Efficient blood sample adding detection device and detection method thereof

Abstract

The invention relates to the technical field of medical detection, and discloses an efficient blood sample adding detection device and a detection method thereof.The efficient blood sample adding detection device comprises an intelligent sample recognition module, a detection module and a detection module, the intelligent sample recognition module is used for obtaining sample label images through a camera on the basis of the image recognition technology, conducting feature extraction and repairing on fuzzy, stained or partially missing labels, and obtaining blood sample information; the full-automatic sample adding system is used for automatically adding a blood sample by controlling a mechanical arm; a self-adaptive sample adding amount control module; a microscale sample adding assembly; a cross contamination prevention device; and an intelligent detection module. The concentration and density of each sample are obtained by means of the sensor before sampling, then the sample adding amount is automatically adjusted, the micro sample adding device is controlled by the mechanical arm to perform sample adding operation, the most appropriate sample adding amount is determined according to the actual characteristics of the samples, the influence of too much or too little sample adding on the detection result is avoided, and the detection accuracy is improved. The accuracy of a detection result is effectively improved, and the quality of detection work is improved.

Description

Technical Field

[0001] The invention relates to the technical field of medical detection, in particular to a high-efficiency blood sampling detection device and a detection method thereof. Background Art

[0002] With the rapid development of modern medicine, clinical diagnosis has put forward higher requirements on the accuracy, efficiency and reliability of blood testing results. Rapid and accurate blood testing is crucial in early disease screening, accurate diagnosis and the formulation of personalized treatment plans. At the same time, the continuous advancement of medical testing technology, such as the emergence of new sensors, artificial intelligence algorithms and micro-electromechanical systems, has provided technical support for the development of more efficient blood sampling and testing devices and methods. Traditional sampling devices mostly rely on manual sampling and testing.

[0003] However, in the current technology, manual sample addition cannot determine the most appropriate sample addition amount according to the actual characteristics of the sample, and it is easy to add too much or too little sample, resulting in reduced accuracy of the test results. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an efficient blood sampling detection device and a detection method thereof, which solves the problem that it is difficult to determine the most appropriate sample addition amount according to the actual characteristics of the sample, resulting in reduced accuracy of the detection result.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an efficient blood sampling detection device, comprising:

[0006] Intelligent sample identification module: Based on image recognition technology, the sample label image is obtained through the camera, and the blurred, stained or partially missing labels are extracted and repaired to obtain blood sample information;

[0007] Fully automatic sample loading system: used to automatically load blood samples by controlling the robotic arm;

[0008] Adaptive sample volume control module: obtains the concentration and density of each sample through the sensor before sampling, and automatically adjusts the sample volume;

[0009] Micro-pipette assembly: It is installed on the robotic arm and uses a micro-pipette equipped with a disposable pipette tip to add blood samples;

[0010] Anti-cross-contamination device: used to set up positive pressure airflow barriers and air filtration devices to isolate each blood sample;

[0011] Intelligent detection module: Based on fluorescent labeling detection technology, the sampled blood is tested, analyzed and managed.

[0012] Preferably, the intelligent sample recognition module includes an image and text recognition unit, an image comparison and verification unit, and a sample classification unit;

[0013] The image character recognition unit is used to extract and recognize character features using a deep convolutional neural network model after graying, denoising and character segmentation preprocessing the image;

[0014] The image comparison and verification unit is used to compare the recognized text information and the appearance features in the image with the sample information pre-stored in the database to identify the sample information;

[0015] The sample classification unit is used to classify the identified sample information into a corresponding detection process queue.

[0016] Preferably, the automatic control of the robotic arm in the fully automatic sample loading system adopts a programmable logic controller, and the sampling path and speed of the robotic arm are planned based on the intelligent sample recognition module, and the camera is arranged on the robotic arm.

[0017] Preferably, the adaptive sample addition amount control module includes a sensor detection unit, a sample addition amount calculation unit and a sample addition amount adjustment execution unit;

[0018] The sensor detection unit is used to perform real-time detection on the blood sample test tube through a density sensor and a concentration sensor;

[0019] The sample addition amount calculation unit is used to establish a mapping relationship between sample characteristics and sample addition amount by using regression analysis through historical detection data and sample addition amount data, and calculate the sample addition amount according to the input sample concentration and density data;

[0020] The sample addition amount adjustment execution unit is used to receive the sample addition amount adjustment instruction calculated by the machine learning model and adjust the sample addition amount of the sample addition head.

[0021] Preferably, the micro-volume sample addition component is installed at the end of the mechanical arm through a mechanical interface, and the micro-volume sample addition device controls its sample addition volume through a sample addition volume adjustment execution unit.

[0022] Preferably, the cross-contamination prevention device comprises an airflow generating unit, an airflow guiding and distributing unit and an air filtering unit;

[0023] The airflow generating unit is used to provide airflow to the sample adding area as an air source for the positive pressure airflow barrier;

[0024] The airflow guiding and distributing unit is used to guide the airflow to the sample adding area evenly and stably through the surrounding air duct and the adjustable guide blades;

[0025] The air filter unit is used to be installed in the sample adding area through an air filter to continuously purify the air.

[0026] Preferably, the intelligent detection module includes a fluorescence excitation and signal acquisition unit, a data analysis unit, a result output unit and a sample data storage module;

[0027] The fluorescence excitation and signal acquisition unit is used to excite the fluorescent substance labeled in the blood sample, capture the fluorescence signal, and convert it into an electrical signal to provide raw data for analysis;

[0028] The data analysis unit is used to extract key features of fluorescence data, including fluorescence intensity, fluorescence peak position and fluorescence lifetime, and identify characteristic patterns related to the disease through a convolutional neural network combined with a pathology library;

[0029] The result output unit is used to generate a detailed test report according to the analysis result of the data analysis unit, including test items, test results, reference ranges and diagnostic suggestions;

[0030] The sample data storage module is used to store the collected original data, characteristic parameters and analysis results, add time stamps to all data, and establish a retrieval database.

[0031] An efficient blood sampling detection method comprises the following steps:

[0032] S1. Sample information recognition: The camera collects sample label images, and the intelligent sample recognition module recognizes characters and compares them to classify the samples into the corresponding detection process queue;

[0033] S2, sample addition amount determination and preparation: the sample is detected by the density and concentration sensor, and the sample addition amount is calculated by the sample addition amount calculation unit and fed back to the sample addition target of the micro-sample addition component;

[0034] S3, sample loading control and planning: The fully automatic sample loading system plans the path and speed of the robotic arm, and moves the micro-sample loading component to the intelligent detection module;

[0035] S4, sample testing process: turn on the anti-cross-contamination device, generate airflow and guide and distribute it, and isolate each blood sample;

[0036] S5. Processing of test results: The sampled blood is tested by fluorescent labeling detection technology, the test results are output, and the test data and test results are timestamped to build a retrieval database.

[0037] The present invention provides an efficient blood sampling detection device and a detection method thereof, which have the following beneficial effects:

[0038] 1. The present invention obtains the concentration and density of each sample before sampling with the help of a sensor, and then automatically adjusts the sample addition amount. It combines a mechanical arm to control the micro-pipette to perform the sample addition operation, thereby determining the most appropriate sample addition amount according to the actual characteristics of the sample, avoiding the influence of too much or too little sample addition on the test results, and effectively improving the accuracy of the test results and the quality of the test work.

[0039] 2. The present invention collects sample label images through the camera on the robotic arm, and uses a deep convolutional neural network model to extract and recognize character features of the images that have been preprocessed by grayscale, noise reduction and character segmentation. The recognized text information and image appearance features are carefully compared with the sample information in the database to ensure that the sample information is accurate. In the face of a large number of sample additions, sample confusion can be effectively avoided, thereby improving the reliability and efficiency of the initial stage of the detection process.

[0040] 3. The present invention isolates each blood sample by setting up a positive pressure airflow barrier and an air filtration device, constructing a relatively independent and clean detection environment, preventing cross contamination between different blood samples due to aerosol transmission, air pollutants and other factors, creating a safe and reliable environment for detection work, and ensuring the authenticity and reliability of each sample detection result.

[0041] 4. The present invention detects target substances in blood through fluorescent labeling detection technology, and uses convolutional neural networks to deeply mine key features such as fluorescence intensity, peak position, and lifetime, converting raw data into diagnostic information, significantly improving the accuracy and efficiency of disease diagnosis, and avoiding human omissions, thereby achieving rapid and accurate detection of target substances in blood samples, providing a strong basis for diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a structural diagram of a highly efficient blood sampling detection device of the present invention;

[0043] Figure 2 This is a diagram of the architecture of an intelligent sample identification module of a highly efficient blood sampling detection device of the present invention;

[0044] Figure 3 This is a diagram of the architecture of an adaptive sample addition volume control module of a high-efficiency blood sample addition detection device of the present invention;

[0045] Figure 4 This is a diagram of the intelligent detection module architecture of a highly efficient blood sampling detection device of the present invention;

[0046] Figure 5 The present invention is a flow chart of an efficient blood sampling detection method. DETAILED DESCRIPTION

[0047] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Please refer to the attached Figure 1 - Attachment Figure 4 The embodiment of the present invention provides an efficient blood sampling detection device, comprising:

[0049] Intelligent sample identification module: Based on image recognition technology, the sample label image is obtained through the camera, and the blurred, stained or partially missing labels are extracted and repaired to obtain blood sample information;

[0050] Fully automatic sample loading system: used to automatically load blood samples by controlling the robotic arm;

[0051] Adaptive sample volume control module: obtains the concentration and density of each sample through the sensor before sampling, and automatically adjusts the sample volume;

[0052] Micro-pipette assembly: It is installed on the robotic arm and uses a micro-pipette equipped with a disposable pipette tip to add blood samples;

[0053] Anti-cross-contamination device: used to set up positive pressure airflow barriers and air filtration devices to isolate each blood sample;

[0054] Intelligent detection module: Based on fluorescent labeling detection technology, the sampled blood is tested, analyzed and managed.

[0055] Specifically, the intelligent sample recognition module uses a camera to obtain sample label images, and extracts and repairs blurred, stained or partially missing labels to obtain blood sample information, avoid sample confusion due to label problems, greatly improve the accuracy and efficiency of sample processing, provide a reliable sample information basis for subsequent testing processes, reduce possible errors in manual identification, and ensure that testing work is carried out in an orderly manner from the source;

[0056] The fully automatic loading system controls the robotic arm to automatically load blood samples, which can replace manual loading operations and realize the automation of the loading process, so that multiple loading operations can be performed simultaneously. The control of the robotic arm can ensure the consistency and accuracy of loading, reduce the loading errors caused by human factors, and improve the stability and reliability of the entire detection process.

[0057] The adaptive sample volume control module uses sensors to obtain the concentration and density of each sample before sampling, and then automatically adjusts the sample volume, so as to determine the most appropriate sample volume according to the actual characteristics of the sample, avoiding the impact of too much or too little sample on the test results, effectively improving the accuracy of the test results, making the test data more scientific and reliable, adapting to the test needs of different samples, and improving the quality of the test work;

[0058] By binding the micro-volume sample addition component to the robotic arm, micro-volume and precise sample addition of blood samples can be achieved. The disposable pipette tip effectively prevents cross-contamination between samples, and ultimately achieves precise control of sample addition volume to meet the test items with strict requirements on sample volume. At the same time, it ensures the safety of the test process and avoids deviations in test results due to cross-contamination.

[0059] Through the anti-cross-contamination device, a positive pressure airflow barrier and an air filter are set up to isolate each blood sample, build a relatively independent and clean testing environment, prevent cross-contamination between different blood samples due to aerosol transmission, air pollutants and other factors, create a safe and reliable environment for testing, and ensure the authenticity and reliability of each sample test result;

[0060] Through the intelligent detection module, the added blood is tested, analyzed and managed based on the fluorescent labeling detection technology, so that the target substances in the blood samples can be detected quickly and accurately. The key information in the samples can be mined through data analysis to provide a strong basis for diagnosis, and a detailed test report can be generated to facilitate doctors to fully understand the patient's blood condition, which is helpful for the early detection and accurate diagnosis of diseases and improve the level of medical testing.

[0061] The intelligent sample recognition module includes an image and text recognition unit, an image comparison and verification unit, and a sample classification unit;

[0062] The image character recognition unit is used to extract and recognize character features using a deep convolutional neural network model after graying, denoising and character segmentation preprocessing of the image;

[0063] The image comparison and verification unit is used to compare the recognized text information and the appearance features in the image with the sample information pre-stored in the database to identify the sample information;

[0064] The sample classification unit is used to classify the identified sample information into the corresponding detection process queue.

[0065] Specifically, the sample label image obtained by the image text recognition unit is grayscaled, and the color image is converted into a grayscale mode that is easy to process. At the same time, the noise reduction algorithm is used to remove the interference noise in the image to make the image clearer. Then, the text in the image is divided into single characters through character segmentation to prepare for subsequent recognition. Finally, the character features are extracted and recognized using a deep convolutional neural network model, and the text information on the sample label is converted into data that the computer can understand. This overcomes the problems of low efficiency and error-proneness of traditional manual recognition, improves the speed and accuracy of sample information extraction, and provides a reliable data foundation for subsequent sample processing processes.

[0066] The image comparison and verification unit compares the text information recognized by the image text recognition unit, together with the appearance features in the image, such as the shape, color, pattern, etc. of the label, with the sample information pre-stored in the database in multiple dimensions, effectively avoiding sample information errors caused by image recognition errors or sample label confusion;

[0067] Through the sample classification unit, based on multiple factors such as test items, sample types, collection time, patient priority, etc., different samples are automatically classified into the corresponding test process queue according to their respective characteristics and test requirements, so that the entire test process can be carried out more orderly and efficiently, thereby improving the overall operating efficiency of the test system and accelerating the output of test results.

[0068] The automatic control of the robotic arm in the fully automatic sampling system adopts a programmable logic controller. The sampling path and speed of the robotic arm are planned based on the intelligent sample recognition module, and the camera is set on the robotic arm.

[0069] Specifically, the robotic arm automation control adopts a programmable logic controller, which plans the robotic arm's sample loading path and speed based on the intelligent sample recognition module. The camera is set on the robotic arm, and the sample label image is obtained and the information is recognized through the camera. The programmable logic controller accurately plans the robotic arm's sample loading path and speed based on this, realizing a seamless connection from sample recognition to sample loading operations, greatly improving the automation level of the sample loading process, avoiding operational errors and time loss caused by human intervention, and at the same time, the robotic arm runs according to the planned path and speed to ensure the accuracy of the sample loading position and the rationality of the sample loading speed.

[0070] The adaptive sample addition amount control module includes a sensor detection unit, a sample addition amount calculation unit and a sample addition amount adjustment execution unit;

[0071] The sensor detection unit is used to perform real-time detection on the blood sample test tube through a density sensor and a concentration sensor;

[0072] The sample addition amount calculation unit is used to establish a mapping relationship between sample characteristics and sample addition amount by using historical detection data and sample addition amount data, and calculate the sample addition amount according to the input sample concentration and density data;

[0073] Specifically, the sensor detection unit uses a density sensor and a concentration sensor to conduct real-time detection of the blood sample tube, which can accurately capture the physical and chemical properties of the blood sample and provide an accurate data source for subsequent sample addition calculation;

[0074] When the sample concentration and density data transmitted by the sensor detection unit is received through the sample addition calculation unit, a mapping relationship between sample characteristics and sample addition amount is established based on regression analysis, and the sample addition amount is calculated, thereby being able to provide a personalized sample addition amount plan based on the unique properties of different samples, effectively meeting diverse detection needs, and laying a solid foundation for obtaining accurate detection results;

[0075] The sample volume adjustment execution unit receives the sample volume adjustment instructions calculated by the machine learning model and converts them into actual mechanical actions to accurately adjust the sample volume of the sample head, ensuring the accuracy and stability of the sample volume process and effectively avoiding the problem of inaccurate sample volume caused by execution deviation. The ultimate effect is to close the entire sample volume process, achieve seamless connection from sample characteristic detection, sample volume calculation to actual sample volume operation, significantly improve the accuracy and detection efficiency of sample volume, and provide strong guarantee for high-quality blood testing.

[0076] The micro-volume sample addition component is installed at the end of the robot arm through a mechanical interface, and the micro-volume sampler controls its sample addition volume through a sample addition volume adjustment execution unit.

[0077] Specifically, the micro-dosing component is firmly installed at the end of the robotic arm with the help of a mechanical interface, which enables the micro-dosing component to be flexibly moved, so that it can quickly reach different blood sample locations and efficiently complete the dosing task. At the same time, the dosing volume adjustment execution unit accurately controls the micro-dosing device based on the calculation results, ensuring the high accuracy of the dosing volume, meeting the strict requirements of various tests on sample volume, and ensuring the accuracy and reliability of the test results.

[0078] The cross-contamination prevention device includes an airflow generating unit, an airflow guiding and distributing unit, and an air filtering unit;

[0079] The airflow generating unit is used to provide airflow to the sample adding area, serving as an air source for the positive pressure airflow barrier;

[0080] The airflow guide distribution unit is used to guide the airflow evenly and stably to the sample loading area through the surrounding air duct and adjustable guide blades;

[0081] The air filtration unit is used to continuously purify the air by installing it in the sample loading area through an air filter.

[0082] Specifically, the airflow generating unit, as the power source of the anti-cross-contamination device, injects strong airflow power into the sample loading area, making it difficult for external pollutants to invade the sample loading area under the action of pressure difference, thereby reducing the risk of contamination from the root;

[0083] The airflow guide distribution unit uses a surround air duct and adjustable guide blades to precisely control the airflow generated by the airflow generation unit, and guides the airflow evenly and stably to the sample loading area, forming an all-round, no-dead-angle positive pressure airflow barrier, so that every part of the sample loading area can be effectively protected to prevent the invasion of external pollutants, greatly improving the effect of anti-cross contamination, and creating a safe and reliable environment for the sample loading operation of blood samples;

[0084] The air in the sampling area is continuously purified through the air filtration unit to intercept aerosols, bacteria, viruses and other tiny pollutants in the air, further reducing the risk of cross-contamination between samples, ensuring that the air quality in the sampling area is always maintained at a high standard, and effectively avoiding detection errors caused by air pollution.

[0085] The intelligent detection module includes a fluorescence excitation and signal acquisition unit, a data analysis unit, a result output unit and a sample data storage module;

[0086] The fluorescence excitation and signal acquisition unit is used to excite the fluorescent substances labeled in the blood sample and capture the fluorescence signal and convert it into an electrical signal to provide raw data for analysis;

[0087] The data analysis unit is used to extract key features of fluorescence data, including fluorescence intensity, fluorescence peak position and fluorescence lifetime, and identify characteristic patterns related to diseases through convolutional neural networks and combined with pathology libraries;

[0088] The result output unit is used to generate a detailed test report according to the analysis result of the data analysis unit, including test items, test results, reference ranges and diagnostic suggestions;

[0089] The sample data storage module is used to store the collected raw data, characteristic parameters and analysis results, add timestamps to all data, and establish a retrieval database.

[0090] Specifically, the fluorescence excitation and signal acquisition unit can accurately excite the fluorescent substances labeled in the blood sample based on a specific excitation light source, such as a laser diode or a high-intensity xenon lamp, and quickly capture the generated fluorescence signal, and convert it into an electrical signal for subsequent processing, ensuring that the detection process can keenly capture the changes of fluorescent substances in the blood sample, providing data for accurate analysis of the sample status, greatly improving the detection efficiency, and laying the foundation for the timeliness and accuracy of subsequent data analysis;

[0091] The data analysis unit can deeply extract the key features of fluorescence data, such as fluorescence intensity, fluorescence peak position and fluorescence lifetime, and with the help of convolutional neural networks, it can convert the original fluorescence data into information with diagnostic value, find clues related to diseases from massive data, and help diagnose the health status of samples quickly and accurately, significantly improving the accuracy and efficiency of disease diagnosis, and effectively avoiding omissions and misjudgments that may occur due to manual analysis;

[0092] The result output unit converts complex test information into detailed reports, providing strong support for doctors to formulate treatment plans, and making it easier for patients to understand their own health conditions, promoting communication and understanding between doctors and patients, and improving the practicality and value of testing services;

[0093] The sample data storage module can be used to store all important information during the testing process, which is convenient for subsequent query, comparison and research. Historical data can be retrieved at any time during diagnosis to track the progression of the patient's condition. Standardized data storage and convenient retrieval functions can be used to achieve efficient management and utilization of data.

[0094] See attached Figure 5 , an efficient blood sampling detection method, comprising the following steps:

[0095] S1. Sample information recognition: The camera collects sample label images, and the intelligent sample recognition module recognizes characters and compares them to classify the samples into the corresponding detection process queue;

[0096] S2, sample addition amount determination and preparation: the sample is detected by the density and concentration sensor, and the sample addition amount is calculated by the sample addition amount calculation unit and fed back to the sample addition target of the micro-sample addition component;

[0097] S3, sample loading control and planning: The fully automatic sample loading system plans the path and speed of the robotic arm, and moves the micro-sample loading component to the intelligent detection module;

[0098] S4, sample testing process: turn on the anti-cross-contamination device, generate airflow and guide and distribute it, and isolate each blood sample;

[0099] S5. Processing of test results: The sampled blood is tested by fluorescent labeling detection technology, the test results are output, and the test data and test results are timestamped to build a retrieval database.

[0100] Specifically, through sample information recognition, the robotic arm camera and intelligent sample recognition module are used to quickly and accurately obtain sample information and classify it, avoiding sample confusion and laying a solid foundation for subsequent testing. The sample volume determination and preparation are based on the characteristics of the sample with the help of sensors and sample volume calculation units. The sample volume is accurately calculated according to the sample characteristics and fed back to the micro-sample component to ensure the accuracy of the sample. Then, the sample control and planning are carried out through the fully automatic sample system to reasonably plan the path and speed of the robotic arm, and the micro-sample component is transferred to the intelligent detection module to improve the efficiency of the sample. In the sample detection process, the anti-cross-contamination device is turned on to effectively isolate the blood sample and ensure the safety of the detection environment. Finally, the test result processing uses fluorescent labeling detection technology to detect blood, output the results, and add timestamps to the data to establish a retrieval database for convenient data management and query. The whole process is automated and precise, and the final effect is to greatly improve the detection efficiency, accuracy and reliability.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient blood sampling detection device, characterized in that: include: Intelligent sample identification module: Based on image recognition technology, the sample label image is obtained through the camera, and the blurred, stained or partially missing labels are extracted and repaired to obtain blood sample information; Fully automatic sample loading system: used to automatically load blood samples by controlling the robotic arm; Adaptive sample volume control module: obtains the concentration and density of each sample through the sensor before sampling, and automatically adjusts the sample volume; Micro-pipette assembly: It is installed on the robotic arm and uses a micro-pipette equipped with a disposable pipette tip to add blood samples; Anti-cross-contamination device: used to set up positive pressure airflow barriers and air filtration devices to isolate each blood sample; Intelligent detection module: Based on fluorescent labeling detection technology, the sampled blood is tested, analyzed and managed.

2. A high-efficiency blood sampling detection device according to claim 1, characterized in that: The intelligent sample recognition module includes an image and text recognition unit, an image comparison and verification unit, and a sample classification unit; The image character recognition unit is used to extract and recognize character features using a deep convolutional neural network model after graying, denoising and character segmentation preprocessing the image; The image comparison and verification unit is used to compare the recognized text information and the appearance features in the image with the sample information pre-stored in the database to identify the sample information; The sample classification unit is used to classify the identified sample information into a corresponding detection process queue.

3. The high-efficiency blood sampling detection device according to claim 1, characterized in that: The automatic control of the robotic arm in the fully automatic sample loading system adopts a programmable logic controller, and the sampling path and speed of the robotic arm are planned based on the intelligent sample recognition module. The camera is arranged on the robotic arm.

4. The high-efficiency blood sampling detection device according to claim 1, characterized in that: The adaptive sample addition amount control module includes a sensor detection unit, a sample addition amount calculation unit and a sample addition amount adjustment execution unit; The sensor detection unit is used to perform real-time detection on the blood sample test tube through a density sensor and a concentration sensor; The sample addition amount calculation unit is used to establish a mapping relationship between sample characteristics and sample addition amount by using regression analysis through historical detection data and sample addition amount data, and calculate the sample addition amount according to the input sample concentration and density data; The sample addition amount adjustment execution unit is used to receive the sample addition amount adjustment instruction calculated by the machine learning model and adjust the sample addition amount of the sample addition head.

5. The high-efficiency blood sampling detection device according to claim 1, characterized in that: The micro-volume sample addition component is installed at the end of the mechanical arm through a mechanical interface, and the micro-volume sample adder controls its sample addition volume through a sample addition volume adjustment execution unit.

6. The high-efficiency blood sampling detection device according to claim 1, characterized in that: The cross-contamination prevention device includes an airflow generating unit, an airflow guiding and distributing unit, and an air filtering unit; The airflow generating unit is used to provide airflow to the sample adding area as an air source for the positive pressure airflow barrier; The airflow guiding and distributing unit is used to guide the airflow to the sample adding area evenly and stably through the surrounding air duct and the adjustable guide blades; The air filter unit is used to be installed in the sample adding area through an air filter to continuously purify the air.

7. The high-efficiency blood sampling detection device according to claim 1, characterized in that: The intelligent detection module includes a fluorescence excitation and signal acquisition unit, a data analysis unit, a result output unit and a sample data storage module; The fluorescence excitation and signal acquisition unit is used to excite the fluorescent substance labeled in the blood sample, capture the fluorescence signal, and convert it into an electrical signal to provide raw data for analysis; The data analysis unit is used to extract key features of fluorescence data, including fluorescence intensity, fluorescence peak position and fluorescence lifetime, and identify characteristic patterns related to the disease through a convolutional neural network combined with a pathology library; The result output unit is used to generate a detailed test report according to the analysis result of the data analysis unit, including test items, test results, reference ranges and diagnostic suggestions; The sample data storage module is used to store the collected original data, characteristic parameters and analysis results, add time stamps to all data, and establish a retrieval database.

8. An efficient blood sampling detection method, characterized in that: An efficient blood sampling detection device for use in claims 1-7 comprises the following steps: S1. Sample information recognition: The camera collects sample label images, and the intelligent sample recognition module recognizes characters and compares them to classify the samples into the corresponding detection process queue; S2, sample addition amount determination and preparation: the sample is detected by the density and concentration sensor, and the sample addition amount is calculated by the sample addition amount calculation unit and fed back to the sample addition target of the micro-sample addition component; S3, sample loading control and planning: The fully automatic sample loading system plans the path and speed of the robotic arm, and moves the micro-sample loading component to the intelligent detection module; S4, sample testing process: turn on the anti-cross-contamination device, generate airflow and guide and distribute it, and isolate each blood sample; S5. Processing of test results: The sampled blood is tested by fluorescent labeling detection technology, the test results are output, and the test data and test results are timestamped to build a retrieval database.

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