Laboratory project management method and system based on modularization

Abstract

The invention relates to the technical field of modularized laboratory project management, and discloses a modularization-based laboratory project management method, which comprises the following steps: S1, system architecture design: adopting a micro-service distributed architecture, and cooperatively working through a plurality of business modules; s2, module management and project creation; the invention also provides a laboratory project management system based on modularization, which comprises a project management engine module, an intelligent key node reminding system, a sample full life cycle tracking management module and a multi-party collaborative feedback management module, the project management engine module comprises three core business modules, namely, a project management module, a sample management module and a contract management module, standardized management and efficient operation of laboratory projects are achieved through a project module engine, an intelligent node reminding mechanism and a full-process tracking algorithm, the project management efficiency can be remarkably improved, and the project management efficiency is improved. And the time cost of a project preparation stage is saved.

Description

Technical Field

[0001] This invention relates to the field of modular laboratory project management technology, specifically to a modular laboratory project management method and system. Background Technology

[0002] With the rapid development of the biopharmaceutical R&D and clinical trial industry, laboratory project management, as a core component of scientific research and testing, faces increasingly higher requirements for standardization and intelligence. Traditional laboratory project management systems suffer from the following problems:

[0003] 1. The project process has a low degree of standardization, with similar management processes being repeatedly created for different projects, resulting in low efficiency;

[0004] 2. Insufficient monitoring of key milestones can easily lead to missing important time points and impacting project progress;

[0005] 3. The management of samples and projects is chaotic, making it difficult to track sample status and leading to sample loss or confusion;

[0006] 4. Low efficiency in multi-party collaboration, with information not synchronized among customers, laboratories, logistics, and other parties, resulting in high communication costs;

[0007] 5. The project modules have poor reusability, making it impossible to effectively utilize past project experience, and reconfiguration is required each time.

[0008] In actual testing, it was found that traditional systems take an average of 2-3 days to set up and configure a project, with a critical node omission rate as high as 15% and a sample tracking accuracy rate of only about 85%. Therefore, the market urgently needs a laboratory project management solution that can be modularly managed, intelligently monitored, and tracked throughout the entire process. Summary of the Invention

[0009] (a) Technical problems to be solved

[0010] To address the shortcomings of existing technologies, this invention provides a modular laboratory project management method and system. This system solves the problems of low standardization in current project processes, repetitive creation of similar management processes across different projects leading to inefficiency, untimely monitoring of key nodes causing missed important deadlines and impacting project progress, chaotic management of samples and project associations, difficulty in tracking sample status leading to sample loss or confusion, low efficiency in multi-party collaboration, asynchronous information among customers, laboratories, and logistics parties resulting in high communication costs, poor reusability of project modules, inability to effectively utilize historical project experience, and the need for reconfiguration each time.

[0011] (II) Technical Solution

[0012] To achieve the above objectives, the present invention provides the following technical solution:

[0013] A modular laboratory project management approach includes the following steps:

[0014] S1: System architecture design, adopting a microservice distributed architecture, with multiple business modules working collaboratively;

[0015] S2: Module Management and Project Creation: Employing a multi-dimensional matching algorithm, the progress management module automatically generates the node sequence for sample reception and report generation, which is loaded in parallel with the attribute management template and quality control module. Combined with a pre-calculation mechanism, the creation time is compressed.

[0016] S3: Sample Management and Tracking: Establish unique identification rules, design multiple state transition mechanisms, integrate environmental monitoring equipment, and realize early warning and impact assessment of temperature and humidity exceeding limits;

[0017] S4: Intelligent node monitoring and reminders, adopting a dual reminder mechanism, integrating time-triggered and status-linked multi-role notification functions, equipped with a three-dimensional risk assessment algorithm, and implementing a graded upgrade process;

[0018] S5: Collaborative Feedback Management: The NLP contract parsing engine automatically identifies terms attached to original or scanned copies of returned documents; logistics API integration synchronizes waybill status in real time and triggers exception handling; the electronic signature system supports multiple confirmation methods.

[0019] S6: Data security and access control adopts the RBAC permission model, which is subdivided into five role levels; the data protection scheme includes AES-256 storage encryption, HTTPS transmission encryption and a four-level backup strategy; full-link auditing records all operation logs, tamper-proof storage and multi-dimensional retrieval.

[0020] Furthermore, the S1 core module architecture design includes: core module architecture design, database architecture optimization, and service registration and discovery mechanism;

[0021] Core module architecture design: The system adopts a microservice distributed architecture, consisting of three core business modules: project management, sample management, and contract management. Each core module contains several sub-modules. The modules exchange data and coordinate business through a unified API interface to achieve collaborative work.

[0022] Database architecture optimization: Establish a project module association table to achieve dynamic combination of the progress management module and the attribute management module through module identifiers; design a sample status transition table to record the complete status change history of the sample from "in preparation" to "completed"; build an access control matrix to achieve fine-grained access control for different roles to different functional modules.

[0023] Service registration and discovery mechanism: When each microservice starts up, it registers metadata information with the service registry, including service name, service address and health check interface. The registry maintains a list of service instances and checks their health status regularly. When a service is called, the optimal service instance is selected and the request is forwarded through a load balancing algorithm.

[0024] Based on the aforementioned scheme, the specific algorithm steps of the multi-dimensional matching algorithm in S2 are as follows:

[0025] Data standardization processing involves standardizing the names of detection methods, establishing feature vectors, classifying sample physical properties, and extracting key parameters.

[0026] Module feature extraction, marking key nodes and dependencies, configuring progress management module, defining sample detection parameter constraints, configuring attribute management module, real-time optimization and conflict resolution, when sample type and detection method conflict, priority is given to the module occupying the instrument's idle time period, realizing dynamic module combination;

[0027] Output and validation: Output the top 3 matching modules and their adaptation parameters, label the differences between the system recommendation and the historical human selection, record the human coverage selection, iteratively optimize the weight allocation, and dynamically adjust the weights by training the weight model with historical project data.

[0028] As a further embodiment of the present invention, the various states in S3 include: in preparation, awaiting handover, signed for, under inspection, completed, and archived;

[0029] In the dual reminder triggering mechanism of S4, when a time-triggered reminder is triggered, the system automatically calculates the reminder time point for each milestone node based on the set advance notification days, and sends a reminder notification according to the set number of days before the expiration date;

[0030] Status linkage reminders: When the status of an upstream node changes, the system automatically analyzes the dependencies of downstream nodes and sends preparation reminders to relevant personnel.

[0031] Multi-role notification: Based on the project role configuration, send differentiated reminder content to multiple roles such as project manager, technical lead, and quality lead at the same time;

[0032] The automatic upgrade mechanism sets milestone timeout thresholds. When a node times out and is not processed, the upgrade process is automatically initiated. Level 1 upgrade: notify the immediate supervisor within 2 hours of timeout; Level 2 upgrade: notify the department head within 6 hours of timeout; Level 3 upgrade: notify senior management within 24 hours of timeout.

[0033] This invention also proposes a modular laboratory project management system. The S5 electronic signature confirmation mechanism supports multiple electronic signature methods, including electronic signature, SMS verification code confirmation, and facial recognition confirmation. During the signature process, the system automatically collects key information such as signature time, signature location, and signature personnel. The system supports attachment upload functionality, allowing signature personnel to upload relevant supporting documents. After signature completion, the system automatically generates a signature voucher and sends it to relevant parties for confirmation. An automated email notification system is also included. The system automatically generates differentiated email notification content based on feedback type and processing status. In the case of original document return, the system automatically sends the return address, packaging requirements, and return deadline information. In the case of scanned document upload, the system automatically sends the upload link, format requirements, and file naming guidelines. Email sending adopts modular management.

[0034] A modular laboratory project management system includes a project management engine module, an intelligent key node reminder system, a sample lifecycle tracking management module, and a multi-party collaborative feedback management module. The project management engine module comprises three core business modules: project management, sample management, and contract management. The project management module includes four sub-modules: progress management, attribute management, quality control, and analytical method library. The sample management module includes three sub-modules: sample preparation management, sample handover management, and anomaly management. The contract management module includes three sub-modules: customer management, contract management, and outsourcing management. All modules exchange data and coordinate business processes through a unified API interface.

[0035] Furthermore, the system enables rapid project creation through the dynamic combination of scheduleTemplate (progress management module) and propertyTemplate (attribute management module). The system pre-configures various project type modules, including "LC-MS quantitative detection", "ELISA immunoassay", and "pharmacokinetic studies". Each module contains standardized progress nodes, attribute configurations, and quality control requirements. When a user creates a new project, the system intelligently recommends the most suitable module combination based on the detection method, sample type, and customer requirements.

[0036] Based on the aforementioned solution, the intelligent key node reminder system performs intelligent monitoring of key project nodes:

[0037] Time-triggered reminders: Automatically calculates the reminder time based on the set number of days in advance;

[0038] Status linkage reminder: When the status of an upstream node changes, a preparation reminder is automatically triggered for the downstream node;

[0039] Multi-role notification: Based on the configured notification role ID, reminders are sent to the project manager, technical lead, and quality lead simultaneously;

[0040] Upgrade mechanism: Nodes that have not been processed within the time limit will be automatically upgraded to the superior administrator.

[0041] As a further aspect of the present invention, the sample lifecycle tracking management module establishes a complete tracking chain from sample preparation to final disposal:

[0042] Sample identification: The unique code of the specimen distinguishes the sample and reagent types, and supports multi-dimensional attribute management such as content, purity, and molecular weight;

[0043] Real-time status monitoring: Track the complete status changes of samples from preparation, handover, testing to completion;

[0044] Temperature and storage condition monitoring: Record key parameters of sample storage temperature and light conditions;

[0045] Anomaly warning mechanism: When the sample condition is abnormal, the system automatically generates an anomaly report and initiates the processing procedure;

[0046] The multi-party collaborative feedback management module integrates a collaborative platform for logistics tracking and electronic signature box feedback management.

[0047] Standardized feedback requirements: Supports automated processing of both original document return and scanned copies of accompanying documents as feedback modes;

[0048] Logistics information synchronization: Real-time synchronization of logistics status via logistics company ID and tracking number; Electronic receipt confirmation: Supports multiple confirmation methods including electronic signature and attachment upload;

[0049] Automated email notifications: Automatically send relevant email notifications based on the type of feedback.

[0050] (III) Beneficial Effects

[0051] Compared with existing technologies, this invention provides a modular laboratory project management method and system, which has the following beneficial effects:

[0052] 1. This invention significantly improves project management efficiency, reducing project creation time to 30 minutes, thus saving time costs during the project preparation phase and enabling projects to enter the execution phase more quickly. Through intelligent module matching algorithms and dynamic module combination strategies, the system can accurately match and combine pre-set modules based on project requirement parameters, reducing repetitive configuration work, avoiding the tediousness of manual repetitive settings, and improving the accuracy and consistency of work. Furthermore, through project progress visualization, managers can more clearly understand project progress, improve decision-making efficiency, and help managers to identify problems and adjust strategies in a timely manner, ensuring the smooth progress of the project.

[0053] 2. This invention enables the system to adopt a microservice distributed architecture, and achieves full lifecycle management of laboratory projects through the collaborative work of multiple business modules.

[0054] 3. This invention can significantly reduce the operational error rate. Through the dual reminder triggering mechanism in the intelligent node monitoring and reminder mechanism, it can effectively monitor each milestone node of the project, thereby reducing the omission rate of key nodes, ensuring the integrity and continuity of the project process, and avoiding project delays or failures due to the omission of key nodes. By adopting measures such as generating unique sample identifiers, designing sample state machines, and real-time monitoring of environmental conditions, the accuracy of sample tracking is improved, and the status and location of samples can be accurately grasped, which facilitates sample management and quality control.

[0055] 4. Through a collaborative feedback management mechanism, the system enables intelligent identification of feedback requirements, synchronous integration of logistics information, electronic signature confirmation, and automated email notifications. This provides customers with a more convenient and efficient service experience, enhancing their trust and recognition of the project. The system also provides internal staff with a collaborative platform that integrates logistics tracking, electronic signature, and feedback management. This enables real-time information sharing and business process automation, reducing communication barriers and coordination costs, and improving the efficiency and quality of team collaboration.

[0056] 5. The system adopts a microservice distributed architecture, divided into multiple core business modules such as project management, sample management, and contract management. Each module contains multiple sub-modules. This modular design makes functional expansion more convenient. When adding new functions or modifying existing functions, operations only need to be performed in the corresponding module, without significantly affecting other modules, thus reducing the complexity of system maintenance.

[0057] Standardized interfaces are set up so that modules can exchange data and coordinate business through a unified API interface. Standardized interfaces make it easier to integrate third-party systems, facilitate connection with other external systems, achieve data sharing and business collaboration, improve the openness and compatibility of the system, and facilitate the application and expansion of the system in different business scenarios. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the architecture of a modular laboratory project management system proposed in this invention.

[0059] Figure 2 This is a schematic diagram of the process structure of a modular laboratory project management method proposed in this invention;

[0060] Figure 3 This is a detailed flowchart illustrating the modular laboratory project management method proposed in this invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] Reference Figures 1-3 This invention proposes a modular laboratory project management method, comprising the following steps:

[0064] S1: System architecture design adopts a microservice distributed architecture, which enables the full lifecycle management of laboratory projects through the collaborative work of multiple business modules;

[0065] The core module architecture design includes: core module architecture design, database architecture optimization, and service registration and discovery mechanisms;

[0066] Core module architecture design: The system adopts a microservice distributed architecture, consisting of three core business modules: project management, sample management, and contract management. Each core module contains several sub-modules. The modules exchange data and coordinate business through a unified API interface to achieve collaborative work.

[0067] Database architecture optimization: Establish a project module association table to achieve dynamic combination of the progress management module and the attribute management module through module identifiers; design a sample status transition table to record the complete status change history of the sample from "in preparation" to "completed"; build an access control matrix to achieve fine-grained access control for different roles to different functional modules.

[0068] Service registration and discovery mechanism: When each microservice starts up, it registers metadata information with the service registry, including service name, service address, and health check interface. The registry maintains a list of service instances and checks their health status periodically. When a service is invoked, the optimal service instance is selected and the request is forwarded using a load balancing algorithm.

[0069] S2: Module Management and Project Creation: Employing a multi-dimensional matching algorithm, the progress management module automatically generates the node sequence for sample reception and report generation, which is loaded in parallel with the attribute management template and quality control module. Combined with a pre-calculation mechanism, the creation time is compressed to within 30 minutes.

[0070] The specific algorithm steps of the multi-dimensional matching algorithm are as follows:

[0071] Data standardization processing involves standardizing the names of detection methods, establishing feature vectors, classifying sample physical properties, and extracting key parameters.

[0072] Module feature extraction, marking key nodes and dependencies, configuring progress management module, defining sample detection parameter constraints, configuring attribute management module, real-time optimization and conflict resolution, when sample type and detection method conflict, priority is given to the module occupying the instrument's idle time period, realizing dynamic module combination;

[0073] Output and validation: Output the top 3 matching modules and their adaptation parameters, label the differences between the system recommendation and the historical human selection, record the human coverage selection, iteratively optimize the weight allocation, and dynamically adjust the weights by training the weight model with historical project data.

[0074] S3: Sample Management and Tracking: Establish unique identification rules, design multiple state transition mechanisms, integrate environmental monitoring equipment, and realize early warning and impact assessment of temperature and humidity exceeding limits;

[0075] Step 1: The system generates a globally unique identifier for each sample. The identifier rule is: sample type prefix - contract number - date stamp - serial number. The sample type prefix uses standardized abbreviations, such as SE for serum, PL for plasma, UR for urine, TI for tissue, etc. The date stamp uses the year-month-day format, and the serial number is a 4-digit number, ensuring that 9999 samples can be uniquely identified on the same day.

[0076] Step 2: Sample State Machine Design

[0077] Establish a complete sample status transfer control mechanism, defining six main statuses: in preparation, awaiting handover, signed for, in testing, completed, and archived. Each status specifies the allowed next status transfer path to prevent abnormal status jumps. During status transfer, key information such as operator, operation time, and transfer reason are automatically recorded to form a complete status change audit chain.

[0078] Step 3: Real-time monitoring of environmental conditions

[0079] The system integrates environmental monitoring equipment such as temperature and humidity sensors to collect key parameters of the sample storage environment, such as temperature, humidity, and light, in real time. It sets standard storage condition thresholds for various types of samples and automatically triggers an early warning mechanism when the monitored values ​​exceed the allowable range. The early warning information includes the degree of deviation, duration, and impact assessment, and automatically notifies the relevant responsible personnel for handling.

[0080] S4: Intelligent node monitoring and reminders, adopting a dual reminder mechanism, integrating time-triggered and status-linked multi-role notification functions, equipped with a three-dimensional risk assessment algorithm, and implementing a graded upgrade process;

[0081] In the dual reminder triggering mechanism, when a time-triggered reminder is triggered, the system automatically calculates the reminder time for each milestone node based on the set advance notification days, and sends a reminder notification a set number of days before the expiration date;

[0082] Status linkage reminders: When the status of an upstream node changes, the system automatically analyzes the dependencies of downstream nodes and sends preparation reminders to relevant personnel.

[0083] Multi-role notification: Based on the project role configuration, send differentiated reminder content to multiple roles such as project manager, technical lead, and quality lead at the same time;

[0084] The automatic upgrade mechanism sets milestone timeout thresholds. When a milestone expires without being processed, the upgrade process is automatically initiated: Level 1 upgrade: notify the immediate supervisor within 2 hours of the timeout; Level 2 upgrade: notify the department head within 6 hours of the timeout; Level 3 upgrade: notify senior management within 24 hours of the timeout.

[0085] The project's overall risk level is generated by comprehensively analyzing three dimensions: time delay risk, resource shortage risk, and quality problem risk. Time delay risk is quantified by statistically analyzing the proportion of overdue milestones to total milestones; resource shortage risk is assessed by analyzing current resource utilization and expected demand; and quality problem risk is calculated by statistically analyzing the frequency of historical quality events. The three dimensions are comprehensively scored with a weight of 4:3:3 to generate the overall risk level of the project.

[0086] The differences from traditional risk assessment methods are as follows:

[0087] 1. A multi-dimensional fusion algorithm is used to quantify discrete laboratory management elements, such as the number of freeze-thaw cycles for samples and the instrument calibration status, into risk parameters. A comprehensive risk assessment is then achieved using the following formula:

[0088] Overall Risk Value = Σ(Dimensional Risk Value × Dynamic Weight) + Urgency Coefficient

[0089] 2. Use laboratory-specific coding rules;

[0090] 3. Deeply integrated with the laboratory information management system, it directly reads and updates risk values ​​in real time, such as instrument status and environmental sensor data, instead of the traditional manual post-event entry and assessment;

[0091] This invention can significantly reduce the operational error rate. Through the dual reminder triggering mechanism in the intelligent node monitoring and reminder mechanism, it can effectively monitor each milestone node of the project, thereby reducing the omission rate of key nodes, ensuring the integrity and continuity of the project process, and avoiding project delays or failures due to the omission of key nodes. By adopting measures such as generating unique sample identifiers, designing sample state machines, and monitoring environmental conditions in real time, the accuracy of sample tracking is improved, and the status and location of samples can be accurately grasped, which facilitates sample management and quality control.

[0092] S5: Collaborative Feedback Management: The NLP contract parsing engine automatically identifies terms attached to original or scanned copies of returned documents; logistics API integration synchronizes waybill status in real time and triggers exception handling; the electronic signature system supports multiple confirmation methods.

[0093] The electronic signature confirmation mechanism supports multiple electronic signature methods, including electronic signature, SMS verification code confirmation, and facial recognition confirmation. During the signing process, the system automatically collects key information such as the signing time, signing location, and signing personnel. The system supports attachment upload, allowing signing personnel to upload relevant supporting documents. After signing is completed, the system automatically generates a signature voucher and sends it to relevant parties for confirmation. An automated email notification system is also set up. The system automatically generates differentiated email notification content based on the feedback type and processing status. In the scenario of returning original documents, the system automatically sends the return address, packaging requirements, and return deadline information. In the scenario of uploading scanned documents, the system automatically sends the upload link, format requirements, and file naming guidelines. Email sending adopts modular management.

[0094] S6: Data security and access control adopts the RBAC access control model, which is subdivided into five levels of role hierarchy; the data protection scheme includes AES-256 storage encryption, HTTPS transmission encryption and a four-level backup strategy; full-link auditing records all operation logs, tamper-proof storage and multi-dimensional retrieval;

[0095] The five-level role hierarchy includes super administrator, project manager, technical personnel, quality personnel, and customer with different role permissions. Each role corresponds to different functional module access permissions and data operation permissions. The system supports dynamic allocation and revocation of permissions, and administrators can temporarily adjust personnel permissions according to project needs. The four-level backup strategy includes backup strategies with different frequencies such as real-time backup, daily backup, weekly backup, and monthly backup.

[0096] Through a collaborative feedback management mechanism, the system enables intelligent identification of feedback requirements, synchronous integration of logistics information, electronic signature confirmation, and automated email notifications. This provides customers with a more convenient and efficient service experience, enhancing their trust and recognition of the project. The system also provides internal staff with a collaborative platform that integrates logistics tracking, electronic signature, and feedback management. This enables real-time information sharing and business process automation, reducing communication barriers and coordination costs, and improving the efficiency and quality of team collaboration.

[0097] This invention also proposes a modular laboratory project management system, including a project management engine module, an intelligent key node reminder system, a sample lifecycle tracking management module, and a multi-party collaborative feedback management module. The project management engine module comprises three core business modules: project management, sample management, and contract management. The project management module includes four sub-modules: progress management, attribute management, quality control, and analytical method library. The sample management module includes three sub-modules: sample preparation management, sample handover management, and anomaly management. The contract management module includes three sub-modules: customer management, contract management, and outsourcing management. All modules exchange data and coordinate business processes through a unified API interface. The system adopts a microservice distributed architecture, divided into multiple core business modules such as project management, sample management, and contract management. Each module contains multiple sub-modules. This modular design makes functional expansion more convenient. When adding new functions or modifying existing functions, operations can be performed only in the corresponding module without significantly affecting other modules, reducing the complexity of system maintenance. Standardized interfaces are set up, and modules exchange data and coordinate business through a unified API interface. Standardized interfaces make third-party system integration simpler, allowing for easy connection with other external systems to achieve data sharing and business collaboration. This enhances the system's openness and compatibility, facilitating its application and expansion in different business scenarios.

[0098] The system enables rapid project creation through the dynamic combination of scheduleTemplate (progress management module) and propertyTemplate (attribute management module). The system comes pre-loaded with various project type modules, including "LC-MS quantitative detection", "ELISA immunoassay", and "pharmacokinetic studies". Each module contains standardized progress nodes, attribute configurations, and quality control requirements. When users create a new project, the system intelligently recommends the most suitable module combination based on the detection method, sample type, and customer requirements.

[0099] The intelligent critical node reminder system intelligently monitors key project nodes: Time-triggered reminders: Automatically calculates reminder times based on the set advance notice days; Status-linked reminders: Automatically triggers preparation reminders for downstream nodes when the status of an upstream node changes; Multi-role notifications: Sends reminders to the project manager, technical lead, and quality lead simultaneously based on the configured notification role ID; Escalation mechanism: Nodes that have not been processed within the time limit are automatically escalated to the superior manager.

[0100] The sample lifecycle tracking and management module establishes a complete tracking chain from sample preparation to final disposal: Sample identification: Distinguishing sample and reagent types through unique specimen codes, supporting multi-dimensional attribute management such as content, purity, and molecular weight; Real-time status monitoring: Tracking the complete status changes of samples from preparation, handover, testing to completion; Temperature and storage condition monitoring: Recording key parameters of sample storage temperature and light conditions; Anomaly warning mechanism: When the sample status is abnormal, the system automatically generates an anomaly report and initiates the processing flow.

[0101] This multi-party collaborative feedback management module integrates a collaborative platform for logistics tracking and electronic signature box feedback management. It features standardized feedback requirements, supporting automated processing of both original document return and scanned accompanying documents feedback modes; synchronized logistics information, enabling real-time synchronization of logistics status via logistics company ID and tracking number; electronic signature confirmation, supporting multiple confirmation methods including electronic signatures and attachment uploads; and automated email notifications, automatically sending relevant email notifications based on feedback type. This invention significantly improves project management efficiency, reducing project creation time to 30 minutes, saving time costs during project preparation, and allowing projects to enter the execution phase more quickly. Through intelligent module matching algorithms and dynamic module combination strategies, the system can accurately match and combine pre-set modules based on project requirement parameters, reducing repetitive configuration work, avoiding tedious manual settings, and improving work accuracy and consistency. Furthermore, project progress visualization allows managers to better understand project progress, improving decision-making efficiency and helping them to promptly identify problems, adjust strategies, and ensure smooth project progress.

[0102] Example 2

[0103] Reference Figures 1-3This invention proposes an intelligent project module management mechanism. Through the dynamic combination of scheduleTemplate (progress management module) and propertyTemplate (attribute management module), it enables rapid project creation. The system pre-configures various project type modules, such as LC-MS quantitative detection, ELISA immunoassay, and pharmacokinetic studies. Each module includes standardized progress nodes, attribute configurations, and quality control requirements. When a user creates a new project, the system intelligently recommends the most suitable module combination based on parameters such as detection method, sample type, and customer requirements, thereby reducing project configuration time from the traditional 2-3 days to less than 30 minutes.

[0104] 2. Intelligent Key Node Alert System

[0105] This invention proposes a dual reminder mechanism based on time triggering and status linkage. The system uses a milestone reminder function module to intelligently monitor key project nodes.

[0106] Time-triggered reminders: Automatically calculates the reminder time based on the set number of days in advance.

[0107] Status linkage reminder: When the status of an upstream node changes, a preparation reminder is automatically triggered for the downstream node;

[0108] Multi-role notification: Based on the configured notification role ID, reminders are sent to the project manager, technical lead, quality manager, etc. simultaneously;

[0109] Upgrade mechanism: Nodes that are not processed within the time limit are automatically upgraded to the superior administrator;

[0110] Tests have shown that this mechanism reduces the critical node omission rate to below 2%.

[0111] 3. Sample lifecycle tracking management

[0112] A complete traceability chain has been established from sample preparation to final disposal:

[0113] Sample identification: The sample and reagent types are distinguished by a unique specificationSn code, and multi-dimensional attribute management such as content, purity, and molecular weight is supported.

[0114] Real-time status monitoring: Track the complete status changes of samples from preparation, handover, testing, and completion;

[0115] Temperature and storage condition monitoring: Record key parameters of sample storage temperature and light conditions;

[0116] Anomaly warning mechanism: When the sample condition is abnormal, the system automatically generates an anomaly report and initiates the processing procedure;

[0117] Tests show that the sample tracking accuracy has been improved to over 99.5%, and the time for handling sample-related anomalies has been reduced by 60%.

[0118] 4. Multi-party collaborative feedback management mechanism

[0119] A collaborative platform integrating logistics tracking, electronic signature, and feedback management has been developed.

[0120] Standardized feedback requirements: Supports automated processing of both "original documents returned" and "scanned copies of accompanying documents" feedback modes;

[0121] Logistics information synchronization: Real-time synchronization of logistics status is achieved through logistics company ID and tracking number;

[0122] Electronic receipt confirmation: Supports multiple confirmation methods such as electronic signature and attachment upload;

[0123] Automated email notifications: Automatically send relevant email notifications based on the feedback type;

[0124] Method implementation steps:

[0125] (1) System architecture design

[0126] The system adopts a microservice distributed architecture, enabling full lifecycle management of laboratory projects through the collaborative work of multiple business modules;

[0127] Step 1: Core Module Architecture Design

[0128] The system is divided into three core business modules: project management, sample management, and contract management. The project management module includes four sub-modules: schedule management, attribute management, quality control, and analysis method library. The sample management module includes three sub-modules: sample preparation management, sample handover management, and anomaly management. The contract management module includes three sub-modules: customer management, contract management, and outsourcing management. All modules exchange data and coordinate business processes through a unified API interface.

[0129] Step 2: Database Architecture Optimization

[0130] Establish a project module association table to dynamically combine the progress management module and the attribute management module through module identifiers. Design a sample status transition table to record the complete status change history of samples from "in preparation" to "completed". Construct an access control matrix based on a role-permission model to achieve fine-grained access control for different roles to different functional modules;

[0131] Step 3: Service Registration and Discovery Mechanism

[0132] Each microservice automatically registers its service metadata information with the service registry upon startup, including service name, service address, and health check interface. The registry maintains a list of service instances and performs periodic health checks. When a service is invoked, a load balancing algorithm selects the optimal service instance for request forwarding.

[0133] (2) Module Management and Project Creation

[0134] Step 1: Intelligent Module Matching Algorithm

[0135] After receiving the project requirements parameters, including information such as testing methods, sample types, and special customer requirements, the system calculates the matching score of each preset module through a multi-dimensional matching algorithm. The matching algorithm assigns 40% weight to the matching of testing methods, 30% weight to the matching of sample types, and 30% weight to the matching of customer requirements. The system returns the top three module combinations with a matching degree of over 70% for the user to choose from.

[0136] The specific algorithm steps of the multi-dimensional matching algorithm are as follows:

[0137] 1. Data standardization processing

[0138] 1) Establish a feature vector by standardizing the detection method name (e.g., LC-MS quantitative detection → LCMS_QUANT encoding).

[0139] 2) Classify the physical characteristics of samples (serum / plasma / tissue) and extract key parameters.

[0140] 2. Module Feature Extraction

[0141] The system marks key nodes and dependencies, configures the progress management module, defines sample testing parameter constraints, configures the attribute management module, optimizes and resolves conflicts in real time, and prioritizes modules that occupy instrument idle time periods when sample type and testing method conflict, thus achieving dynamic module combination.

[0142] 3. Output and Verification

[0143] Output the top 3 matching modules and their adaptation parameters, label the differences between the system recommendation and the historical human selection, record the human coverage selection, and iteratively optimize the weight allocation; train the weight model through historical project data to dynamically adjust the weights.

[0144] Step 2: Module Dynamic Combination Strategy

[0145] The system comprises several modules: a progress management module and a quality control module. The progress management module automatically matches a standardized sequence of progress nodes based on the selected testing method, including key nodes such as sample reception, pretreatment, instrument testing, data analysis, and report generation. The attribute management module dynamically generates attribute configuration forms based on the sample's physicochemical characteristics, including parameters such as concentration range, purity requirements, and molecular weight. The quality control module automatically loads corresponding quality control nodes according to relevant industry standards and regulations.

[0146] The specific implementation steps for dynamic forms are as follows:

[0147] 1. Feature structuring processing;

[0148] 2. Attribute rule matching;

[0149] 3. Dynamic form generation;

[0150] 4. Context-aware optimization.

[0151] Step 3: Quick Project Creation Process

[0152] The system adopts a parallel processing mechanism, simultaneously applying a progress management module, an attribute management module, and a quality control module to avoid time delays caused by serial processing. During the application of the modules, data verification and consistency checks are automatically performed to ensure that the generated project configuration conforms to business rules. The entire project creation process is optimized through pre-calculation and caching mechanisms, keeping the project creation time within 30 minutes.

[0153] (3) Sample Management and Tracking

[0154] Step 1: Generating a unique sample identifier

[0155] The system generates a globally unique identifier for each sample, with the following rule: sample type prefix - contract number - date stamp - serial number. The sample type prefix uses standardized abbreviations, such as SE for serum, PL for plasma, UR for urine, and TI for tissue. The date stamp uses the year-month-day format, and the serial number is a 4-digit number, ensuring that 9,999 samples can be uniquely identified on the same day.

[0156] Step 2: Sample State Machine Design

[0157] Establish a complete sample status transition control mechanism, defining six main statuses: in preparation, awaiting handover, signed for, under testing, completed, and archived. Each status specifies the allowed next status transition path to prevent abnormal status jumps. During status transitions, key information such as operator, operation time, and reason for the transition is automatically recorded, forming a complete status change audit chain.

[0158] Step 3: Real-time monitoring of environmental conditions

[0159] The system integrates environmental monitoring equipment such as temperature and humidity sensors to collect key parameters of the sample storage environment, such as temperature, humidity, and light, in real time; it sets standard storage condition thresholds for various types of samples, and automatically triggers an early warning mechanism when the monitored values ​​exceed the allowable range; the early warning information includes the degree of deviation, duration, impact assessment, etc., and automatically notifies relevant personnel to handle the situation.

[0160] (4) Intelligent node monitoring and alerts

[0161] Step 1: Dual Reminder Trigger Mechanism

[0162] Time-triggered reminders: The system automatically calculates the reminder time for each milestone node based on the set advance notice days, and sends reminder notifications a set number of days before the due date. Status-linked reminders: When the status of an upstream node changes, the system automatically analyzes the dependencies of downstream nodes and sends preparation reminders to relevant personnel. Multi-role notifications: Based on the project role configuration, the system simultaneously sends differentiated reminder content to multiple roles such as project manager, technical lead, and quality lead.

[0163] Step Two: Risk Warning Calculation Algorithm

[0164] The system comprehensively analyzes three dimensions of project risk: time delay risk, resource shortage risk, and quality problem risk. Time delay risk is quantified by statistically analyzing the proportion of overdue milestones to total milestones; resource shortage risk is assessed by analyzing current resource utilization and expected demand; and quality problem risk is calculated by statistically analyzing the frequency of historical quality events. The three dimensions are comprehensively scored with a weight of 4:3:3 to generate the overall risk level of the project.

[0165] The differences from traditional risk assessment methods are as follows:

[0166] 1. Employing a multi-dimensional fusion algorithm, discrete laboratory management elements (such as sample freeze-thaw cycles and instrument calibration status) are quantified into risk parameters, and a comprehensive risk assessment is achieved through the following formula;

[0167] Overall Risk Value = Σ(Dimensional Risk Value × Dynamic Weight) + Urgency Coefficient

[0168] 2. Use laboratory-specific coding rules;

[0169] 3. Deeply integrated with LIMS (Laboratory Information Management System). It directly reads instrument status and environmental sensor data to update risk values ​​in real time, rather than relying on traditional post-event manual entry and assessment.

[0170] Step 3: Automatic Upgrade Processing Mechanism

[0171] The system sets milestone timeout thresholds, and automatically initiates an upgrade process when a node times out without being processed. Level 1 upgrade: Notify the immediate supervisor within 2 hours of timeout; Level 2 upgrade: Notify the department head within 6 hours of timeout; Level 3 upgrade: Notify senior management within 24 hours of timeout. During the upgrade process, information such as the reason for the upgrade, the personnel involved, and the processing time are automatically recorded to provide data support for subsequent process optimization.

[0172] (5) Collaborative Feedback Management

[0173] Step 1: Intelligent Recognition of Feedback Requests

[0174] Based on contract terms, the system utilizes an innovative combination of domain-customized NLP models, a rules engine, and context-aware processing to accurately identify laboratory contract feedback requirements and automatically generate tasks. When a contract includes a "return original documents" clause, the system automatically generates a task to return the original documents, setting the return deadline and address. When a contract includes a "scanned copies to be attached" clause, the system automatically generates a file upload task, limiting allowed file formats and sizes. The system supports combined feedback requirements and simultaneously handles the coordination and prioritization of multiple feedback types.

[0175] Step 2: Synchronous Integration of Logistics Information

[0176] The system integrates with the API interfaces of mainstream logistics companies, and realizes real-time synchronization of logistics status through logistics company number and waybill number. The system periodically calls the logistics interface to obtain information such as the current location, transportation status and estimated arrival time of the package. When the logistics status changes, the system automatically updates the internal status and sends a notification to relevant personnel. When logistics is abnormal, the system automatically records the abnormal information and initiates the abnormal handling process.

[0177] Step 3: Electronic Receipt Confirmation Mechanism

[0178] The system supports multiple electronic signature methods, including electronic signature, SMS verification code confirmation, and facial recognition confirmation. During the signing process, key information such as signing time, signing location, and signing personnel are automatically collected. The system supports attachment upload function, allowing signing personnel to upload relevant supporting documents. After signing is completed, the system automatically generates a signing voucher and sends it to relevant parties for confirmation.

[0179] Step 4: Automated Email Notification System

[0180] The system automatically generates differentiated email notification content based on the feedback type and processing status. In the case of returning original documents, the system automatically sends information such as the return address, packaging requirements, and return deadline. In the case of uploading scanned documents, the system automatically sends guidance information such as the upload link, format requirements, and file naming. Email sending adopts modular management and supports personalized customization and multilingual versions.

[0181] (6) Data security and access control

[0182] Step 1: Hierarchical Access Control Mechanism

[0183] The system establishes a role-based hierarchical permission control system, setting different role permissions such as super administrator, project manager, technical personnel, quality personnel, and customer. Each role corresponds to different functional module access permissions and data operation permissions. The system supports dynamic allocation and revocation of permissions, and administrators can temporarily adjust personnel permissions according to project needs.

[0184] Step Two: Data Encryption and Backup Mechanism

[0185] Sensitive data is stored and encrypted using the AES-256 encryption algorithm. Data transmission is secured using HTTPS protocol and digital certificates. The system establishes a multi-level data backup mechanism, including backup strategies with different frequencies such as real-time backup, daily backup, weekly backup, and monthly backup. Backup data is stored locally and in the cloud to ensure data security and recoverability.

[0186] Step 3: Operation Logs and Audit Trail

[0187] The system records detailed logs of all user operations, including operation time, operator, operation content, and operation results. The logs are stored in an immutable manner to ensure the integrity and authenticity of audit data. The system provides flexible log query and analysis functions, supporting log retrieval and statistical analysis by multiple dimensions such as time, personnel, and operation type.

[0188] In the description herein, it should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A modular laboratory project management method, characterized in that, Includes the following steps: S1: System architecture design, adopting a microservice distributed architecture, with multiple business modules working collaboratively; S2: Module management and project creation. It adopts a multi-dimensional matching algorithm. The progress management module automatically generates the node sequence of sample receiving and report generation. It is loaded in parallel with the attribute management template and quality control module. Combined with the pre-calculation mechanism, the creation time is compressed. S3: Sample management and tracking, establishing unique identification rules, designing multiple state transition mechanisms, integrating environmental monitoring equipment, and realizing early warning and impact assessment of temperature and humidity exceeding limits; S4: Intelligent node monitoring and reminders, adopting a dual reminder mechanism, integrating time-triggered and status-linked multi-role notification functions, equipped with a three-dimensional risk assessment algorithm, and implementing a graded upgrade process; S5: Collaborative feedback management, NLP contract parsing engine automatically identifies original documents returned or scanned documents accompanied by terms, logistics API integration, real-time synchronization of waybill status and triggering of exception handling. The electronic signature system supports multiple confirmation methods; S6: Data security and access control adopts the RBAC access control model, which is subdivided into five role levels; the data protection scheme includes AES-256 storage encryption, HTTPS transmission encryption and a four-level backup strategy. The entire process is audited, recording all operation logs, with tamper-proof storage and multi-dimensional retrieval.

2. The modular laboratory project management method according to claim 1, characterized in that, The S1 core module architecture design includes: core module architecture design, database architecture optimization, and service registration and discovery mechanism; Core module architecture design: The system adopts a microservice distributed architecture, consisting of three core business modules: project management, sample management, and contract management. Each core module contains several sub-modules. The modules exchange data and coordinate business through a unified API interface to achieve collaborative work. Database architecture optimization: Establish a project module association table to achieve dynamic combination of the progress management module and the attribute management module through module identifiers; design a sample status transition table to record the complete status change history of the sample from "in preparation" to "completed"; build an access control matrix to achieve fine-grained access control for different roles to different functional modules. Service registration and discovery mechanism: When each microservice starts up, it registers metadata information with the service registry, including service name, service address and health check interface. The registry maintains a list of service instances and checks their health status regularly. When a service is called, the optimal service instance is selected and the request is forwarded through a load balancing algorithm.

3. The modular laboratory project management method according to claim 1, characterized in that, The specific algorithm steps of the multi-dimensional matching algorithm in S2 are as follows: Data standardization processing involves standardizing the names of detection methods, establishing feature vectors, classifying sample physical properties, and extracting key parameters. Module feature extraction, marking key nodes and dependencies, configuring progress management module, defining sample detection parameter constraints, configuring attribute management module, real-time optimization and conflict resolution, when sample type and detection method conflict, priority is given to the module occupying the instrument's idle time period, realizing dynamic module combination; Output and validation: Output the top 3 matching modules and their adaptation parameters, label the differences between the system recommendation and the historical human selection, record the human coverage selection, iteratively optimize the weight allocation, and dynamically adjust the weights by training the weight model with historical project data.

4. The modular laboratory project management method according to claim 1, characterized in that, The S3 includes multiple states such as preparing goods, awaiting handover, signed for, under inspection, completed, and archived. In the dual reminder triggering mechanism of S4, when a time-triggered reminder is triggered, the system automatically calculates the reminder time point for each milestone node based on the set advance notification days, and sends a reminder notification according to the set number of days before the expiration date; Status linkage reminders: When the status of an upstream node changes, the system automatically analyzes the dependencies of downstream nodes and sends preparation reminders to relevant personnel. Multi-role notifications: Based on project role configuration, differentiated reminder content is sent to multiple roles such as project manager, technical lead, and quality lead simultaneously; The automatic upgrade mechanism sets milestone timeout thresholds. When a node times out and is not processed, the upgrade process is automatically initiated. Level 1 upgrade: notify the immediate supervisor within 2 hours of timeout; Level 2 upgrade: notify the department head within 6 hours of timeout; Level 3 upgrade: notify senior management within 24 hours of timeout.

5. The modular laboratory project management method according to claim 1, characterized in that, The S5 electronic signature confirmation mechanism supports multiple electronic signature methods, including electronic signature, SMS verification code confirmation, and facial recognition confirmation. During the signature process, the system automatically collects key information such as the signature time, signature location, and signature personnel. The system supports attachment upload functionality, allowing signature personnel to upload relevant supporting documents. After the signature is completed, the system automatically generates a signature voucher and sends it to relevant parties for confirmation. An automated email notification system is also set up. The system automatically generates differentiated email notification content based on the feedback type and processing status. In the scenario of returning original documents, the system automatically sends the return address, packaging requirements, and return deadline information. In the scenario of uploading scanned documents, the system automatically sends the upload link, format requirements, and file naming guidelines. Email sending adopts modular management.

6. The modular laboratory project management method according to claim 1, characterized in that, The S6 system has a five-level role hierarchy, including super administrator, project manager, technician, quality personnel, and customer, each with different role permissions. Each role corresponds to different functional module access permissions and data operation permissions. The system supports dynamic allocation and revocation of permissions, and administrators can temporarily adjust personnel permissions according to project needs. The four-level backup strategy includes backup strategies with different frequencies such as real-time backup, daily backup, weekly backup, and monthly backup.

7. A modular laboratory project management system, characterized in that, It includes a project management engine module, an intelligent key node reminder system, a sample lifecycle tracking management module, and a multi-party collaborative feedback management module. The project management engine module comprises three core business modules: project management, sample management, and contract management. The project management module includes four sub-modules: progress management, attribute management, quality control, and analysis method library. The sample management module includes three sub-modules: sample preparation management, sample handover management, and anomaly management. The contract management module includes three sub-modules: customer management, contract management, and outsourcing management. All modules exchange data and coordinate business through a unified API interface.

8. A modular laboratory project management system according to claim 7, characterized in that, The system enables rapid project creation through the dynamic combination of the progress management module and the attribute management module. The system has multiple pre-set project type modules, including LC-MS quantitative detection, ELISA immunoassay, and pharmacokinetic studies. Each module contains standardized progress nodes, attribute configurations, and quality control requirements. When a user creates a new project, the system intelligently recommends the most suitable module combination based on the detection method, sample type, and customer requirements.

9. A modular laboratory project management system according to claim 7, characterized in that, The intelligent key node reminder system intelligently monitors key nodes of the project: Time-triggered reminders: Automatically calculates the reminder time based on the set number of days in advance; Status linkage reminder: When the status of an upstream node changes, a preparation reminder is automatically triggered for the downstream node; Multi-role notification: Based on the configured notification role ID, reminders are sent to the project manager, technical lead, and quality lead simultaneously; Upgrade mechanism: Nodes that have not been processed within the time limit will be automatically upgraded to the superior administrator.

10. A modular laboratory project management system according to claim 7, characterized in that, The sample lifecycle tracking and management module establishes a complete tracking chain from sample preparation to final disposal: Sample identification: The unique code of the specimen distinguishes the sample and reagent types, and supports multi-dimensional attribute management such as content, purity, and molecular weight; Real-time status monitoring: Track the complete status changes of samples from preparation, handover, testing to completion; Temperature and storage condition monitoring: Record key parameters of sample storage temperature and light conditions; Anomaly warning mechanism: When the sample condition is abnormal, the system automatically generates an anomaly report and initiates the processing procedure; The multi-party collaborative feedback management module integrates a collaborative platform for logistics tracking and electronic signature box feedback management. Standardized feedback requirements: Supports automated processing of both original document return and scanned copies of accompanying documents as feedback modes; Logistics information synchronization: Real-time synchronization of logistics status is achieved through logistics company ID and tracking number; Electronic receipt confirmation: Supports multiple confirmation methods including electronic signature and attachment upload; Automated email notifications: Automatically send relevant email notifications based on the type of feedback.

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