Urine sampling device and sampling method for endocrinology department

Abstract

The invention discloses a urine sampling device for the endocrinology department and a sampling method, and belongs to the technical field of medical analysis. Comprising the following modules: an automatic sampling control module for realizing automatic control of the whole urine sampling process, ensuring that sampling accords with a standard process, and supporting automatic sub-packaging and storage management of samples; the sample quality control module is used for monitoring the physical characteristics of the samples in real time, automatically evaluating the effectiveness of the samples in combination with environmental parameter monitoring, and identifying and marking abnormal samples; the data acquisition and processing module is used for comprehensively acquiring physiological data, nutrition metabolism data, operation process data and sample tracing information, and performing cleaning, standardization and fusion processing to generate a standardized data set; the intelligent diagnosis and analysis module is used for intelligently detecting abnormal physiological indexes and carrying out disease risk prediction and early warning; and the man-machine interaction module provides a visual operation interface and integrates multi-level user authority management, automatic report generation and remote monitoring.

Description

Technical Field

[0001] The present application relates to the field of medical analysis technology, and more specifically, to a urine sampling device and a sampling method for endocrinology. Background Art

[0002] With the continuous development of medical diagnostic technology, urine testing, as a common means of monitoring physiological indicators, is widely used in the early screening and diagnosis of endocrine diseases, nutritional metabolic disorders, kidney diseases and other fields. The quality of urine sampling directly affects the accuracy of the test results. Therefore, how to efficiently and accurately collect urine samples and ensure the stability of sample quality has become an important research direction in the field of endocrinology diagnosis.

[0003] Currently, urine sampling usually relies on manual operation, which is a cumbersome process and easily affected by human factors. In traditional sampling methods, sample collection, packaging, storage and other links are often operated by different personnel, which is prone to cross-contamination, sample loss, irregular operation and other problems. In addition, urine samples may be affected by environmental factors, sampling time, flow rate and other variables during the sampling process. These factors will affect the quality and representativeness of the sample, and thus affect the accuracy of subsequent testing. Although some existing equipment attempts to improve sampling efficiency through automation or semi-automation, there are still technical bottlenecks such as incomplete sample quality monitoring, inaccurate sampling process control, and low data processing and analysis efficiency.

[0004] In terms of quality control of urine sampling, although there are some urine testing solutions based on sensor technology that can monitor certain basic physical properties of urine, the existing technology still lacks a system for comprehensive monitoring and intelligent analysis of urine samples, especially in terms of dynamically adjusting urine flow rate, accurately packaging samples, real-time analysis of sample quality, and rapid identification of abnormal samples. In addition, the existing urine sampling equipment lacks association with patient health information and historical data, and cannot perform personalized detection and early warning analysis based on individual differences.

[0005] In summary, how to achieve fully automated control of the urine sampling process, ensure the high quality and stability of the samples, and combine data collection, processing and intelligent analysis to improve the accuracy and efficiency of urine testing in the endocrinology department has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to overcome a series of defects in the prior art, the purpose of this application is to provide a urine sampling device for endocrinology department, including the following modules:

[0007] Automatic sampling control module realizes automatic control of the whole process of urine sampling, ensures that the sampling complies with the standard process, and supports automatic packaging and storage management of samples;

[0008] The sample quality control module monitors the physical characteristics of the sample in real time and automatically evaluates the sample validity in combination with environmental parameter monitoring, and identifies and marks abnormal samples;

[0009] The data collection and processing module comprehensively collects physiological data, nutritional metabolism data, operation process data and sample traceability information, and performs cleaning, standardization and fusion processing to generate a standardized data set;

[0010] Intelligent diagnosis and analysis module, based on standardized data sets, combined with knowledge graphs of endocrine diseases and nutritional metabolic abnormalities, multi-level early warning mechanisms and personalized thresholds, intelligently detects abnormal physiological indicators and performs disease risk prediction and early warning;

[0011] The human-computer interaction module provides an intuitive operation interface, integrating multi-level user authority management, automatic report generation and remote monitoring.

[0012] Preferably, the automatic sampling control module includes the following components:

[0013] The sample detection unit can quickly detect the basic parameters of urine samples as a pre-processing step for sample quality control;

[0014] The flow control unit accurately adjusts the urine flow during the sampling process to ensure that the sampling volume meets the packaging requirements;

[0015] The packaging control unit automatically distributes the collected urine samples to different collection containers according to the preset program, improving the packaging efficiency and accuracy;

[0016] Disinfection control unit, which realizes automatic cleaning and disinfection of sampling pipelines and collection containers, ensuring the sterility of the sampling environment;

[0017] The main control unit coordinates the working sequence of each functional unit to ensure the automated execution of the sampling process.

[0018] Preferably, the sample quality control module includes the following components:

[0019] The environmental monitoring unit is responsible for real-time collection and monitoring of key parameters of the sampling environment, ensuring the stability of environmental conditions through closed-loop control, and providing a reliable environmental basis for sample analysis;

[0020] A multispectral sensor unit collects the spectral characteristics of the sample through a high-precision multispectral sensor array to quantitatively characterize the physical properties and nutritional components of the sample;

[0021] AI visual analysis unit, which integrates deep learning algorithms and computer vision technology to perform real-time image analysis of samples, including morphological feature extraction, size measurement, and colorimetric analysis;

[0022] The sample validity assessment unit conducts automated validity assessment of samples based on preset quality standards by comprehensively analyzing multispectral data and visual features to ensure that the sample quality meets the requirements of endocrinology and nutrition testing;

[0023] The abnormal sample identification unit marks and classifies samples that deviate from the normal range through real-time comparison with the standard sample feature library, and supports automatic elimination mechanism;

[0024] The sample feature analysis unit integrates environmental monitoring, spectral sensor and visual analysis data to generate standardized sample feature descriptions and transmit structured data to the data acquisition and processing module;

[0025] The feedback and adjustment unit adjusts the sampling parameters and environmental conditions in real time through a closed-loop control algorithm based on the sample quality assessment results to achieve dynamic optimization of the sampling process.

[0026] Preferably, the data acquisition and processing module includes the following components:

[0027] The physiological data acquisition unit uses a high-precision sensor array to collect and digitize the patient's key physiological indicators and nutritional metabolic indicators in real time to ensure the timeliness and accuracy of the data;

[0028] The operation process and traceability unit uniformly records the operation steps, timestamps and operator information of the sampling process, and realizes the traceability management of the sample throughout its life cycle through RFID tags or barcode technology;

[0029] The data preprocessing unit performs noise reduction, filtering and outlier detection on the raw data, improves the signal quality through data cleaning, and lays a reliable data foundation for subsequent analysis;

[0030] The data integration unit integrates physiological data, nutritional metabolism data, operation process data, sample traceability information, and sample feature analysis results into a unified multidimensional data set;

[0031] The data verification and consistency check unit implements multi-level data quality control, including data integrity verification, logical relationship verification and cross-validation, to ensure data consistency and reliability;

[0032] The result output unit converts the verified data into a standard format, generates a structured analysis result data package, and supports subsequent module calls or report generation.

[0033] Preferably, the intelligent diagnosis and analysis module includes the following components:

[0034] The data analysis unit conducts in-depth mining of standardized data to extract the characteristic indicator matrix and time series change patterns of endocrine diseases and nutritional metabolic abnormalities;

[0035] The knowledge graph unit constructs a multi-level semantic network, integrates the clinical manifestations, diagnostic criteria and treatment plan knowledge of endocrine diseases and nutritional metabolic abnormalities, forms a structured knowledge reasoning system, and supports intelligent diagnostic decision-making;

[0036] The abnormality detection and diagnosis unit combines personalized thresholds and disease models to automatically identify and diagnose physiological and nutritional metabolic abnormalities and detect potential disease risks in advance;

[0037] The multi-level early warning mechanism unit generates early warning information of different risk levels based on real-time detection results, and provides targeted response strategies and intervention suggestions;

[0038] Personalized threshold adjustment unit, which dynamically optimizes the diagnostic threshold according to the individual characteristics and nutritional status of the patient, improving the accuracy and personalization of the test;

[0039] The risk prediction and trend analysis unit conducts a prospective assessment of disease risks based on the historical trends and real-time monitoring results of physiological data and nutritional metabolism data, and generates predictions of disease development trends and recommendations for the best time to intervene.

[0040] Preferably, the human-computer interaction module includes the following components:

[0041] User interface design unit, providing an intuitive and easy-to-operate interface, supporting touch screen, voice control and graphical operation, ensuring that users can efficiently complete equipment operation and data viewing;

[0042] Multi-level user rights management unit, which assigns access rights according to user roles to ensure data security and operational compliance;

[0043] Automatic report generation unit, based on template engine technology, automatically integrates endocrine test data, nutritional assessment data, diagnostic results and trend analysis to generate structured medical reports;

[0044] Remote monitoring and management unit, which realizes remote monitoring and management of equipment working status, including remote diagnosis, real-time update and fault handling, to ensure efficient operation and timely maintenance of equipment;

[0045] Interactive feedback and prompt unit provides real-time operation feedback and system prompts to guide users in every step of operation, ensuring operation accuracy and smooth process;

[0046] The data visualization unit uses visualization technology to display the collected and processed physiological data, nutritional metabolism data, early warning information and analysis results, helping users to quickly understand and evaluate their health status.

[0047] The purpose of the present application is also to provide a urine sampling method for endocrinology, comprising the following steps:

[0048] Automatically perform environmental parameter detection and equipment self-checking, including temperature and humidity monitoring of the sampling area, confirmation of equipment disinfection status, and operation status inspection of each functional module, to ensure that the entire sampling environment and equipment are in the best working condition;

[0049] Enter the patient's basic information through the human-computer interaction interface and scan the patient's wristband to automatically generate a unique sample number and QR code label, and retrieve the patient's historical sampling records, nutritional assessment records and personalized parameter settings;

[0050] The patient completes the urine sample collection according to the device prompts, and the urine flow rate is adjusted in real time to ensure the accuracy of the sampling volume, while the basic urine parameters are quickly tested;

[0051] Conduct real-time quality assessment of collected samples, including physical property analysis, morphological feature extraction, and abnormal sample identification, to ensure that the endocrine and nutritional assessment of the samples meet the inspection standards;

[0052] Qualified samples are automatically packaged into different collection containers according to the preset plan. Each container is affixed with a corresponding QR code label to simultaneously record the packaging information and sample traceability data;

[0053] Integrate all sampling process data, including environmental parameters, sample characteristics and operation records, and transmit them to the data processing module after standardization, while conducting preliminary detection and risk assessment of endocrine abnormalities and nutritional metabolic abnormalities;

[0054] After sampling is completed, a standardized report is automatically generated, and the cleaning and disinfection procedures of the sampling pipeline and container are executed through the disinfection control unit to prepare for the next sampling.

[0055] Compared with the prior art, this application has the following beneficial effects:

[0056] This application achieves efficient and accurate urine sampling, packaging, storage and traceability management through automatic sampling control, real-time sample quality control, intelligent data analysis and personalized threshold adjustment, ensuring early identification and risk prediction of endocrine and nutritional metabolic abnormalities; in addition, it also integrates a variety of intelligent technologies, such as AI visual analysis, spectral sensors, deep learning diagnosis and multi-level early warning mechanisms, which can optimize sampling parameters in real time and provide personalized and accurate health intervention recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the structure of a urine sampling device for endocrinology disclosed in an embodiment of the present application;

[0058] Figure 2This is a schematic diagram of the flow of a urine sampling method for endocrinology disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0060] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0061] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0062] like Figure 1 As shown, a urine sampling device for endocrinology department includes the following modules:

[0063] Automatic sampling control module realizes automatic control of the whole process of urine sampling, ensures that the sampling complies with the standard process, and supports automatic packaging and storage management of samples;

[0064] The sample quality control module monitors the physical characteristics of the sample in real time and automatically evaluates the sample validity in combination with environmental parameter monitoring, and identifies and marks abnormal samples;

[0065] The data collection and processing module comprehensively collects physiological data, nutritional metabolism data, operation process data and sample traceability information, and performs cleaning, standardization and fusion processing to generate a standardized data set;

[0066] Intelligent diagnosis and analysis module, based on standardized data sets, combined with knowledge graphs of endocrine diseases and nutritional metabolic abnormalities, multi-level early warning mechanisms and personalized thresholds, intelligently detects abnormal physiological indicators and performs disease risk prediction and early warning;

[0067] The human-computer interaction module provides an intuitive operation interface, integrating multi-level user authority management, automatic report generation and remote monitoring.

[0068] In this embodiment, the automatic sampling control module is one of the core components of the urine sampling device, which aims to ensure the standardization and normalization of the urine sampling process through automated means, greatly improving the sampling efficiency and accuracy. Its main function is to accurately control the entire sampling process. Through a highly integrated hardware and software system, it can automatically perform steps such as sample collection, packaging and storage. Specifically, the module detects the state of the sampling bottle through a sensor, automatically starts the sampling process, ensures that the amount of urine collected each time meets the preset standard, and avoids the impact of errors caused by manual intervention or irregular operations. In addition, the module can also automatically distribute samples to different storage containers according to preset storage conditions, and automatically manage according to parameters such as sample type and storage requirements. Through this intelligent automatic control mechanism, it can ensure that every link meets laboratory operating specifications, improve overall sampling efficiency, reduce human interference factors, and improve the reliability and standardization of the workflow.

[0069] In this embodiment, the sample quality control module ensures that each sample collected is effective and meets the requirements of further analysis by monitoring the physical properties of the sample in real time. The core of this module is to automatically evaluate the quality of the sample and reduce errors and omissions in the manual quality control process. The module integrates a variety of sensor technologies, such as real-time detection of physical parameters such as temperature, pH value, turbidity, etc., and can dynamically evaluate the health status of each sample. On this basis, the module can also make adjustments based on environmental factors (such as sampling environment temperature, humidity, air quality, etc.) to ensure that the quality of the sample can be effectively guaranteed even under unstable environmental conditions. Once an abnormal sample is found, an alarm will be automatically issued and the sample will be marked to prevent it from entering the subsequent analysis process. The innovation of this process is that through real-time data monitoring and intelligent evaluation, the effectiveness of the sample is guaranteed, sample contamination, damage, etc. are reduced, and the accuracy and reliability of subsequent analysis are ensured.

[0070] In the present embodiment, the data acquisition and processing module is responsible for comprehensively collecting various types of relevant data and performing necessary processing and analysis. Its core role is to efficiently collect multi-dimensional data and perform standardized processing to ensure data quality and consistency. The module uses a low-power microcontroller unit (MCU) to realize data acquisition, and has the ability to collect multiple data sources such as physiological data, nutritional metabolism data, and operating process data in real time. After preliminary cleaning and filtering, the collected data will be standardized and fused to ensure that all data are consistent in format, unit, and magnitude, so that it meets the requirements of subsequent analysis and modeling. Through data fusion technology, data from different sources can be effectively combined to provide comprehensive information support for subsequent intelligent analysis. In addition, the module also has a data tracing function to ensure that each data sample can be tracked to avoid the risk of data loss or loss. Through this module, standardized data sets can be efficiently generated to provide accurate input for subsequent intelligent diagnosis.

[0071] In this embodiment, the intelligent diagnosis and analysis module is one of the core intelligent functions of the urine sampling device. It analyzes the standardized data, combines the knowledge graph of endocrine diseases and abnormal nutrition metabolism, intelligently detects abnormal conditions of physiological indicators, and predicts potential health risks in advance. Based on deep learning algorithms and big data analysis technology, this module can analyze the collected data from multiple dimensions to discover potential abnormal trends and disease risks. Through multi-level early warning mechanisms and personalized threshold settings, the module can generate personalized health warning reports based on the unique situation of each user. If certain physiological indicators exceed the normal range, a warning will be automatically issued to prompt the user to conduct further inspections or interventions. The intelligent analysis module not only has accurate abnormality detection functions, but also can predict diseases for users based on the potential risks of disease occurrence, and provide early intervention suggestions. This module reflects the important role of intelligent diagnosis in the prevention of endocrine diseases and abnormal nutrition metabolism, and provides intelligent support for users' health management.

[0072] In this embodiment, the human-computer interaction module provides an intuitive and convenient operating interface for the urine sampling device, which greatly improves the ease of use and flexibility. The core of this module is to achieve refined control of the device operation through a clear user interface design and multi-level user authority management. The operation interface adopts a touch screen and visual design, and can directly view the status of various data collection, analysis and quality control through a graphical interface, and quickly understand and operate the system. The human-computer interaction module also supports the automatic generation of analysis reports, and through the remote monitoring function, users can monitor and operate the equipment at any location to ensure that the equipment can operate normally without on-site intervention. Provide different levels of operating permissions according to the authority requirements of different users to ensure that users of different roles can obtain appropriate information and operating permissions, and ensure data security and device stability. Through this module, the operation of the device becomes more user-friendly and intuitive, reducing the difficulty of operation and improving the user experience.

[0073] In summary, the urine sampling device for endocrinology department realizes the full automation, high standardization and precision management of urine sampling process by integrating multiple efficient modules, such as automatic sampling control module, sample quality control module, data acquisition and processing module, intelligent diagnosis and analysis module and human-computer interaction module. Each module ensures the efficient operation of the whole device and the accuracy of data through advanced technical means, avoiding errors and deficiencies in traditional manual operation. Especially in data processing, sample quality control, intelligent analysis and other aspects, it can provide more accurate health risk assessment and disease warning through real-time monitoring and intelligent prediction mechanism. Overall, this device not only greatly improves the efficiency and accuracy of urine sampling process in endocrinology department, but also provides strong technical support for patient health management, and has high application value and development prospects.

[0074] Furthermore, the automatic sampling control module includes the following components:

[0075] The sample detection unit can quickly detect the basic parameters of urine samples as a pre-processing step for sample quality control;

[0076] The flow control unit accurately adjusts the urine flow during the sampling process to ensure that the sampling volume meets the packaging requirements;

[0077] The packaging control unit automatically distributes the collected urine samples to different collection containers according to the preset program, improving the packaging efficiency and accuracy;

[0078] Disinfection control unit, which realizes automatic cleaning and disinfection of sampling pipelines and collection containers, ensuring the sterility of the sampling environment;

[0079] The main control unit coordinates the working sequence of each functional unit to ensure the automated execution of the sampling process.

[0080] In this embodiment, the sample detection unit can monitor the physical and chemical properties of urine in real time, such as color, transparency, pH value, specific gravity and other important parameters, by integrating multiple sensor technologies, such as spectral sensors, turbidity sensors, pH sensors, etc. Through these detection indicators, the validity of urine samples can be quickly judged, abnormal samples (such as contaminated, invalid or unqualified samples) can be identified in time, and these samples can be avoided from being sent to subsequent analysis processes. Furthermore, the sample detection unit can also dynamically adjust the test results according to factors such as ambient temperature and humidity to ensure the accuracy of sample detection under different environmental conditions. The role of this unit is mainly reflected in its efficient and automated functions, which avoids the tediousness and errors of manual detection, improves the accuracy and efficiency of sample quality control, and lays the foundation for the success of the entire sampling process.

[0081] In this embodiment, the flow control unit adopts a high-precision flow meter and intelligent valve control technology, which can monitor the urine flow in real time and adjust the flow rate according to demand. Through precise control, the flow control unit ensures that there will be no excess or insufficient sampling during each sampling process, avoiding sample collection errors or insufficient samples due to uneven flow. At the same time, the flow control unit can also flexibly adjust the flow range according to different urine characteristics and sampling requirements to adapt to the collection requirements of different types of samples. In addition, the flow control unit can realize real-time flow correction in conjunction with the feedback control mechanism, and further improve the accuracy of the sampling process through a closed-loop adjustment mechanism. Through high integration and intelligent control, this unit not only ensures the accuracy of the sampling volume, but also optimizes the flow distribution during the sampling process, improving the stability of the equipment and the reliability of sampling.

[0082] In this embodiment, the role of the packaging control unit is mainly reflected in its efficient packaging operation and extremely high accuracy, ensuring that urine samples can be quickly and accurately distributed between different containers, avoiding errors or waste caused by manual operation. The packaging control unit usually uses actuators such as mechanical arms, precision pumps or valves to adjust the sampling volume of each container through a precise control system to ensure that each sample can be accurately and correctly transferred to the designated container. The built-in intelligent algorithm automatically optimizes the packaging strategy according to the capacity of the container, the required sampling volume and other preset parameters, thereby improving the packaging efficiency and accuracy. During the entire packaging process, each packaging link can also be monitored in real time to ensure that no sample is lost or overflowed, reducing resource waste. The packaging control unit not only improves operational efficiency, but also ensures the integrity and accuracy of each sample, providing a solid guarantee for subsequent experimental analysis.

[0083] In this embodiment, the disinfection control unit uses a variety of disinfection methods such as hot water, ultraviolet rays, and chemical disinfectants, combined with an automated control system, to clean and disinfect pipes and containers as needed. Technically, the unit integrates a variety of sensors to monitor various parameters during the disinfection process, such as temperature, humidity, and chemical concentration, to ensure the comprehensiveness and effectiveness of disinfection. In addition, the disinfection control unit can also adjust the disinfection method and intensity according to different disinfection needs, such as regular high-temperature disinfection for frequently used containers, and ultraviolet irradiation disinfection for occasionally used sample tubes, to ensure that each sampling device is in the best hygienic state. Through this technical means, the disinfection control unit can not only effectively avoid cross-contamination, but also improve the service life of the equipment and the hygienic level of sampling, providing a safe and sterile operating environment for urine sampling.

[0084] In this embodiment, the main control unit is the "brain" of the automatic sampling control module, responsible for coordinating and scheduling the working sequence of each functional unit to ensure that the entire urine sampling process can be executed automatically and efficiently. The main role of this unit is reflected in its powerful computing power and efficient task scheduling function, which can coordinate the work of each link such as sample detection, flow control, packaging operation and disinfection procedure in real time to ensure that each link operates efficiently according to the predetermined process. The main control unit adopts an embedded system and a real-time operating system, which can handle multi-threaded tasks and dynamically adjust the operating state according to real-time data. For example, when an abnormality or failure is detected in a certain link, the main control unit will immediately adjust the working order of other units to ensure the smooth progress of the sampling process. The unit also has data storage and recording functions, which can save the data of each sampling and generate a detailed operation log for later tracing and analysis. Through its powerful computing and scheduling capabilities, the main control unit can ensure the automation, efficiency and stability of the whole process of urine sampling, reduce the need for manual intervention, and greatly improve the work efficiency and system reliability.

[0085] In summary, the automatic sampling control module ensures the full automation, standardization and efficiency of the urine sampling process by integrating multiple efficient and accurate functional units. The sample detection unit, flow control unit, packaging control unit, disinfection control unit and main control unit each play a key role, and together realize the automation of the entire process from sample collection to disinfection and cleaning. The high-precision control and intelligent scheduling of each unit not only ensure the accuracy and stability of the sampling operation, but also improve the overall work efficiency. Through these technical means, the automatic sampling control module provides high-quality and efficient support for the urine sampling process, greatly reducing the errors and risks caused by human intervention, while improving the standardization and traceability of sample collection, and promoting the development and innovation of urine sampling in the medical field, especially in the endocrinology department.

[0086] Furthermore, the sample quality control module includes the following components:

[0087] The environmental monitoring unit is responsible for real-time collection and monitoring of key parameters of the sampling environment, ensuring the stability of environmental conditions through closed-loop control, and providing a reliable environmental basis for sample analysis;

[0088] A multispectral sensor unit collects the spectral characteristics of the sample through a high-precision multispectral sensor array to quantitatively characterize the physical properties and nutritional components of the sample;

[0089] AI visual analysis unit, which integrates deep learning algorithms and computer vision technology to perform real-time image analysis of samples, including morphological feature extraction, size measurement, and colorimetric analysis;

[0090] The sample validity assessment unit conducts automated validity assessment of samples based on preset quality standards by comprehensively analyzing multispectral data and visual features to ensure that the sample quality meets the requirements of endocrinology and nutrition testing;

[0091] The abnormal sample identification unit marks and classifies samples that deviate from the normal range through real-time comparison with the standard sample feature library, and supports automatic elimination mechanism;

[0092] The sample feature analysis unit integrates environmental monitoring, spectral sensor and visual analysis data to generate standardized sample feature descriptions and transmit structured data to the data acquisition and processing module;

[0093] The feedback and adjustment unit adjusts the sampling parameters and environmental conditions in real time through a closed-loop control algorithm based on the sample quality assessment results to achieve dynamic optimization of the sampling process.

[0094] In this embodiment, the environmental monitoring unit is the first line of defense of the sample quality control module, which is mainly responsible for real-time acquisition and monitoring of key parameters of the sampling environment, such as temperature, humidity, air pressure, light intensity, etc. The unit obtains environmental data in real time through a highly accurate sensor array and compares it with the preset standard. The unit ensures the stability of the sampling environment through a closed-loop control mechanism, providing a reliable environmental basis for sample analysis. The sampling environment has an important impact on the quality of the sample, especially in endocrine and nutritional analysis, where slight changes in the environment may cause changes or damage to the sample components. The environmental monitoring unit ensures that the environmental conditions during sample collection and storage are always maintained within the optimal range through continuous monitoring and real-time adjustment. For example, if temperature fluctuations or excessive humidity are detected, environmental control equipment, such as air conditioners or humidifiers, will be automatically adjusted to restore a stable state. Through this module, the high quality of the sample can still be guaranteed in a dynamic environment, providing a solid foundation for subsequent analysis and avoiding the potential impact of environmental factors on sample quality.

[0095] In this embodiment, the multispectral sensor unit is one of the most important components in the sample quality control module, which is responsible for collecting the spectral characteristics of the sample through a high-precision multispectral sensor array. These sensors can cover multiple bands from visible light to near-infrared light, collect spectral information of the sample, and use it to quantitatively characterize the physical properties and nutritional components of the sample. Different substances and components will show different absorption and reflection characteristics at specific wavelengths. Therefore, through multispectral data, the physical and chemical properties of the sample can be analyzed non-invasively. The unit can quickly capture the spectral data of the sample and convert it into meaningful indicators, such as the concentration, specific gravity, pH value, etc. of the components in the urine sample. The unit avoids manual intervention and cumbersome detection steps in traditional methods by efficiently and accurately extracting the spectral characteristics of the sample, greatly improving the speed and accuracy of data acquisition. In addition, by taking advantage of the multispectral sensor, sample analysis can be performed under complex backgrounds, which is particularly suitable for fields such as endocrine and nutritional analysis, providing rich high-dimensional data for subsequent intelligent analysis and quality control.

[0096] In this embodiment, the AI ​​visual analysis unit uses deep learning algorithms and computer vision technology to perform real-time image analysis on samples and extract important information such as morphological features, size, and chromaticity of the samples. The unit can capture images of samples in real time and analyze the samples in the images through high-resolution cameras and image processing technology. Its technical effect is reflected in the automated evaluation of the appearance of samples through advanced computer vision algorithms, reducing manual operations and errors. For example, during the analysis of urine samples, the AI ​​visual analysis unit can detect the color, turbidity, and abnormal impurities of urine, and perform size measurements to determine whether the samples meet the prescribed morphological standards. Through the deep learning model, various possible abnormal features of samples can be learned and identified, and quickly classified. The advantage of this technical module lies in its high degree of automation, accuracy, and flexibility, which not only improves the efficiency of sample analysis, but also reduces the subjective influence of manual operation on sample judgment, providing strong support for sample quality assessment.

[0097] In this embodiment, the sample validity evaluation unit combines the data from the environmental monitoring unit, the spectral sensor unit, and the AI ​​visual analysis unit to conduct a comprehensive evaluation to ensure that the sample quality meets the requirements of endocrine and nutritional tests. The unit provides a scientific and objective sample quality judgment system through multi-dimensional data fusion and intelligent analysis. For example, spectral data can reflect the chemical composition of urine, while visual data can reflect the appearance characteristics of the sample. Combining these two aspects of information, the sample validity evaluation unit can accurately determine whether the sample meets the standard. In this process, by setting different quality thresholds and evaluating according to different types of sample requirements, it is ensured that all collected samples meet the standards of endocrine and nutritional tests, and the impact of invalid samples on subsequent analysis is avoided. This unit not only improves the efficiency of sample quality control, but also reduces the errors of human evaluation through intelligent processing, and improves the automation level of the entire sample management process.

[0098] In this embodiment, the core technical effect of the abnormal sample identification unit is to automatically identify and mark samples that deviate from the normal range through real-time comparison with the standard sample feature library, and support the mechanism of automatically removing unqualified samples. The unit analyzes the multispectral data and visual features of the sample through a machine learning algorithm, and compares it with the feature database of the standard sample to determine whether the sample meets the standard. If the characteristics of the sample are significantly different from the characteristics of the standard sample, it will be automatically marked as an abnormal sample. Further, it can be classified according to the characteristic type of the abnormal sample, such as contaminated samples, expired samples, or abnormal samples caused by sampling errors. Through real-time comparison and intelligent judgment, the detection efficiency and accuracy of abnormal samples are effectively improved. The use of the abnormal sample identification unit can greatly reduce manual intervention and omissions, ensure the accuracy of sample analysis, and reduce the risk of misdiagnosis or misjudgment caused by sample abnormalities. By automatically removing unqualified samples, the unit provides a pure and qualified sample set for subsequent analysis, ensuring the quality of endocrine and nutrition tests.

[0099] In the present embodiment, the sample feature analysis unit integrates data from environmental monitoring, spectral sensors and visual analysis units, generates standardized sample feature descriptions, and transmits these structured data to the data acquisition and processing module. Through efficient data integration and processing, the unit can fully and accurately reflect the various physical and chemical properties of the sample. Through this unit, a complete and standardized feature file can be generated for each sample, including but not limited to spectral features, morphological features, size, chromaticity and other information. The standardized sample feature description provides important data support for subsequent intelligent diagnostic analysis, ensuring the consistency and high quality of the data. In addition, by transmitting the sample feature data to the data acquisition and processing module, the work efficiency and data processing capabilities are further improved, the errors of manual input or manual intervention are avoided, and reliable data support is provided for the final analysis results.

[0100] In this embodiment, the innovation of the feedback and adjustment unit is reflected in its adaptive ability, which can adjust various parameters in the sampling process according to the real-time sample quality and environmental monitoring data to ensure that the sample quality always meets the standard. For example, if it is found that the quality of a batch of samples does not meet the standard, the feedback and adjustment unit can automatically adjust parameters such as ambient temperature, humidity or sampling time to optimize the sampling conditions and ensure the quality of the subsequent sampling process. The closed-loop control algorithm continuously adjusts the sampling strategy through real-time data monitoring and analysis of multiple links to achieve dynamic optimization of the entire process. Through this mechanism, efficiency and quality can be continuously improved in actual operation, sample quality fluctuations caused by environmental changes or equipment abnormalities are avoided, and the stability and efficiency of the sampling process are ensured.

[0101] In summary, the sample quality control module integrates multiple advanced technologies to achieve comprehensive monitoring and intelligent evaluation of urine sample quality. It can not only efficiently collect and analyze sample characteristics, but also automatically identify and eliminate abnormal samples through intelligent algorithms, greatly improving the accuracy and reliability of sample analysis. The closed-loop control mechanism of the feedback and adjustment unit ensures dynamic optimization during the sampling process, so that real-time adjustments can be made according to changes in the environment and sample quality. This module can provide high-quality, standardized samples, provide reliable data support for subsequent endocrine and nutritional tests, and improve the intelligence and automation level of the entire urine sampling process.

[0102] Furthermore, the environmental monitoring, spectral sensor and visual analysis data are integrated to generate standardized sample feature descriptions, including the following steps:

[0103] Time-align and clean environmental monitoring data, perform wavelength calibration and noise elimination on near-infrared and visible spectrum data collected by spectral sensors, and perform denoising and contrast enhancement on visual analysis data;

[0104] Extract key statistical features from environmental data, extract characteristic bands and spectral indices related to endocrine hormones and nutrients from spectral data, extract visual features from visual analysis data, and standardize and fuse all extracted features;

[0105] Based on the fused data, a unified feature matrix was constructed, and a standardized feature description document containing endocrine and nutritional metabolic indicators was generated for each sample.

[0106] In summary, in the process of integrating environmental monitoring, spectral sensors and visual analysis data to generate standardized sample feature descriptions, a series of sophisticated data processing and fusion steps can efficiently integrate multi-source data and provide accurate and comprehensive sample feature analysis. The data cleaning, calibration, denoising and feature extraction in this process not only ensure the high quality of the data, but also improve the accuracy of the analysis. Through the standardized and fused feature matrix, a comprehensive and standardized feature description document can be generated for each sample, greatly improving the efficiency and reliability of subsequent analysis and intelligent diagnosis. This technology not only enhances the operability of sample data, but also provides strong support for disease risk prediction, sample quality control and endocrine nutrition and metabolic analysis, and promotes the cutting-edge applications of intelligence and automation.

[0107] Furthermore, the data acquisition and processing module includes the following components:

[0108] The physiological data acquisition unit uses a high-precision sensor array to collect and digitize the patient's key physiological indicators and nutritional metabolic indicators in real time to ensure the timeliness and accuracy of the data;

[0109] The operation process and traceability unit uniformly records the operation steps, timestamps and operator information of the sampling process, and realizes the traceability management of the sample throughout its life cycle through RFID tags or barcode technology;

[0110] The data preprocessing unit performs noise reduction, filtering and outlier detection on the raw data, improves the signal quality through data cleaning, and lays a reliable data foundation for subsequent analysis;

[0111] The data integration unit integrates physiological data, nutritional metabolism data, operation process data, sample traceability information, and sample feature analysis results into a unified multidimensional data set;

[0112] The data verification and consistency check unit implements multi-level data quality control, including data integrity verification, logical relationship verification and cross-validation, to ensure data consistency and reliability;

[0113] The result output unit converts the verified data into a standard format, generates a structured analysis result data package, and supports subsequent module calls or report generation.

[0114] In this embodiment, the physiological data acquisition unit is the core component of the data acquisition and processing module, which is responsible for obtaining the patient's key physiological indicators and nutritional metabolic indicators in real time through a high-precision sensor array, such as heart rate, blood pressure, body temperature, blood sugar level, cholesterol, etc. The accuracy of these physiological data directly affects the accuracy of the subsequent analysis results. Therefore, the selection and accuracy of the sensor are crucial. The use of a high-precision sensor array can effectively reduce the impact of external noise on data acquisition, while enhancing the stability and anti-interference ability of the sensor, ensuring that the collected data has high timeliness and reliability. For example, real-time monitoring of heart rate and blood pressure is crucial for the early detection of endocrine diseases. If the sensor can capture these data stably and reliably, it can provide sufficient basis for subsequent intelligent diagnosis and analysis. In addition, the collection of nutritional metabolic indicators, such as blood sugar, insulin and other levels, not only reflects the patient's health status, but can also be used to assess the risk of endocrine diseases. Therefore, the technical effect of the physiological data acquisition unit is the basis for quality control of the entire process, ensuring that the entire monitoring process has a high degree of accuracy and real-time performance.

[0115] In this embodiment, the operation process and the traceability unit build a traceability management system for the entire sample life cycle by accurately recording each operation step, timestamp, operator information and other key data in the sample collection process. By adopting RFID tags or barcode technology, it is possible to track the whole process of samples from collection to analysis and then to storage, which not only improves the transparency of laboratory management, but also ensures the reliability and traceability of data. In the field of endocrinology and nutritional metabolism research, the processing and analysis process of samples is often affected by various factors, such as the skill differences of operators, the accuracy of equipment, and the fluctuations of sample processing environment. Therefore, by systematically recording each link, the operation process and the traceability unit can ensure that the data source of each sample is clear, the operation process is correct, and potential operating errors can be discovered and corrected in time. At the same time, the sample traceability function is crucial for the review and quality control of experimental results, especially when the experimental results of multiple samples need to be compared, reliable historical data support can be provided. On the whole, the unit improves the accuracy of sample management and the standardization of laboratory work, ensuring high-quality analysis results.

[0116] In this embodiment, the data preprocessing unit performs processing operations such as cleaning, noise reduction, filtering and outlier detection on the collected raw data to ensure the reliability of data quality. This process provides basic support for subsequent analysis and determines the accuracy and reliability of data analysis results. Physiological data and sample characteristic data usually contain bad information such as noise, missing values, and outliers. If these invalid data are not processed, they will directly affect the analysis results and may even lead to misdiagnosis. Therefore, the technical effect of the data preprocessing unit is very critical. First, the noise reduction and filtering operations eliminate the noise caused by sensor errors, environmental interference or unstable factors in the acquisition process by smoothing the data, ensuring that the data is more accurate. Secondly, the outlier detection function can identify and eliminate abnormal data that deviates significantly from the normal range, which is particularly important for preventing erroneous data from having an adverse effect on the result analysis. Data cleaning technology further improves the integrity and consistency of the data set by filling missing values, removing duplicate data and correcting erroneous data.

[0117] In this embodiment, the data integration unit plays a role in connecting different data sources and optimizing data utilization efficiency in the data acquisition and processing module. It integrates multi-dimensional data from different sensors and modules - physiological data, nutritional metabolism data, operation process data, sample traceability information and sample feature analysis results - in a structured manner to form a unified data set. This process solves the problem that data from different sources and formats cannot be directly and effectively analyzed. Through data integration and normalization, a variety of information can be fused into an easy-to-process format to support subsequent analysis and decision-making. For example, physiological data and nutritional metabolism data often need to be combined with operation process data and sample traceability information in order to comprehensively evaluate the patient's health status. The data integration unit can achieve seamless connection between information through efficient data fusion technology, providing sufficient information basis for subsequent analysis modules. This process ensures the unity of data in cross-domain analysis, making the data not only available, but also greatly enhancing the value of data in practical applications. The technical effect of this unit lays the foundation for the efficient use of multi-source data.

[0118] In this embodiment, the data verification and consistency check unit can ensure that the collected and integrated data are logically correct and meet the predetermined standards. Data integrity verification is used to ensure that the data is not lost or omitted during the collection process, especially for time-sensitive physiological data, any missing data may affect the accuracy of the final diagnosis. Logical relationship verification ensures the internal consistency of the data to avoid unreasonable analysis results caused by data entry errors or deviations generated during the transmission process. Cross-validation further ensures the reliability of the data by comparing data from different sources. For example, there may be a certain correlation between indicators such as heart rate and blood pressure in physiological data and the sample analysis results. Cross-validation can identify potential errors and make corrections in time. Through these verification and inspection measures, the data verification and consistency check unit can significantly improve the credibility of the data set and the accuracy of the analysis results, ensuring that subsequent modules can run on the basis of the most reliable data.

[0119] In this embodiment, the result output unit is responsible for converting the verified and processed data into a standard format, and generating a structured analysis result data packet to support the call or report generation of subsequent modules. The innovation of this unit is that it can format complex and redundant raw data and convert it into structured data that is easy to analyze and understand, thereby providing strong data support for intelligent diagnosis analysis, report generation and decision support. The standardized data format is not only convenient for storage and management, but also can be seamlessly connected with other systems to support further processing and multi-dimensional analysis of data. For example, after physiological data, nutritional metabolism data, sample analysis results, etc. are standardized, analysis reports can be automatically generated in a specified format for direct reference by medical personnel, saving manual processing time and improving work efficiency. In addition, the output of structured data can also provide data support for subsequent intelligent predictions, personalized treatment plan recommendations, etc., and promote the intelligence and precision of endocrine disease and nutritional metabolism management.

[0120] In summary, the careful design of the above units ensures the efficiency and accuracy of data from acquisition, preprocessing to integration, verification and output. These units work together to acquire and process complex data from multiple sources in real time, and ensure the reliability and consistency of the data through strict quality control. Through these technical means, not only the accuracy of diagnostic analysis is improved, but also strong support is provided for personalized medicine and disease early warning. Overall, the data acquisition and processing module provides a solid technical foundation for intelligent endocrine disease monitoring and nutritional metabolism management, and promotes the precision and automation of clinical diagnosis and treatment.

[0121] Furthermore, the intelligent diagnosis and analysis module includes the following components:

[0122] The data analysis unit conducts in-depth mining of standardized data to extract the characteristic indicator matrix and time series change patterns of endocrine diseases and nutritional metabolic abnormalities;

[0123] The knowledge graph unit constructs a multi-level semantic network, integrates the clinical manifestations, diagnostic criteria and treatment plan knowledge of endocrine diseases and nutritional metabolic abnormalities, forms a structured knowledge reasoning system, and supports intelligent diagnostic decision-making;

[0124] The abnormality detection and diagnosis unit combines personalized thresholds and disease models to automatically identify and diagnose physiological and nutritional metabolic abnormalities and detect potential disease risks in advance;

[0125] The multi-level early warning mechanism unit generates early warning information of different risk levels based on real-time detection results, and provides targeted response strategies and intervention suggestions;

[0126] Personalized threshold adjustment unit, which dynamically optimizes the diagnostic threshold according to the individual characteristics and nutritional status of the patient, improving the accuracy and personalization of the test;

[0127] The risk prediction and trend analysis unit conducts a prospective assessment of disease risks based on the historical trends and real-time monitoring results of physiological data and nutritional metabolism data, and generates predictions of disease development trends and recommendations for the best time to intervene.

[0128] In this embodiment, the data analysis unit is one of the core components of the intelligent diagnosis and analysis module, and is responsible for the task of deeply mining the characteristics of endocrine diseases and abnormal nutritional metabolism from standardized data. The unit uses complex algorithms and statistical models, combined with big data analysis technology, to extract key characteristic indicator matrices and time series change patterns from multidimensional data sets. These characteristic indicators include physiological data, nutritional metabolic indicators, and individual differences of patients. Through in-depth mining of data, changes in the patient's health status and potential disease risks can be revealed. For example, by analyzing the time series changes of indicators such as blood sugar, cholesterol, and hormone levels in the patient's body and their historical data, the data analysis unit can identify some potential signs of endocrine disorders, such as early signs of insulin resistance. In addition, the data analysis unit can identify abnormal fluctuations that do not conform to normal physiological states by comparing the data change patterns in different time windows, which is of great significance for early warning of diseases. In general, the unit provides a scientific basis for subsequent intelligent diagnosis, disease risk prediction, and intervention by deeply mining the potential information in the data, and improves the accuracy of medical decision-making.

[0129] In this embodiment, the knowledge graph unit integrates the clinical manifestations, diagnostic criteria and treatment plans of endocrine diseases and nutritional metabolic disorders by constructing a multi-level semantic network to form a structured knowledge reasoning system. This system not only supports intelligent diagnostic decisions, but also provides knowledge-based reasoning and suggestions to help medical personnel better understand and judge the health status of patients. Through the construction of the knowledge graph, various clinical data, treatment plans, symptoms and disease progression can be linked to form a panoramic knowledge view. For example, in the diagnosis of endocrine disorders, by modeling the relationship between relevant clinical manifestations (such as weight changes, metabolic abnormalities, changes in hormone levels, etc.) and diseases, it is possible to quickly infer the diseases that patients may suffer from and provide corresponding treatment recommendations.

[0130] In this embodiment, the abnormal detection and diagnosis unit is a key function in the intelligent diagnosis and analysis module, which is responsible for automatically identifying and diagnosing physiological and nutritional metabolic abnormalities by combining personalized thresholds and disease models, and discovering potential disease risks in advance. The unit uses machine learning, deep learning, and statistical analysis technologies to automatically identify and classify abnormal data through real-time monitoring of physiological and nutritional metabolic data. In traditional disease diagnosis, doctors are often required to make judgments based on experience and standardized diagnostic processes, while the intelligent diagnosis unit can respond immediately when the data deviates from the normal range through real-time analysis to identify potential health risks. For example, for early screening of diabetes, the unit can detect the patient's blood sugar level, insulin secretion, and related physiological indicators in real time, promptly discover abnormal conditions of blood sugar fluctuations in patients, and combine individualized disease models to determine their risk of diabetes. This not only improves the efficiency and accuracy of diagnosis, but also helps doctors intervene in the early stages of the disease to avoid further deterioration of the disease. Through automated abnormal detection and diagnosis functions, the unit significantly reduces the time and error of manual judgment, while improving the effectiveness of disease risk warning and management.

[0131] In this embodiment, the multi-level early warning mechanism unit can identify potential risk points by analyzing the patient's health data in real time, and automatically divide different risk levels according to the severity and probability of the disease. For example, for a patient with long-term hypertension, blood pressure fluctuations will be monitored. If the fluctuation exceeds the safety threshold, a mild or moderate early warning will be triggered, and the patient will be advised to adjust the drug dosage or further examination. For patients with severe metabolic abnormalities or hormonal disorders, a high-risk early warning will be issued and immediate medical treatment will be recommended. Through the multi-level early warning mechanism, accurate response strategies can be provided for different risk situations to help patients take appropriate intervention measures, thereby effectively avoiding acute onset or deterioration of the disease. This mechanism can not only improve the timeliness of the early warning, but also adjust the early warning strategy according to the actual situation, making the intervention measures more personalized and precise.

[0132] In this embodiment, the function of the personalized threshold adjustment unit is to dynamically optimize the diagnostic threshold according to the individual characteristics and nutritional status of the patient, and improve the accuracy and personalization level of the detection. Traditional medical diagnostic standards often use a unified threshold to judge the health status of the patient, but due to differences in the constitution, genes, living habits, etc. of each patient, a fixed threshold is often difficult to accurately reflect the health status of each patient. The personalized threshold adjustment unit dynamically adjusts the threshold of the detection by combining the individual differences of the patient. For example, in the detection of diabetes, some patients may have a normal blood sugar level higher than the general standard due to age, genetic factors, etc., so the unit will intelligently adjust the threshold of blood sugar monitoring according to the patient's historical data, medical history and lifestyle, so that the diagnosis is more accurate and personalized. Personalized threshold adjustment can not only improve the sensitivity of diagnosis, but also avoid misdiagnosis and missed diagnosis of patients, ensuring that each patient can be monitored and treated under the most suitable standard.

[0133] In this embodiment, the risk prediction and trend analysis unit combines a large amount of historical data and real-time monitoring data, and uses time series analysis, machine learning models and big data technology to predict the possible development trend of the disease. For example, based on the changing trends of the patient's past weight, blood sugar, blood lipids and other indicators, it can be predicted whether the patient may develop endocrine-related diseases such as diabetes and cardiovascular disease in the future, and corresponding intervention measures can be formulated in advance. The technical effect of the risk prediction and trend analysis unit is that it can provide a forward-looking perspective on the development of the disease through in-depth analysis of multidimensional data, helping doctors and patients make scientific and reasonable health management decisions. Through this function, patients can take preventive and therapeutic measures before the disease is fully manifested, which greatly improves the effect of early intervention and reduces the probability of major diseases.

[0134] In summary, the intelligent diagnosis and analysis module provides all-round support for the diagnosis, prediction and intervention of endocrine diseases and nutritional metabolic abnormalities through the close collaboration of multiple functional units. The data analysis unit mines potential disease characteristics, the knowledge graph unit integrates medical knowledge, the anomaly detection and diagnosis unit realizes automated diagnosis, the multi-level early warning mechanism unit provides timely early warning response, the personalized threshold adjustment unit optimizes the diagnostic criteria, and the risk prediction and trend analysis unit provides forward-looking suggestions for disease management. The collaborative work of these functions not only improves the accuracy and personalization of diagnosis, but also helps medical institutions identify and intervene in potential health risks in advance, thereby achieving more efficient disease management and health maintenance.

[0135] Furthermore, a multi-level semantic network is constructed to integrate the clinical manifestations, diagnostic criteria and treatment plan knowledge of endocrine diseases and nutritional metabolic disorders to form a structured knowledge reasoning system, including the following steps:

[0136] Collect and organize basic medical knowledge on endocrine diseases and nutritional metabolic abnormalities, clarify core concepts and their relationships, and build a semantic network framework;

[0137] Standardize clinical manifestation data of endocrine diseases and nutritional status, quantify symptom characteristics and analyze the correlation and co-occurrence relationship between symptoms to form a clinical manifestation network;

[0138] Standardize diagnostic criteria, including the hierarchical relationship between essential and auxiliary criteria, indicator weights and thresholds, unify qualitative and quantitative content, and build a reasoning framework for endocrine and nutritional assessment;

[0139] Integrate a variety of treatment methods including drug therapy, nutritional intervention, surgical treatment and lifestyle intervention, clarify indications, contraindications and efficacy evaluation, and formulate treatment decision-making rules;

[0140] Build a multi-level knowledge reasoning mechanism, connect basic medical knowledge, clinical manifestations, diagnostic criteria and treatment methods, define inter-layer logic and reasoning rules, realize intelligent reasoning from symptoms to diagnosis and nutritional assessment, and then to comprehensive treatment plans, and provide support for clinical decision-making.

[0141] In summary, by constructing a multi-level semantic network and integrating the basic medical knowledge, clinical manifestations, diagnostic criteria and treatment methods of endocrine diseases and abnormal nutritional metabolism, accurate knowledge reasoning and personalized treatment decisions can be achieved. Each step provides rich data support and scientific basis, thereby improving the reliability and effectiveness of intelligent diagnosis. From symptom identification to disease diagnosis, and then to the recommendation of treatment plans, the system provides intelligent support for clinical decision-making through layer-by-layer reasoning. Through this comprehensive reasoning framework, the management of endocrine diseases and abnormal nutritional metabolism can be more accurate, efficient and personalized, providing patients with better health management and treatment plans.

[0142] Furthermore, the correlation between symptoms is expressed as: Among them, ρ(S i ,S j ) indicates symptoms S i and S j The Pearson correlation coefficient between the two symptoms is used to measure the linear relationship between the two symptoms; N represents the total number of cases; S i (k) represents the symptom S in the kth case i The standardized value of Indicates symptoms i The mean of the symptom S in all cases i The average of the standardized values ​​of S j (k) represents the symptom S in the kth casej The standardized value of Indicates symptoms j The mean of the symptom S in all cases j The average of the standardized values ​​of

[0143] The co-occurrence relationship between symptoms is expressed as: I(S i ,S j )=∑ i,j P(S i ,S j )log[P(S i ,S j ) / (P(S i )P(S j ))], where I(S i ,S j ) indicates symptoms S i and S j The mutual information between them is used to measure the degree of information sharing between them; P(S i ,S j ) indicates symptoms S i and S j The probability of appearing in the same case at the same time, that is, the symptom S i and S j The frequency of co-occurrence is obtained by calculating the co-occurrence frequency; P(S i ) indicates symptoms S i The probability of appearing in any case, that is, the symptom S i The frequency of occurrence in all cases; P(S j ) indicates symptoms S j The probability of appearing in any case, that is, the symptom S j The frequency of occurrence among all cases.

[0144] In summary, through the combined use of Pearson's correlation coefficient and mutual information, the correlation and co-occurrence relationship between symptoms can be quantified more accurately. The Pearson's correlation coefficient provides a clear measure of the linear relationship between symptoms, while the mutual information reveals the degree of information sharing between symptoms and captures those nonlinear and complex co-occurrence patterns. The combination of the two has higher flexibility and accuracy in analyzing symptom data, providing strong data support for the intelligent diagnosis of endocrine diseases and nutritional metabolic disorders. Ultimately, this multi-angle analysis method can help clinical decision makers accurately identify the characteristics of the disease, optimize the diagnostic process, and provide an important reference for the formulation of personalized treatment plans.

[0145] Furthermore, the automatic identification and diagnosis of physiological and nutritional metabolic abnormalities and the early detection of potential disease risks include the following steps:

[0146] By integrating the user's basic characteristics, nutritional status and medical history, combined with the statistical characteristics of long-term health and nutritional monitoring data, a personalized normal value range of physiological and nutritional metabolic indicators is constructed for each user, and multi-level warning thresholds are set;

[0147] Automatically identify abnormal fluctuations and trend changes in data, and comprehensively identify potential health risk signals by analyzing the correlation between multiple physiological and nutritional metabolic indicators;

[0148] The detected abnormal data will be matched and analyzed with the characteristic models of various endocrine diseases and nutritional metabolic abnormalities to assess potential disease risks and establish an endocrine and nutritional dynamic monitoring and early warning mechanism for high-risk populations.

[0149] In summary, the technical solution for automatically identifying and diagnosing physiological and nutritional metabolic abnormalities and discovering potential disease risks in advance integrates multiple functional modules such as personalized health management, abnormal fluctuation detection, disease feature matching, and dynamic early warning mechanism. Through the collaborative work of these modules, customized health assessment and risk management can be provided to each user to ensure timely intervention measures in the early stages of the disease. In particular, through personalized threshold setting and long-term health monitoring based on big data, the occurrence of diseases such as endocrine and nutritional metabolic abnormalities can be effectively predicted and prevented, providing users with precise health guidance, and ultimately achieving more efficient disease prevention and treatment plans.

[0150] Furthermore, the potential health risk signals are comprehensively identified through the following formula: R total =∑ p＝1 m w p ·Z p +λ·ΔX p +∑ p＝1 m ∑ q>p α p,q ·r p,q +β·Trend t , where R total is the comprehensive health risk signal, which indicates the comprehensive assessment value of health risk based on multi-dimensional data; m is the number of physiological and nutritional metabolic indicators, which indicates the total number of indicators used in the analysis; w p is the weight of the pth indicator, which is used to indicate the importance of the indicator in the comprehensive evaluation; Z p is the Z-score value of the pth indicator; λ represents the importance of trend change in comprehensive risk assessment; ΔX p is the fluctuation of the pth indicator, indicating the variation of the indicator within a certain time range; α p,qis the correlation weight between the pth and qth indicators, indicating the importance of the relationship between these two indicators in health risk assessment; r p,q is the correlation coefficient between the pth and qth indicators; β represents the influence of trend analysis on health risk assessment; Trend t Represents the overall trend change of the indicator at time t.

[0151] In summary, the calculation formula of the comprehensive health risk signal not only focuses on the individual's single physiological and nutritional indicators by introducing a multi-dimensional data analysis method, but also combines the correlation, volatility, trend changes and other information between indicators to build a comprehensive and sophisticated health risk assessment system. This system can conduct a comprehensive assessment of health risks based on the user's personalized data and provide early warnings for potential health problems. Through the combination of multi-dimensional data, users can be provided with more accurate and personalized health guidance, thereby effectively improving the success rate of disease prevention and health intervention.

[0152] Furthermore, the following steps are used to generate disease development trend predictions and optimal intervention timing recommendations:

[0153] Summarize the patient's physiological indicators and nutritional metabolic index history records, test results, medication and nutritional intervention records, and treatment effect feedback information to establish a complete longitudinal database;

[0154] Conduct in-depth mining of historical data to identify key indicators and their dynamic change patterns that are significantly related to disease progression and changes in nutritional status;

[0155] Combine historical trend characteristics with real-time monitoring data to achieve quantitative prediction of disease development and nutritional status trends and generate comprehensive risk scores;

[0156] Based on the prediction results, determine the optimal time window for medical and nutritional intervention and evaluate the expected effects.

[0157] In summary, by building a complete longitudinal database, deeply mining historical data, combining real-time monitoring information for trend prediction, and generating comprehensive risk scores and optimal intervention timing recommendations, personalized and dynamic disease development predictions and intervention recommendations can be achieved. In this process, the application of technologies such as data mining, time series analysis, and machine learning can identify potential risks of diseases and provide accurate health management solutions. Ultimately, this data-based intelligent decision-making mechanism can not only help doctors better formulate treatment plans, but also provide patients with more personalized health guidance, helping them take appropriate intervention measures in the early stages of disease development, thereby effectively improving treatment outcomes and reducing the burden of disease.

[0158] Furthermore, the human-computer interaction module includes the following components:

[0159] User interface design unit, providing an intuitive and easy-to-operate interface, supporting touch screen, voice control and graphical operation, ensuring that users can efficiently complete equipment operation and data viewing;

[0160] Multi-level user rights management unit, which assigns access rights according to user roles to ensure data security and operational compliance;

[0161] Automatic report generation unit, based on template engine technology, automatically integrates endocrine test data, nutritional assessment data, diagnostic results and trend analysis to generate structured medical reports;

[0162] Remote monitoring and management unit, which realizes remote monitoring and management of equipment working status, including remote diagnosis, real-time update and fault handling, to ensure efficient operation and timely maintenance of equipment;

[0163] Interactive feedback and prompt unit provides real-time operation feedback and system prompts to guide users in every step of operation, ensuring operation accuracy and smooth process;

[0164] The data visualization unit uses visualization technology to display the collected and processed physiological data, nutritional metabolism data, early warning information and analysis results, helping users to quickly understand and evaluate their health status.

[0165] In this embodiment, the user interface design unit is one of the most critical components in the human-computer interaction module, which directly determines the efficiency of user-device interaction and user experience. In intelligent medical devices, interface design not only needs to have basic aesthetics, but also should focus on the user's convenience of operation, the readability of information, and the fluency of interaction. In order to achieve this goal, the unit will adopt an intuitive and easy-to-operate design style, supporting multiple interaction methods, such as touch screen, voice control, and graphical operation interface. As the most common interaction method, the touch screen can provide clear navigation, operation buttons, and shortcut menus through a graphical operation interface, allowing users to quickly master the use of the device in a complex medical environment. At the same time, the addition of voice control further enhances the ease of use of the device, especially in some scenarios where manual operation is inconvenient, such as when doctors or caregivers need to operate the device with one hand, voice control can reduce the risk of misoperation and improve efficiency. In addition, the graphical operation method, especially when displaying data or diagnostic results, can present complex physiological data and trend changes in a more intuitive way, allowing users to understand and use the device without too much professional knowledge. In the design process, the needs of different user groups need to be considered. For example, the design of the patient interface focuses on simplicity and clarity to ensure that non-professionals can use it easily; doctors and medical staff require more functions and information levels to support detailed analysis and operations. The design of the user interface should take into account these different usage scenarios and be iteratively optimized through user feedback to ensure the best interactive experience and operational efficiency.

[0166] In this embodiment, the multi-level user rights management unit is the core module to ensure the information security and operational compliance of medical equipment. It ensures that only authorized users can access sensitive data or perform specific operations by assigning different access rights according to different user roles (such as patients, doctors, caregivers, system administrators, etc.). This permission control mechanism is not only crucial to data security, but also plays an important role in the compliance of equipment operation and the guarantee of medical quality. First, the rights management unit sets access levels for different user roles and ensures that each user can only access information related to his or her duties. For example, patients can only view their own health data and analysis reports, while doctors can access the patient's complete medical records and test results, and perform data analysis and diagnosis. The administrator has control over the entire system and can manage operations such as permission configuration, equipment maintenance, and data backup for all users. In this way, through refined permission allocation and management, the risk of data leakage or improper operation can be effectively reduced to ensure legality and compliance. The rights management unit also needs to combine the device's log system to record the user's operation behavior in real time, including login, data access, data modification, etc. Through log auditing, abnormal behavior can be tracked and potential security issues can be discovered in a timely manner. In addition, the design of permission management should also be flexible and able to be quickly adjusted according to changes in the medical environment, such as adding new user roles or modifying permission configurations. Ultimately, the core goal of permission management is to ensure data security and operational compliance, while providing the system with sufficient flexibility to adapt to the needs of different medical scenarios.

[0167] In this embodiment, the automatic report generation unit integrates endocrine test data, nutritional assessment data, diagnostic results and trend analysis into a structured medical report based on template engine technology. The introduction of this unit greatly improves the efficiency of medical report generation, reduces the time cost of doctors and nurses in cumbersome manual writing reports, and also improves the standardization and consistency of report content. At the technical level, the automatic report generation unit uses template engine technology to automatically generate report templates that meet medical industry standards and fill in relevant content according to different test data and analysis results. For example, the patient's endocrine indicators, nutritional metabolism data, health trends and other information will be automatically extracted and classified into the corresponding parts of the report to form a structured data format. This process not only simplifies the doctor's work, but also reduces the report deviation caused by manual input errors, ensuring the accuracy and consistency of the report. At the same time, the automatic report generation unit also needs to have flexible customization capabilities to support users to adjust the report content and format according to specific needs. For example, doctors may need to add or modify certain report items according to the patient's specific condition, or generate different types of reports according to different treatment plans. This customization capability makes the report generation function both efficient and able to meet personalized needs. Ultimately, through the optimization of this unit, medical institutions can improve work efficiency, shorten the time for report generation, and thus improve patients' diagnosis and treatment experience.

[0168] In this embodiment, the remote monitoring and management unit is an important part of modern intelligent medical equipment. It enables the working status, fault handling and maintenance management of the equipment to be realized remotely, thereby improving the efficiency and reliability of the equipment. The core technology of remote monitoring is to realize real-time data transmission and status monitoring of the equipment through the Internet of Things (IoT) technology and cloud platform. Through real-time monitoring, technicians can obtain the working status, performance data and diagnostic information of the equipment at any time, and remotely debug and update the equipment. This not only reduces the cost of manual inspection, but also can handle the fault in the first time when the equipment is abnormal, so as to avoid affecting the treatment effect of the patient due to equipment failure. In addition, remote monitoring also allows equipment managers to obtain the operating data of various types of equipment through the cloud platform for unified monitoring and maintenance. The functions of remote management also include regular inspection, upgrade and fault alarm of the equipment. Technicians can remotely push firmware upgrades to ensure that the software and hardware of the equipment are kept in the best condition and can handle any problems in a timely manner. This remote management capability ensures that the equipment can operate efficiently, reduces human intervention and time delays, and improves the quality and efficiency of medical services.

[0169] In this embodiment, the introduction of the interactive feedback and prompt unit can significantly improve the user experience, especially in the initial stage of device use or when the medical operation is more complicated, the interactive feedback can effectively reduce operational errors and avoid problems caused by the user's unfamiliarity with the operation process. The unit usually combines voice feedback, graphical prompts and tactile feedback to guide the user to complete various operations. For example, when the patient performs a self-examination or enters health data, real-time prompts will be provided according to the progress and accuracy of the operation, such as reminding the user whether a certain step is completed, whether the correct value is entered, or guiding the user to correct the error. In this way, the user can get timely help during the operation of the device and avoid the adverse consequences caused by misoperation. In terms of technical implementation, the interactive feedback and prompt unit relies on natural language processing (NLP) technology, speech recognition technology and intelligent algorithms. Through these technologies, it is possible to understand the user's needs and respond in real time to provide appropriate feedback. At the same time, it is necessary to have a certain intelligence to identify the context and current state of the user's operation, and provide personalized suggestions or warnings according to the specific situation.

[0170] In this embodiment, the data visualization unit displays the collected and processed physiological data, nutritional metabolism data, warning information and analysis results to the user in the form of charts, graphics and animations. Visualization technology can not only help users better understand and evaluate their health status, but also improve the judgment and decision-making efficiency of doctors and nurses on the patient's condition. At the technical level, the data visualization unit uses a variety of graphical display methods, such as linear graphs, bar graphs, pie charts and heat maps, to show the changing trends and distribution of various physiological and nutritional indicators. For example, the time series changes of indicators such as body temperature and blood sugar can be displayed by a line graph to help users understand the fluctuation trend of these indicators over a period of time; while the patient's weight, nutritional components and other data can be intuitively presented through pie charts or bar charts to help doctors or patients identify the problem more quickly. In addition, data visualization is also combined with the display of warning information, and users can quickly understand whether there is a health risk through a graphical interface. In this way, the data visualization unit greatly improves the readability and ease of use of medical data, making complex medical data intuitive and easy to understand, helping users make timely health decisions.

[0171] In summary, the human-computer interaction module is responsible for ensuring that users can operate the equipment efficiently, safely and accurately. Through the well-designed user interface, multi-level permission management, automatic report generation, remote monitoring and management, interactive feedback and data visualization, the module greatly improves the ease of use and intelligence of the equipment, and ensures data security and operational compliance during the medical process. Combined with advanced technical means, especially the application of the Internet of Things, big data and artificial intelligence, it can provide patients and doctors with accurate, efficient and personalized medical services, and promote the development of the medical industry in a more efficient and intelligent direction.

[0172] like Figure 2 As shown, a urine sampling method for endocrinology department comprises the following steps:

[0173] Automatically perform environmental parameter detection and equipment self-checking, including temperature and humidity monitoring of the sampling area, confirmation of equipment disinfection status, and operation status inspection of each functional module, to ensure that the entire sampling environment and equipment are in the best working condition;

[0174] Enter the patient's basic information through the human-computer interaction interface and scan the patient's wristband to automatically generate a unique sample number and QR code label, and retrieve the patient's historical sampling records, nutritional assessment records and personalized parameter settings;

[0175] The patient completes the urine sample collection according to the device prompts, and the urine flow rate is adjusted in real time to ensure the accuracy of the sampling volume, while the basic urine parameters are quickly tested;

[0176] Conduct real-time quality assessment of collected samples, including physical property analysis, morphological feature extraction, and abnormal sample identification, to ensure that the endocrine and nutritional assessment of the samples meet the inspection standards;

[0177] Qualified samples are automatically packaged into different collection containers according to the preset plan. Each container is affixed with a corresponding QR code label to simultaneously record the packaging information and sample traceability data;

[0178] Integrate all sampling process data, including environmental parameters, sample characteristics and operation records, and transmit them to the data processing module after standardization, while conducting preliminary detection and risk assessment of endocrine abnormalities and nutritional metabolic abnormalities;

[0179] After sampling is completed, a standardized report is automatically generated, and the cleaning and disinfection procedures of the sampling pipeline and container are executed through the disinfection control unit to prepare for the next sampling.

[0180] In summary, the endocrinology urine sampling method has achieved full process optimization from environmental monitoring to sample collection, quality assessment, packaging, data integration and report generation through a highly automated design. The implementation of this series of steps not only improves the accuracy and efficiency of urine sample collection, but also achieves early warning and intervention of health risks through intelligent technology and data analysis. The automation and intelligence of each link makes the entire process more accurate and safe, providing doctors with more reliable data support, thereby improving the quality and efficiency of patient diagnosis and treatment.

[0181] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A urine sampling device for endocrinology, characterized in that: Includes the following modules: Automatic sampling control module realizes automatic control of the whole process of urine sampling, ensures that the sampling complies with the standard process, and supports automatic packaging and storage management of samples; The sample quality control module monitors the physical characteristics of the sample in real time and automatically evaluates the sample validity in combination with environmental parameter monitoring, and identifies and marks abnormal samples; The data collection and processing module comprehensively collects physiological data, nutritional metabolism data, operation process data and sample traceability information, and performs cleaning, standardization and fusion processing to generate a standardized data set; Intelligent diagnosis and analysis module, based on standardized data sets, combined with knowledge graphs of endocrine diseases and nutritional metabolic abnormalities, multi-level early warning mechanisms and personalized thresholds, intelligently detects abnormal physiological indicators and performs disease risk prediction and early warning; The human-computer interaction module provides an intuitive operation interface, integrating multi-level user authority management, automatic report generation and remote monitoring.

2. The urine sampling device for endocrinology according to claim 1, characterized in that: The automatic sampling control module includes the following components: The sample detection unit can quickly detect the basic parameters of urine samples as a pre-processing step for sample quality control; The flow control unit accurately adjusts the urine flow during the sampling process to ensure that the sampling volume meets the packaging requirements; The packaging control unit automatically distributes the collected urine samples to different collection containers according to the preset program, improving the packaging efficiency and accuracy; Disinfection control unit, which can realize automatic cleaning and disinfection of sampling pipelines and collection containers, and ensure the sterility of the sampling environment; The main control unit coordinates the working sequence of each functional unit to ensure the automated execution of the sampling process.

3. The urine sampling device for endocrinology according to claim 1, characterized in that: The sample quality control module includes the following components: The environmental monitoring unit is responsible for real-time collection and monitoring of key parameters of the sampling environment, ensuring the stability of environmental conditions through closed-loop control, and providing a reliable environmental basis for sample analysis; A multispectral sensor unit collects the spectral characteristics of the sample through a high-precision multispectral sensor array to quantitatively characterize the physical properties and nutritional components of the sample; AI visual analysis unit, which integrates deep learning algorithms and computer vision technology to perform real-time image analysis of samples, including morphological feature extraction, size measurement, and colorimetric analysis; The sample validity assessment unit conducts automated validity assessment of samples based on preset quality standards by comprehensively analyzing multispectral data and visual features to ensure that the sample quality meets the requirements of endocrinology and nutrition testing; The abnormal sample identification unit marks and classifies samples that deviate from the normal range through real-time comparison with the standard sample feature library, and supports automatic elimination mechanism; The sample feature analysis unit integrates environmental monitoring, spectral sensor and visual analysis data to generate standardized sample feature descriptions and transmit structured data to the data acquisition and processing module; The feedback and adjustment unit adjusts the sampling parameters and environmental conditions in real time through a closed-loop control algorithm based on the sample quality assessment results to achieve dynamic optimization of the sampling process.

4. The urine sampling device for endocrinology according to claim 1, characterized in that: The data acquisition and processing module includes the following components: The physiological data acquisition unit uses a high-precision sensor array to collect and digitize the patient's key physiological indicators and nutritional metabolic indicators in real time to ensure the timeliness and accuracy of the data; The operation process and traceability unit uniformly records the operation steps, timestamps and operator information of the sampling process, and realizes the traceability management of the sample throughout its life cycle through RFID tags or barcode technology; The data preprocessing unit performs noise reduction, filtering and outlier detection on the raw data, improves the signal quality through data cleaning, and lays a reliable data foundation for subsequent analysis; The data integration unit integrates physiological data, nutritional metabolism data, operation process data, sample traceability information, and sample feature analysis results into a unified multidimensional data set; The data verification and consistency check unit implements multi-level data quality control, including data integrity verification, logical relationship verification and cross-validation, to ensure data consistency and reliability; The result output unit converts the verified data into a standard format, generates a structured analysis result data package, and supports subsequent module calls or report generation.

5. The urine sampling device for endocrinology according to claim 1, characterized in that: The intelligent diagnosis and analysis module includes the following components: The data analysis unit conducts in-depth mining of standardized data to extract the characteristic indicator matrix and time series change patterns of endocrine diseases and nutritional metabolic abnormalities; The knowledge graph unit constructs a multi-level semantic network, integrates the clinical manifestations, diagnostic criteria and treatment plan knowledge of endocrine diseases and nutritional metabolic abnormalities, forms a structured knowledge reasoning system, and supports intelligent diagnostic decision-making; The abnormality detection and diagnosis unit combines personalized thresholds and disease models to automatically identify and diagnose physiological and nutritional metabolic abnormalities and detect potential disease risks in advance; The multi-level early warning mechanism unit generates early warning information of different risk levels based on real-time detection results, and provides targeted response strategies and intervention suggestions; Personalized threshold adjustment unit, which dynamically optimizes the diagnostic threshold according to the individual characteristics and nutritional status of the patient, improving the accuracy and personalization of the test; The risk prediction and trend analysis unit conducts a prospective assessment of disease risks based on the historical trends and real-time monitoring results of physiological data and nutritional metabolism data, and generates predictions of disease development trends and recommendations for the best time to intervene.

6. The urine sampling device for endocrinology according to claim 1, characterized in that: The human-computer interaction module includes the following components: User interface design unit, providing an intuitive and easy-to-operate interface, supporting touch screen, voice control and graphical operation, ensuring that users can efficiently complete equipment operation and data viewing; Multi-level user rights management unit, which assigns access rights according to user roles to ensure data security and operational compliance; Automatic report generation unit, based on template engine technology, automatically integrates endocrine test data, nutritional assessment data, diagnostic results and trend analysis to generate structured medical reports; Remote monitoring and management unit, which realizes remote monitoring and management of equipment working status, including remote diagnosis, real-time update and fault handling, to ensure efficient operation and timely maintenance of equipment; Interactive feedback and prompt unit provides real-time operation feedback and system prompts to guide users in every step of operation, ensuring operation accuracy and smooth process; The data visualization unit uses visualization technology to display the collected and processed physiological data, nutritional metabolism data, early warning information and analysis results, helping users to quickly understand and evaluate their health status.

7. A urine sampling method for endocrinology, implemented based on a urine sampling device for endocrinology according to any one of claims 1 to 6, characterized in that: The following steps are involved: Automatically perform environmental parameter detection and equipment self-checking, including temperature and humidity monitoring of the sampling area, confirmation of equipment disinfection status, and operation status inspection of each functional module, to ensure that the entire sampling environment and equipment are in the best working condition; Enter the patient's basic information through the human-computer interaction interface and scan the patient's wristband to automatically generate a unique sample number and QR code label, and retrieve the patient's historical sampling records, nutritional assessment records and personalized parameter settings; The patient completes the urine sample collection according to the device prompts, and the urine flow rate is adjusted in real time to ensure the accuracy of the sampling volume, while the basic urine parameters are quickly tested; Conduct real-time quality assessment of collected samples, including physical property analysis, morphological feature extraction, and abnormal sample identification, to ensure that the endocrine and nutritional assessment of the samples meet the inspection standards; Qualified samples are automatically packaged into different collection containers according to the preset plan. Each container is affixed with a corresponding QR code label to simultaneously record the packaging information and sample traceability data; Integrate all sampling process data, including environmental parameters, sample characteristics and operation records, and transmit them to the data processing module after standardization, while conducting preliminary detection and risk assessment of endocrine abnormalities and nutritional metabolic abnormalities; After sampling is completed, a standardized report is automatically generated, and the cleaning and disinfection procedures of the sampling pipeline and container are executed through the disinfection control unit to prepare for the next sampling.

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