Methods, apparatus, equipment and storage media for determining the effective number of particles

Abstract

This invention discloses a method, apparatus, device, and storage medium for determining the effective particle count. The method includes: acquiring the total number of particles and the pulse signal of each particle in a detection sample; analyzing and calculating based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle, where the characteristic parameters characterize the particle size without the influence of time-domain information, and the time-domain information includes at least: the sampling frequency of the pulse signal and the particle flow velocity; statistically analyzing the characteristic values ​​of each particle to obtain a characteristic parameter distribution map; and calculating the effective particle count in the detection sample based on the characteristic parameter distribution map, a preset compensation coefficient, and the total number of particles. By constructing characteristic parameters without the influence of time-domain information and the characteristic parameter distribution map, the effective particle distribution that eliminates the influence of time-domain information is obtained, and the effective particles ignored in the calculation process are compensated according to the preset compensation coefficient, making the final effective particle count more accurate.

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 determining the effective number of particles. Background Technology

[0002] When collecting samples, the valid particles in the samples are analyzed. Therefore, it is very important to determine whether the particles are valid when collecting them.

[0003] In practice, the number of particles in a sample is determined by collecting the pulse signals generated by the particles. However, the shape of the particles, the sampling frequency when collecting the particle pulse signals, and the particle flow velocity all affect the shape of the particle pulse signals. In addition, the interference of noise signals during collection also affects the shape of the particle pulse signals. This affects the result of determining whether the particles are valid, thus leading to 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 determining the effective number of particles to address the above problems and reduce the error in identifying effective particles.

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

[0006] Acquire the total number of particles and the pulse signal of each particle in the detection sample;

[0007] Based on the pulse signal, the characteristic values ​​of the characteristic parameters of each particle are obtained through analysis and calculation. The characteristic parameters represent the particle size without the influence of time domain information. The time domain information includes at least the sampling frequency of the pulse signal and the flow velocity of the particle.

[0008] The characteristic values ​​of each particle are statistically analyzed to obtain a characteristic parameter distribution map;

[0009] The effective number of particles in the detection sample is calculated based on the feature parameter distribution map, the preset compensation coefficient, and the total number of particles.

[0010] Furthermore, the step of analyzing and calculating based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle specifically includes:

[0011] Based on the analysis of the pulse signal of the target particle, the front peak slope and the back peak slope of the target particle are obtained, wherein the target particle is any particle in the detection sample; the front peak slope is the slope between the peak point of the pulse signal and the first half-peak point; the back peak slope is the slope between the peak point of the pulse signal and the second half-peak point; the first half-peak point is a point in the pulse signal that appears before the peak point and whose ordinate is half of the peak point; the second half-peak point is a point in the pulse signal that appears after the peak point and whose ordinate is half of the peak point.

[0012] The characteristic values ​​of the target particle's characteristic parameters are obtained by calculating the characteristic values ​​based on the slope of the front peak and the slope of the back peak.

[0013] Furthermore, the analysis of the pulse signal based on the target particle to obtain the front peak slope and back peak slope of the target particle specifically includes:

[0014] Obtain the peak point of the pulse signal of the target particle, as well as the first half-peak point and the second half-peak point closest to the peak point;

[0015] The slope is calculated based on the distance between the vertical coordinates of the peak point and the first half-peak point and the distance between their horizontal coordinates to obtain the front peak slope of the target particle;

[0016] The slope is calculated based on the distance between the vertical coordinates of the peak point and the second half-peak point and the distance between their horizontal coordinates, thus obtaining the slope of the target particle's back peak.

[0017] Furthermore, the step of calculating feature values ​​based on the front and back peak slopes of the target particle to obtain the feature values ​​of the target particle's feature parameters specifically includes:

[0018] Calculate the ratio between the slope of the first peak and the slope of the second peak to obtain the first characteristic value;

[0019] Based on the first feature value, the target feature value of the feature parameter of the target particle is obtained by performing a preset mathematical operation.

[0020] Furthermore, before calculating the effective number of particles in the detection sample based on the feature parameter distribution map, the preset compensation coefficient, and the total number of particles, the method further includes:

[0021] Obtain the feature points of the feature parameter distribution map, wherein the feature points include at least extreme points and symmetric points;

[0022] Substituting the feature points into the Gaussian distribution function, the target Gaussian distribution function of the feature parameters is calculated;

[0023] The calculation of the effective particle number in the detection sample based on the feature parameter distribution map, the preset compensation coefficient, and the total particle number specifically includes:

[0024] The effective number of particles in the detection sample is calculated based on the target Gaussian distribution function, the preset compensation coefficient, and the total number of particles.

[0025] Furthermore, obtaining the feature points of the feature parameter distribution map specifically includes:

[0026] 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;

[0027] 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.

[0028] or,

[0029] 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.

[0030] Furthermore, the calculation of the effective particle number in the detection sample based on the target Gaussian distribution function, the preset compensation coefficient, and the total particle number specifically includes:

[0031] Based on the target Gaussian distribution function and the area of ​​the symmetric point, the first ratio of the first effective particle number to the total particle number of the detected sample is obtained.

[0032] The second ratio after compensation is calculated based on the first ratio and the preset compensation coefficient;

[0033] The effective particle number in the detection sample is calculated based on the second ratio and the first effective particle number.

[0034] To achieve the above objectives, a second aspect of this application provides an apparatus for determining the effective number of particles, the apparatus comprising: a signal acquisition unit, a signal processing unit, and a particle compensation unit;

[0035] The signal acquisition unit is used to acquire the total number of particles in the detection sample and the pulse signal of each particle;

[0036] The signal processing unit is used to analyze and calculate based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle. The characteristic parameters characterize the particle size without the influence of time domain information. The time domain information includes at least the sampling frequency of the pulse signal and the flow velocity of the particle.

[0037] The characteristic values ​​of each particle are statistically analyzed to obtain a characteristic parameter distribution map;

[0038] The particle compensation unit is used to calculate the effective number of particles in the detection sample based on the feature parameter distribution map, the preset compensation coefficient, and the total number of particles.

[0039] 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.

[0040] 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.

[0041] The embodiments of the present invention have the following beneficial effects:

[0042] 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; analyzing and calculating based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle, where the characteristic parameters characterize the particle size without the influence of time-domain information, and the time-domain information includes at least: the sampling frequency of the pulse signal and the particle flow velocity; statistically analyzing the characteristic values ​​of each particle to obtain a characteristic parameter distribution map; and calculating the effective particle count in the detection sample based on the characteristic parameter distribution map, a preset compensation coefficient, and the total number of particles. By constructing characteristic parameters without the influence of time-domain information and the characteristic parameter distribution map, the effective particle distribution that eliminates the influence of time-domain information is obtained, and the effective particles ignored in the calculation process are compensated according to the preset compensation coefficient, making the final effective particle count more accurate. Attached Figure Description

[0043] 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.

[0044] in:

[0045] Figure 1 This is a flowchart illustrating the method for determining the effective number of particles in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the working principle of the detection instrument in an embodiment of the present invention;

[0047] 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;

[0048] Figure 4 A characteristic distribution diagram of the pulse signal of the target particle in an embodiment of the present invention;

[0049] Figure 5 A schematic diagram of the feature parameter distribution in an embodiment of the present invention;

[0050] Figure 6 This is a structural block diagram of the device for determining the effective number of particles according to an embodiment of the present invention;

[0051] Figure 7 This is an internal structural diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0052] 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.

[0053] Current methods for identifying valid particles extract the peak value of each particle's pulse signal, discarding pulses with excessively low or high peak values ​​to achieve identification of valid pulses. However, due to the reliance on a single identification feature, a large number of valid particles are discarded, 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. Therefore, there is a significant error in identifying valid particles in a sample.

[0054] Based on this, embodiments of the present invention propose a method for determining the effective number of particles. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating the method for determining the effective number of particles in an embodiment of the present invention. The method includes:

[0055] Step 110: Obtain the total number of particles and the pulse signal of each particle in the detection sample.

[0056] Specifically, medical instruments are used to analyze particles in test samples and identify the effective particles within them. For example, a blood analyzer, also known clinically as a blood cell analyzer or hematology analyzer, is primarily used to test blood samples. It performs qualitative and quantitative analysis of the formed elements in blood and provides relevant information. Specifically, a blood analyzer can be used to analyze the number of effective particles in a blood sample.

[0057] 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, by adding an analysis circuit to the detection instrument, 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, it generates resistance, allowing the acquisition of the voltage pulse signal generated by each particle to determine the total number of particles in the sample.

[0058] 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 generated pulse signal to obtain the size characteristics of particles in the detection sample.

[0059] Step 120: Analyze and calculate based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle. The characteristic parameters represent the particle size without the influence of time domain information. The time domain information includes at least the sampling frequency of the pulse signal and the flow velocity of the particle.

[0060] When acquiring pulse signals, the signals are acquired at a preset acquisition frequency, so the acquisition results will be affected by the acquisition frequency. In addition, the different particle flow velocities will also affect the pulse signal morphology of the particle.

[0061] Therefore, to eliminate the influence of time-domain information, including the acquisition frequency and particle flow velocity, on the pulse signal, the acquired particle pulse signals are analyzed to obtain characteristic parameters representing particle size that have eliminated the influence of time-domain information. This allows for the determination of whether a particle is a valid particle based on these characteristic values, thus improving the accuracy of the determination.

[0062] Step 130: Statistically analyze the characteristic values ​​of each particle to obtain the characteristic parameter distribution map.

[0063] Specifically, since the feature values ​​of the feature parameters of all particles are obtained, all identified particles have corresponding feature values. Because the size of each particle is different, the size of the feature value of each particle is also different. Therefore, by statistically analyzing all feature values, a feature parameter distribution map that reflects the relationship between particle size and particle number is obtained.

[0064] Step 140: Calculate the effective number of particles in the detection sample based on the feature parameter distribution map, the preset compensation coefficient, and the total number of particles.

[0065] After obtaining the feature parameter distribution map, particles that meet the preset threshold are considered valid particles, and the number of valid particles can be counted. Specifically, to obtain the number of valid particles, particles that are too large or too small are deleted as invalid particles, and particles whose volume meets the condition are considered valid particles.

[0066] To prevent errors in the calculation results of feature parameters due to factors such as the detection of particle pulse signals and the calculation of feature parameters, this embodiment of the invention also compensates for the effective particles that have been detected. In order to compensate for the effective particles that are submerged in invalid particles, the number of effective particles after compensation is obtained, so as to make the identification results of effective particles more accurate.

[0067] To eliminate the influence of time-domain information on pulse signals, this embodiment of the invention proposes a method for obtaining feature parameters unaffected by time-domain information. Step 120 involves analyzing and calculating based on the pulse signal to obtain the feature values ​​of the feature parameters for each particle, specifically including:

[0068] Step 1: Analyze the pulse signal of the target particle to obtain the front peak slope and back peak slope of the target particle. The target particle is any particle in the detection sample. The front peak slope is the slope between the peak point of the pulse signal and the first half peak point. The back peak slope is the slope between the peak point of the pulse signal and the second half peak point. The first half peak point is the point in the pulse signal that appears before the peak point and whose ordinate is half of the peak point. The second half peak point is the point in the pulse signal that appears after the peak point and whose ordinate is half of the peak point.

[0069] Since the width of the particle pulse signal is directly proportional to the sampling frequency and inversely proportional to the particle flow velocity, eigenvalues ​​are calculated using the leading and trailing slopes, which are related to the pulse width, to eliminate the influence of time-domain information. For details, please refer to [link to relevant documentation]. Figure 4 , Figure 4 This is a characteristic distribution diagram of the pulse signal of the target particle in an embodiment of the present invention. Figure 4In this equation, P is the peak point of the target particle, A is the first half-peak point, and B is the second half-peak point. Then, the slope of the first peak can be obtained from the coordinates of A and P, and the slope of the second peak can be obtained from the coordinates of B and P.

[0070] To avoid interference between adjacent pulse signals during the analysis, Step 1 involves analyzing the pulse signals to obtain the front and back peak slopes of each particle, specifically including:

[0071] Obtain the peak point of the pulse signal of the target particle, as well as the first half-peak point and the second half-peak point closest to the peak point; calculate the slope based on the distance between the vertical coordinates of the peak point and the first half-peak point and the distance between their horizontal coordinates to obtain the front peak slope of the target particle; calculate the slope based on the distance between the vertical coordinates of the peak point and the second half-peak point and the distance between their horizontal coordinates to obtain the back peak slope of the target particle.

[0072] Since there are several particles during the detection process, several pulse signals will be obtained. Each pulse signal will have two half-peak points. Therefore, in order to prevent the half-peak point of other pulse signals from being selected when selecting the corresponding half-peak point, the first half-peak point and the second half-peak point closest to the peak point of the pulse signal in this embodiment are selected to improve the accuracy of the detection results.

[0073] Step 2: Calculate the characteristic values ​​based on the front peak slope and back peak slope of the target particle to obtain the target characteristic values ​​of the target particle's characteristic parameters.

[0074] After obtaining the front peak slope and back peak slope of the target particle, the characteristic values ​​of the feature parameters that eliminate the influence of the time domain are calculated using the front peak slope and back peak slope. Specifically, Step 2: Calculate the characteristic values ​​based on the front peak slope and back peak slope of the target particle to obtain the characteristic values ​​of the target particle's feature parameters. This includes: calculating the ratio between the front peak slope and the back peak slope to obtain the first characteristic value; and performing calculations based on the first characteristic value according to preset mathematical operations to obtain the feature parameters of the target particle.

[0075] The influence of time-domain information is eliminated by dividing the slopes of the first and second peaks. The result of this division is used as the first eigenvalue. To facilitate subsequent analysis, a pre-defined mathematical operation is performed on the first eigenvalue to obtain the target eigenvalues ​​of the target particle's characteristic parameters. For example, the mathematical operation is to take the square root of the first eigenvalue, i.e. S is the target eigenvalue. Through further mathematical operations, the distribution of eigenparameters of all particles can be made more stable, making it easier to analyze effective particles.

[0076] According to statistical principles, the distribution of any waveform characteristics of a particle pulse signal conforms to a normal distribution. However, due to factors such as noise interference and particle debris, the characteristic parameter distribution of the particle pulse signal becomes non-normal. Therefore, in order to eliminate the influence of noise interference and particle debris, this embodiment of the invention includes step 140, before calculating the effective number of particles in the detection sample based on the characteristic parameter distribution, a preset compensation coefficient, and the total number of particles, the following steps are also included:

[0077] Obtain the feature points of the feature parameter distribution map, including at least extreme points and symmetric points; substitute the feature points into the Gaussian distribution function to calculate the target Gaussian distribution function of the feature parameters.

[0078] 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.

[0079] Furthermore, obtaining feature points from the feature parameter distribution map specifically includes: obtaining the highest point of the feature parameter distribution map and using 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.

[0080] 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.

[0081] 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.

[0082] Furthermore, based on the target Gaussian distribution function, the preset compensation coefficient, and the total number of particles, the effective number of particles in the detection sample is calculated, specifically including:

[0083] Based on the target Gaussian distribution function and the area calculated at the symmetric point, the first ratio of the first effective particle number to the total number of particles in the detection sample is obtained; the second ratio after compensation is calculated based on the first ratio and the preset compensation coefficient; the effective particle number in the detection sample is obtained by calculating based on the second ratio and the first effective particle number.

[0084] Specifically, such as Figure 5 As shown, Figure 5 This is a schematic diagram of the feature parameter distribution in an embodiment of the present invention. Figure 5 In this model, M represents the peak point, L represents the left symmetric point, and R represents the right symmetric point. The particles contained within the region N enclosed by the left and right symmetric points are considered as effective particles, thus obtaining the effective particle count within this region. Based on the effective particle count and the total particle count, the first ratio of the effective particle count to the total particle count is calculated. Then, based on a preset compensation coefficient and the first ratio, the percentage of the actual effective particle count is calculated, which is the second ratio. Finally, the effective particle count is divided by the second ratio to obtain the compensated effective particle count, which is the effective particle count in the detection sample. By compensating for the effective particle count obtained from the detection, a more accurate effective particle count is obtained.

[0085] This invention also proposes a device for determining the effective number of particles; please refer to [link to relevant documentation]. Figure 6 , Figure 6 This is a structural block diagram of the device for determining the effective number of particles according to an embodiment of the present invention. The device includes: a signal acquisition unit 601, a signal processing unit 602, and a particle compensation unit 603.

[0086] The signal acquisition unit 601 is used to acquire the total number of particles in the detection sample and the pulse signal of each particle.

[0087] The signal processing unit 602 is used to perform analysis and calculation based on the pulse signal to obtain the characteristic parameters of each particle without the influence of time-domain information. The time-domain information includes at least the sampling frequency of the pulse signal and the flow velocity of the particle. The characteristic parameters of each particle are statistically analyzed to obtain a characteristic parameter distribution map.

[0088] The particle compensation unit 603 is used to calculate the effective number of particles in the detection sample based on the feature parameter distribution map, the preset compensation coefficient and the total number of particles.

[0089] The effective particle count determination device proposed in this embodiment of the invention obtains the effective particle distribution that eliminates the influence of time domain information by constructing feature parameters and feature parameter distribution map under the influence of no time domain information, and compensates for the effective particles ignored in the calculation process according to the preset compensation coefficient, so that the final effective particle count is more accurate.

[0090] Figure 7 An 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 7 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 7 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 determining the effective number of particles, characterized in that, The method includes: Acquire the total number of particles and the pulse signal of each particle in the detection sample; Based on the pulse signal, the characteristic values ​​of the characteristic parameters of each particle are obtained through analysis and calculation. The characteristic parameters represent the particle size without the influence of time domain information. The time domain information includes at least the sampling frequency of the pulse signal and the flow velocity of the particle. The characteristic values ​​of each particle are statistically analyzed to obtain a characteristic parameter distribution map; The effective number of particles in the detection sample is calculated based on the feature parameter distribution map, the preset compensation coefficient, and the total number of particles. Specifically, the step of analyzing and calculating based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle includes: Based on the analysis of the pulse signal of the target particle, the front peak slope and the back peak slope of the target particle are obtained, wherein the target particle is any particle in the detection sample; the front peak slope is the slope between the peak point of the pulse signal and the first half-peak point; the back peak slope is the slope between the peak point of the pulse signal and the second half-peak point; the first half-peak point is a point in the pulse signal that appears before the peak point and whose ordinate is half of the peak point; the second half-peak point is a point in the pulse signal that appears after the peak point and whose ordinate is half of the peak point. The characteristic values ​​of the characteristic parameters of the target particle are obtained by calculating the characteristic values ​​based on the slope of the front peak and the slope of the back peak of the target particle. Specifically, the analysis of the pulse signal based on the target particle to obtain the front peak slope and back peak slope of the target particle includes: Obtain the peak point of the pulse signal of the target particle, as well as the first half-peak point and the second half-peak point closest to the peak point; The slope is calculated based on the distance between the vertical coordinates of the peak point and the first half-peak point and the distance between their horizontal coordinates to obtain the front peak slope of the target particle; The slope is calculated based on the distance between the ordinate of the peak point and the distance between the abscissa of the second half-peak point, and the slope of the back peak of the target particle is obtained. Specifically, the step of calculating feature values ​​based on the front and back peak slopes of the target particle to obtain the feature parameters of the target particle includes: Calculate the ratio between the slope of the first peak and the slope of the second peak to obtain the first characteristic value; Based on the first feature value, the target feature value of the feature parameter of the target particle is obtained by performing a preset mathematical operation.

2. The method according to claim 1, characterized in that, Before calculating the effective particle count in the detection sample based on the feature parameter distribution map, the preset compensation coefficient, and the total particle count, the method further includes: Obtain the feature points of the feature parameter distribution map, wherein the feature points include at least extreme points and symmetric points; Substituting the feature points into the Gaussian distribution function, the target Gaussian distribution function of the feature parameters is calculated; The calculation of the effective particle number in the detection sample based on the feature parameter distribution map, the preset compensation coefficient, and the total particle number specifically includes: The effective number of particles in the detection sample is calculated based on the target Gaussian distribution function, the preset compensation coefficient, and the total number of particles.

3. The method according to claim 2, characterized in that, The process of obtaining the feature points of the feature parameter distribution map specifically 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 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.

4. The method according to claim 2, characterized in that, The calculation of the effective particle number in the detection sample based on the target Gaussian distribution function, the preset compensation coefficient, and the total particle number specifically includes: Based on the target Gaussian distribution function and the area of ​​the symmetric point, the first ratio of the first effective particle number to the total particle number of the detected sample is obtained. The second ratio after compensation is calculated based on the first ratio and the preset compensation coefficient; The effective particle number in the detection sample is calculated based on the second ratio and the first effective particle number.

5. A device for determining the effective number of particles, characterized in that, The device includes: a signal acquisition unit, a signal processing unit, and a particle compensation 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 used to analyze and calculate based on the pulse signal to obtain the characteristic values ​​of the characteristic parameters of each particle. The characteristic parameters characterize the particle size without the influence of time domain information. The time domain information includes at least the sampling frequency of the pulse signal and the flow velocity of the particle. The characteristic values ​​of each particle are statistically analyzed to obtain a characteristic parameter distribution map; The particle compensation unit is used to calculate the effective number of particles in the detection sample based on the feature parameter distribution map, the preset compensation coefficient and the total number of particles. The signal processing unit is further configured to analyze the pulse signal of the target particle to obtain the front peak slope and the back peak slope of the target particle, wherein the target particle is any particle in the detection sample; the front peak slope is the slope between the peak point of the pulse signal and the first half-peak point; the back peak slope is the slope between the peak point of the pulse signal and the second half-peak point; the first half-peak point is a point in the pulse signal that appears before the peak point and whose ordinate is half of the peak point; the second half-peak point is a point in the pulse signal that appears after the peak point and whose ordinate is half of the peak point; and to calculate feature values ​​based on the front peak slope and the back peak slope of the target particle to obtain the feature values ​​of the feature parameters of the target particle. The signal processing unit is further configured to acquire the peak point of the pulse signal of the target particle, as well as the first half-peak point and the second half-peak point closest to the peak point; calculate the slope based on the distance between the vertical coordinates of the peak point and the first half-peak point and the distance between their horizontal coordinates to obtain the front peak slope of the target particle; and calculate the slope based on the distance between the vertical coordinates of the peak point and the second half-peak point and the distance between their horizontal coordinates to obtain the back peak slope of the target particle. The signal processing unit is further configured to calculate the ratio between the slope of the first peak and the slope of the second peak to obtain a first feature value; and to perform calculations based on the first feature value according to a preset mathematical operation to obtain the target feature value of the feature parameters of the target particle.

6. 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 4.

7. 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 4.

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