Method for determining coagulation time of blood sample and related device

Abstract

The invention discloses a blood sample clotting time determination method and a related device, and relates to the technical field of biomedicines.The method comprises the steps that for a sample of a first abnormal level, first clotting time is obtained through optical detection, then whether the first clotting time is in a corresponding range or not is judged, if not, magnetic bead detection is automatically started to obtain second clotting time, and if yes, the first clotting time is determined; determining target solidification time according to the target solidification time; for the first abnormal grade sample, the target solidification time is determined by fusing the first solidification time obtained by optical detection and the second solidification time obtained by magnetic bead detection, so that the detection result is more reliable, and misjudgment caused by errors possibly caused by a single detection method is avoided. And directly detecting the second abnormal grade sample by using the magnetic beads to obtain the target solidification time. According to the mode of automatically switching the detection scheme, the automation degree of the detection process is greatly improved, the time from sample detection to result report obtaining is shortened, and the determination efficiency and accuracy of the coagulation time of the blood sample are improved.

Description

Technical Field

[0001] The present application relates to the field of biomedical technology, and in particular to a method for determining the coagulation time of a blood sample and a related device. Background Art

[0002] In recent years, blood system diseases such as hemophilia, thrombocytopenia, leukemia, and von Willebrand disease have gradually become important factors affecting people's health. The diagnosis of these diseases relies on the measurement of blood clotting time. For example, before surgery, assessing the patient's coagulation function through clotting time helps predict the risk of postoperative bleeding. During anticoagulant therapy, regular monitoring of clotting time helps ensure the safety and effectiveness of treatment. Therefore, the determination of blood clotting time is particularly important.

[0003] Currently, in traditional blood sample coagulation testing, the coagulation time of the blood sample is determined based on light source irradiation, and manual verification and confirmation are also required.

[0004] However, when there are abnormalities in the blood sample, light source illumination alone cannot accurately reflect its true coagulation status, which can easily lead to inaccurate determination of the coagulation time. Even after manual verification and confirmation, the efficiency of determining the coagulation time is low. Summary of the Invention

[0005] In view of the above problems, this application provides a method and related device for determining the coagulation time of a blood sample in order to improve the accuracy and efficiency of determining the coagulation time of a blood sample. The specific solution is as follows:

[0006] A first aspect of the present application provides a method for determining the coagulation time of a blood sample, comprising:

[0007] Obtaining a target component of a blood sample, and determining a target light source wavelength and a target magnetic field intensity corresponding to the target component of the blood sample, wherein the target component of the blood sample is a component in the blood sample related to coagulation function;

[0008] Determining an abnormality level of the blood sample using a machine learning model, where the abnormality level of the blood sample includes a first abnormality level and a second abnormality level, and the first abnormality level is lower than the second abnormality level;

[0009] If the abnormality level of the blood sample is the first abnormality level, irradiating the blood sample with a light source having the target light source wavelength, taking the time for the light transmittance of the blood sample to change from the first light transmittance to the second light transmittance as a first coagulation time of the blood sample, and determining whether the first coagulation time of the blood sample falls within a coagulation time range corresponding to the first abnormality level, wherein the first light transmittance is determined based on the blood sample in an initially liquid state, and the second light transmittance is determined based on the blood sample after it has completely coagulated;

[0010] If the first coagulation time of the blood sample does not fall within the coagulation time range corresponding to the first abnormality level, determining a second coagulation time of the blood sample using magnetic beads in the blood sample that respond to a magnetic field of the target magnetic field strength, and determining a target coagulation time of the blood sample based on the first coagulation time of the blood sample and the second coagulation time of the blood sample;

[0011] If the abnormality level of the blood sample is the second abnormality level, a third coagulation time of the blood sample is determined using magnetic beads in the blood sample that respond to a magnetic field of the target magnetic field strength, and the third coagulation time of the blood sample is used as the target coagulation time of the blood sample.

[0012] In one possible implementation, determining the target light source wavelength and target magnetic field strength corresponding to the target component of the blood sample includes:

[0013] Performing full-band scanning on the target component of the blood sample to obtain spectral data of the target component;

[0014] The wavelength corresponding to the absorbance peak in the spectral data of the target component is used as the target light source wavelength;

[0015] Marking target components of the blood sample with magnetic beads so that the magnetic beads respond to different magnetic fields in the blood sample, and obtaining response data of the magnetic beads;

[0016] The magnetic field intensity corresponding to the response peak value in the response data of the magnetic beads is used as the target magnetic field intensity.

[0017] In one possible implementation, the machine learning model includes a first abnormality level determination model and a second abnormality level determination model;

[0018] Determining the abnormality level of the blood sample by using a machine learning model includes:

[0019] Obtaining a picture of the blood sample, and inputting the picture of the blood sample into a pre-trained first abnormality level determination model to obtain an abnormality level of the blood sample;

[0020] or,

[0021] The blood sample is irradiated with light sources of multiple wavelengths to obtain the absorbance of the blood sample, and the absorbance of the blood sample is input into a pre-trained second abnormality level determination model to obtain the abnormality level of the blood sample.

[0022] In a possible implementation, the process of determining the first abnormality level determination model includes:

[0023] Obtaining a plurality of historical training samples with historical annotated information, wherein the annotated information includes abnormality levels of historical blood samples, and each of the training samples includes a picture of the historical blood sample;

[0024] The initial model is trained based on the multiple historical training samples to obtain the first abnormality level determination model, the input of the first abnormality level determination model is the image of the blood sample, and the output of the first abnormality level determination model is the abnormality level of the blood sample.

[0025] In a possible implementation, determining the second coagulation time of the blood sample by using magnetic beads in the blood sample that respond to the magnetic field of the target magnetic field strength includes:

[0026] obtaining a swing amplitude of the magnetic beads in the blood sample in response to the magnetic field of the target magnetic field strength;

[0027] The time it takes for the swing amplitude of the magnetic beads to change from a first swing amplitude to a second swing amplitude is used as the second coagulation time of the blood sample, where the first swing amplitude is determined based on the blood sample in an initial liquid state, and the second swing amplitude is determined based on the blood sample after it has completely coagulated.

[0028] In a possible implementation, determining the target coagulation time of the blood sample based on the first coagulation time of the blood sample and the second coagulation time of the blood sample includes:

[0029] calculating a transmittance fluctuation coefficient of the blood sample based on each transmittance of the blood sample, and determining a first reliability of a first coagulation time of the blood sample based on the transmittance fluctuation coefficient of the blood sample;

[0030] calculating a signal-to-noise ratio of the swing amplitudes of the magnetic beads based on the swing amplitudes of the magnetic beads, and determining a second reliability of the second coagulation time of the blood sample based on the signal-to-noise ratio of the swing amplitudes of the magnetic beads;

[0031] assigning a first weight to a first clotting time of the blood sample and assigning a second weight to a second clotting time of the blood sample based on the first confidence level and the second confidence level;

[0032] A target coagulation time of the blood sample is calculated based on the first coagulation time of the blood sample, the first weight, the second coagulation time of the blood sample, and the second weight.

[0033] In a possible implementation, after determining the third coagulation time of the blood sample using the magnetic beads in the blood sample that respond to the magnetic field of the target magnetic field strength, the method further includes:

[0034] determining whether the third coagulation time of the blood sample falls within a coagulation time range corresponding to the second abnormality level;

[0035] If the third coagulation time of the blood sample does not belong to the coagulation time range corresponding to the second abnormality level, irradiating the blood sample with a light source of the target light source wavelength to obtain a fourth coagulation time of the blood sample;

[0036] Correcting the third coagulation time of the blood sample using the fourth coagulation time of the blood sample so that the corrected third coagulation time of the blood sample falls within a coagulation time range corresponding to the second abnormality level;

[0037] The step of using the third coagulation time of the blood sample as the target coagulation time of the blood sample comprises:

[0038] The corrected third coagulation time of the blood sample is used as the target coagulation time of the blood sample.

[0039] A second aspect of the present application provides a computer program product, comprising computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the method for determining the coagulation time of a blood sample according to the first aspect or any implementation of the first aspect.

[0040] A third aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0041] The memory is used to store computer programs;

[0042] The processor is configured to execute the computer program so that the electronic device can implement the method for determining the coagulation time of a blood sample according to the first aspect or any implementation of the first aspect.

[0043] In a fourth aspect, the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can perform the method for determining the coagulation time of a blood sample according to the first aspect or any implementation of the first aspect.

[0044] By means of the above technical solution, the present application provides a method for determining the coagulation time of a blood sample and a related device, the method comprising: for samples of the first abnormal level, first using optical detection to obtain the first coagulation time, then determining whether it is within the corresponding range, if not, automatically enabling magnetic bead detection to obtain the second coagulation time, and determining the target coagulation time accordingly; for samples of the first abnormal level, combining the first coagulation time obtained by optical detection and the second coagulation time obtained by magnetic bead detection to determine the target coagulation time. This multi-dimensional data combination method can verify and supplement each other, making the test results more reliable and avoiding misjudgments caused by errors that may occur due to a single detection method. For samples of the second abnormal level, the target coagulation time is directly obtained by magnetic bead detection. This method of automatically switching detection schemes greatly improves the degree of automation of the detection process, shortens the time from sample detection to result reporting, and improves the efficiency and accuracy of determining the coagulation time of blood samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0046] Figure 1 A schematic flow chart of a method for determining the coagulation time of a blood sample provided in an embodiment of the present application;

[0047] Figure 2 A schematic block diagram of an optical detection method provided in an embodiment of the present application;

[0048] Figure 3 A schematic diagram of a reaction curve of an optical detection provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of the structure of a magnetic bead detection provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of a swing curve of a magnetic bead detection provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of a reaction curve of a magnetic bead assay provided in an embodiment of the present application;

[0052] Figure 7 A schematic diagram of the hardware structure of a device for determining the coagulation time of a blood sample provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following describes the embodiments of the present application in conjunction with the accompanying drawings. The terms used in the implementation methods of the present application are only used to explain the specific embodiments of the present application and are not intended to limit the present application.

[0054] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0055] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, and this is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0056] In order to improve the accuracy and efficiency of determining the coagulation time of a blood sample, the present application provides a method for determining the coagulation time of a blood sample. The method for determining the coagulation time of a blood sample provided in the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0057] Please see the attached Figure 1 , Figure 1 This is a flow chart of a method for determining the coagulation time of a blood sample provided in an embodiment of the present application. The method may include the following steps:

[0058] Step S101: obtaining a target component of a blood sample, and determining a target light source wavelength and a target magnetic field intensity corresponding to the target component of the blood sample.

[0059] It should be noted that the target components of blood samples are those related to coagulation. Blood coagulation involves multiple components, primarily including: coagulation factors, such as factor I (fibrinogen), factor II (prothrombin), factor V, factor VII, factor VIII, factor IX, and factor X. These factors interact during the coagulation process to form thrombin, which in turn converts fibrinogen to fibrin, forming a blood clot. Platelets: Platelets play a key role in the coagulation process. They can aggregate at sites of vascular injury, forming platelet plugs to prevent blood loss. They also release various coagulation-related substances to promote coagulation. Anticoagulants, such as antithrombin III and the protein C system, play an important role in regulating the coagulation process and preventing excessive coagulation. Fibrin degradation products (FDP): Fibrin degradation products are products produced by the decomposition of fibrin or fibrinogen during the fibrinolysis process. Their levels can reflect the fibrinolytic activity in the body and indirectly reflect the balance between coagulation and fibrinolysis.

[0060] There are many ways to obtain target components from blood samples. The following are some of the methods:

[0061] (1) Whole blood separation: After collecting blood samples directly, the blood samples may need to be separated and processed according to the test requirements. For example, blood samples can be separated into blood samples or serum by centrifugation or other methods. Blood samples are the liquid part of the blood after the blood cells are removed. They contain a variety of coagulation factors and proteins and are an important sample source for testing components related to coagulation function. Serum is the liquid that precipitates after blood coagulation and also contains some coagulation-related components.

[0062] (2) Immunoprecipitation: Target components are obtained by using the specific binding reaction between antigen and antibody. Specific antibodies targeting the target component are added to the blood sample, and the antibodies bind to the target component to form immune complexes. Magnetic or precipitable substances (such as protein A / G-coupled magnetic beads) are then added to bind to the antibodies, and the immune complexes are precipitated using a magnetic field or centrifugation, thereby achieving separation and enrichment of the target component.

[0063] (3) Affinity chromatography: A separation technique based on the specific binding between the target component and a specific ligand. The ligand that specifically binds to the target component is fixed to the filler of the chromatography column. When the blood sample passes through the chromatography column, the target component will bind to the ligand and remain in the chromatography column, while other components will flow out with the mobile phase. Subsequently, by changing the elution conditions, the target component is eluted from the ligand, thereby achieving purification and enrichment of the target component.

[0064] In this application, after determining the target component of a blood sample, first, a full-band scan of the target component of the blood sample can be performed to obtain spectral data of the target component. Then, the wavelength corresponding to the absorbance peak in the spectral data of the target component can be used as the target light source wavelength. The target component of the blood sample can then be labeled with magnetic beads so that the magnetic beads respond to different magnetic fields in the blood sample, obtaining magnetic bead response data. Finally, the magnetic field intensity corresponding to the responsivity peak in the magnetic bead response data is used as the target magnetic field intensity.

[0065] Specifically, the blood plasma is first placed in an optically transparent cuvette (typically made of quartz or optical plastic) to ensure that the light transmittance meets the detection requirements. The container material must be transparent to the target light source wavelength and free of fluorescence interference to facilitate subsequent spectral analysis. A spectrometer can be used to scan the target components of the blood sample across the entire wavelength range (e.g., 200-1000 nm), covering the ultraviolet, visible, and near-infrared regions. This light passes through the blood sample, with some absorbed by various components within the sample. The transmitted light is then received by the spectrometer's detector. Based on the intensity of the transmitted light received by the spectrometer, the absorbance value at each wavelength is calculated, resulting in a full-band absorption spectrum curve for the blood sample. The absorbance value reflects the degree of blood absorption at different wavelengths of light and is calculated using the formula: A = lg(I / I0), where I0 is the incident light intensity and I is the transmitted light intensity.

[0066] Each substance has its own unique spectral signature, meaning it strongly absorbs light at specific wavelengths. In a blood sample, the characteristic absorption peaks corresponding to different components are as follows: Hemoglobin: has a characteristic absorption peak at 405nm. If hemolysis occurs in the plasma, hemoglobin is released into the plasma, resulting in a distinct absorption peak at this wavelength. Bilirubin: has a characteristic absorption peak in the 450-500nm range. When the bilirubin content in the plasma increases, such as in the presence of jaundice, the absorbance within this wavelength range increases significantly. Lipids: have characteristic absorption peaks in the near-infrared band, such as at 660nm. In cases of lipemia, the lipid content in the plasma increases, and its absorbance at this wavelength also increases accordingly.

[0067] Next, find the point of highest absorbance on the full-band absorption spectrum curve, i.e., the absorbance peak. The wavelength corresponding to this peak is the characteristic wavelength of the primary absorbing component in the plasma sample and can be used as the target light source wavelength. For example, if the plasma is hemolyzed, the absorbance peak may occur at 405nm; if icterus is present, the peak may be somewhere in the 450-500nm range; if lipemia is present, the peak may be at 660nm, and so on.

[0068] Furthermore, second-derivative spectroscopy involves performing two derivative operations on the original absorption spectrum curve. This mathematical processing amplifies subtle variations in the spectral curve, allowing previously overlapping or close absorption peaks to be separated and clarified. It also eliminates some background interference, such as slowly varying interference caused by light scattering and instrument noise. In practice, professional spectral analysis software is used to perform second-order derivative calculations on spectral data obtained from a full-band scan. The software processes the absorbance values ​​at each wavelength to produce a second-order derivative spectral curve. On this curve, absorption peaks that were previously inconspicuous in the original spectrum become more prominent and sharp, making it easier to accurately determine the wavelength corresponding to the absorbance peak, thereby more precisely targeting the wavelength of the target light source. For example, second-order derivative spectroscopy can help more accurately identify and locate the characteristic absorption peaks of components that are difficult to distinguish in the original spectrum due to background interference or spectral overlap.

[0069] The target components of the blood sample can then be magnetically labeled. Magnetic beads are tiny magnetic particles with a chemically modified surface that can specifically bind to the target component. The magnetic bead labeling process typically involves the following steps: Based on the properties of the target component, magnetic beads with specific antibodies or ligands on their surface are selected. These magnetic beads are capable of specifically binding to the target component (such as coagulation factors, platelets, etc.); the magnetic beads are mixed with the blood sample and reacted under appropriate conditions (such as temperature, pH, reaction time, etc.) to fully bind to the target component; the magnetic beads bound to the target component are separated from the sample using a magnetic field, and then washed with an appropriate buffer to remove unbound impurities and non-specifically bound substances.

[0070] Finally, magnetic bead labeling allows the beads to respond to different magnetic field intensities and frequencies in a blood sample. To obtain data on the magnetic bead response, a magnetic analysis instrument (such as a magnetic bead analyzer or magnetic resonance imaging device) can be used. The specific steps are as follows: A series of magnetic fields of varying strengths are applied to a container containing the labeled sample. The magnetic field strength can be gradually increased from low to high to observe the response of the magnetic beads under different magnetic fields. At each magnetic field strength, the magnetic responsivity of the beads is measured. Responsivity can be measured in various ways, such as bead migration speed, aggregation, and magnetization. These data reflect the characteristics of the magnetic bead's behavior in a magnetic field. The responsivity of the beads at each magnetic field strength is recorded to form a curve showing the relationship between magnetic field strength and responsivity. The recorded response data is plotted on a coordinate system with magnetic field strength on the horizontal axis and bead responsivity on the vertical axis. This typically results in a curve with one or more peaks. On the response curve, the point at which the responsivity reaches its maximum is identified as the responsivity peak. The magnetic field strength corresponding to this peak is the target magnetic field strength. The target magnetic field strength reflects the magnetic response of the magnetic beads at the strongest level after binding to the target component. This indicates that the interaction between the magnetic beads and the target component is most significant at this magnetic field strength, enabling effective detection and analysis of the target component.

[0071] Step S102: using a machine learning model to determine the abnormality level of the blood sample, where the abnormality level of the blood sample includes a first abnormality level and a second abnormality level, and the first abnormality level is lower than the second abnormality level.

[0072] It should be noted that the machine learning model includes a first abnormality level determination model and a second abnormality level determination model.

[0073] As an implementable embodiment, a picture of a blood sample may be obtained and input into a pre-trained first abnormality level determination model to obtain the abnormality level of the blood sample.

[0074] In this application, images of blood samples can be acquired using specialized medical imaging equipment, such as microscope cameras, flow cytometers, or specialized blood analysis imaging systems. These images clearly display information such as the morphology, distribution, and concentration of cells, particles, and other components in the blood sample, including the morphology and aggregation of red blood cells, white blood cells, and platelets. These visual features may be closely related to the degree of blood abnormality. The acquired blood sample images are input into a pre-trained model for determining the first abnormality level. This model has learned to identify and analyze various features in the images using a large number of training samples with historical annotations, and to associate these features with corresponding abnormality levels. The model internally extracts and processes features from the input images, such as identifying abnormal morphology, aggregation, or other key visual indicators of blood cells. Based on these features, the model then determines the abnormality level of the blood sample. Ultimately, the model outputs a predicted result, namely the first abnormality level of the blood sample, providing doctors or test personnel with a reference to the degree of abnormality based on the sample's appearance characteristics.

[0075] The process of determining the first abnormality level determination model may include obtaining multiple historical training samples with historical annotated information, the annotated information including the abnormality level of the historical blood samples, each training sample comprising an image of the historical blood sample. An initial model is trained based on the multiple historical training samples to obtain the first abnormality level determination model, wherein the input of the first abnormality level determination model is the image of the blood sample, and the output of the first abnormality level determination model is the abnormality level of the blood sample.

[0076] Specifically, historical training samples are collected: samples with accurate abnormality level annotations are selected from a large number of historical blood test records as training data. Each training sample consists of two parts: an image of the historical blood sample, acquired using specialized equipment and pre-processed to ensure its quality and clarity meet the requirements of feature analysis; and the corresponding annotation information, namely the actual abnormality level of the historical blood sample, which is typically determined by professional medical examiners or doctors based on clinical diagnosis results.

[0077] Preprocessing and feature extraction: Before training begins, the collected image data needs to be preprocessed. This includes operations such as resizing, grayscaling, or normalizing pixel values ​​to eliminate data discrepancies and improve model training efficiency. Feature extraction from images is also a key step. Traditional image processing techniques (such as edge detection and texture analysis) can be used to extract handcrafted features, or convolutional neural networks (CNNs) within deep learning can be used to automatically learn high-level feature representations from images. These features can more accurately capture visual information related to abnormality levels in blood sample images, such as changes in cell morphology and abnormal distribution of components.

[0078] Training the initial model: The preprocessed image data and its corresponding anomaly level annotations are fed into the initial model for training. The initial model can be built using an artificial neural network, a support vector machine (SVM), or other machine learning algorithms suitable for image classification tasks. During training, the model continuously adjusts its parameters to minimize the error between the predicted anomaly level and the actual anomaly level. This process typically requires multiple epochs. By continuously propagating input data forward, calculating the loss function, and backpropagating parameter updates, the model gradually learns the complex nonlinear relationship between image features and anomaly levels. Regularization techniques and validation sets are also employed during training to prevent overfitting, ensuring the model has good generalization capabilities and can accurately predict anomaly levels on unseen new samples.

[0079] Validation and Optimization: The trained model's performance is evaluated using an independent validation set. This validation set consists of historical samples that were not used in training. By calculating metrics such as precision, recall, and F1 score on the validation set, the model's predictive performance can be objectively measured. If the model performs poorly on the validation set, adjustments to the model structure may be necessary, such as increasing or decreasing the number of network layers, changing the number of neurons, adjusting hyperparameters (such as the learning rate and regularization strength), or adopting different feature extraction methods. After repeated adjustments and optimization, a first-level abnormality level determination model was ultimately developed that accurately and stably predicts abnormality levels based on blood sample images.

[0080] As another possible implementation method, the blood sample can be irradiated with light sources of multiple wavelengths to obtain the absorbance of the blood sample, and the absorbance of the blood sample can be input into a pre-trained second abnormality level determination model to obtain the abnormality level of the blood sample.

[0081] In this application, an optical instrument such as a spectrophotometer can be used to sequentially illuminate a blood sample using light sources of multiple different wavelengths, measuring the sample's absorbance at each wavelength. The selection of these specific wavelengths is typically based on the spectral properties of different blood components. For example, components such as hemoglobin and bilirubin have characteristic absorption peaks at specific wavelengths. By measuring absorbance at multiple wavelengths, information about the relative content and optical properties of various components in the blood sample can be obtained. This data can reflect certain biochemical states and abnormalities in the blood sample. The resulting blood sample absorbance data is input into a pre-trained model for determining a second abnormality level. This model analyzes the absorbance data of new samples by learning the mapping relationship between absorbance features and abnormality levels from historical data. It identifies absorbance patterns associated with abnormality levels, such as abnormal increases or decreases in absorbance at certain wavelengths and correlations between absorbances at different wavelengths. Based on these features, the model calculates and outputs a second abnormality level for the blood sample, providing another dimension for abnormality assessment based on biochemical and optical characteristics.

[0082] Collecting historical training samples involves obtaining multiple historical training samples with historical annotated information, the annotated information including the abnormality level of the historical blood samples, and each training sample including the absorbance of the historical blood samples. An initial model is trained based on the multiple historical training samples to obtain a second abnormality level determination model, where the input of the second abnormality level determination model is the absorbance of the blood samples, and the output of the second abnormality level determination model is the abnormality level of the blood samples.

[0083] Specifically, historical blood sample data with anomaly level annotations is collected. However, this sample data primarily consists of absorbance measurements of blood samples at multiple light source wavelengths and the corresponding anomaly levels. This data can be obtained from historical blood spectral testing records, ensuring that the absorbance data for each sample is accurate and the anomaly level annotations are reliable.

[0084] Data preprocessing and feature engineering: Preprocessing the absorbance data, including noise removal and normalization or standardization, ensures data quality and consistency. Feature engineering also plays a key role in this process and may require feature extraction and transformation of the raw absorbance data. For example, features such as the difference or ratio between absorbances at different wavelengths or the derivative of a smoothed absorbance curve can be calculated to better capture patterns and trends in the absorbance data that are associated with abnormal levels.

[0085] Training the initial model: Preprocessed absorbance feature data and its corresponding anomaly level annotations are fed into the initial model for training. Available model algorithms include linear regression, random forests, neural networks, and other machine learning methods suitable for processing numerical features and classification tasks. During training, the model adjusts internal parameters based on the input data and learns the relationship between absorbance features and anomaly levels. Through multiple iterations of training, the model gradually improves its prediction accuracy for anomaly levels. A validation set is also used to monitor overfitting and ensure good performance on new data.

[0086] Validation and Optimization: Use the validation set to evaluate model performance and calculate relevant evaluation metrics. Based on the evaluation results, make necessary optimization adjustments to the model, such as adjusting model complexity, selecting different feature subsets, or employing ensemble learning methods, to improve the model's predictive power and generalization performance, ultimately resulting in a reliable second-level anomaly determination model.

[0087] When the abnormality level of the blood sample is the first abnormality level, step S103 may be performed.

[0088] Step S103: Irradiate the blood sample with a light source of a target light wavelength, use the time it takes for the transmittance of the blood sample to change from a first transmittance to a second transmittance as a first coagulation time of the blood sample, and determine whether the first coagulation time of the blood sample falls within a coagulation time range corresponding to a first abnormality level.

[0089] It should be noted that when a blood sample's abnormality level is determined to be the first abnormality level, it means the sample's abnormality is relatively mild. In this case, the sample's abnormality will not significantly interfere with coagulation function testing, and testing can be performed using the optical detection channel. The first transmittance is determined based on the initial liquid blood sample, while the second transmittance is determined based on a fully coagulated blood sample.

[0090] In this application, a target light source wavelength for the blood sample has been determined. This wavelength typically matches the spectral characteristics of components in the blood sample that are relevant to coagulation. A light source (such as an LED or laser) with the selected wavelength is then irradiated onto the blood sample. In the optical detection channel, light emitted by the light source passes through the sample, generating signals such as transmitted light and scattered light based on the sample's optical properties. Before the blood sample is added with a coagulation reagent and the coagulation reaction begins, its initial liquid state transmittance, known as the first transmittance, is recorded. This transmittance reflects the optical clarity of the sample before coagulation. The optical detection system continuously monitors changes in the transmitted light intensity and converts this into a transmittance curve. When the blood sample is completely coagulated, its transmittance, known as the second transmittance, is recorded. This transmittance reflects the degree of light scattering and absorption by the fibrin clot formed after blood coagulation.

[0091] As the coagulation reaction proceeds, the fibrinogen in the blood gradually converts into insoluble fibrin, resulting in increased blood turbidity and decreased light transmittance. Figure 2 , Figure 2 This is a block diagram of an optical detection method provided by an embodiment of the present application. The transmitted light is converted into an electrical signal by a photoelectric signal conversion circuit, processed by an I / V conversion circuit and a voltage amplifier circuit, and then input into an AD conversion chip. After being converted into a digital signal, it is collected by an AD acquisition circuit board to form a response curve (AD sequence). For easier understanding, please refer to Figure 3 , Figure 3 A schematic diagram of a reaction curve for optical detection provided in an embodiment of the present application. The reaction curve shows the change in transmitted light intensity over time. The horizontal axis represents the coagulation time, and the vertical axis represents the transmitted light intensity. The starting point of the curve corresponds to the initial transmittance (i.e., the first transmittance), and the plateau of the curve corresponds to the final transmittance (i.e., the second transmittance). From the optical detection reaction curve, the time point at which the transmitted light intensity reaches a predetermined percentage of the coagulation point is found and used as the coagulation time.

[0092] In clinical practice, a coagulation time range corresponding to the first abnormality level can be determined based on coagulation time data from a large number of normal samples and mildly abnormal samples. The measured first coagulation time of the blood sample can be compared with the above coagulation time range. If the first coagulation time falls within this range, it can be preliminarily determined that the sample has a mild abnormality in coagulation function, meeting the characteristics of the first abnormality level. The first coagulation time of the blood sample can be directly used as the target coagulation time for the blood sample.

[0093] If the first coagulation time of the blood sample does not belong to the coagulation time range corresponding to the first abnormality level, step S104 may be executed.

[0094] Step S104: Determine a second coagulation time of the blood sample using magnetic beads in the blood sample that respond to a magnetic field of target magnetic field strength, and determine a target coagulation time of the blood sample based on the first coagulation time of the blood sample and the second coagulation time of the blood sample.

[0095] In the present application, first, the swing amplitude of magnetic beads in a blood sample that respond to a magnetic field of a target magnetic field strength can be obtained. Then, the time it takes for the swing amplitude of the magnetic beads to change from a first swing amplitude to a second swing amplitude can be used as the second clotting time of the blood sample. The first swing amplitude is determined based on the initially liquid blood sample, and the second swing amplitude is determined based on the completely coagulated blood sample. Then, based on the transmittance of the blood sample, a transmittance fluctuation coefficient of the blood sample can be calculated, and based on the transmittance fluctuation coefficient of the blood sample, a first confidence level of the first clotting time of the blood sample can be determined. Then, based on the swing amplitude of the magnetic beads, a swing amplitude signal-to-noise ratio can be calculated, and based on the swing amplitude signal-to-noise ratio, a second confidence level of the second clotting time of the blood sample can be determined. Based on the first and second confidence levels, a first weight is assigned to the first clotting time of the blood sample, and a second weight is assigned to the second clotting time of the blood sample. Finally, a target coagulation time of the blood sample may be calculated based on the first coagulation time of the blood sample, the first weight, the second coagulation time of the blood sample, and the second weight.

[0096] Specifically, first, magnetic beads can be placed in a blood sample, and the beads will respond to changes in the magnetic field. When the blood is in a liquid state, the magnetic beads swing with a large amplitude under the drive of the magnetic field; as the blood coagulates, the movement of the magnetic beads is restricted and the swing amplitude decreases. For easier understanding, please refer to Figure 4 , Figure 4 A schematic diagram of the structure of a magnetic bead detection provided in an embodiment of the present application. Figure 4 The diagram shows a light-emitting LED 1, a light-receiving circuit 2, a driving coil 3, magnetic beads 4, and a reaction cup 5. The light-emitting LED emits light, which passes through the blood sample in the reaction cup. If there are steel balls in the sample, the light will be partially blocked by the steel balls. Then, the transmitted light receiving circuit receives the light after passing through the blood sample and the steel balls, and converts the light signal into an electrical signal. This electrical signal will reflect the situation where the steel balls block the transmitted light path. At the same time, the driving coil will generate an alternating electromagnetic field, which will drive the steel balls to swing in the reaction cup. When the steel ball swings, the time it blocks the transmitted light path will change, and this time is called the light-blocking time t. There is a certain relationship between the swing amplitude A of the steel ball and the light-blocking time t. By monitoring the swing amplitude A, the light-blocking time t can be determined. For ease of understanding, please refer to Figure 5 , Figure 5 A schematic diagram of a swing curve for magnetic bead detection provided in an embodiment of the present application.

[0097] As the blood coagulates, fibrinogen gradually transforms into cross-linked fibrin, the viscosity and fluidity of the reaction system gradually change, and the swing of the steel ball is significantly hindered until it can no longer swing. By analyzing the signal output by the light receiving circuit, the swing amplitude of the magnetic beads can be obtained. The swing amplitude of the magnetic beads will change, and this change reflects the change in the coagulation state of the blood. As the blood coagulates, the swing amplitude of the magnetic beads gradually decreases from the initial larger value (first swing amplitude) until it reaches a stable small value (second swing amplitude). This change process can be monitored in real time by a detection device. For ease of understanding, please refer to Figure 6 , Figure 6 A schematic diagram of a reaction curve for magnetic bead detection provided in an embodiment of the present application. The horizontal axis is the reaction time, and the vertical axis is the light-blocking time of the magnetic beads. In the initial stage of the reaction curve of the magnetic bead detection, the light-blocking time is short and the fluctuation is small. As the blood coagulates, the movement of the magnetic beads is restricted, and the light-blocking time of the magnetic beads gradually increases. When the blood is completely coagulated, the magnetic beads may stop at the position of the light-transmitting hole or within the position range on both sides of the light-transmitting hole. At this time, the light-blocking time of the magnetic beads suddenly changes, indicating that the magnetic beads tend to stop. The time point when the light-blocking time of the magnetic beads changes significantly can be found, that is, the time required for the magnetic bead swing amplitude to change from the first swing amplitude to the second swing amplitude can be determined from the curve. This time is the second coagulation time of the blood sample.

[0098] Based on the changes in transmittance during the blood sample's coagulation process, the transmittance fluctuation coefficient can be calculated. The transmittance fluctuation coefficient reflects the degree of transmittance fluctuation over time and can be used to assess the reliability of the first coagulation time measured based on transmittance. The first reliability level of the first coagulation time can be determined based on the transmittance fluctuation coefficient. A smaller transmittance fluctuation coefficient indicates more stable transmittance changes and higher reliability of the first coagulation time; conversely, a larger transmittance fluctuation coefficient indicates lower reliability of the first coagulation time. Based on the swing amplitude data of the steel ball, the swing amplitude signal-to-noise ratio can be calculated. The signal-to-noise ratio reflects the relative strength of the useful signal and the noise signal and can be used to assess the reliability of the second coagulation time measured based on the swing amplitude of the steel ball. The second reliability level of the second coagulation time can be determined based on the swing amplitude signal-to-noise ratio. A higher signal-to-noise ratio indicates a stronger useful signal and less noise interference, thus increasing the reliability of the second coagulation time; conversely, a lower signal-to-noise ratio indicates lower reliability of the second coagulation time.

[0099] Finally, weights can be assigned to the first and second clotting times based on the magnitude of the first and second confidence levels. Clotting times with higher confidence levels receive larger weights, reflecting their importance in the calculation of the final clotting time. The first clotting time and its weight, along with the second clotting time and its weight, are combined to calculate the final target clotting time for the blood sample using a weighted average or other appropriate mathematical method. This target clotting time comprehensively considers the results of both the transmittance and the steel ball swing amplitude detection methods, resulting in higher reliability and accuracy.

[0100] When the abnormality level of the blood sample is the second abnormality level, step S105 may be executed.

[0101] Step S105: using the magnetic beads in the blood sample that respond to the magnetic field of the target magnetic field strength, determining the third coagulation time of the blood sample, and using the third coagulation time of the blood sample as the target coagulation time of the blood sample.

[0102] In the present application, when the abnormality level of a blood sample is the second abnormality level, the abnormality is more severe, and the interaction between magnetic beads and a magnetic field can be used to more accurately monitor the coagulation process of the blood sample. The magnetic beads are ferromagnetic steel beads that can be magnetized in a magnetic field and respond to changes in the magnetic field. The movement of the magnetic beads in the blood sample can be controlled by an external magnetic field. The oscillation of the magnetic beads in the blood sample can be used to monitor the blood coagulation process. The oscillation amplitude and oscillation time of the magnetic beads vary with the coagulation state of the blood sample. An alternating electromagnetic field is applied via drive coils on both sides of the reaction cup. The magnetic field strength and frequency generated by the drive coils can be adjusted as needed. The alternating electromagnetic field causes the magnetic beads to oscillate within the reaction cup. When the blood sample is not coagulated, the magnetic beads can oscillate freely; as the blood sample coagulates, the oscillation of the magnetic beads is restricted.

[0103] When the blood sample is in a liquid state, magnetic beads, driven by an alternating electromagnetic field, oscillate along the curved track at the bottom of the cuvette. During this period, the beads' oscillation amplitude is large and the oscillation duration is short. An optical detection system (consisting of an LED and a transmitted light receiving circuit) monitors the oscillation of the magnetic beads. As the beads oscillate, they periodically block the transmitted light path, causing variations in the intensity of the transmitted light. The transmitted light receiving circuit converts these variations into electrical signals, recording the duration that the beads block the light-transmitting aperture. As the coagulation reaction in the blood sample progresses, fibrinogen gradually converts to cross-linked fibrin, causing changes in the viscosity and fluidity of the blood sample. The oscillation of the magnetic beads is gradually impeded, resulting in a decrease in oscillation amplitude and a prolonged oscillation duration. Once the blood sample is completely coagulated, the oscillation of the magnetic beads tends to cease. At this point, the magnetic beads may rest at the light-transmitting aperture or within a range of positions on either side of the aperture. Significant changes in the duration that the beads block the aperture indicate that the beads have reached a stationary state. By recording the changes in the duration of the oscillation of the magnetic beads and combining them with algorithmic analysis, the time from oscillation to stationary state can be determined, which is the third clotting time.

[0104] Furthermore, after determining the third clotting time of the blood sample using magnetic beads in the blood sample that respond to a magnetic field of the target magnetic field strength, the method may further include: first, determining whether the third clotting time of the blood sample falls within the clotting time range corresponding to the second abnormality level. Then, if the third clotting time of the blood sample does not fall within the clotting time range corresponding to the second abnormality level, irradiating the blood sample with a light source having a target light source wavelength to obtain a fourth clotting time of the blood sample. Finally, using the fourth clotting time of the blood sample, correcting the third clotting time of the blood sample so that the corrected third clotting time of the blood sample falls within the clotting time range corresponding to the second abnormality level.

[0105] In this case, the third coagulation time of the blood sample is used as the target coagulation time of the blood sample, and the third coagulation time of the blood sample after correction is used as the target coagulation time of the blood sample.

[0106] Specifically, based on extensive clinical data and experimental statistics, the clotting time of blood samples with the second abnormality level (more severe abnormality) falls within a specific range. This range reflects the performance of blood coagulation function under more severe abnormalities. The measured third clotting time is then compared to this range. If the third clotting time is within the range, it is directly used as the target clotting time, indicating that the blood sample's coagulation function is in a more severe abnormal state and no further correction is required.

[0107] If it is out of range, it means that the data may be biased or interfered and needs further processing. The wavelength of the target light source determined in the previous step usually matches the spectral characteristics of the coagulation-related components in the blood sample, and can effectively reflect the coagulation state of the blood sample. Use a light source of selected wavelength to irradiate the blood sample so that it interacts with the coagulation components in the sample to generate a signal that can be used for detection. As the blood coagulates, fibrinogen is converted into fibrin, the turbidity of the blood increases, and the transmittance decreases. The change in the intensity of the transmitted light is monitored by an optical detection system, and the transmittance change curve is recorded. The time when the transmittance level reaches the predetermined coagulation point percentage is found from the transmittance change curve as the fourth coagulation time, which reflects the coagulation function of the blood sample.

[0108] Finally, if the third clotting time exceeds the range corresponding to the second abnormality level, it may indicate interference or measurement error, causing the third clotting time to not accurately reflect the true coagulation state of the blood sample. In this case, using the fourth clotting time as a correction can improve the accuracy of the clotting time. Based on the relationship between the third and fourth clotting times, such as calculating the average, weighted average, or other mathematical methods, the third clotting time is adjusted to fall within the range corresponding to the second abnormality level, resulting in the corrected third clotting time.

[0109] In summary, the present application provides a method for determining the coagulation time of a blood sample, the method comprising: for samples of the first abnormal level, first using optical detection to obtain a first coagulation time, then determining whether it is within the corresponding range, if not, automatically enabling magnetic bead detection to obtain a second coagulation time, and determining the target coagulation time accordingly; for samples of the first abnormal level, combining the first coagulation time obtained by optical detection and the second coagulation time obtained by magnetic bead detection to determine the target coagulation time. This multi-dimensional data combination method can verify and supplement each other, making the test results more reliable and avoiding misjudgments caused by errors that may occur due to a single detection method. For samples of the second abnormal level, the target coagulation time is directly obtained by magnetic bead detection. This method of automatically switching detection schemes greatly improves the degree of automation of the detection process, shortens the time from sample detection to result reporting, and improves the efficiency and accuracy of determining the coagulation time of blood samples.

[0110] An electronic device is also provided in an embodiment of the present application. Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 7 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0111] like Figure 7 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 702 or programs loaded from a storage device 708 into a random access memory (RAM) 703. When the electronic device is powered on, the RAM 703 also stores various programs and data required for the operation of the electronic device. The processing device 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0112] Typically, the following devices may be connected to the I / O interface 705: an input device 706 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 708 including, for example, a memory card, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 7 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0113] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any method for determining the coagulation time of a blood sample provided in the embodiment of the present application.

[0114] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any method for determining the coagulation time of a blood sample provided in an embodiment of the present application.

[0115] It should also be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0116] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course can also be implemented by special hardware including application-specific integrated circuits, special CPUs, special memories, special components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits or special circuits, etc. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a number of instructions to enable a computer device (which can be a personal computer, training equipment, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0117] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0118] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a training device or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website, a computer, a training device or a data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

Claims

1. A method for determining the coagulation time of a blood sample, characterized in that: include: Obtaining a target component of a blood sample, and determining a target light source wavelength and a target magnetic field intensity corresponding to the target component of the blood sample, wherein the target component of the blood sample is a component in the blood sample related to coagulation function; Determining an abnormality level of the blood sample using a machine learning model, where the abnormality level of the blood sample includes a first abnormality level and a second abnormality level, and the first abnormality level is lower than the second abnormality level; If the abnormality level of the blood sample is the first abnormality level, irradiating the blood sample with a light source having the target light source wavelength, taking the time for the light transmittance of the blood sample to change from the first light transmittance to the second light transmittance as a first coagulation time of the blood sample, and determining whether the first coagulation time of the blood sample falls within a coagulation time range corresponding to the first abnormality level, wherein the first light transmittance is determined based on the blood sample in an initially liquid state, and the second light transmittance is determined based on the blood sample after it has completely coagulated; If the first coagulation time of the blood sample does not fall within the coagulation time range corresponding to the first abnormality level, determining a second coagulation time of the blood sample using magnetic beads in the blood sample that respond to a magnetic field of the target magnetic field strength, and determining a target coagulation time of the blood sample based on the first coagulation time of the blood sample and the second coagulation time of the blood sample; If the abnormality level of the blood sample is the second abnormality level, a third coagulation time of the blood sample is determined using magnetic beads in the blood sample that respond to a magnetic field of the target magnetic field strength, and the third coagulation time of the blood sample is used as the target coagulation time of the blood sample.

2. The method for determining the coagulation time of a blood sample according to claim 1, wherein: Determining the target light source wavelength and target magnetic field intensity corresponding to the target component of the blood sample includes: Performing full-band scanning on the target component of the blood sample to obtain spectral data of the target component; The wavelength corresponding to the absorbance peak in the spectral data of the target component is used as the target light source wavelength; Marking target components of the blood sample with magnetic beads so that the magnetic beads respond to different magnetic fields in the blood sample, and obtaining response data of the magnetic beads; The magnetic field intensity corresponding to the response peak value in the response data of the magnetic beads is used as the target magnetic field intensity.

3. The method for determining the coagulation time of a blood sample according to claim 1, wherein: The machine learning model includes a first abnormality level determination model and a second abnormality level determination model; Determining the abnormality level of the blood sample by using a machine learning model includes: Obtaining a picture of the blood sample, and inputting the picture of the blood sample into a pre-trained first abnormality level determination model to obtain an abnormality level of the blood sample; or, The blood sample is irradiated with light sources of multiple wavelengths to obtain the absorbance of the blood sample, and the absorbance of the blood sample is input into a pre-trained second abnormality level determination model to obtain the abnormality level of the blood sample.

4. The method for determining the coagulation time of a blood sample according to claim 3, wherein: The process of determining the first abnormality level determination model includes: Obtaining a plurality of historical training samples with historical annotated information, wherein the annotated information includes abnormality levels of historical blood samples, and each of the training samples includes a picture of the historical blood sample; The initial model is trained based on the multiple historical training samples to obtain the first abnormality level determination model, the input of the first abnormality level determination model is the image of the blood sample, and the output of the first abnormality level determination model is the abnormality level of the blood sample.

5. The method for determining the coagulation time of a blood sample according to claim 1, wherein: The method of determining a second coagulation time of the blood sample by using magnetic beads in the blood sample that respond to a magnetic field of the target magnetic field strength comprises: obtaining a swing amplitude of the magnetic beads in the blood sample in response to the magnetic field of the target magnetic field strength; The time it takes for the swing amplitude of the magnetic beads to change from a first swing amplitude to a second swing amplitude is used as the second coagulation time of the blood sample, where the first swing amplitude is determined based on the blood sample in an initial liquid state, and the second swing amplitude is determined based on the blood sample after it has completely coagulated.

6. The method for determining the coagulation time of a blood sample according to claim 1, wherein: Determining a target coagulation time of the blood sample based on the first coagulation time of the blood sample and the second coagulation time of the blood sample includes: calculating a transmittance fluctuation coefficient of the blood sample based on each transmittance of the blood sample, and determining a first reliability of a first coagulation time of the blood sample based on the transmittance fluctuation coefficient of the blood sample; calculating a signal-to-noise ratio of the swing amplitudes of the magnetic beads based on the swing amplitudes of the magnetic beads, and determining a second reliability of the second coagulation time of the blood sample based on the signal-to-noise ratio of the swing amplitudes of the magnetic beads; assigning a first weight to a first clotting time of the blood sample and assigning a second weight to a second clotting time of the blood sample based on the first confidence level and the second confidence level; A target coagulation time of the blood sample is calculated based on the first coagulation time of the blood sample, the first weight, the second coagulation time of the blood sample, and the second weight.

7. The method for determining the coagulation time of a blood sample according to claim 1, wherein: After determining the third coagulation time of the blood sample using the magnetic beads in the blood sample that respond to the magnetic field of the target magnetic field strength, the method further includes: determining whether the third coagulation time of the blood sample falls within a coagulation time range corresponding to the second abnormality level; If the third coagulation time of the blood sample does not belong to the coagulation time range corresponding to the second abnormality level, irradiating the blood sample with a light source of the target light source wavelength to obtain a fourth coagulation time of the blood sample; Correcting the third coagulation time of the blood sample using the fourth coagulation time of the blood sample so that the corrected third coagulation time of the blood sample falls within a coagulation time range corresponding to the second abnormality level; The step of using the third coagulation time of the blood sample as the target coagulation time of the blood sample comprises: The corrected third coagulation time of the blood sample is used as the target coagulation time of the blood sample.

8. A computer program product, characterized in that The method comprises computer-readable instructions, which, when executed on an electronic device, enable the electronic device to implement the method for determining the coagulation time of a blood sample according to any one of claims 1 to 7.

9. An electronic device, characterized in that: comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is configured to execute the computer program so as to enable the electronic device to implement the method for determining the coagulation time of a blood sample according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the method for determining the coagulation time of a blood sample as claimed in any one of claims 1 to 7.

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