Artificial intelligence-based laboratory automation compliance recording and auditing system and method

Abstract

This invention discloses an AI-based automated compliance recording and auditing system and method for laboratories. The system includes a data acquisition and packaging module, a blockchain evidence storage module, an AI compliance monitoring module, an automatic audit report generation module, and a user interaction module. By providing the AI ​​compliance monitoring module with a trusted data source stored on the blockchain, the input for AI analysis is made authentic and traceable, fundamentally ensuring the reliability of compliance judgments. By packaging the original operational data and the real-time analysis results of the AI ​​compliance monitoring module together on the blockchain, each compliance judgment itself becomes tamper-proof evidence, constructing a dynamic and traceable intelligent compliance evidence chain. Through continuous learning and rule iteration driven by a machine learning engine, and by storing the rule and model changes on the blockchain, the compliance monitoring system can self-evolve, and its evolution process is transparent and trustworthy throughout.

Description

Technical Field

[0001] This invention relates to the field of laboratory quality management and data auditing technology, and more specifically, to an artificial intelligence-based automated compliance recording and auditing system and method for laboratories. Background Technology

[0002] As the core location for pharmaceutical research and development, testing and inspection, and production quality control, the authenticity and integrity of data collected by laboratories directly impact product quality, public safety, and regulatory compliance. To ensure data reliability, laboratories must adhere to a series of stringent regulations and standards, including Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP), and ISO / IEC 17025. However, traditional laboratory quality management faces three major challenges in practice: First, there is a contradiction between unreliable and easily tampered data. Traditional paper-based or centralized electronic record systems (LIMS, ELN) are prone to data loss and internal tampering (such as abuse of privileges or direct modification of the database), making it difficult to provide irrefutable, fully traceable proof of data integrity.

[0003] Second, there is a contradiction between the inefficiency of auditing and its reliance on manual labor. Auditing work relies heavily on manual review of records and verification of logic, which is inefficient, time-consuming, costly, and susceptible to subjective human factors and negligence, making it difficult to handle massive amounts of data and complex processes.

[0004] Third, there is a contradiction between passive response and lack of early warning. Existing systems are mostly "post-event audits," unable to identify risks of deviations from standard operating procedures (SOPs) or regulations in real time during operations, making it difficult to achieve proactive and preventative quality management.

[0005] To address the aforementioned issues, the industry has undertaken a series of technological explorations. For example, CN120543111A discloses a laboratory auditing method and system based on blockchain and artificial intelligence. This method initiates experimental audit requests through a dynamic appointment system, deploys cameras and sensors in the experimental area, analyzes video data through an artificial intelligence behavior recognition module, generates audit logs with timestamps and spatial identifiers, and writes video hash values ​​and sensor data into the blockchain. This solution combines the immutability of blockchain and the behavior recognition capabilities of AI to some extent, but it focuses on the identification and evidence storage of single-point behaviors, lacking continuous and logical compliance monitoring of data throughout the entire experimental process (including multi-source heterogeneous data such as operations, personnel, equipment, and environment). It is difficult to achieve logical correlation verification between multi-step processes and fails to automatically synthesize a complete chain of compliance evidence.

[0006] For example, CN120067213A discloses a method for tracking and multi-party collaborative auditing of electronic archival data based on blockchain smart contracts. This method uses blockchain technology to ensure the authenticity and immutability of archival data, and combines smart contracts to achieve an efficient and transparent auditing process. However, this patent mainly focuses on archival data management and fails to address the issue of automated compliance judgment in laboratory scenarios involving real-time, multi-source, and mixed structured and unstructured data. It also lacks the ability to intelligently analyze and provide early warnings for the experimental operation process itself. Furthermore, this solution still has shortcomings in terms of the execution efficiency and security of smart contracts.

[0007] In summary, while existing technologies have applied blockchain or artificial intelligence to address certain aspects of laboratory quality management, none have systematically solved the challenges of automating, intelligentizing, and ensuring the credibility of laboratory data across the entire lifecycle—from generation, recording, storage, and monitoring to audit report generation. Therefore, constructing a laboratory compliance record and auditing system capable of achieving credible management of the entire data lifecycle, real-time intelligent compliance monitoring, and automated generation of authoritative reports has become a pressing technical problem in this field. Summary of the Invention

[0008] In view of the above-mentioned technical problems in related technologies, the present invention proposes an automated laboratory compliance recording and auditing system and method based on artificial intelligence, which can overcome the above-mentioned shortcomings of the prior art.

[0009] To achieve the above-mentioned technical objectives, the technical solution of the present invention is implemented as follows: An AI-based automated compliance recording and auditing system for laboratories includes: The data acquisition and encapsulation module is used to acquire raw operational data and process metadata from multi-source data in the laboratory in real time or near real time, and encapsulate the operational data and process metadata into structured data blocks. The blockchain evidence storage module is connected to the data acquisition and packaging module. It is used to calculate the cryptographic hash value of the data block and write the hash value and key index information into the blockchain network for distributed evidence storage, generating an immutable data fingerprint certificate. The AI ​​compliance monitoring module connects to the data acquisition and encapsulation module and integrates a rule-based engine and a machine learning engine. The rule-based engine is used to perform real-time logical compliance judgment on incoming data blocks according to predefined compliance rules. The machine learning engine is used to train on historical operation sequences and data to identify complex abnormal patterns and potential risks. The AI ​​compliance monitoring module integrates the analysis results of the two engines to generate real-time warnings for violations or risk trends. The automatic audit report generation module is connected to the blockchain evidence storage module and the artificial intelligence compliance monitoring module respectively. It is used to respond to audit tasks, automatically query operation records, compliance status and corresponding blockchain credentials from the database based on the specified audit template and assertions, and generate a standardized audit report containing data integrity verification and compliance conclusions. The user interaction module provides a visual interface to display the experimental process monitoring view, data traceability chain, compliance warning information, and audit report.

[0010] Furthermore, it also includes a collaborative working mechanism between blockchain and artificial intelligence, which includes: In the data layer collaboration, the AI ​​compliance monitoring module is used to prioritize the use of data with trusted tags anchored by the blockchain notarization module for model training and real-time inference. In collaboration with the business logic layer, after the AI ​​compliance monitoring module completes the compliance judgment, the blockchain evidence storage module packages the original operation data and its corresponding AI analysis results together, calculates the joint hash value and writes it into the blockchain to solidify the dynamic evidence chain containing intelligent interpretation. In the feedback optimization layer collaboration, the machine learning engine is used to identify potential new rules from the audit results, and when compliance rules or AI models are updated, the blockchain evidence storage module stores the changes as transactions to form a closed-loop trusted record of system evolution.

[0011] Furthermore, the machine learning engine in the AI ​​compliance monitoring module is one or more combinations of recurrent neural networks, temporal Transformer models, or graph neural networks; wherein, the recurrent neural network or temporal Transformer model is used to learn normal temporal dependencies between experimental steps to detect process anomalies, and the graph neural network is used to model the personnel, equipment, materials, and environmental elements of the laboratory as dynamic graphs to identify potential cross-contamination risks.

[0012] Furthermore, the automatic audit report generation module is specifically used to: decompose the audit assertion into multiple independently verifiable sub-assertion tasks, distribute them to a distributed computing cluster for parallel evidence retrieval and proof generation, and finally aggregate all local proofs to synthesize the final audit report.

[0013] Furthermore, the system also includes a data persistence layer, which adopts a multi-model database strategy, including: a relational database for storing core business entities and relationships; a time-series database for storing environmental monitoring time-series data; and a document database or object storage for storing audit reports and original files.

[0014] Furthermore, the blockchain evidence storage module adopts a consortium blockchain architecture, allowing laboratories, partners, and regulatory agencies to participate in consensus as nodes or act as observation nodes, so as to achieve cross-institutional data trust verification.

[0015] An AI-based automated compliance recording and auditing method for laboratories, applied to the aforementioned system, includes the following steps: S1 Data Acquisition and Trusted Encapsulation: Real-time acquisition of multi-source laboratory operation data and process metadata, encapsulated into data blocks with timestamps, operator and equipment ID information; S2 Blockchain Fingerprint Evidence: Instantly calculates the hash value of a data block and submits the hash value and key index to the blockchain network. After consensus is reached, an immutable on-chain evidence is formed. S3 Dual-Engine Intelligent Compliance Monitoring: The rule-based engine verifies in real time whether the data complies with standard operating procedures; the machine learning engine asynchronously analyzes operating patterns and data trends to identify hidden anomalies; the results of the two engines are combined to push real-time warnings for medium- and high-risk events. S4 Distributed Audit Report Generation: Receives audit tasks, parses audit scope and assertions; breaks down complex assertions into multiple sub-assertion tasks, distributes them to distributed computing nodes for parallel execution, retrieves business data from the database and integrity credentials from the blockchain, and generates partial proofs; aggregates all partial proofs, combines them with a report template, and automatically generates the final audit report; S5 Visual Interaction and Traceability: Provides full-process visual monitoring of experiments through the user interface, one-click data authenticity verification based on blockchain credentials, and management and export functions for audit reports.

[0016] Furthermore, in step S2, the hash value is generated using a salted hash algorithm, and the salt value consists of a unique laboratory identifier and a timestamp accurate to the millisecond level.

[0017] Furthermore, step S3 further includes: the machine learning engine continuously optimizes the model by analyzing historical compliance and violation data, and feeds newly discovered abnormal patterns back to the rule engine to form a self-evolving compliance knowledge base.

[0018] Furthermore, in step S4, the blockchain certificate is used to prove in the audit report that: the generation time and content of the original data have not been tampered with, and the AI ​​warning event is generated based on credible data that has been stored on the blockchain at that time.

[0019] The beneficial effects of this invention are as follows: By providing a trusted data source with blockchain-verified evidence for the AI ​​compliance monitoring module, this invention ensures the authenticity and traceability of AI analysis inputs, fundamentally guaranteeing the reliability of compliance judgments; by packaging the original operational data and the real-time analysis results of the AI ​​compliance monitoring module together on the blockchain, each compliance judgment itself becomes tamper-proof evidence, constructing a dynamic and traceable intelligent compliance evidence chain; through continuous learning and rule iteration driven by the machine learning engine, and by on-chain evidence of rule and model changes, the compliance monitoring system can self-evolve, and its evolution process is transparent and trustworthy throughout. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a structural diagram of an AI-based laboratory automated compliance recording and auditing system according to an embodiment of the present invention. Detailed Implementation

[0022] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0023] like Figure 1 As shown in the embodiment of the present invention, an artificial intelligence-based laboratory automated compliance recording and auditing system includes: The data acquisition and encapsulation module is used to acquire raw operational data and process metadata from the laboratory information management system, electronic lab notebook, instruments and environmental sensors in real time or near real time, and encapsulate the operational data and process metadata into a structured data block. The data acquisition and encapsulation module acquires laboratory operational data and its corresponding process metadata from at least one data source in real time, including operation timestamps, operator information, equipment numbers, experimental environment parameters, etc. The operational data and process metadata form a data block.

[0024] The blockchain notarization module, connected to the data acquisition and encapsulation module, calculates the cryptographic hash value of the data block and writes this hash value and key index information into the blockchain network for distributed notarization, generating an immutable data fingerprint certificate. The blockchain notarization module employs distributed ledger technology to write the hash value and key metadata index of the data block into the blockchain network for notarization, ensuring the accuracy and immutability of the data through a consensus mechanism among multiple nodes. The hash value is generated using the salted SHA-256 algorithm, and the salt value, combined with a unique laboratory identifier and a timestamp accurate to milliseconds, ensures the uniqueness and stability of the digest.

[0025] The AI ​​compliance monitoring module, connected to the data acquisition and encapsulation module, integrates a rule-based engine and a machine learning engine. The rule-based engine performs real-time logical compliance checks on incoming data blocks based on predefined SOPs and regulatory rules. The machine learning engine trains on historical operation sequences and data to identify complex anomaly patterns and potential risks. The two engines work together to generate real-time warnings for violations or risk trends. The AI ​​compliance monitoring module uses an AI model to analyze the data and determine whether it conforms to predefined compliance rules. The AI ​​model includes both a rule-based engine and a machine learning model. Through learning and analyzing laboratory standard operating procedures (SOPs), it achieves intelligent monitoring and risk warnings of the experimental process. When a potentially deviating operational pattern or abnormal data trend is detected, the system sends a real-time warning to management personnel.

[0026] The automatic audit report generation module connects to both the blockchain evidence storage module and the AI ​​compliance monitoring module. Responding to user requests or scheduled tasks, it automatically queries the database for operation records, compliance status, and corresponding blockchain credentials based on specified audit templates and assertions. It then uses a distributed computing framework to generate proof components in parallel, ultimately synthesizing a standardized audit report containing data integrity verification and compliance conclusions. The automatic audit report generation module, based on a selected regulatory report template, uses Natural Language Processing (NLP) technology to intelligently extract and correlate data, combining blockchain credentials and compliance status to automatically generate the audit report. During the generation process, a distributed computing framework is used to break down complex assertions into multiple sub-assertions, distributing them to multiple computing nodes for simultaneous proof generation. The generated sub-proof components are then merged and verified.

[0027] The user interaction module provides a visual interface to display the experimental process monitoring view, data traceability chain, compliance early warning information, audit report center, and system management configuration functions. It offers web and mobile interfaces for data querying, audit report downloading, and system settings. The system supports multiple report output formats, including common formats such as PDF and Excel, and supports data import and export, facilitating secondary data analysis and in-depth mining.

[0028] The system also features a data recovery mechanism that automatically rolls back to the previous normal state in case of anomalies during data upload or operation, ensuring data integrity and consistency. Additionally, the system supports scheduled or event-triggered automatic auditing, automatically generating audit reports based on preset compliance rules and operation log sets.

[0029] Preferably, the blockchain evidence storage module adopts a consortium blockchain architecture, allowing laboratories, partners, and regulatory agencies to participate in consensus as nodes or as observation nodes, so as to achieve cross-institutional data trust verification.

[0030] Preferably, the machine learning engine in the AI ​​compliance monitoring module is a recurrent neural network or a temporal Transformer model, used to learn the normal temporal dependencies between experimental steps in order to detect process anomalies such as step jumps and reversed order.

[0031] Preferably, the automatic audit report generation module uses natural language processing technology to decompose audit assertions into independently verifiable sub-assertions and distribute them to the computing cluster for parallel evidence retrieval and proof generation.

[0032] Preferably, the system further includes a data persistence layer, employing a multi-model database strategy, including: a relational database for storing core business entities and relationships; a time-series database for storing time-series data such as environmental monitoring; a document database for storing audit reports and unstructured data; and object storage for storing raw files.

[0033] This system adopts a layered, modular microservice architecture to achieve high cohesion, low coupling, easy scalability, and high availability. Logically, the system is divided into five layers: user interaction layer, application service layer, core business layer, data persistence layer, and infrastructure layer. One of the core innovations of this invention lies in not treating blockchain and artificial intelligence as two independent functional modules, but rather designing a deeply collaborative and bidirectionally enhanced working mechanism, achieving a closed loop where "AI analysis ensures business compliance, and blockchain notarization ensures the credibility of AI inputs and conclusions." This collaborative mechanism is reflected in the following three levels: 1. Data Layer: Blockchain provides AI with a trusted, high-quality data source for training and inference. Problem: The performance of AI models, especially machine learning models, is highly dependent on the quality and authenticity of the training data. "Garbage in, garbage out"—if the underlying data used for training and real-time analysis can be tampered with, any conclusions drawn by AI will be baseless and lack legal and regulatory validity.

[0034] Collaboration mechanism: (1) Trusted data supply: The integrity of all raw operational data flowing into the AI ​​compliance monitoring module has been anchored through the blockchain notarization module. When the system provides data to the AI ​​engine (especially the machine learning engine), it can attach the blockchain transaction ID (TxID) of the data block as a "trusted label".

[0035] (2) Anti-tampering training set: When constructing historical training datasets, the system prioritizes data with complete blockchain evidence records to ensure the authenticity of training samples. This fundamentally improves the accuracy of AI models in learning "normal mode" and "abnormal mode".

[0036] (3) Data traceability and auditing: When the AI ​​model makes a warning or judgment, the system can trace back to the original data that led to the judgment and provide proof of the immutability of the data on the blockchain. This makes the AI's decision-making process auditable and verifiable.

[0037] 2. Business Logic Layer: Real-time AI analysis, blockchain-based evidence chain consolidation Problem: Blockchain excels at ensuring the immutability of static data, but lacks the ability to judge the compliance of the data's inherent business logic (such as whether the order of steps is correct and whether the values ​​are reasonable). Traditional auditing requires manual verification afterward.

[0038] Collaboration mechanism: (1) AI as a real-time logic verifier: When a new data block is generated and collected, the AI ​​compliance monitoring module intervenes first, using the rule engine and machine learning model to complete the real-time judgment of its business compliance within milliseconds to seconds (e.g.: "Step B was executed before step A, violating SOP").

[0039] (2) Blockchain as an evidence solidifier: After the AI ​​makes a judgment, the system packages the "raw data" and the "AI analysis results" (including compliance status, risk score, and warning type) together, calculates their joint hash value, and then writes it to the blockchain. This means that not only is the raw data preserved, but also the fact that "at a certain point in time, the system made a judgment on the data based on trusted rules / AI models" is recorded immutably.

[0040] (3) Forming a dynamic evidence chain: The entire experimental process consists of a series of such "data + AI judgment" evidence blocks, forming a dynamic and intelligently interpreted compliant evidence chain on the blockchain, rather than just a raw data log.

[0041] 3. Feedback Optimization Layer: AI learns from audits, and new rules are updated and witnessed by the blockchain. Problem: Compliance rules and risk models are not static. New regulations, new experimental methods, or newly discovered potential risks need to be incorporated into the monitoring system in a timely manner.

[0042] Collaboration mechanism: AI-driven rule discovery: When analyzing massive amounts of historical data, machine learning engines may discover statistically significant anomalous patterns that are not covered by existing rule bases (e.g., a specific device has an unusually high failure rate at night). The system can then submit this as a "potential new rule suggestion" to quality experts.

[0043] Blockchain witnesses rule iteration: When an administrator confirms and approves a new compliance rule or updates an AI model, the rule / model change itself (such as rule content, version number, effective date, and approver) is submitted to the blockchain as a special transaction for record-keeping. This ensures that the evolution of the entire compliance monitoring system is transparent, trustworthy, and undeniable.

[0044] Closed-loop self-evolution: After the new rules take effect, they will be applied to subsequent real-time monitoring. The resulting early warning effects and data will then be fed back to the AI ​​model as new training data, forming a closed-loop self-evolutionary system of "trustworthy data storage -> AI intelligent analysis -> discovery of new knowledge -> trustworthy rule updates -> guidance for a new round of monitoring". In this cycle, blockchain ensures the traceability of every key "decision" and "change".

[0045] 4. Summary: Through the above three-layer collaboration, this invention achieves: Input trustworthiness: Blockchain ensures that the data fed to AI is "real data"; Intelligent processes: AI gives the system a "brain" to understand data and judge compliance; Authority of output: Blockchain solidifies AI's intelligent judgment into an immutable "electronic testimony"; Trustworthy System Evolution: The system's own iteration and optimization process is also recorded by the blockchain, establishing comprehensive trust.

[0046] This synergy enables the system's overall capabilities to transcend the simple sum of blockchain (which can only guarantee that the data remains unchanged) and AI (which can only analyze the data), forming a self-evolving and fully trustworthy "digital quality officer".

[0047] This invention provides an artificial intelligence-based automated compliance recording and auditing method for laboratories, applied to the system described above, characterized in that the method includes the following steps: S1. Data Acquisition and Trusted Encapsulation: Real-time acquisition of multi-source operational data and process metadata from the laboratory, and encapsulation into data blocks with information such as timestamps, operators, and equipment IDs; S2. Blockchain fingerprint evidence storage: The hash value of the data block is calculated in real time, and the hash value and key index are submitted to the blockchain network. After consensus, an immutable on-chain evidence storage is formed. S3. Dual-engine Intelligent Compliance Monitoring: S31. The rule-based engine verifies in real time whether the data conforms to the SOP (such as the order of steps, personnel qualifications, and equipment status). S32. The machine learning engine asynchronously analyzes operational patterns and data trends to identify hidden anomalies; S33. Integrate the results of the two engines to push real-time early warnings for medium- and high-risk events; S4. Distributed Audit Report Generation: S41. Receive audit tasks and analyze the audit scope and assertions; S42. Break down complex assertions into multiple sub-assertion tasks and distribute them to distributed computing nodes; S43. Each node executes in parallel, retrieves business data from the database, obtains integrity credentials from the blockchain, and generates partial proofs; S44. Aggregate all partial evidence and combine it with the report template to automatically generate the final audit report; S5. Visual Interaction and Traceability: The user interface provides visualized monitoring of the entire experimental process, one-click data verification based on blockchain credentials, and management and export functions for audit reports. Preferably, in step S2, the hash value is generated using a salted hash algorithm, where the salt value consists of a unique laboratory identifier and a timestamp accurate to milliseconds. Preferably, in step S3, the machine learning engine continuously optimizes the model by analyzing historical compliance and violation data, and feeds newly discovered anomaly patterns back to the rule engine, forming a self-evolving compliance knowledge base.

[0048] System core process: 1. Trusted Data Pipeline: Data is collected, packaged, and hashed synchronously from the moment it is generated. The hash value is written to the blockchain asynchronously but in a way that ensures eventual consistency. Any tampering with the original data will cause its hash to differ from the on-chain record, and will be immediately detected by the system.

[0049] 2. AI Compliance Monitoring Dual Engine: Rule Engine (Real-time): Based on rule management systems such as Drools, it loads thousands of predefined SOPs and regulatory rules and performs millisecond-level judgments on incoming data.

[0050] Machine learning engine (asynchronous / batch processing): Based on historical data, the model is trained to identify complex patterns (such as implicit correlations between violations in multiple steps, and habitual biases in human operation) and unknown anomalies. The results from both engines are fused at the risk scoring center to generate the final warning.

[0051] 3. Distributed Report Generation: The report generation task is broken down into multiple "assertion proof" subtasks, such as "proving that device A is in a calibration state within a time frame." These subtasks are distributed to the computing cluster for parallel execution, retrieving evidence from the database and blockchain respectively, generating partial proofs, and finally aggregating them to generate a complete report, greatly improving processing efficiency.

[0052] 4. Cross-institutional mutual trust mechanism: By setting regulatory agencies or partners as observer nodes of the consortium blockchain or granting them specific data verification API keys, they can independently verify the authenticity and integrity of the data provided by the laboratory within the authorized scope, without the need for secondary processing or proof by the laboratory, thus building end-to-end trust.

[0053] This invention provides an immutable "digital fingerprint" for each data block using blockchain technology. Combined with real-time analysis and intelligent judgment from an AI monitoring layer, it achieves data integrity assurance and compliance monitoring, effectively solving the problems of easy loss, tampering, and difficulty in traceability of traditional paper / simple electronic records. This significantly improves data credibility and audit accuracy. Automated compliance monitoring and audit report generation based on AI intelligent analysis greatly improves laboratory data processing efficiency, reduces the workload of manual data processing and compilation, lowers labor costs and the risk of human error, thereby achieving high efficiency and standardization in laboratory quality management. Furthermore, it enables real-time collection and analysis of laboratory operational data and its process elements. According to reports, by combining AI intelligent analysis and early warning functions, proactive monitoring and risk warning of the laboratory's production process have been achieved, effectively solving the problem of passively responding to audits in the traditional way and improving the overall quality management level of the laboratory. Based on the distributed evidence storage mechanism of blockchain, a transparent and reliable data sharing and verification channel has been established between the laboratory and its partners, contract research organizations and regulatory agencies, simplifying the data exchange and verification process and promoting information exchange and collaboration between institutions. Through intelligent data extraction, correlation and analysis capabilities, combined with blockchain credentials and compliance status, the automatic generation of audit reports has been achieved, avoiding interference from human subjective factors and improving the objectivity and comprehensiveness of audit results.

[0054] To facilitate understanding of the above technical solutions of the present invention, the following detailed description of the above technical solutions of the present invention will be provided through specific usage methods.

[0055] Example 1: Highly Reliable Data Auditing for Drug Development Laboratories 1. Applicable Scenarios: This embodiment is mainly applicable to laboratories in innovative drug development and biotechnology research that have extremely high requirements for data integrity (ALCOA+ principle) and need to cope with strict regulatory (such as FDA) reviews. Its characteristics are flexible experimental procedures and strong exploratory nature, but the immutability of core data is the lifeline.

[0056] 2. Implementation process: Data acquisition and evidence storage: The system collects operation records, instrument raw data files and environmental data in ELN in real time, encapsulates them and calculates their SHA-256 hash values, and writes them into the Fabric consortium blockchain jointly built by the laboratory and major partners.

[0057] 3. AI Compliance Monitoring: Model type: Compliance monitoring uses a dual-engine approach; Rule engine: Based on Drools, loading thousands of SOPs and 21 CFR Part 11 rules; Machine learning engine: Employs a Long Short-Term Memory (LSTM) network to learn the experimentalist's operational habits and time consumption patterns; Training data source: The training data for the LSTM model comes from all completed experimental operation logs that have been approved by QA in the past two years (the hash values ​​of these logs have been stored on the blockchain). Model update strategy: Every quarter, the system automatically uses new data from the previous quarter to incrementally train the LSTM model. The model update package and its performance evaluation report must be electronically signed by the quality manager, and their hash value must be uploaded to the blockchain before deployment and effectiveness. Example: AI monitoring detected that the time taken for a certain "centrifugation" step was only 30% of the historical average, and the centrifuge logs were not synchronized. The rules engine triggered a "step data incomplete" alert, and the LSTM engine simultaneously provided a high-risk "operational anomaly" score. The system generated a comprehensive alert.

[0058] 4. Distributed audit report generation: Example of assertion decomposition: When generating the "Audit Report on the Integrity of All Experimental Data in Q1 2024", the system decomposes the overall assertion "Proves that all experimental data is complete and compliant" into parallel sub-assertions, for example: Sub-assertion A: SELECT Experiment ID FROM Experiment Record WHERE Time Period = '2024Q1' AND Corresponding Blockchain Transaction ID IS NULL (Proves that all records have been uploaded to the blockchain) Sub-assertion B: For each experiment, verify that all required step fields defined in its SOP have non-null values ​​(to prove data integrity). Sub-assertion C: For each AI alert event, verify that the corresponding original data hash already existed in the blockchain before the alert was generated (proving that the alert is based on trusted data). These subqueries and validations are executed in parallel on each computing node, and a report is automatically generated after the results are aggregated.

[0059] Example 2: Process Traceability and Standards Compliance Audit for Third-Party Testing Organizations 1. Applicable Scenarios: This embodiment is applicable to CMA and CNAS certified testing laboratories and inspection bodies. Its core requirement is to ensure that the testing process strictly adheres to national or industry standards, achieving full traceability from sample receipt, preparation, testing to report issuance, in order to respond to reviews and customer inquiries.

[0060] 2. Implementation characteristics: Data and evidence storage: Focus on collecting and uploading the testing standard version number, sample unique identifier, instrument raw data file (.raw / .dat) hash, and calibration curve data to the blockchain.

[0061] 3. AI Compliance Monitoring: Model type: Combining a temporal Transformer model with a computer vision (CV) model; Time series model: Analyzes the complex time series relationship between "ambient temperature - sample stabilization time - detection results"; CV model: used to identify whether the key parameters entered in the screenshot of the instrument touch screen operation or the scanned copy of the recording paper are consistent with the transmitted electronic data; Training data sources: Transformer model training data comes from the full-process time-series logs of historical qualified inspection projects; CV model training data comes from thousands of manually annotated instrument interface screenshots and corresponding data. Model update strategy: When a new detection standard or instrument is introduced, targeted model fine-tuning is triggered. The hash value of the update basis (new standard document, new instrument manual) is uploaded to the blockchain along with the model version; Audit report generation: The report generation template has built-in CMA / CNAS elements. The system can automatically link and prove that "each result in a test report can be traced back to an instrument with valid calibration status, the original data file that meets the standards, and the corresponding on-chain evidence."

[0062] Example 3: Real-time GMP Compliance Monitoring for Fine Pharmaceutical Manufacturing Workshops 1. Applicable Scenarios: This embodiment is applicable to GMP environments such as pharmaceutical factory production workshops and aseptic preparation production lines. Its characteristics include a fixed process but numerous steps, emphasizing the prevention of contamination, cross-contamination, confusion, and errors, and requiring real-time monitoring and timely handling of deviations.

[0063] 2. Implementation Features: Data and Evidence Storage: High-frequency collection and uploading of cleanroom particle counts, temperature and humidity, equipment operating status, and personnel entry and exit records to the blockchain. The electronic signature hash value of each batch of production instructions is used as a key index and uploaded to the blockchain.

[0064] 3. AI Compliance Monitoring: Model type: Graph Neural Network (GNN) and anomaly detection algorithm (such as Isolation Forest); GNN model: Treats the personnel, equipment, materials and environment of the production workshop as a dynamic graph, learns normal interaction and flow patterns, and is used to discover unconventional cross paths (potential cross-contamination risks).

[0065] Anomaly detection algorithm: Real-time monitoring of key process parameters such as filling weight and pH value to identify "drifts" that exceed traditional control limits but show abnormal trends; Training data source: The training data for GNN comes from the full-element logs of historical normal production batches; the anomaly detection model uses past normal production data to train a "normal" baseline; Model update strategy: After each major process change or workshop layout adjustment, the GNN model must be retrained using simulated operation data. Approval records for change control documents and model update logs must be uploaded to the blockchain. Audit Report Generation: The system supports one-click generation of electronic batch record summary reports by production batch. The report automatically includes a blockchain-based evidence index of all key operations, environmental data, and deviation events for that batch, meeting the FDA's audit trail requirements for electronic batch records.

[0066] In summary, by utilizing the technical solutions described above in this invention, a trusted data source with blockchain-verified evidence is provided to the AI ​​compliance monitoring module, ensuring the authenticity and traceability of AI analysis inputs and fundamentally guaranteeing the reliability of compliance judgments. By packaging the original operational data and the real-time analysis results of the AI ​​compliance monitoring module together on the blockchain, each compliance judgment itself becomes tamper-proof evidence, constructing a dynamic and traceable intelligent compliance evidence chain. Through continuous learning and rule iteration driven by the machine learning engine, and by storing the rule and model change process on the blockchain, the compliance monitoring system can self-evolve, and its evolution process is transparent and trustworthy throughout.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An AI-based automated compliance recording and auditing system for laboratories, characterized in that, include: The data acquisition and encapsulation module is used to acquire raw operational data and process metadata from multi-source data in the laboratory in real time or near real time, and encapsulate the operational data and process metadata into structured data blocks. The blockchain evidence storage module is connected to the data acquisition and packaging module. It is used to calculate the cryptographic hash value of the data block and write the hash value and key index information into the blockchain network for distributed evidence storage, generating an immutable data fingerprint certificate. The AI ​​compliance monitoring module connects to the data acquisition and encapsulation module and integrates a rule-based engine and a machine learning engine; the rule-based engine is used to perform real-time logical compliance judgment on the incoming data blocks according to predefined compliance rules. The machine learning engine is used to train on historical operation sequences and data to identify complex anomaly patterns and potential risks. The AI ​​compliance monitoring module integrates the analysis results of the two engines to generate real-time warnings for violations or risk trends. The automatic audit report generation module is connected to the blockchain evidence storage module and the artificial intelligence compliance monitoring module respectively. It is used to respond to audit tasks, automatically query operation records, compliance status and corresponding blockchain credentials from the database based on the specified audit template and assertions, and generate a standardized audit report containing data integrity verification and compliance conclusions. The user interaction module provides a visual interface to display the experimental process monitoring view, data traceability chain, compliance warning information, and audit report.

2. The AI-based automated laboratory compliance recording and auditing system according to claim 1, characterized in that, It also includes a collaborative working mechanism between blockchain and artificial intelligence, which includes: In the data layer collaboration, the AI ​​compliance monitoring module is used to prioritize the use of data with trusted tags anchored by the blockchain notarization module for model training and real-time inference. In collaboration with the business logic layer, after the AI ​​compliance monitoring module completes the compliance judgment, the blockchain evidence storage module packages the original operation data and its corresponding AI analysis results together, calculates the joint hash value and writes it into the blockchain to solidify the dynamic evidence chain containing intelligent interpretation. In the feedback optimization layer collaboration, the machine learning engine is used to identify potential new rules from the audit results, and when compliance rules or AI models are updated, the blockchain evidence storage module stores the changes as transactions to form a closed-loop trusted record of system evolution.

3. The AI-based automated laboratory compliance recording and auditing system according to claim 1, characterized in that, The machine learning engine in the AI ​​compliance monitoring module is one or more combinations of recurrent neural networks, temporal Transformer models, or graph neural networks; wherein, the recurrent neural network or temporal Transformer model is used to learn normal temporal dependencies between experimental steps to detect process anomalies, and the graph neural network is used to model the personnel, equipment, materials, and environmental elements of the laboratory as dynamic graphs to identify potential cross-contamination risks.

4. The AI-based automated laboratory compliance recording and auditing system according to claim 1, characterized in that, The automatic audit report generation module is specifically used to: decompose audit assertions into multiple independently verifiable sub-assertion tasks, distribute them to a distributed computing cluster for parallel evidence retrieval and proof generation, and finally aggregate all local proofs to synthesize the final audit report.

5. The AI-based automated laboratory compliance recording and auditing system according to claim 1, characterized in that, The system also includes a data persistence layer, which adopts a multi-model database strategy, including: a relational database for storing core business entities and relationships; a time-series database for storing environmental monitoring time-series data; and a document database or object storage for storing audit reports and original files.

6. The AI-based laboratory automated compliance recording and auditing system according to claim 1, characterized in that, The blockchain evidence storage module adopts a consortium blockchain architecture, which allows laboratories, partners and regulatory agencies to participate in consensus as nodes or as observation nodes, so as to achieve cross-institutional data trust verification.

7. An artificial intelligence-based method for automated compliance recording and auditing in laboratories, applied to the system described in any one of claims 1 to 6, characterized in that, Includes the following steps: S1 Data Acquisition and Trusted Encapsulation: Real-time acquisition of multi-source laboratory operation data and process metadata, encapsulated into data blocks with timestamps, operator and equipment ID information; S2 Blockchain Fingerprint Evidence: Instantly calculates the hash value of a data block and submits the hash value and key index to the blockchain network. After consensus is reached, an immutable on-chain evidence is formed. S3 Dual-Engine Intelligent Compliance Monitoring: The rule-based engine verifies in real time whether the data complies with standard operating procedures; The machine learning engine asynchronously analyzes operational patterns and data trends to identify hidden anomalies; By integrating the results from both engines, real-time early warnings are pushed out for medium- and high-risk events; S4 Distributed Audit Report Generation: Receives audit tasks, parses audit scope and assertions; breaks down complex assertions into multiple sub-assertion tasks, distributes them to distributed computing nodes for parallel execution, retrieves business data from the database and integrity credentials from the blockchain, and generates partial proofs; aggregates all partial proofs, combines them with a report template, and automatically generates the final audit report; S5 Visual Interaction and Traceability: Provides full-process visual monitoring of experiments through the user interface, one-click data authenticity verification based on blockchain credentials, and management and export functions for audit reports.

8. The AI-based laboratory automation compliance recording and auditing method according to claim 7, characterized in that, In step S2, the hash value is generated using a salted hash algorithm, and the salt value consists of a unique laboratory identifier and a timestamp accurate to the millisecond level.

9. The artificial intelligence-based laboratory automated compliance recording and auditing method according to claim 7, characterized in that, Step S3 further includes: the machine learning engine continuously optimizes the model by analyzing historical compliance and violation data, and feeds newly discovered abnormal patterns back to the rule engine to form a self-evolving compliance knowledge base.

10. The artificial intelligence-based laboratory automation compliance recording and auditing method according to claim 7, characterized in that, In step S4, the blockchain certificate is used to prove in the audit report that: the generation time and content of the original data have not been tampered with, and the AI ​​warning event is based on credible data that has been stored on the blockchain at that time.

Images