A method for automatically dispensing samples for flow detection

Abstract

This invention provides an automatic sample allocation method for flow cytometry detection, belonging to the field of flow cytometry cell detection technology. The method includes: acquiring basic sample information; pre-analyzing the sample using a microfluidic chip to obtain sample density, viscosity, and ion concentration; measuring the microchannel diameter and length of the microfluidic chip using a laser ranging system and recording the microchannel material properties; setting a target allocation volume, starting a high-precision injection pump system for pre-injection, and obtaining the initial allocation rate by measuring the flow rate using a flow sensor; calculating the corrected allocation rate using fluid dynamics compensation equations; controlling the high-precision injection pump system to allocate the sample according to the corrected allocation rate; monitoring the sample liquid level in real time using an ultrasonic sensor and synchronously acquiring the real-time allocation rate; and performing sample diversion through the microfluidic chip to achieve multi-channel parallel allocation.

Description

Technical Field

[0001] This invention belongs to the field of flow cytometry detection technology, and more specifically, relates to an automatic sample allocation method for flow cytometry detection. Background Technology

[0002] In flow cytometry analysis, accurate and stable sample allocation is a crucial step in ensuring the accuracy of analytical results. However, existing flow cytometry sample allocation techniques face several unresolved issues: First, significant bias and uncertainty exist during sample allocation. Because fluid flow within microchannels is influenced by numerous factors, such as the fluid's physicochemical properties, microchannel geometry, and flow rate, it is difficult to accurately predict the volume and rate of sample allocation. Traditional sample allocation methods typically employ constant flow rates or pressures, failing to dynamically compensate for these interfering factors and hindering the guarantee of high accuracy and repeatability. Second, the level of automation in sample allocation is low. Currently, most flow cytometry systems still rely on manual sample loading and allocation, resulting in low efficiency and significant human error. While some automated sample allocation systems exist, they are mostly limited to specific application scenarios, lacking compatibility with different types and sizes of sample containers, and exhibiting poor flexibility. Third, there is a lack of real-time monitoring and feedback mechanisms for the allocation process. Existing technologies generally employ a black-box approach to sample allocation, meaning that changes in key parameters during allocation cannot be monitored in real time, and anomalies cannot be detected and corrected promptly, leading to instability in analytical results.

[0003] In summary, existing sample allocation techniques for flow cytometry detection suffer from insufficient allocation accuracy. Summary of the Invention

[0004] In view of this, the present invention provides an automatic sample allocation method for flow cytometry detection, which can solve the technical problem that the existing sample allocation technology for flow cytometry detection has insufficient allocation accuracy.

[0005] This invention is implemented as follows:

[0006] This invention provides an automatic sample allocation method for flow cytometry detection, comprising the following steps:

[0007] S10. Obtain basic sample information, including sample number, type, and collection method;

[0008] S20. Pre-analyze the sample using a microfluidic chip to obtain sample density, sample viscosity, and sample ion concentration;

[0009] S30. Measure the microchannel diameter and microchannel length of the microfluidic chip using a laser ranging system, and record the material properties of the microchannel.

[0010] S40. Set the target dispensing volume, start the high-precision injection pump system for pre-injection, and obtain the initial dispensing rate by measuring the flow sensor.

[0011] S50. Calculate the initial distribution rate using the fluid dynamics compensation equations to obtain the corrected distribution rate;

[0012] S60. Control the high-precision injection pump system to dispense samples according to the corrected dispensing rate;

[0013] S70. Use an ultrasonic sensor to monitor the sample liquid level in real time and simultaneously collect the real-time dispensing rate.

[0014] S80. Samples are split through the microfluidic chip to achieve multi-channel parallel allocation, wherein the multi-channel parallel allocation adopts laminar flow control.

[0015] Optionally, step S10 specifically includes:

[0016] Step 101: Place the sample container within the field of view of the machine vision system;

[0017] Step 102: Activate the image acquisition device of the machine vision system to acquire a high-definition image of the sample container;

[0018] Step 103: Preprocess the high-definition image, including image enhancement, noise reduction, and edge extraction;

[0019] Step 104: Use a deep learning model to perform target detection and segmentation on the sample container;

[0020] Step 105: Scan the barcode within the preset area of ​​the sample container;

[0021] Step 106: Parse the barcode information and extract the sample number, sample type, and collection time;

[0022] Step 107: Write the sample number, the sample type, and the collection time into the database.

[0023] The fluid dynamics compensation equation set includes fluid resistance equation, surface tension equation, pressure loss equation, and velocity correction equation;

[0024] The fluid resistance equation is used to calculate the fluid motion resistance coefficient in the microfluidic channel. The inputs include the sample viscosity, the microchannel diameter, and the initial dispensing rate. The output is the fluid motion resistance coefficient.

[0025] The surface tension equation is used to calculate the surface tension correction coefficient. The inputs include the sample density, the sample ion concentration, and the microchannel material characteristic parameters. The output is the surface tension correction coefficient.

[0026] The pressure loss equation is used to calculate the friction pressure loss value. The inputs include the fluid motion resistance coefficient, the microchannel length, and the initial distribution rate. The output is the friction pressure loss value.

[0027] The flow rate correction equation is used to calculate the corrected distribution rate. The inputs include the friction loss value, the surface tension correction coefficient, and the target distribution volume. The output is the corrected distribution rate.

[0028] The relevant equations or mathematical models are explained in detail below:

[0029] 1. The fluid resistance equation is expressed as follows:

[0030] ;

[0031] In the formula, This is the fluid motion drag coefficient; For the sample viscosity; The initial allocation rate; Microchannel diameter; For sample density; The coefficients are undetermined. This is the error term.

[0032] Parameter acquisition method: Obtained through pre-analysis using a microfluidic chip; Obtained by measurement using a flow sensor; Obtained by a laser ranging system; Obtained through pre-analysis using a microfluidic chip; The experimental data were obtained by fitting the data using the least squares method. The range is 0.001 to 0.01. This equation considers the combined effects of laminar flow resistance (first term), turbulent flow resistance (second term), and viscous dissipation (third term). A power-law relationship is used because fluid resistance has a nonlinear relationship with velocity and geometry.

[0033] 2. The surface tension equation is expressed as follows:

[0034] ;

[0035] In the formula, This is the surface tension correction factor; It is the surface tension coefficient; The ion concentration of the sample; Reference ion concentration; Contact angle; The coefficients are undetermined. This is the error term.

[0036] Parameter acquisition method: The surface tension coefficient was obtained by the pendant drop method: Step 1: Drop the sample onto the surface of the microchannel material; Step 2: Use a high-speed camera to photograph the shape of the droplet; Step 3: Obtain the surface tension coefficient through image processing. Obtained through pre-analysis using a microfluidic chip; Use a standard physiological saline solution with a concentration of 0.9%; Obtained by a contact angle measuring instrument; Obtained through multiple regression analysis; The range is 0.01~0.05. This equation considers the relationship between surface tension and density (first term), the effect of ion concentration (second term), and the effect of material wettability (third term). The square root relationship is used because there is a classic square root relationship between surface tension and density.

[0037] 3. The pressure loss equation is expressed as follows:

[0038] ;

[0039] In the formula, This represents the pressure loss along the friction path. The length of the microchannel; This represents the number of local loss points. This is the local loss coefficient; The coefficients are undetermined. This is the error term.

[0040] Parameter acquisition method: Obtained by a laser ranging system; The following steps were obtained through experimental calibration: Step 1: Measure the pressure loss at different flow rates; Step 2: Obtain the local loss coefficient through numerical fitting. Obtained by least squares fitting; The range is 1~5 Pa. This equation is based on the Darcy-Weisbach equation, taking into account friction loss (first term) and local loss (second term). The quadratic relationship is used because the pressure loss is proportional to the square of the velocity.

[0041] 4. The flow velocity correction equation is expressed as follows:

[0042] ;

[0043] In the formula, The corrected allocation rate; Standard atmospheric pressure; The surface tension of water under standard conditions; Allocate volume to the target; The currently allocated volume; The coefficients are undetermined. This is the error term.

[0044] Parameter acquisition method: Take 101325 Pa; Take 72.75 mN / m (20℃); Set value; Earned through points-based traffic: ; Obtained through nonlinear optimization; The range is 0.01~0.05 mm / s. This equation considers the effects of pressure loss (first term), surface tension (second term), and volume compensation (third term). The square relationship is used to enhance the correction effect for large pressure losses.

[0045] 5. The laminar flow control equations for multi-channel parallel allocation are specifically expressed as follows:

[0046] ;

[0047] In the formula, For the first The flow rate of each channel; For the first The target traffic for each channel; Assign matrix elements to the traffic; These are the error terms for each channel.

[0048] Parameter acquisition method: Set according to allocation requirements; The following steps were taken through fluid network analysis: Step 1: Establish a fluid network model; Step 2: Solve the nodal equations; Step 3: Calibrate the matrix parameters; The range is 0.001~0.01 mm / s. This matrix equation describes the flow coupling relationship between multiple channels, considers the mutual influence between channels, and uses a matrix form to facilitate the description of complex fluid networks.

[0049] Compared with existing technologies, this invention provides an automatic sample allocation method for flow cytometry that achieves high precision and repeatability in sample allocation. This method uses a microfluidic chip to pre-analyze the sample's density, viscosity, ion concentration, and other properties, and combines laser ranging technology to measure the geometry of the microchannels, establishing a refined fluid dynamics model. Based on this, through initial allocation rate measurement and flow rate correction algorithms, the influence of factors such as fluid resistance and surface tension is dynamically compensated, enabling precise control of the allocation rate and volume, which is significantly superior to traditional constant flow rate / pressure allocation methods. Simultaneously, a real-time monitoring and feedback mechanism for the allocation process is established. This method uses an ultrasonic sensor to monitor changes in the sample liquid level in real time, calculates the real-time allocation rate and volume, and compares it with preset target values. When a deviation exceeds a threshold, the operating parameters of the syringe pump can be adjusted promptly to ensure the allocation results meet the requirements. This real-time feedback control significantly improves the stability and reliability of the analytical results, solving the technical problem of insufficient allocation precision in existing flow cytometry sample allocation techniques. Attached Figure Description

[0050] Figure 1 A flowchart of the method provided by the present invention;

[0051] Figure 2 The distribution of sample property parameters includes three subplots, from left to right: density, dynamic viscosity, and ion concentration.

[0052] Figure 3 A three-dimensional outline of the microchannel;

[0053] Figure 4 The diagram shows the relationship between fluid dynamic parameters, including two subplots: the left subplot shows the relationship between pressure loss and flow velocity, and the right subplot shows the relationship between surface tension coefficient and ion concentration.

[0054] Figure 5 A graph showing the real-time monitoring of the allocation rate;

[0055] Figure 6 This is a heatmap showing the flow distribution across multiple channels. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0057] like Figure 1 The diagram shown is a flowchart of an automatic sample allocation method for flow cytometry provided by this invention. This method includes the following steps:

[0058] S10. Obtain basic sample information, including sample number, type, and collection method;

[0059] S20. Pre-analyze the sample using a microfluidic chip to obtain sample density, sample viscosity, and sample ion concentration;

[0060] S30. Use a laser ranging system to measure the microchannel diameter and microchannel length of the microfluidic chip, and record the microchannel material property parameters;

[0061] S40. Set the target dispensing volume, start the high-precision injection pump system for pre-injection, and obtain the initial dispensing rate by measuring the flow sensor.

[0062] S50. Calculate the initial distribution rate using the fluid dynamics compensation equations to obtain the corrected distribution rate;

[0063] S60. The high-precision syringe pump system is controlled to dispense samples according to the corrected dispensing rate.

[0064] S70. Use an ultrasonic sensor to monitor the sample liquid level in real time and simultaneously collect the real-time dispensing rate.

[0065] S80: Samples are split through a microfluidic chip to achieve parallel distribution across multiple channels. The parallel distribution across multiple channels is controlled by laminar flow.

[0066] The specific implementation methods of the above steps are described in detail below:

[0067] The specific implementation of step S10 involves directly obtaining basic sample information such as sample number, type, and collection time from the sample itself. Alternatively, machine vision methods can be used for faster acquisition. First, the sample container is placed within the field of view of the machine vision system. Then, the image acquisition device of the machine vision system is activated to acquire a high-resolution image of the sample container. Next, the acquired high-resolution image is preprocessed, including image enhancement, noise reduction, and edge extraction. Then, a deep learning model is used to perform target detection and segmentation on the sample container to determine its location. Barcode scanning is performed within a preset area of ​​the sample container, and the barcode information is parsed to extract basic sample information such as sample number, sample type, and collection time. Finally, this basic sample information is written into a database, providing foundational support for subsequent sample flow tracking and data management.

[0068] The specific implementation of step S20 is as follows: First, the sample is injected into the inlet of the microfluidic chip. Then, the electrode array in the microfluidic chip is controlled to generate an electric field driving force, causing the sample to flow in the microchannel. During this process, the flow time of the sample in the microchannel is measured, and the sample density is calculated based on a pre-calibrated curve. Simultaneously, pressure change data of the sample in the microchannel is collected, and the sample viscosity is calculated based on a rheological model. In addition, the conductivity of the sample is measured using a conductivity sensor, and the ion concentration of the sample is calculated based on a standard curve of conductivity versus ion concentration. Finally, the obtained parameters such as sample density, viscosity, and ion concentration are stored in the control system to provide a basis for subsequent automatic distribution control.

[0069] The specific implementation of step S30 is as follows: First, the laser ranging system is controlled to emit a laser beam. Then, the reflection signal of the laser beam on the surface of the microfluidic chip is acquired, and the lateral dimension of the microchannel is calculated based on the reflection signal to obtain the microchannel diameter. Next, a multi-point scan is performed along the axial direction of the microchannel, and the three-dimensional contour of the microchannel is reconstructed based on the multi-point scan data, and the axial length of the microchannel is calculated to obtain the length of the microchannel. Finally, the surface energy, contact angle, and roughness, and other characteristic parameters of the microchannel material are read from the material database to provide the necessary geometric and material parameters for subsequent fluid dynamics calculations.

[0070] The specific implementation of step S40 is as follows: First, the target distribution volume is input into the control system. Then, the theoretical injection time is calculated based on the target distribution volume, and the injection parameters of the high-precision injection pump system, including the injection rate and injection volume, are set. Next, the high-precision injection pump system is controlled to perform pre-injection, while real-time data from the flow sensor is collected. This real-time data is filtered, and the average value of the real-time data is calculated to obtain the initial distribution rate. The purpose of this step is to obtain the initial distribution rate through actual pre-injection, providing basic data for subsequent fluid dynamics compensation.

[0071] The specific implementation of step S50 is as follows: First, parameters such as sample density, sample viscosity, microchannel diameter, and initial distribution rate are substituted into the fluid resistance equation to solve for the fluid motion resistance coefficient. This fluid resistance equation comprehensively considers factors such as laminar flow resistance, turbulent flow resistance, and viscous dissipation, and is described using a power-law relationship, which can accurately predict the fluid motion resistance. Next, sample density, sample ion concentration, and microchannel material characteristic parameters are substituted into the surface tension equation to solve for the surface tension correction coefficient. This surface tension equation considers the relationship between surface tension and factors such as density, ion concentration, and wettability, and is described using a square root relationship, which can predict the influence of surface tension well. Then, the fluid motion resistance coefficient, microchannel length, and initial distribution rate are substituted into the pressure loss equation to solve for the friction loss value. This pressure loss equation is based on the Darcy-Weisbach equation, comprehensively considers friction loss and local loss, and is described using a quadratic relationship, which can accurately predict pressure loss. Finally, the friction loss value, surface tension correction coefficient, and target distribution volume are substituted into the velocity correction equation to solve for the corrected distribution rate. This flow rate correction equation takes into account the effects of pressure loss, surface tension, and volume compensation, and uses a square relationship to describe it, which can effectively correct the distribution rate.

[0072] The specific implementation of step S60 is as follows: First, the corrected dispensing rate is converted into a control command for the injection pump, and this command is sent to the high-precision injection pump system. Then, the high-precision injection pump system is controlled to dispense samples according to the corrected dispensing rate. During this process, the operating status of the high-precision injection pump system is monitored in real time, and the actual dispensed volume is recorded. Next, the error between the actual dispensed volume and the target dispensed volume is calculated. If the error exceeds a preset threshold (e.g., 5%), an alarm signal is triggered, indicating that the dispensing parameters need to be adjusted. The purpose of this step is to ensure that the actual dispensing result meets the expected target and to optimize the dispensing control through feedback adjustments.

[0073] The specific implementation of step S70 is as follows: First, the ultrasonic sensor is controlled to emit ultrasonic waves towards the sample liquid surface. Then, the ultrasonic signal reflected back from the sample liquid surface is received, and the height of the sample liquid surface is calculated based on the time difference. Next, the real-time volume change is calculated based on the change in the sample liquid surface height, thereby obtaining the real-time dispensing rate. Finally, this real-time dispensing rate is compared with the previously corrected dispensing rate. If the two deviate from each other by more than a preset range (e.g., 10%), the rate adjustment mechanism is triggered, and the operating parameters of the high-precision syringe pump are readjusted. The purpose of this step is to monitor the rate change during the dispensing process in real time, perform timely feedback control, and ensure the accuracy and stability of the dispensing results.

[0074] The specific implementation of step S80 is as follows: First, the number of channels in the microfluidic chip is determined based on the actual distribution requirements. Then, a fluid network model of the microfluidic chip is established, and the flow distribution coefficient of each channel is calculated. Next, the opening degree of the flow control valve of each channel is set according to these flow distribution coefficients, and the flow splitting mechanism of the microfluidic chip is activated. During the distribution process, the flow data of each channel is monitored in real time, and the opening degree of the flow control valve is dynamically adjusted to maintain a stable laminar flow state. The laminar flow control equation for this multi-channel parallel distribution uses a matrix form to describe the flow coupling relationship between each channel, which can better handle complex fluid networks. The purpose of this step is to achieve uniform distribution of samples among multiple channels and ensure that the analysis conditions of each test channel are consistent.

[0075] Specifically, the principle of this invention is to establish a closed-loop dynamic control system, which collects the physicochemical properties of the sample and the geometric dimensions of the microchannel, and combines them with a refined fluid dynamics model to achieve precise control and real-time feedback of the sample distribution process.

[0076] First, before sample allocation, this method uses machine vision technology to identify sample containers and read barcode information to obtain basic sample information, such as sample number, type, and collection time. This provides foundational support for subsequent sample flow tracking and data management.

[0077] Secondly, the sample is pre-analyzed using a microfluidic chip to measure its physicochemical properties, such as density, viscosity, and ion concentration. These parameters are key inputs for fluid dynamics modeling. Simultaneously, laser ranging technology is used to measure the geometric dimensions of the microchannels, including diameter and length, as well as the surface energy and contact angle of the microchannel material. These geometric and materials science parameters are also necessary inputs for fluid dynamics calculations.

[0078] Based on the obtained sample property parameters and microchannel parameters, this method establishes a set of fluid dynamic compensation equations, including fluid resistance equations, surface tension equations, pressure loss equations, and velocity correction equations. This set of equations can accurately predict the flow behavior of samples in the microchannel and dynamically compensate for the effects of fluid motion resistance, surface tension, and other factors. By solving these equations, the corrected optimal allocation rate can be obtained, providing a basis for subsequent precise allocation.

[0079] Next, the method employs a high-precision syringe pump system to dispense samples at a corrected dispensing rate. Simultaneously, an ultrasonic sensor monitors real-time changes in the sample liquid level, calculating the real-time dispensing rate and volume, and comparing this to the target values. If a deviation exceeds a preset range, a rate adjustment mechanism is triggered, resetting the syringe pump's operating parameters to ensure the dispensing results meet the requirements.

[0080] Furthermore, to achieve uniform parallel distribution of samples across multiple channels, this method employs a laminar flow control strategy. By establishing a fluid network model of the microfluidic chip, the flow distribution coefficient of each channel is calculated, and the opening of the flow control valve is dynamically adjusted to maintain a stable laminar flow state.

[0081] The following is a specific embodiment 1 of this method. The detailed implementation of each step in this embodiment 1 is described below: First, in step S10, the method uses a machine vision system to identify the sample container and read the sample barcode information. The specific implementation process is as follows:

[0082] The first step is to place the sample container within the field of view of the machine vision system.

[0083] The second step is to activate the image acquisition device of the machine vision system to acquire high-resolution images of the sample container. The image acquisition process can be described as follows:

[0084] ;

[0085] In the formula, This represents the acquired image. Represents the image acquisition function. Indicates the time of data collection.

[0086] The third step involves preprocessing the acquired high-resolution images, including image enhancement, noise reduction, and edge extraction. The image preprocessing process can be described as follows:

[0087] ;

[0088] In the formula, This represents the preprocessed image. This represents the image preprocessing function.

[0089] The fourth step involves using a deep learning model to perform object detection and segmentation on the sample container. The object detection and segmentation process can be described as follows:

[0090] ;

[0091] In the formula, This represents the sample container region obtained from detection and segmentation. This represents the object detection and segmentation function.

[0092] The fifth step involves scanning the barcode within a pre-defined area of ​​the sample container, parsing the barcode information, and extracting basic sample information such as sample number, sample type, and collection time. The barcode parsing process can be described as follows:

[0093] ;

[0094] In the formula, Indicates the sample number. Indicates the sample type. Indicates the collection time. This represents the barcode decoding function.

[0095] The sixth step is to write the extracted basic sample information into the database, providing foundational support for subsequent sample flow tracking and data management. The database writing process can be described as follows:

[0096] ;

[0097] In the formula, This represents a database of sample information.

[0098] In step S20, the method uses a microfluidic chip to pre-analyze the sample, obtaining parameters such as sample density, viscosity, and ion concentration. The specific implementation process is as follows:

[0099] The first step is to inject the sample into the inlet of the microfluidic chip. The sample injection process can be described as follows:

[0100] ;

[0101] In the formula, This indicates the sample to be analyzed.

[0102] The second step involves controlling the electrode array in the microfluidic chip to generate an electric field driving force, causing the sample to flow within the microchannels. The electric field driving process can be described as follows:

[0103] ;

[0104] In the formula, Represents the driving force of the electric field. Indicates the ion charge of the sample. This indicates the electric field strength within the microfluidic chip.

[0105] The third step involves measuring the flow time of the sample in the microchannel and calculating the sample density based on a pre-calibrated curve. The density calculation process can be described as follows:

[0106] ;

[0107] In the formula, Indicates sample density, Indicates the flow time of the sample in the microchannel. This represents the density calculation function.

[0108] The fourth step involves collecting pressure change data of the sample within the microchannel and calculating the sample viscosity based on a rheological model. The viscosity calculation process can be described as follows:

[0109] ;

[0110] In the formula, Indicates the viscosity of the sample. This indicates the pressure change of the sample in the microchannel. This represents the viscosity calculation function.

[0111] The fifth step involves measuring the sample's conductivity using a conductivity sensor and calculating the sample's ion concentration based on a standard curve of conductivity versus ion concentration. The ion concentration calculation process can be described as follows:

[0112] ;

[0113] In the formula, Indicates the ion concentration of the sample. Indicates the sample conductivity. This represents the function for calculating ion concentration.

[0114] Step 6: Obtain the sample density Viscosity and ion concentration These parameters are stored in the control system to provide a basis for subsequent automatic allocation control. The parameter storage process can be described as follows:

[0115] ;

[0116] In step S30, the method uses a laser ranging system to measure the diameter and length of the microchannels in the microfluidic chip and records the microchannel material properties. The specific implementation process is as follows:

[0117] The first step is to control the laser ranging system to emit a laser beam. The laser emission process can be described as follows:

[0118] ;

[0119] In the formula, Indicates a laser beam. Represents the laser emission function. Indicates the launch time.

[0120] The second step is to acquire the reflection signal of the laser beam on the surface of the microfluidic chip. The reflection signal acquisition process can be described as follows:

[0121] ;

[0122] In the formula, Indicates the reflected signal. This represents the function for detecting reflected signals.

[0123] The third step is to calculate the lateral dimension of the microchannel based on the reflected signal, thus obtaining the microchannel diameter. The diameter calculation process can be described as follows:

[0124] ;

[0125] In the formula, This represents the diameter calculation function.

[0126] The fourth step involves performing multi-point scanning along the axial direction of the microchannel and reconstructing the three-dimensional contour of the microchannel based on the multi-point scanning data. The three-dimensional contour reconstruction process can be described as follows:

[0127] ;

[0128] In the formula, Represents a three-dimensional contour. This represents the contour reconstruction function.

[0129] Fifth step: Calculate the axial projection length of the three-dimensional profile to obtain the microchannel length. The length calculation process can be described as follows:

[0130] ;

[0131] In the formula, This represents a function for calculating length.

[0132] Step 6: Read the surface energy of the microchannel material from the materials database. Contact angle and roughness Material parameter reading process can be described as follows:

[0133] ;

[0134] In the formula, This represents the function for reading material parameters.

[0135] In step S40, the method sets the target dispensing volume, starts the high-precision injection pump system for pre-injection, and obtains the initial dispensing rate by measuring the flow sensor. The specific implementation process is as follows:

[0136] The first step is to input the target allocation volume into the control system. .

[0137] The second step is to calculate the theoretical injection time based on the target allocation volume. :

[0138] ;

[0139] In the formula, This indicates the initial allocation rate.

[0140] The third step is to set the injection parameters of the high-precision infusion pump system, including the injection rate. and injection volume .

[0141] The fourth step involves controlling the high-precision injection pump system for pre-injection. The pre-injection process can be described as follows:

[0142] ;

[0143] In the formula, Indicates the pre-injection volume. Indicates the pre-injection time.

[0144] Step 5: Collect real-time data from the flow sensor. .

[0145] The sixth step is to filter the real-time data to obtain the filtered data. The filtering process can be described as follows:

[0146] ;

[0147] In the formula, This represents the filtering function.

[0148] Step 7: Calculate the average value of the filtered data to obtain the initial allocation rate. :

[0149] ;

[0150] In step S50, the method uses the fluid dynamics compensation equations to calculate the initial distribution rate, obtaining the corrected distribution rate. The specific implementation process is as follows:

[0151] The first step is to determine the sample density. Sample viscosity Microchannel diameter and initial allocation rate Substituting into the fluid resistance equation, the fluid motion drag coefficient is solved. :

[0152] ;

[0153] In the formula, For undetermined coefficients, This is the error term.

[0154] The second step is to adjust the sample density. Sample ion concentration and microchannel material property parameters Substituting into the surface tension equation, the surface tension correction coefficient is obtained. :

[0155] ;

[0156] In the formula, For undetermined coefficients, For the error term, This is the reference ion concentration.

[0157] The third step is to adjust the fluid motion drag coefficient. Microchannel length and initial allocation rate Substituting into the pressure loss equation, the friction loss value is solved. :

[0158] ;

[0159] In the formula, For undetermined coefficients, For the first Local loss coefficients, For the error term, This represents the number of local loss points.

[0160] The fourth step is to calculate the pressure loss value along the friction path. Surface tension correction coefficient and target allocation volume Substituting into the velocity correction equation, the corrected distribution rate is obtained. :

[0161] ;

[0162] In the formula, Standard atmospheric pressure The surface tension of water under standard conditions. For the currently allocated volume, For undetermined coefficients, This is the error term.

[0163] In step S60, the method controls the high-precision syringe pump system to dispense samples according to the corrected dispensing rate. The specific implementation process is as follows:

[0164] The first step is to adjust the allocation rate. Convert to control commands for an infusion pump.

[0165] The second step is to send the control command to the high-precision injection pump system.

[0166] The third step is to control the high-precision injection pump system according to the corrected dispensing rate. Perform sample allocation. The allocation process can be described as follows:

[0167] ;

[0168] In the formula, Indicates the actual allocated volume. This indicates the allocation of time.

[0169] The fourth step is to monitor the operating status of the high-precision injection pump system in real time.

[0170] Step 5: Record the actual dispensing volume of the high-precision syringe pump system. .

[0171] Step 6: Calculate the actual allocated volume Allocation volume with target error :

[0172] ;

[0173] Step 7, if the error If the threshold is exceeded (e.g., 5%), an alarm signal will be triggered.

[0174] In step S70, the method utilizes an ultrasonic sensor to monitor the sample liquid level in real time and simultaneously acquires the real-time dispensing rate. The specific implementation process is as follows:

[0175] The first step is to control the ultrasonic sensor to emit ultrasonic waves toward the sample liquid surface. The ultrasonic emission process can be described as follows:

[0176] ;

[0177] In the formula, Indicates ultrasonic signal, This represents the ultrasonic emission function.

[0178] The second step is to receive the ultrasonic signal reflected from the sample liquid surface. The process of receiving the reflected signal can be described as follows:

[0179] ;

[0180] In the formula, Indicates the reflected signal. This represents the function for detecting reflected signals.

[0181] The third step is to calculate the sample liquid level height based on the time difference of the reflected signal. :

[0182] ;

[0183] In the formula, The speed at which ultrasound propagates in the sample medium. This is the time difference between transmitting and receiving signals.

[0184] Step 4: Based on the sample liquid level height The change in volume was calculated in real time. :

[0185] ;

[0186] In the formula, This represents the cross-sectional area of ​​the sample container.

[0187] Step 5: Based on real-time volume changes Calculate the real-time allocation rate :

[0188] ;

[0189] Step 6: Real-time allocation rate With the corrected allocation rate Compare them.

[0190] Step 7, if This triggers the rate adjustment mechanism, readjusting the infusion pump parameters.

[0191] In step S80, this method employs laminar flow control to achieve multi-channel parallel allocation of the microfluidic chip. The specific implementation process is as follows:

[0192] The first step is to determine the number of channels based on allocation requirements. .

[0193] The second step is to establish a fluid network model of the microfluidic chip and calculate the flow distribution coefficient of each channel. :

[0194] ;

[0195] In the formula, For the first The flow rate of each channel, For the first The target traffic for each channel For the first Error terms for each channel.

[0196] The third step is to determine the flow allocation coefficient. Set the opening degree of the flow control valve for each channel.

[0197] The fourth step is to activate the shunt mechanism of the microfluidic chip.

[0198] Step 5: Monitor the flow data of each channel in real time. .

[0199] The sixth step is to dynamically adjust the valve opening based on the flow data to maintain a stable laminar flow state.

[0200] To better understand and implement this invention, the following is a specific application scenario of the invention, Example 2: When a testing company provides a disease diagnosis and testing service, it uses the automatic sample allocation method for flow cytometry proposed in this invention.

[0201] First, before the samples enter the testing process, staff place the sample containers within the acquisition range of the machine vision system. The system then activates its image acquisition device, capturing high-resolution images of the sample containers. Preprocessing, including image enhancement, noise reduction, and edge extraction, improves the image quality and analyzability.

[0202] The system then uses a deep learning-based object detection model to analyze the image, quickly locating and segmenting the sample container area. Within this pre-defined area, the system activates a barcode scanning module to read the QR code information on the sample container. Through decoding, it extracts basic information such as the sample number, type, and collection time, and writes this information into the central sample information database, laying the foundation for subsequent sample flow tracking and data management.

[0203] After sample information acquisition, the staff injects the sample into a pre-prepared microfluidic chip. This chip contains an electrode array that generates an electric field to drive the sample flow within the microchannels. During the sample flow, the system collects a series of key parameters:

[0204] First, the sample density was calculated by measuring the flow time of the sample in the microchannel and combining it with a pre-established density-flow time calibration curve. The test results showed that the average density of the batch of nucleic acid detection samples for a certain disease was 1.005 g / cm3, with a standard deviation of 0.002 g / cm3.

[0205] Secondly, the system collected pressure change data of the sample in the microchannel and analyzed and calculated the dynamic viscosity of the sample based on a rheological model. The test results showed that the average dynamic viscosity of the sample was 1.002 mPa·s, and the standard deviation was 0.005 mPa·s.

[0206] In addition, the system measured the conductivity of the sample using a conductivity sensor and calculated the ion concentration by referring to a standard curve. The test results showed that the average ion concentration of the sample was 149 mmol / L, with a standard deviation of 1 mmol / L.

[0207] The above sample properties provide the necessary input data for subsequent fluid dynamics modeling.

[0208] Figure 2 The distribution of sample property parameters is shown in the histogram, illustrating the distribution of three key physical parameters: density, dynamic viscosity, and ion concentration. The distribution characteristics of 100 samples are presented in histogram format, with the mean line marked.

[0209] Meanwhile, the center also used a laser ranging system to conduct detailed measurements of the microchannels in the microfluidic chip. First, the system emitted a laser beam to illuminate the chip surface and collected the reflected signals. By analyzing these reflected signals, the lateral dimensions of the microchannel were calculated, yielding a diameter of 50 μm and a standard deviation of 1 μm.

[0210] Next, the system performed multi-point scanning along the axial direction of the microchannel to acquire its three-dimensional contour data. After data processing and reconstruction, the length of the microchannel was determined to be 20 mm, with a standard deviation of 0.1 mm.

[0211] Furthermore, data from a materials database indicates that the microfluidic chip has a surface energy of 72 mN / m, a contact angle of 45°, and a surface roughness of 50 nm. These microchannel geometry and materials science parameters will also serve as inputs for fluid dynamics modeling.

[0212] Figure 3 This is a 3D profile map of the microchannel, using a 3D surface map to show the precise profile of the microchannel, including subtle variations in surface roughness.

[0213] With the sample property parameters and microchannel parameters in hand, the next step for the system is to establish a set of fluid dynamic compensation equations to perform fine modeling and dynamic control of the sample distribution process.

[0214] First, parameters such as sample density, viscosity, microchannel diameter, and initial distribution rate were substituted into the fluid resistance equation to calculate the fluid motion resistance coefficient. Through experimental calibration, the undetermined coefficients of the fluid resistance equation were determined. , , Error term The range is between 0.001 and 0.01.

[0215] Secondly, the sample density, ion concentration, and microchannel material parameters were substituted into the surface tension equation to solve for the surface tension correction coefficient. Through multiple regression analysis, the undetermined coefficients of the surface tension equation were determined. , , Error term The range is between 0.01 and 0.05. Referring to standard physiological saline, the center will use the reference ion concentration. Set to 0.9%.

[0216] Next, parameters such as fluid resistance coefficient, microchannel length, and initial distribution rate were substituted into the pressure loss equation to calculate the pressure loss along the flow path. Through experimental measurements and numerical fitting, the undetermined coefficients of the pressure loss equation were determined. , Local loss coefficient The error terms are 0.5, 0.3, and 0.2 respectively. The range is between 1 and 5 Pa.

[0217] Finally, the friction loss, surface tension correction coefficient, and target distribution volume were substituted into the velocity correction equation to solve for the corrected optimal distribution rate. Through nonlinear optimization, the undetermined coefficients of the velocity correction equation were determined. , , Error term The range is between 0.01 and 0.05 mm / s.

[0218] With the above set of fluid dynamics compensation equations, the system can dynamically calculate the optimal distribution rate based on the actual measured sample property parameters and microchannel parameters, providing a basis for subsequent precise distribution.

[0219] Figure 4 The fluid dynamics parameter relationship diagram shows two important relationships: the relationship between pressure loss and flow velocity, and the relationship between surface tension coefficient and ion concentration.

[0220] Next, the staff input the target dispensing volume of 50 μL into the control system. Based on this target volume, the system calculated the theoretical injection time to be 50 s. Then, the system was set to an injection rate of 1 μL / s and an injection volume of 50 μL for a pre-injection test.

[0221] During the pre-injection process, the system collected real-time data from the flow sensor and filtered it, ultimately calculating the initial dispensing rate to be 0.998 μL / s with a standard deviation of 0.005 μL / s.

[0222] With the initial distribution rate, and combined with the aforementioned fluid dynamics compensation equations, the system calculates the corrected optimal distribution rate as 1.002 μL / s.

[0223] The system then converted this corrected dispensing rate into a control command for the syringe pump and sent it to the high-precision syringe pump. The syringe pump dispensed the sample according to this command. During the dispensing process, the system monitored the operating status of the syringe pump in real time and recorded the actual dispensing volume.

[0224] Meanwhile, the system also uses an ultrasonic sensor to measure the liquid level in the sample in real time. Based on the changes in the liquid level, the real-time distribution rate and volume are calculated. By comparing the corrected target distribution rate with the measured real-time distribution rate, it was found that the deviation between the two was consistently controlled within 5%, meeting the experimental requirements.

[0225] Figure 5 The real-time monitoring graph of the allocation rate shows a real-time comparison between the target allocation rate and the actual allocation rate during a 50-second allocation process, including an error range of ±5%.

[0226] In addition to single-channel allocation, the center's automated allocation system also supports multi-channel parallel allocation. For the 100 COVID-19 nucleic acid test samples collected that day, the system determined a scheme using 10 parallel testing channels.

[0227] First, the engineers at the center established a fluid network model of the microfluidic chip and calculated the flow distribution coefficient matrix for each channel:

[0228] ;

[0229] Based on the flow distribution coefficient matrix, the system sets the opening of the flow control valves for each test channel to ensure that the flow ratio of each channel meets the requirements and maintains a stable laminar flow state.

[0230] Figure 6 The multi-channel traffic distribution heatmap visually displays a 10×10 traffic allocation coefficient matrix, clearly showing the traffic allocation relationship between each channel.

[0231] During the actual distribution process, the system monitored the flow data of each test channel in real time and adjusted the valve opening in a timely manner to ensure that the 100 samples were evenly and orderly distributed among the 10 channels. The entire distribution process took only 15 minutes, significantly improving sample processing efficiency.

[0232] The testing personnel then performed subsequent nucleic acid amplification and fluorescence detection on the allocated samples. All sample test results were satisfactory and met quality control requirements. Compared to the previous manual allocation method, this automated allocation system improved testing efficiency by nearly 50%, significantly reduced human error, and provided strong support for the COVID-19 diagnostic work of testing companies.

[0233] In summary, this testing company fully utilized the automatic sample dispensing method for flow cytometry proposed in this invention when conducting diagnostic testing for a certain disease. By employing machine vision technology for sample information acquisition, microfluidic technology for sample property analysis, laser ranging technology for measuring microchannel parameters, and ultrasonic sensors for real-time monitoring, a closed-loop dynamic control system was established. This system can accurately predict the flow behavior of samples within the microchannels and accordingly control a high-precision syringe pump for optimized sample dispensing. Simultaneously, the system supports multi-channel parallel dispensing, significantly improving the throughput of sample testing.

[0234] In practice, the automatic distribution system achieves a distribution accuracy of within 5%, significantly outperforming traditional constant flow rate / pressure distribution methods. Furthermore, the system automates the entire process, greatly improving efficiency and reducing human error. In addition, real-time monitoring and feedback control ensure the stability and reliability of the analysis results.

[0235] It should be noted that the variable parameters involved in this invention are explained in Table 1 below.

[0236] Table 1. Explanation of Variable Parameters

[0237]

[0238] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An automatic sample allocation method for flow cytometry detection, characterized in that, Includes the following steps: S10. Obtain basic sample information, including sample number, type, and collection time; S20. Pre-analyze the sample using a microfluidic chip to obtain sample density, sample viscosity, and sample ion concentration; S30. Measure the microchannel diameter and microchannel length of the microfluidic chip using a laser ranging system, and record the material properties of the microchannel. S40. Set the target dispensing volume, start the high-precision injection pump system for pre-injection, and obtain the initial dispensing rate by measuring the flow sensor. S50. Substitute the sample density, sample viscosity, sample ion concentration, microchannel diameter, microchannel length, and microchannel material property parameters into the fluid dynamics compensation equation set to calculate the initial distribution rate and obtain the corrected distribution rate. S60. Control the high-precision injection pump system to dispense samples according to the corrected dispensing rate; S70. Use an ultrasonic sensor to monitor the sample liquid level in real time and simultaneously collect the real-time dispensing rate. S80. Samples are split through the microfluidic chip to achieve multi-channel parallel allocation, wherein the multi-channel parallel allocation adopts laminar flow control.

2. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, The fluid dynamics compensation equation set includes fluid resistance equation, surface tension equation, pressure loss equation, and velocity correction equation; The fluid resistance equation is used to calculate the fluid motion resistance coefficient in the microfluidic channel. The inputs include the sample density, the sample viscosity, the microchannel diameter, and the initial dispensing rate, and the output is the fluid motion resistance coefficient. The surface tension equation is used to calculate the surface tension correction coefficient. The inputs include the sample density, the sample ion concentration, and the microchannel material characteristic parameters. The output is the surface tension correction coefficient. The pressure loss equation is used to calculate the friction pressure loss value. The inputs include the fluid motion resistance coefficient, the microchannel length, and the initial distribution rate. The output is the friction pressure loss value. The flow rate correction equation is used to calculate the corrected distribution rate. The inputs include the friction loss value, the surface tension correction coefficient, and the target distribution volume. The output is the corrected distribution rate.

3. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, Step S20 specifically includes: Step 201: Inject the sample into the inlet of the microfluidic chip; Step 202: Control the electrode array in the microfluidic chip to generate an electric field driving force; Step 203: Measure the flow time of the sample in the microchannel and calculate the sample density based on the pre-calibration curve; Step 204: Collect pressure change data of the sample in the microchannel and calculate the viscosity of the sample according to the rheological model; Step 205: Measure the conductivity of the sample using a conductivity sensor; Step 206: Calculate the sample ion concentration based on the standard curve of conductivity versus ion concentration; Step 207: Store the sample density, sample viscosity, and sample ion concentration into the control system.

4. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, Step S30 specifically includes: Step 301: Control the laser ranging system to emit a laser beam; Step 302: Acquire the reflection signal of the laser beam on the surface of the microfluidic chip; Step 303: Calculate the lateral dimension of the microchannel based on the reflected signal to obtain the diameter of the microchannel; Step 304: Perform multi-point scanning along the axial direction of the microchannel; Step 305: Reconstruct the three-dimensional contour of the microchannel based on the multi-point scanning data; Step 306: Calculate the axial projection length of the three-dimensional contour to obtain the microchannel length; Step 307: Read the microchannel material property parameters from the material database, including surface energy, contact angle, and roughness.

5. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, Step S40 specifically includes: Step 401: Input the target allocation volume into the control system; Step 402: Calculate the theoretical injection time based on the target allocated volume; Step 403: Set the injection parameters of the high-precision injection pump system, including injection rate and injection volume; Step 404: Control the high-precision injection pump system to perform pre-injection; Step 405: Collect real-time data from the flow sensor; Step 406: Filter the real-time data; Step 407: Calculate the average value of the real-time data to obtain the initial allocation rate.

6. The automatic sample allocation method for flow cytometry detection according to claim 2, characterized in that, Step S50 specifically includes: Step 501: Substitute the sample density, the sample viscosity, the microchannel diameter, and the initial dispensing rate into the fluid resistance equation; Step 502: Solve the fluid resistance equation to obtain the fluid motion resistance coefficient; Step 503: Substitute the sample density, the sample ion concentration, and the microchannel material characteristic parameters into the surface tension equation; Step 504: Solve the surface tension equation to obtain the surface tension correction coefficient; Step 505: Substitute the fluid motion resistance coefficient, the microchannel length, and the initial distribution rate into the pressure loss equation; Step 506: Solve the pressure loss equation to obtain the friction pressure loss value; Step 507: Substitute the friction loss value, the surface tension correction coefficient, and the target distribution volume into the flow velocity correction equation; Step 508: Solve the flow rate correction equation to obtain the corrected distribution rate.

7. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, Step S60 specifically includes: Step 601: Convert the corrected dispensing rate into an injection pump control command; Step 602: Send the injection pump control command to the high-precision injection pump system; Step 603: Control the high-precision injection pump system to operate according to the corrected distribution rate; Step 604: Monitor the operating status of the high-precision injection pump system in real time; Step 605: Record the actual dispensing volume of the high-precision injection pump system; Step 606: Calculate the error between the actual allocated volume and the target allocated volume; Step 607: When the error exceeds a preset threshold, an alarm signal is triggered.

8. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, Step S70 specifically includes: Step 701: Control the ultrasonic sensor to emit ultrasonic waves toward the sample liquid surface; Step 702: Receive the ultrasonic signal reflected from the sample liquid surface; Step 703: Calculate the height of the sample liquid level based on the time difference of the ultrasonic signals; Step 704: Calculate the real-time volume change based on the change in the sample liquid level. Step 705: Calculate the real-time distribution rate based on the real-time volume change; Step 706: Compare the real-time allocation rate with the corrected allocation rate; Step 707: When the real-time allocation rate deviates from the corrected allocation rate by more than a preset range, the rate adjustment mechanism is triggered.

9. The automatic sample allocation method for flow cytometry detection according to claim 1, characterized in that, Step S80 specifically includes: Step 801: Determine the number of channels based on allocation requirements; Step 802: Establish the fluid network model of the microfluidic chip; Step 803: Calculate the flow distribution coefficient for each channel; Step 804: Set the opening degree of the flow control valve for each channel according to the flow distribution coefficient; Step 805: Activate the shunt mechanism of the microfluidic chip; Step 806: Monitor the traffic data of each channel in real time; Step 807: Dynamically adjust the opening of the flow control valve according to the flow data to maintain laminar flow.

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