Pluggable data adaptation and analysis method for LIMS

Abstract

The invention relates to the technical field of data adaptation, in particular to a pluggable data adaptation and analysis method for LIMS, which comprises the following steps of: determining laboratory instrument types and data output formats by investigation, mapping unstructured data into structured standards by combing ISO / IEC 17025 compliance requirements, and analyzing the unstructured data according to the structured standards; an independent adapter module is designed through a micro-service architecture, a dynamic loading mechanism is adopted, an adapter and an LIMS core are decoupled, original data are obtained through an AI image analysis technology, compliance data are stored through an encrypted database, analysis failure reasons are located through abnormal log analysis, and adapter modification content is recorded through a version management tool. And the problem type and the repair strategy are fed back to the development process, and a continuously optimized adapter version and a problem closed-loop management mechanism are obtained. The problems that laboratory instrument data formats are various and difficult to adapt, data analysis is not comprehensive, formats are not uniform, and adapter maintenance lacks closed-loop management are solved.

Description

Technical Field

[0001] This invention belongs to the field of data adaptation technology, specifically relating to a pluggable data adaptation and parsing method for LIMS. Background Technology

[0002] In the application of Laboratory Information Management Systems (LIMS), efficient data adaptation and accurate data parsing are crucial for ensuring the smooth operation of laboratory work and meeting quality management requirements. Currently, laboratories are equipped with diverse types of instruments, and the data formats output by these instruments vary widely, including both structured and a large amount of unstructured data. This poses a significant challenge to the unified collection and processing of data, making direct integration into the LIMS for subsequent analysis and management difficult. Regarding data compliance, laboratories must adhere to international standards such as ISO / IEC 17025, which impose strict requirements on data fields. However, existing data adaptation methods often lack a systematic approach, making it difficult to accurately extract standard-compliant data fields and effectively map unstructured data to structured standards, resulting in inconsistent data quality and affecting the accuracy and reliability of test results. Furthermore, traditional data parsing methods often employ single technical means, making it difficult to comprehensively and accurately extract effective information from complex and variable raw data, such as data acquired through images. The lack of effective data cleaning and format standardization mechanisms further hinders the integration and utilization of heterogeneous data. Furthermore, in existing technologies, the adapter is tightly coupled to the LIMS core, lacking flexibility and scalability. Once the instrument is updated or the data format changes, a large-scale modification of the entire system is required, resulting in high costs and low efficiency. Moreover, the lack of a robust traceability mechanism during data processing makes it impossible to ensure data integrity and compliance, thus failing to meet audit and regulatory requirements.

[0003] Existing technologies suffer from a lack of systematic standards for adapting to diverse instrument data, difficulty in extending adapters coupled with the LIMS core, incomplete data parsing and inconsistent formats, poor data traceability, and a lack of closed-loop management mechanisms for problems, making it difficult to meet the efficient, accurate, and compliant data processing requirements of LIMS. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a pluggable data adaptation and parsing method for LIMS, which solves the problems of diverse and difficult-to-adapt data formats from laboratory instruments, difficulties in coupling and expanding traditional adapters with the LIMS core, incomplete and inconsistent data parsing formats, poor data traceability, and lack of closed-loop management for adapter maintenance. To achieve the above objectives, this invention adopts the following technical solution:

[0005] The aforementioned pluggable data adaptation and parsing method for LIMS includes the following steps: conducting research to clarify the types of laboratory instruments and data output formats, and reviewing ISO / IEC standards... To meet compliance requirements (17025), data fields were extracted, and unstructured data was mapped to structured standards to obtain a specification document for adapting requirements. Independent adapter modules were designed using a microservice architecture, with each module developing data acquisition interfaces for different device types. A dynamic loading mechanism was used to decouple the adapters from the LIMS core, resulting in an adapter library and interface specifications. Raw data was acquired using AI image analysis technology, and data cleaning algorithms were used to remove null and outlier values, converting heterogeneous data into a unified format to obtain a preprocessed dataset. Device output fields were associated with LIMS business objects through field-level semantic mapping, and a context engine was used to complete environmental information, transforming isolated data into traceable business entities to obtain complete and associated structured data. A rule engine was used to verify whether the data met the detection method standards and result thresholds. Compliant data was stored in an encrypted database, and operation logs were recorded. Raw data was bound to audit information to obtain a traceable data storage solution. Anomaly log analysis was used to locate the cause of parsing failures, and version management tools were used to record adapter modifications. Problem types and remediation strategies were fed back to the development process, resulting in a continuously optimized adapter version and problem closed-loop management mechanism.

[0006] Furthermore, the aforementioned survey clarifies the types of laboratory instruments and data output formats. By reviewing ISO / IEC 17025 compliance requirements, data fields are extracted, and unstructured data is mapped to structured standards to obtain a compliance specification document. This process includes the following steps: A combination of on-site visits and online questionnaires is used to survey the laboratory's existing instrument and equipment list, clarifying the model, communication interface, and data output format of each type of instrument; by analyzing the clauses regarding original records and data integrity in the ISO / IEC 17025 standard, data fields such as testing time, sample number, instrument parameters, and test results are extracted; the unstructured data output by the instruments—text, images, and binary streams—is converted into structured tables according to field mapping rules, clarifying field types, lengths, and validation rules to obtain the compliance specification document.

[0007] Furthermore, the design of independent adapter modules through a microservice architecture, with each module developing a data acquisition interface for a specific device type, employs a dynamic loading mechanism to decouple the adapter from the LIMS core, resulting in an adapter library and interface specifications. This includes the following steps: Adopting a microservice architecture concept, the data adaptation function is split into independent adapter modules, each focusing on the data acquisition needs of a single device type; by analyzing device communication protocols and data output characteristics, acquisition parameters are extracted and standardized interfaces are developed, including data acquisition methods supporting serial communication, network transmission, and file reading; dynamic class loading technology is used to achieve adapter hot-swapping, automatically registering new adapters at runtime without restarting the system; and the modules are encapsulated into an extensible adapter library, resulting in the adapter library and interface specifications.

[0008] Furthermore, the process of obtaining raw data through AI image analysis technology, removing null values ​​and outliers using data cleaning algorithms, and converting heterogeneous data into a unified format to obtain a preprocessed dataset includes the following steps: using AI image analysis technology, information is extracted from image data through object detection and character recognition models to obtain raw detection values ​​and timestamp data; missing fields and outliers deviating from reasonable ranges are identified through data quality assessment algorithms, and data cleaning is completed using mean filling and boundary truncation methods; the text, image analysis results, and electronic data are formatted according to data dictionaries and structured templates, and field naming rules and data types are unified to obtain a standardized preprocessed dataset that meets the analysis requirements.

[0009] Furthermore, the step of associating device output fields with LIMS business objects through field-level semantic mapping, using a context engine to complete environmental information, and transforming isolated data into traceable business entities to obtain associated and complete structured data includes the following steps: using field-level semantic mapping technology to analyze the business meaning of each device output field, and accurately associating them with business objects in the LIMS system by constructing a field mapping table; automatically extracting environmental parameters during data collection using a context-aware engine and associating them with corresponding data records, the environmental parameters including temperature and humidity, operator, and equipment calibration status; integrating the scattered original device data with the completed context information, establishing logical relationships between data through unique identifiers, transforming isolated data into traceable entities with complete business background, and obtaining associated structured data with audit traceability capabilities.

[0010] Furthermore, the step of verifying whether the data conforms to the testing method standards and result thresholds through a rule engine, storing compliant data in an encrypted database, recording operation logs, and binding the original data with audit information to obtain a traceable data storage solution includes the following steps: using rule engine technology, the numerical ranges and unit requirements in the testing method standards are transformed into executable verification rules, and the compliance of the data is determined through real-time comparison; using encryption algorithms to encrypt the database to store the compliant data that has passed verification, ensuring data confidentiality, and recording the data operation time, operator, and modification content through log services to generate an unalterable operation trajectory; and associating and binding the original data, verification results, and audit logs with unique identifiers to construct a traceability chain covering the entire data lifecycle, thereby obtaining a traceable data storage solution that meets CNAS requirements.

[0011] Furthermore, the step of locating the cause of parsing failure through anomaly log analysis, recording adapter modifications using version management tools, and feeding back the problem type and repair strategy to the development process to obtain a continuously optimized adapter version and problem closed-loop management mechanism includes the following steps: using log analysis tools to perform in-depth analysis of anomaly logs, locating the specific link in data parsing failure through keyword matching and context association, and extracting error type, occurrence time, and associated device information; using version management tools to record the adapter code modification history, which includes the modifier, modification time, and changes; synchronizing the analyzed problem type and repair plan to the development task management module to drive iterative optimization, and automatically verifying the repair effect through the continuous integration process; and releasing the tested adapter version to the production environment to obtain a closed-loop management mechanism including problem discovery, attribution, repair, and verification.

[0012] Furthermore, the process of identifying missing fields and outliers deviating from reasonable ranges through a data quality assessment algorithm, and completing data cleaning using mean imputation and boundary truncation, includes the following steps: using a data quality assessment algorithm based on statistical distribution and business rules, missing fields are identified through indicators, and outliers deviating from reasonable ranges are located using standard deviation thresholds; for missing fields, historical averages of similar data are extracted for imputation, and for outliers exceeding equipment range / logical boundaries, maximum / minimum effective value truncation is performed; the cleaning process retains copies of the original data, records cleaning rules and operation logs, and obtains a dataset whose completeness and reasonableness meet the analysis requirements.

[0013] Furthermore, the adoption of rule engine technology transforms the numerical ranges and unit requirements in the detection method standard into executable verification rules. Real-time comparison is used to determine data compliance. This includes the following steps: Using rule engine technology, the detection method standard text is structured and parsed to extract the upper and lower limits of the numerical range and allowed unit types, transforming them into program-recognizable conditional judgment logic; business requirements are mapped to verification rules through a rule configuration interface. These verification rules include setting the effective range of the indicator to 10 to 50 and the unit to be milligrams per liter; real-time verification is triggered during data transmission, comparing the data to be inspected with the conditions in the rule base item by item, marking abnormal data that exceeds the range or has unit mismatches, and obtaining verification results including compliance status indicators and detailed error messages.

[0014] In the technical solution provided by this invention, a survey is conducted to clarify the types of laboratory instruments and data output formats, and the ISO / IEC standards are reviewed. To meet compliance requirements (17025), data fields were extracted, and unstructured data was mapped to structured standards to obtain a specification document for adapting requirements. Independent adapter modules were designed using a microservice architecture, with each module developing data acquisition interfaces for different device types. A dynamic loading mechanism was used to decouple the adapters from the LIMS core, resulting in an adapter library and interface specifications. Raw data was acquired using AI image analysis technology, and data cleaning algorithms were used to remove null and outlier values, converting heterogeneous data into a unified format to obtain a preprocessed dataset. Device output fields were associated with LIMS business objects through field-level semantic mapping, and a context engine was used to complete environmental information, transforming isolated data into traceable business entities to obtain complete and associated structured data. A rule engine was used to verify whether the data met the detection method standards and result thresholds. Compliant data was stored in an encrypted database, and operation logs were recorded. Raw data was bound to audit information to obtain a traceable data storage solution. Anomaly log analysis was used to locate the cause of parsing failures, and version management tools were used to record adapter modifications. Problem types and remediation strategies were fed back to the development process, resulting in a continuously optimized adapter version and problem closed-loop management mechanism. This invention solves the problems of diverse and difficult-to-adapt data formats of laboratory instruments, difficulty in coupling and expanding traditional adapters with the LIMS core, incomplete and inconsistent data parsing, poor data traceability, and lack of closed-loop management for adapter maintenance, thereby achieving efficient adaptation, parsing, and compliant storage of LIMS data. Attached Figure Description

[0015] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0016] Figure 1This is a schematic diagram of a first embodiment of a pluggable data adaptation and parsing method for LIMS according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of a second embodiment of a pluggable data adaptation and parsing method for LIMS according to an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of a third embodiment of a pluggable data adaptation and parsing method for LIMS according to an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram of the fourth embodiment of a pluggable data adaptation and parsing method for LIMS in this invention.

[0020] Figure 5 This is a schematic diagram of the fifth embodiment of a pluggable data adaptation and parsing method for LIMS in this invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] A pluggable data adaptation and parsing method for LIMS, such as Figure 1As shown, the process includes the following steps: conducting research to clarify the types of laboratory instruments and data output formats, and reviewing ISO / IEC standards. To meet compliance requirements (17025), data fields were extracted, and unstructured data was mapped to structured standards to obtain a specification document for adapting requirements. Independent adapter modules were designed using a microservice architecture, with each module developing data acquisition interfaces for different device types. A dynamic loading mechanism was used to decouple the adapters from the LIMS core, resulting in an adapter library and interface specifications. Raw data was acquired using AI image analysis technology, and data cleaning algorithms were used to remove null and outlier values, converting heterogeneous data into a unified format to obtain a preprocessed dataset. Device output fields were associated with LIMS business objects through field-level semantic mapping, and a context engine was used to complete environmental information, transforming isolated data into traceable business entities to obtain complete and associated structured data. A rule engine was used to verify whether the data met the detection method standards and result thresholds. Compliant data was stored in an encrypted database, and operation logs were recorded. Raw data was bound to audit information to obtain a traceable data storage solution. Anomaly log analysis was used to locate the cause of parsing failures, and version management tools were used to record adapter modifications. Problem types and remediation strategies were fed back to the development process, resulting in a continuously optimized adapter version and problem closed-loop management mechanism.

[0024] like Figure 2 As shown, in this embodiment, a combination of on-site visits and online questionnaires was used to survey the existing list of laboratory instruments and equipment, clarifying the model, communication interface, and data output format of each type of instrument; by analyzing the clauses regarding original records and data integrity in the ISO / IEC 17025 standard, data fields such as testing time, sample number, instrument parameters, and test results were extracted; the unstructured data of text, images, and binary streams output by the instruments were converted into structured tables according to field mapping rules, clarifying the field types, lengths, and verification rules, and obtaining the adaptation requirement specification document.

[0025] Through a combination of on-site visits and online questionnaires, a comprehensive and accurate understanding of the laboratory's existing instruments and equipment was obtained, laying a solid foundation for subsequent adaptation work. Key data fields were extracted by analyzing each clause of the ISO / IEC 17025 standard, ensuring that data adaptation strictly adhered to compliance requirements and guaranteeing data quality and integrity. Unstructured data was converted into structured tables, and relevant rules were clearly defined, forming a standardized document outlining the adaptation requirements. This ensured a structured data adaptation process, effectively solving the problem of disorganized instrument data formats and difficulties in unified processing, and providing strong support for the LIMS system to efficiently and accurately receive and process data.

[0026] like Figure 3As shown, in this embodiment, a microservice architecture is adopted, and the data adaptation function is split into independent adapter modules. Each module focuses on the data acquisition needs of a single device type. By analyzing the device communication protocol and data output characteristics, acquisition parameters are extracted and standardized interfaces are developed. The standardized interfaces include data acquisition methods that support serial communication, network transmission, and file reading. Dynamic class loading technology is used to realize adapter hot-swapping. New adapters are automatically registered at runtime without restarting the system. The modules are encapsulated into an extensible adapter library, resulting in the adapter library and interface specifications.

[0027] A microservice architecture was adopted to decompose the data adaptation function into independent modules, making the system structure clearer. Each module focuses on data acquisition from a single device, improving development efficiency and maintainability. Standardized interfaces were developed by analyzing device characteristics, covering multiple data acquisition methods, enhancing compatibility with communication and data output across different devices. Dynamic class loading technology enables hot-swapping of adapters, automatically registering new adapters at runtime without requiring a system restart, greatly improving system flexibility and scalability. The resulting system is encapsulated into an extensible adapter library and standardized with defined interfaces.

[0028] like Figure 4 As shown, in this embodiment, AI image analysis technology is used to extract information from image data through target detection and character recognition models to obtain original detection values ​​and timestamp data; missing fields and outliers deviating from reasonable ranges are identified through data quality assessment algorithms, and data cleaning is completed by mean filling and boundary truncation; the text, image analysis results and electronic data are converted into formats according to data dictionaries and structured templates, and the field naming rules and data types are unified to obtain a standardized preprocessed dataset that meets the analysis requirements.

[0029] Leveraging AI image analysis technology and utilizing object detection and character recognition models, key information such as raw detection values ​​and timestamps can be accurately extracted from image data, solving the problems of low efficiency and error-proneness in manual extraction. Data quality assessment algorithms can effectively identify missing fields and outliers, improving data quality through cleaning methods such as mean imputation and boundary truncation. Format conversion is performed according to data dictionaries and structured templates, unifying field naming and data types to standardize the data and obtain a standardized preprocessed dataset that meets analytical requirements.

[0030] like Figure 5As shown, in this embodiment, field-level semantic mapping technology is used to analyze the business meaning of each field output by the device. By constructing a field mapping table, the data is accurately associated with the business objects in the LIMS system. The environmental parameters during data collection are automatically extracted by the context-aware engine and associated with the corresponding data records. The environmental parameters include temperature and humidity, operators, and device calibration status. The scattered original device data is integrated with the supplemented context information. Logical relationships between data are established through unique identifiers. Isolated data is transformed into traceable entities with complete business background, resulting in associated structured data with audit traceability capabilities.

[0031] Field-level semantic mapping technology deeply analyzes the business meaning of device output fields, constructs mapping tables to achieve precise association with LIMS business objects, and ensures that data accurately corresponds to business scenarios. The context-aware engine automatically extracts environmental parameters such as temperature and humidity and associates them with data records, enriching the data's meaning. By integrating scattered raw data with supplementary contextual information and establishing logical relationships using unique identifiers, isolated data is transformed into traceable entities. This results in structured data with audit traceability capabilities, ensuring both data integrity and accuracy, and providing strong support for subsequent data traceability and auditing.

[0032] In this embodiment, rule engine technology is used to transform the numerical range and unit requirements in the detection method standard into executable verification rules. Real-time comparison is used to determine whether the data is compliant. An encryption algorithm is used to encrypt the database to store the compliant data that has passed the verification, ensuring data confidentiality. The data operation time, operator and modification content are recorded through log service to generate an unalterable operation trajectory. The original data, verification results and audit logs are associated and bound with a unique identifier to build a traceability chain covering the entire data lifecycle, resulting in a traceable data storage solution that meets CNAS requirements.

[0033] By leveraging a rule engine to transform detection standards into executable verification rules, real-time and accurate data compliance assessments are achieved, effectively ensuring data quality. Encryption algorithms encrypt and store compliant data, ensuring confidentiality and security. A log service records detailed operation information, generating an immutable track, enhancing the transparency and auditability of data operations. A traceability chain covering the entire data lifecycle is built by linking original data, verification results, and audit logs using unique identifiers. This results in a traceable data storage solution that meets CNAS requirements.

[0034] In this embodiment, a log analysis tool is used to perform in-depth analysis of the abnormal logs. By using keyword matching and contextual association, the specific steps in the data parsing failure are located, and the error type, occurrence time, and associated device information are extracted. A version management tool is used to record the adapter code modification history, which includes the modifier, modification time, and changes. The analyzed problem types and repair solutions are synchronized to the development task management module to drive iterative optimization. The repair effect is automatically verified through the continuous integration process. The adapter version that passes the test is released to the production environment, resulting in a closed-loop management mechanism that includes problem discovery, attribution, repair, and verification.

[0035] Log analysis tools, leveraging keyword matching and contextual association, can accurately pinpoint data parsing failures and quickly extract key error information, providing strong evidence for troubleshooting. Version management tools meticulously record adapter code modification history, facilitating tracing the source of issues and understanding changes. Issues and fixes are synchronized to the development task management module, driving iterative optimization and automating the continuous integration process to verify fix effectiveness, ensuring effective problem resolution. Tested adapter versions are released, forming a closed-loop management mechanism covering the entire issue lifecycle.

[0036] In this embodiment, a data quality assessment algorithm based on statistical distribution and business rules is adopted. Missing fields are identified by indicators, and outliers deviating from the reasonable range are located using standard deviation thresholds. For missing fields, historical averages of similar data are extracted for filling. For outliers exceeding the equipment range / logical boundaries, the maximum / minimum effective value is used for truncation. The cleaning process retains a copy of the original data, records the cleaning rules and operation logs, and obtains a dataset whose completeness and reasonableness meet the analysis requirements.

[0037] The data quality assessment algorithm, based on statistical distribution and business rules, can accurately identify missing fields and outliers, providing a clear direction for data cleaning. Missing fields are filled with historical averages, and outliers are truncated within a valid range, effectively improving data integrity and reasonableness. Simultaneously, the cleaning process retains copies of the original data and records cleaning rules and operation logs, ensuring data traceability and facilitating subsequent auditing and problem investigation. This results in a dataset that meets the analytical requirements.

[0038] In this embodiment, rule engine technology is used to perform structured parsing of the standard text of the detection method, extract the upper and lower limits of the numerical range and the allowed unit types, and convert them into conditional judgment logic that the program can recognize. The business requirements are mapped to verification rules through the rule configuration interface. The verification rules include setting the effective range of the indicator to be 10 to 50 and the unit must be milligrams per liter. Real-time verification is triggered in the data transmission stage, and the data to be inspected is compared with the conditions in the rule base item by item. Abnormal data that exceeds the range or does not match the unit is marked, and a verification result containing compliance status identifiers and detailed error prompts is obtained.

[0039] By leveraging rule engine technology to perform structured parsing of the standard text of detection methods, key information can be accurately extracted and transformed into program-recognizable logic, ensuring the accuracy and executability of the rules. The rule configuration interface maps business requirements to specific verification rules, offering flexibility and intuitiveness. Real-time verification during data transmission promptly marks abnormal data, allowing issues to surface as early as possible. The resulting verification results include compliance status indicators and detailed error messages, facilitating quick problem identification and resolution by staff.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A pluggable data adaptation and parsing method for LIMS, characterized in that, The pluggable data adaptation and parsing method for LIMS includes the following steps: The survey clarified the types of laboratory instruments and data output formats. By sorting out the ISO / IEC 17025 compliance requirements, data fields were extracted, and unstructured data was mapped to structured standards to obtain a specification document that meets the requirements. Independent adapter modules are designed using a microservice architecture. Each module develops a data acquisition interface for the device type and adopts a dynamic loading mechanism to decouple the adapter from the LIMS core, resulting in an adapter library and interface specifications. The raw data is obtained by using AI image analysis technology, and data cleaning algorithms are used to remove null and outlier values, converting heterogeneous data into a unified format to obtain a preprocessed dataset. By associating device output fields with LIMS business objects through field-level semantic mapping, and using a context engine to complete environmental information, isolated data is transformed into traceable business entities, resulting in complete and associated structured data. By using a rule engine to verify whether the data meets the detection method standards and result thresholds, an encrypted database is used to store compliant data, operation logs are recorded, and raw data is bound to audit information to obtain a traceable data storage solution. By analyzing exception logs to pinpoint the cause of failure, using version management tools to record adapter modifications, and feeding back the problem type and repair strategy to the development process, a continuously optimized adapter version and problem closed-loop management mechanism is achieved.

2. The pluggable data adaptation and parsing method for LIMS according to claim 1, characterized in that, The process involves conducting research to clarify the types of laboratory instruments and data output formats, summarizing ISO / IEC 17025 compliance requirements, extracting data fields, mapping unstructured data to structured standards, and obtaining a required specification document. This includes the following steps: A combination of on-site visits and online questionnaires was used to survey the existing list of instruments and equipment in the laboratory, clarifying the model, communication interface and data output format of each type of instrument; By analyzing the clauses regarding original records and data integrity in the ISO / IEC 17025 standard, data fields such as testing time, sample number, instrument parameters, and test results were extracted. The unstructured data output by the instrument, including text, images, and binary streams, is converted into structured tables according to field mapping rules. The field types, lengths, and validation rules are defined to obtain a specification document that meets the requirements.

3. The pluggable data adaptation and parsing method for LIMS according to claim 1, characterized in that, The method involves designing independent adapter modules using a microservice architecture. Each module develops a data acquisition interface for each device type and employs a dynamic loading mechanism to decouple the adapter from the LIMS core, resulting in an adapter library and interface specifications. This includes the following steps: Adopting the microservice architecture concept, the data adaptation function is split into independent adapter modules, with each module focusing on the data collection needs of a single device type; By analyzing the device's communication protocol and data output characteristics, the acquisition parameters are extracted and a standardized interface is developed. The standardized interface includes data acquisition methods that support serial communication, network transmission, and file reading. The adapter hot-swapping technology is adopted to achieve hot-swapping. New adapters are automatically registered at runtime without restarting the system. The module is encapsulated into an extensible adapter library, resulting in the adapter library and interface specifications.

4. The pluggable data adaptation and parsing method for LIMS according to claim 1, characterized in that, The process of obtaining raw data through AI image analysis technology, removing null and outlier values ​​using data cleaning algorithms, and converting heterogeneous data into a unified format to obtain a preprocessed dataset includes the following steps: Using AI image analysis technology, information is extracted from image data through object detection and character recognition models to obtain raw detection values ​​and timestamp data; Data cleaning is completed by identifying missing fields and outliers that deviate from the reasonable range through data quality assessment algorithms, and by using mean imputation and boundary truncation methods. The text and image parsing results and electronic data are converted into formats according to the data dictionary and structured templates, and the field naming rules and data types are unified to obtain a standardized preprocessed dataset that meets the analysis requirements.

5. A pluggable data adaptation and parsing method for LIMS according to claim 1, characterized in that, The process of associating device output fields with LIMS business objects through field-level semantic mapping, using a context engine to complete environmental information, and transforming isolated data into traceable business entities to obtain complete and structured data includes the following steps: By employing field-level semantic mapping technology, the business meaning of each field output by the device is analyzed one by one, and a field mapping table is constructed to accurately associate it with the business objects in the LIMS system. The context-aware engine automatically extracts environmental parameters during data collection and associates them with the corresponding data records. These environmental parameters include temperature and humidity, operator information, and equipment calibration status. By integrating scattered raw device data with supplementary contextual information, establishing logical relationships between data through unique identifiers, and transforming isolated data into traceable entities with complete business backgrounds, we obtain related structured data with audit traceability capabilities.

6. The pluggable data adaptation and parsing method for LIMS according to claim 1, characterized in that, The step of verifying whether the data meets the detection method standards and result thresholds through a rule engine, storing compliant data in an encrypted database, recording operation logs, and binding raw data with audit information to obtain a traceable data storage scheme includes the following steps: By employing rule engine technology, the numerical ranges and unit requirements in the testing method standards are transformed into executable verification rules, and the compliance of the data is determined through real-time comparison. Encryption algorithms are used to encrypt the database storage of verified compliant data to ensure data confidentiality. Log services are used to record the time of data operations, the operators, and the content modified, generating an unalterable operation track. By associating and binding raw data, verification results, and audit logs with unique identifiers, a traceability chain covering the entire data lifecycle is constructed, resulting in a traceable data storage solution that meets CNAS requirements.

7. A pluggable data adaptation and parsing method for LIMS according to claim 1, characterized in that, The process of analyzing exception logs to locate and resolve failures, using version management tools to record adapter modifications, and feeding back issue types and remediation strategies to the development process results in a continuously optimized adapter version and issue closed-loop management mechanism, including the following steps: Log analysis tools are used to perform in-depth analysis of abnormal logs. By using keyword matching and contextual association, the specific steps in the data parsing failure are located, and the error type, occurrence time, and related device information are extracted. Version control tools are used to record the adapter code modification history, which includes the modifier, modification time, and changes. The identified problem types and remediation solutions are synchronized to the development task management module to drive iterative optimization, and the remediation effect is automatically verified through the continuous integration process. The tested adapter version is released to the production environment, resulting in a closed-loop management mechanism that includes problem discovery, attribution, fixing, and verification.

8. A pluggable data adaptation and parsing method for LIMS according to claim 4, characterized in that, The process of identifying missing fields and outliers that deviate from reasonable ranges using a data quality assessment algorithm, and then cleaning the data using mean imputation and boundary truncation, includes the following steps: A data quality assessment algorithm based on statistical distribution and business rules is adopted to identify missing fields through indicators and to locate outliers that deviate from the reasonable range using standard deviation thresholds. For missing fields, extract the historical average of similar data to fill them in; for outliers that exceed the device's range / logical boundaries, use the maximum / minimum valid value to truncate them. The cleaning process retains a copy of the original data, records cleaning rules and operation logs, and obtains a dataset whose completeness and rationality meet the analysis requirements.

9. A pluggable data adaptation and parsing method for LIMS according to claim 6, characterized in that, The rule engine technology is used to transform the numerical range and unit requirements in the detection method standard into executable verification rules, and to determine whether the data is compliant through real-time comparison. This includes the following steps: Using rule engine technology, the standard text of the detection method is structured and parsed to extract the upper and lower limits of the numerical range and the allowed unit types, which are then transformed into conditional judgment logic that the program can recognize. The business requirements are mapped to verification rules through the rule configuration interface. The verification rules include setting the effective range of the indicator to 10 to 50 and the unit must be milligrams per liter. Real-time verification is triggered during data transmission, comparing the data to be inspected with the conditions in the rule base item by item, marking abnormal data that are out of range or have mismatched units, and obtaining verification results that include compliance status indicators and detailed error messages.

Images