A cloud-based laboratory quality monitoring and inspection system

Abstract

This invention belongs to the field of quality monitoring technology, and in particular to a cloud platform-based laboratory quality monitoring and rectification system. It is characterized by comprising a data acquisition module, a data analysis module, a problem early warning module, a rectification execution module, and a data storage module, all connected to the cloud platform. The data acquisition module collects equipment operation data, environmental monitoring data, test sample information, and personnel operation records during the laboratory testing process and transmits them to the cloud platform. By integrating advanced information technology, it constructs an intelligent quality management platform that integrates data acquisition, analysis, early warning, rectification, and storage, enabling full-process, real-time, and precise monitoring and management of laboratory testing quality, effectively improving laboratory quality management efficiency, and ensuring high-quality output of test results.

Description

Technical Field

[0001] This invention belongs to the field of quality monitoring technology, specifically relating to a cloud-based laboratory quality monitoring and verification system. Background Technology

[0002] In today's context of booming globalized trade and scientific research, laboratories play a crucial role. Customs laboratories, as guardians of national security, bear the heavy responsibility of inspecting and testing import and export commodities. The accuracy and reliability of their test results directly affect national interests, consumer rights, and the fairness and impartiality of international trade. However, traditional laboratory quality management models face numerous severe challenges.

[0003] Data is scattered across different systems and devices, lacking an effective integration mechanism, which makes it difficult to guarantee the integrity and reliability of the data. Data processing relies heavily on manual operation, which is inefficient and prone to errors, making it difficult to achieve in-depth data mining and analysis, and hindering the timely detection of potential quality problems.

[0004] To address these issues, a cloud-based laboratory quality monitoring and verification system was designed. Summary of the Invention

[0005] To address the problems mentioned in the background section, this invention provides a cloud-based laboratory quality monitoring and verification system, which can effectively solve the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a cloud-based laboratory quality monitoring and rectification system, characterized in that it includes a data acquisition module, a data analysis module, a problem early warning module, a rectification execution module, and a data storage module, all of which are connected to the cloud platform, wherein:

[0007] The data acquisition module is used to collect equipment operation data, environmental monitoring data, test sample information and test personnel operation records during the laboratory testing process, and transmit them to the cloud platform.

[0008] The data analysis module analyzes the collected data on the cloud platform. Through data mining technology, statistical analysis methods, and a preset quality assessment model, it evaluates the compliance of the testing process, the accuracy and reliability of the data, the stability of the equipment, and the suitability of the environmental conditions, and identifies potential quality problems and risk factors.

[0009] The problem warning module issues a warning message when the detection quality has potential problems or risks exceeding a preset threshold, based on the results of the data analysis module.

[0010] After receiving the warning information, the investigation and rectification execution module guides relevant personnel to investigate, analyze, and correct the problem according to the preset investigation and rectification strategy and standard operating procedures, and supervises the execution process.

[0011] The data storage module is used to store information such as the collected raw data, analysis process data, early warning records, investigation and correction measures and results.

[0012] As a preferred embodiment of the cloud-based laboratory quality monitoring and inspection system of the present invention, the data acquisition module achieves data acquisition by interfacing with sensors, instruments and equipment and information management systems in the laboratory.

[0013] As a preferred embodiment of the cloud-based laboratory quality monitoring and verification system of the present invention, the data analysis module cleans and preprocesses the data before analysis to remove outliers and erroneous data.

[0014] In a preferred embodiment of the cloud-based laboratory quality monitoring and inspection system of the present invention, the early warning information of the problem early warning module is transmitted to relevant personnel via SMS notification.

[0015] As a preferred embodiment of the cloud-based laboratory quality monitoring and rectification system of the present invention, the rectification execution module feeds back the rectification process and results to the cloud platform after correcting the problem, and the data storage module stores the feedback information, which includes the root cause analysis of the problem, the corrective measures taken, the personnel responsible for the rectification, and the execution time.

[0016] As a preferred embodiment of the cloud-based laboratory quality monitoring and verification system of the present invention, the data storage module classifies and stores data according to data type, time sequence and testing items, and provides data query and export functions, and the data query supports multi-condition combination query.

[0017] As a preferred embodiment of the cloud-based laboratory quality monitoring and verification system of the present invention, the cloud platform has the function of encrypted data transmission.

[0018] As a preferred embodiment of the cloud-based laboratory quality monitoring and investigation system of the present invention, the system further includes a user management module for managing the permissions of users of the system, including permissions for data viewing, data analysis, early warning reception, investigation and investigation execution, and system configuration.

[0019] As a preferred embodiment of the cloud-based laboratory quality monitoring and inspection system of the present invention, the data analysis module can be adjusted according to changes in laboratory testing items and quality requirements.

[0020] As a preferred embodiment of the cloud-based laboratory quality monitoring and inspection system of the present invention, the system can be integrated with other existing management systems in the laboratory to achieve data sharing and collaborative work.

[0021] Compared with the prior art, the beneficial effects of the present invention are: the present invention has a scientific and reasonable structure and is safe and convenient to use.

[0022] By integrating advanced information technology, an intelligent quality management platform is built that integrates data collection, analysis, early warning, investigation and correction and storage. This platform enables full-process, real-time and precise monitoring and management of laboratory testing quality, effectively improving the efficiency of laboratory quality management and ensuring high-quality output of test results. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0024] Figure 1 This is a schematic block diagram of the system of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example: Figure 1 As shown, this invention provides a technical solution: a cloud-based laboratory quality monitoring and rectification system, comprising a data acquisition module, a data analysis module, a problem early warning module, a rectification execution module, and a data storage module. Each module is connected to the cloud platform, wherein:

[0027] The data acquisition module is used to collect equipment operation data, environmental monitoring data, test sample information, and test personnel operation records during the laboratory testing process, and transmit them to the cloud platform;

[0028] The data analysis module analyzes the collected data on the cloud platform. Through data mining technology, statistical analysis methods, and preset quality assessment models, it evaluates the compliance of the testing process, the accuracy and reliability of the data, the stability of the equipment, and the suitability of environmental conditions, and identifies potential quality problems and risk factors.

[0029] Based on the results of the data analysis module, the problem warning module issues a warning message when the detection quality has potential problems or risks exceeding a preset threshold.

[0030] After receiving the warning information, the investigation and rectification module guides relevant personnel to investigate, analyze, and correct the problem according to the preset investigation and rectification strategy and standard operating procedures, and supervises the implementation process.

[0031] The data storage module is used to store information such as collected raw data, analysis process data, early warning records, investigation and correction measures and results.

[0032] The data acquisition module acquires data by interfacing with sensors, instruments, and information management systems in the laboratory. The types of sensors include, but are not limited to, temperature sensors, humidity sensors, pressure sensors, gas sensors, and equipment status sensors.

[0033] The cloud platform has data encryption transmission capabilities to ensure the security of data transmission between the acquisition module and the cloud platform, as well as between various modules within the cloud platform.

[0034] This module, serving as the system's information source, is responsible for establishing extensive connections with various data sources within the laboratory, ensuring the comprehensiveness and timeliness of data acquisition. Through seamless integration with multiple types of sensors (such as temperature, humidity, pressure, gas sensors, and equipment status sensors), it collects real-time laboratory environmental parameters and equipment operating status information. Simultaneously, it deeply interacts with the communication interfaces of various instruments and equipment to obtain detailed data during equipment operation, such as key information like equipment operating parameters, operating modes, and fault codes. Furthermore, it can efficiently integrate with the Laboratory Information Management System (LIMS) to synchronously extract comprehensive information about the tested samples (including sample number, source, properties, and testing requirements) and the operation records of the testing personnel (covering operation steps, time points, and operator identification). After necessary preliminary processing (such as format conversion and data verification), the collected data is uploaded to the cloud platform in an encrypted manner through a secure and reliable data transmission channel, ensuring the confidentiality, integrity, and availability of the data during transmission.

[0035] Before analyzing the data, the data analysis module cleans and preprocesses the data to remove outliers and erroneous data. Preprocessing methods include data filtering, missing value imputation, and data standardization.

[0036] The data analysis module can update and optimize the quality assessment model based on changes in laboratory testing items and adjustments to quality requirements.

[0037] The data analysis module, deployed on the cloud platform, is the core intelligent unit of the system. It first performs rigorous cleaning and preprocessing on the data from the data acquisition module. Using advanced data filtering algorithms, it accurately identifies and removes obviously abnormal data points, effectively preventing erroneous data from interfering with subsequent analysis. To address the issue of missing data, it employs scientifically sound methods for imputing missing values ​​(such as mean imputation based on statistical laws, interpolation, or specific imputation strategies based on data distribution characteristics) to ensure data continuity and integrity. Simultaneously, it standardizes and transforms the data to meet the input requirements of the preset analysis model, improving the accuracy and comparability of the analysis results.

[0038] Leveraging powerful data mining techniques, multivariate statistical analysis methods, and pre-built professional quality assessment models, this module conducts an in-depth review of the compliance of the testing process. Through precise comparison with Standard Operating Procedures (SOPs), it meticulously assesses whether each testing step is strictly executed according to specifications, including rigorous checks on sample processing procedures, instrument operation procedures, and data recording standards. It comprehensively evaluates the accuracy and reliability of data, employing various methods such as data distribution analysis, repeatability testing, and comparison with historical data to ensure that the data truthfully and accurately reflects the actual testing situation and effectively identifies potential deviations and abnormal fluctuations in the data. It closely monitors equipment stability, using dynamic trend analysis of equipment operating data, fluctuation assessment of key performance indicators, and the application of fault prediction models to promptly identify potential risks in equipment operation and predict possible equipment failure points, providing a strong basis for preventative maintenance. Simultaneously, it comprehensively assesses the suitability of environmental conditions, accurately determining whether the current environmental state meets the conditions for ensuring testing accuracy and precision based on the specific requirements of different testing items for environmental parameters (such as temperature, humidity, air pressure, and light), and promptly identifying the potential impact of environmental factors on the testing results. Through multi-dimensional and in-depth data analysis, potential quality problems and risk factors can be accurately identified, providing scientific and reliable decision support for subsequent early warning and investigation.

[0039] The problem warning module delivers warning information to relevant personnel through one or more of the following methods: SMS notification, system pop-up, and email reminder. The warning information includes a problem description, scope of impact, urgency level, and recommended measures.

[0040] The problem early warning module closely monitors the testing quality status in real time based on the evaluation results of the data analysis module. Once a potential quality problem or risk indicator exceeds a preset threshold, the early warning mechanism is immediately activated. Early warning information is delivered to relevant personnel in a timely and accurate manner through various flexible methods (such as SMS notifications, system pop-ups, email alerts, etc.), ensuring efficient and comprehensive information delivery. The early warning information is comprehensive and detailed, covering a detailed description of the problem (including the specific location of the problem, such as the equipment number, the name of the testing process, and the laboratory area; the manifestation of the problem, such as the specific phenomena of equipment failure and the characteristic patterns of data anomalies), clearly indicating the scope of the problem's potential impact (the specific testing items involved, the affected sample batches, and the possible test results affected), clearly identifying the urgency level of the problem (divided into high, medium, and low levels to enable relevant personnel to quickly assess the severity of the problem and take appropriate emergency response measures), and providing targeted and actionable preliminary suggestions (such as preliminary troubleshooting steps, sample handling suggestions in emergency situations, and directions for adjusting environmental parameters), helping relevant personnel to quickly understand the problem overview and take effective preliminary actions to minimize the potential adverse effects of the problem.

[0041] After correcting the problem, the investigation and execution module feeds back the investigation and execution process and results to the cloud platform. The data storage module stores the feedback information, which includes the root cause analysis of the problem, the corrective measures taken, the personnel involved, and the execution time.

[0042] Upon receiving an alert, the troubleshooting module responds swiftly and initiates an efficient problem-solving procedure. It strictly adheres to a pre-defined, detailed troubleshooting strategy and standardized operating procedures, providing clear and explicit action guidelines for relevant personnel. When a problem occurs, it can quickly organize and coordinate a troubleshooting team composed of skilled and experienced personnel to conduct a thorough investigation according to established procedures. Through comprehensive collection and in-depth analysis of relevant information, including detailed historical operating data of the equipment (covering long-term operating trends, past fault records, maintenance records, etc.), recent equipment maintenance operation records, detailed operational recollections from operators (obtained through operation records, video surveillance playback, personnel interviews, etc.), and in-depth clues provided by the data analysis module, it comprehensively utilizes various analytical methods and tools to accurately determine the root cause of the problem. The root cause may involve multiple aspects such as aging and wear of equipment components, sensor calibration deviations, insufficient operator skills or violation of operating procedures, sudden abnormal changes in environmental factors, and improper application of testing methods or standards.

[0043] For different types of root causes, swiftly formulate and decisively implement practical and targeted corrective measures. For example, for equipment malfunctions, immediately contact professional equipment maintenance personnel for emergency repairs or timely replacement of faulty components. After repair, strictly adhere to equipment calibration specifications for comprehensive calibration and performance testing to ensure the equipment returns to normal operation and stable, reliable performance. If the problem is caused by operator error, provide timely one-on-one on-site training and detailed guidance to the relevant operators to correct erroneous operating behaviors. Record this incident in detail in their personal operation training files as an important reference for subsequent training and assessment. If the problem is caused by environmental factors, quickly adjust the parameter settings of environmental control equipment to restore environmental conditions to the normal range that meets testing requirements as soon as possible. Conduct a comprehensive assessment of the testing items affected by the environment and determine whether retesting is necessary based on the assessment results (if necessary, revise the testing plan and strictly implement it). Throughout the entire investigation and rectification process, strictly monitor the progress and effectiveness of implementation to ensure that every corrective measure is effectively implemented and the problem is thoroughly resolved. At the same time, focus on summarizing lessons learned to prevent similar problems from recurring. Upon completion of the investigation and rectification work, a detailed and complete investigation and rectification report should be generated promptly, comprehensively summarizing the problem discovery process, in-depth analysis process, effective handling process, and final results. The investigation and rectification report should be promptly fed back to the cloud platform so that the data storage module can store it completely, providing valuable reference for subsequent data statistical analysis, quality retrospective review, and continuous improvement of quality management.

[0044] The data storage module categorizes and stores data according to data type, time order, and testing items, and provides data query and export functions. Data query supports multi-condition combination query, and export formats include but are not limited to Excel, PDF, and CSV.

[0045] The data storage module constructs a secure, reliable, efficient, and convenient data storage center on the cloud platform, responsible for the long-term storage and effective management of various types of data during system operation. It employs advanced data classification and storage technology, meticulously classifying and storing data according to multiple key dimensions, including data type (such as environmental monitoring data, equipment operation data, test sample data, testing personnel operation data, data analysis results data, early warning record data, and investigation process data), time order (based on precise sorting according to data acquisition time or event occurrence time), and testing items (organically classifying and storing various types of data related to the same testing item). This ensures a clear data storage structure that is easy to query and retrieve. Simultaneously, high-strength data encryption technology is used to encrypt the stored data, effectively guaranteeing data security and confidentiality, and preventing data leakage and unauthorized access. It provides powerful and user-friendly data query and export functions, supporting users to flexibly combine queries based on various conditions (such as single or multiple conditions such as time range, test item name, equipment number, and problem type). It can also conveniently and quickly export query results in various common formats (such as Excel spreadsheets, PDF documents, and CSV comma-separated value files), meeting the data usage needs of different users in different scenarios such as data analysis, report generation, and audit review. It provides rich and accurate data resources to support continuous improvement of laboratory quality management, scientific decision-making, audit work, and scientific research.

[0046] The system also includes a user management module, which is used to manage the permissions of users of the system. Permissions include data viewing, data analysis, early warning reception, investigation and correction execution, and system configuration permissions.

[0047] The user management module is responsible for comprehensive and detailed access control for all users of the system, ensuring the security, standardization, and orderliness of system operations. Based on the different responsibilities, roles, and work needs of laboratory personnel, users are divided into several different categories, such as system administrators, laboratory testing personnel, quality supervisors, equipment maintenance personnel, and data analysis experts. For each user category, a carefully designed set of permissions is assigned, including but not limited to data viewing permissions (limiting the scope and level of data a user can access, such as testing personnel only being able to view data related to the projects they are responsible for, while quality supervisors can view key data from all testing projects), data analysis permissions (granting access to data analysis tools of varying depths and breadths based on user expertise and work needs, such as data analysis experts being able to perform complex data mining and modeling operations, while testing personnel can only perform simple data statistical analysis), early warning receiving permissions (ensuring that relevant personnel can receive timely and accurate early warning information related to their responsibilities, such as equipment maintenance personnel receiving equipment failure warnings, and quality supervisors receiving quality problem warnings), investigation and rectification execution permissions (clearly stipulating that only personnel with professional skills and qualifications can perform problem investigation and rectification operations to prevent misoperation by non-professionals), and system configuration permissions (granting only system administrators the highest authority to configure and manage key system functions such as system parameter settings, model adjustments, and user permission management). Through this rigorous and reasonable permission management mechanism, unauthorized access and operation are effectively prevented, ensuring the security and integrity of system data and guaranteeing the normal and stable operation of system functions.

[0048] The system can be integrated with other existing management systems in the laboratory to achieve data sharing and collaborative work. Existing management systems include Laboratory Information Management System (LIMS) and Equipment Management System.

[0049] This system is equipped with a comprehensive and highly compatible integration interface, designed to achieve deep and seamless integration with other existing key management systems in the laboratory (such as Laboratory Information Management System (LIMS), Equipment Management System, Environmental Management System, etc.), achieving the goal of efficient data sharing and collaborative work. Through tight integration with the LIMS system, it can obtain rich sample information in real time (such as detailed sample attributes, source information, testing items and standard requirements, etc.), precise testing methods and standard specifications, comprehensive customer information, and testing business process-related data from the LIMS. At the same time, it can promptly feed back the testing quality data monitored by this system (such as test result accuracy assessment data, testing process compliance monitoring data, equipment operation stability analysis data, etc.) to the LIMS system, realizing closed-loop management of the testing process and two-way data flow, optimizing the overall testing business process, and improving testing efficiency and quality. After integration with the equipment management system, it can obtain detailed equipment information in real time (such as equipment model, purchase date, maintenance cycle, repair history, etc.), real-time equipment status information (such as online / offline status, real-time values ​​of operating parameters, fault alarm information, etc.), and equipment maintenance plans and records. Simultaneously, it sends early warning information about equipment malfunctions to the equipment management system, realizing the informatization and intelligentization of equipment management, improving equipment utilization and maintenance management levels, ensuring that equipment is always in good operating condition, and providing reliable support for testing work. When integrated with the environmental management system, it can obtain historical data on the laboratory environment, operating status information of environmental control equipment, etc., and simultaneously feed back the environmental data and evaluation results monitored by this system to the environmental management system, enabling precise control and optimization of the laboratory environment, ensuring that the testing environment always meets requirements.

[0050] Working principle:

[0051] Based on the actual layout of the laboratory, the distribution of equipment, and the characteristics and needs of the testing operations, the installation locations of sensors and data acquisition equipment are carefully planned to ensure comprehensive and accurate acquisition of all required data. Stable, high-precision, and highly compatible sensor types (such as high-precision temperature and humidity sensors, sensitive gas sensors, reliable pressure sensors, and advanced equipment status sensors) and data acquisition equipment (such as multi-interface data loggers and high-performance network switches) are selected to ensure their technical parameters meet the laboratory's monitoring requirements. After equipment installation, meticulous configuration is performed, including precisely setting sensor acquisition parameters (such as sampling frequency, measurement range, and accuracy requirements), device communication protocols (such as common protocols like TCP / IP, Modbus, and OPC), and data acquisition module receiving parameters (such as data buffer size and transmission rate limits). A stable and reliable network connection is configured between the data acquisition module and the cloud platform to ensure secure and efficient data upload.

[0052] The system's functional modules are built on a cloud platform. Based on the specific types, technical requirements, and quality management standards of laboratory testing projects, quality assessment models for data analysis modules suitable for different testing scenarios are constructed (e.g., quality control models for chemical analysis testing, equipment stability assessment models based on physical performance testing, etc.). Key parameters and algorithms of the models are determined through analysis and verification of extensive historical data. Warning thresholds are set for the problem warning module, comprehensively considering the impact of different types of problems on test results, their frequency of occurrence, and the laboratory's risk tolerance. Reasonable threshold ranges for high, medium, and low urgency are established (e.g., a deviation of over ±10% for key equipment parameters is a high-risk threshold, a deviation between ±5% and ±10% is a medium-risk threshold, and a deviation within ±5% is a low-risk threshold). Detailed and specific investigation and rectification strategies and standardized operating procedures are entered into the investigation and rectification execution module (e.g., maintenance steps for different types of equipment failures, methods for correcting operational errors, and procedures for handling environmental problems, etc.) to ensure clear and explicit guidance for handling problems when they occur. The data storage module undergoes reasonable capacity planning (determining appropriate storage capacity based on anticipated laboratory data growth and storage cycle requirements), scientific storage structure design (adopting advanced technologies such as tiered storage and distributed storage to improve storage efficiency and data management convenience), and strict security permission settings (such as setting up multi-layered security protection measures like user authentication, access authorization, and data encryption) to ensure secure data storage and convenient access. User accounts for all laboratory personnel are created in the user management module, and corresponding permissions are precisely assigned according to their responsibilities and work needs (e.g., laboratory management personnel have system configuration, data analysis viewing, and user permission management permissions; testing operators have data viewing, partial operation record entry, and simple data analysis permissions). Simultaneously, the integration interface configuration between the system and existing laboratory management systems (such as LIMS, equipment management system, and environmental management system) is completed to ensure smooth and accurate data exchange and sharing (e.g., configuring data interface formats, data synchronization frequency, and interface security authentication methods).

[0053] Data Acquisition and Transmission: During laboratory testing activities, the data acquisition module obtains data from various data sources according to a preset intelligent acquisition strategy. Environmental data is collected in real time by sensors distributed throughout key locations in the laboratory. For example, temperature sensors collect ambient temperature data every 2 minutes, humidity sensors simultaneously collect humidity data, pressure sensors continuously monitor air pressure changes, and gas sensors detect the concentration of specific gases in real time. Equipment operation data is read in real time through dedicated communication interfaces with instruments and equipment, such as weighing data from a high-precision electronic balance, spectral curve data from a spectrometer, and data on rotation speed, centrifugal force, and running time from a centrifuge.

[0054] In routine laboratory testing, the data acquisition module acts like a sensitive sensory organ, continuously collecting rich and diverse data from various data sources (sensors, instruments, information management systems, etc.) and uploading it to the cloud platform in real time via a secure and efficient data transmission channel in an encrypted manner. The data analysis module on the cloud platform, like an intelligent brain, immediately performs a series of meticulous processes on the newly arrived data, including rigorous data cleaning, scientific preprocessing, and in-depth analysis and mining. It uses a pre-set quality assessment model and advanced analytical algorithms to accurately compare the analysis results with stringent quality standards. Once any anomaly or potential risk is detected, the problem early warning module acts as a timely alarm, quickly activating the warning mechanism and accurately conveying detailed warning information to relevant personnel through various notification methods (SMS, pop-ups, emails, etc.). Upon receiving the warning notification, relevant personnel quickly organize and carry out problem investigation and rectification work according to the clear processes and strategies provided by the investigation and rectification module. Like well-trained detectives, they conduct in-depth investigations and comprehensive analyses of the problem, identify the root cause, and take targeted corrective measures. Throughout the process, the data storage module acts as a faithful recorder, comprehensively recording and storing all generated data, including raw data, analysis process data, early warning records, corrective measures, and results. This data not only provides strong support for solving current problems but also lays a solid foundation for subsequent quality retrospective analysis, statistical analysis, continuous improvement, and decision-making. By continuously cycling through this process, the system achieves continuous monitoring, real-time feedback, and timely correction of laboratory testing quality, driving continuous improvement in laboratory quality management and forming a virtuous cycle of quality management.

Claims

1. A cloud-based laboratory quality monitoring and verification system, characterized in that, It includes a data acquisition module, a data analysis module, a problem early warning module, a problem investigation and execution module, and a data storage module. Each module is connected to the cloud platform, among which: The data acquisition module is used to collect equipment operation data, environmental monitoring data, test sample information and test personnel operation records during the laboratory testing process, and transmit them to the cloud platform. The data analysis module analyzes the collected data on the cloud platform. Through data mining technology, statistical analysis methods, and a preset quality assessment model, it evaluates the compliance of the testing process, the accuracy and reliability of the data, the stability of the equipment, and the suitability of the environmental conditions, and identifies potential quality problems and risk factors. The problem warning module issues a warning message when the detection quality has potential problems or risks exceeding a preset threshold, based on the results of the data analysis module. After receiving the warning information, the investigation and rectification execution module guides relevant personnel to investigate, analyze, and correct the problem according to the preset investigation and rectification strategy and standard operating procedures, and supervises the execution process. The data storage module is used to store information such as the collected raw data, analysis process data, early warning records, investigation and correction measures and results; The data acquisition module collects data by interfacing with sensors, instruments, and information management systems in the laboratory. The data analysis module cleans and preprocesses the data before analysis, removing outliers and erroneous data. The problem warning module sends warning information to relevant personnel via SMS. The investigation and rectification module feeds back the investigation process and results to the cloud platform after correcting the problem. The data storage module stores the feedback information, which includes the root cause analysis of the problem, the corrective measures taken, the personnel involved, and the execution time.

2. The cloud-based laboratory quality monitoring and verification system according to claim 1, characterized in that, The data storage module categorizes and stores data according to data type, time sequence, and detection items, and provides data query and export functions. Data query supports multi-condition combination queries.

3. The cloud-based laboratory quality monitoring and verification system according to claim 1, characterized in that, The cloud platform has data encryption transmission capabilities.

4. The cloud-based laboratory quality monitoring and verification system according to claim 1, characterized in that, The system also includes a user management module for managing the permissions of system users. Permissions include data viewing, data analysis, early warning reception, investigation and correction execution, and system configuration permissions.

5. The cloud-based laboratory quality monitoring and verification system according to claim 1, characterized in that, The data analysis module can be adjusted according to changes in laboratory testing items and quality requirements.

6. The cloud-based laboratory quality monitoring and verification system according to claim 1, characterized in that, The system can be integrated with other existing management systems in the laboratory to achieve data sharing and collaborative work.

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