Dimensional data independent audit trace and layered anti-override accounting system

Abstract

The present application belongs to the technical field of financial internal control audit, binary snapshot storage and data tamper-proofing, aiming at the defects of the existing financial system, such as only recording the change of voucher amount, no trace left for dimension adjustment, AI automatic filling data covering manual accurate data, tamperable audit log, and high implicit risk of wrong account, an independent dimension binary snapshot audit system is built, new and old complete data packets are retained and bound MD5 hash tamper-proofing verification every time the label packet is changed, a four-level data source priority algorithm is designed, low priority data is blocked at the bottom to cover high priority effective data, a supporting apportionment amount automatic verification and abnormal voucher marking mechanism is matched, and a snapshot incremental compression optimization module is added. The present application realizes 100% full-link traceability of dimension change, eliminates the implicit wrong account of AI accounting, reduces the audit storage volume by 40%, and is fully suitable for tax inspection, listed audit, AI financial and tax compliance control scenes.

Description

Technical Field

[0001] This invention relates to the fields of financial internal control compliance, binary data snapshot, MD5 hash anti-tampering verification, multi-layer data verification, and AI accounting risk control audit technology, and is applicable to compliance traceability scenarios of all types of AI financial and tax systems. Background Technology

[0002] Existing traditional financial audit logs only record changes in voucher accounts and loan amounts. For operations such as AI batch adjustment of dimensions and manual modification of allocation ratios that only change dimensions and not amounts, there is no log retention or data snapshot. Dimension changes have no traceability basis throughout the process, which cannot meet the requirements of refined tax audits and listing audits.

[0003] Meanwhile, the existing system lacks a data priority control mechanism. Low-priority data automatically matched by AI and filled by system templates directly overwrites high-priority valid data that is accurately entered manually or generated natively from business documents. This leads to a large accumulation of hidden errors over time, frequent false alarms in AI accounting risk control, and extremely high costs for manual reconciliation and investigation. In addition, traditional logs lack anti-tampering mechanisms, have large redundancy in full snapshot storage, low reliability of audit data, and high storage costs, making them unsuitable for high-end compliant financial and tax scenarios. Summary of the Invention

[0004] This invention aims to address the technical shortcomings of existing financial systems, such as the lack of traceability for dimensional changes, AI data overwriting of accurate manual data, the tamperability of audit logs, large redundancy in snapshot storage, and frequent occurrences of hidden errors. It provides a dimensional data independent audit traceability and hierarchical anti-overwriting accounting system.

[0005] The core technical solution of this invention is as follows: A separate binary snapshot auditing system is built, with MD5 hash solidification to prevent tampering; a four-level data priority anti-overwrite mechanism is established to eliminate AI accounting errors from the bottom up; a new snapshot incremental compression module is added to optimize storage costs; and real-time amount verification and anomaly marking are provided to achieve a complete closed loop of accounting, verification, auditing, and traceability.

[0006] The beneficial technical effects of this invention are: achieving 100% traceability of dimensional changes, completely eliminating the problem of AI automatically filling in and overwriting accurate manual data; MD5 hashing ensures that audit data is tamper-proof, meeting the high standards of compliance requirements for listing and taxation; snapshot incremental compression reduces audit storage volume by 40%, improves reconciliation and investigation efficiency by 90%, and is compatible with all AI-driven financial and tax compliance management scenarios. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the dimension-independent audit log snapshot retention mechanism of this invention; 1-Tag packet change trigger node, 2-Binary snapshot collection, 3-Hash-based solidified storage, 4-Audit query terminal; Figure 2This is a schematic diagram illustrating the data source priority layering of the present invention; 1-Business document source layer, 2-Manual entry layer, 3-System template layer, 4-AI matching bottom layer; Figure 3 This is a flowchart of the layered anti-overwriting and anomaly verification closed loop of the present invention; 1-Dimensional writing, 2-Priority verification, 3-Amount balance verification, 4-Normal entry / anomaly marking. Detailed Implementation

[0008] 1. Establish an independent audit snapshot system: Create a read-only tag_audit_log audit table. Automatically collect new and old binary data packets every time the dimension tag package changes, calculate two MD5 hash values ​​and synchronize them into the database, record the operator, timestamp, and operation notes, and prohibit any deletion or modification operations to ensure that the log is permanently fixed.

[0009] 2. Layered anti-overwrite mechanism implementation: Preset four-level data priority rules, when writing dimension data, first verify existing high-priority data, only blank fields are allowed to be filled with low-priority data, AI automatically matches, system template data cannot overwrite manual data, and business document native data.

[0010] 3. Incremental compression optimization: For scenarios where multiple modifications are made consecutively in the same dimension, the system automatically compares the differences between the old and new data packets, stores only the changed fragments, replaces full storage, and significantly reduces redundant data in the audit logs.

[0011] 4. Anomaly Detection and Traceability: Real-time comparison of the allocated amount across dimensions with the total amount of the entries, and automatic marking of vouchers with discrepancies; supports full historical snapshot query, version comparison, and data rollback to achieve accurate traceability of problematic accounts.

[0012] Functional Quantitative Comparison Only dimension change left trace No record Complete snapshot retention Data tamper-proofing capability No check, tamperable MD5 hash solidification, tamper-proof AI data anti-covering No protection, easy to make mistakes Underlying forced interception Audit log storage redundancy Full storage, large volume Incremental compression, volume reduced by 40% Practical application value This invention systematically addresses multiple pain points of existing financial systems in the AI ​​accounting era, including lack of auditing, data corruption, high storage costs, and non-compliance, through an integrated technical architecture encompassing independent snapshot auditing, layered data protection, hash-based anti-tampering, incremental compression, and amount balance verification. It can be widely applied to high-regulatory-requirement business scenarios such as corporate financial accounting, listed company financial bookkeeping, tax compliance auditing, and AI-driven automatic allocation bookkeeping. While meeting the stringent requirements of regulatory agencies and accounting firms for full-process traceability review, it reduces the workload of financial error investigation, lowers server storage and maintenance costs, and balances data security, audit compliance, and business operational efficiency, possessing extremely high industrial application and commercialization value.

Claims

1. A dimensional data independent audit traceability and hierarchical anti-overwrite accounting system, comprising a traditional voucher log audit structure, characterized in that, include: The dimension-independent audit snapshot module is configured with an independent read-only audit data table. Whenever the binary tag package in the tag packaging data table is changed, it automatically collects complete binary snapshots before and after the change, and synchronously stores the operator, timestamp, and operation notes. Each snapshot is bound to a unique MD5 hash value to prevent tampering. The layered source verification module has a built-in four-level data source priority determination algorithm, which divides the priority level according to the data generation source and sets a lock flag for high-priority dimension data. The accounting overwrite verification module executes forced write verification logic, allowing low-priority dimension data to only fill blank dimension fields and prohibiting the overwriting of existing high-priority locked data. The anomaly dimension marking module parses binary tag packets in real time, verifies the integrity of dimensions and the balance of the total allocated amount, and automatically marks vouchers for anomalies and persists them in the log. The snapshot incremental compression optimization module stores only the differences between data segments that have been modified multiple times in the same dimension, replacing the storage of the full data package and reducing the redundant volume of audit logs.

2. The system according to claim 1, characterized in that: The dimension-independent audit snapshot module operates independently of the voucher operation log. Even in scenarios where only the dimension changes and the accounting amount remains unchanged, the audit snapshot is still fully retained without any omissions.

3. The system according to claim 1, characterized in that: The four levels of data priority, from top to bottom, are: original data from business documents, manually entered data, data filled in by subject templates, and AI-matched fallback data. Upper-level data can never be overwritten by lower-level data.

4. The system according to claim 1, characterized in that: It supports querying historical snapshots at the voucher level, comparing differences between multiple versions, and rolling back and tracing historical data.

5. The system according to claim 1, characterized in that: The incremental compression module targets high-frequency, continuous dimension modifications and stores changed fragments in a differentiated manner, significantly reducing the storage pressure on the audit database.

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