Genetic center molecule genetic laboratory management system

Abstract

This invention discloses a molecular genetics laboratory management system for a genetics center, belonging to the field of laboratory information management technology. It provides real-time monitoring of sample status, dynamic scheduling of subsequent tasks, and early warnings for near-expiration and overdue samples. Experimental data is collected through structured electronic task sheets, and the analyzed genetic variation information is dynamically updated to a mutation database. Historical variation data is intelligently recommended for report generation. Report content is automatically populated based on variation data and adapted to project templates. It supports configurable multi-level review processes, enabling online collaboration and operation tracking. Based on review results, first-use acceptance and automatic deduction of inventory for reagents and consumables are performed. Based on resource linkage and traceability, gene disease databases and mutation databases are collaboratively constructed. This system achieves fully automated management from intelligent sample monitoring and scheduling to automatic report generation and review, significantly improving the operational efficiency, data accuracy, and resource utilization of the genetics laboratory.

Description

Technical Field

[0001] This invention relates to the field of laboratory information management technology, specifically a molecular genetics laboratory management system for a genetics center. Background Technology

[0002] With the rapid development of genetic testing technology, the testing operations of genetic center laboratories are becoming increasingly complex, involving a wide variety of sample types, diverse testing methods, massive amounts of data, and high levels of specialization. Traditional manual management or simple information systems are no longer sufficient to meet the demands. Existing technologies suffer from the following pain points: sample status relies on manual tracking, which is prone to time delays and makes it difficult to pinpoint process bottlenecks; experimental data entry is not standardized, genetic variation information lacks structured management, and historical data reuse rate is low; report generation relies on manual integration, the review process is fixed, and it is difficult to adapt to the compliance requirements of different testing projects; reagent and consumable acceptance and inventory management are disconnected, instrument status and task allocation are not linked, and resource waste and usage risks coexist; the system lacks knowledge accumulation and decision support capabilities, and cannot provide data support for laboratory process optimization.

[0003] Therefore, in order to address the above problems, there is an urgent need for a molecular genetics laboratory management system for genetic centers. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a molecular genetics laboratory management system for genetic centers, which solves the problems of low efficiency in manual scheduling, non-standard data collection, cumbersome report generation and review processes, extensive resource management, and untimely knowledge base updates in traditional genetic laboratory information management.

[0005] To achieve the above objectives, this invention provides the following technical solution: a molecular genetics laboratory management system for a genetics center, comprising: a sample intelligent sensing and scheduling module, used to sense sample status in real time through preset status identifiers and time plans, and dynamically schedule subsequent tasks based on task dependencies, while providing early warnings for near-expiration and overdue periods; a detection data and mutation fusion module, which, based on scheduled subsequent tasks, collects experimental data through structured electronic task sheets, dynamically updates the analyzed and entered genetic mutation information to the mutation database, and intelligently recommends historical mutation data for report generation; a report intelligent generation and review module, which automatically fills in report content based on mutation data and adapts to project templates, and supports configurable multi-level review processes, enabling online collaboration and operation traceability; a resource linkage and traceability module, which, based on review results, links instrument status with task allocation, performs first-use acceptance of reagents and consumables and automatic deduction of inventory, and achieves real-time synchronization of sample inventory operations and testing processes; and a knowledge base self-evolution module, which, based on resource linkage and traceability, collaboratively constructs a gene disease database and a mutation database, aggregates and analyzes the entire system's operation logs, and provides data-driven decision support for laboratory resource planning and process optimization.

[0006] Furthermore, the status identifiers specifically include four types: not in progress, in progress, completed, and terminated. These status identifiers, along with a preset time plan, are used to automatically identify and locate the real-time position and status of each sample in the detection process. The task dependency relationships support flexible configuration of serial and parallel processes. Parallel process configuration allows multiple tasks to execute simultaneously and share the same preceding task; parallel tasks are only dynamically released by the system after the preceding task is completed. Serial process configuration requires tasks to be executed sequentially in a preset order; subsequent tasks are only scheduled to the corresponding user workbench after the preceding task is completed.

[0007] Furthermore, the genetic variation information includes at least six core components: genes and transcripts, nucleotide changes, amino acid changes, chromosome location, pathogenicity assessment, and heterozygosity. The structured electronic task sheet is predefined for different detection methods to ensure the standardized entry of experimental data such as concentration, purity, and positive / negative results. The entered structured genetic variation information is used for report generation and dynamic updates to the system's mutation library. It also automatically links to historical test records of the same subject or mutation library data of the same gene locus, providing intelligent recommendation support for report writing and analysis.

[0008] Furthermore, the multi-level review process is specifically a multi-level, multi-person review process that is flexibly configured according to the project type.

[0009] Furthermore, the initial acceptance and automatic deduction of inventory for the reagents and consumables are implemented as follows: when an experimenter selects unaccepted reagents and consumables when creating an experiment task, the experiment task is automatically converted into a reagent and consumables acceptance task. After the test is completed according to the acceptance standards, the test results are automatically stored in the system as the acceptance record of the reagent and consumables. Only reagents and consumables that pass the acceptance can be included in the available inventory for subsequent routine experiment tasks. During the experiment task filling process, the inventory quantity of the corresponding reagents and consumables is automatically deducted according to the consumable usage preset in the experiment plan or the actual consumption entered by the experimenter, ensuring that the inventory data is consistent with the actual consumption in real time.

[0010] Furthermore, the system-wide operation log covers operation records of sample management, task scheduling, data collection, report generation, review and approval processes, and resource usage. Its aggregation and analysis function supports multi-dimensional comprehensive queries by operation time, operator, and operation type. It also supports batch exporting of query results into standardized format files. The exported content includes operation timestamps, operator identity information, operation content, operation results, and associated object identifiers, meeting the audit requirements of the laboratory quality management system and providing complete evidence for quality traceability and compliance inspection.

[0011] Furthermore, the resource linkage and traceability module also includes intelligent sample classification and inventory visualization functions: when samples are put into storage, the sample type, testing items, storage date and other information are automatically parsed according to the preset sample molecule numbering rules, and the samples are classified into the corresponding predefined storage categories; sample storage adopts well position management, and the inventory status of samples in the well position is intuitively distinguished by different colors. Normal inventory samples are marked in green, retrieved samples are marked in yellow, destroyed samples are marked in red, and samples waiting to be put into storage are marked in blue.

[0012] Furthermore, the sample intelligent perception and scheduling module also includes a user task accurate display mechanism: only tasks that the current user is about to execute and whose prerequisites have been met are displayed, and tasks whose prerequisites have not been completed or are not within the scope of the user's responsibilities are not displayed; prerequisite tasks are displayed first to the experimental personnel in charge of the sample, and subsequent tasks are displayed to the corresponding personnel only after the prerequisite tasks are completed and the system confirms that the prerequisites are met, so as to ensure the orderliness and accuracy of task execution.

[0013] The present invention has the following beneficial effects: This molecular genetics laboratory management system for a genetics center features several key modules. The intelligent sample sensing and scheduling module ensures precise task execution according to preset rules, reducing human intervention and increasing processing speed. The detection data and variation fusion module, through structured electronic task sheets, enables real-time acquisition of experimental data and dynamic updates of genetic variation information, providing rich and accurate data sources for report generation. The intelligent report generation and review module not only automatically fills in report content but also supports multi-level review processes, ensuring report standardization and accuracy. It also enables online collaborative editing and operation logging, improving review efficiency and compliance. The resource linkage and traceability module tightly links instrument status, reagents, and task allocation, achieving efficient resource utilization and precise inventory management. It also ensures real-time synchronization of samples and testing processes, enhancing traceability capabilities. The self-evolving knowledge base module collaboratively constructs gene disease and mutation databases and aggregates system-wide operation logs, providing data-driven decision support for laboratory resource planning and process optimization, promoting continuous improvement and innovative development in the laboratory.

[0014] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0015] Figure 1 This is a structural diagram of a molecular genetics laboratory management system at a genetic center.

[0016] Figure 2 This is a flowchart of a method for managing a molecular genetics laboratory at a genetic center according to the present invention. Detailed Implementation

[0017] This application embodiment utilizes a molecular genetics laboratory management system at a genetics center to achieve fully automated management of the entire process, from intelligent sample sensing and scheduling to automatic report generation and review, significantly improving the operational efficiency, data accuracy, and resource utilization of the genetics laboratory.

[0018] The overall concept of this application's embodiments is as follows: The system is divided into several core modules, including sample management, testing management, report management, resource management, and knowledge base management. These modules seamlessly connect through pre-defined interfaces and rules, ensuring real-time data transmission and sharing. The intelligent sample sensing and scheduling module, as the system's core, achieves real-time monitoring of sample status and dynamic task scheduling through pre-defined status indicators and time plans. The testing data and mutation fusion module ensures standardized data collection and dynamic updates of genetic variation information through structured electronic task sheets. The intelligent report generation and review module, combining project templates and multi-level review processes, improves the automation level and efficiency of report generation and review. The resource linkage and traceability module, along with the knowledge base self-evolution module, provide strong support for laboratory management through resource integration and data analysis.

[0019] Please see Figure 1 This invention provides a technical solution: a molecular genetics laboratory management system for a genetics center, comprising: a sample intelligent sensing and scheduling module, used to sense sample status in real time through preset status identifiers and time plans, and dynamically schedule subsequent tasks based on task dependencies, while providing early warnings for near-expiration and overdue periods; a detection data and mutation fusion module, which, based on scheduled subsequent tasks, collects experimental data through structured electronic task sheets, dynamically updates the analyzed and entered genetic mutation information to the mutation database, and intelligently recommends historical mutation data for report generation; a report intelligent generation and review module, which automatically fills in report content based on mutation data and adapts to project templates, and supports configurable multi-level review processes to achieve online collaboration and operation traceability; a resource linkage and traceability module, which, based on the review results, links instrument status with task allocation, performs first-use acceptance of reagents and consumables and automatic deduction of inventory, and achieves real-time synchronization of sample inventory operations and detection processes; and a knowledge base self-evolution module, which, based on resource linkage and traceability, collaboratively constructs a gene disease database and a mutation database, aggregates and analyzes the entire system's operation logs, and provides data-driven decision support for laboratory resource planning and process optimization.

[0020] Specifically, the status indicators include four types: not in progress, in progress, completed, and terminated. The status indicators are automatically identified in conjunction with the preset time plan to locate the real-time position and timeliness status of each sample in the detection process. The task dependency relationship supports flexible configuration of serial and parallel processes. The parallel process configuration allows multiple tasks to be executed simultaneously and share the same preceding task. The parallel task will only be dynamically released by the system after the preceding task is completed. The serial process configuration requires tasks to be executed in a preset order. The subsequent task will only be scheduled to the corresponding user workbench after the preceding task is completed.

[0021] In this implementation plan, the system maintains a state machine for each sample and testing task, with status indicators including not in progress, in progress, completed, and terminated. Status changes are triggered by user actions or system rules. For example, when a person completes and submits information on the gene testing registration interface, the system updates the status field in the database, changing the application form's status from entry to verification.

[0022] After the verification personnel confirm the sample receipt verification management interface, the system will update the status to "verified". At this time, the background will automatically convert the application form into a test form, and the task status under it will be initialized to "not performed".

[0023] After the inspector clicks the "Create Task" button on the experimental task creation interface, the system changes the corresponding task status from "Not in Progress" to "In Progress" by modifying the task status field; once the task is completed, the status changes to "Completed".

[0024] All state transitions are recorded in an operation log to ensure traceability.

[0025] A prerequisite task list field is defined for each detection task to store the IDs of its dependent tasks. When the experimental task creation module generates a pending task, the system executes verification logic: it queries the prerequisite task list of the current task and checks the status of each task to see if it is completed. Only when all prerequisite tasks are completed is the task added to the user workbench. For example, in the DNA extraction and hybridization detection process, the DNA extraction task is a prerequisite task. While the DNA extraction task is not completed, the hybridization task remains in a waiting state; once completed, the system automatically releases the hybridization task to the corresponding user interface. Dependencies support serial and parallel configurations: serial processes require tasks to be executed sequentially (e.g., task B is released only after task A is completed); parallel processes allow multiple tasks to share prerequisite tasks (e.g., tasks B and C both depend on task A; after task A is completed, B and C can be executed simultaneously).

[0026] Parameter acquisition and algorithm process: Status data is read from the database in real time; the time plan is calculated based on the preset standard workload (e.g., the report writing stage is set to 2 days) to calculate the start / end time of the plan, and the comparison with the actual time is used to realize early warning. Dependency verification is realized through SQL query.

[0027] State transitions and dependency checks enable automated process scheduling, avoiding task conflicts or omissions. For example, prenatal diagnostic samples automatically trigger testing tasks after verification, eliminating the need for manual allocation and improving efficiency.

[0028] Specifically, genetic variation information includes at least six core components: genes and transcripts, nucleotide changes, amino acid changes, chromosome location, pathogenicity assessment, and heterozygosity. The structured electronic task sheet is predefined for different detection methods to ensure the standardized entry of experimental data such as concentration, purity, and positive / negative results. The entered structured genetic variation information is used for report generation and dynamic updates to the system's mutation library. At the same time, it automatically links to the historical test records of the same subject or mutation library data of the same gene locus, providing intelligent recommendation support for report writing and analysis.

[0029] In this implementation plan, an electronic task sheet is predefined for each testing method, with field types including numeric, enumeration, and file types, ensuring standardized data entry. For example: For DNA extraction methods, the task sheet includes the following required fields: nucleic acid concentration, purity, volume, and quality control results.

[0030] For the PCR-Sanger sequencing method, the task sheet includes the following upload fields: detection control chart, primer sequence, gel image, and the record field: conclusion.

[0031] The task sheet template is configured in the test item management module. When filling it out, the experimenter needs to enter the information according to the field requirements, and the system will verify the data.

[0032] The implementation rules for intelligent recommendations include: the recommendation logic is based on database queries, rather than complex AI algorithms. The system automatically performs query operations in the report writing or analysis interface. Query condition 1: Using the current examinee's medical record number as the key, query all their historical test records and return data such as mutated genes and results.

[0033] Query condition 2: Using the gene name of the currently analyzed gene as the key, search the mutation database for a completely matching record and return information such as nucleotide changes and pathogenicity assessment.

[0034] The query results are displayed in a list format in the sidebar of the interface, and users can click to refer to them. For example, when an analyst processes a thalassemia sample, the system automatically recommends previous HBA / HBB test results for that medical record number.

[0035] Parameter acquisition and algorithm process: Mutation information is obtained through form input, such as analysts filling in gene names, nucleotide changes, etc. on the experimental task analysis interface; it is recommended to perform queries through SQL statements.

[0036] Structured task sheets ensure data consistency, and intelligent recommendations reduce redundant analysis. For example, in the PGT-M pre-experiment, the system automatically linked historical genetic data, improving report accuracy.

[0037] Specifically, the multi-level review process is a flexible, multi-level, multi-person review process configured according to the project type.

[0038] In this implementation plan, the system provides a graphical configuration interface in the test item management module. Administrators set up approval processes for each test item: Interface elements: a drop-down menu to select the review level (Level 1, Level 2, Level 3), a personnel selector to assign reviewers to each level (multiple selections are allowed), and radio buttons to set the review mode (e.g., "One person passes or all must pass").

[0039] Configuration rules: The system provides pre-defined templates based on project type. For example: G6PD, AZF, Thalassemia Project: Configure a two-level audit. The first level is self-inspection by the inspector, and the second level is to designate one auditor (the mode is "one person passes").

[0040] Other routine items: Level 2 review, which requires approval from all 3 reviewers.

[0041] Prenatal diagnostic projects: three levels of review, Level 1 self-inspection, Level 2 technical review (1 person), Level 3 clinical review (1 person).

[0042] After configuration, the system will automatically trigger the corresponding review process when the report is submitted.

[0043] Parameter Acquisition and Algorithm Process: Review rules are stored in the project configuration table of the database; when a report is submitted, the system queries the configuration based on the project ID to generate a review task queue. For example, changes in review status are driven by the workflow engine, and reviewer actions are recorded in the log table.

[0044] Beneficial effects: Flexible configuration adapts to the compliance requirements of different projects. For example, the three-level audit of prenatal diagnosis ensures the rigor of the report while avoiding over-allocation of resources.

[0045] Specifically, the initial use acceptance and automatic deduction of inventory for reagents and consumables are implemented as follows: When a researcher selects unaccepted reagents and consumables when creating an experimental task, the experimental task is automatically converted into a reagent and consumables acceptance task. After the test is completed according to the acceptance standards, the test results are automatically stored in the system as the acceptance record of the reagent and consumables. Only reagents and consumables that pass the acceptance can be included in the available inventory for subsequent routine experimental tasks. During the experimental task filling process, the inventory quantity of the corresponding reagents and consumables is automatically deducted according to the consumable usage preset in the experimental plan or the actual consumption entered by the researcher, ensuring that the inventory data is consistent with the actual consumption in real time.

[0046] In this implementation plan, the business logic chain includes: Status transition: When a user selects a reagent batch on the experimental task creation interface, the system verifies its acceptance status field. If the status is "not accepted," a task acceptance flag is set for that task in the background.

[0047] Data Association: After a task order is submitted, a system trigger links the test results to the reagent batch file. For example, the test results of an acceptance task order are automatically updated in the acceptance record field of the reagent batch, and the status is changed to qualified or unqualified.

[0048] Inventory deduction: When the lab technician enters the consumption amount on the task sheet, the system deducts the inventory quantity in real time. For example, if 2 μL of reagent is consumed for DNA extraction, the system updates the inventory table to ensure consistency between the records and the actual inventory.

[0049] Acceptance status is read from the reagent inventory table; consumption is obtained from the task order form; deduction logic is implemented through SQL update statements.

[0050] This mechanism embeds acceptance testing into the testing process, eliminating the need for a separate acceptance step. For example, new batches of reagents are automatically accepted upon first use, improving efficiency and ensuring quality.

[0051] Specifically, the system's operation logs cover operation records for sample management, task scheduling, data collection, report generation, review and approval processes, and resource usage. Its aggregation and analysis function supports multi-dimensional comprehensive queries by operation time, operator, and operation type. It also supports batch exporting query results into standardized format files. The exported content includes operation timestamps, operator identity information, operation content, operation results, and associated object identifiers, meeting the audit requirements of the laboratory quality management system and providing complete evidence for quality traceability and compliance inspections.

[0052] In this implementation plan, log events are defined for each module to record operation type, operator, timestamp, and changes. For example: When a sample is put into storage, the sample putting event is recorded, including the sample number, storage location, and operator.

[0053] When reviewing a report, record the review process events, including review comments and results.

[0054] Logs are stored in a central database and can be filtered by time, operator, and type.

[0055] The system provides an operation log query interface, allowing users to combine multiple criteria for queries (such as operation time range or operator name). Query results can be exported in batches to CSV or Excel files, with fields including operation timestamp, operator ID, operation content, operation result, and associated objects. Exported files meet audit requirements such as ISO 15189.

[0056] Log data is captured via database triggers or application-layer code; analytical queries are implemented using SQL. End-to-end logging facilitates quality traceability, such as quickly identifying the responsible party for overdue reports during audits.

[0057] Specifically, the resource linkage and traceability module also includes intelligent sample classification and inventory visualization functions: when samples are put into storage, the sample type, testing items, storage date and other information are automatically parsed according to the preset sample molecule numbering rules, and the samples are classified into the corresponding predefined storage categories; sample storage adopts well position management, and the inventory status of samples in the well position is intuitively distinguished by different colors. Normal inventory samples are marked with green, retrieved samples are marked with yellow, destroyed samples are marked with red, and samples waiting to be put into storage are marked with blue.

[0058] In this implementation scheme, the molecular numbering rules include: sample numbers are automatically generated according to rules, for example: Prefix rules: "M" indicates gene testing of types I-IV, "Y" indicates AZF testing, and "T" indicates prenatal diagnosis.

[0059] Numbering example: New sample numbers are prefixed with a 4-digit serial number, such as M0001; resubmitted samples retain the original number with the suffix "-R1", such as M0001-R1.

[0060] The system automatically categorizes samples into predefined libraries using a prefix-library mapping table (e.g., M→M-DNA-A library).

[0061] Color coding rules include: using color to visually distinguish inventory status. Green: Inventory level ≥ 80% (normal).

[0062] Yellow: Retrieved but not executed (based on the status of the retrieval form).

[0063] Red: Inventory level <20% or already destroyed.

[0064] Blue: Status is "Pending entry into inventory".

[0065] Color rules are rendered in the "Sample Library" interface; for example, the background color of the hole position is automatically updated based on the inventory threshold.

[0066] Numbering rules are implemented through an encoding algorithm; color coding is calculated based on inventory and status fields, with thresholds preset in the system parameters. Intelligent classification improves sample retrieval efficiency, while color coding simplifies inventory management. For example, lab technicians can quickly identify low-inventory samples by color, avoiding stockouts.

[0067] Specifically, the sample intelligent perception and scheduling module also includes a user task accurate display mechanism: only the tasks to be executed by the current user that have been satisfied as prerequisites are displayed, and tasks that have not been completed or are not within the scope of the user's responsibilities are not displayed; prerequisite tasks are displayed first to the experimental personnel in charge of the sample, and subsequent tasks are displayed to the corresponding personnel only after the prerequisite tasks are completed and the system confirms that the prerequisites are satisfied, so as to ensure the orderliness and accuracy of task execution.

[0068] In this implementation plan, in the experimental task creation module, the system only displays tasks that the current user has permission for and whose prerequisites have been met. For example, when querying the task table, additional conditions are added: "Current user ID matches" and "Prerequisite tasks are all completed." The system sorts tasks by creation time, but subsequent tasks remain hidden until prerequisite tasks are completed. For example, the hybridization task only appears on the user's workbench after the DNA extraction task is completed.

[0069] Task data is retrieved from the database, and dependency status is validated via subqueries; the interface dynamically updates the task list using JavaScript. Precise display avoids task confusion, ensuring that inspectors do not see incomplete tasks and reducing accidental operations.

[0070] In summary, this application has at least the following effects: Through the collaborative operation of five modules, the system achieves intelligent management of the entire sample lifecycle, solving problems in traditional management such as difficulty in tracking sample status, non-standard data entry, rigid review processes, disconnected resource management, and insufficient knowledge accumulation. The system not only ensures the accuracy of testing data and the compliance of reports, but also continuously optimizes laboratory operational efficiency and reduces operating costs and risks through knowledge base self-evolution and decision support. It provides strong support for the standardization, digitalization, and intelligent development of genetic testing services, demonstrating significant practical value and promotional significance.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that the combination of each step in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A device for a function specified in one or more processes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0075] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0076] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A molecular genetics laboratory management system for a genetics center, characterized in that, include: The sample intelligent sensing and scheduling module is used to sense the sample status in real time through preset status indicators and time plans, and dynamically schedule subsequent tasks based on task dependencies, while providing early warnings for near-expiration and overdue periods. The detection data and mutation fusion module, based on the scheduled subsequent tasks, collects experimental data through structured electronic task sheets, dynamically updates the analyzed and entered genetic variation information to the mutation library, and intelligently recommends historical variation data for the report generation process. The intelligent report generation and review module automatically fills in report content based on variant data and adapts to project templates. It also supports configurable multi-level review processes, enabling online collaboration and operation tracking. The resource linkage and traceability module, based on the audit results, links the instrument status with the task allocation, performs the first-use acceptance of reagents and consumables and automatic deduction of inventory, and realizes real-time synchronization of sample inventory operations and testing processes. The knowledge base self-evolution module, based on resource linkage and traceability, collaboratively constructs gene disease databases and mutation databases, aggregates and analyzes the operation logs of the entire system, and provides data-driven decision support for laboratory resource planning and process optimization.

2. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The status identifiers specifically include four types: not in progress, in progress, completed, and terminated. The status identifiers are automatically identified in conjunction with the preset time plan to locate the real-time position and time status of each sample in the testing process. The task dependency relationship supports flexible configuration of serial and parallel processes. The parallel process configuration allows multiple tasks to be executed simultaneously and share the same preceding task. The parallel task will only be dynamically released by the system after the preceding task is completed. The serial process configuration requires tasks to be executed in a preset order. Only after the preceding task is completed will subsequent tasks be scheduled to the corresponding user workbench.

3. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The genetic variation information includes at least six core components: genes and transcripts, nucleotide changes, amino acid changes, chromosome position, pathogenicity assessment, and heterozygosity. The structured electronic task sheet is predefined for different detection methods to ensure the standardized entry of experimental process data such as concentration, purity, and positive / negative results. The entered structured genetic variation information is used for report generation and dynamic updates to the system mutation library. At the same time, it automatically links to the historical test records of the same subject or mutation library data of the same gene locus, providing intelligent recommendation support for report writing and analysis.

4. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The multi-level review process is specifically a multi-level, multi-person review process that is flexibly configured according to the project type.

5. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The specific implementation of the first use acceptance and automatic deduction of inventory of the reagents and consumables is as follows: when the experimenter selects unaccepted reagents and consumables when creating an experiment task, the experiment task is automatically converted into a reagent and consumables acceptance task. After the test is completed according to the acceptance standard, the test results are automatically stored in the system as the acceptance record of the reagents and consumables. Only reagents and consumables that pass the acceptance can be included in the available inventory for subsequent routine experiment tasks. During the experimental task reporting process, the corresponding inventory quantity of reagents and consumables is automatically deducted based on the pre-set consumption amount of consumables in the experimental plan or the actual consumption amount entered by the experimenter, ensuring that the inventory data is consistent with the actual consumption in real time.

6. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The system's operation logs cover operation records for sample management, task scheduling, data collection, report generation, review and approval processes, and resource usage. Its aggregation and analysis function supports multi-dimensional comprehensive queries by operation time, operator, and operation type. It also supports batch exporting query results into standardized format files. The exported content includes operation timestamps, operator identity information, operation content, operation results, and associated object identifiers, meeting the audit requirements of the laboratory quality management system and providing complete evidence for quality traceability and compliance inspections.

7. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The resource linkage and traceability module also includes intelligent sample classification and inventory visualization functions: when samples are put into storage, the sample type, testing items, storage date and other information are automatically parsed according to the preset sample molecule numbering rules, and the samples are classified into the corresponding predefined storage categories. Sample storage adopts well location management, which uses different colors to intuitively distinguish the inventory status of samples in the well location. Samples in normal inventory are marked in green, samples that have been retrieved are marked in yellow, samples that have been destroyed are marked in red, and samples waiting to be put into storage are marked in blue.

8. The molecular genetics laboratory management system for a genetic center according to claim 1, characterized in that, The sample intelligent perception and scheduling module also includes a user task accurate display mechanism: only the tasks to be executed by the current user and whose prerequisites have been met are displayed, and tasks whose prerequisites have not been completed or are not within the scope of the user's responsibilities are not displayed. Prioritize showing the preliminary tasks to the experimenters responsible for the samples. Once the preliminary tasks are completed and the system confirms that the prerequisite dependencies are met, then show the subsequent tasks to the corresponding responsible personnel to ensure the orderly and accurate execution of tasks.

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