Checking and correcting system for laboratory quality monitoring based on cloud platform

Abstract

The invention belongs to the technical field of quality monitoring, and particularly relates to a checking and correcting system for laboratory quality monitoring based on a cloud platform, which is characterized by comprising a data acquisition module, a data analysis module, a problem early warning module, a checking and correcting execution module and a data storage module, the data acquisition module is used for acquiring equipment operation data, environment monitoring data, detection sample information and detection personnel operation records in a laboratory detection process and transmitting the data to the cloud platform; by integrating the advanced information technology, an intelligent quality management platform integrating data acquisition, analysis, early warning, checking and correcting and storage is constructed, the whole-process, real-time and precise monitoring and management of the laboratory detection quality are realized, the laboratory quality management efficiency is effectively improved, and the high-quality output of the detection result is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of quality monitoring, and in particular relates to a cloud platform-based laboratory quality monitoring and correction system. Background Art

[0002] In today's booming global trade and scientific research, laboratories play a vital role. As the guardian of national security, customs laboratories are responsible for the inspection and testing of imported and exported goods. The accuracy and reliability of their test results are directly related to national interests, consumer rights and the fairness of international trade. However, the traditional laboratory quality management model faces many severe challenges;

[0003] Data is stored in different systems and devices in a scattered manner, lacking an effective integration mechanism, which makes it difficult to ensure the integrity and reliability of data. Data processing mostly relies on manual operations, which is inefficient and error-prone, making it difficult to achieve in-depth data mining and analysis, and hindering the timely discovery of potential quality issues.

[0004] Therefore, a cloud platform-based laboratory quality monitoring and correction system is designed to solve the above problems. Summary of the invention

[0005] In order to solve the problems raised in the above background technology, the present invention provides a cloud platform-based laboratory quality monitoring and correction system, which can effectively solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a cloud platform-based laboratory quality monitoring and correction system, characterized in that it includes a data acquisition module, a data analysis module, a problem warning module, a correction execution module and a data storage module, each module is 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 laboratory testing, and transmit them to the cloud platform;

[0008] The data analysis module analyzes the collected data on the cloud platform, and 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 through data mining technology, statistical analysis methods, and preset quality assessment models, and identifies potential quality problems and risk factors;

[0009] The problem warning module issues a warning message based on the results of the data analysis module when there are potential problems with the detection quality or the risk exceeds a preset threshold;

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

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

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

[0013] As a preferred cloud platform-based laboratory quality monitoring and correction system of the present invention, the data analysis module cleans and preprocesses the data before analyzing the data to remove abnormal values ​​and erroneous data.

[0014] As a preferred embodiment of the cloud platform-based laboratory quality monitoring and correction system of the present invention, the warning information of the problem warning module is conveyed to relevant personnel via SMS notification.

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

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

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

[0018] As a preferred cloud platform-based laboratory quality monitoring inspection and correction system of the present invention, the system also includes a user management module for performing authority management on personnel using the system, and the authority includes data viewing, data analysis, early warning reception, inspection and correction execution, and system configuration authority.

[0019] As a preferred cloud platform-based laboratory quality monitoring and correction 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 cloud platform-based laboratory quality monitoring and correction 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 invention has the following beneficial effects: the invention has a scientific and reasonable structure and is safe and convenient to use:

[0022] By integrating advanced information technology, we build an intelligent quality management platform that integrates data collection, analysis, early warning, investigation and correction, and storage, to achieve full-process, real-time, and precise monitoring and management of laboratory testing quality, effectively improve laboratory quality management efficiency, and ensure high-quality output of test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0024] Figure 1 A schematic block diagram of the system of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example: Figure 1 As shown, the present invention provides a technical solution, a cloud platform-based laboratory quality monitoring and correction system, including a data acquisition module, a data analysis module, a problem warning module, a correction 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 and 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 through data mining technology, statistical analysis methods, and preset quality assessment models, and identifies potential quality issues and risk factors.

[0029] The problem warning module issues warning information based on the results of the data analysis module when there are potential problems with the detection quality or the risk exceeds the preset threshold;

[0030] After receiving the early 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;

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

[0032] The data acquisition module realizes data acquisition by connecting with sensors, instruments and equipment in the laboratory and the information management system. The sensor types 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 function to ensure the security of data transmission between the acquisition module and the cloud platform and between modules within the cloud platform;

[0034] As the information source of the system, this module is responsible for establishing extensive connections with various data sources within the laboratory to ensure the comprehensiveness and timeliness of data acquisition. Through seamless docking with various types of sensors (such as temperature, humidity, pressure, gas sensors, and equipment status sensors, etc.), laboratory environmental parameters and equipment operating status information are collected in real time. At the same time, it interacts deeply with the communication interfaces of various instruments and equipment to obtain detailed data during the operation of the equipment, such as equipment working parameters, operating mode, fault code and other key information. In addition, it can also be efficiently integrated with the laboratory information management system (LIMS) to simultaneously extract comprehensive information of the test samples (including sample number, source, nature, test requirements, etc.) and the operation records of the test personnel (covering operation steps, time nodes, operator identification, etc.). After the necessary preliminary processing (such as format conversion, data verification, etc.), the collected data is uploaded to the cloud platform in an encrypted manner through a secure and reliable data transmission channel to ensure the confidentiality, integrity and availability of the data during transmission.

[0035] The data analysis module cleans and preprocesses the data before analyzing it to remove outliers and erroneous data. The preprocessing methods include data filtering, missing value filling and data standardization.

[0036] The data analysis module can update and optimize the quality assessment model according to 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 strict cleaning and preprocessing operations on the data from the data acquisition module. Using advanced data filtering algorithms, it accurately identifies and removes obviously abnormal data points, effectively avoiding the interference of erroneous data on subsequent analysis. In response to the problem of missing data, scientific and reasonable missing value filling methods (such as mean filling based on statistical laws, interpolation methods, or specific filling strategies based on data distribution characteristics, etc.) are adopted to ensure the continuity and integrity of the data. At the same time, the data is standardized and converted to meet the input requirements of the preset analysis model, thereby improving the accuracy and comparability of the analysis results.

[0038] Relying on powerful data mining technology, 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 the standard operating procedure (SOP), it carefully determines whether each testing link is strictly carried out in accordance with the specifications, including strict inspections of sample processing steps, testing instrument operation procedures, data recording specifications, etc. Comprehensively evaluate the accuracy and reliability of the data, and use a variety of methods such as data distribution analysis, repeatability test, and comparative analysis with historical data to ensure that the data truly and accurately reflects the actual situation of the test, and effectively identify potential deviations and abnormal fluctuations in the data. Closely monitor the stability of the equipment, based on the dynamic trend analysis of equipment operation data, fluctuation assessment of key performance indicators and the application of fault prediction models, timely discover potential risks in equipment operation, predict possible fault points of the equipment, and provide a strong basis for preventive maintenance. At the same time, comprehensively evaluate the suitability of environmental conditions, based on the specific requirements of different test items for environmental parameters (such as temperature, humidity, air pressure, light, etc.), accurately judge whether the current environmental state meets the guarantee conditions for detection precision and accuracy, and timely discover the potential impact of environmental factors on the test results. Through multi-dimensional and in-depth data analysis, potential quality problems and risk factors can be accurately identified, providing scientific and reliable decision-making support for subsequent early warning and rectification.

[0039] The warning information of the problem warning module is conveyed to relevant personnel through one or more methods including SMS notification, system pop-up window, and email reminder, and the warning information includes problem description, impact scope, urgency and recommended measures;

[0040] The problem warning module closely follows the evaluation results of the data analysis module to conduct real-time dynamic monitoring of the inspection quality status. Once a potential quality problem is detected or the risk indicator exceeds the preset threshold, the warning mechanism is immediately activated. The warning information is promptly and accurately conveyed to relevant personnel through a variety of flexible methods (such as SMS notifications, system pop-ups, email reminders, etc.) to ensure the efficiency and completeness of information transmission. The early warning information is rich 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 test link, the laboratory area, etc.; the manifestation of the problem, such as the specific phenomenon of equipment failure, the characteristic pattern of data anomalies, etc.), clearly indicating the scope of the problem that may be affected (the specific test items involved, the sample batches affected, the test results that may be affected, etc.), and clearly marking the urgency of the problem (divided into three levels: high, medium and low, so that relevant personnel can quickly judge the severity of the problem and take corresponding emergency response measures). At the same time, it provides targeted and operational preliminary recommended measures (such as preliminary troubleshooting steps, sample handling suggestions in emergency situations, environmental parameter adjustment directions, etc.), helping relevant personnel to quickly understand the problem overview and take effective preliminary response actions at the first time, so as to minimize the adverse effects that the problem may bring.

[0041] After correcting the problem, the rectification execution module will feed back the rectification process and results to the cloud platform. The data storage module will store the feedback information, which includes the root cause analysis of the problem, the corrective measures taken, the executors and the execution time.

[0042] After receiving the early warning information, the investigation and correction execution module responds quickly and starts an efficient problem investigation and correction procedure. It strictly follows the detailed investigation and correction strategies and standardized operating procedures that are carefully set in advance to provide clear and unambiguous action guidelines for relevant personnel. When a problem occurs, it can quickly organize and coordinate relevant personnel with professional skills and rich experience to form an investigation and correction team to conduct in-depth and detailed investigation and research on the problem according to the established process. Through comprehensive collection and in-depth analysis of relevant information, including detailed historical operation data of the equipment (covering long-term operation trends, previous fault records, maintenance records, etc.), recent equipment maintenance operation records, detailed operation process recollections of operators (obtained through operation records, video surveillance playback, personnel interviews, etc.) and in-depth clues provided by the data analysis module, a variety of analysis methods and tools are used in combination to accurately determine the root cause of the problem. The root cause may involve aging and wear of equipment components, sensor calibration deviations, insufficient operator skills or violation of operating procedures, sudden abnormal changes in environmental factors, improper application of detection methods or standards, and other aspects.

[0043] For different types of root causes, quickly formulate and decisively implement practical and targeted corrective measures. For example, in the case of equipment failure, immediately contact professional equipment maintenance personnel for emergency repairs or timely replacement of faulty parts, and after the repair is completed, strictly follow the equipment calibration specifications to conduct comprehensive calibration and performance testing to ensure that the equipment returns to normal operation and the performance is stable and reliable; if it is caused by operator error, timely conduct on-site one-on-one training and detailed guidance for relevant operators, correct the wrong operating behavior, and record the incident in detail in the personal operation training file as an important reference for subsequent training and assessment; if the problem is caused by environmental factors, quickly adjust the parameter settings of the environmental control equipment to restore the environmental conditions to the normal range that meets the test requirements as soon as possible, and conduct a comprehensive assessment of the test items affected by the environment, and decide whether to re-test based on the assessment results (if necessary, re-formulate the test plan and strictly implement it). During the entire investigation and correction process, strictly supervise the implementation progress and results to ensure that each corrective measure is effectively implemented and the problem is thoroughly solved. At the same time, pay attention to summarizing experience and lessons to prevent similar problems from happening again. After the rectification work is completed, a detailed and complete rectification report will be generated in a timely manner, which will comprehensively summarize the problem discovery process, in-depth analysis process, effective processing process and the final results. The rectification report will be fed back to the cloud platform in a timely manner 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 stores data by category according to data type, time sequence and test 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 builds a safe, reliable, efficient and convenient data storage center on the cloud platform, which is responsible for the long-term storage and effective management of various types of data during the operation of the system. It uses advanced data classification and storage technology to finely classify and store data according to multiple key dimensions such as data type (such as environmental monitoring data, equipment operation data, test sample data, test personnel operation data, data analysis result data, early warning record data, and correction process data, etc.), time sequence (accurate sorting based on data collection time or event occurrence time) and test items (organically classifying and storing various types of data related to the same test item), ensuring that the data storage structure is clear and easy to query and retrieve. At the same time, high-intensity data encryption technology is used to encrypt the stored data to effectively ensure the security and confidentiality of the data and prevent data leakage and illegal access. It provides powerful and easy-to-use data query and export functions, supports users to flexibly combine queries according to multiple conditions (such as single or multiple combination queries by time range, test item name, equipment number, problem type, etc.), and can export query results in a variety of common formats (such as Excel spreadsheets, PDF documents, CSV comma-separated value files, etc.) conveniently and quickly to meet the data usage needs of different users in different scenarios such as data analysis, report generation, audit review, etc., and provide rich and accurate data resource support for the continuous improvement of laboratory quality management, scientific decision-making, audit work, and scientific research and analysis.

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

[0047] The user management module is responsible for comprehensive and detailed authority management of all personnel using the system to ensure the safety, standardization and orderliness of system operations. According to the different responsibilities, roles and work requirements of laboratory personnel, users are divided into several different categories, such as system administrators, laboratory testers, quality supervisors, equipment maintenance personnel, data analysis experts, etc. For each user category, a matching set of permissions is carefully assigned, including but not limited to data viewing permissions (limiting the scope and level of data accessible to users, such as testers can only view the relevant data of the test project they are responsible for, and quality supervisors can view the key data of all test projects, etc.), data analysis permissions (granting permissions to use data analysis tools of different depths and breadths according to the user's professional capabilities and work needs, such as data analysis experts can perform complex data mining and modeling operations, while testers can only perform simple data statistical analysis), warning receiving permissions (ensuring that relevant responsible personnel can receive problem warning information related to their responsibilities in a timely and accurate manner, such as equipment maintenance personnel receiving equipment failure warnings, quality supervisors receiving quality problem warnings, etc.), investigation and correction execution permissions (clearly stipulating that only personnel with professional skills and qualifications can perform problem investigation and correction operations to prevent non-professionals from operating incorrectly) and system configuration permissions (only granting system administrators the highest permissions to configure and manage key system functions such as system parameter settings, model adjustments, and user permission management). Through a rigorous and reasonable permission management mechanism, unauthorized access and operation can be effectively prevented, the security and integrity of system data can be guaranteed, and the normal and stable operation of system functions can be ensured.

[0048] The system can be integrated with other 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 integrated interface, which is designed to achieve deep and seamless integration with other key management systems already in the laboratory (such as laboratory information management system (LIMS), equipment management system, environmental management system, etc.), and achieve the efficient goal of data sharing and collaborative work. Through close integration with the LIMS system, it can obtain rich test sample information in LIMS in real time (such as detailed sample attributes, source information, test items and standard requirements, etc.), precise test methods and standard specifications, comprehensive customer information and test business process related data. At the same time, the test quality data monitored by this system (such as test result accuracy evaluation data, test process compliance monitoring data, equipment operation stability analysis data, etc.) is fed back to the LIMS system in a timely manner, realizing closed-loop management of the test process and two-way flow of data, optimizing the overall test business process, and improving test efficiency and quality. After integration with the equipment management system, detailed information of the equipment (such as equipment model, purchase date, maintenance cycle, maintenance 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 can be obtained in real time. At the same time, abnormal equipment operation warning information is sent to the equipment management system to realize the informatization and intelligence of equipment management, improve equipment utilization and maintenance management level, ensure that the equipment is always in good operating condition, and provide reliable guarantee for testing work. Integration with the environmental management system can obtain historical data of the laboratory environment, operating status information of environmental control equipment, etc. At the same time, the environmental data and evaluation results monitored by this system are fed back to the environmental management system to achieve precise regulation and optimization of the laboratory environment and ensure that the testing environment always meets the requirements.

[0050] Working principle:

[0051] According to the actual layout of the laboratory, the distribution of equipment, and the characteristics and needs of the testing business, carefully plan the installation location of sensors and data acquisition equipment to ensure that all types of required data can be collected comprehensively and accurately. Select sensor types with stable performance, high accuracy and strong compatibility (such as high-precision temperature and humidity sensors, sensitive gas sensors, reliable pressure sensors, advanced equipment status sensors, etc.) and data acquisition equipment (such as data collectors with multiple interfaces, high-performance network switches, etc.) to ensure that their technical parameters meet the laboratory monitoring requirements. After completing the equipment installation, carry out detailed configuration work, including accurately setting the sensor's acquisition parameters (such as sampling frequency, measurement range, accuracy requirements, etc.), equipment communication protocols (such as TCP / IP, Modbus, OPC and other common protocols) and the receiving parameters of the data acquisition module (such as data cache size, transmission rate limit, etc.). Configure a stable and reliable network connection between the data acquisition module and the cloud platform to ensure that the data can be uploaded to the cloud platform safely and efficiently.

[0052] Build the functional modules of the inspection and correction system on the cloud platform. According to the specific types, technical requirements and quality management specifications of laboratory testing projects, build quality assessment models for data analysis modules suitable for different testing scenarios (such as quality control models for chemical analysis testing, equipment stability assessment models based on physical performance testing, etc.), and determine the key parameters and algorithms of the model through analysis and verification of a large amount of historical data. Set the warning threshold of the problem warning module, comprehensively consider the impact of different types of problems on the test results, the frequency of occurrence and the risk tolerance of the laboratory, and reasonably set the threshold range of high, medium and low urgency (such as the deviation of key equipment parameters exceeding ±10% is a high risk threshold, the deviation between ±5%-±10% is a medium risk threshold, and the deviation within ±5% is a low risk threshold, etc.). Enter detailed and specific inspection and correction strategies and standardized operating procedures (such as maintenance steps for different types of equipment failures, correction methods for operating errors, and environmental problem handling procedures) in the inspection and correction execution module to ensure clear and clear handling instructions when problems occur. Carry out reasonable capacity planning for the data storage module (determine the appropriate storage capacity based on the expected growth of laboratory data and storage cycle requirements), scientific storage structure design (adopt advanced technologies such as hierarchical storage and distributed storage to improve storage efficiency and data management convenience) and strict security permission settings (such as setting up multi-level security protection measures such as user identity authentication, access authorization, and data encryption) to ensure the safe storage and convenient access of data. Create user accounts for all laboratory personnel in the user management module, and accurately assign corresponding permissions according to their responsibilities and work needs (such as laboratory managers have system configuration, data analysis and viewing, and user permission management permissions, and detection operators have data viewing, partial operation record entry, and simple data analysis permissions, etc.). At the same time, complete the integrated interface configuration between the system and the laboratory's existing management systems (such as LIMS, equipment management system, environmental management system, etc.) to ensure that data can be interactively shared smoothly and accurately (such as configuring data interface format, data synchronization frequency, interface security authentication method, etc.).

[0053] Data collection and transmission: During laboratory testing activities, the data collection module obtains data from various data sources according to the preset intelligent collection strategy. Environmental data is collected in real time through sensors distributed in key locations in the laboratory. For example, the temperature sensor collects the ambient temperature every 2 minutes, the humidity sensor collects humidity data synchronously, the pressure sensor continuously monitors the air pressure changes, and the gas sensor detects the concentration of specific gases in real time. Equipment operation data is read in real time through a dedicated communication interface with the instrument and equipment, such as obtaining weighing data from a high-precision electronic balance, obtaining spectral curve data from a spectrometer, and obtaining speed, centrifugal force, and running time data from a centrifuge;

[0054] In the daily testing work of the laboratory, the data acquisition module is like a keen sensory organ, continuously collecting rich and diverse data from various data sources (sensors, instruments and equipment, information management systems, etc.), and uploading them to the cloud platform in real time in an encrypted manner through a secure and efficient data transmission channel. The data analysis module on the cloud platform is like an intelligent brain, immediately performing a series of fine processing on the newly arrived data, including strict data cleaning, scientific preprocessing, and in-depth analysis and mining. It uses the preset quality assessment model and advanced analysis algorithm to accurately compare the analysis results with strict quality standards. Once any abnormal situation or potential risk is detected, the problem warning module is like a timely alarm, quickly starting the warning mechanism, and accurately conveying detailed warning information to the relevant responsible personnel through a variety of notification methods (SMS, pop-up windows, emails, etc.). After receiving the warning notification, the relevant personnel quickly organize and carry out problem investigation and correction work according to the clear process and strategy provided by the investigation and correction execution module. Like well-trained detectives, they conduct in-depth investigation and comprehensive analysis of the problem, find out the root cause of the problem, and take targeted corrective measures. Throughout the entire process, the data storage module acts like a faithful recorder, completely recording and storing all generated data, including raw data, analysis process data, early warning records, investigation and correction measures and results, etc. These data not only provide strong support for solving current problems, but also lay a solid foundation for subsequent quality traceability, statistical analysis, continuous improvement and decision-making. By continuously circulating this process, the system realizes continuous monitoring, real-time feedback and timely correction of laboratory testing quality, promotes the continuous improvement of laboratory quality management level, and forms a benign quality management closed loop.

Claims

1. A cloud platform-based laboratory quality monitoring and correction system, characterized in that: It includes data collection module, data analysis module, problem warning module, investigation and correction execution module and data storage module. Each module is connected to the cloud platform, including: The data acquisition module is used to collect equipment operation data, environmental monitoring data, test sample information and test personnel operation records during laboratory testing, and transmit them to the cloud platform; The data analysis module analyzes the collected data on the cloud platform, and 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 through data mining technology, statistical analysis methods, and preset quality assessment models, and identifies potential quality problems and risk factors; The problem warning module issues a warning message based on the results of the data analysis module when there are potential problems with the detection quality or the risk exceeds a preset threshold; After receiving the early warning information, the investigation and correction execution module guides relevant personnel to investigate, analyze, and correct the problem according to the preset investigation and correction strategy and standard operating procedures, and supervises the execution process; The data storage module is used to store information such as collected original data, analysis process data, early warning records, and correction measures and results.

2. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: The data acquisition module realizes data acquisition by connecting with sensors, instruments and equipment and information management system in the laboratory.

3. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: The data analysis module cleans and preprocesses the data before analyzing it to remove outliers and erroneous data.

4. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: The warning information of the problem warning module is conveyed to relevant personnel via SMS notification.

5. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: After correcting the problem, the correction execution module feeds back the correction process and results to the cloud platform, and the data storage module stores the feedback information, which includes the root cause analysis of the problem, the corrective measures taken, the executors and the execution time.

6. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: The data storage module classifies 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 query.

7. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: The cloud platform has a data encryption transmission function.

8. The cloud platform-based laboratory quality monitoring and correction system according to claim 1 is characterized in that: The system also includes a user management module for managing the permissions of people using the system, including permissions for data viewing, data analysis, early warning reception, inspection and correction execution, and system configuration.

9. The cloud platform-based laboratory quality monitoring and correction 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.

10. The cloud platform-based laboratory quality monitoring and correction 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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