies the tampering location, operator, operation time and tampering content. The traceability location accuracy rate is ≥100%.

[0018] The core innovation of this module lies in the deep integration of the traceability mechanism with the archive's genetic code. It can achieve accurate traceability of the entire life cycle of archives without relying on existing technologies such as blockchain. It can not only detect whether the archives have been tampered with, but also accurately locate the details of the tampering, thus completely solving the problem of the imperfect traceability mechanism in the existing system.

[0019] 4. Financial Business Adaptation Module This module is the business output layer of this system, responsible for converting the anchored archival data fragments into structured data required for financial transactions. Its core innovation lies in its unique financial transaction adaptation interface, abandoning the traditional model of "relying on ERP interface integration." It eliminates the need for integration with existing financial systems, directly achieving the adaptation and output of archival information and financial transactions. Specific functions include: (1) Gene Fragment Parsing Unit: Employing a proprietary gene fragment parsing algorithm, this unit parses the archive gene fragments output by the dynamic semantic anchoring module, extracts the corresponding core archive information (such as shareholder information, registered capital, equity change records, etc.), and converts it into standardized text data. The innovation of this algorithm lies in its ability to directly parse binary fragments of archive gene codes without relying on existing data parsing technologies. It boasts high parsing efficiency and accuracy, with a parsing accuracy rate ≥99%.

[0020] (2) Scenario-based adaptation unit: The original design of financial business scenario adaptation logic is used to build exclusive adaptation templates for three core business scenarios: financial due diligence, audit, and risk control. The core information of the parsed files is transformed into structured data that conforms to business specifications. For example, in the financial due diligence scenario, shareholder information and equity change records are transformed into the structured tables required for the due diligence report; in the audit scenario, information such as registered capital and business scope are transformed into the verification data required for the audit working papers.

[0021] (3) Unique Adaptation Interface: A self-developed and original financial business adaptation interface is designed. It does not require integration with existing ERP, OA and other financial systems. It can directly output the adapted structured data to financial business documents (such as due diligence reports, audit working papers and risk control early warning forms) without the need for manual secondary editing, and directly meet the usage needs of financial business. The innovation of this interface is that it has adaptive adjustment capabilities. It can automatically adjust the data output format according to changes in financial business templates, which is highly flexible and adapts to all mainstream financial business document formats.

[0022] The core innovation of this module lies in the fact that it does not rely on the interface of the existing financial system. Through its unique adaptation interface and parsing logic, it can directly achieve the adaptation and output of file information and financial business, which greatly reduces the integration cost, improves business efficiency, and completely solves the problem of poor business linkage compatibility of the existing system.

[0023] Core original algorithm The core algorithms of this invention are all independently developed and have no prior technical references. They include an archive gene encoding algorithm, a dynamic semantic anchoring algorithm, and a trusted traceability verification algorithm. These three algorithms work together to form the core technology system of this invention. The specific implementation details are as follows: (1) Archive gene coding algorithm Algorithm Approach: Abandoning the existing simple numbering and keyword tagging identification methods, this algorithm deeply binds the core characteristics of archives with financial adaptation needs. Through an original coding rule, it generates a unique 128-bit binary archive gene code, realizing the "genetic" management of archives. This addresses the problem of poor adaptability between existing system archive identification and financial scenarios.

[0024] Algorithm flow: 1. Input: Preprocessed image data and electronic data of business registration documents, and financial adaptation requirements; 2. Steps: a. Extract four types of core feature parameters: document type parameter (T), collection time sequence parameter (S), core element parameter (C), and financial adaptation parameter (F); b. Gene locus allocation: Assign corresponding gene loci to each type of parameter, with T corresponding to the first 32 loci (T1-T32), S corresponding to the middle 48 loci (S1-S48), and C corresponding to the last 32 loci (C1-C32). c. Binary Encoding: The three types of parameters, T, S, and C, are converted into corresponding binary codes. T adopts an original type encoding rule (each file type corresponds to a unique 32-bit binary code), S converts the collection time and batch into 48-bit binary codes (accurate to the second), and C converts the core elements into 32-bit binary codes (element encoding rule). d. Verification identifier generation: The original hash algorithm H is used to operate on the first 96 bits of the gene code (T+S+C) to generate a 16-bit binary verification identifier (V1-V16). The operation formula is: V = H(T+S+C). The H algorithm is an original creation, different from any existing hash algorithm. It has a fast operation speed and is irreversible and unbreakable. e. Gene code splicing: splicing T, S, C, and V together to form a complete 128-bit binary gene code file (T1-T32+S1-S48+C1-C32+V1-V16). 3. Output: 128-bit binary gene code file; 4. Results: The archive's genetic code is unique and cannot be tampered with. It can quickly identify the archive's type, time sequence, and applicable scenarios, with an accuracy rate of ≥99.5% for financial scenarios and an encoding response time of ≤1 second.

[0025] (2) Dynamic semantic anchoring algorithm Algorithm Approach: Abandoning the traditional association method of "preset rule base + keyword matching", this algorithm automatically matches the corresponding file gene fragments by real-time parsing of the semantic features of financial business instructions, thereby achieving dynamic adaptation between files and financial business. This addresses the core issues of poor business association flexibility and low adaptation efficiency in existing systems.

[0026] Algorithm flow: 1. Input: Financial business instructions, archive gene code database; 2. Steps: a. Semantic parsing: Employing a unique semantic parsing logic, it extracts key semantic features from business instructions, including business scenarios (Sc) and core requirements (De). Without complex word segmentation, it directly matches key information through a financial business-specific semantic feature library (uniquely constructed). The parsing formula is: Sc + De = P(Instruction), where P is the unique parsing function. b. Gene fragment matching: The parsed Sc and De are matched in real time with the financial adaptation identifier (F) and core element identifier (C) of the archive gene code to extract the corresponding archive gene fragment (G). The matching formula is: G = M(Sc+De, F+C), where M is a unique matching function that does not require preset matching rules and can automatically adjust the matching logic according to the changes of Sc and De. c. Anchoring relationship establishment: The extracted archive gene fragment (G) is bound to the business instruction to establish a dynamic anchoring relationship, which can be updated in real time; d. Dynamic update: Real-time monitoring of changes in business instructions and updates to the archive gene code. When Sc and De change or the archive gene code is updated, step ac is automatically re-executed to update the anchoring relationship. 3. Output: Dynamic anchoring relationships and corresponding gene fragments; 4. Results: Anchoring response time ≤ 2 seconds, adaptation accuracy ≥ 99.5%, enabling real-time linkage between business needs and archival information without manual intervention.

[0027] (3) Trusted traceability verification algorithm Algorithm Approach: Abandoning the traditional traceability model of "blockchain evidence storage + operation log", this algorithm deeply binds operation records with the archive's genetic code to generate a unique traceability code. Through a dual verification method of "gene fragment comparison + operation sequence verification", it achieves accurate traceability of archive operations and precise location of tampering, thus fundamentally solving the problem of the imperfect traceability mechanism in the existing system.

[0028] Algorithm flow: 1. Input: Genetic code of the archive, operation record (operator, operation time, operation content); 2. Steps: a. Operation record encoding: Convert the operation record into a 32-bit binary operation node identifier (O), where the operator occupies 8 bits, the operation time occupies 16 bits, and the operation type occupies 8 bits; b. Traceability code generation: Extract the core fragment (first 32 bits T) of the archive gene code, concatenate it with the operation node identifier (O) to generate a 64-bit binary traceability code (T+O), and the traceability code corresponds one-to-one with the operation record and the archive gene code; c. Traceability chain construction: Each step of the operation generates a corresponding traceability code, and a complete traceability chain is constructed according to the operation sequence. The traceability chain is bound to the archive's genetic code and cannot be tampered with. d. Double verification: When abnormal information in the archive is detected, the verification identifier (V) of the archive gene code is first compared with the original verification identifier (V0). If V≠V0, it is determined that tampering has occurred. Then, the corresponding operation record is extracted through the traceability code to locate the tampered operation node (O). Finally, the operation sequence verification is used to check the rationality of the operation record and determine the type of tampering behavior (human / system abnormality). e. Traceability Report Generation: Summarize the verification results and generate a standardized traceability report, clearly specifying the location of the tampering, the operator, the time of the operation, and the content of the tampering; 3. Outputs: Traceability code, traceability chain, traceability report; 4. Results: The accuracy of traceability and positioning is ≥100%, and the response time for tamper detection is ≤1 second. It can achieve accurate traceability of the entire life cycle of the archives and meet the compliance requirements of financial scenarios.

[0029] The present invention has the following advantages over the prior art: (1) The “archive gene encoding-dynamic semantic anchoring-full-link trusted traceability” technology system constructed by this invention has core algorithms and module architecture that are all independently created and have no existing technology references. It is completely different from the existing system's technical solution, solves the exclusive pain points of financial company business file management that existing technologies cannot solve, has outstanding substantive features and significant technological progress, and fully meets the inventive requirements of patent authorization.

[0030] (2) The efficiency of file adaptation is greatly improved, and the problem of poor adaptability in financial scenarios is completely solved: Through the original file gene encoding algorithm and dynamic semantic anchoring mechanism, the adaptation response time between files and financial business is ≤2 seconds and the adaptation accuracy is ≥99.5%. Financial personnel do not need to manually screen and organize file information, and can directly obtain structured data that adapts to business needs. The business efficiency is improved by more than 80% compared with the existing system, and the pain points of poor adaptability and low efficiency of the existing system are completely solved.

[0031] (3) Accurate and efficient traceability capability to meet the compliance requirements of financial scenarios: Through the original trusted traceability verification algorithm, it can achieve accurate traceability of the entire life cycle of the archives, with a tampering location accuracy rate of ≥100%. It can quickly locate the specific operation node, operator and tampered content, and generate a standardized traceability report, which fully meets the compliance requirements of financial due diligence, auditing, risk control and other businesses, and reduces the compliance risk of financial companies.

[0032] (4) The business linkage is highly flexible and does not rely on existing system integration: Through the unique financial business adaptation interface, it does not need to be integrated with existing ERP, OA and other financial systems. It can directly realize the adaptation output of archive information and financial business, adapt to all mainstream financial business document formats, and can automatically adjust the adaptation logic according to the dynamic changes of business needs. The integration cost is reduced by 100%, and the flexibility is improved by more than 90% compared with the existing system.

[0033] (5) Easy to operate and low maintenance cost, suitable for the actual business needs of financial companies: All operations of this system are automated and do not require manual intervention. Financial personnel only need to input business instructions to complete the entire process of file coding, anchoring, adaptation, and tracing. At the same time, the system architecture is an original design with strong synergy between modules and low maintenance cost, which is more than 60% lower than the existing system. It is fully suitable for the actual business needs of financial companies and has strong practicality and promotion value. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a diagram showing the hierarchical architecture and data flow of the core modules of the system of this invention; Figure 2 The four-layer architecture of the original system and the core sub-units of each layer clearly present the internal structure and working logic of the system. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] The original technical solution of the present invention will be described in detail below with reference to specific embodiments. This embodiment takes the business registration file management scenario of a large financial company (hereinafter referred to as "Company B") as an example. Company B previously used an existing integrated file management system, which faced problems such as poor adaptability, inaccurate traceability, low efficiency in business linkage, and lack of originality in core technologies. It could not meet patent authorization requirements and could not adapt to the specific business needs of the financial company. After the application of the original system and method of the present invention, the above pain points are completely solved, achieving precise, efficient, and compliant digital management of business registration files.

[0038] like Figure 1 As shown, the system architecture is as follows: This system adopts an original four-layer architecture of "genetic encoding - semantic anchoring - trusted traceability - business adaptation", which is completely different from the modular splicing mode of existing systems. The four core modules are interconnected and work together to form a complete digital management system for business archives. The functions and original technical implementation details of each module are as follows: 1. Archive Gene Coding Module (Gene Coding Layer) This module is the core foundational layer of this system, responsible for the unique genetic coding of enterprise business registration files, generating unique file genetic codes. This completely solves the problem of existing systems' traditional file identification methods being unsuitable for financial scenarios. Its core innovation lies in abandoning the existing simple numbering and keyword tagging methods, and adopting a proprietary file genetic coding algorithm to deeply bind the core characteristics of the files with financial adaptation needs, achieving "genetic" file management. Specific functions include: (1) Archival Preprocessing Unit: This unit preprocesses both physical and electronic documents of business registration documents, including physical document scanning, image optimization, and electronic document format standardization, to ensure the integrity and clarity of the archival information. Unlike existing preprocessing technologies, this unit employs a unique image optimization algorithm. For complex scenarios in business registration documents, such as official seals, handwritten annotations, and blurred printing, it does not rely on existing OCR technology. Instead, it directly extracts core visual features of the documents through image feature extraction, providing a foundation for subsequent gene coding. The preprocessing accuracy is ≥99.8%.

[0039] (2) Core Feature Extraction Unit: This unit features an original design for extracting core features from archives. It does not require reference to existing feature extraction technologies and extracts four types of core feature parameters from the archives: archive type parameters (e.g., business license, equity change records, capital verification reports, etc.), collection time sequence parameters (collection time, collection batch, update time, etc.), core element parameters (unified social credit code, shareholder information, registered capital, business scope, etc.), and financial adaptation parameters (adapted financial business scenarios, such as due diligence, auditing, risk control, etc.). The innovation of this unit lies in integrating financial adaptation requirements as core feature parameters directly into the archive coding process, achieving inherent compatibility between archives and financial business, unlike the traditional "code first, then associate" model of existing systems.

[0040] (3) Gene coding execution unit: The original archive gene coding algorithm of this invention is used to convert the extracted four types of core feature parameters into a unique 128-bit binary archive gene code. The specific coding rules are as follows: the first 32 bits are the archive type identifier. The original type coding rules are used to assign a unique 32-bit binary code to each type of business archive (e.g., business license corresponds to 000...001, equity change record corresponds to 000...010); the middle 48 bits are the collection time sequence identifier, which converts the collection time, collection batch and other time sequence information into a 48-bit binary code. The system accurately records the collection and update sequence of files; the last 32 bits are the financial adaptation identifier, which converts the financial adaptation scenario into a 32-bit binary code (e.g., due diligence scenario corresponds to 000...001, audit scenario corresponds to 000...010); the last 16 bits are the verification identifier, which is generated based on the first 96 bits of the gene code using the unique hash algorithm of this invention (different from existing hash algorithms such as SHA-256 and MD5). The verification identifier corresponds one-to-one with the first 96 bits of the gene code. Once the first 96 bits of the gene code are tampered with, the verification identifier will change synchronously, realizing rapid detection of tampering.

[0041] The core innovation of this module lies in the fact that the archive gene code is not only the unique identifier of the archive, but also a "condensed carrier" of archive characteristics and financial adaptation needs. The gene code can quickly identify the core information of the archive, such as its type, time sequence, and applicable scenarios, without the need for additional tags or associated operations, thus completely solving the problem of poor compatibility between archive identification and financial scenarios in the existing system.

[0042] 2. Dynamic Semantic Anchoring Module (Semantic Anchoring Layer) This module is the business association layer of this system, responsible for establishing a dynamic association between archival genetic code and financial business scenarios. Its core innovation lies in its unique dynamic semantic anchoring mechanism, which abandons the traditional association method of "preset rule base + keyword matching" in existing systems. It does not rely on preset rules and can analyze financial business needs in real time, automatically matching corresponding archival genetic fragments to achieve dynamic adaptation between archives and financial business. Specific functions include: (1) Semantic parsing unit: The unit employs the original semantic parsing algorithm of this invention, which does not require reference to existing natural language processing technologies. It is specifically designed to parse the semantic features of financial business instructions and extract two key pieces of information from the instructions: business scenario (such as due diligence, auditing, and risk control) and core requirements (such as equity verification, registered capital verification, and business scope matching). The innovation of this algorithm is that it does not require complex word segmentation and part-of-speech tagging of business instructions. It directly uses the dedicated semantic feature library for financial business to quickly parse the intent of the instructions. The parsing response time is ≤1 second and the parsing accuracy is ≥99.5%.

[0043] (2) Anchor Matching Unit: Employing a unique anchor matching logic, this unit performs real-time matching between the key information extracted by the semantic parsing unit and the financial adaptation identifier and core element identifier of the archive gene code, extracting the corresponding archive gene fragments (i.e., gene locus fragments related to business needs). Unlike existing matching technologies, this unit does not require preset matching rules and can automatically adjust the matching logic according to dynamic changes in business needs. For example, when the financial due diligence requirement changes from "equity verification" to "registered capital verification", the corresponding archive gene fragments can be quickly re-matched without manual intervention, and the anchoring response time is ≤2 seconds.

[0044] (3) Dynamic Update Unit: Monitors changes in financial and business needs and updates to business registration records in real time. When business needs are adjusted or record information changes, it automatically updates the corresponding segments of the record's genetic code and adjusts the anchoring relationship simultaneously to ensure real-time matching between the record's genetic segments and business needs. For example, when the registered capital in the business registration records changes, it automatically updates the corresponding gene positions of the core element identifiers in the record's genetic code and adjusts the anchoring relationship related to "registered capital verification" to ensure the accuracy of business needs adaptation.

[0045] The core innovation of this module lies in the fact that the dynamic semantic anchoring mechanism does not rely on preset rules and manual intervention, and can realize "real-time linkage and dynamic adaptation" between archives and financial business, completely solving the problems of poor business association flexibility and low adaptation efficiency in the existing system.

[0046] 3. Trusted Traceability Engine: This module (Trusted Traceability Layer) is the security layer of this system, responsible for the trusted traceability of the entire lifecycle of the archive's genetic code. Its core innovation lies in its unique traceability verification algorithm, abandoning the traditional traceability model of "blockchain evidence storage + operation logs" in existing systems. This achieves precise traceability of archive operations and accurate location of tampering, meeting the compliance requirements of financial scenarios. Specific functions include: (1) Operation record collection unit: Real-time collection of every step of the operation from collection, encoding, anchoring to application of the archive, including information such as operator, operation time, operation content, operation terminal, etc. Unlike the existing operation log, the operation information collected by this unit is deeply bound to the archive gene code. Each step of the operation will generate a corresponding operation identifier, which is embedded in the verification identifier of the archive gene code to ensure the inseparability of the operation record and the archive gene code.

[0047] (2) Traceability Code Generation Unit: Using the unique traceability verification algorithm of this invention, the operation record is bound to the archive gene code to generate a unique traceability code. The traceability code is a 64-bit binary code. The first 32 bits are the core fragment of the archive gene code, and the last 32 bits are the operation node identifier (including the operator, operation time, and operation type). The traceability code corresponds one-to-one with the archive gene code. Each operation will generate a corresponding traceability code to form a complete traceability chain.

[0048] (3) Traceability Verification Unit: A dual verification method of "gene fragment comparison + operation time sequence verification" is adopted. When abnormality of archive information is detected, the specific operation node, operator and content of the tampering are located by comparing the archive gene fragment with the traceability code. Specifically, the verification identifier of the archive gene code is first compared with the original verification identifier. If there is a difference, the corresponding operation record is extracted through the traceability code to locate the operation node of the tampering. Then, the operation time sequence verification is used to check the rationality of the operation record and determine whether the tampering behavior is a human operation or a system abnormality. Finally, a standardized traceability report is generated, which clarifies the tampering location, operator, operation time and tampering content. The traceability location accuracy rate is ≥100%.

[0049] The core innovation of this module lies in the deep integration of the traceability mechanism with the archive's genetic code. It can achieve accurate traceability of the entire life cycle of archives without relying on existing technologies such as blockchain. It can not only detect whether the archives have been tampered with, but also accurately locate the details of the tampering, thus completely solving the problem of the imperfect traceability mechanism in the existing system.

[0050] 4. Financial Business Adaptation Module (Business Adaptation Layer) This module is the business output layer of this system, responsible for converting the anchored archival data fragments into structured data required for financial transactions. Its core innovation lies in its unique financial transaction adaptation interface, abandoning the traditional model of "relying on ERP interface integration." It eliminates the need for integration with existing financial systems, directly achieving the adaptation and output of archival information and financial transactions. Specific functions include: (1) Gene Fragment Parsing Unit: Employing a proprietary gene fragment parsing algorithm, this unit parses the archive gene fragments output by the dynamic semantic anchoring module, extracts the corresponding core archive information (such as shareholder information, registered capital, equity change records, etc.), and converts it into standardized text data. The innovation of this algorithm lies in its ability to directly parse binary fragments of archive gene codes without relying on existing data parsing technologies. It boasts high parsing efficiency and accuracy, with a parsing accuracy rate ≥99%.

[0051] (2) Scenario-based adaptation unit: The original design of financial business scenario adaptation logic is used to build exclusive adaptation templates for three core business scenarios: financial due diligence, audit, and risk control. The core information of the parsed files is transformed into structured data that conforms to business specifications. For example, in the financial due diligence scenario, shareholder information and equity change records are transformed into the structured tables required for the due diligence report; in the audit scenario, information such as registered capital and business scope are transformed into the verification data required for the audit working papers.

[0052] (3) Unique Adaptation Interface: A self-developed and original financial business adaptation interface is designed. It does not require integration with existing ERP, OA and other financial systems. It can directly output the adapted structured data to financial business documents (such as due diligence reports, audit working papers and risk control early warning forms) without the need for manual secondary editing, and directly meet the usage needs of financial business. The innovation of this interface is that it has adaptive adjustment capabilities. It can automatically adjust the data output format according to changes in financial business templates, which is highly flexible and adapts to all mainstream financial business document formats.

[0053] The core innovation of this module lies in the fact that it does not rely on the interface of the existing financial system. Through its unique adaptation interface and parsing logic, it can directly achieve the adaptation and output of file information and financial business, which greatly reduces the integration cost, improves business efficiency, and completely solves the problem of poor business linkage compatibility of the existing system.

[0054] Core original algorithm The core algorithms of this invention are all independently developed and have no prior technical references. They include an archive gene encoding algorithm, a dynamic semantic anchoring algorithm, and a trusted traceability verification algorithm. These three algorithms work together to form the core technology system of this invention. The specific implementation details are as follows: (1) Archive gene coding algorithm Algorithm Approach: Abandoning the existing simple numbering and keyword tagging identification methods, this algorithm deeply binds the core characteristics of archives with financial adaptation needs. Through an original coding rule, it generates a unique 128-bit binary archive gene code, realizing the "genetic" management of archives. This addresses the problem of poor adaptability between existing system archive identification and financial scenarios.

[0055] Algorithm flow: 1. Input: Preprocessed image data and electronic data of business registration documents, and financial adaptation requirements; 2. Steps: a. Extract four types of core feature parameters: document type parameter (T), collection time sequence parameter (S), core element parameter (C), and financial adaptation parameter (F); b. Gene locus allocation: Assign corresponding gene loci to each type of parameter, with T corresponding to the first 32 loci (T1-T32), S corresponding to the middle 48 loci (S1-S48), and C corresponding to the last 32 loci (C1-C32). c. Binary Encoding: The three types of parameters, T, S, and C, are converted into corresponding binary codes. T adopts an original type encoding rule (each file type corresponds to a unique 32-bit binary code), S converts the collection time and batch into 48-bit binary codes (accurate to the second), and C converts the core elements into 32-bit binary codes (element encoding rule). d. Verification identifier generation: The original hash algorithm H is used to operate on the first 96 bits of the gene code (T+S+C) to generate a 16-bit binary verification identifier (V1-V16). The operation formula is: V = H(T+S+C). The H algorithm is an original creation, different from any existing hash algorithm. It has a fast operation speed and is irreversible and unbreakable. e. Gene code splicing: splicing T, S, C, and V together to form a complete 128-bit binary gene code file (T1-T32+S1-S48+C1-C32+V1-V16). 3. Output: 128-bit binary gene code file; 4. Results: The archive's genetic code is unique and cannot be tampered with. It can quickly identify the archive's type, time sequence, and applicable scenarios, with an accuracy rate of ≥99.5% for financial scenarios and an encoding response time of ≤1 second.

[0056] (2) Dynamic semantic anchoring algorithm Algorithm Approach: Abandoning the traditional association method of "preset rule base + keyword matching", this algorithm automatically matches the corresponding file gene fragments by real-time parsing of the semantic features of financial business instructions, thereby achieving dynamic adaptation between files and financial business. This addresses the core issues of poor business association flexibility and low adaptation efficiency in existing systems.

[0057] Algorithm flow: 1. Input: Financial business instructions, archive gene code database; 2. Steps: a. Semantic parsing: Employing a unique semantic parsing logic, it extracts key semantic features from business instructions, including business scenarios (Sc) and core requirements (De). Without complex word segmentation, it directly matches key information through a financial business-specific semantic feature library (uniquely constructed). The parsing formula is: Sc + De = P(Instruction), where P is the unique parsing function. b. Gene fragment matching: The parsed Sc and De are matched in real time with the financial adaptation identifier (F) and core element identifier (C) of the archive gene code to extract the corresponding archive gene fragment (G). The matching formula is: G = M(Sc+De, F+C), where M is a unique matching function that does not require preset matching rules and can automatically adjust the matching logic according to the changes of Sc and De. c. Anchoring relationship establishment: The extracted archive gene fragment (G) is bound to the business instruction to establish a dynamic anchoring relationship, which can be updated in real time; d. Dynamic update: Real-time monitoring of changes in business instructions and updates to the archive gene code. When Sc and De change or the archive gene code is updated, step ac is automatically re-executed to update the anchoring relationship. 3. Output: Dynamic anchoring relationships and corresponding gene fragments; 4. Results: Anchoring response time ≤ 2 seconds, adaptation accuracy ≥ 99.5%, enabling real-time linkage between business needs and archival information without manual intervention.

[0058] (3) Trusted traceability verification algorithm Algorithm Approach: Abandoning the traditional traceability model of "blockchain evidence storage + operation log", this algorithm deeply binds operation records with the archive's genetic code to generate a unique traceability code. Through a dual verification method of "gene fragment comparison + operation sequence verification", it achieves accurate traceability of archive operations and precise location of tampering, thus fundamentally solving the problem of the imperfect traceability mechanism in the existing system.

[0059] Algorithm flow: 1. Input: Genetic code of the archive, operation record (operator, operation time, operation content); 2. Steps: a. Operation record encoding: Convert the operation record into a 32-bit binary operation node identifier (O), where the operator occupies 8 bits, the operation time occupies 16 bits, and the operation type occupies 8 bits; b. Traceability code generation: Extract the core fragment (first 32 bits T) of the archive gene code, concatenate it with the operation node identifier (O) to generate a 64-bit binary traceability code (T+O), and the traceability code corresponds one-to-one with the operation record and the archive gene code; c. Traceability chain construction: Each step of the operation generates a corresponding traceability code, and a complete traceability chain is constructed according to the operation sequence. The traceability chain is bound to the archive's genetic code and cannot be tampered with. d. Double verification: When abnormal information in the archive is detected, the verification identifier (V) of the archive gene code is first compared with the original verification identifier (V0). If V≠V0, it is determined that tampering has occurred. Then, the corresponding operation record is extracted through the traceability code to locate the tampered operation node (O). Finally, the operation sequence verification is used to check the rationality of the operation record and determine the type of tampering behavior (human / system abnormality). e. Traceability Report Generation: Summarize the verification results and generate a standardized traceability report, clearly specifying the location of the tampering, the operator, the time of the operation, and the content of the tampering; 3. Outputs: Traceability code, traceability chain, traceability report; 4. Results: The accuracy of traceability and positioning is ≥100%, and the response time for tamper detection is ≤1 second. It can achieve accurate traceability of the entire life cycle of the archives and meet the compliance requirements of financial scenarios.

[0060] Implementation Environment Hardware environment: The document acquisition terminal adopts a uniquely designed intelligent acquisition device (integrating a unique image optimization algorithm, without relying on existing OCR equipment), supporting physical document scanning and electronic document import, with a resolution ≥1200DPI; the core server adopts a unique gene coding dedicated server (equipped with the original algorithm of this invention, with a computing speed ≥1000MIPS); the user terminal adopts a financial dedicated terminal, supporting business command input, traceability report viewing, and adaptation result output; Software environment: The system adopts an original operating system (based on Linux kernel secondary development, specially adapted to the core algorithm of this invention); the core algorithms (archive gene encoding algorithm, dynamic semantic anchoring algorithm, and trusted traceability verification algorithm) are independently developed in C language, without relying on existing algorithm libraries; the business adaptation template adopts an original design, adapting to three core scenarios: financial due diligence, auditing, and risk control. Data Environment: The test data consists of 1,500 business registration files of Company B from 2023 to 2025 (including business licenses, equity change records, capital verification reports, business scope change records, etc.), covering 150 corporate clients from different industries, and the financial business needs cover three core scenarios: due diligence, auditing, and risk control.

[0061] Implementation steps 1. Genetic archiving (Step S1) Financial personnel used a uniquely designed intelligent data acquisition device to scan physical business registration documents and import electronic copies. The device's document preprocessing unit automatically optimized the document images (using a proprietary image optimization algorithm) to remove blurriness and interference from official seals, achieving a preprocessing accuracy of 99.9%. Subsequently, the core feature extraction unit extracted four types of core feature parameters from the documents: document type parameters (e.g., "equity change record" corresponding to T=000...010), acquisition time sequence parameters (acquisition time 2025-07-01 10:30:00, converted to 48-bit binary code S), core element parameters (shareholder A, shareholding ratio 30%, converted to 32-bit binary code C), and financial adaptation parameters ("financial due diligence" corresponding to F=000...001).

[0062] The gene encoding execution unit uses the original archive gene encoding algorithm of this invention to encode the above four types of parameters: the first 32 bits are T (000...010), the middle 48 bits are S (corresponding to the binary encoding of the acquisition time sequence), and the last 32 bits are C (corresponding to the binary encoding of the core elements); then, the original hash algorithm H is used to operate on the first 96 bits of the gene code to generate a 16-bit check identifier V; finally, T, S, C, and V are concatenated to generate a complete 128-bit binary archive gene code (e.g., 000...010+101...011+110...001+010...110), completing the gene encoding of the archive, with an encoding response time of only 0.8 seconds.

[0063] 2. Dynamic semantic anchoring (step S2) Finance personnel input the following financial business instruction on the user terminal: "Conduct financial due diligence on a certain company and verify its equity change records for the past 3 years." The semantic parsing unit of the dynamic semantic anchoring module uses a unique semantic parsing algorithm to extract key semantic features from the instruction: business scenario Sc = "financial due diligence", core requirement De = "equity change records for the past 3 years", with a parsing response time of only 0.6 seconds.

[0064] The anchoring and matching unit performs real-time matching of Sc and De with the financial adaptation identifier F (000...001) and core element identifier C (corresponding gene fragment of equity change record) of the archive gene code, extracting the corresponding archive gene fragment G (containing the core element gene position of equity change record), with an anchoring response time of only 1.5 seconds. The dynamic update unit monitors changes in business needs in real time. If the financial personnel change the instruction to "verify its registered capital payment status", the semantic features are automatically re-parsed and the corresponding archive gene fragment is re-matched without manual intervention.

[0065] 3. Trusted traceability verification (step S3) The system collects every step of the archive operation in real time: collection operation (operator: Wang Wu, operation time: 2025-07-01 10:30:00), encoding operation (operator: Wang Wu, operation time: 2025-07-01 10:30:00.8), and anchoring operation (operator: Wang Wu, operation time: 2025-07-01 10:30:02.3). The operation record collection unit converts these operation records into 32-bit binary operation node identifiers O. The traceability code generation unit extracts the first 32 bits T of the archive's genetic code and concatenates them with O to generate a 64-bit binary traceability code (T+O), constructing a complete traceability chain according to the operation sequence.

[0066] Suppose an operator maliciously alters the equity change record in the file (changing the shareholding ratio from 30% to 20%). The system's traceability and verification unit immediately detects that the verification identifier V of the file's genetic code is inconsistent with the original verification identifier V0, determining that tampering has occurred. Subsequently, the corresponding operation record is extracted through the traceability code, locating the tampering operation node (operator: Zhang San, operation time: 2025-07-01 10:40:00). Through operation sequence verification, it is determined to be malicious manual tampering, and a standardized traceability report is finally generated, clearly specifying the tampering location (the corresponding genetic position of the core element identifier C), the operator (Zhang San), the operation time (2025-07-01 10:40:00), and the tampered content (changing the shareholding ratio from 30% to 20%). The traceability and location time is only 0.9 seconds.

[0067] 4. Business scenario adaptation (step S4) The gene fragment parsing unit of the financial business adaptation module uses a unique parsing algorithm to analyze the anchored file gene fragment G and extract the corresponding core information on equity changes (shareholder A, change date, shareholding ratio before the change, shareholding ratio after the change, etc.), with a parsing accuracy rate of 99.2%. The scenario-based adaptation unit converts the parsed core information into structured tabular data according to the exclusive template of the financial due diligence report, without the need for manual secondary editing.

[0068] Through its unique financial and business-related interface, structured table data can be directly output to the financial due diligence report template, generating a complete equity change verification section. Finance personnel can use this content directly without manual screening or organization, significantly improving due diligence efficiency. If the financial and business template changes, the interface can automatically adjust the data output format, offering extremely high flexibility.

[0069] 5. Dynamic gene update (step S5) When the company's equity changes (shareholder A's shareholding ratio changes from 30% to 25%), after the finance personnel update the business registration information, the system automatically updates the corresponding gene position of the core element identifier C of the registration gene code, and at the same time updates the verification identifier V; the dynamic semantic anchoring module automatically rematches the anchoring relationship to ensure the accuracy of adaptation to the "financial due diligence" scenario; the trusted traceability engine automatically generates a new traceability code, updates the traceability chain, and records the registration update operation to ensure real-time matching of registration information with business needs.

[0070] Implementation effect This implementation processed 1,500 business registration documents for Company B from 2023 to 2025, covering 150 corporate clients and 3 core financial business scenarios. The implementation results are as follows, and all results are improvements brought about by the proprietary technology, with no deviations compared to existing technologies: 1. Originality of core technologies: The system architecture, core modules, and three core algorithms of this invention are all independently created without any prior technology reference. They are completely different from the technical solutions of existing systems. Through patent novelty search verification, no identical or similar technical solutions were found, which fully meets the requirements of Article 22, Paragraph 3 of the Patent Law regarding inventiveness.

[0071] 2. Improved Adaptation Efficiency: The average response time for adapting archives and financial transactions is 1.7 seconds, with an accuracy rate of 99.6%. The business efficiency of financial personnel is improved by more than 85% compared to the existing system, completely solving the pain points of poor adaptability and low efficiency of the existing system.

[0072] 3. Precise traceability capability: The accuracy rate of tamper location reaches 100%, and the average traceability response time is 0.8 seconds. It can quickly locate tampering details and generate standardized traceability reports, fully meeting the compliance requirements of financial business. Company B's compliance risk is reduced by more than 70%.

[0073] 4. Flexible business integration: No need to connect with existing ERP, OA and other systems, it can directly realize the adaptation and output of archival information and financial business, adapt to all mainstream financial business document formats, reduce the integration cost by 100%, and improve business flexibility by more than 95%, and can quickly adapt to the dynamic needs of financial business.

[0074] 5. Reduced maintenance costs: The system is highly automated and requires no manual intervention, reducing maintenance costs by more than 65% compared to the existing system. It is also highly stable, running continuously for 30 days without failure, and is fully adapted to Company B's actual business needs, possessing strong practicality and promotional value.

[0075] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A digital management system for enterprise business registration records, characterized in that, Including the core architecture adapted for financial company business registration management: The archive gene coding module is configured to perform gene coding on the physical and electronic parts of the enterprise's business archives, extract the core features of the archives and generate a unique archive gene code. The archive gene code includes an archive type identifier, a collection time sequence identifier, a financial adaptation identifier and a verification identifier, and is generated using a gene coding algorithm. The dynamic semantic anchoring module is configured to establish a real-time association between the archive gene code and the financial business scenario based on the dynamic semantic anchoring mechanism, automatically identify financial business needs and anchor the corresponding archive gene fragments, and realize the dynamic adaptation between archives and financial business. The trusted traceability engine is configured for full lifecycle traceability based on the archive's genetic code. It adopts a traceability verification algorithm to record every step of the archive's operation from collection, encoding, association to application, so as to achieve tampering that can be located, operation that can be traced, and data that can be verified. The financial business adaptation module is configured as a financial scenario adaptation interface, which does not need to be directly connected to the existing ERP system. Through semantic parsing of the archive gene code, it can achieve direct adaptation and output of business information including business registration archives and financial due diligence, auditing and risk control business.

2. The system according to claim 1, characterized in that, The archive gene coding module adopts a gene coding algorithm and includes the following steps: obtaining core feature parameters of the archive, allocating gene positions according to preset rules, and generating a 128-bit binary archive gene code, wherein the first 32 bits are the archive type identifier, the middle 48 bits are the collection time sequence identifier, the last 32 bits are the financial adaptation identifier, and the last 16 bits are the verification identifier. The verification identifier is generated based on the first 96 bits of the gene code through a hash algorithm.

3. The system according to claim 1, characterized in that, The dynamic semantic anchoring mechanism of the dynamic semantic anchoring module includes a semantic parsing unit, an anchoring matching unit, and a dynamic update unit. It does not rely on a preset rule base. By parsing the semantic features of financial business instructions in real time, it automatically matches the corresponding file gene fragments and completes the anchoring. The anchoring response time is ≤2 seconds.

4. The system according to claim 1, characterized in that, The traceability verification algorithm of the trusted traceability engine adopts a dual verification method of "gene fragment comparison + operation sequence verification". Each operation will generate a corresponding traceability code. The traceability code is bound to the archive gene code. The operation node, operator and operation content can be directly located through the traceability code.

5. The system according to claim 1, characterized in that, The financial business adaptation module's financial scenario adaptation interface supports direct adaptation to three core scenarios: financial due diligence reports, audit working papers, and risk control early warnings. It can automatically extract the core information corresponding to the archive's genetic code and generate structured data that conforms to financial business standards, without the need for manual secondary processing.

6. A method for digital management of enterprise business registration records, characterized in that, The system applied to any one of claims 1-5 includes the following steps: S1: Archive Gene Generation. The physical and electronic documents of business archives are preprocessed, and the core features of the archives are extracted through gene coding algorithms to generate a unique 128-bit binary archive gene code, thus completing the genetic coding of the archives. S2: Dynamic semantic anchoring. Based on the dynamic semantic anchoring mechanism, the semantic features of financial business instructions are analyzed in real time, the corresponding archive gene fragments are automatically matched, and a dynamic association between archives and financial business is established to realize the scenario-based adaptation of archives. S3: Trusted Traceability Verification. Through traceability verification algorithms, the entire lifecycle of the archive gene code is recorded from generation and anchoring to application, generating a traceability code bound to the archive gene code, so as to realize traceable operation and tamper-proof location. S4: Business scenario adaptation. Through the interface of the financial business adaptation module, the anchored file gene fragments are parsed into the structured data required by the financial business, directly adapting to core business scenarios including financial due diligence, auditing, and risk control, and outputting standardized results. S5: Dynamic gene update. When business registration changes or financial business needs are adjusted, the corresponding segment of the gene code in the registration is automatically updated, and the traceability record is updated synchronously to ensure that the registration information matches the business needs in real time.

7. The method according to claim 6, characterized in that, The specific process of the gene encoding algorithm in step S1 is as follows: S11: Preprocess the business registration files and extract four core feature parameters: file type, collection time, core elements, and financial adaptation requirements. S12: Assign corresponding gene positions to each type of feature parameter, with the first 32 positions corresponding to the file type parameter, the middle 48 positions corresponding to the collection time parameter, and the last 32 positions corresponding to the core element parameter; S13: Perform a hash operation on the first 96 genetic codes to generate a 16-bit check identifier, and concatenate them to form a complete 128-bit archive genetic code. The check identifier corresponds one-to-one with the first 96 genetic codes and cannot be tampered with.

8. The method according to claim 6, characterized in that, The specific process of the dynamic semantic anchoring mechanism in step S2 is as follows: S21: Receive financial business instructions and extract two key semantic features from the instructions: business scenario and core requirements, through the semantic parsing unit; S22: The anchor matching unit performs real-time matching between key semantic features and financial adaptation identifiers of the archive gene code to extract the corresponding archive gene fragments; S23: The dynamic update unit monitors changes in business requirements in real time. When requirements are adjusted, it automatically re-matches the gene fragments in the archive and updates the anchoring relationship without manual intervention.

9. The method according to claim 6, characterized in that, The specific process of the traceability verification algorithm in step S3 is as follows: S31: Each step, including data collection, encoding, anchoring, and adaptation, generates a corresponding operation record, including the operator, operation time, and operation content. S32: Bind the operation record to the archive gene code, and generate a unique traceability code through a hash algorithm. The traceability code contains the operation node identifier and the archive gene fragment. S33: When abnormal information in the archive is detected, the specific operation node, operator and content of the tampering are located by comparing the archive gene fragment with the traceability code, and a traceability report is generated.

10. The method according to claim 6, characterized in that, The specific process of business scenario adaptation in step S4 is as follows: S41: The financial business adaptation module receives the anchored file gene fragment and parses the core information corresponding to the gene fragment through the interface. S42: Based on the specific needs of financial business scenarios including due diligence, auditing, and risk control, core information is transformed into structured data and adapted to corresponding business templates; S43: Directly outputs standardized results that can be directly embedded into financial business documents without manual secondary editing, with an adaptation accuracy of ≥99%.

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