Regulatory-oriented data compliance restructuring and question-answering platform and retrieval method

Abstract

The application provides a regulatory-oriented data compliance reconstruction and question-answering platform and a retrieval method, relates to the technical field of regulation science and technology, and comprises a question-answering platform which is formed by mutual coupling of eight core systems and is provided with a total database, wherein: a data traceability reconstruction system realizes reverse data traceability and label supplementing of inventory data through a bidirectional time sequence backtracking algorithm based on an operation dependency chain, and five-element data traceability labels containing regulation clause numbers, obligation types, subjects of burden of proof, storage periods and penalty risk levels are forcibly implanted in front of each processing node of incremental data; in the application, the data traceability reconstruction system realizes reverse label supplementing of inventory data through the bidirectional time sequence backtracking algorithm based on the operation dependency chain, and the five-element data traceability labels are forcibly implanted in front of all writing database paths of incremental data, so that personal information processing activities at any time point in history all have traceable compliance data traceability.

Description

Data compliance restructuring for regulatory purposes, Q&A platform and retrieval methods Technical Field

[0001] This invention relates to the field of regulatory technology, specifically to a data compliance reconstruction and Q&A platform and retrieval method for regulatory purposes. Background Technology

[0002] In current data processing scenarios, data processors handle billions to tens of billions of pieces of personal information daily, with data lifecycles spanning many years. They need to provide verifiable, compliant data support for personal information processing activities of any scale and at any point in history, while ensuring short response times and secure, leak-free data transmission. Currently, traditional data governance, privacy management systems, data mapping, and compliance retrieval tools are widely used, but the following key issues remain when addressing these technical requirements:

[0003] Lacking traceable and compliant data traceability technology for massive historical data, most existing solutions can only process activity records or manage tags for incremental data. For the trillions of historical data accumulated before the system went online, there are no effective reverse tagging technologies, making it impossible to trace the compliance status of personal information processing at any point in history and making it difficult to provide corresponding verification data.

[0004] After the compliance determination rules are changed, the compliance labels of historical data lack automatic linkage and update technology. Existing technologies usually adopt manual assessment or snapshot-based governance methods. When the core parameters related to compliance determination change, it is impossible to complete the batch correction and index synchronization of existing data within an acceptable time. This results in contradictions in the compliance determination results of the same batch of data at different points in time, affecting the verifiability of the data.

[0005] When faced with real-time retrieval needs, it is impossible to complete an interpretable and verifiable response to the full amount of data within seconds, and it is difficult to achieve the security requirement that the data does not leave the domain: existing compliant retrieval systems either have excessively long response times and incomplete evidence of the association between compliance-related data, or lack the technology to quantify the penalty risks for scenarios without direct verification data, or pose a risk of leakage due to reliance on external models or direct transmission of raw data.

[0006] Therefore, data compliance restructuring, Q&A platforms, and retrieval methods oriented towards regulation are needed to solve the above problems. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a data compliance reconstruction and question-and-answer platform and retrieval method for regulatory purposes, thus resolving the problems mentioned in the background section.

[0008] To achieve the above objectives, this invention provides the following technical solution: a data compliance reconstruction and Q&A platform oriented towards regulation, wherein the Q&A platform is composed of eight core systems coupled together, and also includes a central database, wherein:

[0009] Data traceability and reconstruction system: Through a bidirectional time-series backtracking algorithm based on operation dependency chain, the system can perform reverse data traceability and labeling of existing data, and forcibly implant five-element data traceability tags containing the legal clause number, obligation type, subject of burden of proof, retention period, and penalty risk level into incremental data at each processing node.

[0010] Obligation parsing system: Through a hierarchical dependency decomposition algorithm based on conditional logic and exception clauses, regulatory regulations are broken down in real time into four layers of structured metadata: obligation core, triggering conditions, exceptions, and penalty basis.

[0011] Dual-dimensional indexing system: Simultaneously constructs a semantic vector index and a regulatory obligation inverted index with "obligation type + penalty risk level" as the composite primary key, supports joint millisecond-level retrieval of the two indexes and returns retrieval results containing processing activity logs and data traceability tags;

[0012] Evidence Assembly System: Through a dynamic evidence chain reorganization algorithm based on regulatory review habits, the search results are automatically assembled into a structured three-element evidence card consisting of the original text of the legal obligation, the evidence of the company's actual execution, and the basis for responsibility and punishment, including the operation time, responsible person's number and department, the amount of data involved, the directly corresponding penalty clauses, and the statutory amount range.

[0013] Inverted Presumption System: Through a multi-rule parallel inverted reasoning algorithm triggered by the lack of negative evidence, the burden of proof is automatically reversed for obligations that do not meet positive evidence, and the penalty risk level and amount range are output;

[0014] Panoramic Profiling System: Through rapid aggregation of multi-path evidence and risk-weighted ranking algorithms, it generates a structured regulatory profile report within seconds of receiving a regulatory topic, including compliance coverage rate, top risk ranking, and expected penalty amount.

[0015] Regulatory linkage system: Through incremental impact propagation and batch label rewriting algorithms, it can complete the correction of historical data traceability labels and the synchronous reconstruction of dual indexes in a short period of time after regulatory rules are updated;

[0016] Federal Question Answering System: It performs all computations locally using homomorphic encryption combined with a selective proof generation algorithm, and only returns a minimum set of compliance proofs to regulatory agencies.

[0017] Preferably, the total database further includes the following:

[0018] Regulatory obligation clause structured library: used to store the four-layer structured metadata output by the obligation parsing system;

[0019] Enterprise end-to-end processing activity library: used to store complete processing records with timestamps, operators, processing types, and data volume;

[0020] Compliance Data Traceability Tag Library: Used to store the five-element data traceability tags and version history generated and implanted by the data traceability reconstruction system;

[0021] Historical Regulatory Inquiry and Profiling Report Database: This database stores questions from previous inquiries, evidence cards generated by the evidence assembly system, and reports output by the panoramic profiling system. It serves as the foundational data for the continuous optimization and model iteration of the platform's eight core systems.

[0022] Preferably, the data traceability and reconstruction system includes a log reverse parsing module, a cross-system dependency chain stitching module, a five-element tag generation module, and a pre-interception implantation module. The cross-system dependency chain stitching module is used to complete the missing data traceability of fragmented call chains in a distributed environment, and the pre-interception implantation module is used to forcibly write the five-element data traceability tags before the data is written to the database.

[0023] Preferably, the obligation parsing system includes a real-time legal text capture module, a dependency syntax decomposition module, a condition-exception logic extraction module, and a penalty basis mapping module. The condition-exception logic extraction module is used to identify multi-layered nested triggering conditions and exceptions, and the penalty basis mapping module is used to associate penalty clauses with the corresponding obligation trunk.

[0024] Preferably, the dual-dimensional indexing system includes a semantic vector encoding module, an obligation-based inverted index construction module, a composite weight dynamic adjustment module, and an index consistency maintenance module. The composite weight dynamic adjustment module uses the penalty risk level as a real-time weighting factor, and the index consistency maintenance module is used to keep the two indexes synchronized after regulatory changes.

[0025] Preferably, the evidence assembly system includes an evidence fragment extraction module, a timeline alignment module, a ternary structure assembly module, and a visualization rendering module. The timeline alignment module is used to globally sort the retrieval results returned by the dual-dimensional indexing system according to the operation timestamp and link tracing identifier. The ternary structure assembly module is used to generate the ternary evidence card.

[0026] Preferably, the inverted presumption system includes a positive evidence retrieval module, a negative evidence determination module, a multi-regulatory conflict resolution module, and a penalty range calculation module. The multi-regulatory conflict resolution module takes the strictest penalty standard when the same behavior violates multiple regulations, and the penalty range calculation module outputs an amount range based on the severity of the violation and the amount of data.

[0027] Preferably, the panoramic profiling system includes a topic identification module, a multi-path evidence aggregation module, a risk quantification and ranking module, and a report structure generation module. The multi-path evidence aggregation module simultaneously pulls evidence from the compliance data traceability tag library and the enterprise's full-link processing activity library. The risk quantification and ranking module generates a Top Risk List by comprehensively considering the penalty amount, the probability of violation, and the data sensitivity.

[0028] Preferably, the regulatory linkage system includes a change detection module, an impact scope calculation module, a batch label rewriting module, and an index incremental reconstruction module. The impact scope calculation module is used to locate historical data affected by the new regulations. The federated question-and-answer system includes a local computing scheduling module, a homomorphic encryption execution module, a selective proof generation module, and a minimum proof set output module. The selective proof generation module generates verifiable proofs only for obligations of concern to regulatory agencies.

[0029] Preferably, the retrieval method includes the following steps:

[0030] Sp1: Use a data traceability and reconstruction system to complete the implantation of five-element data traceability tags for all data;

[0031] Sp2: Employs a duty resolution system to update the duty inverted index in real time;

[0032] Sp3: After receiving regulatory inquiries, parallel searches are performed in the dual-dimensional indexing system, and the results are merged and reordered with the penalty risk level as the weight.

[0033] Sp4: Employs an evidence assembly system to instantly generate three-element evidence cards;

[0034] SP5: The obligation to provide evidence of non-hit is subject to an inverted presumption system to output a range of penalty amounts;

[0035] SP6: Employs a panoramic profiling system to generate a regulatory profile report within seconds;

[0036] SP7: When regulatory rules change, the regulatory linkage system is used to synchronize the update of data traceability labels and dual indexes.

[0037] Beneficial effects

[0038] This invention provides a data compliance reconstruction and question-and-answer platform and retrieval method oriented towards regulatory oversight. It offers the following advantages:

[0039] 1. This invention effectively solves the key problem of existing technologies lacking traceable and compliant data source tracing for massive historical data. The data source tracing and reconstruction system of this invention relies on a bidirectional time-series backtracking algorithm based on operation dependency chains. By parsing multi-source information such as database logs and application link tracing data, it achieves accurate reverse labeling of trillions of historical data records. Simultaneously, a pre-interception mechanism is implemented at the beginning of all write paths for incremental data, forcibly embedding five-element data source tracing tags containing core information such as regulatory clause numbers and penalty risk levels. This design ensures that personal information processing activities at any point in history have complete and traceable compliant data source tracing.

[0040] 2. Effectively resolves the issue of inconsistent compliance status of historical data following frequent changes in regulatory rules. The obligation analysis system analyzes the latest regulations in real time, and the regulation linkage system combines incremental impact propagation and batch label rewriting algorithms to complete the label correction and dual-index synchronous reconstruction of affected historical data within a short period after the new regulations take effect. This ensures that the compliance status of the same batch of data remains consistent with the current regulations, resulting in consistent evidentiary conclusions and significantly improved credibility of evidence.

[0041] 3. Effectively solves the problems of long response time, incomplete evidence chains, and data leakage risks associated with existing technologies. The platform achieves millisecond-level joint retrieval through a dual-dimensional indexing system, instant generation of structured ternary evidence cards through an evidence assembly system, automatic penalty risk quantification through an inverted presumption system, second-level report generation through a panoramic profiling system, and minimum proof set output based on homomorphic encryption and zero-knowledge proofs through a federated question-answering system. This enables regulatory inquiries to receive complete, explainable, and verifiable responses within a short timeframe, while ensuring that data remains within the enterprise's boundaries throughout the process. Regulators only receive independently verifiable proofs, thus significantly reducing the risk of data leakage while meeting real-time regulatory requirements. Attached Figure Description

[0042] Figure 1 is a framework diagram of the question-and-answer platform of the present invention;

[0043] Figure 2 is a flowchart of the retrieval method of the present invention;

[0044] Figure 3 is a diagram of the overall database structure of this invention;

[0045] Figure 4 is a diagram showing the interaction between the system and the database within the platform of this invention.

[0046] Figure 5 is a schematic diagram demonstrating the platform retrieval method of the present invention. Detailed Implementation

[0047] 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.

[0048] Specific Implementation Example 1:

[0049] As shown in Figures 1 to 5, the data compliance reconstruction and Q&A platform for regulatory purposes consists of eight interconnected core systems, along with a central database.

[0050] The platform adopts a microservice architecture and is deployed on an enterprise private cloud. All systems communicate with each other in real time and through event-driven mechanisms via an enterprise service bus and message queues. The overall database consists of a distributed relational database, a columnar database, a vector database, and a search engine, ensuring high concurrency and hybrid retrieval capabilities.

[0051] Data traceability and reconstruction system: Through a bidirectional time-series backtracking algorithm based on operation dependency chain, the system can perform reverse data traceability and labeling of existing data, and forcibly implant five-element data traceability tags containing the legal clause number, obligation type, subject of burden of proof, retention period, and penalty risk level into incremental data at each processing node.

[0052] Existing data refers to all historical data that existed before the system went live, such as business tables, log tables, profile tables, and model output tables. Reverse labeling is achieved by scanning the database Binlog, RedoLog, audit logs, application link tracing data, and message queue consumption points during daily off-peak hours. Combined with the code version and regulatory mapping table at the time, the five-element tag that should be attached to each historical operation is inferred and written to the extended column of the original table or the bypass data traceability wide table. For incremental data, interceptors are implanted in all write paths (including ORM framework, storage proxy layer, and stream computing tasks). If the data does not carry the complete five-element tag, it will be directly rejected and an alarm will be thrown to ensure that every new data has complete compliant data traceability from the moment it is generated.

[0053] Obligation Parsing System: Through a hierarchical dependency decomposition algorithm based on conditional logic and exception clauses, regulatory provisions are broken down into four layers of structured metadata in real time: obligation core, triggering conditions, exceptions, and penalty basis. The system is automatically updated and solicits opinions daily, supporting PDF, Word, and webpage formats. First, OCR and layout restoration are performed, and then a Chinese dependency parser is used to decompose each clause into a dependency tree. The system recursively identifies and expands up to five levels of nested conditional and exception logic, and finally generates a structured obligation decision tree that can be directly used for retrieval and reasoning.

[0054] The dual-dimensional indexing system simultaneously constructs a semantic vector index and a regulatory obligation inverted index with "obligation type + penalty risk level" as the composite primary key, supporting millisecond-level joint retrieval of the two indexes. The semantic vector index vectorizes each processing log, code snippet, and SQL statement in real time and writes it to the vector database. The regulatory obligation inverted index registers all structured obligations as query rules, enabling reverse retrieval of document matching obligations. The two sets of indexes maintain strong consistency through dual writing via event streams, and are executed in parallel during retrieval, then weighted and merged according to penalty risk level.

[0055] Evidence Assembly System: Utilizing a dynamic evidence chain reorganization algorithm based on regulatory review practices, the system automatically assembles search results into structured ternary evidence cards. These cards consist of the original compliance obligation text, evidence of actual corporate execution, and a section detailing the operation time, responsible employee ID and department, the amount of data involved, directly corresponding penalty clauses, and the legally mandated amount range. Each card is strictly divided into three sections: the first section displays the original regulatory text with highlighted obligation keywords; the second section displays the anonymized actual execution code, SQL statements, API parameters, and Git commit records; and the third section lists the operation time accurate to the second, responsible employee ID and department, the number of data entries involved, directly related penalty clauses, and the legally mandated amount range. Cards can be exported in PDF and Word formats and stamped with the company's electronic seal.

[0056] The inverted presumption system uses a multi-rule parallel inverted reasoning algorithm triggered by the lack of negative evidence to automatically reverse the burden of proof for obligations that lack positive evidence and output the penalty risk level and amount range. When regulators question an obligation and cannot find any positive evidence in the full data traceability tags, the system immediately triggers the burden of proof reversal rule, calculates the amount range according to the strictest penalty standard (the higher of the number of violations processed × the statutory unit price and the upper limit amount), and outputs the risk level and similar penalty case references.

[0057] The panoramic profiling system uses multi-path evidence aggregation and risk-weighted sorting algorithms to generate a structured regulatory profile report within seconds of receiving a regulatory topic. This report includes compliance coverage, top risk ranking, and expected penalty amount. After regulatory personnel input or select an inspection topic, the system completes the aggregation of evidence from the entire database within 3-8 seconds, generating a complete report that includes a compliance dashboard, a heatmap of the top 20 high-risk obligations, upper and lower limits of expected cumulative penalty amount, expandable evidence chains, and suggestions for rectification priorities.

[0058] Regulatory linkage system: Through incremental impact propagation and batch label rewriting algorithms, it can complete the correction of historical data traceability labels and the synchronous reconstruction of dual indexes in a short period of time after the regulatory rules are updated. It automatically simulates the impact scope 30 days before the new rules take effect, and starts hot update at midnight on the day of the effective date. It only performs field-level modifications on the affected table partitions, with no business awareness throughout the process. It can complete the synchronous update of traceability labels and dual indexes for hundreds of billions of data points in a maximum of 4 hours.

[0059] The Federated Question Answering System: By combining homomorphic encryption with a selective proof generation algorithm, all computations are completed locally. Only a minimum set of compliance proofs is returned to regulatory agencies. Regulatory agencies send inquiries through a private network interface. After the platform completes all retrieval, reasoning, and profiling locally, it only returns the conclusion, the homomorphically encrypted evidence hash, and the zero-knowledge proof. The regulator can verify the correctness of the conclusion but cannot obtain any original data or samples.

[0060] The total database further includes the following:

[0061] Regulatory Obligation Clause Structured Library: Used to store four-layer structured metadata output by the obligation parsing system, updated incrementally daily, currently containing approximately 180,000 obligation nodes, and supports version backtracking;

[0062] Enterprise end-to-end processing activity library: used to store complete processing records with timestamps, operators, processing types, and data volume, with 200 million to 1 billion records added daily. Hot data is retained for 6 months, and cold data is archived.

[0063] Compliance Data Traceability Tag Library: Used to store the five-element data traceability tags and version history generated and implanted by the data traceability reconstruction system. Each piece of business data corresponds to one line of data traceability record, supporting billions of queries per second.

[0064] Historical Regulatory Inquiry and Profiling Report Database: This database stores questions from previous inquiries, evidence cards generated by the evidence assembly system, and reports output by the panoramic profiling system. It serves as the foundational data for the continuous optimization and model iteration of the platform's eight core systems, preserving all records from the past five years for subsequent evidence collection and self-optimization.

[0065] The data traceability and reconstruction system includes a log reverse parsing module, a cross-system dependency chain stitching module, a five-element tag generation module, and a pre-interception implantation module. The cross-system dependency chain stitching module is used to complete the missing data traceability in fragmented call chains in a distributed environment. The pre-interception implantation module is used to force the writing of five-element data traceability tags before the data is written to the database. The four modules work together to achieve reverse tagging of existing data and forced implantation of incremental data.

[0066] The obligation parsing system includes a real-time legal text capture module, a dependency syntax decomposition module, a condition-exception logic extraction module, and a penalty basis mapping module. The condition-exception logic extraction module is used to identify multi-layered nested triggering conditions and exceptions, and the penalty basis mapping module is used to associate penalty clauses with the corresponding obligation trunk. The four modules work together to complete the transformation of regulations into a computable obligation tree.

[0067] The dual-dimensional indexing system includes a semantic vector encoding module, an obligation-based inverted index construction module, a composite weight dynamic adjustment module, and an index consistency maintenance module. The composite weight dynamic adjustment module uses the penalty risk level as a real-time weighting factor, and the index consistency maintenance module is used to keep the two indexes synchronized after regulatory changes. The four modules ensure the efficiency and consistency of hybrid retrieval.

[0068] The evidence assembly system includes an evidence fragment extraction module, a timeline alignment module, a ternary structure assembly module, and a visualization rendering module. The timeline alignment module is used to reorder multi-source logs according to the actual operation order, and the ternary structure assembly module is used to generate ternary evidence cards. The four modules complete the transformation from raw logs to regulatory-readable evidence in seconds.

[0069] The reversed presumption system includes a positive evidence retrieval module, a negative evidence determination module, a multi-regulatory conflict resolution module, and a penalty range calculation module. When the same behavior violates multiple regulations, the multi-regulatory conflict resolution module applies the strictest penalty standard. The penalty range calculation module outputs the amount range based on the severity of the violation and the amount of data. These four modules enable the automated execution of the reversed burden of proof.

[0070] The panoramic profiling system includes a topic identification module, a multi-path evidence aggregation module, a risk quantification and ranking module, and a report structure generation module. The multi-path evidence aggregation module simultaneously pulls evidence from the compliance data traceability tag library and the enterprise's full-chain processing activity library. The risk quantification and ranking module generates a top risk list by comprehensively considering the penalty amount, the probability of violation, and the data sensitivity. The four modules enable the generation of a complete profiling report from a regulatory topic in seconds.

[0071] The regulatory linkage system includes a change detection module, an impact scope calculation module, a batch label rewriting module, and an index incremental reconstruction module. The impact scope calculation module is used to locate historical data affected by the new regulations. The federated question and answer system includes a local computation scheduling module, a homomorphic encryption execution module, a selective proof generation module, and a minimum proof set output module. The selective proof generation module only generates verifiable proofs for obligations of concern to regulatory agencies. The eight modules of the two systems work together to achieve zero-time-difference linkage of regulatory changes and secure question and answering of data that does not leave the domain.

[0072] The retrieval method includes the following steps:

[0073] Sp1: Use the data traceability and reconstruction system to complete the implantation of five-element data traceability tags for all data; execute the entire process of reverse labeling of existing data and forced interception of incremental data.

[0074] SP2: The obligation parsing system is used to update the obligation inverted index in real time; the index is refreshed immediately after crawling and parsing the latest compliance information and regulations every day.

[0075] Sp3: After receiving regulatory inquiries, it performs parallel searches in the dual-dimensional indexing system and merges and re-ranks the results with the penalty risk level as the weight; achieving a high hit rate in both semantic relevance and regulatory accuracy.

[0076] SP4: Employs an evidence assembly system to instantly generate three-element evidence cards; transforms hit records into regulatory-readable structured evidence within seconds.

[0077] SP5: The system uses an inverted presumption to output a penalty range for the obligation to provide evidence that is not found; it automatically reverses the burden of proof and quantifies the risk.

[0078] SP6: Employs a panoramic profiling system to generate regulatory profile reports within seconds; completes full evidence aggregation and risk profiling.

[0079] SP7: When regulatory rules change, the regulatory linkage system is used to synchronize the update of data traceability labels and dual indexes; thus achieving zero-time-difference correction of the impact of regulatory changes on historical data.

[0080] Specific Implementation Example 2:

[0081] As shown in Figures 1 to 5, the following is a description of the data flow and control flow design of this Q&A platform:

[0082] The platform adopts an event-driven + request-response dual-mainline architecture. All eight core systems achieve millisecond-level collaboration through a unified Kafka enterprise event bus and gRPC synchronous calls, ensuring that the entire closed-loop process, from regulatory changes to historical data correction, from regulatory inquiries to the final return of only the minimum set of proofs, is traceable, auditable, and reproducible.

[0083] I. Main Data Flow (divided into four closed loops based on time dimension):

[0084] First, a closed loop of automatic reconstruction of all data from regulatory changes (daily routine operation or triggered by new regulations). After the obligation analysis system captures and analyzes the latest regulations daily, it writes the structured nodes of the changed obligations into the structured regulatory obligation clause library and publishes the change event. The regulatory linkage system subscribes to the event in real time and immediately calculates the range of affected historical data. Subsequently, it calls the data traceability and reconstruction system to perform batch five-element tag rewriting. After the rewriting is completed, the two-dimensional indexing system performs incremental reconstruction. The execution record and report of the entire linkage process are written to the enterprise's full-link processing activity library in real time. Finally, the regulatory linkage report is archived by the historical regulatory inquiry and profile report library.

[0085] Second, a closed-loop system (real-time, millisecond-level) is established from the generation of business data to the mandatory implantation of real-time data traceability. All write requests initiated by business systems first reach the pre-interception and implantation module of the data traceability reconstruction system. This module forcibly generates five-element data traceability tags based on the current operation context and the latest regulatory mapping and attaches them to the records. Only records with complete tags are allowed to be written to the database. At the same time, the tagged records are synchronously written to both the enterprise's end-to-end processing activity database and the compliance data traceability tag database. An event is pushed to trigger the two-dimensional indexing system to complete real-time vectorization encoding and inverted index registration. The entire process has an end-to-end latency of less than 50 milliseconds, ensuring that every newly generated data has complete and regulatory traceability from the moment it is created.

[0086] Third, the regulatory inquiry achieves a closed-loop, explainable response within seconds (3-8 seconds per inquiry). After an external initiator sends a natural language or structured inquiry through the dedicated network interface of the federated question-answering system, the local computing and scheduling module of the federated question-answering system immediately converts it into an internal standard query task and distributes it. The dual-dimensional indexing system performs semantic vector retrieval and obligation inverted index retrieval in parallel and returns the hit records after fusion and sorting. The evidence assembly system then generates a structured ternary evidence card. The inverted presumption system automatically reverses the burden of proof for zero-hit obligations and calculates the penalty amount range. The panoramic profiling system integrates all evidence and inverted results to generate a complete regulatory profile report. Finally, the federated question-answering system performs homomorphic encryption and zero-knowledge proof packaging on the conclusion, report, and evidence hashes, returning only the minimum proof set to the regulator. At the same time, the full record of this inquiry, evidence card, and profile report are added to the historical regulatory inquiry and profile report database, realizing an explainable closed loop where regulators can ask questions at any time, respond within seconds, and the data does not leave the domain.

[0087] Fourth, a closed loop of continuous optimization is established between historical inquiry data and the platform itself (executed weekly / monthly). Historical regulatory inquiries and profiling reports are regularly fed back to the data traceability and reconstruction system regarding the accuracy of real-time labeling, which is used to optimize the accuracy of reverse backtracking; to the obligation analysis system regarding regulatory interpretation deviations, which is used to optimize dependency syntax strategies; to the two-dimensional indexing system regarding retrieval and ranking effects, which is used to dynamically adjust fusion weights; to the inverted presumption system regarding the estimated penalty amount deviation, which is used to calibrate the calculation model; and to the panoramic profiling system regarding report usability, which is used to optimize templates and ranking algorithms. This forms a complete self-learning and continuous capability improvement closed loop for the platform based on real regulatory scenarios.

[0088] II. Core Control Flow and Coordination Mechanism:

[0089] Unified Task Orchestration Center:

[0090] The platform has a built-in lightweight Orchestrator (based on Apache DolphinScheduler) that is responsible for coordinating all long-term cross-system transactions, including batch tasks for regulatory linkage, reverse labeling of existing data, and orchestration of the entire process of regulatory inquiries.

[0091] Event Bus (Kafka) Topic Division:

[0092] `regulation.change`: The obligation parsing system publishes regulatory changes; `lineage.tag.missing`: Data tracing interception failure alert; `query.in`: The federated question-and-answer system receives external queries; `query.out`: The federated question-and-answer system returns the minimum proof set; `lineage.rewrite.complete`: Regulatory linkage completion event; All systems subscribe / publish by topic to achieve loose coupling.

[0093] Anomaly and Rollback Protection:

[0094] For any task involving batch data tracing and rewriting, the regulatory linkage system first conducts a simulation in the shadow partition. Only after a successful simulation is the task actually executed, and an immutable execution report with a hash chain is generated.

[0095] If a regulatory inquiry times out or any module malfunctions, the Federal Q&A system will uniformly return the standard response "System busy, please try again later" and record the complete error chain for internal auditing.

[0096] Priority and resource isolation:

[0097] Regulatory inquiry tasks have the highest priority and are bound to a separate high-priority thread pool with CPU affinity.

[0098] The reverse labeling and regulatory linkage task is only executed during the off-peak business window from 00:00 to 06:00, and is automatically limited to no more than 30% of the cluster resources.

[0099] Through the above four closed-loop data flows and rigorous control flow design, this platform achieves:

[0100] Regulations change daily, and historical data is corrected on the same day; business data is subject to mandatory traceability as soon as it is generated; regulators can ask any question at any time, and a complete, explainable, and verifiable conclusion can be provided within 3-8 seconds without the data leaving the domain; all operations are traceable, auditable, verifiable, and self-optimizable.

[0101] The entire coordination process can degrade without relying on any single system outage (for example, if the obligation parsing system is temporarily unavailable, yesterday's cached obligations can continue to respond to inquiries), ensuring high oversight.

[0102] Specific Implementation Example 3:

[0103] As shown in Figures 1 to 5, the following is a further explanation of the algorithm in this scheme:

[0104] A bidirectional temporal backtracking algorithm based on operation dependency chains (core of the data source tracing and reconstruction system):

[0105] This algorithm is a dedicated backtracking method that reconstructs the entire data processing chain from both "forward" and "backward" directions. Input data includes database Binlog / RedoLog, application OpenTelemetry links, Kafka consumer points, audit logs, and the Git version of the code at the time. The algorithm first traces the call stack and business context backward using the timestamp of each write operation as an anchor point, and then traces the subsequent propagation path backward. It then precisely stitches together fragmented logs across systems using call fingerprints (method signature + key parameter hashes), ultimately outputting the five-element metadata traceability tag that each historical data entry should have. Specifically, in the platform, it is applied to the reverse tagging of existing data in the data traceability and reconstruction system, enabling data generated at any point in history to be accurately assigned the legal obligations that should have been followed at that time.

[0106] Calculation steps: ① Using the write timestamp T0 of a single existing record as the anchor point, extract the original SQL or change event corresponding to the record from the Binlog / RedoLog; ② Backtrack forward: Extract the OpenTelemetryspan_id of the thread where the SQL is located, trace back up to 50 hops of the call chain to obtain the complete business context (e.g., "user profile calculation → recommendation ranking → advertising placement"); ③ Backtrack backward: Starting from the primary key of the record, search for read / propagation behaviors with the same primary key in all logs within the next 30 minutes; ④ Merge the front and back links into a complete operation dependency graph; ⑤ Map each node in the graph to the "code Git version → legal obligations mapping table in effect at that time" to obtain all legal clauses actually triggered by the operation; ⑥ Generate the final five-element data traceability tag by combining the highest penalty level, retention period, and responsible person's number, write it into the extended column of the original table or the bypass data traceability wide table and establish a foreign key; ⑦ Generate the MerkleTree hash notarization of this supplementary tag record.

[0107] A hierarchical dependency decomposition algorithm based on conditional logic and exception clauses (core of the obligation resolution system):

[0108] This algorithm refers to a structured parsing method that recursively decomposes natural language legal provisions into a four-layer computable dependency tree. The input is the original legal text (PDF / Word / webpage). The algorithm first uses LTP or Stanford CoreNLP to perform Chinese dependency parsing to obtain a syntax tree, then recursively identifies conditional-exception trigger words such as "if…then…", "unless…", and "but…", supporting up to five levels of nested expansion. Finally, each provision is converted into a four-layer structured JSON: "obligation core - triggering condition - exception situation - penalty basis". Specifically applied to the obligation parsing system within the platform, this automatically transforms hundreds of constantly updated regulations into machine-executable obligations that can be directly used for inverted index registration and burden of proof reversal reasoning.

[0109] Calculation steps: ① Input the original legal text → OCR + layout restoration → obtain plain text; ② Use LTP for dependency parsing to construct a syntax tree; ③ Traverse the syntax tree and identify all conditional trigger words (if, as long as, unless, however, under certain circumstances); ④ Use the main clause as the "obligation trunk", recursively push trigger word clauses into the "trigger condition layer", and push exception word clauses into the "exception case layer", with a maximum depth of 5 layers; ⑤ Search for keywords such as penalty amount and penalty subject in the tree nodes of the syntax tree and populate the "penalty basis layer"; ⑥ Output a four-layer structured JSON, and generate a unique obligation ID and version number; ⑦ Write the regulatory obligation clause structure library and publish the regulation.change event.

[0110] A dynamic evidence chain reconstruction algorithm based on regulatory review practices (core of the evidence assembly system):

[0111] This algorithm refers to a specialized method for dynamically assembling evidence according to the actual case-handling reading order of regulatory agencies (first reviewing regulations, then examining how the company acted, and finally determining who is responsible and how the penalty is imposed). The input is the original hit log list returned by the dual-dimensional indexing system; the algorithm first aligns all multi-source fragments chronologically, then fixes them into a three-part structure: "Original description of regulations → Actual execution fragments by the company (anonymized SQL + code + parameters) → Operation time / responsible person / data volume / basis for penalty," and finally renders it as a visual card; in this platform, it is specifically applied to the evidence assembly system, enabling regulatory personnel to complete a closed-loop judgment of "regulation-behavior-responsibility" within one second of receiving each card.

[0112] Calculation steps: ① Input N original logs hit by the dual-dimensional index; ② Globally sort by event timestamp + trace_id to reconstruct the complete timeline; ③ Extract the first segment: original legal text + highlighted obligation keywords; ④ Extract the second segment: anonymized real SQL / code snippets + Git commit hash; ⑤ Extract the third segment: operation time (accurate to milliseconds), responsible person's ID, department, number of data entries involved, and basis for punishment (clause + amount range); ⑥ Combine the three segments into an HTML card according to a fixed template, render the PDF and affix the CFCA seal; ⑦ The entire process for a single card is <800ms.

[0113] Multi-rule parallel inverted inference algorithm triggered by lack of negative evidence (core of the inverted presumption system):

[0114] This algorithm is specifically designed to automatically trigger a reverse burden of proof inference engine when "no positive evidence can be found". The input is the hit count (0 or >0) of a certain obligation in the full data tracing; when the hit count is 0, the algorithm applies the reversed clauses of all applicable laws in parallel, automatically selects the strictest penalty standard, and outputs the penalty risk level (level 1-5) and the amount range (the higher of the number of violations × unit price and the upper limit). In this platform, it is specifically applied to the reverse presumption system, so that when the regulator asks "whether consent has been obtained" and the company cannot provide evidence, the system directly gives the conclusion of "presumed violation + estimated fine amount".

[0115] Calculation steps: ① Input an obligation ID; ② Perform a precise query in the compliance data traceability tag library and count the number of positive hits; ③ If the hit count = 0, immediately load all inverted clauses associated with the obligation in parallel; ④ Take the strictest penalty standard: Amount = max(number of violations × 50 yuan, 50 million yuan); ⑤ Simultaneously output risk level 5 and attach 3 similar penalty case document numbers;

[0116] ⑥ The results are written into the profile report and returned to the federal question and answer system.

[0117] Multi-path evidence rapid aggregation and risk-weighted ranking algorithm (core of the panoramic portrait system):

[0118] This algorithm aggregates relevant evidence from the entire database and generates a risk profile within 3-8 seconds. The input is regulatory topic keywords. The algorithm simultaneously retrieves evidence from three parallel sources: a compliance data traceability tag library (structured path), an enterprise end-to-end processing activity library (log path), and a semantic vector index (fuzzy path). It then performs a top-ranking algorithm using a comprehensive weighting of penalty risk level × violation probability × data sensitivity, ultimately outputting a compliance dashboard, heatmap, and expected penalty amount range. In this platform, it is specifically applied to a panoramic profile system, enabling regulators to obtain a complete regulatory profile report within seconds by inputting a single topic.

[0119] Calculation steps: ① Input regulatory keywords; ② Initiate three parallel queries simultaneously: ① Semantic vector Top-500, ② Obligation inverted index for precise matching, ③ Structured scanning of data traceability tag library; ③ Union of the three results for deduplication; ④ Calculate the comprehensive risk score for each record = penalty risk level × log (data volume) × sensitivity coefficient; ⑤ Select the Top 20 records in descending order of risk score; ⑥ Summarize and generate compliance rate, expected penalty amount range, and heat map; ⑦ The entire report is rendered in 3-8 seconds.

[0120] Incremental impact propagation and batch label rewriting algorithm (core of the regulatory linkage system):

[0121] This algorithm is used to rewrite only the affected historical data when new regulations take effect. The input is the obligation change difference output by the obligation parsing system; the algorithm first constructs a dependency graph of "new obligation → old obligation → data traceability label field", calculates the set of least affected partitions, and then modifies only the retention period, penalty level and other fields of these partitions in a hot update manner; in this platform, it is specifically applied to the regulatory linkage system to complete the compliance correction of hundreds of billions of historical data on the day the new regulations take effect.

[0122] Calculation steps: ① Subscribe to the regulation.change event to obtain the obligation change difference; ② Construct a dependency graph of "new obligation → old obligation → data source label field"; ③ Calculate the minimum affected partition set (usually <5% of total data); ④ Practice the update in the shadow partition first; ⑤ Execute the UPDATE at midnight, modifying only the affected fields (e.g., retention period changed from permanent to 3 years); ⑥ Trigger incremental rebuilding of the two-dimensional index; ⑦ Generate an execution report with MerkleTree.

[0123] Homomorphic encryption combined with selective proof generation algorithm (core of federated question answering system):

[0124] This algorithm is designed to perform all computations locally, allowing regulators to verify the correctness of the conclusions without disclosing any original data. The inputs are the final conclusion and the evidence hash. The algorithm first performs Paillier homomorphic encryption on the evidence hash, then uses zk-SNARK to generate a zero-knowledge proof that only proves "I did perform the search on the full dataset and the conclusion is correct." In this platform, it is specifically applied to a federated question-answering system, achieving true "data not leaving the domain, regulated but not stolen."

[0125] Calculation steps:

[0126] ① Use Paillier homomorphic encryption on the final conclusion and all evidence hashes; ② Construct an arithmetic circuit to prove "I performed a two-dimensional search on the full data and it has not been tampered with"; ③ Generate a zero-knowledge proof of <1KB using zk-SNARK; ④ Return only: plaintext conclusion + homomorphic ciphertext hash + proof; ⑤ The regulator can locally verify that the proof is valid and the conclusion is correct, but cannot decrypt any original evidence.

[0127] Specific Implementation Example 4:

[0128] As shown in Figures 1 to 5, the module data flow of each system in Embodiment 1 is described below:

[0129] The log reverse parsing module of the data traceability and reconstruction system first reads the original operation event stream from the database Binlog / RedoLog, audit logs, OpenTelemetry links, and Kafka consumer points as input, and outputs a structured operation event sequence to the cross-system dependency chain stitching module. The cross-system dependency chain stitching module then outputs a complete business dependency graph to the five-element label generation module. The five-element label generation module simultaneously reads the current valid regulatory mapping from the latest four-layer obligation tree written into the regulatory obligation clause structure library of the obligation parsing system, and generates the final five-element data traceability label. The pre-interception and implantation module directly intercepts all write requests (ORM, storage proxy, Flink task). If the request does not carry the label generated by the five-element label generation module, it refuses to write and throws the event lineage.tag.missing. At the same time, it synchronously writes the complete tagged record into the enterprise full-link processing activity library and the compliance data traceability label library, and pushes the event-triggered dual-dimensional index system's semantic vector encoding module and obligation inverted index construction module to update the two sets of indexes in real time.

[0130] The regulatory text capture module of the obligation parsing system outputs the latest original regulatory text to the dependency syntax decomposition module daily. The latter outputs the syntax tree to the condition-exception logic extraction module. Then, the penalty basis mapping module outputs the complete four-layer structured obligation JSON, writes it into the regulatory obligation clause structure library, and publishes the regulation.change event. This event is subscribed to by the change detection module and the impact scope calculation module of the regulatory linkage system for subsequent rewriting.

[0131] The semantic vector encoding module of the dual-dimensional indexing system consumes the labeled records generated by the data traceability and reconstruction system in real time, and the output vector falls into Milvus; the obligation inverted index construction module consumes the latest obligations written into the regulatory obligation clause structured library by the obligation parsing system in real time and registers them as Percolator rules; the composite weight dynamic adjustment module reads the penalty risk level in the data traceability tags and adjusts the retrieval weight in real time; the index consistency maintenance module subscribes to the rewrite events completed by the regulatory linkage system, triggers incremental reconstruction, and ensures that the two sets of indexes are always consistent.

[0132] The evidence fragment extraction module of the evidence assembly system directly receives the original hit record list returned by the parallel retrieval of the dual-dimensional indexing system as input. The timeline alignment module sorts these records globally by trace_id and timestamp and outputs an ordered event chain. The ternary structured assembly module then pulls the corresponding original text of the regulations from the structured library of regulatory obligation clauses and the anonymized execution details from the enterprise's full-link processing activity library to assemble a complete ternary evidence card. The visualization rendering module outputs the final PDF / Word card and writes it into the historical regulatory inquiry and profile report library.

[0133] The positive evidence retrieval module of the inverted presumption system reuses the results of the dual-dimensional index system. If the hit count is 0, the negative evidence judgment module is triggered. The latter reads all the inverted clauses of the obligation from the structured library of regulatory obligation clauses. The multi-regulatory conflict resolution module takes the strictest penalty standard and then hands it over to the penalty range calculation module to output the final amount range and risk level, which are directly written into the panoramic profile report.

[0134] The panoramic profiling system's topic identification module receives regulatory inquiry topics forwarded by the federated question-and-answer system. The multi-path evidence aggregation module simultaneously pulls evidence from three sources: the compliance data traceability tag library, the dual-dimensional index system, and the evidence assembly system. The risk quantification and ranking module calculates a comprehensive score by integrating the penalty risk level, data volume, and sensitivity in the data traceability tags. Finally, the report structure generation module outputs a complete profiling report and writes it into the historical regulatory inquiry and profiling report library.

[0135] The change detection module and impact scope calculation module of the regulatory linkage system analyze the regulation.change event of the consumer obligation parsing system, output the list of affected partitions to the batch label rewriting module, which calls the label generation capability of the data traceability and reconstruction system to complete the hot update, and then notifies the dual-dimensional indexing system to perform incremental reconstruction. The whole process is recorded in the enterprise's full-link processing activity library.

[0136] The local computation scheduling module of the Federal Question Answering System serves as the sole external entry point. After receiving regulatory inquiries, it sequentially or in parallel calls the dual-dimensional indexing system, evidence assembly system, inverted presumption system, and panoramic profiling system to complete local computations. The homomorphic encryption execution module and the selective proof generation module encrypt and package the final conclusion and evidence hashes. The minimum proof set output module only returns the proof package. All inquiry records, cards, and reports are synchronously stored in the historical regulatory inquiry and profiling report library for platform self-optimization.

[0137] At this point, all module inputs come from explicit outputs of upstream modules or the overall database, and all outputs are consumed by downstream modules or the overall database, forming a complete closed-loop data transmission path from regulatory changes → historical data correction → real-time data traceability and implantation → regulatory inquiries → minimum proof set return → platform self-optimization, with no module function operating independently.

[0138] Specific Implementation Example 5:

[0139] As shown in Figures 1 to 5, the following is a further explanation of the retrieval method in this technical solution:

[0140] The retrieval method of this invention strictly follows the seven steps Sp1-Sp7 sequentially or in parallel. All steps are completed locally within the enterprise, and the data never leaves the domain. The response time for a single complete regulatory inquiry is consistently within 7-10 seconds. The specific implementation steps are as follows:

[0141] Sp1: Employ a data traceability and reconstruction system to implant five-element data traceability tags into all data.

[0142] First, within the first month after launch, reverse tagging of existing data is performed: the data traceability and reconstruction system scans the database Binlog / RedoLog, audit logs, OpenTelemetry links, Kafka consumer positions, and historical Git code versions in batches during the off-peak hours of 00:00-06:00 daily. Using a bidirectional time-series backtracking algorithm based on operation dependency chains (see item 1 of Example 3), five-element data traceability tags are added to approximately 30,000-50,000 historical tables and trillions of records. After tagging is completed, an immutable report with Merkle Tree hash is generated. At the same time, for incremental data, the pre-interception and implantation module implants AOP aspects in all write paths such as MyBatis interceptor, ShardingSphere proxy layer, and Flink SQL Gateway to force the writing of five-element data traceability tags (regulatory clause number, obligation type, subject of burden of proof, retention period, and penalty risk level). If missing, the data is directly rejected from being written to the database and a Kafka event lineage.tag.missing is thrown. Ultimately, all data (existing + incremental) carries complete compliant data traceability.

[0143] SP2: Employs a duty resolution system to update the duty inverted index in real time;

[0144] The obligation parsing system automatically obtains the latest compliance information regulations and draft for comments daily. It generates a four-layer structured obligation JSON using a hierarchical dependency decomposition algorithm based on conditional logic and exception clauses (see item 2 of Example 3). After writing the regulatory obligation clause structured library, it immediately publishes a regulation.change event. The obligation inverted index building module of the dual-dimensional index system consumes this event in real time and registers the newly added or changed obligations as Elasticsearch Percolator reverse query rules to ensure that the inverted index is strongly consistent with the latest regulations.

[0145] SP3: Upon receiving regulatory inquiries, parallel searches are performed in the dual-dimensional indexing system, and the results are merged and reordered using the penalty risk level as the weight.

[0146] The local computation scheduling module of the federated question-answering system receives natural language or structured queries from regulatory agencies via a dedicated network interface, immediately converts them into standard query tasks, and simultaneously distributes them to the two-dimensional indexing system. The semantic vector encoding module uses the BGE-large-zh-v1.5 model to vectorize the queries, performs ANN retrieval in Milvus, and returns the Top-500 semantically relevant records. The obligation inverted index construction module synchronously performs Percolator reverse matching, returning all precisely matched regulatory obligations. The composite weight dynamic adjustment module reads the penalty risk level (level 1-5) from the five-element metadata traceability tag of each matched record, calculates the final fusion score = semantic similarity × 0.4 + penalty risk level × 0.6, and returns the weighted and sorted results from both paths to the downstream system. The average retrieval time is 0.8-1.2 seconds.

[0147] SP4: Employs an evidence assembly system to instantly generate three-element evidence cards;

[0148] The evidence assembly system receives an ordered list of hit records returned by SP3. The evidence fragment extraction module and the timeline alignment module globally sort and reconstruct the complete event chain according to trace_id and timestamp. The ternary structured assembly module pulls the corresponding original text of the regulations from the structured library of regulatory obligation clauses, pulls the anonymized real SQL / code fragments / Git commit records from the enterprise's full-link processing activity library, and pulls the responsible person / time / data volume / penalty basis from the compliance data traceability tag library. Through the evidence chain dynamic reorganization algorithm based on regulatory review habits (see item 3 of embodiment 3 for the steps), each ternary evidence card of "Original description of obligation → Actual execution fragment of the enterprise → Operation time / responsible person / data volume / penalty basis" is generated within <800ms. The visualization rendering module outputs PDF and affixes the CFCA electronic seal.

[0149] SP5: The obligation to provide evidence of non-hit is subject to an inverted presumption system, which outputs a range of penalty amounts.

[0150] The positive evidence retrieval module of the inverted presumption system reuses the SP3 results. For obligations with a hit count of 0, it immediately triggers the multi-rule parallel inverted reasoning algorithm for missing negative evidence (see item 4 of Example 3). It loads all related inverted clauses in parallel from the structured library of regulatory obligation clauses. The multi-regulatory conflict resolution module takes the strictest penalty standard. The penalty range calculation module calculates the final amount range according to the higher of "number of violations × 50 yuan and 50 million yuan" and outputs a 5-level risk level, which is directly written into the subsequent profile report.

[0151] SP6: Employs a panoramic profiling system to generate a regulatory profile report within seconds;

[0152] The panoramic portrait system's topic identification module receives the original inquiry topic, while the multi-path evidence aggregation module simultaneously pulls all relevant evidence from three sources: the compliance data traceability tag library (structured path), the enterprise's full-link processing activity library (log path), and the evidence cards already generated by SP4. Through multi-path evidence rapid aggregation and risk weighted sorting algorithm (see item 5 of Example 3), the system completes the Top 20 risk ranking, compliance rate calculation, and summary of the expected penalty amount range within 3-8 seconds. The report structure generation module outputs a complete PDF report that includes a dashboard, heat map, evidence chain that can be expanded with one click, and rectification suggestions.

[0153] SP7: When regulatory rules change, the regulatory linkage system is used to synchronize the update of data traceability labels and dual indexes;

[0154] After the obligation parsing system detects a regulatory change, it issues a regulation.change event. The regulatory linkage system's change detection module and impact scope calculation module immediately construct a dependency graph and calculate the minimum affected partition (usually <5%). The batch label rewriting module performs a hot update from 00:00 to 04:00 on the effective day after the shadow partition exercise, using incremental impact propagation and the batch label rewriting algorithm (see item 6 of Example 3). Only the affected fields (such as retention period and penalty risk level) are modified. After completion, the dual-dimensional indexing system is notified to perform incremental reconstruction and generate an execution report with Merkle Tree, achieving zero-time-difference correction of historical data by regulatory changes.

[0155] Specific Implementation Example Six:

[0156] As shown in Figures 1 to 5, the following are specific application instructions for this solution:

[0157] This document describes a predictable real-world application scenario in a typical data processing company with end-to-end data processing capabilities (processing over 10 billion pieces of personal information daily, involving user registration and login, profile calculation, algorithm recommendation, and other data operation scenarios). This scenario is entirely based on existing mature technology components and universally recognized production environment parameters. With the support of the content described in this specification and conventional technical means, those skilled in the art can stably reproduce this scenario and achieve the corresponding technical effects.

[0158] The company plans to deploy this platform on its own private cloud Kubernetes cluster (estimated 2000+ CPU cores and 10PB of storage). The total database will adopt a four-database separation architecture: TiDB (hot storage), ClickHouse (cold archiving), Milvus 2.4 (vector library), and Elasticsearch 8.x (inverted index library). All business systems (Java, Python, Flink, Spark, etc.) will be connected to the pre-interception and implantation module of the data traceability and reconstruction system through AOP interceptors or Sidecar proxies to enable incremental data to carry five-element data traceability tags.

[0159] During the data supplementation phase, the data traceability and reconstruction system is expected to process approximately 30,000 to 50,000 historical tables and trillions of records during the off-peak window (00:00-06:00) in the first month after its launch. By using a two-way time-series backtracking algorithm, the system will supplement historical data with five-element data traceability labels, enabling all personal information processing activities over the past few years to have complete data traceability that can be questioned by regulators.

[0160] The obligation parsing system automatically captures and structures all currently effective central and local regulations daily (estimated at 180,000-200,000 obligation nodes). When a new regulation or judicial interpretation takes effect, the regulation linkage system completes an impact scope simulation 30 days in advance. On the day of the effective date, it is expected to complete the hot update of affected historical data partitions (usually involving 1%-8% of the total data) within 2-4 hours, and simultaneously complete the incremental reconstruction of dual indexes, achieving zero business awareness.

[0161] In foreseeable real-world regulatory inquiry scenarios, when regulatory agencies send inquiries via dedicated network interfaces (such as "Have all automated decisions in the past 6 months been made with individual consent?"), the Federated Question Answering System is expected to complete the entire process response within 7-10 seconds: the dual-dimensional indexing system retrieves relevant records in parallel, the evidence assembly system generates thousands to tens of thousands of ternary evidence cards, the inverted presumption system automatically reverses the burden of proof for zero-hit obligations and provides a range of statutory penalty amounts, and the panoramic profiling system outputs a complete profile report including a compliance dashboard, a Top 20 risk heatmap, and an expandable chain of evidence. Finally, it only returns plaintext conclusions + homomorphic encrypted hashes + zero-knowledge proofs to the regulator. The regulator can verify the correctness of the conclusions but cannot obtain any original data or samples. Throughout the entire process, the data never leaves the enterprise's boundaries.

[0162] The retrieval methods Sp1-Sp7 will be executed stably in the following order after actual deployment: Sp1 will complete the full data traceability and integration in the first month after launch; Sp2 will be completed automatically every day with the analysis of regulations; Sp3-Sp6 will be completed in parallel within 7-10 seconds during each regulatory inquiry; and Sp7 will complete the historical data correction within 2-4 hours during each regulatory change. This will enable all potential administrative penalty amounts to be accurately estimated in advance and internal rectification to be completed. As a result, when facing on-site inspections or written inquiries initiated by institutions with data compliance regulatory authority, a complete answer that is verifiable, verifiable, and acceptable penalty amount estimation can be given in a shorter time.

[0163] The above application scenarios are derived entirely from existing mature technology components and production practice parameters recognized in the field. Those skilled in the art can directly achieve the same technical effects in a similar-sized enterprise environment by following the architecture, algorithm, data flow between modules and retrieval methods provided in this specification, without any creative effort.

[0164] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data compliance restructuring and question-and-answer system for regulatory purposes, including a question-and-answer platform, characterized in that: The question-and-answer platform consists of eight interconnected core systems, along with a central database. Among them is a data tracing and reconstruction system: This system uses a bidirectional time-series backtracking algorithm based on operational dependency chains to perform reverse data tracing and labeling of existing data. For incremental data, it forcibly implants five-element data tracing tags at each processing node, including the legal clause number, obligation type, burden of proof entity, retention period, and penalty risk level. The data tracing and reconstruction system includes a log reverse parsing module, a cross-system dependency chain stitching module, a five-element tag generation module, and a pre-interception and implantation module. The chain stitching module is used to supplement the missing data traceability of fragmented call chains in a distributed environment. The pre-interception implantation module is used to forcibly write the five-element data traceability tags before the data is written to the database. The obligation parsing system: through a hierarchical dependency decomposition algorithm based on conditional logic and exception clauses, regulatory provisions are decomposed in real time into four layers of structured metadata: obligation backbone, triggering conditions, exceptions, and penalty basis. The dual-dimensional indexing system: simultaneously constructs a semantic vector index and a regulatory obligation inverted index with "obligation type + penalty risk level" as the composite primary key, supporting millisecond-level retrieval and return of the two indexes. The system includes: a search result system for processing activity logs and data traceability tags; an evidence assembly system that automatically assembles search results into a structured ternary evidence card based on a dynamic evidence chain reorganization algorithm based on regulatory review habits. This card consists of the original compliance obligation text, evidence of actual corporate execution, and the basis for responsibility and penalty, including operation time, responsible employee number and department, data volume involved, directly corresponding penalty clauses, and statutory amount ranges; an inverted presumption system that automatically inverts the burden of proof for obligations that do not have positive evidence, and outputs the penalty risk level and amount range; a panoramic portrait system that generates a structured regulatory portrait report containing compliance coverage, risk top ranking, and expected penalty amount within seconds of receiving a regulatory topic, using a multi-path evidence rapid aggregation and risk weighting sorting algorithm; a regulatory linkage system that completes the correction of historical data traceability tags and synchronous reconstruction of dual indexes within a short time after regulatory rule updates, using incremental impact propagation and batch tag rewriting algorithms; and a federated question-and-answer system that completes all calculations locally using homomorphic encryption combined with selective proof generation algorithms, returning only the minimum compliance proof set to the regulatory agency.

2. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 1, characterized in that, The overall database further includes the following: The regulatory obligation clause structured library is used to store the four-layer structured metadata output by the obligation parsing system; the enterprise end-to-end processing activity library is used to store complete processing records with timestamps, operators, processing types, and data volume. Compliance Data Traceability Tag Library: Used to store the five-element data traceability tags and version history generated and implanted by the data traceability reconstruction system; Historical Regulatory Inquiry and Profiling Report Library: Used to store the questions from previous inquiries, the evidence cards generated by the evidence assembly system, and the reports output by the panoramic profiling system, serving as the basic data for the continuous optimization and model iteration of the platform's eight core systems.

3. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 1, characterized in that, The obligation parsing system includes a real-time legal text capture module, a dependency syntax decomposition module, a condition-exception logic extraction module, and a penalty basis mapping module. The condition-exception logic extraction module is used to identify multi-layered nested triggering conditions and exceptions, and the penalty basis mapping module is used to associate penalty clauses with the corresponding obligation trunk.

4. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 1, characterized in that, The dual-dimensional indexing system includes a semantic vector encoding module, an obligation-based inverted index construction module, a composite weight dynamic adjustment module, and an index consistency maintenance module. The composite weight dynamic adjustment module uses the penalty risk level as a real-time weighting factor, and the index consistency maintenance module is used to keep the two indexes synchronized after regulatory changes.

5. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 1, characterized in that, The evidence assembly system includes an evidence fragment extraction module, a timeline alignment module, a ternary structure assembly module, and a visualization rendering module. The timeline alignment module is used to globally sort the retrieval results returned by the dual-dimensional indexing system according to the operation timestamp and link tracing identifier. The ternary structure assembly module is used to generate the ternary evidence card.

6. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 1, characterized in that, The inverted presumption system includes a positive evidence retrieval module, a negative evidence determination module, a multi-regulatory conflict resolution module, and a penalty range calculation module. When the same behavior violates multiple regulations, the multi-regulatory conflict resolution module takes the most stringent penalty standard. The penalty range calculation module outputs a range of amounts based on the severity of the violation and the amount of data.

7. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 2, characterized in that, The panoramic profiling system includes a topic identification module, a multi-path evidence aggregation module, a risk quantification and ranking module, and a report structure generation module. The multi-path evidence aggregation module simultaneously pulls evidence from the compliance data traceability tag library and the enterprise's full-link processing activity library. The risk quantification and ranking module generates a Top Risk List by comprehensively considering the penalty amount, the probability of violation, and the data sensitivity.

8. The data compliance reconstruction and question-answering system for regulatory purposes according to claim 1, characterized in that, The regulatory linkage system includes a change detection module, an impact scope calculation module, a batch label rewriting module, and an index incremental reconstruction module. The impact scope calculation module is used to locate historical data affected by the new regulations. The federated question-and-answer system includes a local computing scheduling module, a homomorphic encryption execution module, a selective proof generation module, and a minimum proof set output module. The selective proof generation module generates verifiable proofs only for obligations of concern to regulatory agencies.

9. A retrieval method for a regulatory-oriented data compliance reconstruction and question-answering system based on any one of claims 1-8, characterized in that, The process includes the following steps: Sp1: Using a data traceability reconstruction system to implant five-element traceability tags into all data; Sp2: Using an obligation parsing system to update the obligation inverted index in real time; Sp3: After receiving regulatory inquiries, parallel retrieval is performed in the dual-dimensional index system, and the data is fused and reordered with the penalty risk level as the weight; Sp4: Using an evidence assembly system to instantly generate ternary evidence cards; Sp5: For obligations for which no evidence is found, an inverted presumption system is used to output the penalty amount range; Sp6: Using a panoramic profiling system to generate a regulatory profile report within seconds; Sp7: When regulatory rules change, a regulatory linkage system is used to synchronize the update of data traceability tags and dual indexes.

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