Biological detection data management method

Abstract

The invention relates to a biological detection data management method, and belongs to the technical field of data management, and the method comprises the steps: carrying out the feature extraction of obtained sample data, obtaining a sample attribute and a detection item, and carrying out the matching of a target detection department in combination with a preset department business range rule base; according to the detection item, a sample preparation parameter instruction is called from a preset dynamic preparation standard library, a sample preparation completion instruction is finally obtained, a detection task is established in combination with a target detection department, detection personnel, detection equipment, consumables, a preset detection method and a detection environment are verified based on the detection task and a preset detection requirement, and a verification result is obtained; according to the method, the detection equipment starting instruction is generated, the original equipment data of the target detection equipment in the detection process is obtained, the detection result is generated in combination with the preset threshold value, the detection report is generated according to the detection result and the sample data, and the detection efficiency, the result reliability and the quality control capability are remarkably improved through digitization, automation and standardization of the whole process.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and more specifically to a method for managing biological detection data. Background Technology

[0003] Currently, existing technologies mainly employ rule-based laboratory information management systems or traditional manual recording processes. These systems register sample information, assign tasks, and input results through pre-set forms and fixed procedures. They rely on operators to manually transfer information and paper documents between different stages and to manually verify and approve at key points.

[0004] However, in existing technologies, the system struggles to automatically process heterogeneous test data from multiple sources. Information silos exist at each stage, and there is a lack of real-time verification and dynamic linkage mechanisms for the core quality elements of "man, machine, material, method, and environment." This makes it impossible to ensure the standardization of the testing process and the authenticity and traceability of the data, resulting in low overall testing efficiency and difficulty in supporting rapid and accurate public health decision-making. Summary of the Invention

[0005] In view of the shortcomings of related technologies, the purpose of this invention is to provide a biological detection data management method to solve the technical problems of existing technologies, which lack real-time intelligent collaborative verification and closed-loop control of all detection elements, resulting in process breakpoints, low data reliability, and difficulty in traceability, thus failing to meet the needs of high-quality and efficient prevention and control decision-making.

[0006] This invention provides a method for managing biological detection data, comprising the following steps: Data acquisition steps: Obtain sample data through the API data interface platform, extract features from the sample data to obtain sample attributes and detection items; Department matching step: Based on the sample attributes and the testing items, department matching is performed according to the preset department business scope rule base to obtain the target testing department; Task creation steps: Based on the test item, retrieve the sample preparation parameter instruction from the preset dynamic preparation standard library; in response to the preparation completion confirmation operation triggered by the sample preparation parameter instruction, generate the sample preparation completion instruction; and create the test task based on the sample preparation completion instruction and the target testing department. Task allocation steps: Based on the detection task and preset detection requirements, verify the detection personnel, detection equipment, consumables, preset detection methods and detection environment, obtain the verification results, and generate task allocation instructions based on the verification results; Result generation steps: Generate a detection device start command based on the task allocation command; obtain the original device data of the target detection device during the detection process based on the detection device start command; generate the detection result based on the original device data and a preset threshold; and generate a detection report based on the detection result and the sample data.

[0007] By establishing a series of mandatory and coherent digital steps—data acquisition, intelligent department matching, task creation and allocation, and automatic result generation—the discrete operations that traditionally relied on manual coordination and offline processes are integrated into a continuous process driven by the system automatically. This eliminates the human intervention points in the sample transfer process from a mechanism perspective, and ensures the standardization and controllability of the process through preset rules and real-time verification. As a result, the overall efficiency and reliability of the testing process are directly improved, effectively addressing the dual requirements of speed and quality for large-scale epidemiological investigations.

[0008] In some embodiments of the present invention, the data acquisition step specifically includes: Acquire multi-source heterogeneous test data, and standardize and parse the multi-source heterogeneous test data to obtain epidemiological investigation sample test data; The sample data is obtained by associating and binding the epidemiological investigation sample submission data with the sample attachment information acquired in real time. Based on the sample data, a data verification instruction is generated, and in response to the verification confirmation operation triggered by the data verification instruction, a label generation instruction is generated. A unique identification code is generated for the sample data based on the label generation instructions.

[0009] By performing standardized cleaning, parsing, and attachment association and binding operations after the multi-source heterogeneous test data is accessed, and by introducing a mechanism for manual verification to trigger label generation, the problems of chaotic original test data format and information disconnection are effectively solved. The acquisition of sample data ensures that the sample data entering the core process of the system has complete, accurate and uniform structured characteristics, laying a high-quality data foundation from the source and providing reliable input for subsequent intelligent allocation and precise control. Through the generation of a unique identification code, a unique identifier is established for the precise traceability of the sample throughout its entire life cycle, which is the primary technical guarantee for achieving data authenticity and full controllability.

[0010] In some embodiments of the present invention, the task allocation step specifically includes: Based on the standard operating procedures corresponding to the testing items, obtain the testing requirements for the testing personnel, the testing equipment, the consumables, the preset testing methods, and the testing environment; Based on the aforementioned testing requirements, the testing qualifications of the testing personnel, the operating data of the testing equipment, the information of the consumables, the standard specifications of the preset testing method, and the environmental parameters of the testing environment are queried to obtain status data. The status data is examined based on the aforementioned detection requirements, and the examination results are generated.

[0011] By binding the testing items with standard operating procedures and conducting unified inspections of the real-time status data of various resources based on the testing requirements generated by the standard operating procedures, a deep integration of quality control rules and business processes has been achieved. By transforming the traditional resource allocation process, which relies on human experience and judgment, into an objective and unified automated verification process driven by digital SOPs, it ensures that the five key elements of people, machines, materials, methods, and environment meet the preset quality standards before the task is executed. This eliminates quality risks caused by non-compliant resources from the source of decision-making and is the core mechanism for achieving standardization of the testing process and reliability of results.

[0012] In some embodiments of the present invention, the task allocation step further includes: If all the status data meet the corresponding detection requirements, the inspection result is "passed". Based on the inspection result, the target inspection personnel and target inspection equipment are determined, the inspection task is bound to the target inspection personnel and target inspection equipment, and the task allocation instruction is generated. If any of the aforementioned status data fails to meet the corresponding detection requirements, the detection result is deemed as failing the inspection, triggering an early warning signal and suspending the allocation of the detection task.

[0013] By defining the application rules for the verification results—that is, executing task binding and allocation when all requirements are met, and suspending the process and issuing an alert when any requirement is not met—an unavoidable quality gate can be established in the system. This elevates quality requirements from soft guidance to hard constraints, ensuring that any testing task that does not meet the testing requirements cannot enter the execution stage. This achieves synchronization between the business flow and the quality flow, fundamentally guaranteeing that only fully prepared and compliant tasks will be initiated, greatly enhancing the quality and safety level of the testing process and the authority of decision-making.

[0014] In some embodiments of the present invention, the task allocation step further includes: Based on the standard operating procedure, determine the testing requirements for the testing personnel needed to perform the testing task; wherein, the testing requirements for the testing personnel include instrument usage qualifications, testing item qualifications, and training qualifications; Based on the testing needs of the testing personnel, their digital qualification files are queried to obtain their testing qualifications. Compare the testing requirements of the testing personnel with their testing qualifications, and calculate the qualification matching degree of the testing personnel; If the qualification matching degree is greater than the preset qualification threshold, the inspector passes the inspection; Otherwise, the inspector failed the inspection.

[0015] By extracting specific qualification requirement categories from standard operating procedures and performing quantitative matching calculations with the actual qualifications in personnel digital files, a precise comparison between personnel capabilities and task requirements was achieved. By refining the previously vague certification requirements into multi-dimensional and quantifiable entry standards for specific instruments, projects, and training, and by using preset thresholds, the system can automatically and objectively screen out personnel who do not meet the qualification requirements. This ensures that personnel performing testing tasks have the precise matching technical capabilities, thus strengthening the first line of defense for testing quality from the core element of "people" and reducing the risk of human error.

[0016] In some embodiments of the present invention, the task allocation step further includes: Based on the testing requirements of the testing personnel, query the current task load of the testing personnel; The task allocation score of each inspector is calculated based on the qualification matching degree and the current task load using a preset multi-factor weight allocation algorithm. The inspector with the highest task assignment score is selected as the target inspector.

[0017] By incorporating consideration of the current task load of personnel and using a multi-factor weighting algorithm to comprehensively calculate task allocation scores to select the best personnel, task allocation can be elevated from a simple question of whether someone is qualified to an optimization decision-making level of who is more suitable. By balancing qualification compliance and workload, tasks can be dynamically assigned to the optimal personnel who have the corresponding abilities and are relatively available. This optimizes the utilization of human resources and smooths the workload of each person while ensuring quality, which helps to improve the overall efficiency and stability of the testing task.

[0018] In some embodiments of the present invention, the task allocation step further includes: The testing requirements for the testing equipment needed to perform the testing task are determined according to the standard operating procedure; wherein, the testing requirements for the testing equipment include the equipment calibration cycle; Based on the aforementioned testing requirements, the operating status of the testing equipment is monitored, and usage time, calibration counts, and maintenance records are recorded to obtain the operating data. If the calibration of the testing equipment is determined based on the operating data to be within the calibration cycle of the equipment, then the testing equipment passes the inspection, and the testing equipment that passes the inspection is identified as the target testing equipment; Otherwise, the testing equipment failed the inspection.

[0019] By adhering to the calibration cycle requirements in the standard operating procedures and combining continuous monitoring data on equipment operating status, usage history, and maintenance records to determine equipment availability, equipment management is transformed from passive, fixed-cycle planned maintenance to proactive, preventative control based on real-time status and historical data. It can automatically identify equipment that is about to exceed its calibration cycle or has an abnormal history and exclude it before task allocation, thereby ensuring that testing tasks are only assigned to equipment in a well-controlled state. From the perspective of the "machine," this guarantees the accuracy of the testing process and the reliability of the data generated.

[0020] In some embodiments of the present invention, the task allocation step further includes: The testing requirements for consumables needed to perform the testing task are determined according to the standard operating procedure; wherein, the testing requirements for consumables include requirements for the type, specifications, quantity, and batch of consumables. Based on the testing requirements of the consumables, the corresponding consumables and their real-time inventory are obtained to obtain information about the consumables; If the real-time inventory of consumables is higher than the required quantity, the consumables pass inspection. Otherwise, the consumables fail the inspection, triggering an inventory replenishment warning signal.

[0021] By clarifying the specific types, specifications, quantities, and batch requirements of consumables according to standard operating procedures and verifying inventory information in real time, refined management of key materials required for testing has been achieved. This ensures that the compliance and sufficiency of the required consumables can be accurately verified before the task is executed, avoiding experimental interruptions or result deviations caused by incorrect consumable models, insufficient inventory, or unqualified batches. By setting up inventory warnings, we have achieved proactive management of consumable supply, ensuring the continuity and stability of the testing process, and improving the quality assurance system from the perspective of "materials".

[0022] In some embodiments of the present invention, the task allocation step further includes: According to the test items, retrieve the latest standard specification version corresponding to the preset method standard library; If the version of the standard specification for the preset testing method in the standard operating procedure is consistent with the latest version of the standard specification, then the preset testing method passes the inspection. Otherwise, the preset detection method fails the test and triggers a method standard update reminder.

[0023] By establishing a link between testing items and a pre-defined standard library of methods, and automatically verifying whether the testing methods referenced in the standard operating procedures used to execute tasks are the latest versions, dynamic monitoring of the standard compliance of testing methods is achieved. This solves the problem of inconsistent testing methods and incomparable results caused by lagging standard updates. It can automatically detect and remind users of version differences between standard operating procedures and the latest standards, prompting timely updates of methods and ensuring that all testing activities comply with the latest recognized technical specifications. From a legal perspective, this safeguards the authority, advancement, and legal validity of testing work and results.

[0024] In some embodiments of the present invention, the task allocation step further includes: The required parameter standard range for the testing environment is determined according to the standard operating procedure described above; The environmental parameters of the target detection department are collected in real time using IoT sensors. If the environmental parameters do not exceed the standard range, the testing environment passes the inspection. Otherwise, the detection environment fails the test and an environmental anomaly warning signal is triggered.

[0025] By automatically comparing the standard range of environmental parameters specified in the standard operating procedures with the laboratory environmental data collected in real time by IoT sensors, continuous online monitoring and instant verification of the testing environment are achieved, and the monitoring of key environmental factors such as temperature and humidity is upgraded from manual timed recording to fully automatic and uninterrupted intelligent control. If environmental parameters deviate from the standard range, the system can immediately issue an alarm and trigger task allocation decisions to prevent testing under unsuitable environmental conditions. This effectively avoids potential interference from environmental factors with the test results, ensuring that testing activities are always carried out under controlled and compliant conditions from the perspective of the "environment". Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 A flowchart of a biological detection data analysis method provided in an embodiment of the present invention; Figure 2 A flowchart of another biological detection data analysis method provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application. It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus. In the field of biological testing quality control, in the face of the urgent need for rapid response and routine precise prevention and control of sudden animal public health events, it is imperative to build an integrated intelligent control system that can deeply integrate data, processes, quality and traceability to provide core technical support.

[0028] The current situation of animal public health prevention and control is severe, characterized by rapid cross-regional transmission, large sample size for epidemiological investigations, diverse testing needs, and complex quality control. This makes the traditional epidemiological investigation and testing management model difficult to adapt to modern prevention and control requirements. Its core technical pain points are concentrated in the following aspects: The existing system is inadequate in the collaborative processing of multi-source data. This is mainly manifested in the lack of a unified data access standard, which leads to fragmented data formats and data classification that is highly dependent on manual operation. This not only causes problems such as high data processing delays and high rates of misclassification and omission, but also causes sample attachment information to be disconnected from core data, forming data silos that are difficult to break down.

[0029] There are significant deficiencies in process intelligence and closed-loop management. There are multiple information gaps in the entire process from sample receipt, preparation, testing, review to report generation. Key links such as label generation, task allocation, and process flow still require manual intervention, which is very easy to introduce human error. At the same time, the lack of a standardized intelligent approval link for sample information changes makes it difficult to trace data corrections, which seriously affects the continuity of the testing process and the accuracy of the results.

[0030] The authenticity and traceability of the test data are also relatively weak. Traditional systems rely on manual input of test equipment data, which poses a risk of data tampering and recording deviations, and lacks an effective correlation mechanism between equipment, data and samples. The instrument operation and maintenance status and consumable usage trajectory are not deeply linked to the testing process, making it impossible to achieve the full-chain traceability requirements of "data that can be checked, process that can be traced, and responsibility that can be investigated".

[0031] The comprehensive quality control system encompassing "human, machine, material, method, and environment" is not yet perfect. The existing system has failed to build an intelligent quality control model and lacks the ability to verify and dynamically monitor key quality elements such as the qualifications of testing personnel, the calibration status of equipment, the compliance of consumables, the standards of testing methods, and environmental parameters in real time. Therefore, it is difficult to meet the stringent requirements of laboratory quality standards and public health testing.

[0032] Furthermore, the report generation and decision support are inefficient, with test results requiring manual compilation and summarization, and multi-level review relying on offline processes, resulting in a lengthy report generation cycle. At the same time, the system lacks big data analysis and visualization capabilities, making it impossible to link test data with epidemiological trends and regional transmission characteristics for analysis, thus failing to provide real-time and accurate decision-making basis for prevention and control decision-makers.

[0033] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other. The technical solution of the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0034] like Figure 1 and Figure 2 As shown, the present invention provides a method for managing biological detection data, comprising the following steps: Data acquisition step S1: Obtain sample data through the API data interface platform, extract features from the sample data, and obtain sample attributes and detection items; Department matching step S2: Based on the sample attributes and the test items, perform department matching according to the preset department business scope rule base to obtain the target test department; In some embodiments, the data acquisition step S1 specifically includes: Acquire multi-source heterogeneous test data, standardize and clarify the format of the multi-source heterogeneous test data, and obtain epidemiological investigation sample test data; The sample data is obtained by associating and binding the epidemiological investigation sample submission data with the sample attachment information acquired in real time. Data verification instructions are generated based on sample data, and label generation instructions are generated in response to the verification confirmation operation triggered by the data verification instructions. Generate a unique identification code for the sample data based on the label generation instructions.

[0035] By performing standardized cleaning, parsing, and attachment association and binding operations after the multi-source heterogeneous test data is accessed, and by introducing a mechanism for manual verification to trigger label generation, the problems of chaotic original test data format and information disconnection are effectively solved. The acquisition of sample data ensures that the sample data entering the core process of the system has complete, accurate and uniform structured characteristics, laying a high-quality data foundation from the source and providing reliable input for subsequent intelligent allocation and precise control. Through the generation of a unique identification code, a unique identifier is established for the precise traceability of the sample throughout its entire life cycle, which is the primary technical guarantee for achieving data authenticity and full controllability.

[0036] Furthermore, by building an API data interface platform, a parsing and processing channel compatible with multiple data formats such as JSON and XML was established to cope with the input of multi-source heterogeneous inspection data in actual business. The API data interface platform's built-in data cleaning and preprocessing algorithms enable efficient compatibility and unified storage of epidemiological investigation sample submission data from different channels. At the same time, by automatically synchronizing and associating sample attachment information generated during the sample transfer process, such as sampling site images, electronic scans of submission forms, and related case data, a complete and traceable digital archive of the sample is constructed, and sample data is obtained. By automatically extracting sample attributes and testing items from sample data and matching them with departments according to a pre-defined departmental business scope rule base, the target testing department can be obtained, thus achieving automated and precise departmental matching of sample data. Preferably, the sample data is subjected to deep analysis and feature vectorization through natural language processing or a predefined mapping table to extract sample attributes, which may include species: pig; type: serum; origin: a certain province and city; at the same time, the detection items are extracted, which may include African swine fever virus fluorescent PCR detection. The keywords in the description are uniformly mapped to terms in the standard project library, and features are supplemented or verified from the associated sample attachment information to form a standardized, multi-dimensional sample feature vector. The sample feature vectors are compared with the digitized and structured departmental business scope rule base. Each department is defined as one or more sets of feature rules in the departmental business scope rule base. For example, the rules for molecular diagnostics can be stated as including PCR, sequencing, and nucleic acid testing. The rules for serological testing stipulate that the test items must include antibodies or antigens, or the sample type must include serum or plasma. The rules of regional testing centers can be supplemented with geographical constraints that the source of the product belongs to a certain region. The matching engine calculates the matching degree between the features of the sample data and the rules of each department, and generates a matching degree ranking list using Boolean logic, weighted scoring or machine learning models. For example, the matching degree of the molecular diagnostics department is 95%, the matching degree of the serological testing department is 60%, or the suggested target testing department can be directly output based on the preset threshold; Data verification instructions are generated based on sample data. The matching results of departments and complete sample data will be displayed to the back-end staff through a visual interface. The back-end staff will then verify the completeness and logical accuracy of the data online based on the data verification instructions to complete the verification and confirmation operation. After the verification and confirmation operation is completed, a label generation instruction is generated to generate a unique identification code for the sample data, thus completing the transformation from data to physical task.

[0037] To ensure the accuracy and traceability of data throughout its entire lifecycle, a standardized information change and traceability process has been established. When errors are found in the entered sample data, a fully electronic approval process can be initiated. A change request form is automatically generated and transferred to the relevant authorized personnel for review. The change operation can only be executed after the review is approved. At the same time, the system will fully record the content before and after the change, the operator, and the timestamp, which can ensure the rigor of the data and realize the detectability and traceability of any data modification behavior, thus preventing the risk of data tampering from both institutional and technical perspectives.

[0038] Task creation step S3: Based on the testing item, retrieve the sample preparation parameter instructions from the preset dynamic preparation standard library, respond to the preparation completion confirmation operation triggered by the sample preparation parameter instructions, generate the sample preparation completion instruction, and create the testing task based on the sample preparation completion instruction and the target testing department; Furthermore, after successfully processing the sample data and generating a unique identification code, the process enters the standardized sample preparation and testing task creation stage to ensure the scientific nature and repeatability of the preparation process and to prepare resources for subsequent testing. Based on the identified testing items, the system automatically retrieves and matches the corresponding sample preparation parameter instructions from the preset dynamic preparation standard library. Based on the specific combination of testing items, the system automatically calculates the number of samples to be prepared and generates precise preparation instructions, thereby transforming abstract testing requirements into specific, quantifiable operational steps that can be executed by the laboratory. A rigid binding relationship is established between the preparation instructions and the testing items, and the instructions are automatically synchronized to the dedicated work interface of the target testing department. This ensures that the preparation requirements are accurately transmitted and received, eliminating operational errors caused by misunderstandings or missing information from the source.

[0039] Once the laboratory staff completes the sample preparation according to the preparation instructions and confirms it in the system, the process will proceed to the formal creation of the testing task and the allocation of resources. Based on the generated unique identification code, the target testing department, and the confirmed testing items, a structured electronic testing task sheet is created. The electronic testing task sheet gathers all the background information and testing requirements of the sample, becoming the core carrier driving all subsequent activities in the digital space. After a testing task is generated, the department head can clearly understand the status of all pending testing tasks in the department, as well as the real-time workload and qualifications of testing personnel, through an integrated visual task management dashboard.

[0040] Task allocation step S4: Based on the testing task and preset testing requirements, verify the testing personnel, testing equipment, consumables, preset testing methods and testing environment, obtain the verification results, and generate task allocation instructions based on the verification results; In some embodiments, task allocation step S4 specifically includes: Based on the standard operating procedures corresponding to the testing items, obtain the testing requirements for testing personnel, testing equipment, consumables, preset testing methods, and testing environment; Based on testing requirements, query the testing qualifications of testing personnel, the operating data of testing equipment, the information of consumables, the standards and specifications of preset testing methods, and the environmental parameters of the testing environment to obtain status data; The status data is examined based on the detection requirements, and the examination results are generated.

[0041] By binding the testing items with standard operating procedures and conducting unified inspections of the real-time status data of various resources based on the testing requirements generated by the standard operating procedures, a deep integration of quality control rules and business processes has been achieved. By transforming the traditional resource allocation process, which relies on human experience and judgment, into an objective and unified automated verification process driven by digital SOPs, it ensures that the five key elements of people, machines, materials, methods, and environment meet the preset quality standards before the task is executed. This eliminates quality risks caused by non-compliant resources from the source of decision-making and is the core mechanism for achieving standardization of the testing process and reliability of results.

[0042] In some embodiments, the task assignment step S4 further includes: If all status data meet the corresponding detection requirements, the inspection result is "passed". Based on the inspection result, the target inspection personnel and target inspection equipment are determined, the inspection task is bound to the target inspection personnel and target inspection equipment, and a task allocation instruction is generated. If any state data fails to meet the corresponding detection requirements, the detection result will be deemed as failing the inspection, triggering an early warning signal and suspending the allocation of detection tasks.

[0043] By defining the application rules for the verification results—that is, executing task binding and allocation when all requirements are met, and suspending the process and issuing an alert when any requirement is not met—an unavoidable quality gate can be established in the system. This elevates quality requirements from soft guidance to hard constraints, ensuring that any testing task that does not meet the testing requirements cannot enter the execution stage. This achieves synchronization between the business flow and the quality flow, fundamentally guaranteeing that only fully prepared and compliant tasks will be initiated, greatly enhancing the quality and safety level of the testing process and the authority of decision-making.

[0044] In some embodiments, the task assignment step S4 further includes: Based on standard operating procedures, determine the testing requirements for the testing personnel needed to perform the testing tasks; among these requirements are the testing personnel's qualifications in using instruments, testing items, and training. Based on the testing needs of the testing personnel, their digital qualification files can be queried to obtain their testing qualifications. Compare the testing needs of testing personnel with their testing qualifications, and calculate the qualification matching degree of the testing personnel; If the qualification matching degree is greater than the preset qualification threshold, the inspector passes the inspection; Otherwise, the inspectors failed the inspection.

[0045] By extracting specific qualification requirement categories from standard operating procedures and performing quantitative matching calculations with the actual qualifications in personnel digital files, a precise comparison between personnel capabilities and task requirements was achieved. By refining the previously vague certification requirements into multi-dimensional and quantifiable entry standards for specific instruments, projects, and training, and by using preset thresholds, the system can automatically and objectively screen out personnel who do not meet the qualification requirements. This ensures that personnel performing testing tasks have the precise matching technical capabilities, thus strengthening the first line of defense for testing quality from the core element of "people" and reducing the risk of human error.

[0046] In some embodiments, the task assignment step S4 further includes: Based on the testing needs of the testing personnel, query the current workload of the testing personnel; The task allocation score for each inspector is calculated based on the qualification matching degree and the current task load using a preset multi-factor weight allocation algorithm. The inspector with the highest task assignment score is selected as the target inspector.

[0047] By incorporating consideration of the current task load of personnel and using a multi-factor weighting algorithm to comprehensively calculate task allocation scores to select the best personnel, task allocation can be elevated from a simple question of whether someone is qualified to an optimization decision-making level of who is more suitable. By balancing qualification compliance and workload, tasks can be dynamically assigned to the optimal personnel who have the corresponding abilities and are relatively available. This optimizes the utilization of human resources and smooths the workload of each person while ensuring quality, which helps to improve the overall efficiency and stability of the testing task.

[0048] Furthermore, after completing the sample preparation and testing tasks, the process will enter a stage of rigorous verification and intelligent allocation of personnel, machines, materials, methods, and environment, fundamentally ensuring the quality and efficiency of the testing process. During the inspection of testing personnel, the first step is to conduct mandatory access verification of candidate testing personnel through the integrated digital qualification files of testing personnel; The digital qualification file centrally manages key qualification information of all testing personnel, such as instrument operation certificates, testing project authorizations, and training records. When it is necessary to assign testing personnel to a specific testing task, the digital qualification file of the candidate testing personnel will be automatically queried and compared according to the testing requirements specified in the standard operating procedures bound to the testing task. Only when a candidate's digital qualification profile fully covers and meets all the mandatory qualification requirements for the testing task is the tester considered to have basic access qualifications, thus systematically eliminating the possibility of unqualified personnel being involved in the testing task.

[0049] For all qualified testing personnel, their current workload will be obtained in real time. Through a preset multi-factor weight allocation algorithm, the system will comprehensively and quantitatively calculate the qualification matching degree of each candidate testing personnel and their current workload to obtain a dynamic task allocation score. The task assignment score intuitively reflects the optimal combination of quality and efficiency for which testing personnel should perform the task at any given moment. Department heads can confirm the intelligent recommendations with a single click on the system's visual task dashboard, or manually fine-tune and finalize the assignment based on the actual situation. This ensures a high degree of compliance in task assignment and significantly improves the overall utilization efficiency of laboratory resources and the smoothness of task flow.

[0050] In some embodiments, the task assignment step S4 further includes: The testing requirements for the testing equipment needed to perform the testing task are determined according to the standard operating procedures; among which, the testing requirements for the testing equipment include the equipment calibration cycle; Based on testing requirements, monitor the operating status of testing equipment, record usage time, calibration times, and maintenance records to obtain operational data; If the calibration of the testing equipment is determined based on the operating data to be within the calibration cycle, then the testing equipment passes the inspection, and the testing equipment that passes the inspection is identified as the target testing equipment. Otherwise, the testing equipment failed the inspection.

[0051] By adhering to the calibration cycle requirements in standard operating procedures and combining this with continuous monitoring data on equipment operating status, usage history, and maintenance records, equipment availability is determined. This transforms equipment management from passive, fixed-cycle planned maintenance to proactive, preventative control based on real-time status and historical data. It automatically identifies equipment nearing the end of its calibration cycle or exhibiting abnormal historical data, excluding it before task allocation. This ensures that testing tasks are assigned only to equipment in a well-controlled state, guaranteeing the accuracy of the testing process and the reliability of data generation from the perspective of the "machine" itself. Furthermore, during the inspection of testing equipment, the operating status of each testing device is continuously monitored and recorded through the Internet of Things interface and the equipment data gateway, including usage time, calibration times, and maintenance records. Combined with the equipment calibration cycle, a complete digital ledger of the entire equipment lifecycle is constructed. When it is necessary to allocate testing equipment for a testing task, its verification logic deeply integrates the ideas of preventive maintenance and real-time status management. Based on the testing requirements in the standard operating procedure of the testing task, it determines whether the type and accuracy of the candidate testing equipment match. Then, check in real time whether the testing equipment is currently in an idle or standby available state, whether the testing equipment is in a locked state automatically determined and triggered based on historical operating data, and whether the calibration of the testing equipment is still within the specified equipment calibration cycle; Among them, the lockout state, which is automatically determined and triggered based on historical operating data, is manifested as the system prohibiting allocation due to abnormal data, exceeding the calibration cycle, or the existence of serious alarms that have not been cleared; Only testing equipment that simultaneously meets the four conditions of type matching, real-time availability, not being locked by the system, and valid calibration can pass the system's collaborative verification and enter the allocable resource pool. This upgrades traditional periodic planned maintenance to preventive proactive management based on real-time data and preset rules. The system can automatically exclude potentially risky equipment at key decision points in task allocation, ensuring that every testing task is performed by the equipment in the best condition. This builds a solid quality defense line at the "machine" level and greatly improves the utilization efficiency of equipment resources and the level of refined management.

[0052] In some embodiments, the task assignment step S4 further includes: The testing requirements for consumables needed to perform the testing task are determined according to the standard operating procedures. The testing requirements for consumables include the type, specifications, quantity, and batch requirements of the consumables. Based on the testing requirements of consumables, obtain the corresponding consumables and their real-time inventory to obtain information about the consumables; If the real-time inventory of consumables exceeds the quantity requirement, the consumables will pass inspection. Otherwise, the consumables fail the inspection, triggering an inventory replenishment warning signal.

[0053] By clarifying the specific types, specifications, quantities, and batch requirements of consumables according to standard operating procedures and verifying inventory information in real time, refined management of key materials required for testing has been achieved. This ensures that the compliance and sufficiency of the required consumables can be accurately verified before the task is executed, avoiding experimental interruptions or result deviations caused by incorrect consumable models, insufficient inventory, or unqualified batches. By setting up inventory warnings, we have achieved proactive management of consumable supply, ensuring the continuity and stability of the testing process, and improving the quality assurance system from the perspective of "materials".

[0054] Furthermore, during the inspection of consumables, the required types, specifications, quantities, batches, or expiration dates of the consumables are precisely extracted based on the standard operating procedures bound to the testing task. Automatic querying of real-time data deeply integrated with the inventory management system verifies whether the target testing department has sufficient compliant consumables that meet all requirements. If the inventory is sufficient and compliant, this part of the resources can be logically locked in advance; If the inventory is below the quantity requirement or cannot meet the batch requirements, the verification will fail and an inventory replenishment warning will be triggered immediately, causing the task allocation process to be suspended.

[0055] In some embodiments, the task assignment step S4 further includes: Based on the testing items, retrieve the latest corresponding standard and specification version from the preset method standard library; If the version of the standard specification for the preset test method in the standard operating procedure is consistent with the latest version of the standard specification, then the preset test method passes the inspection; Otherwise, the preset testing method fails the test and triggers a method standard update reminder.

[0056] By establishing a link between testing items and a pre-defined standard library of methods, and automatically verifying whether the testing methods referenced in the standard operating procedures used to execute tasks are the latest versions, dynamic monitoring of the standard compliance of testing methods is achieved. This solves the problem of inconsistent testing methods and incomparable results caused by lagging standard updates. It can automatically detect and remind users of version differences between standard operating procedures and the latest standards, prompting timely updates of methods and ensuring that all testing activities comply with the latest recognized technical specifications. From a legal perspective, this safeguards the authority, advancement, and legal validity of testing work and results.

[0057] Furthermore, to ensure the authority, advancement, and comparability of the testing work, a dynamic testing method standard control system has been constructed. By deeply binding external, continuously updated industry technical specifications with internally implemented testing processes, the system ensures at the methodological level that all testing activities follow the currently recognized best practices.

[0058] During the verification process of the testing methods, a preset method standard library synchronized with authoritative standards is built-in, and the latest standard specification version corresponding to the test item is retrieved from the preset method standard library; Automatically compare the standard specifications of the testing methods referenced in the standard operating procedures on which the testing task is to be performed with the latest version of the standard specifications; If it is found that the method version referenced in the standard operating procedure is outdated compared to the latest standard, an alarm is immediately triggered, and the testing task is marked as pending method confirmation. At the same time, the relevant person in charge is notified for review and decision-making. This effectively solves the problem of testing method version drift caused by delays in the transmission of standard update information or human negligence, ensuring that the laboratory always keeps pace with the forefront of the industry in terms of technology, and fundamentally improves the standardization level and compliance assurance capabilities of laboratory technical management.

[0059] In some embodiments, the task assignment step S4 further includes: Determine the required parameter standard range for the testing environment according to standard operating procedures; Environmental parameters of the target detection department are collected in real time using IoT sensors; If the environmental parameters do not exceed the standard range, the environmental test passes. Otherwise, the testing environment fails the inspection and an environmental anomaly warning signal is triggered.

[0060] By automatically comparing the standard range of environmental parameters specified in the standard operating procedures with the laboratory environmental data collected in real time by IoT sensors, continuous online monitoring and instant verification of the testing environment are achieved, and the monitoring of key environmental factors such as temperature and humidity is upgraded from manual timed recording to fully automatic and uninterrupted intelligent control. If environmental parameters deviate from the standard range, the system can immediately issue an alarm and trigger task allocation decisions to prevent testing under unsuitable environmental conditions. This effectively avoids potential interference from environmental factors with the test results, ensuring that testing activities are always carried out under controlled and compliant conditions from the perspective of the "environment".

[0061] Furthermore, in order to achieve precise control over environmental factors in testing departments that have a significant impact on testing quality, the environmental management system, which previously relied on manual recording and passive response, has been upgraded to a fully automated, early warning-enabled, and deeply integrated proactive intelligent control system that fundamentally eliminates the risk of data deviation caused by environmental inaccuracies.

[0062] During the inspection of the testing environment, temperature and humidity sensors were integrated and deployed in key areas of the testing department to achieve continuous and automatic collection of environmental parameters, including temperature and humidity. Based on the standard operating procedures for the testing task, the parameter standard range of the testing environment is clearly defined, and the real-time environmental parameters of the target testing department are automatically compared and analyzed. The system comprehensively evaluates the stability and trend of environmental parameters in the near term. Only when the environmental parameters remain within the parameter standard range for a preset stable period will the system determine that the environmental conditions meet the requirements, allow the assignment of detection tasks, and start the detection process. If environmental parameters exceed the standard, an audible and visual or text message warning will be immediately triggered, and the allocation of relevant testing tasks to the testing department will be automatically delayed or suspended until the environment returns to a stable and compliant state. This achieves intelligent collaboration between business processes and the physical environment, providing tamper-proof environmental evidence for test results. When it is necessary to verify data or investigate anomalies, the specific environmental state during the experiment can be accurately traced back, thus forming a complete chain of quality evidence and a traceability loop in the "loop" dimension, greatly enhancing the credibility of test results and the ability to defend against challenges.

[0063] Result generation step S5: Generate a detection device start command based on the task allocation command, obtain the original device data of the target detection device during the detection process based on the detection device start command, generate the detection result based on the original device data and the preset threshold, and generate a detection report based on the detection result and the sample data.

[0064] By establishing a series of mandatory and coherent digital steps—data acquisition, intelligent department matching, task creation and allocation, and automatic result generation—the discrete operations that traditionally relied on manual coordination and offline processes are integrated into a continuous process driven by the system automatically. This eliminates the human intervention points in the sample transfer process from a mechanism perspective, and ensures the standardization and controllability of the process through preset rules and real-time verification. As a result, the overall efficiency and reliability of the testing process are directly improved, effectively addressing the dual requirements of speed and quality for large-scale epidemiological investigations.

[0065] Furthermore, a real-time communication link is established with key testing equipment such as PCR instruments and nucleic acid sequencers through a pre-built standardized equipment data interface gateway; Specifically, a PCR instrument is a laboratory instrument used to perform polymerase chain reaction (PCR). It rapidly cycles between high temperature, low temperature and suitable temperature by programmably controlling the temperature of the reaction module, thereby achieving rapid and large-scale replication of specific DNA sequences in vitro. Once the target inspection personnel confirm the start of the inspection program in the system, the system automatically drives the inspection equipment and begins to collect the core parameters and raw output data of the equipment in real time at the millisecond level. The data is then directly synchronized to a secure central database to obtain the raw equipment data. This bypasses the traditional manual observation, copying, or secondary entry process, realizing end-to-end automation of data from generation to storage. It eliminates the risk of data tampering, recording errors, or information loss caused by human negligence or intervention from the very source, laying the technical foundation for data integrity. While the testing process proceeds automatically, the system simultaneously performs refined consumable and data association operations; Based on the testing task, the system can automatically retrieve or confirm specific batches of consumables that have been pre-locked during the preparation stage, and automatically or in a guided manner record the precise specifications, quantity and batch of the consumables consumed during the experiment. This enables each test result to form an inseparable digital association with a set of specific material information at the moment it is generated. The real-time collected raw equipment data, consumable batches, preset thresholds for testing items, and accumulated historical data characteristics are all input into the intelligent result judgment model for fusion analysis. The result determination model can automatically complete calculations, comparisons, and logical judgments, output objective test results, and automatically mark any abnormal data that deviates from expectations, triggering a review process, thereby greatly improving efficiency and building a solid and verifiable full-chain digital traceability path from samples, equipment, consumables to result data.

[0066] Furthermore, based on the preset standardized report template, the system automatically integrates all dimensions of data from this testing task, including sample data, testing items, test results, original equipment data, batches of consumables used, and environmental parameters, and intelligently fills them into the corresponding chapters of the report, generating a professional test report draft with a rigorous structure and complete content. The draft of the professional testing report is input into a multi-level online review process, and the opinions and signatures of reviewers at all levels are recorded in the system in real time; After final approval, the system can automatically generate a standard format test report with an electronic signature, and can further perform visual analysis on the core data in the test report, transforming it into intuitive forms such as charts, directly serving the judgment of epidemic trends and prevention and control decisions; Based on blockchain or tamper-proof log technology, from the moment sample data is connected to the system, every key operation and status change is recorded in real time and continuously. This includes the trajectory of each sample data transfer, the login and actions of each operator, the start and stop and parameters of each test equipment operation, the issuance and consumption of each batch of consumables, and the submission and adoption of each audit opinion. All these fragmented events in the dimensions of "people, machine, material, method, and environment" are encrypted and time-stamped, and then strongly associated with the unique identification code of the sample data. This ultimately forms a complete and reliable audit log covering "sample, data, and process". By quickly searching and tracing the audit log, true full-process review, verification, and accountability are achieved, raising quality control and data credibility to a new level.

[0067] It should be noted that the above is a reference method for managing biological detection data, and the present invention is not limited thereto.

[0068] The embodiments of the present invention improve the overall efficiency and reliability of the detection process, effectively address the dual requirements of speed and quality for large-scale epidemiological investigations, and solve the technical problems of existing technologies that lack real-time intelligent collaborative verification and closed-loop management of all elements of detection, resulting in process breakpoints, low data credibility, and difficulty in traceability, thus failing to meet the needs of high-quality and efficient prevention and control decision-making.

[0069] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A biological detection data management method characterized by, The method comprises the following steps: a data acquisition step: obtaining sample data through an API data interface platform, extracting features from the sample data to obtain sample attributes and detection items; a department matching step: based on the sample attributes and the detection items, matching departments according to a preset department business scope rule library to obtain a target detection department; a task creation step: based on the detection items, retrieving sample preparation parameter instructions from a preset dynamic preparation standard library, generating sample preparation completion instructions in response to preparation completion confirmation operations triggered according to the sample preparation parameter instructions, and creating a detection task based on the sample preparation completion instructions and the target detection department; a task allocation step: based on the detection task and preset detection requirements, verifying detection personnel, detection equipment, consumables, a preset detection method, and a detection environment to obtain a verification result, and generating a task allocation instruction based on the verification result; a result generation step: generating a detection equipment start instruction based on the task allocation instruction, obtaining original equipment data of a target detection equipment during a detection process based on the detection equipment start instruction, generating a detection result based on the original equipment data and a preset threshold, and generating a detection report based on the detection result and the sample data.

2. The biological test data management method according to claim 1, wherein, The data acquisition step specifically comprises: obtaining multi-source heterogeneous submission data, standardizing and clarifying the multi-source heterogeneous submission data, and obtaining flow survey sample submission data; associating and binding the flow survey sample submission data with real-time obtained sample accessory information to obtain the sample data; generating a data verification instruction based on the sample data, and generating a label generation instruction in response to a verification confirmation operation triggered according to the data verification instruction; generating a unique identification code for the sample data based on the label generation instruction.

3. The biological test data management method of claim 1, wherein, The task allocation step specifically comprises: obtaining detection requirements for the detection personnel, the detection equipment, the consumables, the preset detection method, and the detection environment according to a standard operating procedure corresponding to the detection items; based on the detection requirements, querying detection qualifications of the detection personnel, operation data of the detection equipment, information of the consumables, standard specifications of the preset detection method, and environmental parameters of the detection environment to obtain state data; verifying the state data based on the detection requirements to generate the verification result.

4. The biological test data management method according to claim 3, wherein, The task allocation step further comprises: if all the state data meet the corresponding detection requirements, the verification result is passed, and based on the verification result, a target detection personnel and a target detection equipment are determined, the detection task is bound to the target detection personnel and the target detection equipment, and the task allocation instruction is generated; if any of the state data does not meet the corresponding detection requirement, the detection result is not passed, a warning signal is triggered, and the allocation of the detection task is suspended.

5. The biological test data management method of claim 4, wherein, The task allocation step further comprises: determining detection requirements of detection personnel required to perform the detection task according to the standard operating procedure; wherein the detection requirements of the detection personnel include instrument use qualifications, detection item qualifications, and training qualifications; querying a digital qualification archive of the detection personnel based on the detection requirement of the detection personnel, to obtain a detection qualification of the detection personnel; comparing the detection requirement of the detection personnel with the detection qualification, to calculate a qualification matching degree of the detection personnel; if the qualification matching degree is greater than a preset qualification threshold, the detection personnel passes the inspection; otherwise, the detection personnel fails the inspection.

6. The biological test data management method according to claim 5, wherein, The task allocation step further comprises: querying a current task load of the detection personnel based on the detection requirement of the detection personnel; calculating a task allocation score of each detection personnel based on the qualification matching degree and the current task load by a preset multi-factor weight allocation algorithm; selecting a detection personnel with the highest task allocation score as the target detection personnel.

7. The biological test data management method according to claim 3 or 4, characterized by, The task allocation step further comprises: determining a detection requirement of a detection device required for performing the detection task according to the standard operating procedure; wherein the detection requirement of the detection device comprises a device calibration period; monitoring an operating state of the detection device based on the detection requirement, to record usage time, calibration times and maintenance records, to obtain the operating data; if it is determined according to the operating data that calibration of the detection device does not exceed the device calibration period, the detection device passes the inspection, and the detection device that passes the inspection is determined as the target detection device; otherwise, the detection device fails the inspection.

8. The biological test data management method according to claim 3 or 4, characterized by, The task allocation step further comprises: determining a detection requirement of a consumable required for performing the detection task according to the standard operating procedure; wherein the detection requirement of the consumable comprises a type requirement, a specification requirement, a quantity requirement and a batch requirement of the consumable; based on the detection requirement of the consumable, obtaining corresponding consumables and real-time inventory quantities thereof, to obtain information of the consumables; if the real-time inventory quantity of the consumable is higher than the quantity requirement, the consumable passes the inspection; otherwise, the consumable fails the inspection, and an inventory replenishment early warning signal is triggered.

9. The biological test data management method according to claim 3 or 4, characterized by, The task allocation step further comprises: according to the detection item, calling a corresponding latest standard specification version from a preset method standard library; if a version of a standard specification of the preset detection method in the standard operating procedure is consistent with the latest standard specification version, the preset detection method passes the inspection; otherwise, the preset detection method fails the inspection, and a method standard update reminder is triggered.

10. The biological test data management method according to claim 3 or 4, characterized by, The task allocation step further comprises: determining a parameter standard range of a required detection environment according to the standard operating procedure; real-time collection of environmental parameters of the target detection department by an Internet of Things sensor; if the environmental parameters do not exceed the parameter standard range, the detection environment passes the inspection; otherwise, the detection environment fails the inspection, and an environmental abnormality early warning signal is triggered.

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