Method and device for confirming effective particle number, equipment and storage medium

Abstract

Embodiments of the present application disclose a method for determining the number of effective particles, the method comprising: obtaining the total number of particles of a detection sample and pulse signals of each particle; performing waveform feature analysis based on the pulse signals to obtain waveform feature parameters of each particle, the waveform feature parameters at least including: a waveform feature type of the particle and a waveform feature value corresponding to the waveform feature type; generating a feature parameter distribution map of the particles in the detection sample according to the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type; determining an effective area of the feature parameter distribution map and the number of effective particles in the effective area. The waveform feature parameters of all the particles in the detection sample are counted to obtain the feature parameter distribution map, and the number of effective particles in the effective area of the feature parameter distribution map is further obtained, so that the number of particles that are misdetected as effective particles and not in the effective area is excluded, and a more accurate number of effective particles is obtained.

Description

Technical Field

[0001] This invention relates to the field of sample detection technology, and in particular to methods, apparatus, equipment and storage media for confirming the effective particle count. Background Technology

[0002] When collecting samples, the effective particles in the samples are analyzed. Therefore, it is very important to identify whether the particles are effective when collecting sample particles.

[0003] In practice, the number of particles in a sample is determined by collecting the pulse signals generated by the particles. However, the collection process is affected by various factors, which can lead to inaccurate particle counts. For example, different particle shapes, different sampling frequencies when collecting particle pulse signals, and different particle flow velocities can all affect the shape of the particle pulse signals. Furthermore, noise interference during collection can also affect the shape of the particle pulse signals. This can affect the determination of whether a particle is valid, resulting in a large error in identifying valid particles. Summary of the Invention

[0004] Therefore, it is necessary to propose methods, devices, equipment, and storage media for confirming the number of effective particles in order to reduce the identification error of effective particles.

[0005] To achieve the above objectives, the first aspect of this application provides a method for confirming the effective number of particles, the method comprising:

[0006] Obtain the total number of particles in the sample being tested, as well as the pulse signal of each particle;

[0007] Based on the pulse signal, waveform feature analysis is performed to obtain the waveform feature parameters of each particle. The waveform feature parameters include at least: the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type.

[0008] Based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type, a feature parameter distribution map of the particles in the detection sample is generated;

[0009] The effective region of the feature parameter distribution map is determined, and the number of effective particles within the effective region is determined, wherein the area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio.

[0010] Furthermore, the waveform feature type includes one or more of the following: peak height, peak width, half-peak width, front peak width, back peak width, front peak slope, and back peak slope;

[0011] The step of generating a feature parameter distribution map of particles in the detection sample based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type specifically includes:

[0012] Based on the waveform feature parameters of each particle, waveform feature values ​​belonging to the same target waveform feature type are extracted from the waveform feature parameters of each particle to form a feature value set corresponding to the target waveform feature type. The target waveform feature parameter is any one of all types of waveform feature parameters.

[0013] Using the waveform feature value set corresponding to the target waveform feature type, a feature parameter distribution map corresponding to each waveform feature type of the particles in the detection sample is generated.

[0014] Furthermore, the step of generating a feature parameter distribution map corresponding to each waveform feature type of the particles in the detection sample using the waveform feature value set corresponding to the target waveform feature type specifically includes:

[0015] The waveform feature values ​​of the target waveform feature type are grouped according to a preset threshold to obtain a distribution histogram of the target waveform feature type. The distribution histogram includes the relationship between the size of the waveform feature value containing particles and the number of particles.

[0016] Furthermore, determining the effective region of the feature parameter distribution map and determining the effective number of particles within the effective region specifically includes:

[0017] When the waveform feature type is one type, obtain the effective region of the feature parameter distribution map of the target waveform feature type, and confirm the number of particles in the effective region to obtain the effective particle number.

[0018] Furthermore, determining the effective region of the feature parameter distribution map and determining the effective number of particles within the effective region specifically includes:

[0019] When the waveform feature type is of multiple types, obtain the effective region of the feature parameter distribution map of the target waveform feature type;

[0020] Identify the effective particles in the effective region and obtain the effective particle set for each waveform feature type;

[0021] The effective particle sets of each waveform feature type are combined to obtain the target effective particle set.

[0022] The number of effective particles within the target effective particle set is counted to obtain the number of effective particles within the effective region.

[0023] Furthermore, before determining the effective region of the feature parameter distribution map, the method further includes:

[0024] Obtain the feature points of the feature parameter distribution map, wherein the feature points include at least extreme points and symmetric points;

[0025] Substitute the feature points into a preset Gaussian distribution function to calculate the target Gaussian distribution function of the waveform feature type;

[0026] Determining the effective region of the feature parameter distribution map specifically includes:

[0027] Determine the effective region of the target Gaussian distribution function, wherein the effective region is the region symmetrical about the vertical axis based on the highest point of the target Gaussian distribution function.

[0028] Furthermore, obtaining the feature points of the feature parameter distribution map specifically includes:

[0029] Obtain the highest point of the feature parameter distribution map and take the highest point as the extreme point, and obtain the left symmetric point and the right symmetric point based on the highest point;

[0030] Wherein, the distance from the x-coordinate of the left symmetric point to the x-coordinate of the highest point is equal to the distance from the x-coordinate of the right symmetric point to the highest point, and the difference between the y-coordinates of the left symmetric point and the right symmetric point is not greater than a preset first threshold.

[0031] or,

[0032] The left symmetrical point and the right symmetrical point have the same ordinate, and the difference between the distance from the left symmetrical point to the highest point and the distance from the right symmetrical point to the highest point is not greater than a preset second threshold.

[0033] To achieve the above objectives, a second aspect of this application provides an effective particle count confirmation device, the device comprising: a signal acquisition unit, a signal processing unit, and a particle determination unit;

[0034] The signal acquisition unit is used to acquire the total number of particles in the detection sample and the pulse signal of each particle;

[0035] The signal processing unit is used to perform waveform feature analysis based on the pulse signal to obtain waveform feature parameters of each particle. The waveform feature parameters include at least: the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type.

[0036] Based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type, a feature parameter distribution map of the particles in the detection sample is generated;

[0037] The particle determination unit is used to determine the effective region of the feature parameter distribution map and the number of effective particles within the effective region, wherein the area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio.

[0038] To achieve the above objectives, a third aspect of this application provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, the processor performs the steps of the method described in the first aspect.

[0039] To achieve the above objectives, a fourth aspect of this application provides a computer device including a memory and a processor, characterized in that the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method described in the first aspect.

[0040] The embodiments of the present invention have the following beneficial effects:

[0041] This invention discloses a method for determining the effective particle count. The method includes: acquiring the total number of particles in a detection sample and the pulse signal of each particle; performing waveform feature analysis based on the pulse signal to obtain waveform feature parameters for each particle, wherein the waveform feature parameters include at least: the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type; generating a feature parameter distribution map of particles in the detection sample based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type; determining the effective region of the feature parameter distribution map and determining the effective particle count within the effective region, wherein the area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio. By statistically analyzing the waveform feature parameters of all particles in the detection sample to obtain the feature parameter distribution map, the effective particle count within the effective region of the feature parameter distribution map is further obtained, eliminating the values ​​of particles outside the effective region that were falsely detected as effective particles, thus obtaining a more accurate effective particle count. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] in:

[0044] Figure 1 This is a flowchart illustrating the method for confirming the effective number of particles according to an embodiment of the present invention.

[0045] Figure 2This is a schematic diagram illustrating the working principle of the detection instrument in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the pulse signals generated when particles of different sizes pass through the small hole in an embodiment of the present invention;

[0047] Figure 4 This is a waveform feature distribution diagram of the pulse signal of the target particle in an embodiment of the present invention;

[0048] Figure 5 This is a structural block diagram of the effective particle count confirmation device according to an embodiment of the present invention;

[0049] Figure 6 This is a diagram showing the internal structure of a computer device in an embodiment of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The purpose of identifying effective particles in a sample is to detect the sample concentration. Therefore, obtaining an accurate number of effective particles is essential for obtaining a more precise sample concentration. Existing methods for identifying effective particles mainly involve extracting the peak value of the pulse signal for each particle and discarding pulses with excessively low or high peak values ​​to identify effective pulses. However, this method discards a large number of effective particles, and the identification results are also affected by other factors such as the sampling frequency of the pulse signal and the particle flow velocity in the sample, leading to significant errors in the detection results.

[0052] Based on this, embodiments of the present invention propose a method for confirming the effective number of particles. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating a method for confirming the effective number of particles according to an embodiment of the present invention. The method includes:

[0053] Step 110: Obtain the total number of particles in the detection sample and the pulse signal of each particle.

[0054] Specifically, medical instruments are used to analyze particles in test samples and identify the effective particles in the sample. For example, a blood analyzer, also known clinically as a blood cell analyzer or blood cytometer, is mainly used to test blood samples. It is an instrument that performs qualitative and quantitative analysis of the formed elements in blood and provides relevant information. Specifically, a blood analyzer can be used to analyze blood samples to obtain the effective particle count of the blood sample.

[0055] In this embodiment of the invention, a stable current is applied to both ends of the aperture of the detection instrument used to identify effective particles in the detection sample. When effective particles in the detection sample pass through the aperture, several pulse signals are generated, as detailed in [reference needed]. Figure 2 , Figure 2 The diagram shows the working principle of the testing instrument, such as... Figure 2 As shown, an analysis circuit is added to the detection instrument, and a constant current source is added to both ends of the orifice. The sample is diluted and then added to the instrument for particle detection. Each time a particle in the sample passes through the orifice, a resistance is generated. The voltage pulse signal generated by each particle can then be acquired. The total number of particles in the sample is determined based on the number of detected voltage pulse signals.

[0056] Please also see Figure 3 , Figure 3 This diagram illustrates the pulse signals generated when particles of different sizes pass through a small hole in an embodiment of the present invention. It shows that the larger the particle volume, the greater the resistance, and consequently, the larger the peak value of the pulse signal. Therefore, the present invention utilizes the characteristic that the particle volume is proportional to the peak value of the pulse signal it generates to determine whether the particles in the detection sample are effective.

[0057] Step 120: Perform waveform feature analysis based on the pulse signal to obtain the waveform feature parameters of each particle. The waveform feature parameters include at least: the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type.

[0058] Specifically, waveform characteristic parameters can reflect the waveform characteristics of a pulse signal. Waveform characteristic types can include the extreme values ​​of the pulse signal, the slope between two points on the pulse signal, the width between two points on the pulse signal, etc., all of which can represent the waveform characteristics of the pulse signal. Simultaneously, the waveform characteristic values ​​corresponding to each waveform characteristic type of the pulse signal are recorded for classification and analysis.

[0059] Step 130: Generate a distribution map of the characteristic parameters of the particles in the detection sample based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type.

[0060] After obtaining the waveform feature values ​​corresponding to the waveform feature types of each particle, since each particle has a different size and generates different pulse signals, the waveform feature values ​​of each particle are also different under the same type of waveform feature parameters. Therefore, the waveform feature values ​​are statistically analyzed to obtain the feature parameter distribution diagram of the relationship between waveform feature values ​​and particle number.

[0061] Step 140: Determine the effective region of the feature parameter distribution map and the number of effective particles within the effective region. The area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio.

[0062] Because various factors can occur during the detection process, leading to deviations in the detected pulse signals, some filtering of the pulse signals is necessary. Furthermore, since waveform characteristics reflect the shape of the pulse signal, the magnitude of the waveform feature values ​​can be used for filtering, with particles corresponding to feature values ​​that meet preset standards being considered valid particles. Specifically, the size of the valid region is determined by the area of ​​the product of the total area formed by the feature parameter distribution map and the preset area percentage, and the number of particles included within the valid region is obtained, thus yielding the number of valid particles.

[0063] By statistically analyzing the waveform characteristic parameters of all particles in the detection sample, a characteristic parameter distribution map is obtained. The number of effective particles within the effective area of ​​the characteristic parameter distribution map is then obtained, eliminating the values ​​of particles that are not in the effective area and are falsely detected as effective particles, thus obtaining a more accurate number of effective particles.

[0064] Furthermore, waveform feature types include one or more of the following: peak height, peak width, half-peak width, front peak width, back peak width, front peak slope, and back peak slope.

[0065] For details, please refer to Figure 4 , Figure 4 This is a waveform feature distribution diagram of the pulse signal of the target particle in an embodiment of the present invention. Figure 4 In the middle, P is the peak point of the target particle, that is, the height of point P is the peak height, the peak width is the distance between the x-coordinates of the starting point and the ending point, points A and B are half peak points, the half peak width is the distance between the x-coordinates of the two half peak points A and B, the front peak width is the distance between the x-coordinates of the half peak point A and the peak point P, the front peak width is the distance between the x-coordinates of the half peak point B and the peak point P, the front peak slope is the slope between the half peak point A and the peak point P, and the back peak slope is the slope between the half peak point B and the peak point P.

[0066] Based on the waveform feature type of each particle and the corresponding waveform feature value, a feature parameter distribution map of the particles in the detection sample is generated. Specifically, this includes: extracting waveform feature values ​​belonging to the same target waveform feature type from the waveform feature parameters of each particle, forming a feature value set corresponding to the target waveform feature type. The target waveform feature parameter can be any one of all waveform feature parameter types. Using the waveform feature value set corresponding to the target waveform feature type, a feature parameter distribution map corresponding to each waveform feature type of the particles in the detection sample is generated. It can be understood that, to obtain more accurate detection results for effective particles, more waveform feature types can be selected for detection, and feature value sets for each type can be obtained by classification according to the waveform feature type. Analysis of these feature value sets is then performed to determine the number of effective particles.

[0067] This invention provides a method for generating a feature parameter distribution map. Specifically, it uses the waveform feature value set corresponding to the target waveform feature type to generate a feature parameter distribution map corresponding to each waveform feature type of particles in the detection sample. The method includes: grouping the waveform feature values ​​of the target waveform feature type according to a preset threshold to obtain a distribution histogram of the target waveform feature type. The distribution histogram includes the relationship between the magnitude of the waveform feature value of the particles and the number of particles.

[0068] The threshold value is determined based on the magnitude of the characteristic values ​​of all particles. The characteristic values ​​are then divided into segments according to their range. For example, if the characteristic value range for a certain waveform feature type is 1-100, the threshold can be set to 10, resulting in 10 segments: [1-10], [11-20], [21-30], ..., [91-100]. The number of particles within each segment is counted to obtain a distribution histogram of the waveform feature parameters. It is understood that the smaller the threshold, the more closely the distribution histogram approximates a curve.

[0069] According to statistical principles, regardless of the waveform characteristic type of the pulse signal, the resulting distribution map conforms to a normal distribution. However, due to the influence of noise interference signals, particle debris, sampling frequency, and flow velocity, the characteristic parameter distribution map of the particle pulse signal exhibits a non-normal distribution. Therefore, to reduce the bias, before determining the effective region of the characteristic parameter distribution map in step 140, the following steps are included: obtaining the characteristic points of the characteristic parameter distribution map, including at least extreme points and symmetrical points; substituting the characteristic points into a preset Gaussian distribution function to calculate the target Gaussian distribution function for the waveform characteristic type.

[0070] Since the normal distribution is characterized by symmetry, we can select representative feature points of the feature parameter distribution map for analysis, such as extreme points and symmetric points. Substituting the feature values ​​of the representative feature parameter distribution map into a preset Gaussian distribution function yields a target Gaussian function with a normal distribution. By correcting the non-normally distributed feature parameter distribution map, we can eliminate the influence of noise interference signals and particle debris.

[0071] Further embodiments of the present invention also propose a method for obtaining feature points, namely, obtaining feature points from a feature parameter distribution map, specifically including: obtaining the highest point of the feature parameter distribution map and taking the highest point as an extreme point, and obtaining left and right symmetrical points based on the highest point; wherein, the distance from the x-coordinate of the left symmetrical point to the x-coordinate of the highest point is equal to the distance from the x-coordinate of the right symmetrical point to the highest point, and the difference between the y-coordinates of the left and right symmetrical points is not greater than a preset first threshold; or, the y-coordinates of the left and right symmetrical points are the same, and the difference between the distance from the x-coordinate of the left symmetrical point to the x-coordinate of the highest point and the distance from the x-coordinate of the right symmetrical point to the x-coordinate of the highest point is not greater than a preset second threshold.

[0072] Considering the possibility that two points may not be perfectly symmetrical about the peak point, two points whose x-coordinates are symmetrical about the peak point and whose y-coordinates differ by less than a preset first threshold can be selected as symmetrical points. Alternatively, two points with the same y-coordinate and whose x-coordinates differ from the peak point's x-coordinate by less than a preset second threshold can be selected as symmetrical points. The first threshold can be y = τ * Y, where τ is a constant and Y is the difference between the y-coordinates of the left and right symmetrical points. The second threshold can also be x = τ * X, where τ is a constant and X is the difference between the x-coordinates of the left and right symmetrical points.

[0073] After substituting the left and right symmetric points into the Gaussian distribution function to obtain the target Gaussian function, the effective number of particles in the detection sample can be calculated based on the target Gaussian distribution function, the preset compensation coefficient, and the total number of particles.

[0074] After determining the target Gaussian distribution function, the effective region can be determined based on the target Gaussian distribution function. Specifically, the effective region of the characteristic parameter distribution map is determined, which includes: determining the effective region of the target Gaussian distribution function. The effective region is the region symmetrical to the vertical axis of the highest point of the target Gaussian distribution function.

[0075] The following section introduces methods for obtaining the effective particle count by selecting one waveform feature type and multiple waveform feature types.

[0076] 1. When the waveform feature type is of one type, obtain the effective area of ​​the feature parameter distribution map of the target waveform feature type, and confirm the number of particles in the effective area to obtain the effective particle count.

[0077] Specifically, when selecting one of the waveform feature types—peak height, peak width, half-peak width, front peak width, back peak width, front peak slope, and back peak slope—after determining the location of the effective region in the feature parameter distribution map, the particles contained within the effective region are taken as effective particles, and the number of these effective particles is counted to obtain the effective particle count.

[0078] 2. When there are multiple waveform feature types, obtain the effective region of the feature parameter distribution map of the target waveform feature type; identify the effective particles in the effective region and obtain the effective particle set of each waveform feature type; perform union processing on the effective particle sets of each waveform feature type to obtain the target effective particle set; count the number of particles in the target effective particle set to obtain the number of effective particles in the effective region.

[0079] Specifically, when selecting multiple waveform feature types such as peak height, peak width, half-peak width, front peak width, back peak width, front peak slope, and back peak slope, the effective region corresponding to each waveform feature type, and the effective particle set within each effective region, are first calculated. To avoid some effective particles being misclassified as invalid particles, all particles within each effective particle set are considered effective particles. That is, the union of all effective particle sets is obtained, and the number of particles in the union is taken as the number of effective particles.

[0080] This invention also proposes a device for confirming the effective particle count; please refer to [link to relevant documentation]. Figure 5 , Figure 5 This is a structural block diagram of an effective particle count confirmation device according to an embodiment of the present invention. The device includes: a signal acquisition unit 501, a signal processing unit 502, and a particle determination unit 503.

[0081] The signal acquisition unit 501 is used to acquire the total number of particles in the detection sample and the pulse signal of each particle.

[0082] The signal processing unit 502 is used to perform waveform feature analysis based on pulse signals to obtain waveform feature parameters of each particle. The waveform feature parameters include at least the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type.

[0083] Based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type, a feature parameter distribution map of the particles in the detection sample is generated.

[0084] The particle determination unit 503 is used to determine the effective region of the feature parameter distribution map and the number of effective particles within the effective region. The area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio.

[0085] The effective particle count confirmation device proposed in this embodiment of the invention obtains a feature parameter distribution map by statistically analyzing the waveform feature parameters of all particles in the detection sample, and further obtains the effective particle count within the effective area of ​​the feature parameter distribution map. This eliminates the values ​​of particles that are not in the effective area and are falsely detected as effective particles, thus obtaining a more accurate effective particle count.

[0086] Figure 6An internal structural diagram of a computer device according to one embodiment of the present invention is shown. This computer device can specifically be a terminal or a system. Figure 6 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program causes the processor to perform the steps in the above-described method embodiments. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the steps in the above-described method embodiments. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps in the above method embodiments.

[0088] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps in the above method embodiments.

[0089] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for confirming the effective number of particles, characterized in that, The method includes: Obtain the total number of particles in the sample being tested, as well as the pulse signal of each particle; Based on the pulse signal, waveform feature analysis is performed to obtain the waveform feature parameters of each particle. The waveform feature parameters include at least: the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type. Based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type, a feature parameter distribution map of the particles in the detection sample is generated; Determine the effective region of the feature parameter distribution map and the effective number of particles within the effective region, wherein the area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio; The waveform feature types include one or more of the following: peak height, peak width, half-peak width, front peak width, back peak width, front peak slope, and back peak slope. The step of generating a feature parameter distribution map of particles in the detection sample based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type specifically includes: Based on the waveform feature parameters of each particle, waveform feature values ​​belonging to the same target waveform feature type are extracted from the waveform feature parameters of each particle to form a feature value set corresponding to the target waveform feature type. The target waveform feature type is any one of all types of waveform feature parameters. Using the waveform feature value set corresponding to the target waveform feature type, a feature parameter distribution map corresponding to each waveform feature type of the particles in the detection sample is generated; Specifically, determining the effective region of the feature parameter distribution map and determining the effective number of particles within the effective region includes: When the waveform feature type is of one type, obtain the effective region of the feature parameter distribution map of the target waveform feature type, and confirm the number of particles in the effective region to obtain the effective particle number; The method further includes the following steps before determining the effective region of the feature parameter distribution map: Obtain the feature points of the feature parameter distribution map, wherein the feature points include at least extreme points and symmetric points; Substitute the feature points into a preset Gaussian distribution function to calculate the target Gaussian distribution function of the waveform feature type; Determining the effective region of the feature parameter distribution map specifically includes: Determine the effective region of the target Gaussian distribution function, wherein the effective region is the region symmetrical about the vertical axis based on the highest point of the target Gaussian distribution function; Specifically, obtaining the feature points of the feature parameter distribution map includes: Obtain the highest point of the feature parameter distribution map and take the highest point as the extreme point, and obtain the left symmetric point and the right symmetric point based on the highest point; Wherein, the distance from the x-coordinate of the left symmetric point to the x-coordinate of the highest point is equal to the distance from the x-coordinate of the right symmetric point to the x-coordinate of the highest point, and the difference between the y-coordinates of the left symmetric point and the right symmetric point is not greater than a preset first threshold. or, The left symmetrical point and the right symmetrical point have the same ordinate, and the difference between the distance from the left symmetrical point to the highest point and the distance from the right symmetrical point to the highest point is not greater than a preset second threshold.

2. The method according to claim 1, characterized in that, The step of generating a feature parameter distribution map corresponding to each waveform feature type of particles in the detection sample using the waveform feature value set corresponding to the target waveform feature type specifically includes: The waveform feature values ​​of the target waveform feature type are grouped according to a preset threshold to obtain a distribution histogram of the target waveform feature type. The distribution histogram contains the relationship between the size of the waveform feature value of the particle and the number of particles.

3. The method according to claim 1, characterized in that, The determination of the effective region of the feature parameter distribution map and the determination of the effective number of particles within the effective region specifically include: When the waveform feature type is of multiple types, obtain the effective region of the feature parameter distribution map of the target waveform feature type; Identify the effective particles in the effective region and obtain the effective particle set for each waveform feature type; The effective particle sets of each waveform feature type are combined to obtain the target effective particle set. The number of effective particles within the target effective particle set is counted to obtain the number of effective particles within the effective region.

4. A device for confirming the effective number of particles, characterized in that, The device includes: a signal acquisition unit, a signal processing unit, and a particle determination unit; The signal acquisition unit is used to acquire the total number of particles in the detection sample and the pulse signal of each particle; The signal processing unit is configured to perform waveform feature analysis based on the pulse signal to obtain waveform feature parameters of each particle. The waveform feature parameters include at least: the waveform feature type of the particle and the waveform feature value corresponding to the waveform feature type; and generate a feature parameter distribution map of the particles in the detection sample based on the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type. The particle determination unit is used to determine the effective region of the feature parameter distribution map and the number of effective particles within the effective region, wherein the area of ​​the effective region is the product of the total area of ​​the feature parameter distribution map and a preset area ratio; The waveform feature types include one or more of the following: peak height, peak width, half-peak width, front peak width, back peak width, front peak slope, and back peak slope. The signal processing unit is further configured to extract waveform feature values ​​belonging to the same target waveform feature type from the waveform feature parameters of each particle based on the waveform feature parameters of each particle, forming a feature value set corresponding to the target waveform feature type, wherein the target waveform feature type is any one of all types of waveform feature parameters; and generate a feature parameter distribution map corresponding to each waveform feature type of particles in the detection sample using the waveform feature value set corresponding to the target waveform feature type. The particle determination unit is further configured to, when the waveform feature type is of one type, obtain the effective region of the feature parameter distribution map of the target waveform feature type, and confirm the number of particles in the effective region to obtain the effective particle count; The device is also used to acquire feature points of the feature parameter distribution map, the feature points including at least extreme points and symmetric points; and to substitute the feature points into a preset Gaussian distribution function to calculate the target Gaussian distribution function of the waveform feature type. The particle determination unit is further configured to determine the effective region of the target Gaussian distribution function, wherein the effective region is a region symmetrical about the longitudinal axis based on the highest point of the target Gaussian distribution function; The device is further configured to obtain the highest point of the feature parameter distribution map and use the highest point as the extreme point, and to obtain a left symmetrical point and a right symmetrical point based on the highest point; wherein the distance from the x-coordinate of the left symmetrical point to the x-coordinate of the highest point is equal to the distance from the x-coordinate of the right symmetrical point to the x-coordinate of the highest point, and the difference between the y-coordinates of the left symmetrical point and the right symmetrical point is not greater than a preset first threshold; or, the y-coordinates of the left symmetrical point and the right symmetrical point are the same, and the difference between the distance from the x-coordinate of the left symmetrical point to the x-coordinate of the highest point and the distance from the x-coordinate of the right symmetrical point to the x-coordinate of the highest point is not greater than a preset second threshold.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 3.

6. A computer device comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 3.

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