Capillary serum protein electrophoresis pattern partition method, device, medium and equipment

Abstract

The application discloses a capillary serum protein electrophoresis atlas partition method, device, medium and equipment, and belongs to the M protein analysis field. The application firstly acquires a serum protein electrophoresis curve of M protein and performs effectiveness judgment, calculates a candidate peak group of the serum protein electrophoresis curve and an abscissa position of the candidate peak group after the effectiveness judgment passes, then determines three candidate regions according to the number and the abscissa position of the candidate peak group, and calculates the ordinate minimum value of the three candidate regions and corresponding abscissa positions fl, sl and tl, and determines the beta region and the gamma region according to the abscissa positions fl, sl and tl. The application can automatically divide the beta region and the gamma region of a serum protein electrophoresis control atlas according to the serum protein electrophoresis curve, is more intelligent and efficient, and does not need manual adjustment, and when analyzing a complex serum protein electrophoresis control atlas, the beta region and the gamma region can be more accurately divided.

Description

Technical Field

[0001] This invention relates to the field of M protein analysis, and in particular to a method, apparatus, medium and equipment for partitioning capillary serum protein electrophoresis patterns. Background Technology

[0002] Monoclonal immunoglobulins, or M proteins, are immunoglobulin molecules or fragments with the same molecular structure and electrophoretic mobility produced by the clonal proliferation of monoclonal plasma cells.

[0003] Capillary immunophenotyping for quantitative M protein requires the addition of five control antibodies (IgA heavy chain (α chain) antibody, IgM heavy chain (μ chain) antibody, IgG heavy chain (γ chain) antibody, κ light chain antibody, and λ light chain antibody) to the serum being tested, followed by simultaneous capillary electrophoresis. The control serum protein electrophoresis does not require the addition of any antibodies and proceeds directly with electrophoresis, resulting in six capillary electrophoresis patterns. The presence and content of M protein are determined by the changes in the electrophoretic peak patterns before and after the addition of different antibodies.

[0004] The vast majority of M proteins migrate to the β-globulin region (i.e., the β region or beta region) and the γ-globulin region (i.e., the γ region or gamma region) in serum protein electrophoresis. The γ region exhibits polyclonal background, while the β region does not. Therefore, the methods for quantifying M proteins in these two regions differ. Accurately dividing the β and γ regions is crucial for accurate M protein quantification. Partitioning is the first step in the entire analytical process and directly affects the accuracy of subsequent analyses.

[0005] Current methods for partitioning serum protein electrophoresis patterns are not very accurate. They cannot automatically and accurately divide the beta and gamma regions in serum protein electrophoresis patterns, requiring manual adjustment and intervention. This results in low intelligence and low efficiency. Summary of the Invention

[0006] To address the problems in the prior art, this invention provides a capillary serum protein electrophoresis pattern partitioning method, apparatus, medium, and equipment that can automatically partition the beta and gamma regions, making the analysis of M proteins more efficient and accurate.

[0007] The technical solution provided by this invention is as follows:

[0008] In a first aspect, the present invention provides a method for partitioning capillary serum protein electrophoresis patterns, the method comprising:

[0009] Obtain serum protein electrophoresis curves from capillary immunophenotyping experiments; wherein, the serum protein electrophoresis curves are obtained by fitting several data points;

[0010] The validity of the serum protein electrophoresis curve is determined based on the degree of protrusion of the peaks in the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate in the candidate region of the serum protein electrophoresis curve.

[0011] Calculate all peaks on the electrophoresis curve of serum proteins that pass the validity judgment and whose protrusion degree is greater than a first set value to obtain a candidate peak group, and determine the abscissa position of each peak in the candidate peak group;

[0012] Based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group, the first candidate region, the second candidate region, and the third candidate region are determined.

[0013] Calculate the minimum ordinate values ​​on the serum protein electrophoresis curves in the first, second, and third candidate regions, respectively, and determine the abscissa positions fl, sl, and tl corresponding to the three calculated minimum ordinate values;

[0014] The x-axis range [fl, sl] is defined as the beta region, and the x-axis range [sl, tl] is defined as the gamma region.

[0015] Furthermore, the degree of peak protrusion is calculated using the following method:

[0016] Set the size of the sampling window;

[0017] The center point of the sampling window is aligned with the current data point of the serum protein electrophoresis curve. The minimum vertical coordinate of the serum protein electrophoresis curve in the sampling windows on the left and right sides of the current data point is calculated respectively. The larger of the two minimum vertical coordinate values ​​is selected as the reference value.

[0018] The difference between the ordinate of the current data point and the reference value is calculated as the degree of protrusion of the peak corresponding to the current data point.

[0019] Furthermore, the step of determining the validity of the serum protein electrophoresis curve based on the prominence of the peaks in the candidate region and the minimum value of the ordinate in the candidate region includes:

[0020] The first 100 data points of the serum protein electrophoresis curve were selected and fitted to obtain the first curve segment;

[0021] The center point of the sampling window is used to traverse all data points of the first curve segment, and the protrusion degree of the peak corresponding to each data point of the first curve segment is calculated; wherein, the width of the sampling window is 50;

[0022] Based on the degree of protrusion of the peak corresponding to each data point of the first curve segment, determine whether there is a peak in the first curve segment with a protrusion degree greater than 1300;

[0023] The first 10 data points of the serum protein electrophoresis curve were selected and fitted to obtain the second curve segment;

[0024] Determine whether the minimum value of the ordinate on the second curve segment is less than 250;

[0025] If the first curve segment has a peak with a protrusion greater than 1300 and the minimum value of the ordinate on the second curve segment is less than 250, then the serum protein electrophoresis curve passes the validity judgment.

[0026] Furthermore, the calculation, based on the validity assessment, identifies all peaks on the serum protein electrophoresis curve whose spike intensity exceeds a first set value, thus obtaining a candidate peak group, including:

[0027] The sampling window is used to iterate through all data points of the serum protein electrophoresis curve, and the degree of peak protrusion corresponding to each data point of the serum protein electrophoresis curve is calculated; wherein, the width of the sampling window is 20.

[0028] Based on the degree of peak protrusion corresponding to each data point of the serum protein electrophoresis curve, peaks with a protrusion degree greater than 10 are retained as the candidate peak group.

[0029] Furthermore, determining the first candidate region, the second candidate region, and the third candidate region based on the abscissa position of each peak in the candidate peak group and the total number of peaks in the candidate peak group includes:

[0030] If the total number of peaks in the candidate peak group is greater than or equal to 6, then the horizontal coordinate position from the fourth to the last peak to the third to the last peak is taken as the first candidate region, the horizontal coordinate position from the second to the last peak to the last peak is taken as the second candidate region, and the horizontal coordinate position from the last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region.

[0031] If the total number of peaks in the candidate peak group is less than 6, then the x-coordinate position from the third-to-last peak to the second-to-last peak is taken as the first candidate region, the x-coordinate position from the second-to-last peak to the last peak is taken as the second candidate region, and the x-coordinate position from the last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region.

[0032] Further, the step of calculating the minimum ordinate values ​​on the serum protein electrophoresis curves within the first, second, and third candidate regions, and determining the corresponding abscissa positions fl, sl, and tl for the three calculated minimum ordinate values, includes:

[0033] Calculate the minimum ordinate values ​​on the serum protein electrophoresis curves in the first, second, and third candidate regions, respectively, and determine the relative indexes of the abscissas of the three calculated minimum ordinate values ​​in the first, second, and third candidate regions, respectively.

[0034] Combining the left boundaries of the first, second, and third candidate regions, the three relative abscissa positions are converted into abscissa positions fl, sl, and tl on the serum protein electrophoresis curve.

[0035] Furthermore, the method also includes:

[0036] If fl is less than 150, and the total number of peaks in the candidate peak group is greater than or equal to 6, then the x-coordinate position from the third to last peak to the second to last peak is taken as the first candidate region after re-division; and the minimum ordinate value and its corresponding x-coordinate position fl on the serum protein electrophoresis curve of the first candidate region are recalculated.

[0037] In a second aspect, the present invention provides a capillary serum protein electrophoresis mapping device, the device comprising:

[0038] The data acquisition module is used to acquire the serum protein electrophoresis curve in the capillary immunophenotyping experiment; wherein the serum protein electrophoresis curve is obtained by fitting several data points;

[0039] The validity judgment module is used to judge the validity of the serum protein electrophoresis curve based on the degree of protrusion of the peaks on the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate on the candidate region of the serum protein electrophoresis curve.

[0040] The candidate peak group calculation module is used to calculate all peaks on the serum protein electrophoresis curve that have passed the validity judgment and whose protrusion degree is greater than a first set value, to obtain a candidate peak group, and to determine the abscissa position of each peak in the candidate peak group.

[0041] The candidate region calculation module is used to determine the first candidate region, the second candidate region, and the third candidate region based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group.

[0042] The partition node calculation module is used to calculate the minimum value of the ordinate on the serum protein electrophoresis curve in the first candidate region, the second candidate region and the third candidate region respectively, and determine the abscissa positions fl, sl and tl corresponding to the three calculated minimum ordinate values;

[0043] The partitioning module is used to define the x-axis range [fl, sl] as the beta region and the x-axis range [sl, tl] as the gamma region.

[0044] Furthermore, the degree of peak prominence is calculated through the following process:

[0045] Set the size of the sampling window;

[0046] The center point of the sampling window is aligned with the current data point of the serum protein electrophoresis curve. The minimum vertical coordinate of the serum protein electrophoresis curve in the sampling windows on the left and right sides of the current data point is calculated respectively. The larger of the two minimum vertical coordinate values ​​is selected as the reference value.

[0047] The difference between the ordinate of the current data point and the reference value is calculated as the degree of protrusion of the peak corresponding to the current data point.

[0048] Furthermore, the validity determination module includes:

[0049] The first curve segment acquisition unit is used to select the first 100 data points of the serum protein electrophoresis curve and fit them to obtain the first curve segment.

[0050] The first protrusion degree calculation unit is used to traverse all data points of the first curve segment by the center point of the sampling window, and calculate the protrusion degree of the peak corresponding to each data point of the first curve segment; wherein, the width of the sampling window is 50.

[0051] The first judgment unit is used to determine whether there is a peak with a protrusion degree greater than 1300 in the first curve segment based on the degree of protrusion of the peak corresponding to each data point of the first curve segment.

[0052] The second curve segment acquisition unit is used to select the first 10 data points of the serum protein electrophoresis curve and fit them to obtain the second curve segment.

[0053] The second judgment unit is used to determine whether the minimum value of the ordinate on the second curve segment is lower than 250;

[0054] The validity judgment unit is used to determine the validity of the serum protein electrophoresis curve if the first curve segment has a peak with a protrusion degree greater than 1300 and the minimum value of the ordinate on the second curve segment is less than 250.

[0055] Furthermore, the candidate peak group calculation module includes:

[0056] The second protrusion degree calculation unit is used to traverse all data points of the serum protein electrophoresis curve by the center point of the sampling window, and calculate the protrusion degree of the peak corresponding to each data point of the serum protein electrophoresis curve; wherein, the width of the sampling window is 20.

[0057] The candidate peak group determination unit is used to retain peaks with a protrusion degree greater than 10 as the candidate peak group based on the protrusion degree of the peaks corresponding to each data point of the serum protein electrophoresis curve.

[0058] Furthermore, the candidate region calculation module includes:

[0059] The first calculation unit is used to determine the following if the total number of peaks in the candidate peak group is greater than or equal to 6: the x-coordinate position from the fourth-to-last peak to the x-coordinate position of the third-to-last peak is taken as the first candidate region, the x-coordinate position from the second-to-last peak to the x-coordinate position of the first-to-last peak is taken as the second candidate region, and the x-coordinate position from the first-to-last peak to the x-coordinate position at the end of the serum protein electrophoresis curve is taken as the third candidate region.

[0060] The second calculation unit is used to determine the following if the total number of peaks in the candidate peak group is less than 6: the x-coordinate position from the third-to-last peak to the second-to-last peak is designated as the first candidate region, the x-coordinate position from the second-to-last peak to the first-to-last peak is designated as the second candidate region, and the x-coordinate position from the first-to-last peak to the end of the serum protein electrophoresis curve is designated as the third candidate region.

[0061] Furthermore, the partition node calculation module includes:

[0062] The relative position index calculation unit is used to calculate the minimum value of the ordinate on the serum protein electrophoresis curve in the first candidate region, the second candidate region and the third candidate region respectively, and to determine the relative position index of the abscissa of the three calculated minimum values ​​of the ordinate in the first candidate region, the second candidate region and the third candidate region respectively.

[0063] The partition node determination unit is used to combine the left boundaries of the first candidate region, the second candidate region, and the third candidate region to convert the three relative horizontal coordinate positions obtained into horizontal coordinate positions fl, sl, and tl on the serum protein electrophoresis curve.

[0064] Furthermore, the device also includes:

[0065] The re-division module is used to, if fl is less than 150 and the total number of peaks in the candidate peak group is greater than or equal to 6, take the abscissa position of the third to last peak to the abscissa position of the second to last peak as the first candidate region after re-division; and recalculate the minimum ordinate value and the corresponding abscissa position fl on the serum protein electrophoresis curve of the first candidate region.

[0066] Thirdly, the present invention provides a computer storage medium for partitioning capillary serum protein electrophoresis patterns, including a memory for storing processor-executable instructions, which, when executed by the processor, implement steps including the capillary serum protein electrophoresis pattern partitioning method described in the first aspect.

[0067] Fourthly, the present invention provides an electronic device for partitioning capillary serum protein electrophoresis patterns, comprising at least one processor and a memory storing computer-executable instructions, wherein the processor executes the instructions to implement the steps of the capillary serum protein electrophoresis pattern partitioning method described in the first aspect.

[0068] The present invention has the following beneficial effects:

[0069] This invention first obtains the serum protein electrophoresis curve of M protein and performs validity assessment. After passing the validity assessment, it calculates the candidate peak groups and their abscissa positions of the serum protein electrophoresis curve. Then, based on the number of candidate peak groups and their abscissa positions, it determines three candidate regions and calculates the minimum ordinate value of the three candidate regions and their corresponding abscissa positions fl, sl, and tl. Based on the abscissa positions fl, sl, and tl, the beta and gamma regions can be determined. This invention divides the beta and gamma regions based on the serum protein electrophoresis curve, providing a universal automatic partitioning method for serum protein electrophoresis control patterns in capillary immunophenotyping experiments for M protein quantification. It is universally applicable to capillary serum protein electrophoresis patterns in this application scenario; it can automatically partition the beta and gamma regions of the serum protein electrophoresis control pattern, making it more intelligent and efficient without manual adjustment; and it can more accurately partition the beta and gamma regions when analyzing complex serum protein electrophoresis control patterns. Attached Figure Description

[0070] Figure 1 This is a flowchart of the capillary serum protein electrophoresis pattern partitioning method of the present invention;

[0071] Figure 2 This is a schematic diagram showing the partitioning of serum protein electrophoresis curves and IgA curves on the same graph;

[0072] Figure 3 This is a schematic diagram showing the partitioning of serum protein electrophoresis curves and IgG curves on the same graph;

[0073] Figure 4 This is a schematic diagram showing the partitioning of serum protein electrophoresis curves and IgM curves on the same graph;

[0074] Figure 5 This is a schematic diagram showing the partitioning of serum protein electrophoresis curves and K-curves on the same graph;

[0075] Figure 6 This is a schematic diagram showing the partitioning of the serum protein electrophoresis curve and the L-curve on the same graph;

[0076] Figure 7 This is a schematic diagram of the capillary serum protein electrophoresis pattern partitioning device of the present invention. Detailed Implementation

[0077] To make the technical problems, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. The components of the embodiments of this invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0078] Example 1:

[0079] This invention provides a method for partitioning capillary serum protein electrophoresis patterns, such as... Figure 1 As shown, the method includes:

[0080] S100: Obtain serum protein electrophoresis curves from capillary immunophenotyping experiments.

[0081] The serum protein electrophoresis curve is the curve obtained by electrophoresis of M protein without the addition of any antibody in capillary immunophenotyping experiments. It is also called the control curve or ELP curve. This serum protein electrophoresis curve is obtained by fitting several (usually 300) data points.

[0082] S200: The validity of the serum protein electrophoresis curve is determined based on the degree of protrusion of the peaks in the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate in the candidate region of the serum protein electrophoresis curve.

[0083] This step is used to determine the validity of serum protein electrophoresis curves based on certain characteristics of the curves themselves. For serum protein electrophoresis curves that pass the validity assessment (i.e., valid data), the subsequent analysis process continues; for serum protein electrophoresis curves that fail the validity assessment (i.e., invalid data), the analysis is terminated.

[0084] Specifically, in this embodiment of the invention, the validity of the serum protein electrophoresis curve is determined by two features: the degree of peak protrusion on the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate.

[0085] S300: Calculate all peaks on the electrophoresis curve of serum proteins that pass the validity judgment and whose protrusion degree is greater than the first set value, obtain the candidate peak group, and determine the abscissa position of each peak in the candidate peak group.

[0086] S400: Determine the first candidate region, the second candidate region, and the third candidate region based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group.

[0087] The first, second, and third candidate regions are determined to be ranges of a horizontal axis.

[0088] S500: Calculate the minimum ordinate values ​​on the serum protein electrophoresis curves in the first, second, and third candidate regions, respectively, and determine the corresponding abscissa positions fl, sl, and tl for the three calculated minimum ordinate values.

[0089] Within the first, second, and third candidate regions, the serum protein electrophoresis curves each have a minimum value on the ordinate. There are a total of three minimum values ​​on the ordinate across the three candidate regions. Each minimum value on the ordinate corresponds to an x-coordinate position, and the x-coordinate positions corresponding to the three minimum values ​​on the ordinate are denoted as fl, sl, and tl, respectively.

[0090] S600: Define the x-axis range [fl, sl] as the beta region and the x-axis range [sl, tl] as the gamma region.

[0091] Draw a vertical line at each of the three horizontal coordinate positions fl, sl, and tl. The area between the vertical line of fl and the vertical line of sl is the beta region, and the area between the vertical line of sl and the vertical line of tl is the gamma region.

[0092] After identifying the beta and gamma regions on the serum protein electrophoresis curves, these curves can be plotted on the same graph as the IgA curve (after adding α heavy chain antibody), IgG curve (after adding γ heavy chain antibody), IgM curve (after adding μ heavy chain antibody), K curve (after adding κ light chain antibody), and L curve (after adding λ light chain antibody) from capillary immunophenotyping assays. Figure 2-6 As shown; the region between the first and second vertical lines is the beta region, and the region between the second and third vertical lines is the gamma region.

[0093] This invention first obtains the serum protein electrophoresis curve of M protein and performs validity assessment. After passing the validity assessment, it calculates the candidate peak groups and their abscissa positions of the serum protein electrophoresis curve. Then, based on the number of candidate peak groups and their abscissa positions, it determines three candidate regions and calculates the minimum ordinate value of the three candidate regions and their corresponding abscissa positions fl, sl, and tl. Based on the abscissa positions fl, sl, and tl, the beta and gamma regions can be determined. This invention divides the beta and gamma regions based on the serum protein electrophoresis curve, providing a universal automatic partitioning method for serum protein electrophoresis control patterns in capillary immunophenotyping experiments for M protein quantification. It is universally applicable to capillary serum protein electrophoresis patterns in this application scenario; it can automatically partition the beta and gamma regions of the serum protein electrophoresis control pattern, making it more intelligent and efficient without manual adjustment; and it can more accurately partition the beta and gamma regions when analyzing complex serum protein electrophoresis control patterns.

[0094] In this embodiment of the invention, both S200 and S300 involve the degree of peak protrusion. In this invention, the degree of peak protrusion can be calculated by the following method:

[0095] S1: Set the size of the sampling window.

[0096] S2: Align the center point of the sampling window with the current data point of the serum protein electrophoresis curve, calculate the minimum ordinate of the serum protein electrophoresis curve in the sampling windows on the left and right sides of the current data point, and select the larger of the two minimum ordinate values ​​as the reference value.

[0097] S3: Calculate the difference between the ordinate of the current data point and the reference value, which is used as the degree of protrusion of the peak corresponding to the current data point.

[0098] Serum protein electrophoresis curves have multiple data points. By traversing all data points of the serum protein electrophoresis curve through the center point of the sampling window, the degree of prominence of all peaks in the serum protein electrophoresis curve can be calculated.

[0099] As an improvement to an embodiment of the present invention, the aforementioned S200 may include:

[0100] S210: Select the first 100 data points of the serum protein electrophoresis curve and fit them to obtain the first curve segment.

[0101] S220: Traverse all data points of the first curve segment by sampling the center point of the sampling window, and calculate the protrusion degree of the peak corresponding to each data point of the first curve segment; wherein, the width of the sampling window is set to 50.

[0102] S230: Based on the degree of protrusion of the peak corresponding to each data point of the first curve segment, determine whether there is a peak with a protrusion degree greater than 1300 in the first curve segment.

[0103] S240: Select the first 10 data points of the serum protein electrophoresis curve and fit them to obtain the second curve segment.

[0104] S250: Determine whether the minimum value of the ordinate on the second curve segment is lower than 250.

[0105] S260: If the first curve segment has a peak with a protrusion greater than 1300 and the minimum value of the ordinate on the second curve segment is less than 250, then the serum protein electrophoresis curve passes the validity judgment.

[0106] Accordingly, the aforementioned S300 may include:

[0107] S310: Traverse all data points of the serum protein electrophoresis curve by sampling the center point of the sampling window, calculate the protrusion degree of the peak corresponding to each data point of the serum protein electrophoresis curve, and obtain all the peaks of the serum protein electrophoresis curve; wherein, the width of the sampling window is set to 20.

[0108] S320: Based on the degree of peak protrusion corresponding to each data point of the serum protein electrophoresis curve, retain peaks with a protrusion degree greater than 10 as candidate peak groups.

[0109] S330: Determine the x-coordinate position of each peak in the candidate peak group.

[0110] As another improvement to the embodiment of the present invention, the aforementioned S400 may include:

[0111] S410: If the total number of peaks in the candidate peak group is greater than or equal to 6, then the x-coordinate position from the fourth to the last peak to the third to the last peak is taken as the first candidate region, the x-coordinate position from the second to the last peak to the last peak is taken as the second candidate region, and the x-coordinate position from the last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region.

[0112] S420: If the total number of peaks in the candidate peak group is less than 6, then the x-coordinate position from the third to last peak to the second to last peak is taken as the first candidate region, the x-coordinate position from the second to last peak to the first to last peak is taken as the second candidate region, and the x-coordinate position from the first to last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region.

[0113] Accordingly, the S500 includes:

[0114] S510: Calculate the minimum ordinate values ​​on the serum protein electrophoresis curves in the first, second, and third candidate regions, respectively, and determine the relative position indices of the abscissas of the three calculated minimum ordinate values ​​in the first, second, and third candidate regions.

[0115] S520: Combining the left boundaries of the first, second, and third candidate regions, the three relative positions of the horizontal coordinates are converted into horizontal coordinate positions fl, sl, and tl on the serum protein electrophoresis curve.

[0116] In special cases, specifically when fl is less than 150 and the total number of peaks in the candidate peak group is greater than or equal to 6, the first candidate region needs to be redefined. The x-coordinate position from the third-to-last peak to the second-to-last peak is taken as the redefined first candidate region, while the second and third candidate regions remain unchanged. The minimum ordinate value and its corresponding x-coordinate position fl on the serum protein electrophoresis curve of the first candidate region are then recalculated.

[0117] The invention will be explained in more detail and more intuitively below with a specific example:

[0118] 1. Data Acquisition:

[0119] We obtained 300 data points from the serum protein electrophoresis experiment in capillary immunophenotyping and fitted them to obtain the serum protein electrophoresis curve.

[0120] 2. Validity assessment:

[0121] (1) Select the first 100 data points of the serum protein electrophoresis curve and plot the first segment of the curve. Set the sampling window size to 50. The first segment of the curve has a peak with a height greater than 1300. The horizontal coordinate position of the peak is 43.

[0122] (2) Select the first 10 data points of the serum protein electrophoresis curve and plot the second curve. The minimum value of the second curve is 11, which is lower than 250.

[0123] Since the serum protein electrophoresis curve meets the above two conditions, it is a valid sample based on the validity assessment.

[0124] 3. Calculate candidate peak groups:

[0125] With the sampling window size set to 20, the serum protein electrophoresis curve has 6 peaks with a protrusion degree greater than 10, which are the candidate peak groups; the horizontal coordinates of these peaks are 43, 119, 150, 179, 199, and 234, respectively.

[0126] 4. Determine the candidate region:

[0127] Candidate regions are determined by using the abscissa position of the peaks obtained in step 3. Since there are 6 peaks that meet the conditions in this example, the S410 method is selected to determine the candidate regions. The first candidate region is [150, 179], the second candidate region is [199, 234], and the third candidate region is [234, 299].

[0128] 5. Determine the x-coordinate position of the minimum y-coordinate value:

[0129] Within the three candidate regions obtained from step 4, calculate the relative position index of the x-coordinate corresponding to the minimum ordinate value in each candidate region. In the first candidate region, the relative position index of the x-coordinate of the minimum ordinate value is 18; in the second candidate region, the relative position index of the x-coordinate of the minimum ordinate value is 7; and in the third candidate region, the relative position index of the x-coordinate of the minimum ordinate value is 18.

[0130] Based on the left boundaries of the three candidate regions, calculate the absolute indices of the three minimum ordinate positions, i.e., the x-coordinate positions fl, sl, and tl corresponding to the three minimum ordinate positions, where fl is 168, sl is 206, and tl is 252.

[0131] 6. Divide the region into beta and gamma regions:

[0132] Based on the calculated fl, sl, and tl values ​​from step 5, the region is divided into beta and gamma regions. The final range of the beta region is [168, 206], and the range of the gamma region is [206, 252]. See the partitioning results below. Figure 2-6 Serum protein electrophoresis curves were plotted on the same graph as IgA, IgG, IgM, K, and L curves. The region between the first and second vertical lines is the beta region, and the region between the second and third vertical lines is the gamma region.

[0133] Example 2:

[0134] This invention provides a capillary serum protein electrophoresis pattern partitioning device, such as... Figure 7 As shown, the device includes:

[0135] Data acquisition module 1 is used to acquire serum protein electrophoresis curves in capillary immunophenotyping experiments; wherein, the serum protein electrophoresis curves are obtained by fitting several data points.

[0136] The validity judgment module 2 is used to judge the validity of the serum protein electrophoresis curve based on the degree of protrusion of the peaks on the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate on the candidate region of the serum protein electrophoresis curve.

[0137] The candidate peak group calculation module 3 is used to calculate all peaks on the serum protein electrophoresis curve that have passed the validity judgment and whose protrusion degree is greater than the first set value, to obtain the candidate peak group, and to determine the horizontal coordinate position of each peak in the candidate peak group.

[0138] Candidate region calculation module 4 is used to determine the first candidate region, the second candidate region, and the third candidate region based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group.

[0139] The partition node calculation module 5 is used to calculate the minimum value of the ordinate on the serum protein electrophoresis curve in the first candidate region, the second candidate region and the third candidate region respectively, and to determine the abscissa positions fl, sl and tl corresponding to the three calculated minimum values ​​of the ordinate.

[0140] Partitioning module 6 is used to define the horizontal coordinate range [fl, sl] as the beta region and the horizontal coordinate range [sl, tl] as the gamma region.

[0141] This invention first obtains the serum protein electrophoresis curve of M protein and performs validity assessment. After passing the validity assessment, it calculates the candidate peak groups and their abscissa positions of the serum protein electrophoresis curve. Then, based on the number of candidate peak groups and their abscissa positions, it determines three candidate regions and calculates the minimum ordinate value of the three candidate regions and their corresponding abscissa positions fl, sl, and tl. Based on the abscissa positions fl, sl, and tl, the beta and gamma regions can be determined. This invention divides the beta and gamma regions based on the serum protein electrophoresis curve, providing a universal automatic partitioning method for serum protein electrophoresis control patterns in capillary immunophenotyping experiments for M protein quantification. It is universally applicable to capillary serum protein electrophoresis patterns in this application scenario; it can automatically partition the beta and gamma regions of the serum protein electrophoresis control pattern, making it more intelligent and efficient without manual adjustment; and it can more accurately partition the beta and gamma regions when analyzing complex serum protein electrophoresis control patterns.

[0142] In this invention, the degree of peak protrusion is calculated through the following process:

[0143] Set the size of the sampling window.

[0144] Align the center point of the sampling window with the current data point of the serum protein electrophoresis curve, calculate the minimum ordinate of the serum protein electrophoresis curve in the sampling windows on the left and right sides of the current data point, and select the larger of the two minimum ordinate values ​​as the reference value.

[0145] Calculate the difference between the ordinate of the current data point and the reference value, and use this difference as the degree of prominence of the peak corresponding to the current data point.

[0146] As an improvement to this embodiment of the invention, the aforementioned validity determination module includes:

[0147] The first curve segment acquisition unit is used to select the first 100 data points of the serum protein electrophoresis curve and fit them to obtain the first curve segment.

[0148] The first protrusion degree calculation unit is used to traverse all data points of the first curve segment by sampling the center point of the sampling window, and calculate the protrusion degree of the peak corresponding to each data point of the first curve segment; wherein, the width of the sampling window is 50.

[0149] The first judgment unit is used to determine whether there is a peak with a protrusion degree greater than 1300 in the first curve segment based on the degree of protrusion of the peak corresponding to each data point of the first curve segment.

[0150] The second curve segment acquisition unit is used to select the first 10 data points of the serum protein electrophoresis curve and fit them to obtain the second curve segment.

[0151] The second judgment unit is used to determine whether the minimum value of the ordinate on the second curve segment is lower than 250.

[0152] The validity judgment unit is used to determine the validity of the serum protein electrophoresis curve if the first curve segment has a peak with a protrusion degree greater than 1300 and the minimum value of the ordinate on the second curve segment is less than 250.

[0153] Accordingly, the candidate peak group calculation module includes:

[0154] The second protrusion degree calculation unit is used to traverse all data points of the serum protein electrophoresis curve by the center point of the sampling window, and calculate the protrusion degree of the peak corresponding to each data point of the serum protein electrophoresis curve; wherein, the width of the sampling window is 20.

[0155] The candidate peak group determination unit is used to retain peaks with a protrusion degree greater than 10 as candidate peak groups based on the protrusion degree of the peaks corresponding to each data point of the serum protein electrophoresis curve.

[0156] As another improvement to this embodiment of the invention, the aforementioned candidate region calculation module includes:

[0157] The first calculation unit is used to determine the following if the total number of peaks in the candidate peak group is greater than or equal to 6: the x-coordinate position from the fourth to the last peak to the third to the last peak is taken as the first candidate region, the x-coordinate position from the second to the last peak to the last peak is taken as the second candidate region, and the x-coordinate position from the last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region.

[0158] The second calculation unit is used to determine the following if the total number of peaks in the candidate peak group is less than 6: the x-coordinate position from the third to last peak to the x-coordinate position of the second to last peak is used as the first candidate region, the x-coordinate position from the second to last peak to the x-coordinate position of the first to last peak is used as the second candidate region, and the x-coordinate position from the first to last peak to the x-coordinate position at the end of the serum protein electrophoresis curve is used as the third candidate region.

[0159] Accordingly, the partition node calculation module includes:

[0160] The relative position index calculation unit is used to calculate the minimum value of the ordinate on the electrophoresis curve of serum protein in the first candidate region, the second candidate region and the third candidate region respectively, and to determine the relative position index of the abscissa of the three calculated minimum ordinate values ​​in the first candidate region, the second candidate region and the third candidate region respectively.

[0161] The partition node determination unit is used to combine the left boundaries of the first candidate region, the second candidate region, and the third candidate region, and convert the three relative abscissa position indices into abscissa positions fl, sl, and tl on the serum protein electrophoresis curve.

[0162] In special circumstances, the apparatus of the present invention may further include:

[0163] The re-division module is used to determine the first candidate region after re-division if fl is less than 150 and the total number of peaks in the candidate peak group is greater than or equal to 6. The module takes the x-coordinate position of the third to last peak to the x-coordinate position of the second to last peak as the first candidate region after re-division. The module also recalculates the minimum ordinate value and the corresponding x-coordinate position fl on the serum protein electrophoresis curve of the first candidate region.

[0164] The device provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment 1. For the sake of brevity, any parts not mentioned in this device embodiment can be referred to the corresponding content in the aforementioned method embodiment 1. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the aforementioned device and unit can all be referred to the corresponding processes in the aforementioned method embodiment 1, and will not be repeated here.

[0165] Example 3:

[0166] The method described in Embodiment 1 of this invention can implement business logic through a computer program and record it on a storage medium. This storage medium can be read and executed by a computer, achieving the effects of the scheme described in Embodiment 1 of this specification. Therefore, this invention also provides a computer storage medium for capillary serum protein electrophoresis pattern partitioning, including a memory for storing processor-executable instructions. When the instructions are executed by the processor, they implement the steps of the capillary serum protein electrophoresis pattern partitioning method of Embodiment 1.

[0167] This invention first obtains the serum protein electrophoresis curve of M protein and performs validity assessment. After passing the validity assessment, it calculates the candidate peak groups and their abscissa positions of the serum protein electrophoresis curve. Then, based on the number of candidate peak groups and their abscissa positions, it determines three candidate regions and calculates the minimum ordinate value of the three candidate regions and their corresponding abscissa positions fl, sl, and tl. Based on the abscissa positions fl, sl, and tl, the beta and gamma regions can be determined. This invention divides the beta and gamma regions based on the serum protein electrophoresis curve, providing a universal automatic partitioning method for serum protein electrophoresis control patterns in capillary immunophenotyping experiments for M protein quantification. It is universally applicable to capillary serum protein electrophoresis patterns in this application scenario; it can automatically partition the beta and gamma regions of the serum protein electrophoresis control pattern, making it more intelligent and efficient without manual adjustment; and it can more accurately partition the beta and gamma regions when analyzing complex serum protein electrophoresis control patterns.

[0168] The storage medium may include a physical device for storing information, typically digitizing the information and then storing it using electrical, magnetic, or optical methods. The storage medium may include: devices that store information using electrical energy, such as various types of memory, like RAM and ROM; devices that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memory, bubble memory, and USB flash drives; and devices that store information using optical methods, such as CDs or DVDs. Of course, there are other readable storage media, such as quantum memories and graphene memories.

[0169] The storage medium described above, according to the description of method embodiment 1, may also include other implementation methods. Specific implementation methods can be found in the description of the relevant method embodiment 1, and will not be elaborated upon here.

[0170] Example 4:

[0171] This invention also provides an electronic device for partitioning capillary serum protein electrophoresis patterns. The electronic device may be a standalone computer, or it may include an actual operating device that uses one or more of the methods or embodiments described in this specification. The electronic device for partitioning capillary serum protein electrophoresis patterns may include at least one processor and a memory storing computer-executable instructions. When the processor executes the instructions, it implements the steps of the capillary serum protein electrophoresis pattern partitioning method described in any one or more embodiments 1 above.

[0172] This invention first obtains the serum protein electrophoresis curve of M protein and performs validity assessment. After passing the validity assessment, it calculates the candidate peak groups and their abscissa positions of the serum protein electrophoresis curve. Then, based on the number of candidate peak groups and their abscissa positions, it determines three candidate regions and calculates the minimum ordinate value of the three candidate regions and their corresponding abscissa positions fl, sl, and tl. Based on the abscissa positions fl, sl, and tl, the beta and gamma regions can be determined. This invention divides the beta and gamma regions based on the serum protein electrophoresis curve, providing a universal automatic partitioning method for serum protein electrophoresis control patterns in capillary immunophenotyping experiments for M protein quantification. It is universally applicable to capillary serum protein electrophoresis patterns in this application scenario; it can automatically partition the beta and gamma regions of the serum protein electrophoresis control pattern, making it more intelligent and efficient without manual adjustment; and it can more accurately partition the beta and gamma regions when analyzing complex serum protein electrophoresis control patterns.

[0173] The electronic device described above may also include other implementation methods according to the description of the method or device embodiments. For specific implementation methods, please refer to the description of the relevant method embodiment 1, which will not be repeated here.

[0174] It should be noted that the apparatus or system described above in this specification may include other implementation methods based on the description of the relevant method embodiments. Specific implementation methods can be referred to the description of the method embodiments, and will not be elaborated upon here. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for hardware + program and storage medium + program embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0175] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0176] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation of an electronic device is a computer. Specifically, a computer can be a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0177] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, when implementing one or more of these specifications, the functions of each module can be implemented in one or more software and / or hardware components, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0178] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0179] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0183] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element.

[0184] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0185] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0186] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0187] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for partitioning capillary serum protein electrophoresis patterns, characterized in that, The method includes: Obtain serum protein electrophoresis curves from capillary immunophenotyping experiments; wherein, the serum protein electrophoresis curves are obtained by fitting several data points; The validity of the serum protein electrophoresis curve is determined based on the degree of protrusion of the peaks in the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate in the candidate region of the serum protein electrophoresis curve. Calculate all peaks on the electrophoresis curve of serum proteins that pass the validity judgment and whose protrusion degree is greater than a first set value to obtain a candidate peak group, and determine the abscissa position of each peak in the candidate peak group; Based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group, the first candidate region, the second candidate region, and the third candidate region are determined. Calculate the minimum ordinate values ​​on the serum protein electrophoresis curves in the first, second, and third candidate regions, respectively, and determine the abscissa positions fl, sl, and tl corresponding to the three calculated minimum ordinate values; The x-axis range [fl, sl] is defined as the beta region, and the x-axis range [sl, tl] is defined as the gamma region.

2. The capillary serum protein electrophoresis pattern partitioning method according to claim 1, characterized in that, The degree of peak prominence is calculated using the following method: Set the size of the sampling window; The center point of the sampling window is aligned with the current data point of the serum protein electrophoresis curve. The minimum vertical coordinate of the serum protein electrophoresis curve in the sampling windows on the left and right sides of the current data point is calculated respectively. The larger of the two minimum vertical coordinate values ​​is selected as the reference value. The difference between the ordinate of the current data point and the reference value is calculated as the degree of protrusion of the peak corresponding to the current data point.

3. The capillary serum protein electrophoresis pattern partitioning method according to claim 2, characterized in that, The step of determining the validity of the serum protein electrophoresis curve based on the prominence of the peaks in the candidate region and the minimum value of the ordinate in the candidate region includes: The first 100 data points of the serum protein electrophoresis curve were selected and fitted to obtain the first curve segment; The sampling window is used to iterate through all data points of the first curve segment, and the degree of peak protrusion corresponding to each data point of the first curve segment is calculated; wherein, the width of the sampling window is 50. Based on the degree of protrusion of the peak corresponding to each data point of the first curve segment, determine whether there is a peak in the first curve segment with a protrusion degree greater than 1300; The first 10 data points of the serum protein electrophoresis curve were selected and fitted to obtain the second curve segment; Determine whether the minimum value of the ordinate on the second curve segment is less than 250; If the first curve segment has a peak with a protrusion greater than 1300 and the minimum value of the ordinate on the second curve segment is less than 250, then the serum protein electrophoresis curve passes the validity judgment.

4. The capillary serum protein electrophoresis pattern partitioning method according to claim 3, characterized in that, The calculation, based on the validity assessment, identifies all peaks on the serum protein electrophoresis curve whose spike intensity exceeds a first set value, resulting in a candidate peak group, including: The sampling window is used to iterate through all data points of the serum protein electrophoresis curve, and the degree of peak protrusion corresponding to each data point of the serum protein electrophoresis curve is calculated; wherein, the width of the sampling window is 20. Based on the degree of peak protrusion corresponding to each data point of the serum protein electrophoresis curve, peaks with a protrusion degree greater than 10 are retained as the candidate peak group.

5. The capillary serum protein electrophoresis pattern partitioning method according to any one of claims 1-4, characterized in that, The step of determining the first candidate region, the second candidate region, and the third candidate region based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group includes: If the total number of peaks in the candidate peak group is greater than or equal to 6, then the horizontal coordinate position from the fourth to the last peak to the third to the last peak is taken as the first candidate region, the horizontal coordinate position from the second to the last peak to the last peak is taken as the second candidate region, and the horizontal coordinate position from the last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region. If the total number of peaks in the candidate peak group is less than 6, then the x-coordinate position from the third-to-last peak to the second-to-last peak is taken as the first candidate region, the x-coordinate position from the second-to-last peak to the last peak is taken as the second candidate region, and the x-coordinate position from the last peak to the end of the serum protein electrophoresis curve is taken as the third candidate region.

6. The capillary serum protein electrophoresis pattern partitioning method according to claim 5, characterized in that, The step of calculating the minimum ordinate values ​​on the serum protein electrophoresis curves within the first, second, and third candidate regions, and determining the corresponding abscissa positions fl, sl, and tl for the three calculated minimum ordinate values, includes: Calculate the minimum ordinate values ​​on the serum protein electrophoresis curves in the first, second, and third candidate regions, respectively, and determine the relative indexes of the abscissas of the three calculated minimum ordinate values ​​in the first, second, and third candidate regions, respectively. Combining the left boundaries of the first, second, and third candidate regions, the three relative abscissa positions are converted into abscissa positions fl, sl, and tl on the serum protein electrophoresis curve.

7. The capillary serum protein electrophoresis pattern partitioning method according to claim 6, characterized in that, The method further includes: If fl is less than 150, and the total number of peaks in the candidate peak group is greater than or equal to 6, then the x-coordinate position from the third to last peak to the second to last peak is taken as the first candidate region after re-division; and the minimum ordinate value and its corresponding x-coordinate position fl on the serum protein electrophoresis curve of the first candidate region are recalculated.

8. A capillary serum protein electrophoresis pattern partitioning device, characterized in that, The device includes: The data acquisition module is used to acquire the serum protein electrophoresis curve in the capillary immunophenotyping experiment; wherein the serum protein electrophoresis curve is obtained by fitting several data points; The validity judgment module is used to judge the validity of the serum protein electrophoresis curve based on the degree of protrusion of the peaks on the candidate region of the serum protein electrophoresis curve and the minimum value of the ordinate on the candidate region of the serum protein electrophoresis curve. The candidate peak group calculation module is used to calculate all peaks on the serum protein electrophoresis curve that have passed the validity judgment and whose protrusion degree is greater than a first set value, to obtain a candidate peak group, and to determine the abscissa position of each peak in the candidate peak group. The candidate region calculation module is used to determine the first candidate region, the second candidate region, and the third candidate region based on the horizontal coordinate position of each peak in the candidate peak group and the total number of peaks in the candidate peak group. The partition node calculation module is used to calculate the minimum value of the ordinate on the serum protein electrophoresis curve in the first candidate region, the second candidate region and the third candidate region respectively, and determine the abscissa positions fl, sl and tl corresponding to the three calculated minimum values ​​of the ordinate; The partitioning module is used to define the x-axis range [fl, sl] as the beta region and the x-axis range [sl, tl] as the gamma region.

9. A computer storage medium for partitioning capillary serum protein electrophoresis patterns, characterized in that, It includes a memory for storing processor-executable instructions, which, when executed by the processor, implement the steps of the capillary serum protein electrophoresis pattern partitioning method according to any one of claims 1-7.

10. An electronic device for partitioning capillary serum protein electrophoresis patterns, characterized in that, It includes at least one processor and a memory storing computer-executable instructions, wherein the processor executes the instructions to implement the steps of the capillary serum protein electrophoresis pattern partitioning method according to any one of claims 1-7.

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