An automated coagulation detection system

Abstract

The present invention relates to the field of automated medical testing technologies, and specifically to an automated coagulation testing system. The system includes a sample state regulation module, a light source equalization imaging module, a dynamic image extraction module, a reaction process quantification module, and a risk trend identification module. In the present invention, through the interval mapping of the sample concentration and specific gravity value, combined with the temperature and pH feedback to screen dynamic parameters, the precise regulation of sedimentation and component state is achieved, the imaging stability is improved, the light source irradiation angle and power are dynamically matched according to the intensity distribution and boundary coincidence degree, the brightness consistency is enhanced, the brightness and boundary offset are synchronously analyzed during image acquisition, the consistent region is extracted, the image continuity and quantification accuracy are improved, the reaction trend is based on the superposition of section sequences, the accuracy of timing judgment is enhanced, combined with the trajectory and sample feature classification to form a stable mapping, the abnormal identification ability is improved, a closed-loop detection process is constructed, and the detection stability and individual adaptability are strengthened.

Description

Technical Field

[0001] The present invention relates to the technical field of automated medical testing, and particularly to an automated coagulation testing system. Background Art

[0002] The technical field of automated medical testing includes technologies related to the automated processing, analysis, and testing of medical samples. The core content of this technical field is to complete the efficient analysis and quantitative testing of biological samples such as blood and urine through the coordinated action of mechanical equipment, sensors, and control systems, thereby improving testing efficiency and data consistency. Automated medical testing systems involve multiple consecutive links such as sample collection, sample separation, reagent addition, reaction control, and result reading, and achieve process standardization through program control, being widely used in laboratories, biomedical research, and public health monitoring.

[0003] Among them, an automated coagulation testing system refers to a system specifically used for the automated testing of blood coagulation function and the informatization management of the entire testing process. It mainly focuses on links such as sample information entry, reagent preparation, testing step control, data collection, result recording, and traceability of testing records during the coagulation testing process. It uses barcode recognition technology to automatically match samples with testing tasks, uses temperature control technology to regulate the reaction environment, obtains the time parameters of the coagulation reaction through optical detection methods, executes testing steps in sequence according to the preset process control logic, and stores each testing parameter in a structured format through a data recording module after the testing is completed to achieve systematic management of testing data.

[0004] Although the prior art has achieved standardized process operations, there are still significant limitations when faced with high-precision and multi-variant samples. In the sample processing link, it is unable to effectively distinguish changes in sample concentration and specific gravity, resulting in inconsistent states of samples in the pre-reaction processing stage and affecting the accuracy of subsequent testing. The imaging light source configuration generally uses fixed irradiation parameters, making it difficult to cope with the dynamic changes in the boundary distribution and light intensity response of different samples during the reaction process, leading to a decrease in imaging signal-to-noise ratio. There is a lack of a judgment mechanism for the synchronous fluctuations of brightness and boundaries in fluorescence image acquisition, making abnormal frames or non-uniform regions interfere with the coherence of the detection results. The analysis of the reaction process mostly relies on static time-point data and lacks the ability to continuously track the overall trend of the reaction, making it difficult to reflect the true dynamic rhythm of the coagulation process. In terms of sample recognition, existing methods rely on rule templates for classification, ignoring the non-linear relationship between individual characteristics and detection trajectories, resulting in insufficient ability to identify differences in samples of the same batch. For example, some low-concentration samples are misjudged as abnormal states due to severe imaging boundary drift, indicating that the existing process has problems of response lag and insufficient accuracy in sample adaptation and reaction dynamic recognition. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose an automated coagulation detection system.

[0006] To achieve the above object, the present invention adopts the following technical solution: An automated coagulation detection system includes:

[0007] The sample state regulation module obtains the initial concentration value and specific gravity distribution value of the whole blood sample, performs interval mapping according to the data fed back by the temperature control unit, identifies the dynamic regulation parameters for controlling sedimentation and component balance, and generates a sample state balance parameter set;

[0008] The light source balance imaging module calls the sample state balance parameter set, collects the intensity distribution of the light source component and the boundary positioning of the reaction cup, compares the coincidence degree of the imaging area distribution, selects a suitable irradiation angle and light source power configuration, and generates a uniform light projection configuration group;

[0009] The dynamic image extraction module, based on the uniform light projection configuration group, collects the fluorescence imaging brightness change and the boundary position offset trajectory, screens the consistent area within the imaging cycle, and generates a continuous image feature trajectory set;

[0010] The reaction process quantification module calls the continuous image feature trajectory set, extracts the fluorescence brightness interval in the image frame sequence, matches the reaction cycle change paragraph, judges the reaction process trend through section superposition, and obtains the coagulation process time sequence trajectory trend.

[0011] As a further solution of the present invention, the sample state balance parameter set includes a concentration mapping coefficient, a specific gravity adjustment factor, a temperature and pH regulation weight, a sedimentation balance threshold, and a component distribution balance value. The uniform light projection configuration group includes an irradiation angle parameter, a light source power configuration, a boundary matching coefficient, and an imaging area balance degree. The continuous image feature trajectory set includes a brightness change curve, a boundary offset trajectory, a synchronous offset index, and a consistency area label. The coagulation process time sequence trajectory trend includes a fluorescence intensity section sequence, a stage evolution mode, a change trend path, and a time sequence matching result.

[0012] As a further solution of the present invention, the sample state regulation module includes:

[0013] The sample state acquisition sub-module obtains the initial concentration value and specific gravity distribution value of the whole blood sample, extracts the parameter difference deviating from the specific gravity reference interval and the concentration standard interval according to the temperature value and pH value range fed back by the temperature control unit of the reaction cup loading cavity, and generates a sample initial offset parameter group;

[0014] The adjustment parameter extraction sub-module, based on the sample initial offset parameter group and combined with the adjustment limit under the current temperature control condition, screens the temperature adjustment factor and pH correction factor intervals that can be used to control the offset difference, extracts the adjustment combination matching the current offset level, and generates a coagulation adjustment matching parameter;

[0015] The detection process mapping sub-module, based on the coagulation regulation matching parameters, combines the current concentration value and the specific gravity distribution value of the sample, identifies the function mapping group for sedimentation and equilibrium regulation, determines the constraint boundaries of the loading and reaction rhythms, and generates a sample state equilibrium parameter set.

[0016] As a further solution of the present invention, the light source equilibrium imaging module includes:

[0017] The detection response sub-module calls the sample state equilibrium parameter set, collects the light source power response values and angle mapping values under multi-directional illumination, compares the light intensity response and the illumination angle matching degree, and obtains the light overlap rate index;

[0018] The boundary positioning sub-module calls the light overlap rate index, extracts the image boundary gradient intensity, jointly screens the angle set that meets the boundary clarity requirements, and obtains the illumination angle selection set;

[0019] The uniform light configuration sub-module combines the light source power groups according to the illumination angle selection set, identifies the light intensity and boundary pixel density of each group, calculates the light balance error rate, selects the configuration with the smallest error rate group, and generates a uniform light projection configuration group.

[0020] As a further solution of the present invention, the dynamic image extraction module includes:

[0021] The brightness trajectory extraction sub-module, based on the uniform light projection configuration group, collects the brightness values of each unit area of each frame of the image, records the time series and identifies the brightness difference between consecutive frames, and obtains the brightness change time trajectory;

[0022] The boundary offset determination sub-module extracts the boundary coordinates according to the corresponding frames of the brightness change time trajectory, identifies the pixel position change vector of each frame, matches the brightness and the boundary difference, calculates the synchronous offset degree value, determines whether it is lower than the boundary change reference, and obtains the synchronous offset frame set;

[0023] The feature trajectory construction sub-module calls the frame sequence in the synchronous offset frame set, extracts the joint vector of brightness and boundary, sorts and constructs the trajectory, and determines the consistency according to the Euclidean distance between adjacent frames, and generates a continuous image feature trajectory set.

[0024] As a further solution of the present invention, the reaction process quantification module includes:

[0025] The brightness interval extraction sub-module calls the time sequence frame segments in the continuous image feature trajectory set, extracts the fluorescence pixel brightness values in the reaction area according to the set time window, counts the maximum and minimum brightness of each segment, delimits the brightness change interval, and obtains the brightness distribution set of the reaction period;

[0026] The section matching and judging sub-module identifies the fitting difference between the brightness amplitude deviation and the preset amplitude mean value according to each brightness interval range in the concentrated brightness distribution during the reaction period, and combines the change characteristic sections set in the reaction stage, and calculates the section offset trend value in the reaction stage;

[0027] The trend trajectory generation sub-module identifies the sections with continuous same-type change directions according to the section offset trend value in the reaction stage, accumulates the trends of adjacent same-direction segments, classifies and labels the trend directions, and obtains the trend of the coagulation process time series trajectory.

[0028] As a further solution of the present invention, the system further includes a risk trend identification module:

[0029] Based on the trend of the coagulation process time series trajectory, the risk trend identification module classifies the trajectory trends of the samples in the same round, collects the sample feature categories recorded in the sample label information group, judges the stability degree of the associated interval between it and the trajectory classification, and generates an automated coagulation detection status mapping group;

[0030] The automated coagulation detection status mapping group includes trajectory classification results, sample feature correlation degrees, classification stable intervals, and detection status labels.

[0031] As a further solution of the present invention, the risk trend identification module includes:

[0032] The trajectory interval classification sub-module identifies the trajectory trend vector according to the start and end points and amplitude difference of the trajectory change based on the trend of the coagulation process time series trajectory, delimits the trajectory change interval according to the positions of adjacent inflection points in the time series, marks the corresponding interval numbers of the sample trajectories, and generates a trajectory interval marking table;

[0033] The category interval association sub-module calls the trajectory interval marking table, collects the feature category items in the sample label information, analyzes the category frequency through the sample distribution quantity between the interval number and the feature category, and screens the association combinations with frequencies higher than the category interval matching threshold to obtain the category interval corresponding relationship group;

[0034] The process status configuration sub-module sets the process label mapping rule between the trajectory interval and the feature category according to the category interval corresponding relationship group, configures the detection process status field for the interval samples that meet the mapping rule, automatically marks the sample process management field, and generates an automated coagulation detection status mapping group.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0036] In the present invention, through interval mapping of the concentration value and specific gravity distribution value of a whole blood sample, and combined with temperature and pH feedback of the reaction environment for dynamic parameter screening, pre-identification and precise regulation of the sample sedimentation and component equilibrium state are achieved, avoiding imaging deviation caused by sample state fluctuations. The irradiation angle and power configuration of the imaging light source no longer rely on a fixed template, but are matched based on the coincidence degree between the actual light source intensity distribution and the reaction cup boundary, realizing two-way optimization of irradiation uniformity and imaging area coverage rate, and improving the brightness consistency of fluorescence images. A synchronization comparison mechanism of boundary offset and brightness change trajectory is introduced during the acquisition of the image frame sequence, ensuring accurate extraction of the consistent area within the imaging cycle, and enhancing the continuity and quantifiability of the image feature trajectory. The reaction process analysis is based on the matching of brightness sections and preset change intervals, and a trend trajectory is constructed by superimposing section sequences, enabling the judgment of the reaction process to shift from single-point data to overall trend recognition, and improving the ability to accurately depict the reaction timing. In the sample difference evaluation, by combining the trajectory trend and sample feature classification information, a corresponding relationship of a stable interval is formed, expanding the ability to identify potential abnormal states in samples of the same round, strengthening the mapping consistency between the automated detection state and the actual characteristics of the samples. With the cooperation of multi-dimensional participation items, a closed-loop detection chain from sample regulation to image extraction, then to reaction quantification and result recognition is constructed, overall improving the stability, continuity and individual adaptation ability of coagulation detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is the system flow chart of the present invention;

[0038] Figure 2 is the flow chart of the sample state regulation module in the present invention;

[0039] Figure 3 is the flow chart of the light source balanced imaging module in the present invention;

[0040] Figure 4 is the flow chart of the dynamic image extraction module in the present invention;

[0041] Figure 5 is the flow chart of the reaction process quantification module in the present invention;

[0042] Figure 6 is the flow chart of the risk trend identification module in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0045] Please refer to Figure 1 , an automated coagulation detection system includes:

[0046] The sample state regulation module obtains the initial concentration value and specific gravity distribution value of the whole blood sample, screens the adjustment value according to the temperature and pH value range fed back by the temperature control unit of the reaction cup loading chamber, performs interval mapping in combination with the concentration and specific gravity distribution value, identifies the dynamic regulation parameters for controlling sedimentation and component balance, and generates a sample state balance parameter set;

[0047] The light source balance imaging module calls the sample state balance parameter set, collects the intensity distribution of the detection light source component and the reaction cup boundary positioning, compares the coincidence degree of their distributions in the imaging area, selects the corresponding irradiation angle and light source power group configuration, and generates a uniform light projection configuration group;

[0048] The dynamic image extraction module, based on the uniform light projection configuration group, collects the brightness change time trajectory and the boundary position offset trajectory of the fluorescence imaging frame sequence, judges the synchronous offset amplitude between the trajectories, screens the consistent area within the imaging period, and generates a continuous image feature trajectory set;

[0049] The reaction process quantification module calls the continuous image feature trajectory set, extracts the fluorescence brightness interval of each period in the image frame sequence, matches it with the set change paragraph interval in the reaction cycle, and judges the trend of the reaction process by superimposing the section sequences to obtain the time sequence trajectory trend of the coagulation process;

[0050] The risk trend identification module, based on the time sequence trajectory trend of the coagulation process, classifies the trajectory trends of the samples in the same round, collects the sample feature categories recorded in the sample label information group, judges the stability degree of the association interval between it and the trajectory classification, and generates an automated coagulation detection state mapping group.

[0051] The sample status equilibrium parameter set includes the concentration mapping coefficient, the specific gravity adjustment factor, the temperature and pH regulation weights, the sedimentation equilibrium threshold, and the component distribution balance value. The uniform light projection configuration group includes the irradiation angle parameter, the light source power configuration, the boundary matching coefficient, and the imaging area equilibrium degree. The continuous image feature trajectory set includes the brightness change curve, the boundary offset trajectory, the synchronous offset index, and the consistency area label. The blood coagulation process time sequence trajectory trend includes the fluorescence intensity section sequence, the stage evolution mode, the change trend path, and the time sequence matching result. The automated blood coagulation detection status mapping group includes the trajectory classification result, the sample feature correlation degree, the classification stable interval, and the detection status label.

[0052] Please refer to Figure 2 , the sample status regulation module includes:

[0053] The sample status acquisition sub-module obtains the initial concentration value and specific gravity distribution value of the whole blood sample, extracts the parameter difference deviated from the specific gravity reference interval and concentration standard interval according to the temperature value and pH value range fed back by the temperature control unit of the reaction cup loading cavity, and generates the sample initial offset parameter group;

[0054] First, the collector extracts a certain amount of whole blood sample from the sample and uses precise measuring tools to determine the initial concentration value and specific gravity distribution value of the sample. The initial concentration value represents the solute concentration contained in the blood sample, which is measured using a densitometer or an optical sensor, while the specific gravity distribution value is obtained by detecting the precipitation distribution of different solutes in the sample. These two data reflect the current physical characteristics of the sample and help to determine whether the sample meets the preset reaction standards. The temperature control unit monitors and feeds back the current ambient temperature and pH value range in the reaction cup loading cavity. The temperature value affects the activity and reaction rate of the reaction, while the pH value is related to the solubility of the solutes in the sample and the blood coagulation process. During this process, the fed-back temperature and pH data will be used to compare with the specific gravity reference interval and concentration standard interval. If the specific gravity or concentration deviates from the preset standard interval, it is necessary to calculate the deviation value to determine whether the reaction conditions need to be adjusted. The deviated parameter difference can help to identify potential processing requirements during the sample processing. For example, when the concentration of the whole blood sample exceeds the upper limit of the standard interval, it is necessary to determine whether to adjust the temperature or pH value according to the deviation value in order to restore the sample to a suitable reaction state. Through a series of measurements, comparisons, and calculations, the sample initial offset parameter group is generated to provide a basis for subsequent adjustment steps.

[0055] The adjustment parameter extraction sub-module, based on the sample initial offset parameter group and combined with the adjustment limit values under the current temperature control conditions, screens the temperature adjustment factor and pH correction factor intervals that can be used to control the offset difference, extracts the adjustment combination that matches the current offset level, and generates the blood coagulation adjustment matching parameter;

[0056] Evaluate each piece of data in the initial offset parameter group of the sample. The offset parameter group includes the concentration offset of the sample, the specific gravity offset, and the differences between the temperature and pH values fed back by the temperature control unit and the reference values. By calculating the offset values and comparing them with the preset intervals of the temperature adjustment factor and the pH correction factor, the ranges of the temperature adjustment factor and the pH correction factor are determined through historical data analysis and experiments. For example, assuming the current temperature is 30°C and the standard temperature is 37°C, then the temperature control unit needs to calculate the appropriate temperature adjustment range according to the preset limit value. Assuming the adjustment range is ±3°C, the interval of the pH correction factor is set from 6.5 to 7.5, and the current pH value is 6.8. Based on this data and combined with the offset of the sample, the adjustment factor extracts the optimal adjustment combination to ensure that the blood sample can be processed under the best reaction conditions in the subsequent process, and can screen out appropriate adjustment parameters to make the sample better meet the experimental requirements, generate coagulation regulation matching parameters, and be used for the sample processing in the next stage.

[0057] The detection process mapping sub-module, according to the coagulation regulation matching parameters, combines the current concentration value and specific gravity distribution value of the sample, identifies the function mapping group for sedimentation and equilibrium regulation, judges the constraint boundaries of the loading and reaction rhythms, and generates the sample state equilibrium parameter set;

[0058] Conduct an analysis of the sample state. According to the current concentration and specific gravity distribution values of the sample, identify the function mapping group for the sedimentation characteristics and equilibrium regulation of the sample. The sedimentation characteristics refer to the distribution of solutes or particles in the blood sample under the action of gravity, and the equilibrium regulation function is used to adjust the uniformity of the solute distribution during the reaction process. According to the characteristics, further judge whether the loading and reaction rhythms of the sample meet the specified boundary conditions. For example, if the concentration value exceeds a certain range, it will cause uneven solute distribution, thus affecting the accuracy of the subsequent coagulation reaction. By analyzing the sample loading conditions, judge whether it is necessary to adjust the rhythm to ensure the consistency and stability of the sample processing during the experiment. Combining the current state of the sample and the regulation matching parameters, a sample state equilibrium parameter set will be generated to provide data support for further experimental steps and ensure that the reaction can proceed according to the set rhythm.

[0059] Please refer to Figure 3 , the light source equilibrium imaging module includes:

[0060] The detection response sub-module calls the sample state equilibrium parameter set, collects the light source power response values and angle mapping values under multi-directional irradiation, compares the light intensity response and the irradiation angle matching degree, and obtains the light overlap rate index;

[0061] According to the sample state equilibrium parameter set, including sample light absorption characteristics, sample surface reflectivity, and liquid surface disturbance degree, etc., the response coefficient of the reaction cup under different wavelength illuminations is extracted through a preset equilibrium parameter table, and then combined with the various light source combination angles configured in the illumination matrix index. The illuminance sensor is used to synchronously record the light source output power value and the angle mapping value under each configuration. For example, the sample is placed at the detection position, and the light source module irradiates at multiple incident angles such as 10°, 20°, 30°, etc. Each irradiation records the corresponding power as 30mW, 45mW, 50mW, etc., to form a configuration matrix. Then, the collected power response values and angle mapping values are paired. After fixing the angle, the numerical comparison of the edge change trend of the illumination area under different powers is carried out to form a light intensity change curve. The edge coincidence degree of the illumination area contours corresponding to each angle is statistically analyzed. The overlapping ratio is obtained by dividing the overlapping area by the total illumination area, and a coincidence rate interval is set. For example, a coincidence rate less than 0.6 is a low overlap interval, between 0.6 and 0.85 is a medium overlap interval, and greater than 0.85 is a high overlap interval. If the overlapping area under the 30° irradiation angle is 4 cm² and the total area is 4.5 cm², then the overlapping rate is 4 / 4.5 ≈ 0.89, which is classified as a high overlap interval. At the same time, the difference between this data and the set illumination reference value is determined. The reference value is set as the corresponding intensity value of 120 lux when the mean illumination intensity and the actual pixel recognition rate are maximally matched through a large number of sample pre-experiments. If the illuminance is less than this value, it is not adopted. Finally, the configurations with a high overlapping rate and a light intensity response higher than the reference are screened and output as the illumination overlapping rate index.

[0062] The boundary positioning sub-module calls the illumination overlapping rate index, extracts the image boundary gradient intensity, and jointly screens the angle set that meets the boundary clarity requirement to obtain the illumination angle selection set;

[0063] For each group of irradiations, the pixel gradient of the reaction cup edge image is calculated. The edge detection operator is used to extract the region with the most drastic gray change in the image, and the relative position and the corresponding gradient amplitude of the region where the maximum pixel gradient value is located are recorded. By comparing the changes in the boundary images under different irradiation conditions, the pixel gradient maps are stacked frame by frame and normalized to obtain the pixel density distribution map. Screening is carried out with reference to the average gradient value of the pixel concentration region at each angle. For example, the average edge pixel gradient value at the 20° angle is 35, and at the 30° angle is 42. The average gradient values of each angle are divided into intervals. For example, if the boundary clarity reference is set to 40, then those greater than 40 are included in the clear interval. Finally, the angle combinations with high clarity and an overlapping rate index in the medium-high interval are combined into the illumination angle selection set, denoted as the illumination angle selection set.

[0064] The uniform light configuration sub-module combines the light source power groups according to the illumination angle selection set, identifies the light intensity and boundary pixel density of each group, and uses the formula:

[0065] ;

[0066] Calculate the illumination balance error rate, select the configuration group with the smallest error rate, and generate the uniform light projection configuration group;

[0067] Among them, represents the light source power value of the group, represents the boundary pixel density of the group, is the target light intensity value of the group, is the illumination balance error rate, represents the total number of light source configuration groups,

[0068] The illumination balance error rate refers to the numerical deviation degree between the actual illumination effect and the set target illumination intensity under the combination of multiple light source power configurations and the corresponding image boundary pixel density. By calculating the difference between the product result of the light intensity and the pixel density under each group of configurations and the target illumination intensity, taking the absolute value, squaring and summing, and finally performing a square root operation, the overall error level of the overall lighting configuration in terms of illumination uniformity is reflected. The smaller this index, the closer the illumination uniformity of the current light source combination is to the set standard when meeting the imaging requirements, and it has higher imaging stability and regional illuminance consistency;

[0069] According to several selected illumination angle configurations in the illumination angle selection set, extract the corresponding light source power groups. The power value of each group is measured by an illuminance sensor with the light intensity per unit area (unit: mW / cm²). It is set that within the irradiation area at a fixed distance (such as 5 cm) from the sample surface for each group of light sources, the effective irradiation area is 1 cm². Record the illuminance of each group of light sources obtained as 42 mW / cm², 48 mW / cm², and 45 mW / cm², and set them as , , ;

[0070] Extract the pixel density of the area in the reaction cup edge image, calculate the number of edge pixels per unit area (set as 0.5 cm²), and use the gray-scale change edge recognition method to scan the image contour area point by point to count the number of edge pixels. Suppose the number of edge pixels in the corresponding areas under the 3 groups of configurations are 120 points, 135 points, and 128 points respectively. Normalize them to match the power dimension, and the normalization standard is the maximum pixel density of 255 points per unit. Calculate the density proportionality coefficients as:

[0071] ;

[0072] ;

[0073] ;

[0074] Illumination target value Based on the pixel density - illuminance mapping relationship corresponding to the best illumination uniformity during the imaging of the reaction cuvette, extract the point value of the maximum recognition efficiency of illuminance from a large number of sample experimental data, and set it as the target illumination intensity of 125 mW / cm² (unify the unit, consistent with the power), that is, for each group ;

[0075] Calculate item by item:

[0076] , error term , the square is 11078.6;

[0077] , error term , the square is 9917.8;

[0078] , error term , the square is 10489.6;

[0079] ;

[0080] The final error rate is 177.4, corresponding to the overall error level in the three - group combination. Subsequently, select the single - group configuration with the lowest error to independently compare each group value. For example, the group with the smallest error within a single group is the second group, and output the configuration of this group as the uniform light projection configuration group;

[0081] : The light source power (mW / cm²) under the irradiation configuration of the th group;

[0082] : The pixel density (unit - normalized value, between 0 and 1) under the configuration of the th group;

[0083] : The target illumination intensity (mW / cm²), and the value is taken from the best - boundary imaging point of the experimental sample;

[0084] By introducing the product error calculation of the normalized pixel density and the target illumination value, the numerical measurement and optimal group selection between the light intensity and the image recognition accuracy for the multi - angle irradiation configuration are realized. Moreover, during the operation process, the dimension of each participating value is unified, and the result has a direct guiding role. This result shows that the error of the current configuration group is 177.4. Compared with the set illumination error tolerance (such as setting the threshold to 200), it meets the balance requirement of the projection configuration, indicating that this group can be selected to generate the final uniform light projection configuration group.

[0085] Please refer to Figure 4 , the dynamic image extraction module includes:

[0086] The brightness trajectory extraction sub-module, based on the uniform light projection configuration group, collects the brightness values of the unit areas of each frame of the image, records the time series and identifies the brightness difference between consecutive frames, and obtains the brightness change time trajectory;

[0087] Divide the preset area (such as the central area of the reaction cup) in each frame of the image into unit sampling blocks, obtain the brightness gray value corresponding to each sampling block through the image sensor, set the gray range to 0 to 255, take the average value of the gray values in a single frame as the brightness value of the current frame, construct a brightness time series according to the sampling time order, and set the time interval to 0.2 seconds and the total number of sampled frames to 20 frames to form a brightness sequence within 4 seconds. After constructing the sequence, calculate the change amplitude between the brightness values of any two frames in the sequence, that is, the absolute value of the difference between the brightness value of the subsequent frame and the brightness value of the previous frame, to obtain the brightness change intensity distribution of the entire sequence. Then, count all the brightness differences, construct a brightness change vector. For example, if the brightness of the 3rd frame is 180 and the 4th frame is 192, then the corresponding brightness difference is |192 - 180| = 12. Concatenate the differences of all frame pairs to form a brightness change time trajectory, and exclude the frames with brightness values lower than the set sensing sensitivity threshold (such as gray levels lower than 20) to ensure the stability of the data source of the change trajectory.

[0088] The boundary offset determination sub-module extracts the boundary coordinates according to the frames corresponding to the brightness change time trajectory, identifies the pixel position change vector of each frame, matches the brightness and the boundary difference, and uses the formula:

[0089] ;

[0090] Calculate the synchronous offset degree value, determine whether it is lower than the boundary change reference, and obtain the synchronous offset frame set;

[0091] Among them, is the synchronous offset degree value, is the total number of frames, is the boundary pixel coordinate of the th frame, is the boundary pixel coordinate of the th frame, is the brightness value of the th frame, is the boundary recognition intensity value of the th frame;

[0092] The synchronous offset value is a comprehensive indicator that measures the coordination of the boundary position changes and brightness changes in each frame of a fluorescence image sequence in the time dimension. This value is obtained by calculating the Euclidean distance between the boundary center coordinates of adjacent frames and the normalized difference between the brightness values and the boundary recognition intensity, and fuses the spatial position changes and gray-scale response changes for evaluation. The smaller the value, the higher the degree of synchronization between the boundary position and the brightness response changes, indicating that the displacement of the imaging boundary in this frame sequence is stable and the brightness changes regularly, and it can be used to extract continuous and stable frame segments in the image sequence;

[0093] According to the frame index corresponding to the time trajectory of the brightness change, the pixel coordinate data of the reaction cup boundary position is extracted frame by frame from the original fluorescence image sequence. The process of obtaining the boundary position is to identify the gray-scale mutation area in edge detection, set the gray-scale gradient threshold to 20, extract the coordinates of all pixel points where the first-order gray-scale difference at the boundary is greater than the threshold, and obtain the boundary center position coordinates of this frame through the centroid calculation method , for example, the centroid of the first frame is (120, 145), the second frame is (123, 147), and the third frame is (125, 150);

[0094] At the same time, the overall brightness value of the corresponding image area of each frame is collected , which is obtained by averaging the gray-scale values of all pixels in the target area (for example, the central 50×50 pixels). Suppose the first frame is 132, the second frame is 138, and the third frame is 140;

[0095] Boundary recognition intensity is the average absolute difference in gray-scale change within the area where the boundary pixels are located (within the range of ±2 pixels). This value is obtained by sampling the gray-scale change of each boundary line, statistically taking the absolute value of the first-order gray-scale difference between sample points and then averaging, and getting 135 for the first frame, 136 for the second frame, and 139 for the third frame;

[0096] To achieve the dimensional consistency of the brightness value and the boundary intensity, both are normalized to the range of [0, 1]. The normalization reference range is 0–255 (gray-scale value range), for example:

[0097] 、 、 ;

[0098] 、 、 ;

[0099] Then calculate the synchronous offset value according to the formula:

[0100] Assume that 3 frames of data are processed, that is , and calculate item by item as follows:

[0101] The second frame:

[0102] The coordinate change is ;

[0103] The difference between the brightness and the boundary intensity is ;

[0104] The total is: 3.6056 + 0.0079 = 3.6135;

[0105] Frame 3:

[0106] The coordinate change is ;

[0107] The difference is ;

[0108] The total is: 3.6056 + 0.0039 = 3.6095;

[0109] Taking the average of the two results, we get: ;

[0110] The set boundary change reference value is 4.0 (taking the upper limit of the 95% confidence interval in the first 100 segments of the training sample analysis). Since 3.6115 is less than this threshold, it indicates that the synchronization degree between the brightness and the boundary change in this sequence is relatively high. Finally, the synchronized offset frame set is output, that is, Frame 2 and Frame 3 are synchronized consistent frame segments;

[0111] The fusion error amount is constructed by the square root of the horizontal and vertical coordinate displacement amounts and the cumulative average of the normalized brightness differences, taking into account the reflection of the position change and the gray scale information on the imaging change, providing a quantitative standard for subsequent image screening. The result shows that the current inter-frame change fluctuation is below the boundary reference, which can be used as the basis for continuous screening paragraphs.

[0112] The feature trajectory construction sub-module calls the frame sequence in the synchronized offset frame set, extracts the joint vector of brightness and boundary, sorts and constructs the trajectory, and judges the consistency based on the Euclidean distance between adjacent frames to generate a continuous image feature trajectory set;

[0113] Extract the average image brightness of each frame and the coordinate parameters of the boundary pixel contour frame by frame. The average image brightness is calculated by statistically calculating the gray value of the unit area, and the boundary contour coordinates are marked by the position aggregation extraction method of the image edge pixel points. For example, in a certain frame of image, the average brightness of the central detection area is 185, and the central point coordinates of the main line of the boundary pixels are (98, 205). Then the joint feature vector corresponding to this frame can be expressed as a triple of brightness and position. Process all the image frames in the synchronous frame set in this way, and construct a joint vector data set frame by frame according to the frame sequence order. Subsequently, for all the constructed vector pairs, perform a continuity judgment operation. The judgment method is whether the change in the distance between the joint vector values between two frames is stable. If the brightness difference between a certain frame and the previous frame is within 10, and at the same time the boundary coordinate offset is within 3 pixel points, then it is considered that there is continuity between these two frames. Scan the entire frame set through a sliding window, merge and count the frame pairs with qualified continuity to generate continuous frame segments, and integrate and classify all continuous frame segments. During the integration process, if a certain frame has a sudden brightness change (such as the difference exceeds 30) or a drastic boundary jump (such as the boundary center offset between adjacent frames exceeds 8 pixels), then interrupt the construction of the continuous segment, remove this frame and use it as a breakpoint. In actual operation, set the brightness change interval threshold to 10 and the boundary displacement threshold to 3 pixels as the continuous judgment criteria. Once the continuous number of frames in a frame segment reaches more than 5 frames, it is regarded as a stable frame segment in the imaging stage, and this frame segment is incorporated into the continuous image structure. Finally, integrate all the frame segments that meet the stable feature criteria to construct a feature trajectory set of each key frame segment in the time series. Through the unified characterization of the two-dimensional features of brightness and boundary coordinates, it is concatenated into a complete frame trajectory chain in the time dimension to form a key data set that can be used for subsequent time-series image analysis, and finally obtain a continuous image feature trajectory set.

[0114] Please refer to Figure 5 , the reaction process quantification module includes:

[0115] The brightness interval extraction sub-module calls the time-series frame segments in the continuous image feature trajectory set, extracts the fluorescence pixel brightness values of the reaction area according to the set time window, statistically calculates the maximum and minimum brightness of each segment, delimits the brightness change interval, and obtains the brightness distribution set of the reaction period;

[0116] According to the principle of fixed-time segmentation in the automated blood coagulation detection process, a reaction period is constructed for every consecutive 10 frames of images. Within each period, the fluorescence gray values of all pixels within a 50×50 pixel rectangular area at the center of the reaction area are extracted, the brightness value distribution of this area in each frame of the image is recorded, and the maximum and minimum brightness values of this area within this period are calculated, which are respectively marked as the upper limit brightness and the lower limit brightness values. In this way, a brightness distribution interval for the reaction period is established. To avoid deviations in subsequent calculations caused by differences in brightness ranges in different image acquisition batches, the brightness data is processed using a normalization method, which is: the original gray value divided by the gray upper limit of 255, converted into a value within the range of [0, 1]. In the example, the maximum brightness value from the 1st frame to the 10th frame is 210, and the minimum value is 125, so the normalized interval is [0.4902, 0.8235]. This interval is recorded and assigned a period number, such as "section 01". Each time segment is processed in this way to construct a sequence of brightness intervals. The sequence structure is time label, upper limit brightness value, lower limit brightness value, and normalized interval value. The results are saved as a structured array for use in subsequent section fluctuation judgment, and finally a reaction period brightness distribution set is obtained.

[0117] The section matching judgment sub-module, based on the brightness interval range of each section in the reaction period brightness distribution set and combined with the characteristic section set for changes in the reaction stage, identifies the fitting difference between the brightness amplitude deviation and the preset amplitude mean value, using the formula:

[0118] ;

[0119] Calculate the section offset trend value for the reaction stage;

[0120] Among them, represents the section offset trend value for the reaction stage, is the total number of brightness sections, is the maximum brightness value of the th section, is the preset interval upper limit value of the th section,

[0121] The reaction stage section offset trend value refers to the average difference obtained by comparing the amplitude of fluorescence brightness fluctuations extracted for each reaction time period with the brightness change range of the theoretical section in the preset reaction cycle in the automated coagulation detection process. It is used to quantify the deviation degree between the current reaction paragraph and the standard template. This value is calculated by taking the average of the differences between the actual brightness interval amplitudes of multiple sections and the template interval mean. The closer the value is to zero, the stronger the consistency of the brightness change trend of this reaction stage with the preset template. A positive value indicates that the actual brightness fluctuation is higher than expected, and a negative value indicates lower than expected. This trend value can be used as a key basis for subsequent determination of whether the reaction process is abnormal or sectional identification;

[0122] According to the fluorescence brightness range information recorded for the concentration of the brightness distribution in the reaction period, combined with the reaction cycle change paragraph template set in the automated coagulation detection process, the maximum and minimum values of each brightness interval are extracted one by one, the brightness fluctuation amplitude within the current section is calculated, and an offset amount judgment is made with the mean value of the theoretical fluctuation amplitude in the corresponding stage of the template. The brightness value of each section is statistically analyzed using the gray-scale mean within a 50×50 pixel central area, and the gray-scale range is from 0 to 255;

[0123] To eliminate the acquisition differences between devices, the maximum brightness value of each section and the minimum value need to be normalized. The normalization formula is , so as to unify all participating items to the range of 0 to 1. For example, for the first section with a maximum value of 210 and a minimum value of 125, after normalization: , , and the amplitude within the section is ;

[0124] At the same time, the upper and lower limits of the reference brightness fluctuation range defined in the system template for this section are 190 to 120. After normalization: , , and its mean value is ;

[0125] Perform the offset trend operation according to the formula:

[0126] Suppose there are currently 3 sections of data, and their parameters are:

[0127] The first section: , , , ;

[0128] The second section: maximum 192, minimum 135 → , , and the template is [180, 130] → , ;

[0129] Paragraph 3: Maximum 185, minimum 132 → , , the template is [170, 120] → , ;

[0130] The operation process is as follows:

[0131] Paragraph 1: , mean = ;

[0132] Paragraph 2: ;

[0133] Paragraph 3: ;

[0134] Substitute all results into the formula: ;

[0135] According to the judgment criteria set by the system, when the absolute value is less than 0.50, it is judged as offset stable. This value is the upper limit within the 95% confidence interval derived from statistical derivation of empirical data; therefore, the calculation result of this paragraph, -0.3399, indicates that the brightness section fluctuation conforms to the expectation of the reference paragraph, and it is judged that the offset trend is consistent;

[0136] By directly comparing the actual brightness fluctuation amplitude of the current reaction period with the mean value of the preset theoretical paragraph interval, a standard quantification judgment mechanism for the dynamic trajectory comparison template is established, avoiding the traditional fuzzy processing mode based on boundary judgment. This result shows that the brightness fluctuation in this period is within the expected change rhythm, and the result is classified as a normal component of the offset trend in the reaction stage section.

[0137] The trend trajectory generation sub-module identifies the sections with continuous similar change directions according to the offset trend value in the reaction stage section, accumulates the trends of adjacent same-direction segments, classifies and marks the trend directions, and obtains the coagulation process time-sequence trajectory trend;

[0138] Mark the positive and negative of the section trend value to represent the brightness change direction of each section. If multiple consecutive sections have the same-direction offset trend, they are regarded as a type of trend area, record the start and end section index numbers and the number of continuous sections, classify and mark this change trend area. For example, the offset values of paragraphs 1 to 3 are negative, marked as "descending section A", and the offset values of paragraphs 4 to 5 are positive, marked as "ascending section B", construct a trend sequence structure body, which contains: section identification, trend direction, continuous length, change interval value, arrange all trend sequences in chronological order, form the basic data for characterizing the trend evolution of each reaction stage in the automated coagulation detection process, and finally generate a coagulation reaction trend sequence table.

[0139] Please refer to Figure 6 , the risk trend identification module includes:

[0140] The trajectory interval classification sub-module, based on the time-series trajectory trend of the blood coagulation process, identifies the trajectory trend vector according to the start and end points and the amplitude difference of the trajectory change, delimits the trajectory change interval according to the positions of adjacent inflection points in the time series, marks the corresponding interval serial numbers of the sample trajectories, and generates a trajectory interval marking table;

[0141] The determination of the start and end points of the trajectory depends on the comprehensive analysis of the time-series data. In this process, the time-series data of the sample is extracted, which contains the relationship between the measured index value and time. The difference method is used to calculate the amplitude. When the change of the trajectory in the time series exceeds the set amplitude threshold, it is determined as the starting point of the trajectory change. Based on the subsequent data, the amplitude change of adjacent time periods is continuously compared until the amplitude difference returns to the initial level or is lower than the set threshold, and then it is determined as the end point of the trajectory. For example, when the amplitude difference is greater than 0.5, it is judged as the starting point, and when the amplitude difference returns to below 0.1, it is judged as the end point. According to the starting point and the end point, multiple intervals of the trajectory change are divided according to the time sequence. For each interval, by calculating the time difference between the positions of adjacent inflection points, the division is further refined to ensure that the interval delimitation conforms to the actual situation. Using the delimited interval information, a unique serial number is assigned to each interval, and this serial number is marked on the trajectory sample. Finally, a trajectory interval marking table is generated, and each sample data will correspond to an interval serial number. For example, for the sample trajectory points (time T1, value V1), (time T2, value V2), if the amplitude change between T1 and T2 exceeds the set threshold, the interval number is defined as 1, and when the amplitude change ends, the start point of interval 2 is delimited at time T3.

[0142] The category interval association sub-module calls the trajectory interval marking table, collects the feature category items in the sample label information, analyzes the category frequency through the sample distribution quantity between the interval serial number and the feature category, and screens the association combinations with a frequency higher than the category interval matching threshold to obtain the category interval corresponding relationship group;

[0143] Call the trajectory interval marking table to obtain the interval numbers of sample data and the corresponding sample label information. Collect the characteristic category items in each sample data, where the category items represent different coagulation characteristics, such as coagulation time, thrombin generation rate, etc. Analyze the distribution of the characteristic category items within each interval number, count the occurrence frequencies of each characteristic category in different intervals, use a frequency analysis tool to calculate the category frequency, and compare it with the set category interval matching threshold to screen out the category interval combinations whose frequency exceeds the threshold. Frequency analysis includes counting the sample data of each category. When the occurrence times of a certain category within an interval reach the set threshold, it is considered that there is an effective association between this category and this interval. For example, if the characteristic category A appears 30 times in interval 1 and 5 times in interval 2, and the set frequency threshold is 10 times, then the association between interval 1 and category A is strong, and the association between interval 2 and category A is weak. The final obtained category interval correspondence group will include the strong association between interval 1 and category A.

[0144] According to the category interval correspondence group, the process status configuration sub-module sets the process label mapping rules between the trajectory interval and the characteristic category, configures the detection process status field for the interval samples that meet the mapping rules, and automatically marks the sample process management field to generate an automated coagulation detection status mapping group;

[0145] Set the mapping rules between the trajectory interval and the characteristic category. The setting of the mapping rules depends on the actual detection standard. For example, it is set that when the frequency of a certain characteristic category A in a certain interval exceeds a certain threshold, the process status of the samples in this interval is "normal", otherwise it is "abnormal". The setting of the mapping rules depends on historical data or empirical rules, and a reasonable mapping range is obtained through experiments. For each interval sample, apply this mapping rule to detect its status and automatically configure the status field. If an interval meets the mapping rule, it is automatically marked as the "normal" or "abnormal" status. For example, if the frequency of category A in interval 1 exceeds the threshold of 10, the corresponding process status of this interval is "normal", and if the frequency of category B is lower than the set threshold, it is marked as "abnormal". Through an automated algorithm, the status information of all intervals is summarized to generate an automated coagulation detection status mapping group, which will be used for subsequent detection and processing, and the automatically marked process status field will also reflect the management situation of the samples;

[0146] Table 1: Example of frequency analysis of category interval association:

[0147]

[0148] As shown in Table 1, the table lists the occurrence frequencies of different categories within certain intervals. For example, in Interval 1, Category A appears 30 times, Category B appears 5 times, Category C appears 10 times. The frequency of Category A is higher than the set matching threshold by 10 times. Therefore, the association between Interval 1 and Category A is relatively strong. While in Interval 2, the frequency of Category A is lower than the threshold, so the association is relatively weak.

[0149] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An automated coagulation detection system, characterized in that, The system includes: The sample state regulation module obtains the initial concentration value and specific gravity distribution value of the whole blood sample, performs interval mapping according to the data fed back by the temperature control unit, identifies the dynamic regulation parameters for controlling sedimentation and component equilibrium, and generates a sample state equilibrium parameter set; The light source equilibrium imaging module calls the sample state equilibrium parameter set, collects the intensity distribution of the light source component and the boundary positioning of the reaction cup, compares the coincidence degree of the imaging area distribution, selects appropriate irradiation angles and light source power configurations, and generates a uniform light projection configuration group; The dynamic image extraction module, based on the uniform light projection configuration group, collects the fluorescence imaging brightness change and the boundary position offset trajectory, screens the consistent areas within the imaging cycle, and generates a continuous image feature trajectory set; The reaction process quantification module calls the continuous image feature trajectory set, extracts the fluorescence brightness interval in the image frame sequence, matches the reaction cycle change paragraphs, and judges the reaction process trend through section superposition to obtain the coagulation process time sequence trajectory trend.

2. The automated coagulation detection system according to claim 1, wherein, The sample state equilibrium parameter set includes a concentration mapping coefficient, a specific gravity adjustment factor, temperature and pH regulation weights, a sedimentation equilibrium threshold, and a component distribution balance value. The uniform light projection configuration group includes irradiation angle parameters, light source power configurations, boundary matching coefficients, and imaging area equilibrium degrees. The continuous image feature trajectory set includes a brightness change curve, a boundary offset trajectory, a synchronous offset index, and a consistent area label. The coagulation process time sequence trajectory trend includes a fluorescence intensity section sequence, a stage evolution mode, a change trend path, and a time sequence matching result.

3. The automated coagulation detection system according to claim 1, wherein The sample state regulation module includes: The sample state acquisition sub-module obtains the initial concentration value and specific gravity distribution value of the whole blood sample, extracts the parameter differences deviated from the specific gravity reference interval and the concentration standard interval according to the temperature value and pH value range fed back by the temperature control unit of the reaction cup loading cavity, and generates a sample initial offset parameter group; The adjustment parameter extraction sub-module, based on the sample initial offset parameter group, combines the adjustment limit values under the current temperature control conditions, screens the temperature adjustment factor and pH correction factor intervals that can be used to control the offset difference, extracts the adjustment combination matching the current offset level, and generates a coagulation adjustment matching parameter; The detection process mapping sub-module, according to the coagulation adjustment matching parameter, combines the current concentration value and specific gravity distribution value of the sample, identifies the function mapping group for sedimentation and equilibrium regulation, judges the constraint boundary of the loading and reaction rhythms, and generates a sample state equilibrium parameter set.

4. The automated coagulation detection system according to claim 3, wherein The light source equilibrium imaging module includes: The detection response sub-module calls the sample state equilibrium parameter set, collects the light source power response values and angle mapping values under multi-directional irradiation, compares the light intensity response and irradiation angle matching degree, and obtains the light overlap rate index; The boundary positioning sub-module calls the light overlap rate index, extracts the image boundary gradient intensity, jointly screens the angle set that meets the boundary clarity requirement, and obtains the illumination angle selection set; The uniform light configuration sub-module combines the light source power groups according to the illumination angle selection set, identifies the light intensity and boundary pixel density of each group, calculates the light balance error rate, selects the configuration with the smallest error rate group, and generates a uniform light projection configuration group.

5. The automated coagulation detection system according to claim 4, wherein The dynamic image extraction module includes: The brightness trajectory extraction sub-module collects the brightness values of unit areas of each frame of image based on the uniform light projection configuration group, records the time series and identifies the brightness difference between consecutive frames, and obtains the brightness change time trajectory; The boundary offset determination sub-module extracts the boundary coordinates according to the frames corresponding to the brightness change time trajectory, identifies the pixel position change vector of each frame, matches the brightness and the boundary difference, calculates the synchronous offset value, determines whether it is lower than the boundary change reference, and obtains the synchronous offset frame set; The feature trajectory construction sub-module calls the frame sequence in the synchronous offset frame set, extracts the combined vector of brightness and boundary, sorts and constructs the trajectory, and determines the consistency based on the Euclidean distance between adjacent frames, and generates a set of continuous image feature trajectories.

6. The automated coagulation detection system according to claim 5, wherein The reaction process quantification module includes: The brightness interval extraction sub-module calls the time-sequential frame segments in the set of continuous image feature trajectories, extracts the brightness values of the fluorescent pixels in the reaction area according to the set time window, counts the maximum and minimum brightness of each segment, delimits the brightness change interval, and obtains the brightness distribution set of the reaction period; The section matching and judgment sub-module, according to the brightness interval range of each section in the brightness distribution set of the reaction period, combines the change characteristic sections set in the reaction stage, identifies the fitting difference between the brightness amplitude deviation and the preset amplitude mean value, and calculates the section offset trend value of the reaction stage; The trend trajectory generation sub-module, according to the section offset trend value of the reaction stage, identifies the sections with continuous same-type change directions, accumulates the trends of adjacent same-direction segments, classifies and labels the trend directions, and obtains the time-sequence trajectory trend of the blood coagulation process.

7. The automated coagulation detection system according to claim 1, characterized in that, The system further includes a risk trend identification module: The risk trend identification module classifies the trajectory trends of samples in the same round based on the time-sequence trajectory trend of the blood coagulation process, collects the sample feature categories recorded in the sample label information group, judges the stability degree of the association interval between it and the trajectory classification, and generates an automated blood coagulation detection status mapping group; The automated blood coagulation detection status mapping group includes trajectory classification results, sample feature correlation degrees, classification stable intervals, and detection status labels.

8. The automated coagulation detection system according to claim 7, wherein The risk trend identification module includes: The trajectory interval classification sub-module, based on the time-sequence trajectory trend of the blood coagulation process, identifies the trajectory trend vector according to the start and end points and amplitude difference of the trajectory change, delimits the trajectory change interval according to the positions of adjacent inflection points in the time series, labels the corresponding interval serial numbers of the sample trajectories, and generates a trajectory interval marking table; The category interval association sub-module calls the trajectory interval marking table, collects the feature category items in the sample label information, analyzes the category frequency through the sample distribution quantity between the interval serial number and the feature category, and screens out the association combinations with frequencies higher than the category interval matching threshold to obtain the category interval correspondence group; The process status configuration sub-module, according to the category interval correspondence group, sets the process label mapping rule between the trajectory interval and the feature category, configures the detection process status field for the interval samples that meet the mapping rule, and automatically marks the sample process management field to generate an automated blood coagulation detection status mapping group.

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