Laboratory information management system based on LIMS and management method thereof

Abstract

The invention provides an LIMS-based laboratory information management system and a management method thereof, relates to the technical field of laboratory information management, adopts a hierarchical decoupling architecture combining a technical platform with micro-service, and comprises a unified interaction layer, a micro-service application layer, a technical platform layer and an infrastructure layer. According to the invention, diversified seamless access entrances are provided through the unified interaction layer, high cohesion, independent deployment and on-demand expansion of business capability are realized depending on the domain micro-service of the micro-service application layer, and reusable components such as a low-code development platform, an integrated bus, an AI model service platform and the like of the platform layer in the technology are utilized. And the general capability is precipitated and standardized, so that the business response speed, the system integration efficiency and the intelligent level are greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of laboratory information management, in particular to a laboratory information management system based on LIMS and a management method thereof. BACKGROUND

[0002] LIMS is a software system specially used for managing and standardizing the core business processes of laboratories. Through the digital management of samples, personnel, equipment, methods and data, it realizes the full-process tracking and control from sample registration, task allocation, experiment execution, data collection to report generation, mainly to ensure the integrity, traceability and compliance of data, and to improve the operation efficiency and quality management level of laboratories.

[0003] Laboratory information management refers to the comprehensive management practice of generating, collecting, processing, analyzing and reporting information in a laboratory environment using various technologies, strategies and processes including LIMS. It emphasizes the transformation of data into valuable decision support information through information technology to optimize the overall operation of the laboratory. LIMS is a key tool for efficient laboratory information management.

[0004] The laboratory information management systems in the prior art generally have the problem of rigid architecture, using monolithic or strong coupling design, which makes the system difficult to expand and iterate, and the function update is complex and risky. In terms of integration, there is a lack of standardized and visual connection capability for heterogeneous instruments and external systems, and data collection and exchange rely on high-cost customized development, which is prone to information silos. Moreover, the system has poor business adaptability, and process or rule changes must be implemented through code modification, which is slow to respond and cannot allow business personnel to directly participate in adjustments, which needs to be improved. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art. By providing a unified interaction layer to provide diversified seamless access portals, relying on the domain-oriented microservices of the microservice application layer to realize high cohesion, independent deployment and on-demand expansion of business capabilities, and using reusable components such as low-code development platforms, integrated integration buses and AI model service platforms in the technology middle platform layer to deposit and standardize general capabilities, the business response speed, system integration efficiency and intelligent level are greatly improved.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a laboratory information management system based on LIMS adopts a layered decoupling architecture combining technology middle platform and microservice, including: a unified interaction layer, a microservice application layer, a technology middle platform layer and a infrastructure layer.

[0007] The unified interaction layer is used to provide diversified access and interaction portals for different user roles and external systems.

[0008] The microservice application layer is composed of a group of loosely coupled, independently developed, deployed and extended business capability microservices, each of which is based on a multi-tenant architecture for data and logical isolation to support parallel use of multiple independent user units; The services of the microservice application layer include: a multi-tenant resource management microservice for unified management of laboratory core assets, a sample management microservice for driving sample full life cycle state management, a detection process microservice for driving and executing standardized detection procedures, and a data insight microservice for in-depth analysis and presentation of experimental data;

[0009] The technology platform layer provides reusable and shareable capability components, including: a low-code development platform for allowing business personnel to configure business rules in a non-programming manner, an integrated integration bus for connecting with heterogeneous laboratory instruments and external information systems, an AI model service platform for encapsulating and managing artificial intelligence models in a service-oriented manner, and a process rule engine for parsing and executing business processes;

[0010] The infrastructure layer is built based on a container cloud platform, which provides elastic computing, storage and network resources for the upper layer services, and includes a microservice governance center, a distributed database cluster and an object storage service.

[0011] As a preferred embodiment, the multi-tenant resource management microservice is divided into a plurality of logically independent and data-related sub-information libraries, including:

[0012] A CMA capability information library is established and supported according to CMA specifications for public inquiries;

[0013] A unit basic information library, a personnel information library and a quality control information library isolated by user unit;

[0014] A physical resource library for managing instruments, standard substances and consumables, wherein the standard substance information can be shared within the authorized range after the standard value is blurred by the AI model service platform, but the associated unit name information is not disclosed;

[0015] An environmental information library for managing laboratory environmental requirements and monitoring records;

[0016] A supplier information library for integrating supplier evaluation records.

[0017] As a preferred embodiment, the low-code development platform specifically includes:

[0018] A visual process designer module for defining a complete business process including task nodes, branch conditions and role assignments through a drag-and-drop method, and publishing the process definition to the process rule engine;

[0019] a visual form designer module for dynamically designing sample information registration forms, detection record forms and result report templates, and binding data models and verification rules;

[0020] a configuration management center for storing and managing all process, form and data model metadata defined through the low-code development platform, and supporting version management.

[0021] As a preferred embodiment, the integrated bus specifically includes:

[0022] an instrument driver warehouse pre-provisioned with standardized instrument driver programs for multiple communication protocols;

[0023] a zero-code configuration interface for configuring instrument connection and data field mapping through a graphical wizard;

[0024] an enterprise service bus module serving as a hub for data exchange inside and outside the system;

[0025] an open API gateway module providing standardized API interfaces and integrating security management functions.

[0026] As a preferred embodiment, the AI model service platform specifically includes:

[0027] a model containerized runtime environment for encapsulating AI models as independently deployable microservice instances;

[0028] a model service management module for providing model lifecycle management functions;

[0029] a standardized inference interface providing a unified calling method for upper-layer applications.

[0030] As a preferred embodiment, it further includes an electronic signature service module, which includes:

[0031] a digital certificate management submodule for managing user digital certificates;

[0032] a signature generation and verification submodule for generating and verifying electronic signatures at key business nodes;

[0033] a signature audit and binding submodule for binding signature operations with business data, user identity and timestamps in a tamper-proof manner.

[0034] A laboratory information management method based on LIMS, including a system implementation and deployment phase and a system online operation and continuous optimization phase, the system implementation and deployment phase adopts a progressive delivery strategy, specifically including:

[0035] S101: Business blueprint planning and microservice splitting: Sort out the core business processes and multi-tenant data strategies of the laboratory, split the system functions into independent microservices, and determine the implementation priority;

[0036] S102: Incremental implementation in stages: Plan multiple implementation stages, and deploy a group of core microservices that can deliver independent business value in each stage;

[0037] S103: Short-cycle iterative development: In each stage, use the agile development framework to develop, test and integrate in 2-4 week cycles;

[0038] S104: Continuous feedback and adaptive optimization: Regularly demonstrate the function increment to key users, collect feedback and incorporate it into the subsequent development cycle, and continuously optimize the system design.

[0039] As a preferred embodiment, the system online operation continuous optimization stage emphasizes data-driven converged operation, specifically including:

[0040] S201: Form a business-IT converged operation team, with business backbone as product manager;

[0041] S202: Establish and maintain a network of key users, and give them the responsibility of participating in optimization design, test feedback and internal training;

[0042] S203: Set up an excellent operation center responsible for monitoring system performance, analyzing user feedback and evaluating function effect;

[0043] S204: Prioritize the implementation of configuration optimization through the low-code development platform to quickly respond to business changes.

[0044] As a preferred embodiment, the system online operation continuous optimization stage emphasizes data-driven converged operation, and further includes:

[0045] S301: Automatically collect multi-dimensional operation data such as sample turnaround time, instrument utilization rate, personnel load and user satisfaction;

[0046] S302: Use the data insight microservice and AI model service platform to conduct in-depth analysis and visualization of operation data;

[0047] S303: Form a technical or process optimization scheme based on the analysis results and execute it;

[0048] S304: Quantitatively evaluate the actual effect of the optimization measures, and form a continuous optimization closed loop of "measurement-analysis-decision-improvement-verification".

[0049] Compared with the prior art, the advantages and positive effects of the present application are:

[0050] The present application provides diversified seamless access entry through a unified interaction layer, relies on domainized microservices of a microservice application layer to realize high cohesion, independent deployment and on-demand expansion of business capabilities, and uses reusable components such as a low-code development platform, an integrated integration bus and an AI model service platform of a technology middle platform layer to deposit general capabilities and standardize, greatly improves business response speed, system integration efficiency and intelligent level, and a container cloud platform and distributed facilities based on an infrastructure layer provide elastic resource support and excellent operation and management capabilities, so that the entire system can be smoothly implemented in a progressive delivery strategy, and continuously self-optimized through a data-driven fusion operation mechanism, finally realizes automatic, fine and intelligent management of the whole process of laboratory operation under the premise of ensuring multi-tenant data isolation and electronic signature legal effect, greatly improves operation efficiency, decision-making scientificity and compliance reliability. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 An architecture schematic diagram of a laboratory information management system based on LIMS is provided for the present application.

[0052] Figure 2 A flowchart of a laboratory information management method based on LIMS is provided for the present application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Embodiment 1

[0055] As shown in Figure 1 The present application provides a technical solution: a laboratory information management system based on LIMS, which adopts a layered decoupling architecture combining technology middle platform and microservice, specifically including: a unified interaction layer, a microservice application layer, a technology middle platform layer, an infrastructure layer and an electronic signature service module.

[0056] The unified interaction layer is used to provide diversified access and interaction entrances for different user roles and external systems, which adopts the mechanism of separating front end from gateway, request routing and protocol conversion, constructs various channels such as Web portal, mobile application and standardized Open API, and integrates API gateway or load balancer internally to uniformly perform identity authentication, permission verification, protocol adaptation and routing distribution on all incoming requests, and accurately and safely guide access traffic of different sources and forms to the corresponding business capability unit of the backend microservice application layer.

[0057] The microservice application layer is composed of a group of loosely coupled, independently developed, deployed and extended business capability microservices, each microservice is based on multi-tenant architecture for data and logic isolation to support parallel use of multiple independent user units; the services of the microservice application layer include: a multi-tenant resource management microservice for unified management of laboratory core assets, a sample management microservice for driving sample full life cycle state management, a detection process microservice for driving and executing standardized detection procedures, and a data insight microservice for deep analysis and presentation of experimental data, which follows the domain-driven design and single responsibility principle, and splits the laboratory information management system into a group of autonomous and clear boundary microservices according to core business domains, each service encapsulates specific business capabilities, has independent data storage and communicates through defined API, and the whole layer adopts containerized deployment and orchestration technology to realize independent development, deployment and elastic scaling of services; the multi-tenant architecture mainly realizes data and logic isolation through data storage strategy and mandatory embedding of tenant context identifier in business logic;

[0058] The multi-tenant resource management microservice is divided into a plurality of logically independent and data-related sub-information libraries, including: a CMA capability information library established according to CMA specification and supporting public query, a unit basic information library isolated according to user unit, a personnel information library and a quality control information library, a physical resource library for managing instruments, standard substances and consumables, an environment information library for managing laboratory environment requirements and monitoring records, and a supplier information library for integrating supplier evaluation records, wherein the standard substance information can be shared within the authorized range after the standard value is blurred by the AI model service platform, but the associated unit name information is not public;

[0059] The design adopts a refined data architecture design based on business domain and data sensitivity, further divides the data storage behind the unified resource management business logic into multiple logically independent and interrelated sub-information libraries through key identifiers, wherein through data classification and policy-based storage, public data, strictly tenant-isolated private data and conditionally shared data are physically or logically managed differently, and for standard substance information in the physical resource library, an AI model service platform from the third layer technology center is introduced to perform intelligent fuzzy desensitization processing of the standard values, to achieve a balance between data availability and security, and the entire microservice integrates these sub-information libraries through a unified service interface and data model at the business logic layer, providing coherent resource management capabilities for upper-layer applications.

[0060] In the above, the complex business processes of the laboratory are disassembled into a series of business capability microservices with clear boundaries and autonomy, such as multi-tenant resource management, sample management, detection process and data insight, each service independently encapsulating its business logic and data storage, cooperating through API, and achieving independent development, deployment and elastic scaling through containerization and orchestration technology, while embedding a unified data storage strategy and tenant context identifier to build multi-tenant isolation capabilities for all microservices at the logical and data level, thereby supporting parallel use of multi-tenancy at the architectural level;

[0061] Under this framework, the multi-tenant resource management microservice further adopts a refined data architecture based on business domain and data sensitivity, and according to different strategies such as publicity, privacy and sharing, the data of the core field of resource management is physically or logically divided into multiple logically independent and interrelated sub-information libraries such as CMA capability information library, unit information library, personnel information library, quality control information library, physical resource library, environment information library and supplier information library, and especially for the standard substance information in the physical resource library which has sharing value but is sensitive, a third layer AI model service platform is introduced for intelligent fuzzy desensitization processing, and then at the business logic layer, these sub-libraries are integrated through a unified service interface to provide coherent services to the outside.

[0062] The technology platform layer provides reusable and shareable capability components, including a low-code development platform for allowing business personnel to configure business rules in a non-programming manner, an integrated integration bus for realizing connection with heterogeneous laboratory instruments and external information systems, an AI model service platform for encapsulating and managing artificial intelligence models in a service-oriented manner, and a process rule engine for parsing and executing business processes. In the design, the low-code development platform converts the definition of business rules, interfaces, and processes into a metadata model that can be stored and executed by providing a visual process and form designer and a configuration management center, thereby allowing business personnel to quickly build or modify application logic through drag-and-drop configuration rather than writing code. The integrated integration bus plays the roles of protocol conversion, message routing, and unified interface portal within and outside the system by providing a zero-code configuration interface, as well as enterprise service bus and API gateway modules, and realizes standardized management and data exchange of connection with heterogeneous laboratory instruments and external information systems. The AI model service platform abstracts the development, deployment, invocation, and operation process of artificial intelligence models into a set of manageable microservices through a model containerized runtime environment, a service management module, and a standardized inference interface, realizing the loose coupling integration of AI capabilities and business systems. The process rule engine is responsible for parsing and executing business process logic defined by the low-code platform or other means, driving the automated flow of tasks in the system.

[0063] The low-code development platform specifically includes:

[0064] The visual process designer module is used to define a complete business process including task nodes, branch conditions, and role allocation through a drag-and-drop manner, and publish the process definition to the process rule engine.

[0065] The visual form designer module is used to dynamically design sample information registration forms, detection record forms, and result report templates, and bind data models and validation rules.

[0066] The configuration management center is used to store and manage all process, form, and data model metadata defined through the low-code development platform, and supports version management.

[0067] In the design, a business application rapid construction and iteration system with metadata-driven as the core is constructed through the visual process designer module, the visual form designer module and the configuration management center module. The visual process designer module abstracts the business process of the laboratory into visual task nodes, branch conditions and role allocation graphics, allows users to arrange logic through drag and link, and publishes the finally generated process model as a standardized definition for the process rule engine to parse and execute. The visual form designer module provides rich form controls and layout tools, enabling users to intuitively design complex interfaces such as sample information registration forms and detection record forms, and simultaneously bind back-end data models and front-end verification rules to realize dynamic association of interfaces and data. The configuration management center module serves as a unified metadata warehouse, storing and managing the configuration information of processes, forms and data models created by the first two designers in a structured manner, and recording each change through version management functions.

[0068] Moreover, the integrated bus specifically includes: an instrument driver warehouse preloaded with standardized instrument driver programs of multiple communication protocols, a zero-code configuration interface for configuring instrument connection and data field mapping through a graphical wizard, and an enterprise service bus module as the hub of internal and external data exchange of the system.

[0069] An open API gateway module providing standardized API interfaces and integrated security control functions is provided externally.

[0070] In the design, the integrated bus provides protocol abstraction and adaptation capabilities for the underlying diverse instruments through the instrument driver warehouse preloaded with standardized instrument driver programs of multiple communication protocols, and enables non-developers to intuitively complete the configuration of instrument access in combination with the zero-code configuration interface for configuring instrument connection and data field mapping through a graphical wizard. The enterprise service bus module as the hub of internal and external data exchange of the system is responsible for internal message routing, conversion and reliable transmission. The open API gateway module providing standardized API interfaces and integrated security control functions is externally provided, and the open API gateway module providing standardized API interfaces and integrated security control functions is externally provided.

[0071] Moreover, the AI model service platform specifically includes: a model containerized runtime environment for encapsulating AI models into independently deployable microservice instances, a model service management module for providing model full life cycle management functions, and a standardized inference interface for providing a unified calling method for upper-layer applications.

[0072] In this design, the AI model is encapsulated as a model containerized running environment of an independently deployable micro-service instance, which ensures the environmental isolation and portability of model running through container technology. The model service management module, which is used to provide model full life cycle management functions, realizes the centralized control of model version, deployment, monitoring and retirement, etc. The standardized inference interface, which provides a unified calling mode for the upper layer application, abstracts the complex calling details of different models.

[0073] In the above, through the collaborative construction of the four core platform components of the low-code development platform, the integrated integration bus, the AI model service platform and the process rule engine, the general and complex business construction, system integration, intelligent analysis and process execution capabilities are thoroughly abstracted, encapsulated and standardized.

[0074] Among them:

[0075] The low-code development platform abstracts the business process into a graph model that can be dragged and arranged through the visual process designer module, realizes the dynamic binding of interface and data model through the visual form designer module, and relies on the configuration management center module to store and manage the metadata of all design products. version management, thus forming a rapid application construction system driven by business personnel and based on metadata model definition as the core;

[0076] The integrated integration bus standardizes the encapsulation of various instrument communication protocols through its instrument driver warehouse, realizes graphical connection and field mapping configuration through a zero-code configuration interface, uses the enterprise service bus module for reliable message routing and conversion within the system, and finally provides a unified and secure service portal through the open API gateway module. service portal, thus building a full-stack, configuration-based integration pipeline from the device layer to the system layer;

[0077] The AI model service platform ensures that the AI model can be delivered in the form of isolated and portable micro-services through the model containerized running environment, realizes the centralized control of the full life cycle from development to retirement through the model service management module, and provides a consistent calling mode for the upper layer application through the standardized inference interface., thus converting AI capabilities into on-demand platform services;

[0078] The process rule engine, as the underlying executor, is specially designed to analyze and execute business process metadata.

[0079] The infrastructure layer is built based on a container cloud platform, and is used for providing elastic computing, storage and network resources for the upper layer services, and comprises a micro-service governance center, a distributed database cluster and an object storage service; in the design, all micro-services and their dependent environments in the upper layer are packaged into standard and lightweight deployable units through containerization encapsulation, and the container orchestration engine is used for dynamically scheduling, elastically scaling and highly ensuring the resources; meanwhile, the micro-service governance center integrated in the layer provides the upper layer micro-service application layer with systematic runtime governance capabilities, the distributed database cluster realizes highly available and scalable storage of massive structured data through data sharding, multi-replica replication and consistency protocol, and the object storage service provides massive and secure storage space and access interface for unstructured experimental data.

[0080] The electronic signature service module comprises a digital certificate management submodule for managing user digital certificates, a signature generation and verification submodule for generating and verifying electronic signatures at key business nodes, and a signature audit and binding submodule for binding signature operations with business data, user identity and time stamp in a non-tamperable manner; in the design, the digital certificate management submodule is used for managing the digital certificates of users throughout their life cycles; the signature generation and verification submodule is used for automatically generating electronic signatures with legal effect at key business nodes such as sample review and report issuance, and verifying the authenticity of the electronic signatures, so as to ensure the non-repudiation and data integrity of operation behaviors; the signature audit and binding submodule is used for one-way encryption and hash binding of original business data, execution user identity and accurate time stamp associated with each signature operation by using blockchain or high-security-strength digital digest and time stamp technology, so as to generate non-tamperable audit tracking records.

[0081] In this embodiment, a clear layered and decoupled architecture achieves separation of concerns and capability reuse. The unified interaction layer serves as the system boundary, employing a front-end and gateway separation mechanism and request routing conversion to uniformly manage and distribute all access. The microservice application layer, based on domain-driven design, decomposes the core laboratory business into a set of autonomous microservices such as multi-tenant resource management, sample management, testing processes, and data insights. Each service is independently developed and deployed, achieving elastic scaling through containerized orchestration. Simultaneously, multi-tenant isolation is achieved through data policies and tenant context embedding. The multi-tenant resource management microservice further adopts a refined architecture based on data sensitivity, dividing its data into multiple logically related sub-information databases and introducing an AI module from the technology platform layer. The service platform intelligently desensitizes standard material information to achieve secure and controllable data sharing. The technology middleware layer, acting as a capability hub, uses a low-code development platform to metadataize business rules for rapid configuration, standardizes all internal and external connections through an integrated bus, encapsulates AI capabilities as services through an AI model service platform, and drives automated workflows through a process rule engine. These platform components collectively abstract common and complex capabilities into reusable services. The infrastructure layer, based on a container cloud platform, microservice governance center, distributed database, and object storage, provides elastic resources and data support for the upper layers. The electronic signature service module, based on digital certificates and asymmetric encryption technology, achieves full-process legal validity signatures and tamper-proof audit traceability through its sub-modules.

[0082] Example 2

[0083] like Figure 2 As shown, based on Embodiment 1, a LIMS-based laboratory information management system is provided, along with a LIMS-based laboratory information management method, including a system implementation and deployment phase and a system online operation and continuous optimization phase.

[0084] The system implementation and deployment phase adopts a progressive delivery strategy, which specifically includes the following steps:

[0085] S101: Business Blueprint Planning and Microservice Decomposition: Analyze the core business processes and multi-tenant data strategies of the laboratory, decompose system functions into independent microservices and determine implementation priorities. Through workshops and interviews, identify end-to-end core business processes such as sample registration, task allocation, testing execution, data review, and report publication. Clarify data isolation, sharing, and compliance strategies in multi-tenant scenarios. Then, using the bounded context partitioning method of domain-driven design, decompose system functions into highly cohesive and loosely coupled microservices. Use a priority matrix to determine the implementation order of microservices and form a detailed architecture roadmap.

[0086] S102: Incremental implementation in stages: Plan multiple implementation stages, each of which deploys a set of core microservices that can deliver independent business value. Based on the priority of S101, plan the overall project into multiple consecutive release stages, and focus on deploying a set of independently running, clearly defined business value delivering microservices in each stage. Ensure that a usable system increment is formed at the end of each stage, so that value is generated early and project risk is reduced;

[0087] S103: Short-cycle iterative development: Within each stage, use agile development frameworks to develop, test, and integrate in cycles of 2 to 4 weeks, based on feature branch code management, automated unit / integration testing, continuous integration, and containerized builds. At the end of each iteration, complete code development, testing, and integration into the stage target version through a continuous integration pipeline, ensuring that the software is always in an integrable and testable state;

[0088] S104: Continuous feedback and adaptive optimization: Regularly demonstrate feature increments to key users, collect feedback and incorporate it into subsequent development cycles, and continuously optimize system design. At each iteration or stage milestone, demonstrate the latest feature increments to key business users through a demo environment, collect user experience, business process matching, and feature defect feedback through prototype demonstration and user acceptance testing, then structure the feedback into product backlog, prioritize it by the product owner, and use it as a core input for subsequent iteration planning, driving continuous optimization of system design, interaction, and functionality to ensure that the delivered product closely matches actual business needs;

[0089] In this design, step S101 identifies core business areas through systematic business process analysis and multi-tenant data strategy definition, and uses the concept of bounded context to scientifically decompose the complex system function cluster into high-cohesion, well-defined microservice units such as multi-tenant resource management and sample management in the microservice application layer. Then, through the value and risk trade-off of the priority matrix, an architecture roadmap is formed to guide all subsequent technical activities;

[0090] Step S102 converts the roadmap output by step S101 into a series of consecutive technical release stages that aim to deliver independent business value. Each stage focuses on deploying a set of independently running core microservices, which directly takes advantage of the independent deployment capabilities supported by the microservice architecture and the container cloud platform at the infrastructure layer;

[0091] Step S103 iterates through short cycles of 2 to 4 weeks within each phase, and forces the adoption of engineering practices such as feature branch management, automated testing, continuous integration pipelines, and containerized builds, to ensure that the code of microservices can be continuously integrated into deliverable versions in a high-frequency and high-quality manner, which relies on the containerized packaging and build environment provided by the infrastructure layer;

[0092] Step S104 realizes the continuous optimization of system design in the development process by demonstrating the environment, prototype, and user acceptance test, structuring the feedback of key users' experience of functional increments and business matching degree into product to-do lists and driving the adjustment of subsequent development plans.

[0093] The system online operation continuous optimization phase emphasizes data-driven converged operation, specifically including the following steps:

[0094] S201: Assemble a business-IT converged operation team, with business backbone as product manager, members including backbone familiar with laboratory business, IT system architect, operation and maintenance engineer, and data analyst, the team adopts productized operation mode, and is jointly responsible for the function evolution, performance optimization and user experience improvement of the system, to ensure that the technology investment is aligned with the business goals;

[0095] S202: Establish and maintain a network of key users, and give the network the responsibility of participating in optimization design, test feedback, and internal training. Select frontline users from various business departments to form a network, and give frontline users the following responsibilities: participate in new function prototype design review, test in pre-release environment and provide feedback, assist in system usage training and problem collection in their own departments, by establishing online community, regularly holding optimization seminars, and providing test sandbox environment;

[0096] S203: Establish a superior operation center responsible for monitoring system performance, analyzing user feedback, and evaluating function effectiveness. The superior operation center is responsible for monitoring the health, response time, and exceptions of the infrastructure layer container cloud platform and each service in the microservice application layer using application performance monitoring tools; collecting function usage frequency and path through user behavior analysis tools; collecting user satisfaction feedback through service desks and surveys, and regularly analyzing system performance reports, user feedback data, and business function usage effectiveness reports to identify bottlenecks and improvement opportunities;

[0097] S204: Prioritize configuration optimization through the low-code development platform to quickly respond to business changes. When business rules, forms, or simple processes need to be changed, the operation team should prioritize using the low-code development platform in the technical middle platform layer. Business personnel or operation team can make configuration modifications through visual form designers and visual process designers, and publish them through the configuration management center, without waiting for the traditional software development cycle, thus achieving rapid response;

[0098] S301: Automatically collect multi-dimensional operation data of sample turnaround time, instrument utilization rate, personnel load and user satisfaction, and automatically collect structured operation data through system burying, database log analysis, and instrument interface data docking, such as calculating sample turnaround time from sample management microservice logs, calculating instrument utilization rate from instrument connection data, calculating personnel load from task allocation data, collecting user satisfaction through online questionnaire or interactive scoring, and synchronizing data to data warehouse regularly;

[0099] S302: Deep analysis and visualization of operation data using data insight microservice and AI model service platform, operation team uses data insight microservice in microservice application layer to aggregate, analyze and analyze multi-dimensionally the data collected in S301, and for complex pattern recognition and predictive analysis, calls the corresponding analysis model hosted on the AI model service platform in the technical middle platform layer, and the analysis results are visualized through the dashboard and report provided by the microservice;

[0100] S303: Form technical or process optimization scheme and execute according to analysis results, based on the analysis insight of S302, the outstanding operation center leads the integration of operation team to diagnose and form specific optimization scheme;

[0101] S304: Quantitatively evaluate the actual effect of optimization measures, after the implementation of optimization measures, restart the data collection of S301, and use the analysis method of S302 again to compare the changes of key indicators before and after optimization, and perform quantitative evaluation through hypothesis testing or trend comparison to form a clear effect report. Whether successful or not, the experience and lessons will be fed back to the operation to-do list, so as to solidify a complete "measurement-analysis-decision and improvement-verification" data-driven continuous optimization closed loop;

[0102] In this design, step S201 breaks down the barriers between business demand and technical implementation at the organizational level by forming a fusion team composed of business backbone and IT experts and adopting productized operation mode, ensuring the alignment of optimization direction and business target;

[0103] Step S202 builds a structured and sustainable front-line business feedback collection and penetration channel by establishing a key user network and assigning them the responsibilities of design review and test feedback;

[0104] Step S203 realizes the all-round and data-based monitoring and analysis of the running state of infrastructure layer and microservice application layer and user experience by setting up outstanding operation center and using application performance monitoring and user behavior analysis tools;

[0105] Step S204 specifically provides a technical path for configuration modification using the low-code development platform of the technical middle platform, converting common business changes into code-free quick release activities;

[0106] Step S301 automatically collects multi-dimensional operation data from business services such as sample management microservices and instrument interfaces through system burying, log analysis, etc., providing fuel for analysis;

[0107] Step S302 calls the data insight microservice in the microservice application layer for regular statistical analysis, and calls the prediction or diagnosis model on the AI model service platform of the technical middle platform for deep analysis, converting data into insights;

[0108] Step S303 forms specific technical or process optimization schemes based on data insights and executes them;

[0109] Step S304 quantitatively verifies the optimization effect by restarting data collection and analysis, and feeds back the results to the operation to-do list, thus completing the "Step S301 measurement - Step S302 analysis - Step S303 decision and improvement - Step S304 verification" closed loop.

[0110] In this embodiment, in the implementation and deployment phase, through the business blueprint planning and microservice splitting of step S101, the domain-driven design is used to analyze the complex business domain of the laboratory, so as to scientifically define the independent service units such as multi-tenant resource management microservice, sample management microservice, etc. in the microservice application layer with high cohesion and clear boundaries, and form a guiding architecture roadmap; then through the step S102 of phased incremental implementation, the roadmap is converted into a series of value-oriented release stages, making full use of the independent deployment capability of the microservice application layer services and the container cloud platform of the infrastructure layer, to deliver value early in the form of minimum viable product set and control risks; the short-cycle iterative development of step S103 relies on the containerized build environment and continuous integration pipeline provided by the infrastructure layer, through engineering practices such as feature branch and automated testing, to ensure that the code of each service can be continuously integrated with high quality and high frequency; the continuous feedback mechanism established in step S104 structures user insights through prototype demonstration and user acceptance testing, and feeds back to the development cycle, realizing adaptive optimization of the microservice application layer service interface and business logic;

[0111] In the online operation continuous optimization phase, the technical principle of the method turns to building an autonomous optimization engine linked with organization, data and platform: steps S201-S204 first establish the organization and rapid response mechanism of fusion operation, among which step S204 clearly stipulates that configuration change is preferentially implemented by using the low-code development platform of the technical middle platform layer, and a large amount of business optimization is converted into rapid release without coding; subsequently, steps S301-S304 build a rigorous data-driven closed loop, through step S301, multi-dimensional operation data is automatically collected from sample management microservices and other business units, through step S302, statistical analysis is performed by calling the data insight microservice of the microservice application layer, and intelligent prediction is performed by linking the AI model service platform of the technical middle platform layer, so as to convert data into insight; further, through step S303, an optimization scheme is formed and executed, and finally through step S304, data collection and analysis are restarted to quantitatively verify the effect, so as to solidify the complete closed loop of'measurement-analysis-decision-improvement-verification'.

[0112] Working principle:

[0113] As shown in Figures 1-2 , in operation, based on business blueprint planning and microservice splitting, the project team clearly defines the core business boundary and multi-tenant strategy through domain analysis, so as to split the system function into independent service units corresponding to the microservice application layer, and form an architecture roadmap; then enter the incremental implementation in stages, according to the roadmap priority, plan multiple release stages, and in each stage, independently deploy a set of minimum microservices that can deliver business value relying on the container cloud platform of the infrastructure layer; in each stage, agile development is carried out through short-cycle iterative development, code integration and containerization construction are completed by using the continuous integration pipeline of the infrastructure layer; and the user is demonstrated with the increment through continuous feedback and adaptive optimization, and feedback is collected to drive the optimization of the service design of the microservice application layer in subsequent iterations;

[0114] After the system is put into operation, enter the operation continuous optimization phase: the business-IT fusion operation team and the key user network jointly ensure that the optimization direction is aligned with the business; the excellent operation center continuously monitors the performance and user experience of each service of the infrastructure layer and the microservice application layer by using the monitoring tool; when there is a business change requirement, preferentially perform visual configuration and rapid release by using the low-code development platform of the technical middle platform layer; at the same time, the system automatically collects operation data from sample management microservices and other business units, and calls the data insight microservice of the microservice application layer and the AI model service platform of the technical middle platform layer for in-depth analysis and visualization; based on the analysis insight, an optimization scheme is formed and executed; finally, the effect is quantitatively evaluated through re-measurement and analysis, the closed loop of'measurement-analysis-decision-improvement-verification' is completed, and the experience is fed back to the operation to-do list, so as to drive the system into the next optimization cycle;

[0115] During the entire operation process, the unified interaction layer always serves as a unified entrance to process all access requests of users to external systems and route them to corresponding services of the micro-service application layer; when processing business logic, the services of the micro-service application layer can call reusable capabilities of the technical middle platform layer, such as the low-code development platform, the integrated integration bus, the AI model service platform and the process rule engine; and the infrastructure layer provides elastic containerized resources, micro-service governance and data storage support for all upper-layer services, to ensure that the entire system evolves efficiently, stably and sustainably.

[0116] The above merely describes preferred embodiments of the present application, and is not intended to limit the present application in other forms. Any person skilled in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, in accordance with the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A LIMS-based laboratory information management system, characterized by: A layered decoupling architecture combining technology middle platform and microservices is adopted, including a unified interaction layer, a microservice application layer, a technology middle platform layer, and an infrastructure layer; The unified interaction layer is configured to provide diversified access and interaction entrances for different user roles and external systems; The microservice application layer is composed of a group of loosely coupled, independently developed, deployed, and extended business capability microservices, each of which is based on a multi-tenant architecture for data and logic isolation to support parallel use of multiple independent user units; the services of the microservice application layer include a multi-tenant resource management microservice for unified management of laboratory core assets, a sample management microservice for driving sample lifecycle state management, a detection process microservice for driving and executing standardized detection procedures, and a data insight microservice for in-depth analysis and presentation of experimental data; The technology middle platform layer provides reusable and shareable capability components, including a low-code development platform for allowing business personnel to configure business rules in a non-programming manner, an integrated integration bus for realizing connection with heterogeneous laboratory instruments and external information systems, an AI model service platform for encapsulating and managing artificial intelligence models in a service-oriented manner, and a process rule engine for parsing and executing business processes; The infrastructure layer is built based on a container cloud platform and is configured to provide elastic computing, storage, and network resources for the upper-layer services, and includes a microservice governance center, a distributed database cluster, and an object storage service.

2. A LIMS based laboratory information management system as claimed in claim 1, wherein: The multi-tenant resource management microservice is divided into a plurality of logically independent and data-associated sub-information bases, including: A CMA capability information base established in accordance with CMA specifications and supporting public query; A unit basic information base, a personnel information base, and a quality control information base isolated according to user units; A physical resource base for managing instruments, equipment, standard substances, and consumables, wherein the standard substance information can be shared within an authorized range after the standard value thereof is blurred by the AI model service platform, but the associated unit name information is not disclosed; An environment information base for managing laboratory environmental requirements and monitoring records; A supplier information base for integrating supplier evaluation records.

3. A LIMS based laboratory information management system as claimed in claim 1, wherein: The low-code development platform specifically includes: A visual process designer module configured to define a complete business process including task nodes, branch conditions, and role allocation through a drag-and-drop manner, and publish the process definition to the process rule engine; A visual form designer module configured to dynamically design sample information registration forms, detection record forms, and result report templates, and bind data models and verification rules; A configuration management center configured to store and manage all process, form, and data model metadata defined through the low-code development platform, and support version management.

4. A LIMS based laboratory information management system as claimed in claim 1, wherein: The integrated integration bus specifically includes: An instrument driver warehouse pre-stored with standardized instrument driver programs of multiple communication protocols; A zero-code configuration interface configured to configure instrument connection and data field mapping through a graphical wizard; An enterprise service bus module serving as a hub for data exchange within and outside the system; An open API gateway module providing standardized API interfaces to the outside and integrating security management and control functions.

5. A LIMS based laboratory information management system as claimed in claim 1, wherein: The AI model service platform specifically includes: A model containerized runtime environment for encapsulating AI models as independently deployable microservice instances; A model service management module for providing model lifecycle management functions; A standardized inference interface for providing a unified calling method for upper-layer applications.

6. A LIMS based laboratory information management system as claimed in claim 1, wherein: It also includes an electronic signature service module, which includes: A digital certificate management submodule for managing user digital certificates; A signature generation and verification submodule for generating and verifying electronic signatures at key business nodes; A signature audit and binding submodule for binding signature operations with business data, user identity, and timestamps in a tamper-proof manner.

7. A LIMS-based laboratory information management method, the LIMS-based laboratory information management system according to any one of claims 1-6, characterized by: It includes a system implementation deployment phase and a system online operation continuous optimization phase. The system implementation deployment phase adopts a gradual delivery strategy, specifically including: S101: Business blueprint planning and microservice splitting: combing the core business processes and multi-tenant data strategies of the laboratory, the system functions are split into independent microservices and the implementation priority is determined; S102: Incremental implementation in stages: plan multiple implementation stages, and deploy a group of core microservices that can deliver independent business value in each stage; S103: Short-cycle iterative development: within each stage, use agile development frameworks to develop, test, and integrate in 2-4 week cycles; S104: Continuous feedback and adaptive optimization: regularly demonstrate function increments to key users, collect feedback and incorporate it into subsequent development cycles to continuously optimize system design.

8. A LIMS-based laboratory information management method according to claim 7, characterized in that: The system online operation continuous optimization phase emphasizes data-driven converged operations, specifically including: S201: Form a business-IT converged operations team, with business backbone as product manager; S202: Establish and maintain a network of key users, giving them the responsibility to participate in optimization design, test feedback, and internal training; S203: Establish an excellent operation center responsible for monitoring system performance, analyzing user feedback, and evaluating function effectiveness; S204: Prioritize configuration optimization through the low-code development platform to quickly respond to business changes.

9. The LIMS-based laboratory information management method of claim 7, wherein: The system online operation continuous optimization phase emphasizes data-driven converged operations, and also includes: S301: Automatically collect multi-dimensional operation data such as sample turnaround time, instrument utilization rate, personnel load, and user satisfaction; S302: Use the data insight microservice and AI model service platform to conduct in-depth analysis and visualization of operation data; S303: Form technical or process optimization schemes based on analysis results and implement them; S304: Quantitatively evaluate the actual effect of optimization measures to form a continuous optimization closed loop of "measurement-analysis-decision-improvement-verification".

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