Method and device for evaluating consistency of urine protein indexes

Abstract

The invention relates to the field of medical care informatics, in particular to a consistency evaluation method and device for urine protein indexes, and the method comprises the following steps: constructing a paper-based micro-fluidic biochip of a centrifugal urine sample, constructing a micro-fluid driving model of the paper-based micro-fluidic biochip, controlling the centrifugal urine sample to flow on the paper-based micro-fluidic biochip, and evaluating the consistency of the urine protein indexes through the micro-fluid driving model of the paper-based micro-fluidic biochip. A flowing urine sample is obtained; reacting with a channel receptor in a corresponding reaction area of the paper-based micro-fluidic biochip to obtain a reaction compound of the flowing urine sample, and separating the reaction compound in a corresponding separation area of the micro-fluidic channel to obtain a target analyte; collecting an analyte image of the target analyte, and calculating an image reliability coefficient of the analyte image; and constructing a feature map of the analyte image, identifying the urine protein index content of the urine sample by using the protein index identification model, and analyzing the consistency of the urine protein indexes through the urine protein index content. According to the method, the accuracy of evaluating the consistency of the urine protein indexes can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical care informatics, and in particular to a consistency evaluation method and device for urine protein indicators. Background Art

[0002] The consistency evaluation of urine protein index refers to the process of measuring and analyzing protein-related indicators (such as the concentration and activity of specific proteins) in urine samples in scientific research or clinical experiments to verify whether the results obtained under different experimental conditions are consistent and repeatable. The consistency evaluation of urine protein index can ensure the effectiveness and credibility of urine protein testing in clinical diagnosis and treatment monitoring, especially in the diagnosis and management of diseases such as chronic kidney disease. Through consistency evaluation, the comparability of urine protein testing in different laboratories and different equipment can be improved, thereby providing doctors with more accurate diagnosis and treatment basis.

[0003] Currently, the consistency assessment of urine protein indicators mainly relies on laboratory technicians using urine analyzers and test strips for testing. These methods usually include collecting urine samples, using test strips for preliminary screening, and then performing more detailed quantitative analysis through urine analyzers. However, these traditional methods rely too much on manual operation and are prone to subjective errors, resulting in limited accuracy in the consistency assessment of urine protein indicators. Summary of the invention

[0004] The present invention provides a method and a device for evaluating the consistency of urine protein indicators, the main purpose of which is to improve the accuracy of the consistency evaluation of urine protein indicators.

[0005] To achieve the above object, the present invention provides a method for evaluating the consistency of urine protein indicators, comprising: Obtaining a urine sample, centrifuging the urine sample to obtain a centrifuged urine sample, determining a urine protein index of the centrifuged urine sample, and constructing a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyzing the protein detection material of the centrifuged urine sample, and determining a channel receptor of the microfluidic channel; Based on the microfluidic channel, protein detection material and channel receptor, a paper-based microfluidic biochip of the centrifuged urine sample is constructed, a microfluidic driving model of the paper-based microfluidic biochip is constructed, and the microfluidic driving model is used to control the centrifuged urine sample to flow on the paper-based microfluidic biochip to obtain a flowing urine sample; Using the flowing urine sample to react with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain a reaction compound of the flowing urine sample, calculating the molecular charge of the reaction compound, and separating the reaction compound in the corresponding separation area of ​​the microfluidic channel based on the molecular charge to obtain a target analyte; collecting an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculating an image reliability coefficient of the analyte image; When the image reliability coefficient meets the preset reliability threshold, a feature map of the analyte image is constructed, and based on the feature map, the urine protein index content of the urine sample is identified using a trained protein index recognition model, and the consistency of the urine protein index is analyzed through the urine protein index content.

[0006] Optionally, the microfluidic channel for centrifuging the urine sample is constructed based on the urine protein index, comprising: Based on the urine protein index, determining the microfluidic channel structure of the centrifuged urine sample, wherein the microfluidic channel structure includes a reaction structure, a separation structure and a detection structure; Based on the microfluidic channel structure, using the preset Fluent software to create a microfluidic channel geometric model of the centrifuged urine sample; Defining boundary conditions of the microfluidic channel geometric model; Constructing a flow physics model of the microfluidic channel geometric model; Analyzing the flow coefficient of the microfluidic channel geometry model based on the boundary conditions and the flow physics model; Based on the flow coefficient, the microfluidic channel structure is adjusted to obtain the microfluidic channel for the centrifuged urine sample.

[0007] Optionally, analyzing the flow coefficient of the microfluidic channel geometric model based on the boundary conditions and the flow physics model includes: analyzing urine characteristics of the microfluidic channel geometric model corresponding to the centrifuged urine sample; Based on the urine characteristics, constructing a urine simulation fluid of the centrifuged urine sample; Based on the boundary conditions, the Reynolds number of the urine simulation fluid in the microfluidic channel geometry model is calculated using the Reynolds number function corresponding to the flow physics model; wherein the Reynolds number function is: ; in, represents the Reynolds number of urine simulation fluid in the microfluidic channel geometry model, represents the density of urine simulation fluid, represents the average flow rate of urine simulation fluid in the boundary condition, represents the characteristic length corresponding to the boundary condition, that is, the channel diameter of the microfluidic channel, Indicates the dynamic viscosity of urine simulation fluid; Based on the boundary conditions, the resistance coefficient of the urine simulation fluid in the microfluidic channel geometry model is calculated using the Hagen-Poiseuille function corresponding to the flow physics model; wherein the Hagen-Poiseuille function is: ; in, represents the resistance coefficient of urine simulation fluid in the microfluidic channel geometry model, represents the density of urine simulation fluid, represents the average flow rate of urine simulation fluid in the boundary condition, represents the characteristic length corresponding to the boundary condition, that is, the channel diameter of the microfluidic channel, represents the dynamic viscosity of urine simulation fluid, represents the pressure difference of urine simulation fluid in boundary conditions when passing through the channel of microfluidic channel geometry model; Based on the Reynolds number and the resistance coefficient, the flow coefficient of the microfluidic channel geometry model is analyzed.

[0008] Optionally, the paper-based microfluidic biochip for centrifuged urine samples is constructed based on the microfluidic channel, protein detection material and channel receptor, comprising: Determining a channel pattern of the microfluidic channel, and converting the channel pattern into a Gerber format to obtain a channel pattern Gerber file; Covering the protein detection material with photoresist to obtain a photoresist protein detection material; Using a preset photolithography machine to construct the ultraviolet light of the photoresist protein detection material; Calculating the light intensity of the ultraviolet light, and when the light intensity meets the preset light intensity requirement, photolithography the photoresist protein detection material using the ultraviolet light and the channel pattern Gerber file to obtain a photolithography paper-based microfluidic biochip; Developing the photolithographic paper-based microfluidic biochip to obtain a developed paper-based microfluidic biochip; The channel receptor and the developing paper-based microfluidic biochip are integrated to obtain the paper-based microfluidic biochip of the centrifuged urine sample.

[0009] Optionally, the construction of the microfluidic driving model of the paper-based microfluidic biochip comprises: Identifying chip features of the paper-based microfluidic biochip; Determining fluid driving parameters of the fluid dynamic equation based on the chip characteristics; Based on the fluid driving parameters, the fluid driving function of the microfluidic biochip is constructed using the following formula: ; in, represents the derivative, represents the flow velocity vector corresponding to the fluid driving parameter, represents the flow motion time corresponding to the fluid driving parameters, represents the divergence corresponding to the fluid driving parameter, that is, the flux of the velocity field, represents the fluid density corresponding to the fluid driving parameter, which is equal to the density of the urine simulation fluid, represents the fluid stress tensor corresponding to the fluid driving parameters, represents the external force of the fluid corresponding to the fluid driving parameters, represents the fluid pressure corresponding to the fluid driving parameter, Represents the dot product of tensors; Based on the fluid driving function, a microfluidic driving model of the paper-based microfluidic biochip is constructed.

[0010] Optionally, the calculating the molecular charge of the reaction compound comprises: collecting the electrical signal of the reaction compound; extracting electrical signal features of the electrical signal; constructing a signal-charge relationship model of the electrical signal and the reaction compound; Based on the electrical signal characteristics, the molecular charge of the reaction compound is calculated using the signal-charge relationship model.

[0011] Optionally, calculating the image reliability coefficient of the analyte image includes: performing degradation processing on the analyte image to obtain a degraded analyte image; calculating a mean square error between the degraded analyte image and the analyte image; Based on the mean square error and the degraded analyte image, the image PSNR of the analyte image is calculated using the following formula: ; in, represents the image PSNR of the analyte image, represents the analyte image, represents the degraded analyte image corresponding to the analyte image, represents the mean square error between the degraded analyte image and the analyte image, represents the maximum gray value of the analyte image; Based on the image PSNR, an image reliability factor of the analyte image is determined.

[0012] Optionally, when the image reliability coefficient meets a preset reliability threshold, constructing a characteristic graph of the analyte image includes: When the image reliability coefficient meets a preset reliability threshold, filtering the analyte image to obtain a filtered analyte image; Performing multi-scale division on the filtered analyte image to obtain a multi-scale analyte image; Constructing a feature extraction function corresponding to the scale of the multi-scale analyte image; extracting multi-scale features of the multi-scale analyte image based on the feature extraction function; Converting the multi-scale features into an initial feature map; The initial feature map is optimized to obtain the feature map of the multi-scale analyte image.

[0013] Optionally, analyzing the consistency of the urine protein index through the urine protein index content includes: Constructing a control group of the urine protein index content; Identifying the control urine protein index content of the control group; Respectively analyzing the protein statistical list of the urine protein index content and the control urine protein index content and the control protein statistical list; Calculating the coefficient of variation of the protein statistical list and the control protein statistical list; Based on the coefficient of variation, the consistency of the urine protein index was analyzed.

[0014] To achieve the above object, the present invention also provides a device for evaluating the consistency of urine protein indicators, comprising: A microfluidic channel construction module, used for obtaining a urine sample, centrifuging the urine sample to obtain a centrifuged urine sample, determining a urine protein index of the centrifuged urine sample, and constructing a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyzing the protein detection material of the centrifuged urine sample, and determining a channel receptor of the microfluidic channel; A biochip construction module, which is used to construct the paper-based microfluidic biochip of the centrifuged urine sample based on the microfluidic channel, protein detection material and channel receptor, and to construct a microfluidic driving model of the paper-based microfluidic biochip, and to control the centrifuged urine sample to flow on the paper-based microfluidic biochip using the microfluidic driving model to obtain a flowing urine sample; A target analyte acquisition module, used to react the flowing urine sample with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain a reaction compound of the flowing urine sample, calculate the molecular charge of the reaction compound, and separate the reaction compound in the separation area corresponding to the microfluidic channel based on the molecular charge to obtain a target analyte; A reliability coefficient calculation module, used to collect an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculate an image reliability coefficient of the analyte image; The consistency calculation module is used to construct a feature map of the analyte image when the image reliability coefficient meets a preset reliability threshold value, and based on the feature map, use a trained protein index recognition model to identify the urine protein index content of the urine sample, and analyze the consistency of the urine protein index through the urine protein index content.

[0015] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: a memory storing at least one instruction; and The processor executes the instructions stored in the memory to implement the above-mentioned consistency assessment method for urine protein indicators.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned method for consistency assessment of urine protein indicators.

[0017] The present invention can effectively separate different components in the urine sample by centrifuging the urine sample to obtain a centrifuged urine sample, thereby improving the accuracy of urine protein index detection; the present invention constructs a microfluidic channel for the urine sample based on the urine protein index to ensure that the chip can achieve the expected performance in actual operation, thereby improving the accuracy and reliability of detection; the present invention constructs a paper-based microfluidic biochip for the urine sample based on the microfluidic channel, protein detection material and channel receptor, which can achieve rapid and accurate extraction of specific proteins in urine, improve the efficiency of protein index analysis of urine samples, and further, the present invention By constructing the microfluidic drive model of the paper-based microfluidic biochip to describe the flow behavior of the fluid on the microfluidic chip, the reliability of urine protein data is improved. Furthermore, the present invention can provide a basis for the subsequent separation of reaction compounds by calculating the molecular charge of the reaction compound to ensure the separation efficiency and purity of the target analyte. Finally, the present invention can determine the quality of the analyte image by calculating the image reliability coefficient of the analyte image, thereby improving the accuracy of protein index analysis. The present invention analyzes the consistency of the urine protein index through the urine protein index content, which helps to improve the accuracy of urine detection. Therefore, the present invention can improve the accuracy of the consistency evaluation of urine protein index. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a flow chart of a method for evaluating the consistency of urine protein indicators provided by one embodiment of the present invention; Figure 2 A functional module diagram of a device for evaluating consistency of urine protein indicators provided by one embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for evaluating the consistency of urine protein indicators provided by one embodiment of the present invention.

[0019] Description of reference numerals: 100. A consistency evaluation device for urine protein index; 101. Microfluidic channel construction module; 102. Biochip construction module; 103. Target analyte acquisition module; 104. Reliability coefficient calculation module; 105. Consistency calculation module; 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] The embodiment of the present application provides a consistency assessment method for urine protein indicators. The execution subject of the consistency assessment method for urine protein indicators includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the consistency assessment method for urine protein indicators can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Reference Figure 1 FIG. 1 is a flow chart of a method for evaluating the consistency of urine protein indicators provided by an embodiment of the present invention. In this embodiment, the method for evaluating the consistency of urine protein indicators includes: S1. Obtain a urine sample, centrifuge the urine sample to obtain a centrifuged urine sample, determine the urine protein index of the centrifuged urine sample, and based on the urine protein index, construct a microfluidic channel of the centrifuged urine sample, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyze the protein detection material of the centrifuged urine sample, and determine the channel receptor of the microfluidic channel.

[0024] It should be explained that the urine sample refers to the urine discharged from the target body, which is a carrier of various biochemical and physiological indicators and can reflect the health status of the target body. The urine protein index usually refers to the concentration or presence of specific proteins in the urine, such as albumin, transferrin and other proteins.

[0025] The present invention can effectively separate different components in the urine sample by centrifuging the urine sample to obtain a centrifuged urine sample, thereby improving the accuracy of urine protein index detection. The centrifuged urine sample refers to a urine sample that has been centrifuged. In detail, the centrifugation of the urine sample can be achieved by a centrifuge.

[0026] The present invention constructs the microfluidic channel of the urine sample based on the urine protein index to ensure that the chip can achieve the expected performance in actual operation, thereby improving the accuracy and reliability of detection.

[0027] In detail, the microfluidic channel for centrifuging urine samples based on the urine protein index comprises: Based on the urine protein index, determining the microfluidic channel structure of the centrifuged urine sample, wherein the microfluidic channel structure includes a reaction structure, a separation structure and a detection structure; Based on the microfluidic channel structure, using the preset Fluent software to create a microfluidic channel geometric model of the centrifuged urine sample; Defining boundary conditions of the microfluidic channel geometric model; Constructing a flow physics model of the microfluidic channel geometric model; Analyzing the flow coefficient of the microfluidic channel geometry model based on the boundary conditions and the flow physics model; Based on the flow coefficient, the microfluidic channel structure is adjusted to obtain the microfluidic channel for the centrifuged urine sample.

[0028] Wherein, the microfluidic channel structure refers to the channel network designed on the microfluidic chip, including the structure of the reaction area, separation area and detection area. These areas are designed into specific geometric shapes and sizes to control the flow and processing of fluids. The microfluidic channel geometry model refers to a three-dimensional model created in professional software (such as Fluent), which accurately represents the layout of the microfluidic chip and the shape of the channel. The boundary conditions refer to the inlet and outlet conditions defined in the microfluidic channel geometry model, including the flow rate, pressure, temperature, etc. of the fluid, which determine the flow behavior of the fluid in the channel. The flow physics model refers to the physical laws applied in the microfluidic channel geometry model, such as the Navier-Stokes equation, which describe the motion and dynamic characteristics of the fluid in the channel. The flow coefficient refers to various flow parameters obtained by simulation analysis when the fluid flows in the microfluidic channel, such as Reynolds number, friction factor, etc. The reaction area, separation area and detection area of ​​the microfluidic channel refer to the areas in the microfluidic channel used for reaction, separation and detection with centrifuged urine samples.

[0029] Further, the analyzing the flow coefficient of the microfluidic channel geometric model based on the boundary conditions and the flow physics model includes: analyzing urine characteristics of the microfluidic channel geometric model corresponding to the centrifuged urine sample; Based on the urine characteristics, constructing a urine simulation fluid of the centrifuged urine sample; Based on the boundary conditions, the Reynolds number of the urine simulation fluid in the microfluidic channel geometry model is calculated using the Reynolds number function corresponding to the flow physics model; wherein the Reynolds number function is: ; in, represents the Reynolds number of urine simulation fluid in the microfluidic channel geometry model, represents the density of urine simulation fluid, represents the average flow rate of urine simulation fluid in the boundary condition, represents the characteristic length corresponding to the boundary condition, that is, the channel diameter of the microfluidic channel, Indicates the dynamic viscosity of urine simulation fluid; Based on the boundary conditions, the resistance coefficient of the urine simulation fluid in the microfluidic channel geometry model is calculated using the Hagen-Poiseuille function corresponding to the flow physics model; wherein the Hagen-Poiseuille function is: ; in, represents the resistance coefficient of urine simulation fluid in the microfluidic channel geometry model, represents the density of urine simulation fluid, represents the average flow rate of urine simulation fluid in the boundary condition, represents the characteristic length corresponding to the boundary condition, that is, the channel diameter of the microfluidic channel, represents the dynamic viscosity of urine simulation fluid, represents the pressure difference of urine simulation fluid in boundary conditions when passing through the channel of microfluidic channel geometry model; Based on the Reynolds number and the resistance coefficient, the flow coefficient of the microfluidic channel geometry model is analyzed.

[0030] Among them, the urine characteristics refer to the characteristics of urine including its physical and chemical properties, such as density, dynamic viscosity, surface tension, etc., the urine simulation fluid refers to a fluid obtained through experiments or computational simulations based on the characteristics of urine, the Reynolds number function refers to a function used to calculate the Reynolds number of the urine simulation fluid in the microfluidic channel, the Reynolds number refers to a dimensionless number used to describe the flow of fluid, the dynamic viscosity refers to a property of the fluid, which describes the nature of the internal friction inside the fluid, the Hagen-Poiseuille function refers to a function used to calculate the resistance coefficient of the urine simulation fluid in the microfluidic channel, the resistance coefficient refers to a value that describes the flow resistance of the fluid in the microfluidic channel, and the pressure difference refers to the pressure difference caused by the flow of the fluid when the fluid flows in the microfluidic channel.

[0031] It should be explained that the protein detection material refers to the base material of the chip constructed by the microfluidic channel, and the channel receptor refers to the molecules fixed on the microfluidic chip that can specifically recognize the target protein in urine. These receptors are usually antibodies, which can bind to the target protein in urine and trigger the subsequent detection steps.

[0032] S2. Based on the microfluidic channel, protein detection material and channel receptor, a paper-based microfluidic biochip of the centrifuged urine sample is constructed, a microfluidic driving model of the paper-based microfluidic biochip is constructed, and the microfluidic driving model is used to control the flow of the centrifuged urine sample on the paper-based microfluidic biochip to obtain a flowing urine sample.

[0033] The present invention constructs the paper-based microfluidic biochip of the urine sample based on the microfluidic channel, protein detection material and channel receptor, which can realize the rapid and accurate extraction of specific proteins in urine and improve the efficiency of protein index analysis of urine samples.

[0034] In detail, the paper-based microfluidic biochip for centrifuged urine samples is constructed based on the microfluidic channel, protein detection material and channel receptor, including: Determining a channel pattern of the microfluidic channel, and converting the channel pattern into a Gerber format to obtain a channel pattern Gerber file; Covering the protein detection material with photoresist to obtain a photoresist protein detection material; Using a preset photolithography machine to construct the ultraviolet light of the photoresist protein detection material; Calculating the light intensity of the ultraviolet light, and when the light intensity meets the preset light intensity requirement, photolithography the photoresist protein detection material using the ultraviolet light and the channel pattern Gerber file to obtain a photolithography paper-based microfluidic biochip; Developing the photolithographic paper-based microfluidic biochip to obtain a developed paper-based microfluidic biochip; The channel receptor and the developing paper-based microfluidic biochip are integrated to obtain the paper-based microfluidic biochip of the centrifuged urine sample.

[0035] Wherein, the channel pattern refers to the layout of the microfluidic channel designed on the paper material, which defines the area of ​​fluid flow and chemical reaction on the chip, the channel pattern Gerber file refers to an industrial standard file format used to define the coating area of ​​the photoresist, the ultraviolet light refers to an electromagnetic wave with a shorter wavelength, the light intensity refers to the intensity of the ultraviolet light emitted by the photolithography machine, which has a direct impact on the curing effect of the photoresist, and the light intensity requirement refers to the specific intensity that the ultraviolet light must reach during the photolithography process. This helps to ensure the curing effect of the photoresist and the accuracy of the microfluidic channel, the photolithography paper-based microfluidic biochip refers to a chip with a microfluidic channel formed on the paper material by a photolithography process, but has not yet been developed and functionalized, the developed paper-based microfluidic biochip refers to a paper-based chip that removes the uncured photoresist through a development process after photolithography to form a microfluidic channel, and the paper-based microfluidic biochip refers to a microfluidic chip that integrates microfluidic channels, protein detection materials (such as antibodies or receptors) and channel receptors on a paper material.

[0036] The present invention constructs a microfluidic driving model of the paper-based microfluidic biochip to describe the flow behavior of the fluid on the microfluidic chip, thereby improving the reliability of urine protein data.

[0037] In detail, the microfluidic driving model of the paper-based microfluidic biochip is constructed, including: Identifying chip features of the paper-based microfluidic biochip; Determining fluid driving parameters of the fluid dynamic equation based on the chip characteristics; Based on the fluid driving parameters, the fluid driving function of the microfluidic biochip is constructed using the following formula: ; in, represents the derivative, represents the flow velocity vector corresponding to the fluid driving parameter, represents the flow motion time corresponding to the fluid driving parameters, represents the divergence corresponding to the fluid driving parameter, that is, the flux of the velocity field, represents the fluid density corresponding to the fluid driving parameter, which is equal to the density of the urine simulation fluid, represents the fluid stress tensor corresponding to the fluid driving parameters, represents the external force of the fluid corresponding to the fluid driving parameters, represents the fluid pressure corresponding to the fluid driving parameter, Represents the dot product of tensors; Based on the fluid driving function, a microfluidic driving model of the paper-based microfluidic biochip is constructed.

[0038] Among them, the chip feature refers to the characteristics or structural parameters of the paper-based microfluidic biochip, such as the width, length, depth, etc. of the microchannel; the fluid drive parameter refers to the parameter that controls the flow of fluid in the microfluidic channel, such as an external electric field, pressure difference, temperature gradient, etc.; the fluid drive function refers to a mathematical formula or model that describes the movement law of the fluid in the microfluidic system, and may include functions that describe related parameters such as the fluid velocity field, fluid pressure, and fluid stress; the flow velocity vector refers to the magnitude and direction of the velocity of the fluid at a specific position in space, usually expressed as a vector; the divergence refers to an operator that describes the rate of change of velocity in the velocity field, and is used to describe the convergence or divergence of the flow field in fluid dynamics; the fluid stress tensor refers to a tensor that describes the internal stress distribution of the fluid, including components such as shear stress and normal stress; the fluid external force refers to the external force applied to the fluid, which may include electric field force, gravitational force, etc. in a microfluidic chip; the fluid pressure refers to the pressure inside the fluid; the dot product of the tensor refers to the dot multiplication operation of two tensors, which is usually used to calculate the interaction between tensors.

[0039] The present invention uses the microfluidic driving model to control the centrifuged urine sample to flow on the paper-based microfluidic biochip to obtain a flowing urine sample to ensure the flow efficiency and quality of the sample in the chip. In detail, according to the microfluidic driving model, the fluid driving parameters such as flow rate, pressure, etc. are adjusted, and the microfluidic driving model is used to control the flow path and speed of the centrifuged urine sample in the chip to ensure that the sample can effectively flow through various areas in the chip.

[0040] S3. Use the flowing urine sample to react with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain the reaction compounds of the flowing urine sample, calculate the molecular charges of the reaction compounds, and separate the reaction compounds in the corresponding separation area of ​​the microfluidic channel based on the molecular charges to obtain the target analyte.

[0041] The flowing urine sample is reacted with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain the reaction compounds of the flowing urine sample, which can be used as the basis for protein data extraction later. The reaction compounds refer to molecules in the urine sample that bind to the channel receptors on the chip. These molecules can be target proteins, metabolites or other biological molecules that specifically bind to receptors in urine. In detail, the reaction of the flowing urine sample with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip can involve biochemical reactions, such as immune binding, enzymatic reactions, etc.

[0042] For example, albumin in a flowing urine sample binds to a specific antibody on a paper-based microfluidic biochip, and the reaction compound may be an immune complex.

[0043] The present invention can provide a basis for the subsequent separation of the reaction compounds by calculating the molecular charges of the reaction compounds, so as to ensure the separation efficiency and purity of the target analyte.

[0044] In detail, the calculating the molecular charge of the reaction compound comprises: collecting the electrical signal of the reaction compound; extracting electrical signal features of the electrical signal; constructing a signal-charge relationship model of the electrical signal and the reaction compound; Based on the electrical signal characteristics, the molecular charge of the reaction compound is calculated using the signal-charge relationship model.

[0045] Among them, the electrical signal refers to the change of electrical parameters such as current, potential or conductivity caused by the electrochemical reaction of the reaction compound on the electrode surface in the electrochemical detection area of ​​the microfluidic chip. These characteristics may include current size, potential change, etc. The electrical signal characteristics refer to the key parameters extracted from the electrical signal, which are used to distinguish different types of reaction compounds. The signal-charge relationship model refers to the relationship between the electrical signal characteristics and the molecular charge of the reaction compound. The molecular charge refers to the charge distribution on the reaction compound molecule, which determines the charge state of the molecule in the aqueous solution.

[0046] Furthermore, the construction of the signal-charge relationship model of the electrical signal and the reaction compound can be achieved by using a machine learning algorithm or a statistical method to construct a mathematical model between the electrical signal characteristics and the molecular charge.

[0047] The present invention is based on the molecular charge, and the reaction compounds are separated in the separation area corresponding to the microfluidic channel to obtain the target analyte. The target analyte can be separated by utilizing the difference in molecular charge. The separation process can be carried out by fluid dynamics principles, such as capillary action, pumps, etc. The target analyte refers to a specific molecule or compound separated according to the difference in molecular charge during the electrophoretic separation process. The target analyte can be a target protein, metabolite or other biological molecule in a urine sample that binds to a channel receptor on the chip.

[0048] S4. Collect an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculate an image reliability coefficient of the analyte image.

[0049] Collecting the analyte image of the target analyte using the digital microscope corresponding to the detection area can achieve detailed recording and analysis of the morphology, size, distribution and other information of the target analyte. These image data can provide important input for subsequent intelligent analysis models and improve the accuracy of predicting and analyzing the protein content in urine. Among them, the digital microscope refers to a high-resolution microscope that can capture and display very small objects and structures, and the analyte image refers to the image of the target analyte captured by the digital microscope.

[0050] The present invention can determine the quality of the analyte image by calculating the image reliability coefficient of the analyte image, thereby improving the accuracy of protein index analysis.

[0051] In detail, the calculating the image reliability coefficient of the analyte image includes: performing degradation processing on the analyte image to obtain a degraded analyte image; calculating a mean square error between the degraded analyte image and the analyte image; Based on the mean square error and the degraded analyte image, the image PSNR of the analyte image is calculated using the following formula: ; in, represents the image PSNR of the analyte image, represents the analyte image, represents the degraded analyte image corresponding to the analyte image, represents the mean square error between the degraded analyte image and the analyte image, represents the maximum gray value of the analyte image; Based on the image PSNR, an image reliability factor of the analyte image is determined.

[0052] It should be explained that the degraded analyte image refers to an image obtained by simulating the image degradation process in the real world, the mean square error refers to a measure of the difference between the original analyte image and the degraded analyte image, the image PSNR refers to a commonly used objective evaluation index for measuring image quality, namely, the peak signal-to-noise ratio. The higher the PSNR value, the smaller the difference between the degraded analyte image and the original analyte image, and the better the image quality. The maximum grayscale value refers to the upper limit of the pixel value in the image, and the image reliability coefficient refers to a coefficient determined based on the image PSNR and used to evaluate the quality and reliability of image data.

[0053] Furthermore, the degradation processing of the analyte image to obtain the degraded analyte image is achieved by adding noise, blurring, geometric transformation, etc.

[0054] Further, the calculating of the mean square error between the degraded analyte image and the analyte image may be performed by calculating the pixel difference between the degraded analyte image and the analyte image, squaring the pixel difference to obtain the square difference, summing the square difference and dividing the sum by the number of pixels.

[0055] Furthermore, the image reliability coefficient of the analyte image can be determined based on the image PSNR by setting a reliability coefficient level of an image reliability coefficient list and comparing it with the image PSNR. For example, the reliability coefficient level can be , , When the image PSNR is 0.1, the image reliability coefficient is 0.8; when the image PSNR is 0.7, the image reliability coefficient is 0.3.

[0056] S5. When the image reliability coefficient meets a preset reliability threshold, a feature map of the analyte image is constructed. Based on the feature map, the urine protein index content of the urine sample is identified using a trained protein index recognition model. The consistency of the urine protein index is analyzed through the urine protein index content.

[0057] The present invention constructs a feature map of the analyte image as input to the AI ​​model for further analysis and identification when the image reliability coefficient meets a preset reliability threshold. The construction of the feature map needs to consider the quality and reliability of the image to ensure the accuracy and reliability of the model.

[0058] In detail, when the image reliability coefficient meets a preset reliability threshold, constructing a characteristic graph of the analyte image includes: When the image reliability coefficient meets a preset reliability threshold, filtering the analyte image to obtain a filtered analyte image; Performing multi-scale division on the filtered analyte image to obtain a multi-scale analyte image; Constructing a feature extraction function corresponding to the scale of the multi-scale analyte image; extracting multi-scale features of the multi-scale analyte image based on the feature extraction function; Converting the multi-scale features into an initial feature map; The initial feature map is optimized to obtain the feature map of the multi-scale analyte image.

[0059] Among them, the reliable threshold refers to a preset standard used to determine whether the quality of an image is good enough, the filtered analyte image refers to an image obtained by filtering the original analyte image, the multi-scale analyte image refers to a scale hierarchy formed by dividing the filtered analyte image into multiple scales, the feature extraction function refers to an algorithm or model used to extract features from an image, the multi-scale feature refers to a set of features extracted at different scales, the initial feature map refers to a multi-scale feature converted into a matrix form, each feature corresponds to a value, and the feature map refers to a feature map after optimization processing.

[0060] Furthermore, the optimization of the initial feature map can be obtained through methods such as feature selection, feature fusion, and feature space transformation.

[0061] Based on the feature graph, the urine protein index content of the urine sample can be accurately identified and quantified by using a trained protein index recognition model. The protein index recognition model is a machine learning or deep learning model for identifying and quantifying specific protein indexes in urine samples. For example, the SVM model can learn the nonlinear relationship between the feature graph and the protein index content through a large amount of historical feature graph data, and is used to predict new urine samples. The protein index content refers to the concentration of a specific protein index in a urine sample.

[0062] Finally, the present invention helps to improve the accuracy of urine testing by analyzing the consistency of the urine protein index through the content of the urine protein index.

[0063] In detail, analyzing the consistency of the urine protein index through the urine protein index content includes: Constructing a control group of the urine protein index content; Identifying the control urine protein index content of the control group; Respectively analyzing the protein statistical list of the urine protein index content and the control urine protein index content and the control protein statistical list; Calculating the coefficient of variation of the protein statistical list and the control protein statistical list; Based on the coefficient of variation, the consistency of the urine protein index was analyzed.

[0064] Among them, the control group refers to a group of urine samples, which are selected for comparison with the urine samples of the experimental group, the control urine protein index content refers to the actual measured value of the urine protein index in the urine samples of the control group, the protein statistical list is a set of statistical indicators used to describe the distribution characteristics of the urine protein index content, including the mean and standard deviation, the control protein statistical list refers to a list of statistical indicators used to describe the urine protein index content of the control group, the coefficient of variation refers to a coefficient used to measure the degree of variation of a set of data, and the consistency refers to the similarity of statistical characteristics between the urine protein index content of the experimental group and the urine protein index content of the control group.

[0065] The present invention can effectively separate different components in the urine sample by centrifuging the urine sample to obtain a centrifuged urine sample, thereby improving the accuracy of urine protein index detection; the present invention constructs a microfluidic channel for the urine sample based on the urine protein index to ensure that the chip can achieve the expected performance in actual operation, thereby improving the accuracy and reliability of detection; the present invention constructs a paper-based microfluidic biochip for the urine sample based on the microfluidic channel, protein detection material and channel receptor, which can achieve rapid and accurate extraction of specific proteins in urine, improve the efficiency of protein index analysis of urine samples, and further, the present invention By constructing the microfluidic drive model of the paper-based microfluidic biochip to describe the flow behavior of the fluid on the microfluidic chip, the reliability of urine protein data is improved. Furthermore, the present invention can provide a basis for the subsequent separation of reaction compounds by calculating the molecular charge of the reaction compound to ensure the separation efficiency and purity of the target analyte. Finally, the present invention can determine the quality of the analyte image by calculating the image reliability coefficient of the analyte image, thereby improving the accuracy of protein index analysis. The present invention analyzes the consistency of the urine protein index through the urine protein index content, which helps to improve the accuracy of urine detection. Therefore, the present invention can improve the accuracy of the consistency evaluation of urine protein index.

[0066] like Figure 2 , which is a functional module diagram of a device for evaluating consistency of urine protein indicators provided by one embodiment of the present invention.

[0067] The consistency evaluation device 100 for urine protein index of the present invention can be installed in an electronic device. According to the functions to be implemented, the consistency evaluation device 100 for urine protein index can include a microfluidic channel construction module 101, a biochip construction module 102, a target analyte acquisition module 103, a reliability coefficient calculation module 104 and a consistency calculation module 105. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0068] The microfluidic channel construction module 101 is used to obtain a urine sample, centrifuge the urine sample to obtain a centrifuged urine sample, determine the urine protein index of the centrifuged urine sample, and construct a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyze the protein detection material of the centrifuged urine sample, and determine the channel receptor of the microfluidic channel; The biochip construction module 102 is used to construct the paper-based microfluidic biochip of the centrifuged urine sample based on the microfluidic channel, protein detection material and channel receptor, and to construct a microfluidic driving model of the paper-based microfluidic biochip, and to control the centrifuged urine sample to flow on the paper-based microfluidic biochip using the microfluidic driving model to obtain a flowing urine sample; The target analyte acquisition module 103 is used to use the flowing urine sample to react with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain the reaction compound of the flowing urine sample, calculate the molecular charge of the reaction compound, and separate the reaction compound in the separation area corresponding to the microfluidic channel based on the molecular charge to obtain the target analyte; The reliability coefficient calculation module 104 is used to collect an analyte image of the target analyte using the digital microscope corresponding to the detection area, and calculate the image reliability coefficient of the analyte image; The consistency calculation module 105 is used to construct a feature map of the analyte image when the image reliability coefficient meets a preset reliability threshold value, and based on the feature map, use a trained protein index recognition model to identify the urine protein index content of the urine sample, and analyze the consistency of the urine protein index through the urine protein index content.

[0069] In detail, each module in the apparatus 100 for evaluating the consistency of urine protein index in the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the consistency evaluation method for urine protein indicators described in and can produce the same technical effects will not be repeated here.

[0070] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a method for evaluating the consistency of urine protein indicators provided by an embodiment of the present invention.

[0071] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a consistency evaluation method program for urine protein indicators.

[0072] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the consistency evaluation method program for urine protein indicators, but also can be used to temporarily store data that has been output or is to be output.

[0073] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a consistency assessment method program for urine protein indicators, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0074] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0075] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0076] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0077] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0078] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0079] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0080] The consistency evaluation method program for urine protein index stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: Obtaining a urine sample, centrifuging the urine sample to obtain a centrifuged urine sample, determining a urine protein index of the centrifuged urine sample, and constructing a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyzing the protein detection material of the centrifuged urine sample, and determining a channel receptor of the microfluidic channel; Based on the microfluidic channel, protein detection material and channel receptor, a paper-based microfluidic biochip of the centrifuged urine sample is constructed, a microfluidic driving model of the paper-based microfluidic biochip is constructed, and the microfluidic driving model is used to control the centrifuged urine sample to flow on the paper-based microfluidic biochip to obtain a flowing urine sample; Using the flowing urine sample to react with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain a reaction compound of the flowing urine sample, calculating the molecular charge of the reaction compound, and separating the reaction compound in the corresponding separation area of ​​the microfluidic channel based on the molecular charge to obtain a target analyte; collecting an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculating an image reliability coefficient of the analyte image; When the image reliability coefficient meets the preset reliability threshold, a feature map of the analyte image is constructed, and based on the feature map, the urine protein index content of the urine sample is identified using a trained protein index recognition model, and the consistency of the urine protein index is analyzed through the urine protein index content.

[0081] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0082] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0083] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement: Obtaining a urine sample, centrifuging the urine sample to obtain a centrifuged urine sample, determining a urine protein index of the centrifuged urine sample, and constructing a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyzing the protein detection material of the centrifuged urine sample, and determining a channel receptor of the microfluidic channel; Based on the microfluidic channel, protein detection material and channel receptor, a paper-based microfluidic biochip of the centrifuged urine sample is constructed, a microfluidic driving model of the paper-based microfluidic biochip is constructed, and the microfluidic driving model is used to control the centrifuged urine sample to flow on the paper-based microfluidic biochip to obtain a flowing urine sample; Using the flowing urine sample to react with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain a reaction compound of the flowing urine sample, calculating the molecular charge of the reaction compound, and separating the reaction compound in the corresponding separation area of ​​the microfluidic channel based on the molecular charge to obtain a target analyte; collecting an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculating an image reliability coefficient of the analyte image; When the image reliability coefficient meets the preset reliability threshold, a feature map of the analyte image is constructed, and based on the feature map, the urine protein index content of the urine sample is identified using a trained protein index recognition model, and the consistency of the urine protein index is analyzed through the urine protein index content.

[0084] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0085] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0087] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0088] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The second and other words are used to indicate names, but not to indicate any particular order.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for evaluating the consistency of urine protein index, characterized in that: The method comprises: Obtaining a urine sample, centrifuging the urine sample to obtain a centrifuged urine sample, determining a urine protein index of the centrifuged urine sample, and constructing a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyzing the protein detection material of the centrifuged urine sample, and determining a channel receptor of the microfluidic channel; Based on the microfluidic channel, protein detection material and channel receptor, a paper-based microfluidic biochip of the centrifuged urine sample is constructed, a microfluidic driving model of the paper-based microfluidic biochip is constructed, and the microfluidic driving model is used to control the centrifuged urine sample to flow on the paper-based microfluidic biochip to obtain a flowing urine sample; Using the flowing urine sample to react with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain a reaction compound of the flowing urine sample, calculating the molecular charge of the reaction compound, and separating the reaction compound in the corresponding separation area of ​​the microfluidic channel based on the molecular charge to obtain a target analyte; collecting an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculating an image reliability coefficient of the analyte image; When the image reliability coefficient meets the preset reliability threshold, a feature map of the analyte image is constructed, and based on the feature map, the urine protein index content of the urine sample is identified using a trained protein index recognition model, and the consistency of the urine protein index is analyzed through the urine protein index content.

2. The method for evaluating the consistency of urine protein index according to claim 1, characterized in that: The microfluidic channel for centrifuging the urine sample based on the urine protein index comprises: Based on the urine protein index, determining the microfluidic channel structure of the centrifuged urine sample, wherein the microfluidic channel structure includes a reaction structure, a separation structure and a detection structure; Based on the microfluidic channel structure, using the preset Fluent software to create a microfluidic channel geometric model of the centrifuged urine sample; Defining boundary conditions of the microfluidic channel geometric model; Constructing a flow physics model of the microfluidic channel geometric model; Analyzing the flow coefficient of the microfluidic channel geometry model based on the boundary conditions and the flow physics model; Based on the flow coefficient, the microfluidic channel structure is adjusted to obtain the microfluidic channel for the centrifuged urine sample.

3. The method for evaluating the consistency of urine protein index according to claim 2, characterized in that: The step of analyzing the flow coefficient of the microfluidic channel geometric model based on the boundary conditions and the flow physics model includes: analyzing urine characteristics of the microfluidic channel geometric model corresponding to the centrifuged urine sample; Based on the urine characteristics, constructing a urine simulation fluid of the centrifuged urine sample; Based on the boundary conditions, the Reynolds number of the urine simulation fluid in the microfluidic channel geometry model is calculated using the Reynolds number function corresponding to the flow physics model; wherein the Reynolds number function is: ； in, represents the Reynolds number of urine simulation fluid in the microfluidic channel geometry model, represents the density of urine simulation fluid, represents the average flow rate of urine simulation fluid in the boundary condition, represents the characteristic length corresponding to the boundary condition, that is, the channel diameter of the microfluidic channel, Indicates the dynamic viscosity of urine simulation fluid; Based on the boundary conditions, the resistance coefficient of the urine simulation fluid in the microfluidic channel geometry model is calculated using the Hagen-Poiseuille function corresponding to the flow physics model; wherein the Hagen-Poiseuille function is: ； in, represents the resistance coefficient of urine simulation fluid in the microfluidic channel geometry model, represents the density of urine simulation fluid, represents the average flow rate of urine simulation fluid in the boundary condition, represents the characteristic length corresponding to the boundary condition, that is, the channel diameter of the microfluidic channel, represents the dynamic viscosity of urine simulation fluid, represents the pressure difference of urine simulation fluid in boundary conditions when passing through the channel of microfluidic channel geometry model; Based on the Reynolds number and the resistance coefficient, the flow coefficient of the microfluidic channel geometry model is analyzed.

4. The method for evaluating the consistency of urine protein index according to claim 3, characterized in that: The paper-based microfluidic biochip for centrifuged urine samples is constructed based on the microfluidic channel, protein detection material and channel receptor, comprising: Determining a channel pattern of the microfluidic channel, and converting the channel pattern into a Gerber format to obtain a channel pattern Gerber file; Covering the protein detection material with photoresist to obtain a photoresist protein detection material; Using a preset photolithography machine to construct the ultraviolet light of the photoresist protein detection material; Calculating the light intensity of the ultraviolet light, and when the light intensity meets the preset light intensity requirement, photolithography the photoresist protein detection material using the ultraviolet light and the channel pattern Gerber file to obtain a photolithography paper-based microfluidic biochip; Developing the photolithographic paper-based microfluidic biochip to obtain a developed paper-based microfluidic biochip; The channel receptor and the developing paper-based microfluidic biochip are integrated to obtain the paper-based microfluidic biochip of the centrifuged urine sample.

5. The method for evaluating the consistency of urine protein index according to claim 4, characterized in that: The microfluidic driving model of the paper-based microfluidic biochip is constructed, comprising: Identifying chip features of the paper-based microfluidic biochip; Determining fluid driving parameters of the fluid dynamic equation based on the chip characteristics; Based on the fluid driving parameters, the fluid driving function of the microfluidic biochip is constructed using the following formula: ； in, represents the derivative, represents the flow velocity vector corresponding to the fluid driving parameter, represents the flow motion time corresponding to the fluid driving parameters, represents the divergence corresponding to the fluid driving parameter, that is, the flux of the velocity field, represents the fluid density corresponding to the fluid driving parameter, which is equal to the density of the urine simulation fluid, represents the fluid stress tensor corresponding to the fluid driving parameters, represents the external force of the fluid corresponding to the fluid driving parameters, represents the fluid pressure corresponding to the fluid driving parameter, Represents the dot product of tensors; Based on the fluid driving function, a microfluidic driving model of the paper-based microfluidic biochip is constructed.

6. The method for evaluating the consistency of urine protein index according to claim 5, characterized in that: The calculating the molecular charge of the reaction compound comprises: collecting the electrical signal of the reaction compound; extracting electrical signal features of the electrical signal; constructing a signal-charge relationship model of the electrical signal and the reaction compound; Based on the electrical signal characteristics, the molecular charge of the reaction compound is calculated using the signal-charge relationship model.

7. The method for evaluating the consistency of urine protein index according to claim 6, characterized in that: The calculating the image reliability coefficient of the analyte image comprises: performing degradation processing on the analyte image to obtain a degraded analyte image; calculating a mean square error between the degraded analyte image and the analyte image; Based on the mean square error and the degraded analyte image, the image PSNR of the analyte image is calculated using the following formula: ； in, represents the image PSNR of the analyte image, represents the analyte image, represents the degraded analyte image corresponding to the analyte image, represents the mean square error between the degraded analyte image and the analyte image, represents the maximum gray value of the analyte image; Based on the image PSNR, an image reliability factor of the analyte image is determined.

8. The method for evaluating the consistency of urine protein index according to claim 7, characterized in that: When the image reliability coefficient meets a preset reliability threshold, constructing a characteristic graph of the analyte image includes: When the image reliability coefficient meets a preset reliability threshold, filtering the analyte image to obtain a filtered analyte image; Performing multi-scale division on the filtered analyte image to obtain a multi-scale analyte image; Constructing a feature extraction function corresponding to the scale of the multi-scale analyte image; extracting multi-scale features of the multi-scale analyte image based on the feature extraction function; Converting the multi-scale features into an initial feature map; The initial feature map is optimized to obtain the feature map of the multi-scale analyte image.

9. The method for evaluating the consistency of urine protein index according to claim 8, characterized in that: The analysis of the consistency of the urine protein index by the urine protein index content includes: Constructing a control group of the urine protein index content; Identifying the control urine protein index content of the control group; Respectively analyzing the protein statistical list of the urine protein index content and the control urine protein index content and the control protein statistical list; Calculating the coefficient of variation of the protein statistical list and the control protein statistical list; Based on the coefficient of variation, the consistency of the urine protein index was analyzed.

10. A consistency assessment device for urine protein index, characterized in that: The device comprises: A microfluidic channel construction module, used for obtaining a urine sample, centrifuging the urine sample to obtain a centrifuged urine sample, determining a urine protein index of the centrifuged urine sample, and constructing a microfluidic channel of the centrifuged urine sample based on the urine protein index, wherein the microfluidic channel includes a reaction area, a separation area, and a detection area, analyzing the protein detection material of the centrifuged urine sample, and determining a channel receptor of the microfluidic channel; A biochip construction module, which is used to construct the paper-based microfluidic biochip of the centrifuged urine sample based on the microfluidic channel, protein detection material and channel receptor, and to construct a microfluidic driving model of the paper-based microfluidic biochip, and to control the centrifuged urine sample to flow on the paper-based microfluidic biochip using the microfluidic driving model to obtain a flowing urine sample; A target analyte acquisition module, used to react the flowing urine sample with the channel receptors in the corresponding reaction area of ​​the paper-based microfluidic biochip to obtain a reaction compound of the flowing urine sample, calculate the molecular charge of the reaction compound, and separate the reaction compound in the separation area corresponding to the microfluidic channel based on the molecular charge to obtain a target analyte; A reliability coefficient calculation module, used to collect an analyte image of the target analyte using a digital microscope corresponding to the detection area, and calculate an image reliability coefficient of the analyte image; The consistency calculation module is used to construct a feature map of the analyte image when the image reliability coefficient meets a preset reliability threshold value, and based on the feature map, use a trained protein index recognition model to identify the urine protein index content of the urine sample, and analyze the consistency of the urine protein index through the urine protein index content.

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