Fully automated laboratory sample testing and identification management system and testing and identification method

Abstract

The application discloses a full-automatic laboratory sample test identification management system and a test identification method, relates to the technical field of laboratory information management systems, and aims at the problems of fragmented process, subjective judgment and low-efficiency record in the existing system.The sample test task is generated through an experiment management module, and the sample test task is dispatched and controlled safely; a test configuration scheme is output through an experiment configuration module; a sample test pretreatment operation process matched with a test instrument is generated through a pretreatment module; visual sample feeding setting is performed through a 3D virtual sample feeding system of a machine detection module, and a sample feeding sequence file matched with the test instrument is generated; a test result output by the test instrument is received through a result analysis module, data analysis is performed, and a test report is generated.The system can realize full-process automation and intelligent management from task acceptance to report generation, and significantly improves the test efficiency, accuracy and data security of the laboratory.

Description

Technical Field

[0001] This invention relates to the field of laboratory information management system technology, and in particular to a fully automated laboratory sample testing and identification management system and testing and identification method. Background Technology

[0002] Laboratory information management systems (ILS) serve as core management tools for modern laboratories, aiming to achieve comprehensive management of samples, data, personnel, and instruments. While existing systems have played a fundamental role in promoting paperless and centralized data management in laboratories, they still have significant limitations in architecture and functionality, making it difficult to meet the current needs of testing and identification laboratories for intelligent, automated, and closed-loop control of the entire process. Specifically, the functional modules are independent of each other, and the data and workflow are not well connected, resulting in low overall operational efficiency. At the same time, key aspects such as task scheduling, resource allocation, and testing operations lack objective and quantitative decision support, relying heavily on manual experience and judgment, which affects the accuracy of data and the consistency of processes. In addition, information on the testing process, environment, and resources still relies on manual or scattered records, making it difficult to achieve complete and traceable electronic quality control, and also bringing difficulties to problem investigation and result reproduction.

[0003] Therefore, there is an urgent need to develop a new management system that can deeply integrate intelligent scheduling, visual operation guidance, automated resource allocation, proactive environmental monitoring, and closed-loop data flow, so as to promote the laboratory testing and identification management system to a true stage of full-process automation and intelligence. Summary of the Invention

[0004] To address the aforementioned problems, this invention aims to provide a fully automated laboratory sample testing and identification management system and testing and identification method. It schedules sample testing tasks through a dynamic priority evaluation model, manages personnel permissions using a composite control mechanism, determines testing configuration schemes through an experimental configuration module, provides visual operation guidance through a preprocessing module and a 3D virtual sample introduction system, and automatically processes data and generates test reports using a result analysis module. This achieves fully automated and intelligent management of the entire process from task acceptance to report generation, significantly improving testing efficiency, accuracy, and data security.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] On one hand, the present invention provides a fully automated laboratory sample testing and identification management system, the system comprising:

[0007] The experiment management module is used to interface with external case acceptance systems, generate sample testing tasks, schedule sample testing tasks through a dynamic priority evaluation model, and manage personnel permissions based on a composite control mechanism of role access control and dynamic permission adjustment.

[0008] The experiment configuration module is used to determine the test configuration plan based on the sample testing task by querying the standard test method knowledge base and using Internet of Things technology to match and manage the status of reagents, consumables, standards and instruments.

[0009] The pre-processing module generates a sample pre-processing operation flow that matches the testing instruments, based on the sample testing task and testing configuration scheme.

[0010] The on-machine testing module communicates with the testing instrument, performs visual sample introduction settings through the 3D virtual sample introduction system, and generates a sample introduction sequence file that matches the testing instrument.

[0011] The results analysis module is connected to the output of the testing instrument. It is used to receive the test results output by the testing instrument, perform data analysis, and generate test reports.

[0012] Optionally, the experiment management module includes:

[0013] The task docking unit is used to dock with the external case acceptance system, obtain sample information, match the corresponding standard inspection method according to the sample information, and generate a sample inspection task containing the standard inspection method number.

[0014] The intelligent scheduling unit receives sample testing tasks, determines the priority of sample testing tasks based on a dynamic priority evaluation model, and performs task scheduling and testing resource allocation.

[0015] The security management unit employs a composite management mechanism based on role-based access control and dynamic permission adjustment to manage personnel permissions.

[0016] Optionally, the priority of the sample testing task is determined based on the dynamic priority evaluation model as follows:

[0017] ;

[0018] In the formula, Score the task priority. Due to the urgency of the case, Due to the urgency of the preservation period for the samples, This indicates the instrument's load status. As a weighting coefficient for the urgency of the case, This is a weighting coefficient for the retention period of the test sample. This is the weighting coefficient for the instrument load status.

[0019] Optionally, the experiment configuration module includes:

[0020] The method management subsystem is used to build and maintain a structured knowledge base of standard testing methods, and to retrieve the corresponding list of method resources from the knowledge base of standard testing methods according to the sample testing task.

[0021] The reagent, consumable and standard product management subsystem is used to match reagents, consumables and standards according to the method resource list, generate a reagent, consumable and standard product matching result report, and perform full life cycle inventory, expiration date and traceability management for all reagents, consumables and standards.

[0022] The instrument and equipment management subsystem is used to match testing instruments according to the method resource list, generate instrument and equipment matching result reports, and manage the files, status monitoring, online reservation and maintenance plan management of all instruments and equipment.

[0023] The experimental configuration module generates the testing configuration scheme by integrating the method resource list, the reagent and consumable matching result report, and the instrument and equipment matching result report.

[0024] Optionally, the operation process of the 3D virtual sample introduction system specifically includes:

[0025] Load a 3D sample loading disk model that matches the testing instrument model;

[0026] It provides a visual interactive interface that allows users to drag and drop virtual sample vials to the target injection position in the 3D injection tray model and set injection parameters.

[0027] The status of each injection point is rendered in real time using different colors;

[0028] The status of each injection point and the injection parameters are converted into a structured injection sequence file and transmitted to the testing instrument.

[0029] Optionally, the result analysis module receives the test results output by the testing instrument, performs data analysis, and generates a test report, specifically including:

[0030] The built-in multi-format parsing engine automatically reads and parses the raw data files output by the testing instruments;

[0031] Perform statistical analysis on the parsed raw data file, compare the analysis results with the quality control rule base for compliance, and mark the abnormal test results;

[0032] The system automatically selects a structured report template based on the type of sample testing task and generates the final test report.

[0033] Optionally, the system further includes:

[0034] The environmental management module is used for real-time monitoring and early warning of laboratory environmental parameters.

[0035] Optionally, the environment management module includes:

[0036] The ventilation monitoring unit is used to monitor the status of the ventilation system in real time and issue an alarm when there is an abnormality.

[0037] Access control and security units are used to dynamically manage access permissions and record entry and exit logs for the laboratory.

[0038] An environmental monitoring unit is used to continuously monitor and record environmental status parameters of the laboratory through a sensor network;

[0039] The instrument and equipment supply monitoring unit is used to monitor the supply parameters of the testing instruments and provide early warnings when the supply is abnormal.

[0040] The early warning and linkage unit is used to realize risk early warning and task linkage control based on preset rules.

[0041] On the other hand, the present invention also provides a sample testing and identification method, based on the system described above, the method comprising:

[0042] The experiment management module generates sample testing tasks and schedules and manages the safety of these tasks.

[0043] Based on the scheduling results, the corresponding inspector prepares to begin the inspection;

[0044] The inspector prepares the corresponding inspection resources based on the inspection configuration plan output by the experimental configuration module;

[0045] Perform the corresponding preprocessing operations according to the sample testing preprocessing operation flow output by the preprocessing module;

[0046] Inspectors can visualize the sample injection settings for the inspection process through the on-machine testing module, and the on-machine testing module generates sample injection sequence files;

[0047] The testing instrument automatically tests the samples based on the injection sequence file, and the test results are output to the result analysis module for data analysis to generate a test report.

[0048] The beneficial effects of this invention are:

[0049] (1) This invention uses a quantitative dynamic priority evaluation model to automatically classify, sort, and assign sample testing tasks by comprehensively considering the urgency of cases, the retention period of samples, and the instrument load status. Combined with a Gantt chart-heatmap fusion view, it achieves dynamic scheduling through human-machine collaboration, significantly improving laboratory throughput, resource utilization, and response speed for high-priority tasks. A composite management and control mechanism based on role-based access control and dynamic permission adjustment is adopted to achieve refined and real-time management of personnel permissions, enhancing system security and operational standardization.

[0050] (2) This invention transforms standard operations into intuitive visual interactive steps through a guided pretreatment process and a high-fidelity 3D virtual sample introduction system, reducing reliance on personnel experience, reducing operational errors, and ensuring the consistency, traceability and standardization of the detection process.

[0051] (3) This invention transforms static standard documents into dynamic, structured, and associative method knowledge graphs through a method management subsystem, and combines them with the full lifecycle electronic traceability system of reagents, consumables, and standards to ensure the compliance and authority of testing activities, and support the continuous accumulation, rapid novelty search, and accurate retrieval of laboratory knowledge.

[0052] (4) The environmental management module of the present invention continuously monitors and provides intelligent early warning for key parameters such as laboratory temperature, humidity, ventilation and gas supply 24 hours a day. All environmental data are strongly correlated with sample testing tasks and instrument and equipment status, forming an unalterable environmental evidence chain, which ensures the reproducibility and compliance of test results.

[0053] (5) The result analysis module of the present invention realizes one-click generation from raw data to standardized report through automatic parsing of multi-format data, intelligent algorithm calculation and automatic comparison of quality control rules, which greatly improves the accuracy of data interpretation and the efficiency of report preparation. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the overall architecture of the fully automated laboratory sample testing and identification management system of the present invention.

[0055] Figure 2 This is a schematic diagram of the user interface of the fully automated laboratory sample testing and identification management system of the present invention.

[0056] Figure 3 This is a schematic diagram of the operation interface of the reagent, consumables and standard product management subsystem in this invention.

[0057] Figure 4 This is a schematic diagram of the operation interface of the instrument and equipment management subsystem in this invention.

[0058] Figure 5 This is a schematic diagram of the interface display of the environment management module in this invention.

[0059] Figure 6 This is a flowchart of the method for sample testing and identification based on a fully automated laboratory sample testing and identification management system in this invention.

[0060] Figure 7 This is a detailed flowchart of the sample testing and identification process based on the fully automated laboratory sample testing and identification management system of this invention. Detailed Implementation

[0061] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments, and not all of the embodiments.

[0062] Example 1: Example 1 provides a fully automated laboratory sample testing and identification management system, see attached document. Figure 1 As shown, the system is deployed at a municipal forensic science center. As the core operating platform, the system includes an experiment management module, an experiment configuration module, a pre-processing module, an on-machine testing module, an environment management module, and a result analysis module.

[0063] Specifically, the experiment management module interfaces with an external case handling system to generate sample testing tasks, schedules these tasks using a dynamic priority evaluation model, and manages personnel permissions based on a composite control mechanism of role-based access control and dynamic permission adjustment. The experiment configuration module determines the testing configuration scheme based on the sample testing tasks by querying a standard testing method knowledge base and using IoT technology to match and manage the status of reagents, consumables, standards, and instruments. The pre-processing module generates a sample pre-processing operation flow matching the testing instruments based on the sample testing tasks and the testing configuration scheme. The on-machine testing module communicates with the testing instruments, performs visualized sample introduction settings through a 3D virtual sample introduction system, and generates an introduction sequence file matching the testing instruments. The environment management module monitors and provides early warnings for laboratory environmental parameters in real time. The result analysis module connects to the output of the testing instruments, receives the test results output by the instruments, performs data analysis, and generates test reports. The system's user interface is shown in the attached figure. Figure 2 As shown.

[0064] In this embodiment, the samples in the sample testing task refer to various physical specimens (hereinafter referred to as specimens) that require laboratory testing and identification. The system automatically associates the specimens with preset testing categories and their corresponding standard testing methods based on their physical morphology and chemical properties, thereby achieving intelligent management. Examples of the main specimen types supported by the system and their automatically associated standard testing methods are as follows:

[0065] (1) For biological samples such as blood (spots), saliva (spots), semen stains, tissues, bones, teeth, and hair (with follicles), the system automatically associates them with the "DNA testing" category, which includes standard testing methods such as STR typing test, Y-STR test, and mitochondrial DNA sequencing.

[0066] (2) For samples such as urine, gastric contents, and liver tissue, the system automatically associates them with the "Toxic Analysis" category, which includes standard testing methods such as routine drug screening (e.g., morphine, methamphetamine), pesticide residue analysis, and detection of volatile toxins (e.g., ethanol).

[0067] (3) For fingerprint (potential, visible, imprint) and other trace evidence, the system automatically associates it with the "fingerprint display and comparison" category, which includes physical display methods (such as powder method), chemical display methods (such as ninhydrin method, 502 fumigation method) and subsequent image acquisition and automatic comparison process.

[0068] (4) For trace evidence such as fibers, paint fragments, and glass fragments, the system automatically associates them with the category of “physicochemical property analysis of trace evidence”. The standard testing methods under this category include microscopic morphological examination, Fourier transform infrared spectroscopy (FTIR) analysis, Raman spectroscopy (Raman) analysis, and scanning electron microscopy / energy dispersive spectroscopy (SEM-EDS) elemental analysis.

[0069] (5) For samples such as explosive residues and gunshot residues, the system automatically associates them with the category of "Analysis of Explosives and Gunshot Residues". The standard test methods under it include ion chromatography (IC) to analyze anions, gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) to analyze organic explosive components.

[0070] (6) For samples such as tool marks, shoe prints, and tire prints, the system automatically associates them with the "trace morphology comparison" category, which includes three-dimensional scanning modeling, feature point extraction and pattern matching algorithm comparison.

[0071] Through the above methods, the system can automatically call the corresponding standard testing methods according to the type of evidence, realizing the full-process automated and intelligent testing and identification management of evidence in multiple scenarios such as criminal investigation and physical evidence identification, identification of public safety hazard substances, and identification of biological genetic material.

[0072] Furthermore, the experimental management module is the intelligent scheduling and safety control center of the system, including a task docking unit, an intelligent scheduling unit, and a safety management unit.

[0073] (1) The task docking unit is used to dock with the external case acceptance system, obtain sample information, and match the corresponding standard testing method according to the sample information to generate a sample testing task containing the standard testing method number. The experiment management module achieves real-time data docking with the existing "smart case handling platform" (i.e., case acceptance system) of the judicial appraisal center through a standardized RESTful API interface. After the case handler completes the entrustment registration, evidence information entry and approval in the case acceptance system, the task docking unit automatically matches the corresponding standard testing method according to the evidence type and generates a corresponding sample testing task. Each standard testing method corresponds to a unique standard testing method number, and each sample testing task is a task instruction containing the standard testing method number. The task instruction also includes information such as task number, case type, evidence list, storage requirements, suggested time limit, and contact person. After the task instruction is generated, it will be automatically pushed to the central task pool to provide a data source for subsequent intelligent scheduling.

[0074] (2) The intelligent scheduling unit receives sample testing tasks and determines their priority based on a dynamic priority evaluation model, then schedules the tasks and allocates testing resources. Specifically, testing resources include reagents, consumables, standards, instruments, and personnel. The core of the scheduling is a quantitative dynamic priority evaluation model. For each task in the central task pool, the model automatically collects and calculates three core dimension parameters:

[0075] urgency of the case The system assigns values ​​based on the "statutory time limit" and "case nature" provided in the task, with a value range of 1-10. For example, "evidence from summary procedure" can be assigned a value of 9, and "evidence from petition review" can be assigned a value of 3. The system supports manual fine-tuning by administrators according to policies.

[0076] Urgency of sample retention period The system incorporates stable period models for different sample types (such as blood, fingerprints, and easily degradable plants). Based on the sample reception time, current time, and preset validity period, it calculates the reciprocal of the remaining valid time and maps it to the [1-10] interval using a linear normalization method. The shorter the remaining time, the better. The higher the value.

[0077] Instrument load status The experiment management module initiates a real-time data query request to the experiment configuration module through the system's internally defined application programming interface (API) or service call mechanism. This retrieves the current task queue occupancy rate and future reservation saturation of key instruments and equipment required for sample testing (such as triple quadrupole gas chromatography-mass spectrometry, gas chromatography-mass spectrometry, etc.), and maps this data to the [1-10] interval using a load factor calculation model. The load factor calculation model is constructed as follows:

[0078] First, the current task queue occupancy rate is defined as the ratio of the current task queue length to the maximum capacity of the equipment. The future reservation saturation rate is defined as the ratio of the total reserved time of the equipment within a future time period T (e.g., 8 or 24 hours) to T. The comprehensive load factor F is calculated based on the current task queue occupancy rate and the future reservation saturation rate.

[0079] F (1)

[0080] In the formula, This represents the current task queue occupancy rate. To ensure future booking saturation, and Configurable weighting coefficients ( + =1), used for adjustment and The proportion of impact on the overall load. If more attention is paid to real-time throughput, it can be set to... > If a greater focus is placed on availability over a future timeframe, it can be set... > .

[0081] Then the overall load factor F is mapped to the instrument load status. :

[0082] By using a piecewise function, F is mapped to the interval [1, 10] when F <= F low When L=1 (instrument load is very light); when F low <F<F high At that time, L = 1 + 9 × (FF) low ) / (F high -F low (Medium instrument load, linear growth); when F>=F high At that time, L=10 (the instrument was under extremely heavy load). Where F low and F high The preset instrument load threshold can be calibrated based on actual operating experience of laboratory instruments.

[0083] Task priority score The calculation formula is:

[0084] (2)

[0085] In the formula, As a weighting coefficient for the urgency of the case, This is a weighting coefficient for the retention period of the evidence. This is the weighting coefficient for the instrument load status.

[0086] In this embodiment, the weight coefficients of the dynamic priority evaluation model are set as follows: , , This weight may be dynamically adjusted according to laboratory policies.

[0087] according to The task is automatically categorized into three levels and triggers different scheduling strategies:

[0088] Red Mission ( ≥8): Execute immediately. The system automatically locks the required scarce instrument and equipment resources, prioritizes the allocation of qualified inspectors, reduces the scheduling of related non-critical tasks, and sends an immediate alarm to the head of the business department.

[0089] Yellow missions (5≤) <8): Medium priority. The system follows the principle of "resource balance and efficiency maximization," calculates the optimal allocation plan based on each inspector's current workload and professional matching degree, and automatically assigns the task.

[0090] Green mission ( <5): Can be delayed. The task is included in the pre-planned queue as a buffer task to fill the "low production capacity" of the laboratory, and is dynamically launched according to the availability of instruments, equipment and personnel in the future.

[0091] The scheduling results are visualized using a Gantt chart-heatmap fusion view. The view uses time as the horizontal axis and personnel and equipment as the vertical axis. Tasks are displayed as colored bars, with colors based on... The system dynamically renders values ​​(red, yellow, and green). Administrators can manually intervene by dragging and dropping task bars. The system will automatically calculate the impact on subsequent tasks and will pop up warnings and solution suggestions (such as suggesting the use of backup instruments) when conflicts occur (e.g., causing delays to other high-priority tasks), thus achieving dynamic optimization scheduling through human-machine collaboration.

[0092] (3) The security control unit adopts a composite control mechanism combining role-based access control (RBAC) and dynamic permission adjustment to achieve refined and real-time management of laboratory personnel's operating permissions. The implementation of this mechanism specifically includes the following core aspects:

[0093] First, the system pre-defines eight roles covering the core functions of the laboratory, and defines a precise matrix of operational permissions and data access scopes for each role to achieve separation of duties and mutual checks and balances. These roles include:

[0094] Inspectors are responsible for performing specific inspection tasks and filling out the electronic laboratory record book (ELN).

[0095] Senior inspectors are responsible for the preliminary review and technical guidance of inspectors' records and data.

[0096] The reagent and consumables administrator is responsible for the entire lifecycle management of reagents, standards, and other materials, including procurement, inventory, and requisition.

[0097] The authorized signatory, as the final signatory of the inspection report, bears legal responsibility for the results.

[0098] The head of the business unit is responsible for the overall allocation of tasks and process supervision within the department.

[0099] The technical lead is responsible for the development, verification, and maintenance of standard testing methods.

[0100] The quality manager is responsible for overseeing the effective operation of the entire quality management system.

[0101] The system administrator is responsible for the user, configuration, and operation and maintenance management of the system itself.

[0102] The permissions for each role are finely allocated from three dimensions: system functions, the scope of accessible data, and operation level (such as create, modify, and read-only), which together constitute a structured permission management system.

[0103] Secondly, the system dynamically adjusts the operational permissions of laboratory personnel based on a finite state machine model. The system explicitly defines the lifecycle of a sample testing task as a series of discrete states, such as "Pending Assignment," "Under Testing," "Pending Initial Review," and "Archived." Each state transition is triggered by a specific action performed by a specific role (e.g., an inspector clicking "Submit for Review"). When a task's state changes, the system automatically applies the permission rule set bound to that state, adjusting the permissions of relevant personnel in real time. For example, when a task enters the "Pending Initial Review" state, the system automatically revokes the original inspector's modification permissions on the relevant ELN (Elastic Data Record) and grants review permissions to a senior inspector; when a task enters the "Archived" state, the system revokes the modification and deletion permissions for relevant data for all roles, retaining only read-only permissions, thus ensuring the permanence and immutability of the original records and forming a complete operational loop.

[0104] Furthermore, the experimental configuration module is the resource hub of the system, including a method management subsystem, a reagent and consumables management subsystem, and an instrument and equipment management subsystem.

[0105] (1) Method Management Subsystem

[0106] The method management subsystem is used to build and maintain a structured knowledge base of standard testing methods, and to retrieve the corresponding method resource list from the knowledge base based on the sample testing task. The knowledge base of standard testing methods adopts a knowledge graph architecture, which transforms standard testing method documents into a semantic network composed of entities and relationships. The core entities include standard testing methods, samples, target objects, instruments and equipment, reagents, standards, consumables, pretreatment steps, and environmental conditions.

[0107] The standard testing method knowledge base is populated through a semi-automated multi-path process: For internal standard testing methods, structured forms are provided for technical personnel to create manually; for external standard testing method documents (PDF / Word), the system initiates an intelligent parsing pipeline, converting them into text via OCR (Optical Character Recognition), and then using NLP (Natural Language Processing) models for the chemical and biological fields for named entity recognition and relation extraction, populating the forms with the results, which are then manually verified and confirmed. Simultaneously, the system can be configured with an API to synchronize with national or international standard testing method databases, automatically acquiring standard metadata. Each standard testing method's structured form includes at least: basic information (standard testing method number, name, standard number, publication date, etc.), technical information (applicable sample scope, list of target substances, measurement range, detection limit / quantitation limit, etc.), and resource associations (mandatory association of required pretreatment equipment, testing instrument models, key reagents and standards, and necessary environmental conditions).

[0108] When the experiment management module pushes a task instruction containing the standard testing method number to the experiment configuration module, the method management subsystem first retrieves the corresponding method resource list from the standard testing method knowledge base based on the standard testing method number. This list is then simultaneously sent to the reagent, consumables, and standards management subsystem and the instrument and equipment management subsystem to initiate the subsequent resource matching process. The system has a built-in standard testing method novelty search engine that regularly monitors standard testing method updates, automatically marks older standard testing methods, and triggers review tasks to ensure the current validity and traceability of changes to standard testing methods.

[0109] (2) Reagent, Consumables and Standards Management Subsystem

[0110] The reagent, consumable, and standard product management subsystem matches reagents, consumables, and standards according to the method resource list, generates a matching result report, and performs full lifecycle inventory, expiration date, and traceability management for all reagents, consumables, and standards. This subsystem is implemented using an integrated architecture of "Internet of Things hardware + software platform."

[0111] The IoT hardware platform includes: smart shelves integrated with UHF (ultra-high frequency) RFID and weighing sensors for material identification and balance estimation; smart storage cabinets equipped with electromagnetic locks, RFID readers and cameras for secure access and process traceability of controlled chemicals; temperature and humidity sensors, differential pressure sensors and VOC sensors distributed in key areas; and dedicated work terminals for mobile operations.

[0112] The IoT software platform includes: an IoT device management platform responsible for device driving, status monitoring, and remote upgrades; a digital master data engine that establishes a unique traceability file for each material, including CAS number, MSDS, storage conditions, etc., with all files forming a digital master database; a real-time inventory and warehouse management system that updates material quantity, location, and status through a complex event processing engine and provides a visual warehouse map; an intelligent early warning and compliance rule engine that automatically triggers early warnings based on expiration dates, inventory thresholds, and environmental data, and blocks non-compliant requisition requests; and an API integration and data service layer that provides real-time inventory queries for other modules within the system and interfaces with external ERP and other systems.

[0113] Upon receiving the method resource list, the reagent, consumables, and standard product management subsystem queries the digital master database in real time to verify the inventory quantity, location, expiration date, and control level of each material, generating a matching result report with statuses such as "Available," "Insufficient Inventory, Request Required," "Near Expiration Date Warning," or "No Permission." The user interface of the reagent, consumables, and standard product management subsystem is shown in the attached figure. Figure 3 As shown.

[0114] (3) Instrument and Equipment Management Subsystem

[0115] The instrument and equipment management subsystem matches testing instruments according to the method resource list, generates an instrument and equipment matching result report, and manages the files, status monitoring, online reservation, and maintenance plans for all instruments and equipment. The instrument and equipment management subsystem includes the following collaborative modules:

[0116] Equipment File Digital Management Module: This module assigns a unique asset number and QR code label to each instrument / equipment, maintaining a structured electronic file containing basic identity information, technical capability details, legal and compliance information, relationship network information, and operational documents. Inspectors can access and update the file in real time by scanning the code.

[0117] Real-time status monitoring and data acquisition module: Based on the Internet of Things architecture, it collects the operating status and parameters of instruments and equipment through sensor and instrument interfaces to realize real-time mapping and visualization of status.

[0118] Intelligent Reservation Scheduling and Conflict Resolution Module: Provides a visualized online reservation calendar. Upon receiving a reservation request, it monitors conflicts across multiple dimensions, including instrument and equipment status, necessary consumable inventory, and maintenance plans, and dynamically schedules reservations based on preset strategies.

[0119] Preventative maintenance and compliance management module: Automatically generates calibration, maintenance, and periodic verification plans based on preset schedules, tracks task execution, and records results. When instruments or equipment are overdue for maintenance or have not been maintained, the system automatically marks them as "unavailable" and blocks usage requests.

[0120] Intelligent matching and resource allocation module: It analyzes the instrument and equipment requirements in the resource list, comprehensively queries the archive, real-time status and reservation queue, and intelligently recommends the best testing instruments and available time windows from the pool of available instruments and equipment according to rules such as load balancing and optimal performance.

[0121] Upon receiving the task instruction, the instrument and equipment management subsystem initiates the matching process: the intelligent matching and resource allocation module parses the requirements and queries the database for initial screening; it then collaborates with the real-time status monitoring and data acquisition module and the intelligent reservation scheduling and conflict resolution module to verify real-time availability; finally, it generates an instrument and equipment matching result report. The user interface of the instrument and equipment management subsystem is attached. Figure 4 As shown.

[0122] Finally, the experimental configuration module integrates the method resource list generated by the method management subsystem, the reagent, consumable and standard matching result report generated by the reagent, consumable and standard management subsystem, and the instrument and equipment matching result report generated by the instrument and equipment management subsystem to generate a visual test configuration plan to guide the testers in subsequent operations.

[0123] Furthermore, the pretreatment module, based on the test configuration scheme generated by the sample testing task and the experimental configuration module, generates and displays a sample pretreatment operation flow that matches the testing instrument, used for guided pretreatment operations on standard solutions and test samples. The pretreatment module for standard solutions includes: fully digital management of the entire process of receiving, storing, weighing, dissolving, adjusting volume, gradient dilution, and dispensing; the pretreatment for test samples includes: standardized guidance for multiple steps such as registration, sample separation, extraction, purification, and concentration. Taking the preparation of standard solutions as an example, the specific workflow is as follows:

[0124] When the experimental configuration module issues a task involving the preparation of a specific standard, the task will be automatically loaded into the preprocessing module. The inspector scans the QR code on the original standard bottle and the workstation markings on the dedicated workstation terminal, and the system immediately initiates the guided process. The screen will display the operating instructions step-by-step according to the preset standard operating procedure: first, prompting the use of a designated, networked electronic balance for weighing (the system automatically records the balance ID and weighing data); then prompting the use of a specific solvent and a calibrated pipette for dissolution and transfer, requiring the inspector to confirm by scanning the QR code on the solvent bottle; next, guiding the user to a volumetric flask of a specified capacity for volume adjustment, and requiring the user to scan the internal serial number of the container. For steps requiring gradient dilution, the system will automatically calculate and display the dilution scheme. In the final stage of the process, the system automatically generates and prints a unique traceability QR code label for each dispensed standard solution. This label is associated with the original standard information, the preparer, the time, the concentration, and the data records of the entire operation process.

[0125] Furthermore, the core of the on-machine testing module is a 3D virtual sample introduction system. The 3D virtual sample introduction system adopts a front-end and back-end separated B / S (browser / server) architecture, including a front-end rendering and interaction layer, a back-end business logic and service layer, and an instrument interface and adapter layer.

[0126] The front-end rendering and interaction layer utilizes mainstream WebGL-based 3D graphics libraries such as Three.js or Babylon.js. These libraries encapsulate complex WebGL APIs, providing rich functionality for 3D object creation, materials, lighting, and camera control, without requiring any browser plugins and offering excellent cross-platform compatibility. The front-end rendering and interaction layer also integrates with modern front-end frameworks such as Vue.js, React, or Angular to build a visual interactive interface, such as the sample list on the left and the parameter configuration panel on the right, and is responsible for data communication with the back-end. The core components of the front-end rendering and interaction layer include a model loader, a scene manager, an interaction controller, and a state renderer. The model loader is responsible for asynchronously loading and parsing 3D model files obtained from the server; the scene manager manages all objects in the 3D scene, such as the 3D sample tray model, sample vials, lights, and cameras; the interaction controller enables mouse translation, scaling, and rotation of the 3D scene, as well as dragging and selecting sample vials; and the state renderer updates the color of the sample injection position in real time based on state data pushed from the back-end or calculated by the front-end.

[0127] The backend business logic and service layer adopt mature backend technologies such as Java (Spring Boot), Python (Django / Flask), or Node.js, including asset management services, task and sample data services, sequence verification services, and sequence generation services. The asset management service provides an API for the frontend to query and obtain the corresponding 3D model file and sample tray layout metadata (such as the total number of wells, coordinates, and naming rules) based on the testing instrument model. The task and sample data service provides an API to obtain the list of samples to be tested under the current task. When the frontend submits the virtual plate arrangement result (a structured JSON object), the backend will start the sequence verification service to strictly verify the submitted result, including but not limited to: whether the sample ID is unique, whether the method file exists, whether the injection volume is within a reasonable range, and whether the quality control materials are inserted as required. After the verification is passed, the sequence generation service calls the instrument adapter to convert the general JSON plate arrangement data into a format that can be recognized by the specific instrument control software.

[0128] The instrument interface and adapter layer creates a dedicated adapter for each instrument family. Each adapter internally defines the format template for the instrument sequence file (such as the column header order of CSV and the node structure of XML). When the adapter receives standardized JSON layout data from the backend, it populates the template to generate the final sequence file, and then transmits the file to the instrument control computer via a shared network folder (SMB / CIFS).

[0129] The operation procedure of the 3D virtual sample introduction system is as follows:

[0130] (1) Load the 3D sample tray model that matches the testing instrument model. Specifically, after the inspector selects the testing instrument model, the front end requests the resource package for that model from the asset management service. The resource package contains not only the .glb model file, but also a .json metadata file, which defines information such as {"total_wells": 100, "layout": "10x10", "well_coordinates": [...]}.

[0131] (2) Provides a visual interactive interface that supports dragging and dropping virtual sample vials to the target injection position in the 3D injection tray model and setting injection parameters. Specifically, when a sample is dragged to the 3D injection tray model, the system will automatically snap to the nearest available well and highlight it; the system supports selecting multiple samples and automatically filling them into the 3D injection tray model in a specified order (e.g., starting from A1, row first, column last); after the virtual sample vials are placed, the injection parameters are set, including the type of virtual sample vial, injection volume, standard test method to be called, and washing procedure; the system also provides an "Insert Quality Control (QC)" function, where the inspector selects the QC type (e.g., blank, parallel sample), and the system can automatically insert QC samples into the sequence according to preset rules (e.g., insert one blank for every 10 samples).

[0132] (3) The visual interactive interface renders the status of each injection point in real time using different colors. Different colors represent different statuses:

[0133] Gray (Idle): Default state, indicating that no virtual vial is placed at this injection position.

[0134] Blue (Ready): The sample has been placed and the parameters have been configured. It has also passed the initial verification at the front end.

[0135] Yellow (Warning): There is a potential configuration problem, but it does not block operation. For example, the method file is associated with an older version, or the sample comments are incomplete.

[0136] Red (Abnormal): A blocking error has occurred, and the sequence could not be generated. For example, sample ID conflict, invalid method file path, or injection volume of 0.

[0137] Green (in operation): Indicates that the testing instrument is testing the sample at this location.

[0138] (4) The status and injection parameters of each injection point are converted into a structured injection sequence file and transmitted to the testing instrument. Specifically, when the inspector clicks "Generate Sequence" on the visual interactive interface, the front end sends the complete arrangement information (JSON format) to the back end sequence verification service for final and comprehensive business logic verification. After the verification is passed, the sequence generation service starts an asynchronous task, calls the matching instrument adapter to generate a structured injection sequence file that the instrument can recognize. After completion, the front end provides a download link or automatically triggers the distribution process to transmit the injection sequence file to the testing instrument for controlling the testing instrument to perform the testing task.

[0139] Furthermore, the results analysis module incorporates a multi-format compatible parsing engine that can automatically identify and read raw data files generated by various instruments such as chromatography, mass spectrometry, and spectroscopy. All parsed data is converted into a unified JSON data format within the system, providing standard input for subsequent processing. The module also includes a built-in calculation engine that automatically executes quantitative calculations, concentration conversions, uncertainty assessments, and other analytical processes by calling predefined data processing algorithms (such as baseline correction, peak identification, integration, and calculation). The analysis results are compared with a preset quality control rule library (such as recovery range, parallel sample deviation, and standard curve correlation coefficient), automatically marking abnormal data or results exceeding standards. Finally, based on the sample testing task type (such as testing items and client) and the user's preset customized requirements, the results analysis module selects a corresponding template from the structured report template library, automatically extracts and associates sample information, pretreatment records, testing instruments, standard testing methods, environmental data (from the environmental management module), and analysis results, generates a test report, and pushes it to the review process.

[0140] Specifically, the test reports generated by the results analysis module support user-defined report template fields, formats, logos, and approval workflows, and can automatically apply different report templates and data presentation formats according to different client requirements or certification standards (such as CNAS and CMA).

[0141] Furthermore, the environmental management module includes a ventilation monitoring unit, an access control and security unit, an environmental monitoring unit, an instrument and equipment supply monitoring unit, and an early warning and linkage unit. The ventilation monitoring unit monitors the airflow velocity, differential pressure, and operating status of the fume hood in real time, issuing alarms and interlocking control of testing activities in case of abnormalities. The access control and security unit integrates laboratory access control and personnel positioning information to achieve dynamic control of access permissions and entry / exit logs for specific areas (such as hazardous chemical warehouses and instrument rooms). The environmental monitoring unit continuously monitors and records temperature, humidity, differential pressure, and VOC concentration data through a sensor network distributed throughout the laboratory's functional areas. The instrument supply monitoring unit monitors the carrier gas and auxiliary gas pressure and flow rate of key instruments and equipment required for sample testing tasks (such as gas chromatography-mass spectrometry and inductively coupled plasma mass spectrometry), providing early warnings when the supply is insufficient or interrupted. The early warning and linkage unit has a built-in intelligent early warning engine that analyzes the above monitoring data based on preset rules, providing graded early warnings through various methods such as sound and light, SMS, and in-app messages. It can also push early warning information to the experimental management module, adjusting the priority, scheduling status, or resource allocation of affected tasks based on the early warning information, such as pausing tasks, reassigning tasks to other available instruments, or triggering emergency procedures to ensure laboratory safety and the reliability of testing data.

[0142] Specifically, the environmental management module integrates various sensors and controllers through IoT technology to continuously monitor and intelligently warn of key laboratory environmental and supply parameters. Monitoring data is linked to testing tasks, instrument status, and record management. For example, it verifies fume hood status in real time during task scheduling and execution, or automatically binds environmental data to electronic records during operations such as weighing. When critical supplies (such as carrier gas pressure) are insufficient, the system will issue an early warning to avoid analysis interruption. The environmental management module can also automatically generate environmental monitoring reports and provide complete environmental data packages for specific time periods during audits, supporting data validity. The interface of the environmental management module is shown in the attached figure. Figure 5 As shown.

[0143] Example 2: Example 2 provides a sample testing and identification method, based on the system described in Example 1, as shown in the attached figure. Figure 6 As shown, the method includes:

[0144] The experiment management module generates sample testing tasks and schedules and manages the safety of these tasks.

[0145] Based on the scheduling results, the corresponding inspector prepares to begin the inspection;

[0146] The inspector prepares the corresponding inspection resources based on the inspection configuration plan output by the experimental configuration module;

[0147] Perform the corresponding preprocessing operations according to the sample testing preprocessing operation flow output by the preprocessing module;

[0148] Inspectors can visualize the sample injection settings for the inspection process through the on-machine testing module, and the on-machine testing module generates sample injection sequence files;

[0149] The testing instrument automatically tests the samples based on the injection sequence file, and the test results are output to the result analysis module for data analysis to generate a test report.

[0150] As attached Figure 7 As shown, the detailed workflow of the sample testing and identification method in this embodiment is as follows:

[0151] (1) Creation and initialization of inspection task: The user selects the business type corresponding to this inspection (such as "criminal identification" or "commissioned testing") in the case acceptance system and enters or associates the information of the evidence. The experimental management module creates a corresponding sample inspection task accordingly, and the inspection and identification process is officially started.

[0152] (2) Intelligent pre-configuration of experiments: The experiment configuration module matches the corresponding method resource list according to the sample testing task, and automatically matches the required reagents, consumables, standards and instrument resources according to the method resource list to generate a visual testing configuration plan.

[0153] (3)Guided Pretreatment and Preparation of Inspection Instruments: The pretreatment module generates an operation process for sample pretreatment before inspection based on the sample inspection task and inspection configuration plan; the inspector prepares the corresponding reagents, consumables, standards, and inspection instruments according to the inspection configuration plan, and completes the sample pretreatment operation under the guidance of the sample inspection pretreatment operation process.

[0154] (4)Complete Sampling Configuration: The inspector loads a 3D sampling tray model matching the inspection instrument model through the 3D virtual sampling system, places the virtual sample bottle at the target sampling position through drag-and-drop operations on the visual interaction interface, sets the sampling parameters, and the visual interaction interface renders and displays the status of each sampling position in different colors.

[0155] (5)Automated Inspection and Data Collection: After the inspector completes the configuration, the on-machine detection module converts the status of each sampling position and the sampling parameters into a structured sampling sequence file, sends it to the corresponding inspection instrument, starts the automated analysis program, and the inspection instrument collects raw data in real time during operation and automatically uploads it to the result analysis module.

[0156] (6)Intelligent Result Analysis and Judgment: The result analysis module automatically analyzes the uploaded raw data file, calls the built-in algorithm for integration, calculation, and concentration conversion. Subsequently, the calculation results are automatically compared with the preset quality control rule library, and an objective judgment of "qualified" or "unqualified" is made on the analysis results.

[0157] (7)Closed-loop Processing and Report Generation:

[0158] If the result is judged to be qualified: The result analysis module automatically associates and uploads representative analysis spectra, and performs branch processing according to the initially registered business type: If it is an external commissioned inspection, the result analysis module automatically summarizes all process data and results, generates a detailed inspection record as the delivery result, and the process ends; if it is an internal monitoring or R & D task, after generating a complete data packet, the result analysis module automatically submits it to the review and approval process. After the approval is passed, the data is automatically archived and the inspection report is generated synchronously, and the process ends.

[0159] If the result is judged to be unqualified: The result analysis module automatically marks the abnormality and prompts to trace the reason. According to the preset rules or manual judgment, the process feedback mechanism can be triggered, and it can be returned to the data upload link, guiding the inspector to check the status of the inspection instrument or re-prepare the sample, and then perform data collection and analysis again to form a quality control closed loop.

[0160] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A fully automated laboratory sample testing and identification management system, wherein the system is deployed in a forensic identification center, characterized in that, The system includes: The experiment management module is used to interface with external case acceptance systems, generate sample testing tasks, schedule sample testing tasks through a dynamic priority evaluation model, and manage personnel permissions based on a composite control mechanism of role access control and dynamic permission adjustment. The experiment configuration module is used to determine the test configuration plan based on the sample testing task by querying the standard test method knowledge base and using Internet of Things technology to match and manage the status of reagents, consumables, standards and instruments. The pretreatment module generates a sample pretreatment operation flow that matches the testing instruments, based on the sample testing task and testing configuration scheme. It is used to perform guided pretreatment operations on standard solutions and test samples. The pretreatment module for standard solutions includes: digital management of the entire process of receiving, storing, weighing, dissolving, making up to volume, gradient dilution and dispensing; and standardized guidance for the pretreatment of test samples includes: registration, sample separation, extraction, purification and concentration steps. The on-machine testing module communicates with the testing instrument, performs visual sample introduction settings through the 3D virtual sample introduction system, and generates a sample introduction sequence file that matches the testing instrument. The results analysis module is connected to the output of the testing instrument. It is used to receive the test results output by the testing instrument, perform data analysis, and generate test reports. The experimental configuration module includes: The method management subsystem is used to build and maintain a structured knowledge base of standard testing methods, and to retrieve the corresponding list of method resources from the knowledge base of standard testing methods according to the sample testing task. The reagent, consumable and standard product management subsystem is used to match reagents, consumables and standards according to the method resource list, generate a reagent, consumable and standard product matching result report, and perform full life cycle inventory, expiration date and traceability management for all reagents, consumables and standards. The instrument and equipment management subsystem is used to match testing instruments according to the method resource list, generate instrument and equipment matching result reports, and manage the files, status monitoring, online reservation and maintenance plan management of all instruments and equipment. The experimental configuration module generates the testing configuration plan by integrating the method resource list, the reagent and consumable matching result report, and the instrument and equipment matching result report; The specific operation process of the 3D virtual sample introduction system includes: Load a 3D sample loading disk model that matches the testing instrument model; The system provides a visual interactive interface, allowing users to drag and drop virtual sample vials to target injection positions in the 3D injection tray model and set injection parameters. When a sample is dragged to the 3D injection tray model, the system automatically snaps to the nearest available well and highlights it. The system supports selecting multiple samples and automatically filling them into the 3D injection tray model in a specified order. After the virtual sample vials are placed, injection parameters can be set, including the type of virtual sample vial, injection volume, standard testing method to be called, and washing procedure. The system also provides an "Insert Quality Control" function, where inspectors select the quality control type, and the system automatically inserts quality control samples into the sequence according to preset rules. The status of each injection station is rendered in real time with different colors, and different colors represent different statuses: gray represents idle: the default status, indicating that no virtual bottle is placed at this injection station; Blue indicates ready: the sample has been placed, the parameters have been configured, and the initial verification has been passed; Yellow indicates a warning: there is a potential configuration problem, but it does not block operation; Red indicates an error: a blocking error has occurred, and the sequence cannot be generated; Green indicates that the instrument is in operation, meaning it is testing the sample at that location. The status and injection parameters of each injection point are converted into a structured injection sequence file and transmitted to the testing instrument. When the inspector clicks "Generate Sequence" on the visual interactive interface, the front end sends the complete layout information to the back end sequence verification service for final and comprehensive business logic verification. After the verification is passed, the sequence generation service starts an asynchronous task, calls the matching instrument adapter to generate a structured injection sequence file that the instrument can recognize. After completion, the front end provides a download link or automatically triggers the distribution process to transmit the injection sequence file to the testing instrument for controlling the testing instrument to perform the testing task.

2. The fully automated laboratory sample testing and identification management system according to claim 1, characterized in that, The experiment management module includes: The task docking unit is used to dock with the external case acceptance system, obtain sample information, match the corresponding standard inspection method according to the sample information, and generate a sample inspection task containing the standard inspection method number. The intelligent scheduling unit receives sample testing tasks, determines the priority of sample testing tasks based on a dynamic priority evaluation model, and performs task scheduling and testing resource allocation. The security management unit employs a composite management mechanism based on role-based access control and dynamic permission adjustment to manage personnel permissions.

3. The fully automated laboratory sample testing and identification management system according to claim 2, characterized in that, The priority of the sample testing task is determined based on the dynamic priority evaluation model as follows: ； In the formula, Score the task priority. Due to the urgency of the case, Due to the urgency of the preservation period for the samples, This indicates the instrument's load status. As a weighting coefficient for the urgency of the case, This is a weighting coefficient for the retention period of the evidence. This is the weighting coefficient for the instrument load status.

4. The fully automated laboratory sample testing and identification management system according to claim 3, characterized in that, The result analysis module receives the test results output by the testing instrument, performs data analysis, and generates a test report, specifically including: The built-in multi-format parsing engine automatically reads and parses the raw data files output by the testing instruments; Perform statistical analysis on the parsed raw data file, compare the analysis results with the quality control rule base for compliance, and mark the abnormal test results; The system automatically selects a structured report template based on the type of sample testing task and generates the final test report.

5. The fully automated laboratory sample testing and identification management system according to claim 4, characterized in that, The system also includes: The environmental management module is used for real-time monitoring and early warning of laboratory environmental parameters.

6. The fully automated laboratory sample testing and identification management system according to claim 5, characterized in that, The environment management module includes: The ventilation monitoring unit is used to monitor the status of the ventilation system in real time and issue an alarm when there is an abnormality. Access control and security units are used to dynamically manage access permissions and record entry and exit logs for the laboratory. An environmental monitoring unit is used to continuously monitor and record environmental status parameters of the laboratory through a sensor network; The instrument and equipment supply monitoring unit is used to monitor the supply parameters of the testing instruments and provide early warnings when the supply is abnormal. The early warning and linkage unit is used to realize risk early warning and task linkage control based on preset rules.

7. A sample testing and identification method, based on the system described in any one of claims 1-6, characterized in that, The method includes: The experiment management module generates sample testing tasks and schedules and manages the safety of these tasks. Based on the scheduling results, the corresponding inspector prepares to begin the inspection; The inspector prepares the corresponding inspection resources based on the inspection configuration plan output by the experimental configuration module; Perform the corresponding preprocessing operations according to the sample testing preprocessing operation flow output by the preprocessing module; Inspectors can visualize the sample injection settings for the inspection process through the on-machine testing module, and the on-machine testing module generates sample injection sequence files; The testing instrument automatically tests the samples based on the injection sequence file, and the test results are output to the result analysis module for data analysis to generate a test report.

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