CN-121979877-A - Pluggable data adaptation and analysis method for LIMS

CN121979877ACN 121979877 ACN121979877 ACN 121979877ACN-121979877-A

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 adopting investigation to define laboratory instrument types and data output formats, mapping unstructured data into structural standards by combing ISO/IEC 17025 compliance requirements, designing an independent adapter module by a micro-service architecture, decoupling an adapter from a LIMS core by adopting a dynamic loading mechanism, acquiring original data by adopting an AI image analysis technology, storing compliance data by adopting an encryption database, analyzing and positioning failure reasons by adopting an abnormal log, adopting a version management tool to record the content of the adapter modification, feeding back the problem types and the restoration strategy to a development flow, and obtaining a continuously optimized adapter version and a continuously optimized problem closed-loop management mechanism. The invention solves the problems of various and difficult adaptation of laboratory instrument data formats, incomplete data analysis, non-uniform formats and lack of closed-loop management for adapter maintenance.

Inventors

ZHANG WENQUAN
YU WEIYI
ZHANG YONG
ZHANG LEI
XI PENGGUO
ZHOU XIANG

Assignees

陕西金合信息科技股份有限公司

Dates

Publication Date: 20260505
Application Date: 20260327

Claims (9)

1. The pluggable data adaptation and analysis method for the LIMS is characterized by comprising the following steps of: the laboratory instrument type and the data output format are clarified through investigation, data fields are extracted through combing ISO/IEC 17025 compliance requirements, unstructured data are mapped into structural standards, and an adaptation requirement specification document is obtained; Designing independent adapter modules through a micro-service architecture, developing a data acquisition interface by each module according to the type of equipment, and decoupling an adapter from a LIMS core by adopting a dynamic loading mechanism to obtain an adapter library and an interface specification; Acquiring original data through an AI image analysis technology, removing null values and abnormal values by adopting a data cleaning algorithm, and converting heterogeneous data into a unified format to obtain a preprocessed data set; the method comprises the steps of associating an equipment output field to a LIMS service object through field-level semantic mapping, complementing environmental information by a context engine, and converting isolated data into a traceable service entity to obtain structural data with complete association; checking whether the data accords with the detection method standard and the result threshold value through a rule engine, storing the compliance data by adopting an encryption database, recording an operation log, and binding the original data with audit information to obtain a traceable data storage scheme; Analyzing, positioning and analyzing failure reasons through the abnormal logs, recording adapter modification contents by using a version management tool, and feeding back the problem types and the repair strategies to a development process to obtain continuously optimized adapter versions and a problem closed-loop management mechanism.
2. The pluggable data adaptation and analysis method for LIMS according to claim 1, wherein the adapting requirement specification document is obtained by adopting investigation to define laboratory instrument type and data output format, extracting data fields by carding ISO/IEC 17025 compliance requirements, mapping unstructured data to structured standards, and comprising the steps of: the method is characterized in that a mode of combining field visit with an online questionnaire is adopted to research the existing instrument and equipment list of a laboratory, and the model, the communication interface and the data output form of each type of instrument are defined; Extracting data fields of detection time, sample number, instrument parameters and detection results by analyzing clause requirements on original records and data integrity in the ISO/IEC 17025 standard one by one; and converting unstructured data of text, images and binary streams output by the instrument into a structured table according to a field mapping rule, and defining field types, lengths and verification rules to obtain an adaptation requirement specification document.
3. The pluggable data adaptation and analysis method for LIMS according to claim 1, wherein the independent adapter modules are designed through a micro-service architecture, each module develops a data acquisition interface for a device type, and a dynamic loading mechanism is adopted to decouple the adapter from a LIMS core, so as to obtain an adapter library and an interface specification, and the method comprises the following steps: splitting the data adaptation function into independent adapter modules by adopting a micro-service architecture concept, wherein each module focuses on the data acquisition requirement of a single equipment type; Extracting acquisition parameters and developing a standardized interface by analyzing equipment communication protocol and data output characteristics, wherein the standardized interface comprises a data acquisition mode supporting serial port communication, network transmission and file reading; The adapter hot plug is realized by adopting a dynamic class loading technology, a new adapter is automatically registered in the running process without restarting a system, and the module is packaged into an extensible adapter library, so that the adapter library and the interface specification are obtained.
4. The pluggable data adaptation and analysis method for LIMS according to claim 1, wherein the raw data is obtained by AI image analysis technology, null values and outliers are removed by a data cleaning algorithm, and heterogeneous data is converted into a unified format to obtain a preprocessed data set, comprising the following steps: adopting an AI image analysis technology, extracting information by a target detection and character recognition model aiming at image data, and obtaining original detection numerical value and timestamp data; Identifying missing fields and abnormal values deviating from a reasonable range through a data quality evaluation algorithm, and completing data cleaning by adopting a mean filling and boundary cutting mode; and converting the text, the image analysis result and the electronic data according to the format of the data dictionary and the structured template, and unifying field naming rules and data types to obtain a standardized preprocessing data set meeting analysis requirements.
5. The pluggable data adaptation and parsing method for LIMS according to claim 1, wherein the associating the device output field to the LIMS service object through field level semantic mapping, complementing the environmental information with a context engine, converting the isolated data into traceable service entities, and obtaining the associated complete structured data, includes the following steps: Analyzing the business meaning of the output fields of the equipment one by adopting a field-level semantic mapping technology, and accurately relating the business meaning to the business object in the LIMS system by constructing a field mapping table; automatically extracting environmental parameters during data acquisition by a context awareness engine, and associating the environmental parameters to corresponding data records, wherein the environmental parameters comprise temperature and humidity, operators and equipment calibration states; And integrating the scattered original data of the equipment with the completed context information, establishing a logic relationship between the data through the unique identifier, and converting the isolated data into a traceable entity comprising a complete business background to obtain associated structured data with audit trail capability.
6. The pluggable data adaptation and analysis method for LIMS according to claim 1, wherein the checking whether the data meets the detection method standard and the result threshold by the rule engine, storing the compliance data by using an encryption database, recording an operation log, binding the original data with the audit information, and obtaining a traceable data storage scheme includes the following steps: the method comprises the steps of converting a numerical range and unit requirements in a detection method standard into executable check rules by adopting a rule engine technology, and judging whether data are compliant or not through real-time comparison; The encryption algorithm is adopted to encrypt the database to store the checked compliance data, so that the confidentiality of the data is ensured, the operation time of the data, an operator and the modification content are recorded through the log service, and an operation track which cannot be tampered is generated; And (3) associating and binding the original data, the verification result and the audit log through a unique identifier, and constructing a traceable chain covering the whole life cycle of the data to obtain a traceable data storage scheme meeting the CNAS requirement.
7. The pluggable data adapting and parsing method for LIMS according to claim 1, wherein the analyzing and locating the failure cause by exception log, recording the content of the modification of the adapter by using a version management tool, feeding back the type of the problem and the repair policy to the development process, and obtaining a continuously optimized adapter version and a problem closed-loop management mechanism, includes the following steps: carrying out deep analysis on the abnormal log by adopting a log analysis tool, and extracting error types, occurrence time and associated equipment information through specific links of keyword matching and context associated positioning data analysis failure; recording an adapter code modification history by using a version management tool, wherein the modification history comprises a modifier, modification time and change content; Synchronizing the analyzed problem types and the repairing schemes to a development task management module, driving iterative optimization, and automatically verifying the repairing effect through a continuous integration flow; And releasing the adapter version passing the test to a production environment to obtain a closed-loop management mechanism comprising problem discovery, attribution, repair and verification.
8. The pluggable data adaptation and analysis method for LIMS according to claim 4, wherein the step of identifying missing fields and outliers deviating from a reasonable range through a data quality evaluation algorithm, and performing data cleaning by means of mean filling and boundary truncation includes the steps of: Adopting a data quality evaluation algorithm based on statistical distribution and business rules, identifying missing fields through indexes, and positioning abnormal values deviating from a reasonable interval by using a standard deviation threshold; Extracting historical average values of similar data for filling according to the missing fields, and adopting maximum/minimum effective value cutoff processing according to abnormal values exceeding the range/logic boundary of the equipment; and the cleaning process keeps original data copies, records cleaning rules and operation logs, and obtains a data set with integrity and rationality meeting analysis requirements.
9. The pluggable data adaptation and analysis method for LIMS according to claim 6, wherein the rule engine technology is adopted to convert a numerical range and a unit requirement in a detection method standard into an executable check rule, and the method comprises the following steps of: Carrying out structural analysis on a standard text of a detection method by adopting a rule engine technology, extracting upper and lower limits of a numerical range and allowable unit types, and converting the upper and lower limits and the allowable unit types into condition judgment logic which can be identified by a program; Mapping the service requirement into a check rule through a rule configuration interface, wherein the check rule comprises the steps of setting the effective range of an index to be 10-50 and the unit of the index must be milligrams per liter; Triggering real-time verification in a data transmission link, comparing the to-be-detected data with the conditions in the rule base item by item, and marking abnormal data which are beyond the range and are not matched in units to obtain a verification result comprising a compliance state identifier and a detailed error prompt.

Description

Pluggable data adaptation and analysis method for LIMS Technical Field The invention belongs to the technical field of data adaptation, and particularly relates to a pluggable data adaptation and analysis method for LIMS. Background In application of a Laboratory Information Management System (LIMS), efficient adaptation and accurate analysis of data are key links for ensuring that laboratory work is smoothly carried out and quality management requirements are met. Currently, laboratory-equipped instruments are of various types, and the data formats output by different instruments are quite different, so that structured data exists, and a large amount of unstructured data exists. This presents a significant challenge for unified collection and processing of data, which is difficult to integrate directly into LIMS for subsequent analysis and management. In terms of data compliance, laboratories need to follow international standards such as ISO/IEC 17025, which have stringent requirements on data fields. However, the existing data adaptation method often lacks systematic carding, is difficult to accurately extract data fields meeting standards, and cannot effectively map unstructured data into structured standards, so that the quality of the data is uneven, and the accuracy and reliability of detection results are affected. Meanwhile, the traditional data analysis mode mostly adopts a single technical means, faces to complex and changeable original data, such as data acquired through images, is difficult to comprehensively and accurately extract effective information, and lacks an effective data cleaning and format unifying mechanism, so that heterogeneous data is difficult to integrate and utilize. In addition, in the prior art, the adapter is tightly coupled with the LIMS core, the flexibility and the expandability are lacking, and once the instrument is updated or the data format is changed, the whole system needs to be modified in a large scale, so that the cost is high and the efficiency is low. In addition, in the data processing process, a perfect traceability mechanism is lacked, the integrity and compliance of data cannot be ensured, and audit and supervision requirements are difficult to meet. The prior art has the defects of lack of systematic specification for data adaptation of various instruments, difficult expansion of the coupling between an adapter and a LIMS core, incomplete data analysis, difficult unification of formats, poor data traceability, lack of a problem closed-loop management mechanism, and difficulty in meeting the requirements of LIMS on efficient, accurate and compliant data processing. Disclosure of Invention Aiming at the current situation, the invention provides a pluggable data adaptation and analysis method for LIMS, which can solve the problems of various and difficult adaptation of laboratory instrument data formats, difficult coupling expansion of a traditional adapter and a LIMS core, incomplete data analysis, non-uniform format, poor data traceability and lack of closed-loop management of adapter maintenance. In order to achieve the above purpose, the present invention adopts the following technical scheme: The pluggable data adapting and analyzing method for the LIMS comprises the steps of adopting investigation to define a laboratory instrument type and a data output format, extracting data fields through combing ISO/IEC 17025 compliance requirements, mapping unstructured data into structural standards to obtain an adapting requirement specification document, designing an independent adapter module through a micro-service architecture, each module developing a data acquisition interface aiming at a device type, adopting a dynamic loading mechanism to decouple an adapter from a LIMS core to obtain an adapter library and interface specifications, acquiring original data through an AI image analyzing technology, adopting a data cleaning algorithm to remove empty values and abnormal values, converting the heterogeneous data into a unified format to obtain a preprocessed data set, associating the device output fields to LIMS service objects through field-level semantic mapping, adopting a context engine to complement environmental information, converting the isolated data into a service entity to obtain associated complete structured data, checking whether the data accords with a detection method standard and a result threshold through a rule engine, adopting an encryption database to store the adapting specification data, recording operation log, binding the original data with the information, adopting a data storage strategy to obtain an error management scheme, and continuously correcting the error management strategy, and adopting a closed-loop analysis scheme to obtain a closed-loop management error management problem. Further, the method comprises the steps of adopting investigation to define laboratory instrument types and data output formats, extracting data fie