Pollutant control method and system for hemoglobinuria flow cytometry

Abstract

The embodiment of the invention provides a pollutant control method and system for hemoglobinuria flow cytometry, and belongs to the technical field of biomedicine. The method comprises the following steps: determining a target pipeline during flow cytometry of hemoglobinuria, obtaining target residues from the target pipeline, and carrying out photoelectric data measurement on the target residues to obtain first measurement data; carrying out photoelectric data measurement on a target reference agent for hemoglobinuria flow cytometry to obtain second measurement data; performing data filtering processing on the first measurement data to obtain first target data, and performing data filtering processing on the second measurement data to obtain second target data; calculating a target similarity between the first target data and the second target data, and determining a dynamic threshold according to the first target data and the second target data; determining a pollutant detection result of the target pipeline according to the target similarity and a dynamic threshold value; and performing data analysis according to the pollutant detection result to determine a pollutant control strategy of the target pipeline.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to a method and system for controlling pollutants in flow cytometry detection of hemoglobinuria. Background Art

[0002] Paroxysmal nocturnal hemoglobinuria (PNH) is a rare hematopoietic stem cell clonal disease, manifested as paroxysmal intravascular hemolysis, paroxysmal hemoglobinuria, bone marrow hematopoietic failure, and venous thrombosis. Its pathogenesis is somatic mutation of the PIG-A gene on the X chromosome, resulting in disorders in GPI anchor synthesis and expression, and the inability of anchored proteins to bind and thus deletion. Flow cytometry (FCM) has become the gold standard for PNH detection because it can detect the absence of GPI-anchored proteins and anchors, quantify the size and characteristics of PNH clones, and has high sensitivity for detecting small clones. However, the detection frequency and positive detection rate of PNH in China are low, and the introduction of pollutants (such as reagent impurities, microbial contamination, dust in the operating environment, etc.) during the FCM detection process will interfere with the detection results. However, there are insufficient effective control measures for these pollutants in the prior art, which further leads to false positives in the detection results, thus resulting in problems of low detection accuracy and reliability.

[0003] Therefore, it is urgent to design a technical solution to solve at least one of the above technical problems. Summary of the Invention

[0004] The main objective of the embodiments of the present invention is to provide a method and system for controlling pollutants in flow cytometry detection of hemoglobinuria, aiming to solve the problem in the related art that there are insufficient effective control measures for pollutants during the FCM detection process, which further leads to false positives in the detection results, thus resulting in problems of low detection accuracy and reliability.

[0005] In a first aspect, the embodiments of the present invention provide a method for controlling pollutants in flow cytometry detection of hemoglobinuria, including:

[0006] Determine a target pipeline corresponding to the flow cytometry detection of hemoglobinuria, obtain a target residue corresponding to the detection from the target pipeline, and perform optoelectronic data measurement on the target residue to obtain first measurement data;

[0007] Determine a target reference agent corresponding to the flow cytometry detection of hemoglobinuria, and perform optoelectronic data measurement on the target reference agent to obtain second measurement data;

[0008] Perform data filtering processing on the first measurement data to obtain first target data and perform data filtering processing on the second measurement data to obtain second target data;

[0009] Calculate the target similarity between the first target data and the second target data, and determine a dynamic threshold according to the first target data and the second target data;

[0010] Determine the pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold;

[0011] Perform data analysis according to the pollutant detection result to determine the pollutant control strategy corresponding to the target pipeline.

[0012] In a second aspect, an embodiment of the present invention provides a pollutant control system for hemoglobinuria flow cytometry detection, including:

[0013] A first acquisition module, configured to determine a target pipeline corresponding to hemoglobinuria flow cytometry detection, obtain a target residue corresponding to the detection from the target pipeline, and perform optoelectronic data measurement on the target residue to obtain first measurement data;

[0014] A second acquisition module, configured to determine a target reference agent corresponding to the hemoglobinuria flow cytometry detection, and perform optoelectronic data measurement on the target reference agent to obtain second measurement data;

[0015] A data processing module, configured to perform data filtering processing on the first measurement data to obtain first target data and perform data filtering processing on the second measurement data to obtain second target data;

[0016] A data determination module, configured to calculate the target similarity between the first target data and the second target data, and determine a dynamic threshold according to the first target data and the second target data;

[0017] A detection determination module, configured to determine the pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold;

[0018] A strategy determination module, configured to perform data analysis according to the pollutant detection result to determine the pollutant control strategy corresponding to the target pipeline.

[0019] In a third aspect, an embodiment of the present invention further provides a terminal device, which includes a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection communication between the processor and the memory. When the computer program is executed by the processor, the steps of any one of the pollutant control methods for hemoglobinuria flow cytometry detection provided in the specification of the present invention are implemented.

[0020] In a fourth aspect, an embodiment of the present invention further provides a storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the pollutant control methods for flow cytometry detection of hemoglobinuria provided in the specification of the present invention.

[0021] An embodiment of the present invention provides a pollutant control method and system for flow cytometry detection of hemoglobinuria. The method includes: determining a target pipeline for hemoglobinuria detection, obtaining a target residue and a target reference agent corresponding to the target pipeline, and then respectively performing optoelectronic data measurement to obtain first measurement data and second measurement data, providing a reliable basis for subsequent data analysis. Then, filter the first measurement data and the second measurement data to remove noise and interference, and obtain purer first target data and second target data, improving the credibility of the data. Thus, by calculating the target similarity between the first target data and the second target data, the consistency of the data can be dynamically evaluated, further improving the sensitivity and specificity of the detection. And set a dynamic threshold according to the first target data and the second target data, making the detection result more in line with the characteristics of the actual sample, reducing false positives and false negatives. Then, according to the target similarity and the dynamic threshold, determine the pollutant detection result of the target pipeline, comprehensively evaluating the presence and impact of pollutants. Finally, through data analysis of the pollutant detection result, formulate corresponding pollutant control strategies to ensure the cleanliness and safety of the detection process, so as to be able to timely detect and handle whether there are pollutants in the FCM detection process, ensure the continuity and reliability of the detection process, and further improve the accuracy and reliability of flow cytometry detection of hemoglobinuria. It can also monitor and control pollutants in real time to ensure the safety and efficiency of the experimental process. It also solves the problem in the related art that there are insufficient effective control measures for pollutants in the FCM detection process, which further leads to false positives in the detection result, resulting in low detection accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic flowchart of a pollutant control method for flow cytometry detection of hemoglobinuria provided by an embodiment of the present invention;

[0024] Figure 2 It is a schematic module structure diagram of a pollutant control system for flow cytometry detection of hemoglobinuria provided by an embodiment of the present invention;

[0025] Figure 3 This is a schematic block diagram of the structure of a terminal device provided by an embodiment of the present invention. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] The flowchart shown in the accompanying drawings is only an example illustration, and does not necessarily include all contents and operations / steps, nor does it necessarily execute in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged, so the actual execution order may be changed according to the actual situation.

[0028] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in this specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0029] An embodiment of the present invention provides a method and system for controlling pollutants in flow cytometry detection of hemoglobinuria. Among them, the method for controlling pollutants in flow cytometry detection of hemoglobinuria can be applied to a terminal device, and the terminal device can be an electronic device such as a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The terminal device can be a server or a server cluster.

[0030] Next, some embodiments of the present invention will be described in detail with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0031] Please refer to Figure 1 , Figure 1 This is a schematic flowchart of a method for controlling pollutants in flow cytometry detection of hemoglobinuria provided by an embodiment of the present invention.

[0032] As Figure 1 shown, the method for controlling pollutants in flow cytometry detection of hemoglobinuria includes steps S101 to S106.

[0033] Step S101: Determine the target pipeline corresponding to the flow cytometry detection of hemoglobinuria, obtain the corresponding target residue after detection from the target pipeline, and perform optoelectronic data measurement on the target residue to obtain the first measurement data.

[0034] Exemplarily, before performing the flow cytometry detection of hemoglobinuria, it is first necessary to determine the target pipeline according to the experimental design or standard operating procedure. The target pipeline is usually used for the detection of specific types of samples (such as blood, urine, etc.). Thus, after completing the flow cytometry detection of hemoglobinuria, collect the target residue in the target pipeline. The target residue may include the sample that has not been completely consumed, washing solution, waste liquid, etc. Then, use a dedicated optoelectronic detection device (such as a spectrometer, photodiode array detector, etc.) to perform optoelectronic data measurement on the collected target residue to obtain the first measurement result, and the first measurement result includes but is not limited to data such as light intensity and wavelength.

[0035] Step S102: Determine the target reference agent corresponding to the flow cytometry detection of hemoglobinuria, and perform optoelectronic data measurement on the target reference agent to obtain the second measurement data.

[0036] Exemplarily, before performing the flow cytometry detection of hemoglobinuria, determine the target reference agent according to the experimental design and standard operating procedure. The target reference agent is usually a standard substance with known concentration and composition, which is used to calibrate and verify the accuracy of the detection method. Then, use the same optoelectronic detection device as the target residue (such as a spectrometer, photodiode array detector, etc.) to perform optoelectronic data measurement on the prepared target reference agent sample to obtain the second measurement result, and the second measurement result includes but is not limited to data such as light intensity and wavelength.

[0037] Step S103: Perform data filtering processing on the first measurement data to obtain the first target data and perform data filtering processing on the second measurement data to obtain the second target data.

[0038] Exemplarily, use filtering algorithms such as low-pass filtering, high-pass filtering, band-pass filtering, median filtering, Kalman filtering, etc. to perform data filtering processing on the first measurement data to obtain the first target data, and use filtering algorithms such as low-pass filtering, high-pass filtering, band-pass filtering, median filtering, Kalman filtering, etc. to perform data filtering processing on the second measurement data to obtain the second target data.

[0039] In some embodiments, the data filtering process for the first measurement data to obtain the first target data and the data filtering process for the second measurement data to obtain the second target data include: performing multi-level decomposition on the first measurement data using a first wavelet basis to obtain first high-frequency information and first low-frequency information; determining a first threshold, and performing threshold quantization on the first high-frequency information according to the first threshold to obtain a first sensitive value corresponding to the first measurement data in the first wavelet basis; determining a first target basis corresponding to the first measurement data from the first wavelet basis according to the first sensitive value; decomposing the first measurement data according to the first target basis to obtain second high-frequency information and second low-frequency information; performing data reconstruction according to the second high-frequency information and the second low-frequency information to implement filtering of the first measurement data to obtain the first target data; performing multi-level decomposition on the second measurement data using a second wavelet basis to obtain third high-frequency information and third low-frequency information; determining a second threshold, and performing threshold quantization on the third high-frequency information according to the second threshold to obtain a second sensitive value corresponding to the second measurement data in the second wavelet basis; determining a second target basis corresponding to the second measurement data from the second wavelet basis according to the second sensitive value; decomposing the second measurement data according to the second target basis to obtain fourth high-frequency information and fourth low-frequency information; performing data reconstruction according to the fourth high-frequency information and the fourth low-frequency information to implement filtering of the second measurement data to obtain the second target data.

[0040] Exemplarily, the first wavelet basis and the second wavelet basis are selected such as Daubechies, Haar, Symlets, etc., and multi-level wavelet decomposition is performed on the first measurement data using the selected first wavelet basis. The multi-level decomposition can extract high-frequency information and low-frequency information in the data, which are respectively stored as the first high-frequency information and the first low-frequency information. Thus, according to the statistical characteristics (such as standard deviation, mean, etc.) of the first high-frequency information, the first threshold is calculated. The setting of the first threshold is usually based on the noise level. If it is too high, useful information may be lost; if it is too low, noise may be retained. Then, threshold quantization is performed on the first high-frequency information according to the first threshold. The quantization process can remove the noise in the high-frequency information and generate a first sensitive value corresponding to the first measurement data in the first wavelet basis.

[0041] Exemplarily, according to the first sensitive value, the first wavelet basis corresponding to the minimum first sensitive value is selected from the first wavelet basis and determined as the first target basis. The selection of the first target basis should ensure that it retains useful information while removing noise.

[0042] Exemplarily, the first measurement data is decomposed using the first target basis to obtain second high-frequency information and second low-frequency information. Then, data reconstruction is performed based on the second high-frequency information and the second low-frequency information to implement filtering processing of the first measurement data and obtain first target data.

[0043] Exemplarily, multi-level wavelet decomposition is performed on the second measurement data using a selected second wavelet basis to extract third high-frequency information and third low-frequency information. According to the statistical characteristics of the third high-frequency information, a second threshold is calculated. The setting of the second threshold should be similar to the first threshold to ensure an appropriate noise removal level. Threshold quantization processing is performed on the third high-frequency information according to the second threshold to generate a second sensitive value corresponding to the second measurement data in the second wavelet basis.

[0044] Exemplarily, the second wavelet basis corresponding to the minimum second sensitive value is selected from the second wavelet basis according to the second sensitive value and determined as the second target basis. The selection of the second target basis should ensure that it retains useful information while removing noise. Then, the second measurement data is decomposed using the second target basis to obtain fourth high-frequency information and fourth low-frequency information. Data reconstruction is performed based on the fourth high-frequency information and the fourth low-frequency information to implement filtering processing of the second measurement data and obtain second target data.

[0045] Step S104: Calculate the target similarity between the first target data and the second target data, and determine a dynamic threshold based on the first target data and the second target data.

[0046] Exemplarily, the target similarity between the first target data and the second target data is calculated using methods such as cosine similarity, Euclidean distance, and Manhattan distance.

[0047] Exemplarily, the data distributions of the first target data and the second target data are analyzed to obtain statistical characteristics such as the range, mean, and standard deviation of the data. Then, a statistical-based method (such as the standard deviation multiple method), a historical data-based method (such as the moving average method), or a machine learning-based method is used to calculate and obtain the dynamic threshold based on the first target data and the second target data.

[0048] For example, in the machine learning-based method, data prediction is performed on the first target data and the second target data to determine at what value the preset similarity is reached when it can be determined that the first target data and the second target data are similar, that is, to determine the minimum similarity when the first target data and the second target data are recognized as similar, thereby obtaining the dynamic threshold.

[0049] In some embodiments, calculating the target similarity between the first target data and the second target data includes: obtaining the first similarity according to the data distribution between the first target data and the second target data; performing data alignment on the first target data and the second target data to obtain the maximum number corresponding to the same or similar data in the first target data and the second target data; performing an intersection calculation on the first target data and the second target data to obtain a target intersection result, and obtaining the intersection number corresponding to the target intersection result; calculating the structural similarity between the first target data and the second target data according to the maximum number and the intersection number to obtain a second similarity; determining a segmentation interval, and determining the first distribution information corresponding to the first target data according to the segmentation interval and determining the second distribution information corresponding to the second target data according to the segmentation interval; determining the data span similarity between the first target data and the second target data according to the first distribution information and the second distribution information to obtain a third similarity; and fusing the first similarity, the second similarity, and the third similarity to determine the target similarity between the first target data and the second target data.

[0050] Exemplarily, perform statistical analysis on the first target data and the second target data to understand their distribution characteristics, including mean, variance, skewness, kurtosis, etc. Then, based on the statistical characteristics of the data distribution, use similarity measurement methods (such as KL divergence, JS divergence, etc.) to calculate the distribution similarity between the first target data and the second target data, so as to obtain the first similarity.

[0051] Exemplarily, align the first target data and the second target data to ensure the consistency of the first target data and the second target data in terms of time and dimension, so as to obtain the maximum number corresponding to the same or similar data in the first target data and the second target data.

[0052] Exemplarily, perform an intersection calculation on the aligned first target data and the second target data to obtain a target intersection result, and count the number of data in the target intersection result to obtain the intersection number.

[0053] Exemplarily, obtain the first number corresponding to the first target data and the second number corresponding to the second target data, then add the first number and the second number to obtain the first target number, and add the maximum number and the intersection number to obtain the second target number. Further, divide the second target number by the first target number to obtain the second similarity, and the second similarity can reflect the degree of closeness between the first target data and the second target data in terms of structure and composition.

[0054] Exemplarily, according to the distribution characteristics of the data and business requirements, the data is divided into several segmented intervals. For the first target data and the second target data, the distribution information within each segmented interval is calculated respectively to obtain the first distribution information and the second distribution information. Then, based on the first distribution information and the second distribution information, the data span similarity between the first target data and the second target data is calculated, that is, the third similarity is obtained. The span similarity reflects the distribution differences and overlapping degrees of the first target data and the second target data in different intervals.

[0055] Exemplarily, the first similarity, the second similarity and the third similarity are weighted and fused to determine the comprehensive similarity between the first target data and the second target data, that is, the target similarity. The weights can be adjusted according to the importance of different similarities to comprehensively reflect the overall similarity of the data.

[0056] Specifically, by comprehensively analyzing the similarities of the first target data and the second target data in terms of distribution, structure and span, the target similarity between them is finally determined. This process helps to comprehensively evaluate the similarity of the data and provides a reliable basis for subsequent data analysis, classification and prediction.

[0057] In some embodiments, obtaining the first similarity according to the data distribution between the first target data and the second target data includes: performing data discretization on the first target data to obtain first discrete data and performing data discretization on the second target data to obtain second discrete data; determining an adjustment parameter according to the first data volume of the first discrete data and the second data volume of the second discrete data, determining the first probability corresponding to each first sub - data in the first discrete data and the first density corresponding to the first sub - data, and determining the second probability corresponding to each second sub - data in the second discrete data and the second density corresponding to the second sub - data; using the adjustment parameter to combine the first probability, the first density, the second probability and the second density to determine the data difference value between the first target data and the second target data; determining the first similarity according to the data difference value; wherein, the data difference value is obtained according to the following formula:

[0058]

[0059] where diff represents the data difference value, count 1 represents the first data volume of the first discrete data, count 2 represents the second data volume of the second discrete data, n represents the total data volume corresponding to the first sub - data, m represents the total data volume corresponding to the second sub - data, p 1i represents the first probability corresponding to the i - th first sub - data, γ 1iDenote the first density corresponding to the i-th first sub-data as p 2j Denote the second probability corresponding to the j-th second sub-data as γ 2j Denote the second density corresponding to the j-th second sub-data, and α represents the adjustment parameter.

[0060] Exemplarily, perform discretization processing on the first target data, convert continuous data into a discrete data sequence, and obtain the first discrete data. Similarly, perform discretization processing on the second target data to obtain the second discrete data.

[0061] Exemplarily, calculate the first data volume of the first discrete data and the second data volume of the second discrete data respectively. When the first data volume is greater than or equal to the second data volume, the adjustment parameter is the second data volume divided by the first data volume; when the second data volume is greater than the first data volume, the adjustment parameter is the first data volume divided by the second data volume.

[0062] Exemplarily, when the first data volume is greater than or equal to the second data volume, count the first frequency corresponding to each first sub-data in the first discrete data, thereby obtaining the first probability corresponding to the first sub-data by dividing the first frequency by the first data volume, and obtaining the second frequency of the first sub-data in the second discrete data. Then, sum the first frequency and the second frequency, divide by the first data volume, and then divide by 2 to obtain the first density. Also, count the third frequency corresponding to each second sub-data in the second discrete data, thereby obtaining the second probability corresponding to the second sub-data by dividing the third frequency by the second data volume, and obtaining the fourth frequency of the second sub-data in the first discrete data. Then, sum the third frequency and the fourth frequency, divide by the first data volume, and then divide by 2 to obtain the second density.

[0063] Exemplarily, when the first data volume is less than the second data volume, count the first frequency corresponding to each first sub-data in the first discrete data, thereby obtaining the first probability corresponding to the first sub-data by dividing the first frequency by the first data volume, and obtaining the second frequency of the first sub-data in the second discrete data. Then, sum the first frequency and the second frequency, divide by the second data volume, and then divide by 2 to obtain the first density. Also, count the third frequency corresponding to each second sub-data in the second discrete data, thereby obtaining the second probability corresponding to the second sub-data by dividing the third frequency by the second data volume, and obtaining the fourth frequency of the second sub-data in the first discrete data. Then, sum the third frequency and the fourth frequency, divide by the second data volume, and then divide by 2 to obtain the second density.

[0064] Exemplarily, use the adjustment parameter, combine the first probability, the first density, the second probability, and the second density, and calculate the data difference value between the first target data and the second target data according to the following formula. Among them, obtain the data difference value according to the following formula:

[0065]

[0066] Among them, diff represents the data difference value, count 1 represents the first data volume of the first discrete data, count 2 represents the second data volume of the second discrete data, n represents the total data volume corresponding to the first sub-data, m represents the total data volume corresponding to the second sub-data, p 1i represents the first probability corresponding to the i-th first sub-data, γ 1i represents the first density corresponding to the i-th first sub-data, p 2j represents the second probability corresponding to the j-th second sub-data, γ 2j represents the second density corresponding to the j-th second sub-data, and α represents the adjustment parameter.

[0067] Exemplarily, by adjusting the parameter, it is possible to adapt to different data volumes of the first target data and the second target data, ensuring the stability and consistency of the calculation results under different data volumes. The adjustment parameter can balance the influence of different data volumes, making the calculation results more objective. The calculation of probability and density can accurately quantify the distribution characteristics of the first target data and the second target data, providing a more detailed basis for data analysis. Thus, through probability and density, the characteristics of different data can be discovered, which helps to deeply understand the distribution law and internal relationship of the data. Thus, the difference between the first target data and the second target data is quantified by the data difference value.

[0068] Exemplarily, after obtaining the data difference value, then subtract the data difference value from 1 to obtain the first similarity between the first target data and the second target data.

[0069] In some embodiments, fusing the first similarity, the second similarity, and the third similarity to determine the target similarity between the first target data and the second target data includes: multiplying the second similarity and the third similarity to obtain a product similarity; determining a first weight corresponding to the first similarity and a second weight corresponding to the product similarity; performing data fusion on the first similarity and the product similarity according to the first weight and the second weight to obtain the target similarity between the first target data and the second target data.

[0070] Exemplarily, multiply the second similarity and the third similarity to obtain a product similarity. The product similarity comprehensively considers the influence of the second similarity and the third similarity, and reflects the comprehensive similarity of the first target data and the second target data in terms of data structure.

[0071] Exemplarily, consider the contribution degrees of different similarities and the actual application scenarios to determine the first weight corresponding to the first similarity and the second weight corresponding to the product similarity.

[0072] Exemplarily, according to the first weight and the second weight, perform weighted fusion on the first similarity and the product similarity, and then through weighted fusion, obtain the target similarity between the first target data and the second target data. The target similarity comprehensively considers the contributions of different similarities and can more comprehensively and accurately reflect the overall similarity between the first target data and the second target data, thereby providing strong support for subsequent data processing and decision-making.

[0073] In some embodiments, the determining the dynamic threshold according to the first target data and the second target data includes: arranging the first target data in order to obtain the first arranged data and arranging the second target data in order to obtain the second arranged data; determining the first segmentation threshold of the first target data relative to the second target data according to the first arranged data and the second arranged data; calculating the proximity distance of the first target data to obtain the adjustable parameter corresponding to the first segmentation threshold; determining the second segmentation threshold corresponding to the first target data according to the first segmentation threshold and the adjustable parameter; screening the first target data according to the second segmentation threshold to obtain the third target data; calculating the data similarity between the third target data and the second target data, and determining the dynamic threshold according to the data similarity.

[0074] Exemplarily, arrange the first target data in ascending or descending order to obtain the first arranged data. Similarly, arrange the second target data in the same order to obtain the second arranged data.

[0075] Exemplarily, compare the first arranged data and the second arranged data, find the corresponding data points of the two, and determine the first segmentation threshold of the first target data relative to the second target data. The first segmentation threshold can be the difference value, proportional relationship or other statistical indicators between the first arranged data and the second arranged data.

[0076] Exemplarily, calculate the proximity distance of the first target data. Usually, select a specific reference point (such as the median, mean, etc.) as the benchmark and calculate the distance between other data points and this reference point. The proximity distance can reflect the distribution density and discrimination degree of the data points, and thus determine the adjustable parameter according to the calculated proximity distance in combination with the first segmentation threshold.

[0077] Exemplarily, an adjustable range is determined according to a first segmentation threshold and an adjustable parameter, and the adjustable range is determined as the second segmentation threshold corresponding to the first target data. The second segmentation threshold is a flexible range of the first segmentation threshold with respect to the adjustable parameter. Thus, according to the second segmentation threshold, the first target data is screened to obtain the third target data. The screening process may be to retain the data points greater than the second segmentation threshold.

[0078] Exemplarily, based on a certain distance metric (such as Euclidean distance, cosine similarity, etc.) or similarity of probability distribution, the data similarity between the third target data and the second target data is calculated, and the calculated data similarity is determined as the dynamic threshold.

[0079] In some embodiments, the obtaining the adjustable parameter corresponding to the first segmentation threshold by performing a nearest neighbor distance calculation on the first target data includes: obtaining the first nearest neighbor data corresponding to the first segmentation threshold and the second nearest neighbor data progressively corresponding under the first nearest neighbor data from the first target data; calculating a first nearest neighbor distance between the first nearest neighbor data and the first segmentation threshold and calculating a second nearest neighbor distance between the second nearest neighbor data and the first segmentation threshold; and performing a distance averaging calculation according to the first nearest neighbor distance and the second nearest neighbor distance to obtain the adjustable parameter corresponding to the first segmentation threshold.

[0080] Exemplarily, from the first target data, the data point closest to the first segmentation threshold is found, that is, the first nearest neighbor data. The first nearest neighbor data should have the smallest difference from the first segmentation threshold, reflecting the distribution of the first target data near the segmentation point. Based on the first nearest neighbor data, the data point that is the second closest to the first segmentation threshold is continuously found, that is, the second nearest neighbor data. The second nearest neighbor data is progressively selected based on the first nearest neighbor data, reflecting the further distribution of the first target data near the segmentation point.

[0081] Exemplarily, the distance between the first nearest neighbor data and the first segmentation threshold is calculated to obtain the first nearest neighbor distance. The first nearest neighbor distance reflects the smallest difference of the first target data near the segmentation point. The distance between the second nearest neighbor data and the first segmentation threshold is calculated to obtain the second nearest neighbor distance. The second nearest neighbor distance reflects the second smallest difference of the first target data near the segmentation point.

[0082] Exemplarily, the first nearest neighbor distance and the second nearest neighbor distance are averaged to obtain a comprehensive distance average value, and the calculated distance average value is used as the adjustable parameter corresponding to the first segmentation threshold. The adjustable parameter reflects the overall distribution and the degree of difference of the first target data near the first segmentation threshold.

[0083] Step S105. Determine the pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold.

[0084] Exemplarily, compare the target similarity with the dynamic threshold. When the target similarity is greater than or equal to the dynamic threshold, the pollutant detection result corresponding to the target pipeline is that there is no pollutant or the pollutant does not affect the FCM detection; when the target similarity is less than the dynamic threshold, the pollutant detection result corresponding to the target pipeline is that there is a pollutant or the pollutant affects the FCM detection.

[0085] Step S106. Determine the pollutant control strategy corresponding to the target pipeline through data analysis according to the pollutant detection result.

[0086] Exemplarily, when the pollutant detection result is that there is no pollutant or the pollutant does not affect the FCM detection, the pollutant control strategy corresponding to the target pipeline is to allow the input, gating, and analysis of the next batch of FCM test substances, so as to improve the accuracy of the flow cytometry detection of paroxysmal nocturnal hemoglobinuria. When the pollutant detection result is that there is a pollutant or the pollutant affects the FCM detection, the pollutant control strategy is to conduct a detailed detection of the pollutants in the target pipeline to determine their types, concentrations, and sources. Then, according to the pollutant detection results, corresponding treatment measures are taken, such as cleaning the pipeline, replacing reagents, adjusting detection parameters, etc. After the pollutants are treated, the target pipeline is verified again to confirm that the pollutants have been effectively removed or their impacts have been minimized. Furthermore, after confirming that the pollutants have been effectively treated, the FCM detection is carried out again to ensure that the detection results are not interfered by the pollutants. Thus, through the pollutant control strategy, it is possible to effectively cope with the presence and impacts of pollutants without affecting the accuracy of FCM detection, ensuring the accuracy and reliability of the flow cytometry detection of paroxysmal nocturnal hemoglobinuria.

[0087] Please refer to Figure 2 , Figure 2A pollutant control system 200 for flow cytometry detection of hemoglobinuria provided by an embodiment of the present application. The pollutant control system 200 for flow cytometry detection of hemoglobinuria includes a first acquisition module 201, a second acquisition module 202, a data processing module 203, a data determination module 204, a detection determination module 205, and a policy determination module 206. Among them, the first acquisition module 201 is configured to determine a target pipeline corresponding to flow cytometry detection of hemoglobinuria, obtain a target residue corresponding to the detection from the target pipeline, and perform optoelectronic data measurement on the target residue to obtain first measurement data; the second acquisition module 202 is configured to determine a target reference agent corresponding to the flow cytometry detection of hemoglobinuria, and perform optoelectronic data measurement on the target reference agent to obtain second measurement data; the data processing module 203 is configured to perform data filtering processing on the first measurement data to obtain first target data and perform data filtering processing on the second measurement data to obtain second target data; the data determination module 204 is configured to calculate a target similarity between the first target data and the second target data, and determine a dynamic threshold according to the first target data and the second target data; the detection determination module 205 is configured to determine a pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold; the policy determination module 206 is configured to perform data analysis according to the pollutant detection result to determine a pollutant control policy corresponding to the target pipeline.

[0088] In some embodiments, during the process of performing data filtering processing on the first measurement data to obtain first target data and performing data filtering processing on the second measurement data to obtain second target data, the data processing module 203 performs the following:

[0089] Perform multi-level decomposition processing on the first measurement data using a first wavelet basis to obtain first high-frequency information and first low-frequency information;

[0090] Determine a first threshold, and perform threshold quantization processing on the first high-frequency information according to the first threshold to obtain a first sensitive value corresponding to the first measurement data in the first wavelet basis;

[0091] Determine a first target basis corresponding to the first measurement data from the first wavelet basis according to the first sensitive value;

[0092] Decompose the first measurement data according to the first target basis to obtain second high-frequency information and second low-frequency information;

[0093] Perform data reconstruction according to the second high-frequency information and the second low-frequency information to implement filtering processing on the first measurement data to obtain the first target data;

[0094] Performing multi-level decomposition processing on the second measurement data using a second wavelet basis to obtain third high-frequency information and third low-frequency information;

[0095] Determining a second threshold, and performing threshold quantization processing on the third high-frequency information according to the second threshold to obtain a second sensitive value corresponding to the second measurement data under the second wavelet basis;

[0096] Determining a second target basis corresponding to the second measurement data from the second wavelet basis according to the second sensitive value;

[0097] Decomposing the second measurement data according to the second target basis to obtain fourth high-frequency information and fourth low-frequency information;

[0098] Performing data reconstruction according to the fourth high-frequency information and the fourth low-frequency information to implement filtering processing on the second measurement data to obtain the second target data.

[0099] In some embodiments, during the process of calculating the target similarity between the first target data and the second target data, the data determination module 204 performs:

[0100] Obtaining the first similarity according to the data distribution between the first target data and the second target data;

[0101] Aligning the first target data and the second target data to obtain the maximum number corresponding to the same or similar data in the first target data and the second target data;

[0102] Performing an intersection calculation on the first target data and the second target data to obtain a target intersection result, and obtaining the intersection number corresponding to the target intersection result;

[0103] Calculating the structural similarity between the first target data and the second target data according to the maximum number and the intersection number to obtain a second similarity;

[0104] Determining a segmentation interval, and determining first distribution information corresponding to the first target data according to the segmentation interval and determining second distribution information corresponding to the second target data according to the segmentation interval;

[0105] Determining the data span similarity between the first target data and the second target data according to the first distribution information and the second distribution information to obtain a third similarity;

[0106] Fusing the first similarity, the second similarity, and the third similarity to determine the target similarity between the first target data and the second target data.

[0107] In some embodiments, during the process of obtaining the first similarity based on the data distribution between the first target data and the second target data, the data determination module 204 performs the following:

[0108] Perform data discretization on the first target data to obtain first discretized data and perform data discretization on the second target data to obtain second discretized data;

[0109] Determine an adjustment parameter according to the first data volume of the first discretized data and the second data volume of the second discretized data,

[0110] Determine the first probability corresponding to each first sub - data in the first discretized data and the first density corresponding to the first sub - data, and determine the second probability corresponding to each second sub - data in the second discretized data and the second density corresponding to the second sub - data;

[0111] Use the adjustment parameter to combine the first probability, the first density, the second probability, and the second density to determine the data difference value between the first target data and the second target data;

[0112] Determine the first similarity according to the data difference value;

[0113] Among them, the data difference value is obtained according to the following formula:

[0114]

[0115] Among them, diff represents the data difference value, count 1 represents the first data volume of the first discretized data, count 2 represents the second data volume of the second discretized data, n represents the total data volume corresponding to the first sub - data, m represents the total data volume corresponding to the second sub - data, p 1i represents the first probability corresponding to the i - th first sub - data, γ 1i represents the first density corresponding to the i - th first sub - data, p 2j represents the second probability corresponding to the j - th second sub - data, γ 2j represents the second density corresponding to the j - th second sub - data, and α represents the adjustment parameter.

[0116] In some embodiments, during the process of fusing the first similarity, the second similarity, and the third similarity to determine the target similarity between the first target data and the second target data, the data determination module 204 performs the following:

[0117] Multiply the second similarity and the third similarity to obtain a product similarity;

[0118] Determine a first weight corresponding to the first similarity and a second weight corresponding to the product similarity;

[0119] Perform data fusion on the first similarity and the product similarity according to the first weight and the second weight to obtain the target similarity between the first target data and the second target data.

[0120] In some embodiments, during the process of the data determination module 204 determining the dynamic threshold according to the first target data and the second target data, execute:

[0121] Perform sequential arrangement on the first target data to obtain first arranged data and perform sequential arrangement on the second target data to obtain second arranged data;

[0122] Determine a first segmentation threshold of the first target data corresponding to the second target data according to the first arranged data and the second arranged data;

[0123] Perform calculation of the nearest neighbor distance on the first target data to obtain an adjustable parameter corresponding to the first segmentation threshold;

[0124] Determine a second segmentation threshold corresponding to the first target data according to the first segmentation threshold and the adjustable parameter;

[0125] Screen the first target data according to the second segmentation threshold to obtain third target data;

[0126] Calculate the data similarity between the third target data and the second target data, and determine the dynamic threshold according to the data similarity.

[0127] In some embodiments, during the process of the data determination module 204 performing calculation of the nearest neighbor distance on the first target data to obtain an adjustable parameter corresponding to the first segmentation threshold, execute:

[0128] Obtain first nearest neighbor data corresponding to the first segmentation threshold from the first target data and second nearest neighbor data corresponding in progression under the first nearest neighbor data;

[0129] Calculate a first nearest neighbor distance between the first nearest neighbor data and the first segmentation threshold and calculate a second nearest neighbor distance between the second nearest neighbor data and the first segmentation threshold;

[0130] Perform distance average calculation according to the first nearest neighbor distance and the second nearest neighbor distance to obtain the adjustable parameter corresponding to the first segmentation threshold.

[0131] In some embodiments, the pollutant control system 200 for flow cytometry detection of hemoglobinuria can be applied to a terminal device.

[0132] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the pollutant control system 200 for flow cytometry detection of hemoglobinuria described above can refer to the corresponding process in the foregoing embodiments of the pollutant control method for flow cytometry detection of hemoglobinuria, and will not be described herein again.

[0133] Please refer to Figure 3 , Figure 3 which is a schematic block diagram of the structure of a terminal device provided by an embodiment of the present invention.

[0134] As Figure 3 shown, the terminal device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected through a bus 303, and this bus is, for example, an I2C (Inter - integrated Circuit) bus.

[0135] Specifically, the processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. The processor 301 can be a central processing unit (CPU), and this processor 301 can also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general - purpose processor can be a microprocessor or this processor can also be any conventional processor, etc.

[0136] Specifically, the memory 302 can be a Flash chip, read - only memory (ROM), magnetic disk, optical disc, USB flash drive or mobile hard disk, etc.

[0137] Those skilled in the art can understand that Figure 3 the structure shown in

[0138] The processor is configured to run a computer program stored in the memory and, when executing the computer program, implement any one of the pollutant control methods for hemoglobinuria flow cytometry detection provided by the embodiments of the present invention.

[0139] In one embodiment, the processor is configured to run a computer program stored in the memory and, when executing the computer program, implement the following steps:

[0140] Determine the target pipeline corresponding to the hemoglobinuria flow cytometry detection, obtain the target residue corresponding to the detection from the target pipeline, and perform optoelectronic data measurement on the target residue to obtain first measurement data;

[0141] Determine the target reference agent corresponding to the hemoglobinuria flow cytometry detection, and perform optoelectronic data measurement on the target reference agent to obtain second measurement data;

[0142] Perform data filtering processing on the first measurement data to obtain first target data and perform data filtering processing on the second measurement data to obtain second target data;

[0143] Calculate the target similarity between the first target data and the second target data, and determine a dynamic threshold according to the first target data and the second target data;

[0144] Determine the pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold;

[0145] Determine the pollutant control strategy corresponding to the target pipeline according to the pollutant detection result through data analysis.

[0146] In some embodiments, during the process of performing data filtering processing on the first measurement data to obtain first target data and performing data filtering processing on the second measurement data to obtain second target data, the processor 301 executes:

[0147] Perform multi-level decomposition processing on the first measurement data using a first wavelet basis to obtain first high-frequency information and first low-frequency information;

[0148] Determine a first threshold, and perform threshold quantization processing on the first high-frequency information according to the first threshold to obtain a first sensitive value corresponding to the first measurement data under the first wavelet basis;

[0149] Determine a first target basis corresponding to the first measurement data from the first wavelet basis according to the first sensitive value;

[0150] Decompose the first measurement data according to the first target basis to obtain second high-frequency information and second low-frequency information;

[0151] Perform data reconstruction based on the second high-frequency information and the second low-frequency information to implement filtering processing of the first measurement data and obtain the first target data;

[0152] Perform multi-level decomposition processing on the second measurement data using a second wavelet basis to obtain third high-frequency information and third low-frequency information;

[0153] Determine a second threshold, and perform threshold quantization processing on the third high-frequency information according to the second threshold to obtain a second sensitive value corresponding to the second measurement data under the second wavelet basis;

[0154] Determine a second target basis corresponding to the second measurement data from the second wavelet basis according to the second sensitive value;

[0155] Decompose the second measurement data according to the second target basis to obtain fourth high-frequency information and fourth low-frequency information;

[0156] Perform data reconstruction based on the fourth high-frequency information and the fourth low-frequency information to implement filtering processing of the second measurement data and obtain the second target data.

[0157] In some embodiments, during the process of calculating the target similarity between the first target data and the second target data, the processor 301 executes:

[0158] Obtain the first similarity according to the data distribution between the first target data and the second target data;

[0159] Perform data alignment on the first target data and the second target data to obtain the maximum number corresponding to the same or similar data in the first target data and the second target data;

[0160] Perform an intersection calculation on the first target data and the second target data to obtain a target intersection result, and obtain the intersection number corresponding to the target intersection result;

[0161] Perform a structural similarity calculation on the first target data and the second target data according to the maximum number and the intersection number to obtain a second similarity;

[0162] Determine a segmentation interval, and determine first distribution information corresponding to the first target data according to the segmentation interval and determine second distribution information corresponding to the second target data according to the segmentation interval;

[0163] Determine a data span similarity between the first target data and the second target data according to the first distribution information and the second distribution information to obtain a third similarity;

[0164] Determine the target similarity between the first target data and the second target data by integrating the first similarity, the second similarity, and the third similarity.

[0165] In some embodiments, when obtaining the first similarity according to the data distribution between the first target data and the second target data, the processor 301 performs:

[0166] Perform data discretization on the first target data to obtain first discrete data and perform data discretization on the second target data to obtain second discrete data;

[0167] Determine an adjustment parameter according to the first data volume of the first discrete data and the second data volume of the second discrete data,

[0168] Determine the first probability corresponding to each first sub - data in the first discrete data and the first density corresponding to the first sub - data, and determine the second probability corresponding to each second sub - data in the second discrete data and the second density corresponding to the second sub - data;

[0169] Use the adjustment parameter to combine the first probability, the first density, the second probability, and the second density to determine the data difference value between the first target data and the second target data;

[0170] Determine the first similarity according to the data difference value;

[0171] Wherein, the data difference value is obtained according to the following formula:

[0172]

[0173] Wherein, diff represents the data difference value, count 1 represents the first data volume of the first discrete data, count 2 represents the second data volume of the second discrete data, n represents the total data volume corresponding to the first sub - data, m represents the total data volume corresponding to the second sub - data, p 1i represents the first probability corresponding to the i - th first sub - data, γ 1i represents the first density corresponding to the i - th first sub - data, p 2j represents the second probability corresponding to the j - th second sub - data, γ 2j represents the second density corresponding to the j - th second sub - data, and α represents the adjustment parameter.

[0174] In some embodiments, when the processor 301 determines the target similarity between the first target data and the second target data by fusing the first similarity, the second similarity, and the third similarity, it performs the following:

[0175] Multiply the second similarity and the third similarity to obtain a product similarity;

[0176] Determine a first weight corresponding to the first similarity and a second weight corresponding to the product similarity;

[0177] Perform data fusion on the first similarity and the product similarity according to the first weight and the second weight to obtain the target similarity between the first target data and the second target data.

[0178] In some embodiments, when the processor 301 determines a dynamic threshold based on the first target data and the second target data, it performs the following:

[0179] Arrange the first target data in order to obtain first arranged data, and arrange the second target data in order to obtain second arranged data;

[0180] Determine a first segmentation threshold of the first target data relative to the second target data according to the first arranged data and the second arranged data;

[0181] Calculate the nearest neighbor distance of the first target data to obtain an adjustable parameter corresponding to the first segmentation threshold;

[0182] Determine a second segmentation threshold corresponding to the first target data according to the first segmentation threshold and the adjustable parameter;

[0183] Filter the first target data according to the second segmentation threshold to obtain third target data;

[0184] Calculate the data similarity between the third target data and the second target data, and determine the dynamic threshold according to the data similarity.

[0185] In some embodiments, when the processor 301 calculates the nearest neighbor distance of the first target data to obtain an adjustable parameter corresponding to the first segmentation threshold, it performs the following:

[0186] Obtain first nearest neighbor data corresponding to the first segmentation threshold from the first target data, and obtain second nearest neighbor data corresponding to the first nearest neighbor data in a progressive manner;

[0187] Calculate a first nearest neighbor distance between the first nearest neighbor data and the first segmentation threshold and calculate a second nearest neighbor distance between the second nearest neighbor data and the first segmentation threshold;

[0188] Perform distance averaging calculation based on the first nearest neighbor distance and the second nearest neighbor distance to obtain the adjustable parameter corresponding to the first segmentation threshold.

[0189] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the above-described terminal device can refer to the corresponding process in the embodiment of the pollutant control method for hemoglobinuria flow cytometry detection described above, and will not be elaborated here.

[0190] The embodiment of the present invention also provides a storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the pollutant control methods for hemoglobinuria flow cytometry detection provided in the specification of the embodiment of the present invention.

[0191] Among them, the storage medium can be an internal storage unit of the terminal device in the foregoing embodiment, such as the hard disk or memory of the terminal device. The storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device.

[0192] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware embodiment, the division of functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be executed by several physical components in cooperation. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile disk (DVD), or other optical disk storage, magnetic cartridges, tapes, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that a communication medium typically contains computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0193] It should be understood that the term "and / or" used in the specification and appended claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. It should be noted that, in this article, the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or system that comprises the element.

[0194] The serial numbers of the embodiments of the present invention above are only for description and do not represent the superiority or inferiority of the embodiments. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for controlling pollutants in flow cytometric detection of hemoglobinuria, characterized in that: The method comprises: Determine a target pipeline corresponding to the hemoglobinuria flow cytometric detection, obtain a target residue corresponding to the detection from the target pipeline, and perform photoelectric data measurement on the target residue to obtain first measurement data; Determining a target reference agent corresponding to the hemoglobinuria flow cytometric detection, and performing photoelectric data measurement on the target reference agent to obtain second measurement data; Performing data filtering processing on the first measurement data to obtain first target data and performing data filtering processing on the second measurement data to obtain second target data; Calculating target similarity between the first target data and the second target data, and determining a dynamic threshold value according to the first target data and the second target data; Determine a pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold; Data analysis is performed based on the pollutant detection results to determine the pollutant control strategy corresponding to the target pipeline.

2. The method according to claim 1, characterized in that The performing data filtering processing on the first measurement data to obtain first target data and the performing data filtering processing on the second measurement data to obtain second target data include: Using a first wavelet basis to perform multi-level decomposition processing on the first measurement data to obtain first high-frequency information and first low-frequency information; Determine a first threshold, and perform threshold quantization processing on the first high-frequency information according to the first threshold to obtain a first sensitivity value corresponding to the first measurement data under the first wavelet basis; Determine a first target basis corresponding to the first measurement data from the first wavelet basis according to the first sensitive value; Decomposing the first measurement data according to the first target basis to obtain second high-frequency information and second low-frequency information; Performing data reconstruction according to the second high-frequency information and the second low-frequency information to filter the first measurement data to obtain the first target data; Using a second wavelet basis to perform multi-level decomposition processing on the second measurement data to obtain third high-frequency information and third low-frequency information; Determine a second threshold, and perform threshold quantization processing on the third high-frequency information according to the second threshold to obtain a second sensitivity value corresponding to the second measurement data under the second wavelet basis; Determine a second target basis corresponding to the second measurement data from the second wavelet basis according to the second sensitive value; Decomposing the second measurement data according to the second target basis to obtain fourth high-frequency information and fourth low-frequency information; Data reconstruction is performed according to the fourth high-frequency information and the fourth low-frequency information to implement filtering processing on the second measurement data to obtain the second target data.

3. The method according to claim 1, characterized in that The calculating the target similarity between the first target data and the second target data includes: Obtaining the first similarity according to data distribution between the first target data and the second target data; Performing data alignment on the first target data and the second target data to obtain a maximum number corresponding to when data in the first target data and the second target data are identical or similar; Performing intersection calculation on the first target data and the second target data to obtain a target intersection result, and obtaining the number of intersections corresponding to the target intersection result; Calculate the structural similarity of the first target data and the second target data according to the maximum number and the intersection number to obtain a second similarity; Determine a segmentation interval, and determine first distribution information corresponding to the first target data according to the segmentation interval and determine second distribution information corresponding to the second target data according to the segmentation interval; Determine the data span similarity between the first target data and the second target data according to the first distribution information and the second distribution information to obtain a third similarity; The first similarity, the second similarity, and the third similarity are integrated to determine the target similarity between the first target data and the second target data.

4. The method according to claim 3, characterized in that The obtaining the first similarity according to the data distribution between the first target data and the second target data includes: Performing data discretization on the first target data to obtain first discrete data and performing data discretization on the second target data to obtain second discrete data; determining an adjustment parameter according to a first data amount of the first discrete data and a second data amount of the second discrete data, Determine a first probability corresponding to each first sub-data in the first discrete data and a first density corresponding to the first sub-data, and determine a second probability corresponding to each second sub-data in the second discrete data and a second density corresponding to the second sub-data; Determine a data difference value between the first target data and the second target data by using the adjustment parameter in combination with the first probability, the first density, the second probability and the second density; Determining the first similarity according to the data difference value; The data difference value is obtained according to the following formula: Wherein, diff represents the data difference value, count1 represents the first data amount of the first discrete data, count2 represents the second data amount of the second discrete data, n represents the total amount of data corresponding to the first sub-data, m represents the total amount of data corresponding to the second sub-data, p 1i represents the first probability corresponding to the i-th first sub-data, γ 1i represents the first density corresponding to the i-th first sub-data, p 2j represents the second probability corresponding to the jth second sub-data, γ 2j represents the second density corresponding to the j-th second sub-data, and α represents the adjustment parameter.

5. The method according to claim 3, characterized in that: The fusing the first similarity, the second similarity, and the third similarity to determine the target similarity between the first target data and the second target data includes: Multiplying the second similarity and the third similarity to obtain a product similarity; Determine a first weight corresponding to the first similarity and a second weight corresponding to the product similarity; The target similarity between the first target data and the second target data is obtained by performing data fusion on the first similarity and the product similarity according to the first weight and the second weight.

6. The method according to claim 1, characterized in that The determining a dynamic threshold according to the first target data and the second target data comprises: Arranging the first target data in sequence to obtain first arrangement data and arranging the second target data in sequence to obtain second arrangement data; Determine a first segmentation threshold corresponding to the first target data relative to the second target data according to the first arrangement data and the second arrangement data; Performing neighbor distance calculation on the first target data to obtain an adjustable parameter corresponding to the first segmentation threshold; Determine a second segmentation threshold corresponding to the first target data according to the first segmentation threshold and the adjustable parameter; Filtering the first target data according to the second segmentation threshold to obtain third target data; The data similarity between the third target data and the second target data is calculated, and the dynamic threshold is determined according to the data similarity.

7. The method according to claim 6, characterized in that The step of performing neighbor distance calculation on the first target data to obtain an adjustable parameter corresponding to the first segmentation threshold includes: Obtaining, from the first target data, first neighbor data corresponding to the first segmentation threshold and second neighbor data corresponding progressively under the first neighbor data; Calculating a first neighbor distance between the first neighbor data and the first segmentation threshold and calculating a second neighbor distance between the second neighbor data and the first segmentation threshold; The adjustable parameter corresponding to the first segmentation threshold is obtained by performing distance average calculation according to the first nearest neighbor distance and the second nearest neighbor distance.

8. A pollutant control system for hemoglobinuria flow cytometric detection, characterized in that: include: A first acquisition module is used to determine a target pipeline corresponding to the hemoglobinuria flow cytometric detection, obtain a target residue corresponding to the detection from the target pipeline, and perform photoelectric data measurement on the target residue to obtain first measurement data; A second acquisition module is used to determine a target reference agent corresponding to the hemoglobinuria flow cytometric detection, and perform photoelectric data measurement on the target reference agent to obtain second measurement data; A data processing module, configured to perform data filtering processing on the first measurement data to obtain first target data and perform data filtering processing on the second measurement data to obtain second target data; a data determination module, configured to calculate a target similarity between the first target data and the second target data, and determine a dynamic threshold value according to the first target data and the second target data; A detection and determination module, used to determine the pollutant detection result corresponding to the target pipeline according to the target similarity and the dynamic threshold; A strategy determination module is used to perform data analysis based on the pollutant detection results to determine the pollutant control strategy corresponding to the target pipeline.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the contaminant control method for hemoglobinuria flow cytometric detection according to any one of claims 1 to 7 when executing the computer program.

10. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the contaminant control method for hemoglobinuria flow cytometric detection according to any one of claims 1 to 7.

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