Closed-loop controlled digital microfluidics system and method thereof

Abstract

The application discloses a kind of closed loop control digital microfluidic system and method thereof, it is related to microfluidic technical field, including, initialization digital microfluidic chip electrode array, set electrode spacing, electrode size and voltage upper limit, and collect empty load state signal, generate initial reference data;Droplet is formed in the predetermined position of digital microfluidic chip, and according to initial reference data, collect droplet initial position, volume and morphological information, generate droplet initial state data set;Droplet initial state data set is input after pre-training closed loop prediction model, carries out dynamics analysis and trajectory prediction calculation, generates droplet expected motion trajectory, and compares with droplet real-time state data flow, generates droplet deviation feedback data;According to droplet deviation feedback data, dynamically adjust preliminary driving voltage parameter, generate closed loop driving voltage sequence.The application improves droplet trajectory control precision, reduces environmental disturbance and the motion error brought by electrode response difference.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of microfluidic technology, and particularly to a closed-loop control digital microfluidic system and method thereof. BACKGROUND

[0002] Microfluidic technology is a cutting-edge technology that precisely manipulates fluid at a microscale, widely used in biological analysis, chemical reactions, drug screening, and laboratory automation. With the development of digital microfluidic chips, electrode array-driven droplet manipulation has become a key method for high-throughput and multifunctional droplet processing. Existing digital microfluidic methods typically rely on pre-set voltage sequences to drive droplets on the chip surface for distribution, mixing, or transfer, achieving experimental operations through droplet generation, movement, and merging.

[0003] Existing digital microfluidic technology still has room for optimization in droplet generation, trajectory prediction, and state feedback. In the initial stage of droplet generation, the non-uniformity of droplet volume, shape, and initial position directly affects the stability of the subsequent movement trajectory, while the open-loop control method is difficult to calibrate deviations in time, leading to a decrease in droplet operation precision. In multi-droplet parallel operation or complex path planning, the lack of systematic and closed-loop prediction and adjustment mechanisms limits the scalability and automation level of droplet operation. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a closed-loop control digital microfluidic method to solve the problems of insufficient droplet movement precision and real-time adjustment difficulty.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a closed-loop control digital microfluidic method, comprising,

[0008] Initializing the electrode array of the digital microfluidic chip, setting the electrode spacing, electrode size, and voltage upper limit, and collecting the empty load state signal to generate initial reference data;

[0009] Forming a droplet at a predetermined position on the digital microfluidic chip, and collecting the initial position, volume, and shape information of the droplet according to the initial reference data to generate a droplet initial state data set;

[0010] Inputting the droplet initial state data set into a pre-trained closed-loop prediction model for dynamic analysis and trajectory prediction calculation to generate a droplet expected movement trajectory, and comparing it with the real-time state data stream of the droplet to generate droplet deviation feedback data;

[0011] According to the droplet deviation feedback data, the preliminary driving voltage parameters are dynamically adjusted to generate a closed-loop driving voltage sequence;

[0012] The closed-loop driving voltage sequence is applied to the electrode array, real-time droplet state data is collected, droplet operation instructions are generated, and integration is performed to form a global droplet state matrix;

[0013] According to the global droplet state matrix, the droplet operation is recorded, and a droplet operation record file is generated.

[0014] As a preferred scheme of the closed-loop control digital microfluidic method of the application, wherein: the initial reference data is generated by the following specific steps,

[0015] Read the digital microfluidic chip model and electrode array design parameters, calculate the electrode spacing, size and material characteristics, and generate electrode layout configuration data;

[0016] Load the electrode layout configuration data to the controller, set the initial voltage upper limit and apply a low amplitude test voltage, and collect the no-load state sensor signal to generate no-load reference data;

[0017] According to the no-load reference data, calculate the local response of each electrode and the difference of the dielectric layer, generate the electrode calibration parameters, and integrate with the voltage distribution table to generate the initial reference data.

[0018] As a preferred scheme of the closed-loop control digital microfluidic method of the application, wherein: the initial droplet state data set is generated by the following specific steps,

[0019] According to the initial reference data, calculate the target position of the droplet generated on the digital microfluidic chip and the initial voltage configuration to obtain the droplet generation control instruction;

[0020] According to the droplet generation control instruction, a voltage pulse is applied at the predetermined position of the digital microfluidic chip to form a droplet, and at the same time, real-time position, volume and shape sensor signals during the droplet formation process are collected to generate preliminary droplet formation data;

[0021] The preliminary droplet formation data is analyzed, the actual position, volume and shape information of the droplet are extracted, and integration is performed to generate the initial droplet state data.

[0022] As a preferred scheme of the closed-loop control digital microfluidic method of the application, wherein: the pre-trained closed-loop prediction model is constructed based on the initial droplet state data set, historical droplet motion data and electrode calibration parameters through dynamics analysis and trajectory prediction algorithm, and is pre-trained through the historical droplet motion data.

[0023] As a preferred scheme of the closed-loop control digital microfluidic method, the method comprises the following steps:

[0024] The droplet initial state data set is input into the pre-trained closed-loop prediction model to establish a droplet dynamics prediction environment.

[0025] In the droplet dynamics prediction environment, the droplet initial state data set is subjected to dynamics analysis to generate preliminary driving voltage parameters.

[0026] The preliminary driving voltage parameters are subjected to trajectory prediction calculation to generate a droplet expected motion trajectory.

[0027] As a preferred scheme of the closed-loop control digital microfluidic method, the method comprises the following steps:

[0028] Real-time acquisition of droplet current position, volume and morphology information generates a droplet real-time state data stream.

[0029] The droplet real-time state data stream is compared with the droplet expected motion trajectory to extract the deviation of actual droplet position, volume and morphology from the expected trajectory, and to integrate droplet trajectory deviation data.

[0030] According to the droplet trajectory deviation data, the deviation amount and deviation direction of the droplet are calculated to generate droplet deviation feedback data.

[0031] As a preferred scheme of the closed-loop control digital microfluidic method, the method comprises the following steps:

[0032] The droplet deviation feedback data is subjected to deviation amount and deviation direction analysis to generate feedback analysis data.

[0033] According to the feedback analysis data, a voltage adjustment algorithm is adopted to calculate the correction value of the preliminary driving voltage parameters to generate a voltage adjustment scheme.

[0034] According to the voltage adjustment scheme, the preliminary driving voltage parameters are dynamically modified to generate a closed-loop driving voltage sequence.

[0035] As a preferred scheme of the closed-loop control digital microfluidic method, the method comprises the following steps:

[0036] The closed-loop driving voltage sequence is loaded to the electrode array to analyze the voltage amplitude, pulse timing and corresponding application sequence of each electrode to generate electrode driving task instructions.

[0037] According to the electrode driving task instruction, a closed-loop driving voltage is applied to the electrode array, and the droplet position, volume and shape are collected in real time to generate a droplet state data stream;

[0038] The droplet state data stream is analyzed and integrated to generate droplet operation instructions, and the state of each droplet is summarized to form a global droplet state matrix.

[0039] As a preferred scheme of the closed-loop control digital microfluidic method, the droplet operation record file is generated, and the specific steps are as follows,

[0040] According to the global droplet state matrix, the operation events of each droplet are numbered, classified and time-labeled to generate droplet operation log entries;

[0041] The droplet operation log entries are summarized, formatted and saved to generate a droplet operation record file.

[0042] In a second aspect, the present application provides a closed-loop control digital microfluidic system, comprising,

[0043] The electrode initialization module is used for initializing the electrode array of the digital microfluidic chip, setting the electrode spacing, electrode size and voltage upper limit, and collecting the empty load state signal to generate initial reference data;

[0044] The droplet generation module is used for forming droplets at a predetermined position of the digital microfluidic chip, and collecting the initial position, volume and shape information of the droplets according to the initial reference data to generate a droplet initial state data set;

[0045] The trajectory prediction module is used for inputting the droplet initial state data set into a pre-trained closed-loop prediction model to perform dynamic analysis and trajectory prediction calculation, generating a droplet expected motion trajectory, and comparing it with the droplet real-time state data stream to generate droplet deviation feedback data;

[0046] The voltage optimization module is used for dynamically adjusting the preliminary driving voltage parameters according to the droplet deviation feedback data to generate a closed-loop driving voltage sequence;

[0047] The state control module is used for applying the closed-loop driving voltage sequence to the electrode array, collecting the droplet state data in real time, generating droplet operation instructions, and integrating them to form a global droplet state matrix;

[0048] The record generation module is used for recording the droplet operation according to the global droplet state matrix to generate a droplet operation record file.

[0049] The present application has the beneficial effects that: through deviation amount and deviation direction analysis on droplet deviation feedback data, and dynamic modification of the preliminary driving voltage parameter by using voltage adjustment algorithm, a closed-loop driving voltage sequence is generated, real-time closed-loop regulation and control in the droplet movement process is realized, self-adaptive deviation correction can be performed immediately when the droplet deviates from the expected trajectory, the droplet is ensured to move stably along the planned path, so that the droplet trajectory control precision is improved, the motion error caused by environmental disturbance and electrode response difference is reduced, and the droplet operation process has the effects of high precision, dynamic adjustment and high repeatability. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Fig. 1 Flow chart of the closed-loop control digital microfluidic method.

[0052] Fig. 2 Schematic diagram of the closed-loop control digital microfluidic system.

[0053] Fig. 3 Flow chart for generating the initial state data set of the droplet.

[0054] Fig. 4 Flow chart for generating the closed-loop driving voltage sequence. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0056] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0057] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment that excludes other embodiments.

[0058] REFERENCE Figs. 1-4For an embodiment of the present application, the embodiment provides a closed-loop control digital microfluidic method, comprising the following steps:

[0059] S1, initialize the digital microfluidic chip electrode array, set the electrode spacing, electrode size and voltage upper limit, and collect the empty load state signal to generate the initial reference data.

[0060] S1.1, read the digital microfluidic chip model and electrode array design parameters, calculate the electrode spacing, size and material properties, and generate electrode layout configuration data.

[0061] Specifically, the digital microfluidic chip model and electrode array design parameters are read, and parameters such as the number of electrodes, array row and column number, unit spacing, allowable minimum feature size, dielectric layer thickness and material type are parsed and extracted; using a unit-by-unit coordinate calculation method, the array row and column number is used as an index to calculate the row number and column number of each electrode in the array, and the center coordinates of adjacent electrodes are calculated according to the unit spacing; using a geometric quantization method based on adjacent units, the width and length of the electrode are determined according to the allowable minimum feature size and the space ratio (for example, when the space ratio is 0.6, the electrode width is the allowable minimum feature size x 0.6), and the electrode layer thickness and the conductive layer thickness are calculated combined with the dielectric layer thickness and the material type (for example, the conductive layer thickness is 200 nanometers); the center coordinates, width, length, height and material properties of each electrode are calculated and recorded by field, and the records are combined to generate electrode layout configuration data.

[0062] It should also be noted that the unit-by-unit coordinate calculation method refers to using the row number and column number of the electrode in the array as an index to calculate the center coordinates of each electrode one by one; the adjacent unit geometric quantization method refers to determining the geometric dimensions (such as width and length) of adjacent electrodes to specific values through quantization calculation according to the electrode spacing and the allowable minimum feature size.

[0063] S1.2, load the electrode layout configuration data to the controller, set the initial voltage upper limit and apply a low amplitude test voltage, while collecting the empty load state sensor signal to generate the empty load reference data.

[0064] Specifically, after loading the electrode layout configuration data into the controller, the electrode coordinates, area parameters and connection modes in the electrode layout configuration data are read in sequence according to the electrode serial numbers, and the initial voltage upper limit is set in the controller. A low-amplitude test voltage, for example, 0.5V, is applied to each electrode according to the electrode serial numbers, and the voltage quantization value, current quantization value and impedance quantization value of the idle state sensor signal are obtained by synchronously calling the sampling instruction of the controller during the application of the low-amplitude test voltage. After the application of the low-amplitude test voltage to all electrodes is completed, the corresponding voltage quantization value, current quantization value and impedance quantization value of the idle state sensor signal are arranged according to the electrode serial numbers to generate idle reference data.

[0065] S1.3, according to the idle reference data, calculating the local response of each electrode and the difference of the dielectric layer, generating the electrode calibration parameter, and integrating with the voltage distribution table to generate the initial reference data.

[0066] Specifically, according to the idle reference data, the voltage quantization value, current quantization value and impedance quantization value in the idle reference data are read in sequence according to the electrode serial numbers. The idle response characteristics of each electrode under the same low-amplitude test voltage are used to obtain the local response of each electrode by the difference ratio method, for example, the difference value of the impedance quantization value of adjacent electrodes as the local response quantization result. The local response of each electrode and the dielectric layer thickness quantization value and material parameter quantization value recorded in the electrode layout configuration data are compared, the dielectric layer difference quantization value is calculated according to the dielectric layer difference evaluation rule, and the local response quantization value and the dielectric layer difference quantization value of each electrode are combined to form the electrode calibration parameter. After generating the electrode calibration parameter, the target voltage quantization value in the voltage distribution table is read according to the electrode serial number, the local response quantization value and the dielectric layer difference quantization value in the electrode calibration parameter are superimposed to the target voltage quantization value to generate the adjusted voltage quantization value, and all the adjusted voltage quantization values are arranged according to the electrode serial numbers to generate the initial reference data.

[0067] It should also be noted that the difference ratio method is to calculate the signal difference value of the idle response of adjacent electrodes, and to perform ratio operation on the signal difference value and the reference signal, so as to quantify the response difference between adjacent electrodes to obtain the local calibration parameter of each electrode, which is convenient for subsequent voltage adjustment and accurate control of droplet movement.

[0068] The dielectric layer difference evaluation rule is to compare the response signals of each electrode in the idle state, combine the material dielectric constant and layer thickness information, and quantitatively evaluate the characteristics of the dielectric layer covered by different electrodes, so as to determine the influence of the dielectric layer difference on the local response of the electrode, which is used to generate the electrode calibration parameter to ensure the accuracy of voltage application and the controllability of droplet movement.

[0069] The voltage distribution table is a voltage mapping table prepared in advance according to the chip design and the target droplet operation process, and is used to distribute a basic driving voltage and an application time parameter to each electrode, and can be obtained through experimental calibration or simulation calculation.

[0070] S2. Forming a droplet at a predetermined position of the digital microfluidic chip, and collecting initial position, volume and morphology information of the droplet according to initial reference data to generate a droplet initial state data set.

[0071] S2.1. Calculating a target position of droplet generation on the digital microfluidic chip and an initial voltage configuration according to the initial reference data to obtain a droplet generation control instruction.

[0072] Specifically, according to the electrode layout configuration data, the electrode calibration parameters and the no-load response information in the initial reference data, the target coordinate mapping method is used to calculate the coordinates of the droplet generation position of the digital microfluidic chip unit by unit, and the initial voltage configuration corresponding to each droplet is calculated in combination with the droplet volume and the generation sequence, and the droplet generation control instruction is integrated, and the voltage amplitude and the action time length are exemplarily allocated to each target position.

[0073] It should be noted that the target coordinate mapping method refers to a method of mapping the target spatial coordinates of each droplet to the corresponding electrode array electrode according to the predetermined droplet generation position on the digital microfluidic chip, and allocating the corresponding voltage amplitude and action time length to each electrode array electrode by considering the electrode geometric configuration, electrode spacing and droplet volume, so as to form the droplet generation control instruction.

[0074] The droplet volume and the generation sequence refer to setting a specific volume size for each droplet on the digital microfluidic chip to ensure that the droplets are generated according to the path and functional requirements, and determining the sequence of the droplets in the generation process, wherein the droplet volume is used to calculate the amplitude and duration of the required applied voltage, and the generation sequence is used to arrange the time sequence of the electrode driving, so as to ensure that the droplets are formed in sequence according to the target position and do not interfere with each other.

[0075] S2.2. According to the droplet generation control instruction, a voltage pulse is applied at the predetermined position of the digital microfluidic chip to form a droplet, and at the same time, real-time collection of position, volume and morphology sensor signals in the droplet formation process is performed to generate a droplet formation preliminary data.

[0076] Specifically, according to the droplet generation control instruction, a voltage pulse with a set amplitude and duration is applied to the electrode array electrode at the predetermined position of the digital microfluidic chip, for example, a voltage pulse of 5 volts and 20 milliseconds is applied, so that the droplet is separated from the liquid storage area and generated at the target position, while the position information, volume change and morphological feature signal of the droplet in the generation process are collected in real time by using the position sensor, volume sensor and morphological sensor, and integrated in time sequence to generate droplet formation preliminary data, which includes droplet image frames collected by high-speed imaging and analyzed position information / volume / morphological features.

[0077] It should also be noted that the electrode array electrode at the predetermined position refers to a specific electrode arranged according to the droplet operation plan on the digital microfluidic chip for applying voltage to generate or move the droplet, each electrode has a determined spatial coordinate and electrical connection for accurately controlling the formation position and initial motion direction of the droplet on the chip.

[0078] The voltage pulse with a set amplitude and duration refers to an electrical pulse signal with a determined voltage size (for example, in volts) and duration length (for example, in milliseconds) applied to a specific electrode of the digital microfluidic chip, which is used to drive the droplet to form or move at the predetermined position, the amplitude controls the force on the droplet, and the duration determines the time of the force on the droplet, thereby realizing accurate generation and motion control of the droplet.

[0079] S2.3, analyze the droplet formation preliminary data, extract the actual position, volume and morphological information of the droplet, and integrate to generate droplet initial state data.

[0080] Specifically, by using image processing method, the position information of the droplet formation preliminary data is analyzed by coordinates to extract the actual position of each droplet on the chip; the volume change of the droplet formation preliminary data is calculated to obtain the actual volume of the droplet; the morphological feature signal of the droplet formation preliminary data is identified by contour and the morphological feature is extracted to obtain the actual morphology of the droplet; the actual position, volume and morphological information of the droplet are integrated to generate the droplet initial state data.

[0081] It should also be noted that the image processing method refers to a method of obtaining the spatial position, size, morphology or other characteristic information of the target object by preprocessing, segmentation, feature extraction and analysis of the collected digital image.

[0082] S3, input the droplet initial state data set into the pre-trained closed-loop prediction model, perform dynamics analysis and trajectory prediction calculation to generate the expected motion trajectory of the droplet, and compare it with the real-time state data stream of the droplet to generate droplet deviation feedback data.

[0083] S3.1, the pre-trained closed-loop prediction model is based on the droplet initial state data set, historical droplet motion data and electrode calibration parameters, constructed through dynamic analysis and trajectory prediction algorithm, and obtained through pre-training of historical droplet motion data.

[0084] Specifically, the droplet initial state data set, historical droplet motion data and electrode calibration parameters are standardized, and the data format and dimension are unified; based on the droplet dynamics analysis method, the trajectory analysis and motion feature extraction of the historical droplet motion data are carried out to form the training sample set; the trajectory prediction algorithm is used to learn the training sample set, the droplet motion prediction relationship is constructed, and the prediction parameters are adjusted through iteration to minimize the deviation between the actual motion and the expected trajectory; the prediction parameters obtained by training, the droplet initial state data set and the electrode calibration parameters are combined to form the pre-trained closed-loop prediction model, which is used for subsequent droplet dynamics analysis and trajectory prediction.

[0085] It should also be noted that the historical droplet motion data refers to the motion information set of the droplet collected and recorded in real time during the past operation on the digital microfluidic chip, including the specific position coordinates, volume change and morphological characteristics of the droplet at different time points, which is used to reflect the motion law and response characteristics of the droplet under the action of different electrodes, and can provide reference for dynamics analysis and trajectory prediction;

[0086] The droplet dynamics analysis method refers to a method for modeling and calculating the motion law of the droplet driven by the electrode on the microfluidic chip based on the droplet initial state data set, historical droplet motion data and electrode calibration parameters, by considering the droplet position, volume, morphological change and electrode electric field effect, the motion trend and dynamic response characteristics of the droplet are obtained;

[0087] The trajectory prediction algorithm refers to a method for numerically predicting and calculating the motion path of the droplet in the future time period based on the historical droplet motion law and the current initial state, generating the expected motion trajectory of the droplet, and providing reference for closed-loop control.

[0088] S3.2, input the droplet initial state data set into the pre-trained closed-loop prediction model to establish the droplet dynamics prediction environment.

[0089] Specifically, the droplet initial state data set is loaded into the pre-trained closed-loop prediction model one by one according to the position, volume and morphological information of each droplet, the historical droplet motion data and electrode calibration parameters are read, the motion response of the droplet under the action of the electrode is calculated according to the droplet dynamics analysis method, the initial state of each droplet and the historical motion law are mapped into the droplet dynamics prediction environment, the droplet dynamics prediction environment is established, for example, each droplet is assigned with corresponding electrode force parameters and time step, forming an initial calculation environment that can be used for trajectory prediction.

[0090] It should also be noted that the droplet dynamics analysis method refers to a calculation method combining fluid mechanics and electro-dynamics based on the initial state data set of the droplet, the physical properties of the droplet and the electrode calibration parameters, to model and simulate the motion law of the droplet driven by the electric field on the digital microfluidic chip. Specifically, it includes: calculating the position, velocity and shape change of the droplet under the action of driving force, surface tension and viscous resistance generated by the voltage applied by each electrode, iteratively updating the droplet state step by step, predicting the dynamic response of the droplet on the microfluidic channel or open surface, thereby providing accurate dynamic basis for subsequent trajectory prediction and closed-loop control.

[0091] S3.3, in the droplet dynamics prediction environment, the initial state data set of the droplet is analyzed dynamically to generate preliminary driving voltage parameters.

[0092] Specifically, in the droplet dynamics prediction environment, the initial position, volume and shape information of each droplet in the initial state data set of the droplet are calculated by using the droplet dynamics analysis method, combined with the electrode calibration parameters and historical droplet motion data, the force situation of the droplet under the action of the voltage applied by each electrode is simulated step by step, including electric driving force, surface tension and viscous resistance, and the droplet position and shape are updated, the dynamic response of the droplet along the predetermined path is calculated in turn, and finally the preliminary driving voltage parameters capable of driving the droplet to move along the expected trajectory are determined, such as the voltage amplitude and application sequence of each electrode.

[0093] It should also be noted that the droplet dynamics prediction environment refers to a simulation calculation environment constructed by the dynamic analysis method and the trajectory prediction algorithm based on the initial state data set of the droplet, the historical droplet motion data and the electrode calibration parameters, which is used to simulate the motion behavior and shape change of the droplet under the action of different electrode voltages, and can predict and verify the expected motion trajectory of the droplet under virtual conditions, providing bias analysis basis and driving voltage adjustment reference for closed-loop control.

[0094] S3.4, trajectory prediction calculation is performed on the preliminary driving voltage parameters to generate the expected motion trajectory of the droplet.

[0095] Specifically, in the droplet dynamics prediction environment, the trajectory prediction algorithm is used to apply the preliminary driving voltage parameters to each droplet in the initial state data set of the droplet in turn, calculate the position change and shape evolution of the droplet under the action of the voltage of each electrode according to the time step, combine the droplet dynamics analysis result, perform interpolation calculation and trajectory reconstruction on the future motion path of the droplet, generate the expected motion trajectory of the droplet, including the spatial coordinates, volume and shape state of each droplet at each time point, such as recording the droplet position change curve and volume change curve at millisecond level time interval.

[0096] It should also be noted that the trajectory prediction algorithm refers to a method for calculating and deducing the future movement path of the droplet on the microfluidic chip using the initial state data set of the droplet, the historical droplet movement data and the electrode calibration parameters. It usually includes steps such as time series analysis, dynamic model solving and numerical integration, and is used to generate trajectory data of the expected position, volume and morphology of the droplet changing with time, to provide reference and deviation feedback basis for closed-loop control.

[0097] S3.5, real-time acquisition of droplet current position, volume and morphology information, and generation of droplet real-time state data stream.

[0098] Specifically, a high-speed image acquisition method is used to continuously photograph the current position of the droplet on the digital microfluidic chip, and image processing is performed on each frame of image to extract the spatial coordinate position of the droplet, the volume size of the droplet and the morphology characteristics of the droplet. The droplet information of each frame is integrated in time sequence to generate continuous time sequence data containing the position, volume and morphology of the droplet, and a droplet real-time state data stream is generated.

[0099] It should also be noted that the high-speed image acquisition method refers to continuously acquiring the movement image of the droplet on the microfluidic chip by a high-speed camera or a high-speed imaging machine at a high frame rate (for example, thousands of frames per second or higher), and storing each frame of image in a digital form, so as to ensure that the small position, volume and morphology changes of the droplet in a short time can be captured, and the whole process of the droplet movement can be continuously and accurately recorded.

[0100] S3.6, comparing the droplet real-time state data stream with the droplet expected movement trajectory, extracting the deviation of the actual droplet position, volume and morphology from the expected trajectory, and integrating to generate droplet trajectory deviation data.

[0101] Specifically, the Euclidean distance calculation method is used to compare each droplet position point in the droplet real-time state data stream with the position of the corresponding time point of the droplet expected movement trajectory point by point, calculate the position deviation vector, at the same time, difference operation is performed on the droplet real-time volume and the droplet expected volume, extract the volume deviation, difference calculation is performed on the droplet real-time morphology characteristics (such as aspect ratio and roundness) and the droplet expected morphology characteristics, extract the morphology deviation, and integrate the position deviation, volume deviation and morphology deviation in time sequence to generate droplet trajectory deviation data. Each droplet at each time point contains position deviation, volume deviation and morphology deviation record.

[0102] S3.7, according to the droplet trajectory deviation data, calculating the deviation amount and deviation direction of the droplet, and generating droplet deviation feedback data.

[0103] Specifically, based on the droplet trajectory deviation data, the Euclidean distance calculation method is used to calculate the square root of the sum of the squares of the coordinate differences between the actual position and the expected position of each droplet to obtain the droplet deviation amount. The droplet deviation direction is determined by vector direction calculation. Combining the droplet deviation amount and deviation direction information, droplet deviation feedback data is generated, such as recording the deviation amount and corresponding direction of each droplet in tabular form.

[0104] It should be noted that introducing a pre-trained closed-loop prediction model based on initial droplet state data, historical droplet motion data, and electrode calibration parameters into the digital microfluidic control process upgrades droplet motion control from the traditional method that relies on fixed rules, empirical parameters, or static path planning to an intelligent closed-loop control mode with active prediction, real-time comparison, and error quantification capabilities. In existing technologies, droplet motion trajectories typically rely on fixed driving voltage sequences or simple empirical correction methods, which are insufficient to cope with trajectory deviations caused by droplet volume fluctuations, differences in local electrode responses, and dielectric layer inhomogeneities.

[0105] S4. Based on the droplet deviation feedback data, the initial driving voltage parameters are dynamically adjusted to generate a closed-loop driving voltage sequence.

[0106] S4.1 Analyze the droplet deviation feedback data for deviation amount and deviation direction, and generate feedback analysis data.

[0107] Specifically, based on the droplet deviation feedback data, the deviation amount and deviation direction information of each droplet are extracted, the deviation amount is numerically normalized, and the deviation direction is angularly standardized. The processed deviation amount and deviation direction are integrated according to the droplet sequence to form feedback analysis data, which includes a complete dataset containing the deviation amount and deviation direction of each droplet, and arranged according to the droplet arrangement order.

[0108] S4.2 Based on the feedback analysis data, a voltage adjustment algorithm is used to calculate the correction value of the initial drive voltage parameters and generate a voltage adjustment scheme.

[0109] Specifically, a voltage adjustment algorithm is used, taking the deviation amount and direction of each droplet as input, to calculate the correction value of the initial driving voltage parameters, expressed as:

[0110] ;

[0111] in, Represents droplets Preliminary drive voltage parameter correction values ​​(in V). This represents the vertical voltage adjustment factor (unit: V / μm). Represents droplets The deviation (in μm). Represents droplets a deviation direction (unit: rad) of the droplet, represents a horizontal direction voltage adjustment coefficient (unit: V / μm), represents an index of the droplet, represents a horizontal direction;

[0112] and the preliminary driving voltage parameter correction value of all droplets is integrated according to the electrode arrangement order to generate a voltage adjustment scheme containing the corrected voltage of each droplet.

[0113] It should also be noted that the electrode arrangement order represents the fixed arrangement of each electrode in the spatial position of the digital microfluidic chip, and the order is formed by sequentially numbering the positions of the electrodes from the starting position to the ending position, for example, sequentially from left to right along the row direction of the digital microfluidic chip, or sequentially from top to bottom along the column direction; the electrode arrangement order is used to clearly determine the position sequence of each electrode on the digital microfluidic chip, so that the subsequent processing steps can process and output the electrode driving voltage according to the consistent spatial arrangement basis;

[0114] The vertical direction voltage adjustment coefficient is used to convert the vertical deviation amount of the droplet into a voltage correction value, which can be determined by experiment calibration or numerical simulation, for example, by applying a known voltage pulse, collecting the vertical displacement of the droplet, and calculating the linear relationship between the voltage and the deviation; the horizontal direction voltage adjustment coefficient is used to convert the horizontal deviation amount of the droplet into a voltage correction value, which is determined by experiment calibration or numerical simulation, and the corresponding relationship between the known voltage input and the horizontal displacement of the droplet is fitted.

[0115] S4.3, according to the voltage adjustment scheme, dynamically modifying the preliminary driving voltage parameter to generate a closed-loop driving voltage sequence.

[0116] Specifically, after obtaining the voltage adjustment scheme, the preliminary driving voltage parameter correction value of the corresponding electrode in the voltage adjustment scheme is read according to the electrode arrangement order, the original driving voltage in the preliminary driving voltage parameter is numerically added to the corresponding preliminary driving voltage parameter correction value to obtain the updated electrode driving voltage value; the same numerical addition is performed for each electrode in the arrangement order, so that all original driving voltages are replaced by the corresponding updated electrode driving voltage values; after the update processing of all electrodes is completed, all updated electrode driving voltage values arranged according to the electrode arrangement order are integrated to generate a closed-loop driving voltage sequence.

[0117] It should be noted that the data-driven voltage adaptive control method based on real-time deviation feedback makes the driving voltage no longer a preset fixed sequence, but is dynamically, continuously and accurately modified according to the droplet deviation amount and deviation direction; in the prior art, digital microfluidics usually relies on a static driving voltage table or an adjustment method based on simple rules, when the droplet position deviates, the volume changes or is disturbed by the environment, due to the lack of real-time feedback closed loop, the control cannot be point-by-point modified according to the specific deviation, resulting in that the droplet is easy to deviate from the trajectory and even the operation fails.

[0118] S5, the closed-loop driving voltage sequence is applied to the electrode array, the droplet state data is collected in real time, the droplet operation instruction is generated, and the integration is formed to form a global droplet state matrix.

[0119] S5.1, the closed-loop driving voltage sequence is loaded to the electrode array, the voltage amplitude, pulse timing and corresponding application sequence of each electrode are analyzed, and the electrode driving task instruction is generated.

[0120] Specifically, the closed-loop driving voltage sequence is read item by item according to the electrode arrangement order, the voltage amplitude, pulse start time, pulse duration and corresponding electrode position contained in each item of the closed-loop driving voltage sequence are analyzed in turn, and the voltage amplitude, pulse start time and pulse duration obtained by analysis are matched with the corresponding electrodes in the electrode array one by one according to the electrode arrangement order; according to the analyzed corresponding electrode position, pulse start time and pulse duration, the electrode driving task instruction is generated, which includes specific application sequence, application time and application amplitude.

[0121] S5.2, according to the electrode driving task instruction, the closed-loop driving voltage is applied to the electrode array, and the droplet position, volume and morphology are collected in real time to generate a droplet state data stream.

[0122] Specifically, according to the electrode position, application time and application amplitude recorded in the electrode driving task instruction, the closed-loop driving voltage is applied to the corresponding electrodes in the electrode array one by one; during the application of the closed-loop driving voltage, the image data of the region where the droplet is located is continuously acquired at a preset sampling frequency through high-speed imaging, each frame of image collected is input into an image processing method, target contour extraction, boundary positioning and area estimation are performed on each frame of image respectively, the position, volume and morphology parameters of the droplet in each frame of image are calculated in turn, and the droplet position, volume and morphology parameters obtained from each frame of image are integrated in the order of image acquisition time to form a data sequence containing continuous droplet state information, which is output as a droplet state data stream.

[0123] It should be noted that the specific steps of presetting the sampling frequency include: determining the sampling frequency range according to the droplet movement speed and the processing time of the image processing method before the closed-loop driving voltage is applied, for example, when the droplet movement speed is fast, the sampling frequency range is set to 100 frames per second to 300 frames per second; selecting a target frequency for collecting image data in the sampling frequency range, and recording the target frequency as the preset sampling frequency; writing the preset sampling frequency into the image collection instruction for constraining the image acquisition interval of the high-speed imaging mode in the droplet state collection process, so that the high-speed imaging mode performs continuous image collection operation according to the preset sampling frequency, and the sampling frequency should satisfy at least N frames (such as N≥10) per droplet movement cycle, and the specific value can be determined according to the droplet speed and the image processing time;

[0124] The image processing method is a processing method for extracting droplet position, droplet volume and droplet morphology from the droplet image frame obtained by the high-speed imaging mode, by using image preprocessing, target contour recognition and feature quantization calculation steps, wherein the image preprocessing generates an image containing only the droplet region by grayscale, binarization and background elimination; the target contour recognition extracts the droplet contour pixels by edge detection; the feature quantization calculation obtains the numerical information of the droplet position, the droplet volume and the droplet morphology by area statistics, barycenter coordinate calculation and shape feature parameter calculation on the contour pixels.

[0125] S5.3, analyze and integrate the droplet state data stream, generate droplet operation instructions, and summarize each droplet state to form a global droplet state matrix.

[0126] Specifically, the time series analysis method is used to analyze the time sequence of the droplet state data stream, and each frame of droplet position, volume and morphology information is classified according to the time stamp and droplet index to generate a time sequence of each droplet; the moving average method and the outlier elimination method are used to perform trajectory smoothing and outlier elimination on the time sequence of each droplet to obtain the cleaned droplet state sequence; according to the cleaned droplet state sequence, the threshold comparison method and the state machine determination method are used to determine the operation events to be executed to generate corresponding droplet operation instructions, the droplet operation instructions including operation type, target electrode, application time and operation parameters, for example, when the droplet position changes more than 80% of the center distance between adjacent electrodes and the volume changes less than 5%, it is determined as a moving event, when the volume changes more than 10%, it is determined as a generation or merging event, when the deviation exceeds the set threshold and the droplet is near the expected path, it is determined as a correction event; at the same time, the latest state of each droplet at the current time point is recorded as a droplet state entry; all droplet operation instructions are combined into an operation instruction set according to the execution sequence, and all droplet state entries are summarized according to the droplet index and time stamp to form a global droplet state matrix.

[0127] It should also be noted that time series analysis is a method that analyzes data arranged in chronological order to identify the trends, periodicity, and regularity of data changes over time, thereby generating a continuous state sequence for each droplet.

[0128] The moving average method calculates the average value of consecutive data points in a time series by setting a window size, which smooths short-term fluctuations in the droplet state sequence and reduces noise interference.

[0129] Outlier removal methods identify and exclude droplet state data that deviate from the normal range based on statistical characteristics or set thresholds to ensure the accuracy of sequence analysis.

[0130] The threshold comparison method is to set upper and lower thresholds for various state parameters of the droplet and judge the parameters such as the current position, volume or shape of the droplet. If the parameters exceed or fall below the upper or lower thresholds, the droplet is determined to be in a specific state or a corresponding operation is triggered.

[0131] The state machine decision method defines the droplet's operational state as a finite set of states, and performs state transitions based on the droplet's current state and input conditions (such as position, volume, and shape) to determine the operation that the droplet should perform.

[0132] S6. Based on the global droplet state matrix, record the droplet operation status and generate a droplet operation log file.

[0133] S6.1. Based on the global droplet state matrix, number, classify, and time-mark the operation events of each droplet to generate droplet operation log entries.

[0134] Specifically, the state information of each droplet is read sequentially in the global droplet state matrix. Each operation event is uniquely numbered according to the droplet's current position, volume, and shape, and is classified according to the event category. At the same time, a timestamp is generated for each event according to the preset sampling frequency. The number, category, and timestamp are integrated to generate a droplet operation log entry. For example, the numbers are 001 to n, the category examples are generation, movement, and correction, and the timestamp example is a millisecond-level timestamp.

[0135] S6.2 Summarize, format, and save the droplet operation log entries to generate a droplet operation record file.

[0136] Specifically, the droplet operation log entries are read in sequence according to the droplet number, and the number, category and time mark of each log entry are uniformly formatted, for example, the number is formatted as fixed-length characters, the category uses a uniform string identifier, and the time mark uses a millisecond-level timestamp; all log entries are summarized according to the droplet number and time sequence to generate a droplet operation record set, and the droplet operation record set is saved to generate a droplet operation record file, for example, stored as a CSV or JSON format file, for subsequent query and analysis.

[0137] The embodiment also provides a closed-loop control digital microfluidic system, comprising: an electrode initialization module for initializing an electrode array of a digital microfluidic chip, setting electrode spacing, electrode size and voltage upper limit, and collecting an empty load state signal to generate initial reference data; a droplet generation module for forming a droplet at a predetermined position of the digital microfluidic chip, and collecting droplet initial position, volume and morphology information according to the initial reference data to generate a droplet initial state data set; a trajectory prediction module for inputting the droplet initial state data set into a pre-trained closed-loop prediction model to perform dynamic analysis and trajectory prediction calculation, generating a droplet expected motion trajectory, and comparing it with a droplet real-time state data stream to generate droplet deviation feedback data; a voltage optimization module for dynamically adjusting preliminary driving voltage parameters according to the droplet deviation feedback data to generate a closed-loop driving voltage sequence; a state control module for applying the closed-loop driving voltage sequence to the electrode array, collecting droplet state data in real time, generating droplet operation instructions, and integrating to form a global droplet state matrix; and a record generation module for recording droplet operation according to the global droplet state matrix to generate a droplet operation record file.

[0138] In summary, the present application realizes real-time closed-loop regulation and control during droplet motion by analyzing the deviation amount and direction of droplet deviation feedback data and dynamically modifying preliminary driving voltage parameters using a voltage adjustment algorithm to generate a closed-loop driving voltage sequence, which can adaptively correct deviations when droplets deviate from the expected trajectory, ensure stable movement of droplets along the planned path, improve droplet trajectory control accuracy, reduce motion errors caused by environmental disturbances and electrode response differences, and ensure high precision, dynamic adjustability and high repeatability in the droplet operation process.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A closed-loop control digital microfluidic method, characterized in that: include, Initialize the digital microfluidic chip electrode array, set the electrode spacing, electrode size and voltage limit, and acquire no-load state signals to generate initial reference data; A droplet is formed at a predetermined location on the digital microfluidic chip, and the initial position, volume and morphology information of the droplet are collected based on the initial reference data to generate a droplet initial state dataset. The initial state dataset of the droplets is input into the pre-trained closed-loop prediction model for dynamic analysis and trajectory prediction calculation to generate the expected trajectory of the droplets. The specific steps are as follows. Input the initial state dataset of droplets into the pre-trained closed-loop prediction model to establish a droplet dynamics prediction environment; In the droplet dynamics prediction environment, dynamic analysis is performed on the initial state dataset of droplets to generate preliminary driving voltage parameters; The initial driving voltage parameters are used to perform trajectory prediction calculations to generate the expected trajectory of the droplet. This trajectory is then compared with the real-time droplet status data stream to generate droplet deviation feedback data. The specific steps are as follows. Real-time acquisition of droplet current position, volume and morphology information, generating droplet real-time status data stream; The real-time status data stream of the droplet is compared with the expected trajectory of the droplet to extract the deviation between the actual position, volume and shape of the droplet and the expected trajectory, and then integrated to generate droplet trajectory deviation data. Based on the droplet trajectory deviation data, the amount and direction of the droplet deviation are calculated, and droplet deviation feedback data is generated. The pre-trained closed-loop prediction model is constructed based on the droplet initial state dataset, historical droplet motion data, and electrode calibration parameters, through dynamic analysis and trajectory prediction algorithms, and is obtained through pre-training using historical droplet motion data. Based on the droplet deviation feedback data, the initial driving voltage parameters are dynamically adjusted to generate a closed-loop driving voltage sequence; A closed-loop driving voltage sequence is applied to the electrode array, droplet state data is collected in real time, droplet operation commands are generated, and integrated to form a global droplet state matrix. Based on the global droplet state matrix, droplet operation status is recorded, and a droplet operation log file is generated.

2. The closed-loop control digital microfluidic method as described in claim 1, characterized in that: The specific steps for generating the initial reference data are as follows: Read the digital microfluidic chip model and electrode array design parameters, calculate the electrode spacing, size and material properties, and generate electrode layout configuration data; The electrode layout configuration data is loaded into the controller, the initial voltage upper limit is set and a low amplitude test voltage is applied, and at the same time, the no-load state sensor signal is acquired to generate no-load reference data. Based on the no-load reference data, the local response and dielectric layer differences of each electrode are calculated to generate electrode calibration parameters, which are then integrated with the voltage distribution table to generate initial reference data.

3. The closed-loop control digital microfluidic method as described in claim 1, characterized in that: The specific steps for generating the initial state dataset of the droplets are as follows. Based on the initial reference data, the target position and initial voltage configuration for droplet generation on the digital microfluidic chip are calculated to obtain droplet generation control commands. According to the droplet generation control command, a voltage pulse is applied at a predetermined position on the digital microfluidic chip to form a droplet. At the same time, the position, volume and morphology sensor signals of the droplet are collected in real time during the droplet formation process to generate preliminary data on droplet formation. The preliminary data on droplet formation are analyzed to extract the actual position, volume, and morphology of the droplets, and then integrated to generate the initial state data of the droplets.

4. The closed-loop control digital microfluidic method as described in claim 1, characterized in that: The specific steps for generating the closed-loop driving voltage sequence are as follows. Analyze the droplet deviation feedback data for deviation amount and direction to generate feedback analysis data; Based on the feedback analysis data, a voltage adjustment algorithm is used to calculate the correction value of the initial drive voltage parameters and generate a voltage adjustment scheme. Based on the voltage adjustment scheme, the initial driving voltage parameters are dynamically modified to generate a closed-loop driving voltage sequence.

5. The closed-loop control digital microfluidic method as described in claim 1, characterized in that: The specific steps for forming the global droplet state matrix are as follows: The closed-loop driving voltage sequence is loaded onto the electrode array, and the voltage amplitude, pulse timing and the corresponding application order of each electrode are analyzed to generate electrode driving task instructions. According to the electrode driving task instructions, a closed-loop driving voltage is applied to the electrode array, and the droplet position, volume and morphology are collected in real time to generate a droplet state data stream. The droplet state data stream is analyzed and integrated to generate droplet operation instructions, and the state of each droplet is summarized to form a global droplet state matrix.

6. The closed-loop control digital microfluidic method as described in claim 1, characterized in that: The specific steps for generating the droplet operation log file are as follows. Based on the global droplet state matrix, the operation events of each droplet are numbered, classified, and time-stamped to generate droplet operation log entries; The droplet operation log entries are summarized, formatted, and saved to generate a droplet operation record file.

7. A closed-loop control digital microfluidic system, based on the closed-loop control digital microfluidic method according to any one of claims 1 to 6, characterized in that: include, The electrode initialization module is used to initialize the electrode array of the digital microfluidic chip, set the electrode spacing, electrode size and voltage limit, and collect no-load state signals to generate initial reference data. The droplet generation module is used to form droplets at predetermined positions on the digital microfluidic chip, and to collect the initial position, volume and morphology information of the droplets based on the initial reference data to generate a droplet initial state dataset. The trajectory prediction module is used to input the initial state dataset of the droplet into the pre-trained closed-loop prediction model, perform dynamic analysis and trajectory prediction calculation, generate the expected motion trajectory of the droplet, and compare it with the real-time state data stream of the droplet to generate droplet deviation feedback data. The voltage optimization module is used to dynamically adjust the initial driving voltage parameters based on droplet deviation feedback data to generate a closed-loop driving voltage sequence. The state control module is used to apply the closed-loop drive voltage sequence to the electrode array, collect droplet state data in real time, generate droplet operation commands, and integrate them to form a global droplet state matrix. The record generation module is used to record droplet operation information based on the global droplet state matrix and generate droplet operation record files.

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