Microfluidic Droplet Formation Detection and Control Method and System

Abstract

This invention relates to the field of microfluidic control technology and discloses a method and system for detecting and controlling microfluidic droplet formation. The method first constructs an ideal concentric ring geometric model by acquiring the target parameters of the microcapsule and establishes a force-shape coupling response matrix. Based on this, a preset velocity triplet is solved to generate the initial droplet. Then, droplet image sequences are acquired and fitted with real-time concentric ring groups. These are geometrically superimposed with the ideal concentric ring geometric model to generate a deviation ring domain, which is then decomposed hierarchically to obtain geometric deformation characteristic components. Finally, a coordinated velocity adjustment vector is calculated using the force-shape coupling response matrix, a composite fluid correction field is constructed, and the deviation ring ablation cycle is iteratively executed until geometric convergence is achieved. This invention effectively solves the oscillation problem caused by strong parameter coupling in multiphase flow fields by constructing a quantitative mapping and inverse decoupling mechanism between fluid and geometric shape, achieving coordinated and precise control of the microcapsule's external dimensions and internal structure.

Description

Technical Field

[0001] This invention relates to the field of microfluidic control technology, and more specifically, to a method and system for detecting and controlling microfluidic droplet formation. Background Technology

[0002] Microfluidics, a technique for precisely manipulating fluids at the micrometer scale, has been widely applied in fields such as biomedicine, new material synthesis, and fine chemicals due to its high controllability over the microdroplet generation process. Particularly in microcapsule preparation, the construction of core-shell structured microcapsule droplets using multiphase laminar flow technology enables efficient encapsulation and controlled release of active pharmaceutical ingredients.

[0003] In the prior art, Chinese Patent CN113145190B discloses a microfluidic device for the large-scale controllable preparation of multi-structured composite microdroplets. This device, through three functional areas—stacked valve control, droplet preparation, and fluid distribution—utilizes a multi-level cascaded microchannel structure and fluid distribution logic to address the challenges of high-throughput preparation and structural diversity control of composite microdroplets. Chinese Patent CN110237877B discloses a microfluidic device and droplet control method. This technology integrates a heat-sensitive layer and a heating layer, utilizes thermoelectric signal feedback to locate the droplet position, and controls the drive components based on the position information, achieving efficient and precise droplet transport and position control within the flow channel.

[0004] However, the aforementioned existing technologies mainly focus on the structural flux design of microfluidic chips or the transport position control after droplet generation, and have not yet addressed the complex dynamic mapping problem between multiphase fluid dynamic parameters and microscopic geometry during microcapsule formation. Specifically, microcapsule formation is essentially a nonlinear fluid dynamics game involving internal phase support, external phase film formation, and mobile phase shear within a microchannel. In this process, there is a very strong shear-expansion coupling effect, meaning that any change in the velocity of any phase will affect the pressure distribution and shear stress field of the entire flow field. For example, when the control system attempts to adjust the mobile phase velocity to correct the overall particle size of the microcapsule, the change in velocity not only adjusts the shear cutoff force but also simultaneously squeezes or stretches the film-forming interface of the external phase fluid, causing an unexpected shift in the originally acceptable shell thickness. This strong fluid dynamic coupling characteristic renders traditional single-variable feedback control strategies ineffective. The system cannot distinguish the differences in sensitivity of overall size changes and internal structural proportion changes to the velocities of each phase, thus falling into a continuous oscillation trap where correcting the particle size leads to excessive shell thickness, and correcting the shell thickness leads to uncontrolled particle size. This multi-objective competitive deadlock not only makes it difficult for the system to converge to a steady state, but also causes a huge waste of expensive core reagents during the long parameter debugging process, which restricts the continuous steady-state mass production of high-precision microcapsules. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of existing technologies, this invention provides a method and system for detecting and controlling microfluidic droplet formation. By constructing an ideal concentric ring geometric model and a force-shape coupling response matrix, a decoupling mapping mechanism from the velocity vector space to the geometric shape vector space is established. This method can generate a cooperative velocity adjustment vector using an inverse solution algorithm, constructing a composite fluid correction field in the physical field. This effectively overcomes the problem of strong parameter coupling oscillation in multiphase flow fields, achieving synchronous and precise convergence of the microcapsule's external dimensions and internal structure, significantly improving preparation accuracy and yield.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Methods for detecting and controlling microfluidic droplet formation include:

[0008] The target parameters of the microcapsule are obtained to construct an ideal concentric ring geometric model, and a force-shape coupling response matrix is ​​established. The preset flow velocity triplet is solved based on the ideal concentric ring geometric model and the force-shape coupling response matrix. The initial droplet is generated based on the preset flow velocity triplet.

[0009] The droplet image sequence of the initial droplet is acquired, and a real-time concentric circle group is fitted according to the droplet image sequence. The real-time concentric circle group is geometrically superimposed with the ideal concentric ring geometric model to generate a deviation ring domain. The deviation ring domain is decomposed in layers to obtain the geometric deformation feature components.

[0010] The coordinated flow velocity adjustment vector is calculated based on the geometric deformation characteristic components and the force-shape coupling response matrix. A composite fluid correction field is formed based on the coordinated flow velocity adjustment vector, and the deviation loop ablation cycle is iteratively executed to achieve the geometric convergence of the microcapsule.

[0011] The target parameters of the microcapsules include the target total particle size and the target shell thickness;

[0012] The method for constructing the ideal concentric annular geometric model includes:

[0013] The target kernel diameter is calculated based on the total target particle size and the target shell thickness. An ideal outer circle is constructed with the total target particle size as the diameter, and an ideal inner circle is constructed with the target kernel diameter as the diameter. The ideal outer circle and the ideal inner circle together form an ideal concentric ring geometric model.

[0014] The method for establishing the force-shape coupling response matrix includes:

[0015] Define a ternary force field vector space, which includes a core expansion vector generated by the inner phase velocity, a shell support vector generated by the outer phase velocity, and a shear compression vector generated by the mobile phase velocity. Establish the partial derivative relationships of the inner phase velocity, outer phase velocity, and mobile phase velocity with respect to the total particle size and core diameter ratio, and construct the force-shape coupling response matrix.

[0016] The method for solving the preset velocity triplet includes:

[0017] Based on the target total particle size, the initial value of the mobile phase velocity is determined; based on the determined initial value of the mobile phase velocity, a reference velocity triplet is constructed; and a geometric target deviation vector is constructed based on the reference velocity triplet, the target total particle size, and the target core diameter ratio.

[0018] Based on a defined initial flow velocity of the mobile phase, the target total particle size and target core diameter ratio in the ideal concentric ring geometric model are used as constraints. The geometric target deviation vector is mapped to a flow velocity adjustment vector using the generalized inverse matrix of the force-shape coupling response matrix. The flow velocity adjustment vector is then superimposed on the reference flow velocity triplet to obtain a preset flow velocity triplet containing the initial values ​​of the inner phase flow velocity, the outer phase flow velocity, and the initial values ​​of the mobile phase flow velocity.

[0019] The method for fitting a real-time set of concentric circles includes:

[0020] The centroid coordinates, outer contour boundary, and inner phase interface of a single droplet are extracted from the droplet image sequence. The outer contour boundary is fitted to the actual outer circle, and the inner phase interface is fitted to the actual inner circle. The actual outer circle and the actual inner circle together form a real-time concentric circle group.

[0021] The method for generating the deviation annular domain includes:

[0022] An outer diameter deviation ring is generated based on the regional difference between the actual outer circle and the ideal outer circle, and a structural deviation ring is generated based on the difference between the actual shell region and the ideal shell region. The actual shell region is the annular region between the actual outer circle and the actual inner circle, and the ideal shell region is the annular region between the ideal outer circle and the ideal inner circle. The deviation annular domain is jointly formed by the outer diameter deviation ring and the structural deviation ring.

[0023] The method for generating the geometric deformation feature components includes:

[0024] The actual outer diameter is measured as the actual total particle size, and the actual inner diameter is measured as the actual core diameter. The actual core diameter ratio is calculated based on the ratio of the actual core diameter to the actual total particle size. The difference between the actual total particle size and the target total particle size is calculated as the overall scale deviation, and the difference between the actual core diameter ratio and the target core diameter ratio is calculated as the structural proportion deviation. The overall scale deviation and the structural proportion deviation together constitute the geometric deformation characteristic component.

[0025] The calculation method for the coordinated flow velocity adjustment vector includes:

[0026] A generalized inverse operation is performed on the force-shape coupling response matrix to obtain the inverse coupling deconstruction matrix. The geometric deformation characteristic components are constructed as deviation vectors. The inverse coupling deconstruction matrix and the deviation vector are subjected to matrix operation, and an anti-oscillation gain coefficient is introduced to calculate the coordinated flow velocity regulation vector, which includes the internal phase flow velocity regulation, the external phase flow velocity regulation, and the mobile phase flow velocity regulation.

[0027] The method for generating the composite fluid correction field includes:

[0028] The internal phase velocity regulation amount in the coordinated velocity regulation vector is converted into an internal phase pump control command, the external phase velocity regulation amount is converted into an external phase pump control command, and the mobile phase velocity regulation amount is converted into a mobile phase pump control command. The internal phase pump control command is sent to the internal phase pump, the external phase pump control command is sent to the external phase pump, and the mobile phase pump control command is sent to the mobile phase pump. The three-phase pumps execute the control commands synchronously, forming a composite fluid correction field within the microfluidic chip channel.

[0029] A microfluidic droplet formation detection and control system, used to implement the above-mentioned microfluidic droplet formation detection and control method, the system comprising:

[0030] Preset flow rate generation module: used to obtain the target parameters of microcapsules, construct an ideal concentric ring geometric model, establish a force-shape coupling response matrix, solve the preset flow rate triplet based on the ideal concentric ring geometric model and the force-shape coupling response matrix, and generate initial droplets based on the preset flow rate triplet;

[0031] Deformation feature extraction module: used to acquire droplet image sequence of initial droplet, fit real-time concentric circle group according to droplet image sequence, geometrically superimpose real-time concentric circle group with ideal concentric ring geometric model to generate deviation ring domain, and perform hierarchical decomposition of deviation ring domain to obtain geometric deformation feature components.

[0032] The collaborative flow rate control module is used to calculate the collaborative flow rate adjustment vector based on the geometric deformation characteristic components and the force-shape coupling response matrix, form a composite fluid correction field based on the collaborative flow rate adjustment vector, and iteratively execute the deviation loop ablation cycle to achieve the geometric convergence of the microcapsule.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] This invention establishes a precise mapping benchmark from the physical flow field to the digital geometric space in microcapsule preparation by constructing an ideal concentric ring geometric model and establishing a force-shape coupling response matrix. Utilizing the hierarchical decomposition technique of the deviation ring domain, implicit multiphase fluid dynamics errors are transformed into visible and independently calculable geometric deformation characteristic components, achieving decoupled characterization of the overall droplet size deviation and internal structural proportion deviation. Based on this, a coordinated velocity adjustment vector is calculated using the geometric deformation characteristic components and the force-shape coupling response matrix, driving the synchronous action of the multiphase fluid to form a composite fluid correction field. This correction field can simultaneously compensate for the velocities of the inner phase, outer phase, and mobile phase according to a specific vector ratio, fundamentally overcoming the problem of strong coupling oscillation of shear-expansion parameters caused by the interference of velocity, pressure, and shear force in microfluidic multiphase flow fields. Through iterative execution of the deviation ring ablation cycle, the geometric morphology of the microcapsules converges rapidly and stably to the target state under closed-loop control, significantly improving the geometric accuracy and yield of microcapsule preparation. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a microfluidic droplet formation detection and control method provided in an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the microcapsule droplet generation process within the microfluidic chip channel provided in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of an ideal concentric ring geometric model provided in an embodiment of the present invention;

[0039] Figure 4 This is a flowchart illustrating the principle of generating a preset flow rate triplet provided in an embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram of a droplet image acquisition scenario provided in an embodiment of the present invention;

[0041] Figure 6 A functional block diagram of the microfluidic droplet forming detection and control system provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1:

[0044] Please see Figure 1 As shown, this embodiment provides a method for detecting and controlling microfluidic droplet formation, including:

[0045] Step S10: Obtain the target parameters of the microcapsule, construct an ideal concentric ring geometric model, establish a force-shape coupling response matrix, solve the preset flow velocity triplet based on the ideal concentric ring geometric model and the force-shape coupling response matrix, and generate the initial droplet based on the preset flow velocity triplet.

[0046] Specifically, step S10 transforms the physical process of microcapsule preparation into a computable, quantifiable, and controllable mathematical model. Please refer to [link / reference]. Figure 2 , Figure 2 This is a schematic diagram illustrating the microcapsule droplet generation process within the microfluidic chip channel provided in this embodiment. The generation process involves complex interactions between the internal phase fluid, the external phase fluid, and the mobile phase fluid within the microfluidic chip channel. The internal phase fluid flows from... Figure 2 The inner phase enters through the chip tip injection port marked "Inner Phase." The outer phase fluid surrounds the inner phase fluid from both the top and bottom sides of the inner phase injection port, forming a bilayer fluid structure. The mobile phase fluid, marked "Mobile Phase," flows along the direction of the dotted arrow in the diagram on the outside of the bilayer fluid structure and, through shearing action, breaks the bilayer fluid into discrete core-shell bilayer structures, as shown in the diagram. Figure 2 The particles are labeled "microcapsule droplets". Traditional microfluidic preparation methods rely solely on empirical parameters to set the flow rates of each phase, lacking a quantitative understanding of the relationship between droplet geometry and fluid parameters. This results in a lengthy debugging period after system startup before the droplet morphology approaches the target state. Step S10 establishes a digital standard mold for microcapsule droplets by constructing an ideal concentric ring geometric model. It quantifies the influence of changes in flow rates of each phase on droplet geometric parameters by establishing a force-shape coupling response matrix. By solving the preset flow rate triplet, it achieves precise initialization of the system, bringing it close to the target state upon startup. These three elements together constitute a complete mapping benchmark from the physical world to the digital geometric world, providing an indispensable mathematical basis for deviation detection in step S20 and coordinated control in step S30.

[0047] Further, step S10 includes:

[0048] Step S11: Obtain the target total particle size and target shell thickness of the microcapsules; calculate the target core diameter based on the target total particle size and target shell thickness; calculate the ratio of the target core diameter to the target total particle size to obtain the target core diameter ratio; and construct an ideal concentric ring geometric model based on the target total particle size and target core diameter.

[0049] Specifically, microcapsule droplets, as particles with a core-shell bilayer structure, can have their geometry fully characterized by two independent scalar parameters: the target total particle size and the target shell thickness. The target total particle size characterizes the overall outer diameter of the microcapsule droplet, directly determining its physical scale characteristics in subsequent applications. The target shell thickness characterizes the radial dimension of the microcapsule's outer shell, determining its encapsulation strength and release rate. A thicker shell results in stronger encapsulation but slower release, while a thinner shell leads to a more sensitive response but lower mechanical strength. After the user presets the target total particle size and target shell thickness according to specific application requirements, the system automatically calculates the target core diameter. The target core diameter equals the target total particle size minus twice the target shell thickness. The target core-to-diameter ratio is calculated using a dimensionless ratio, specifically as the target core-to-diameter ratio equals the target core diameter divided by the target total particle size. As a dimensionless structural parameter, the target core-to-diameter ratio is limited to a value between zero and one, representing the relative proportion of the microcapsule core in the overall size. When the target core diameter ratio is close to one, it indicates that the shell is extremely thin relative to the total particle size, and the microcapsule is close to a monolayer droplet; when the target core diameter ratio is close to zero, it indicates that the shell is extremely thick relative to the total particle size, and the microcapsule is close to a solid microsphere. The technical significance of introducing the target core diameter ratio is that it decouples the absolute size of the microcapsule from the proportion of its internal structure, so that the deviation analysis in the subsequent step S20 can independently judge the two different dimensions of quality indicators, namely "whether the overall size meets the standard" and "whether the internal structure meets the standard", avoiding the confusion of two different types of deviations.

[0050] The construction of the ideal concentric ring geometric model is completed in a virtual coordinate system. Please refer to [link / reference]. Figure 3 , Figure 3 This illustration shows the ideal concentric annular geometric model constructed in this embodiment and its various components. The specific construction method is as follows: Figure 3 As shown, an ideal outer circle is constructed with the coordinate origin marked in the figure as the center and the target total particle size as the diameter. An ideal inner circle is constructed with the same coordinate origin as the center and the target core diameter as the diameter. The ideal outer and inner circles together form an ideal concentric annular geometric model. Combined with... Figure 3 It can be seen that the ideal outer circle represents the outer contour boundary of the microcapsule in a perfectly formed state, and the ideal inner circle represents the ideal interface between the internal and external phases of the microcapsule. Figure 3The annular region between the ideal outer circle and the ideal inner circle represents the ideal shell region. Projecting the three-dimensional spherical shell structure of the microcapsule into a two-dimensional concentric ring structure is based on the physical characteristics of droplet formation within the microfluidic chip channel: the droplet flows along the channel axis in the observation area, and the microscopic imaging component acquires images from a direction perpendicular to the channel axis. The acquired droplet image is the projection of the droplet at its maximum cross-section, which presents a concentric ring shape. By using a concentric ring geometric model instead of a three-dimensional spherical shell model, the three-dimensional geometric comparison is simplified to a two-dimensional geometric comparison while preserving the complete information of the core-shell structure. This allows the edge detection and contour fitting in the subsequent step S21 to be completed directly in the two-dimensional image plane, avoiding the computational complexity and error accumulation caused by three-dimensional reconstruction.

[0051] Step S12: Define a ternary force field vector space, which includes a core expansion vector generated by the inner phase velocity, a shell support vector generated by the outer phase velocity, and a shear compression vector generated by the mobile phase velocity. Establish the partial derivative relationships of the inner phase velocity, outer phase velocity, and mobile phase velocity with respect to the total particle size and core diameter ratio, and construct the force-shape coupling response matrix.

[0052] Specifically, the droplet formation process within the microfluidic chip channel is essentially the result of multiple hydrodynamic forces competing and ultimately reaching a dynamic equilibrium. To transform this complex hydrodynamic process into a solvable control problem, step S12 defines a ternary force field vector space, mapping the three-phase fluids involved in droplet formation to three force field vectors with distinct physical meanings. The core expansion vector is generated by the inner phase velocity, and its physical mechanism is as follows: the inner phase fluid is continuously injected from the tip of the microfluidic chip, constantly filling the droplet core region with material during the droplet formation cycle, generating a radial expansion effect radiating outward from the droplet's center of mass. The greater the inner phase velocity, the more fluid volume is injected into the core per unit time, the greater the magnitude of the core expansion vector, leading to an increase in the diameter of the droplet core. The direction of the core expansion vector is defined as the radial direction radiating outward from the center, characterizing its tendency to cause the core to expand outward.

[0053] The shell support vector is generated by the outer phase flow velocity. Its physical mechanism is as follows: the outer phase fluid is injected simultaneously from both the upper and lower sides of the inner phase injection port, forming a shell structure that surrounds the inner phase fluid. During the droplet formation cycle, material is continuously added to the shell region, generating a radial support effect radiating from the inner boundary to the outer boundary. The greater the outer phase flow velocity, the more fluid volume is injected into the shell per unit time, the greater the magnitude of the shell support vector, leading to an increase in shell thickness. The direction of the shell support vector is defined as the radial direction radiating from the inner circle to the outer circle, characterizing its tendency to cause the shell to expand outward.

[0054] The shear compression vector is generated by the flow velocity of the mobile phase. Its physical mechanism is as follows: the mobile phase flows at high speed on the outside of the bilayer fluid structure, forming a velocity gradient between itself and the bilayer fluid. This velocity gradient generates shear stress acting on the outer surface of the bilayer fluid, while the dynamic pressure of the mobile phase exerts lateral compression on the bilayer fluid. The effect of the shear compression vector is to truncate the continuous bilayer fluid into discrete droplets and compress the overall droplet size. The higher the flow velocity of the mobile phase, the stronger the shear stress and dynamic pressure, and the larger the magnitude of the shear compression vector, resulting in a smaller overall droplet size and an increased generation frequency. The direction of the shear compression vector is defined as the radial direction perpendicular to the droplet flow direction and pointing towards the droplet's center of mass, characterizing its tendency to cause the droplet to contract inward. The definition of the ternary force field vector space transforms the fluid dynamics effects that cannot be directly observed in microfluidic systems into vector forms with clear direction and magnitude, laying the physical foundation for establishing a quantitative force-shape correspondence. Traditional microfluidic control methods treat the flow velocities of each phase as independent variables, mistakenly assuming that the inner phase velocity only affects the core size, the outer phase velocity only affects the shell thickness, and the mobile phase velocity only affects the total particle size. This simplistic assumption ignores the mutual interference between fluids, leading to oscillations where "correcting one parameter deviates from another" during single-variable control. Step S12 defines a ternary force field vector space, clarifying that the three-phase flow velocities all have cross-influences on multiple geometric parameters of the droplet, thus preparing the conceptual foundation for constructing a coupled model that reflects these cross-influences.

[0055] Based on the physical analysis of the aforementioned ternary force field vector space, the total particle size and core diameter ratio of microcapsules are jointly influenced by the internal phase velocity, external phase velocity, and mobile phase velocity. This multi-input, multi-output mapping relationship needs to be uniformly described using matrix form. The overall mathematical framework of the force-shape coupling response matrix is ​​expressed as follows: the geometric parameter change vector equals the force-shape coupling response matrix multiplied by the velocity change vector. The geometric parameter change vector is a two-dimensional column vector containing the total particle size change and the core diameter ratio change, while the velocity change vector is a three-dimensional column vector containing the internal phase velocity change, external phase velocity change, and mobile phase velocity change. This matrix relationship integrates the cross-influence of the three-phase velocity on the two geometric parameters into a single matrix operation, avoiding the tedious process of establishing independent equations for each geometric parameter.

[0056] The force-shape coupling response matrix is ​​defined as a 2x3 matrix. The rows correspond to the output dimensions of the droplet's geometric parameters, and the columns correspond to the input dimensions of the flow velocities of each phase. The matrix elements are structured as follows: The element in the first row and first column is the partial derivative of the total particle size with respect to the internal phase velocity, representing the change in total particle size caused by a unit change in the internal phase velocity. A positive value indicates that increasing the internal phase velocity will increase the total particle size. The element in the first row and second column is the partial derivative of the total particle size with respect to the external phase velocity, representing the change in total particle size caused by a unit change in the external phase velocity. A positive value indicates that increasing the external phase velocity will increase the total particle size. The element in the first row and third column is the partial derivative of the total particle size with respect to the mobile phase velocity, representing the change in total particle size caused by a unit change in the mobile phase velocity. A negative value indicates that increasing the mobile phase velocity will decrease the total particle size. The element in the second row and first column... The element in the second row and second column of the matrix is ​​the partial derivative of the core diameter ratio with respect to the inner phase velocity, representing the change in core diameter ratio caused by a unit change in the inner phase velocity. Since increasing the inner phase velocity causes the core to expand and the shell to become relatively thinner, this value is positive. The element in the second row and second column of the matrix is ​​the partial derivative of the core diameter ratio with respect to the outer phase velocity, representing the change in core diameter ratio caused by a unit change in the outer phase velocity. Since increasing the outer phase velocity causes the shell to thicken and the core to become relatively thinner, this value is negative. The element in the second row and third column of the matrix is ​​the partial derivative of the core diameter ratio with respect to the mobile phase velocity, representing the change in core diameter ratio caused by a unit change in the mobile phase velocity. The effect of the mobile phase velocity change on the core diameter ratio depends on the difference in the degree of compression of the core and shell.

[0057] The partial derivatives of the force-shape coupling response matrix can be obtained using the following method: During system debugging, small-amplitude perturbations are performed on the internal phase velocity, external phase velocity, and mobile phase velocity, while keeping the other two phase velocities constant. The changes in the total droplet diameter and core diameter ratio are recorded, and the ratio of these changes to the perturbation is calculated to obtain the corresponding partial derivative values. For example, the internal phase velocity is increased by 5% near a reference value, and the change in total droplet diameter from the reference value in micrometers is measured. The partial derivative of the total droplet diameter with respect to the internal phase velocity is obtained by dividing the change in total droplet diameter by the change in internal phase velocity. This method is based on the first-order approximation principle of Taylor expansion and has good linear approximation accuracy within a small perturbation range. For different chip structures, fluid formulations, and operating conditions, the element values ​​of the force-shape coupling response matrix need to be recalibrated to ensure that the matrix accurately reflects the force-shape coupling characteristics of the current system.

[0058] The force-shape coupling response matrix constructed in step S12 clearly quantifies the cross-influence of three-phase flow velocities on geometric morphology. This quantification directly reveals the mathematical reason for the failure of traditional single-variable control: each row of the matrix contains three non-zero elements, indicating that both the total particle size and the core diameter ratio are affected by the three-phase flow velocities. Adjusting any one phase flow velocity alone will inevitably change both geometric parameters simultaneously, making it impossible to achieve independent control such as "adjusting only the particle size while keeping the core diameter ratio unchanged" or "adjusting only the shell thickness while keeping the particle size unchanged." The existence of the force-shape coupling response matrix provides the mathematical premise for achieving decoupling control through the generalized inverse matrix in step S31: only by clarifying the forward coupling relationship can the reverse decoupling relationship be solved through inverse operation. Without step S12, the system would lack a quantitative understanding of the multivariable coupling relationship, the inverse coupling deconstruction matrix in step S31 would be impossible to calculate, the solution of the cooperative flow velocity adjustment vector would degenerate into a tentative single-variable adjustment, and the control system would fall into an inefficient cycle of "adjustment-oscillation-readjustment."

[0059] Step S13: Using the target total particle size and target core diameter ratio in the ideal concentric ring geometric model as constraints, and combining the force-shape coupling response matrix for inverse solution, a preset velocity tripartite consisting of the initial values ​​of the inner phase velocity, the outer phase velocity, and the mobile phase velocity is obtained. Initial droplets are generated based on the preset velocity tripartite.

[0060] Specifically, the purpose of solving the preset velocity triplet is to make the initial droplets generated after system startup as close as possible to the target shape defined by the ideal concentric ring geometric model, thereby shortening the transition time from system startup to steady state and reducing the amount of defective droplets generated during the transition period. Traditional microfluidic preparation methods usually use empirical values ​​or trial-and-error methods to set the initial velocity parameters. The shape of the initial droplets often deviates significantly from the target shape, and the system needs to undergo multiple rounds of iterative adjustments to gradually approach the target state. Step S13 solves inversely by using the target geometric parameters as constraints, allowing the calculation of the velocity parameter combination that matches the target shape before system startup, thus achieving accurate system initialization. (See also...) Figure 4 The constraints for the inverse solution are provided by the ideal concentric ring geometric model constructed in step S11: the target total particle size serves as a constraint on the overall droplet size, and the target core diameter ratio serves as a constraint on the proportion of the droplet's internal structure. The mapping relationship for the inverse solution is provided by the force-shape coupled response matrix established in step S12, which describes the forward mapping from the velocity change to the geometric parameter change. The task of the inverse solution is: given the desired geometric parameter values, to reverse-calculate the velocity combinations that produce those geometric parameter values.

[0061] Solving the pre-set flow rate ternary set involves two stages. The first stage is determining the initial value of the mobile phase flow rate: the mobile phase flow rate not only affects the droplet size but also directly determines the droplet formation frequency, which is directly related to production efficiency. Based on the target total particle size and production efficiency requirements, and referring to the empirical correlation curve between mobile phase flow rate and droplet diameter, the initial value of the mobile phase flow rate is selected as the benchmark value. This empirical correlation curve can be obtained through pre-experimentation, recording the average droplet size and formation frequency at different mobile phase flow rates, thus establishing the correspondence between mobile phase flow rate and droplet size. For example, for the microcapsule preparation requirement with a target total particle size of 300 micrometers, the initial value of the mobile phase flow rate is selected based on the empirical correlation curve, ensuring that the average droplet size at this mobile phase flow rate falls within a reasonable range near the target total particle size.

[0062] The second stage involves simultaneously solving for the initial values ​​of the internal and external phase velocities: given a fixed initial value for the mobile phase velocity, both the target total particle size constraint and the target core diameter ratio constraint must be satisfied. Since the force-shape coupling response matrix establishes a linear mapping between velocity changes and geometric parameter changes, this problem can be transformed into solving a system of linear equations. A reference velocity triplet is set as the starting point. The difference between the target total particle size and the predicted particle size under the reference state is taken as the total particle size target deviation, and the difference between the target core diameter ratio and the predicted core diameter ratio under the reference state is taken as the core diameter ratio target deviation, thus constructing a geometric target deviation vector. Using the generalized inverse matrix of the force-shape coupling response matrix, the geometric target deviation vector is mapped to a velocity adjustment vector. Superimposing the velocity adjustment vector onto the reference velocity triplet yields the preset velocity triplet. The method for determining the reference flow rate triplets is as follows: The initial flow rate of the mobile phase selected in the first stage is used as the mobile phase component. The internal and external flow rates recommended based on empirical correlation curves or system default values ​​at this mobile phase flow rate are used as the internal and external phase components, respectively. These three components constitute the reference flow rate triplets. The method for obtaining the predicted particle size and predicted core diameter ratio under the reference state is as follows: Consult the system's historical calibration database for this chip and fluid formulation combination. Read the previously measured average total droplet size under the corresponding flow rate combination of the reference flow rate triplets as the predicted particle size under the reference state, and read the previously measured average core diameter ratio of the droplets as the predicted core diameter ratio under the reference state. If this is the first time using this chip formulation combination and there is no historical calibration data, a short-term trial run can be conducted using the reference flow rate triplets, and a small number of droplet samples can be collected to measure their average total particle size and average core diameter ratio as the predicted values ​​under the reference state.

[0063] The generalized inverse matrix is ​​used to handle the non-square matrix of the force-shape coupling response matrix. This matrix is ​​a 2x3 matrix with three input dimensions and two output dimensions, belonging to an underdetermined system where infinitely many velocity solutions produce the same geometric parameters. The generalized inverse matrix selects the solution with the smallest modulus among all feasible solutions, i.e., minimizing the adjustment range of each phase velocity. This characteristic is beneficial for maintaining the stability and controllability of the system. For example, the generalized inverse matrix can be calculated using the Moore-Ponrose pseudo-inverse method.

[0064] After the preset velocity tripartite calculation is completed, the system sends the initial values ​​of the inner phase velocity, outer phase velocity, and mobile phase velocity to the inner phase pump, outer phase pump, and mobile phase pump in the flow supply module, respectively. The three-phase pumps start synchronously according to the preset velocity tripartite, driving the three-phase fluid into the microfluidic chip channel to begin generating initial droplets. Since the preset velocity tripartite is obtained by inversely solving based on the target geometric parameters, the deviation between the shape of the initial droplets and the ideal concentric ring geometric model is significantly reduced compared to the case of randomly setting the initial velocity. The closed-loop control in subsequent steps S20 and S30 only requires fine correction rather than large-scale adjustment, resulting in shorter control convergence time and a smoother control process.

[0065] Step S13 works closely in synergy with steps S11 and S12: the target total particle size and target core diameter ratio provided in step S11 constitute the constraints for the inverse solution, while the force-shape coupling response matrix established in step S12 provides the mathematical mapping tool for the inverse solution. Without the target parameter definition in step S11, the inverse solution would lose its optimization objective; without the coupling matrix construction in step S12, the inverse solution would be unable to quantify the correspondence between flow velocity and geometric parameters, degenerating into an empirical setting without any basis. Step S13 integrates and applies the results of the first two steps, outputting flow velocity parameters that can directly drive the physical device, completing the transformation from mathematical model to physical execution. The accuracy of the preset flow velocity triplet directly affects the efficiency of subsequent closed-loop control. If the initial droplet deviates too much from the target shape, step S30 needs to perform more rounds of iterative adjustment to converge to a steady state; if the initial droplet deviates only slightly from the target shape, step S30 only needs a small amount of fine adjustment to achieve convergence, improving the overall system response speed and reducing material waste.

[0066] Step S10 establishes a mapping benchmark from physical space to digital space for microcapsule preparation by constructing an ideal concentric ring geometric model, establishing a force-shape coupling response matrix, and solving for the preset flow velocity triplet. First, by introducing a target core diameter ratio and transforming scalar parameters into an ideal concentric ring geometric model, the overall droplet scale and internal structural proportions are decoupled, providing a quantitative geometric mold for subsequent deviation detection. Second, the force-shape coupling response matrix is ​​constructed, and the cross-influence of the ternary force field vector space on the geometric morphology is quantitatively analyzed through partial derivatives, making the implicit multiphase flow coupling mechanism explicit and solving the problem of strong coupling oscillation of the "shear-expansion" parameters caused by traditional single-variable feedback control. Finally, the preset flow velocity triplet is generated by inverse matrix solving, ensuring that the system is in the linear neighborhood of the target state from startup, shortening the steady-state convergence time and reducing the consumption of expensive reagents, providing a precise physical starting point for subsequent closed-loop coordinated control.

[0067] Step S20: Acquire droplet image sequence of initial droplet, fit real-time concentric circle group according to droplet image sequence, geometrically superimpose real-time concentric circle group with ideal concentric ring geometric model to generate deviation ring domain, and perform hierarchical decomposition of deviation ring domain to obtain geometric deformation feature components.

[0068] Microcapsule droplets are continuously generated at extremely high frequencies within the channels of a microfluidic chip. The generation cycle of a single droplet is typically on the order of milliseconds, and the droplet size is on the order of micrometers. The subtle morphological changes of the droplets cannot be directly observed by the human eye, and traditional offline sampling methods suffer from severe time lag. By the time the detection results are fed back to the operator, a large number of defective droplets have already been produced. Step S20 continuously acquires droplet images using a high-speed microscopic imaging component, solidifying the transient fluid morphology into a digital image sequence. Geometric contour information is extracted from the droplet images using image processing algorithms and fitted into a standardized concentric circle structure. This structure is then geometrically superimposed with the ideal concentric ring geometric model constructed in step S11 to generate a visualized deviation ring domain. By performing hierarchical decomposition on the deviation ring domain, i.e., quantifying the radial deviations of the outer diameter deviation ring and the structural deviation ring separately, geometric deformation feature components that can be directly involved in mathematical calculations are obtained. This step-by-step transformation process, from "physical form" to "digital image" to "geometric model" and finally to "numerical deviation," enables machine vision to accurately capture and quantify fluid dynamic errors, providing real-time, precise, and computable input data for the coordinated control of step S30.

[0069] Further, step S20 includes:

[0070] Step S21: Continuously acquire the time-stamped droplet image sequence of the initial droplet, extract the centroid coordinates, outer contour boundary and inner phase interface of a single droplet from the droplet image sequence, fit the outer contour boundary as an actual outer circle, fit the inner phase interface as an actual inner circle, and the actual outer circle and the actual inner circle together form a real-time concentric circle group.

[0071] Specifically, please refer to Figure 5 , Figure 5 This is a schematic diagram of a droplet image acquisition scenario provided in this embodiment. The acquisition of the droplet image sequence is achieved through a microscopic imaging component, such as... Figure 5 As shown, the microscopic imaging assembly includes a high-magnification microscope objective and a high-speed industrial camera. The microscope objective is aligned with... Figure 5 The observation area on the microfluidic chip magnifies the micron-sized droplets (marked as droplets) at the location indicated by the dashed line within the observation area to a scale that can be effectively resolved by the camera sensor. A high-speed industrial camera continuously captures images at a frame rate much higher than the droplet generation frequency, ensuring that each droplet is captured with at least one clear image within the observation area. The frame rate of the high-speed industrial camera is selected based on several times the droplet generation frequency. For example, when the droplet generation frequency is one hundred per second, the camera frame rate is set to five hundred to one thousand frames per second to ensure that each droplet is captured with multiple images as it passes through the observation area. From these, frames in the optimal observation position of the droplet are selected for subsequent analysis. Each image is accompanied by a timestamp, generated by the system clock and stored in conjunction with the image data. The introduction of the timestamp gives the droplet image sequence temporal correlation, allowing the tracking of the droplet morphology at a specific moment and providing a time reference for the iterative convergence process in subsequent step S33.

[0072] Preprocessing of the droplet images in the droplet image sequence includes grayscale conversion, denoising filtering, and contrast enhancement. Grayscale conversion converts the color image into a single-channel grayscale image, reducing the computational load of subsequent processing. Denoising filtering uses Gaussian filtering or median filtering to eliminate random noise introduced during image acquisition. Contrast enhancement uses histogram equalization to improve the grayscale difference between the droplet edge and the background, creating favorable conditions for edge detection. The centroid coordinates of a single droplet are extracted using an image moment calculation method. Specifically, the preprocessed droplet image is binarized to separate the droplet region from the background region, obtaining a binary mask image of the droplet. The zeroth-order moment and the first-order moment are calculated on the binary mask image. The zeroth-order moment represents the total number of pixels in the droplet region, and the first-order moment represents the weighted sum of the pixel coordinates in the droplet region. Dividing the first-order moment by the zeroth-order moment yields the position coordinates of the droplet centroid in the image coordinate system. The centroid coordinates serve as the origin of the coordinates for subsequent geometric comparison, and their positioning accuracy directly affects the accuracy of generating the deviation ring domain. The image moment method is used instead of the simple bounding box center method because the image moment method can more accurately reflect the geometric center of the droplet. Even if the droplet shape is slightly irregular, the centroid position can remain stable, avoiding the drift of the coordinate origin due to local deformation.

[0073] The outer contour boundary is extracted using an edge detection algorithm, which locates object edges by identifying pixels with abrupt changes in grayscale values ​​in the image. The outer contour boundary of the microcapsule droplet corresponds to the interface between the droplet's outer surface and the flowing phase. Due to the difference in refractive indices between the two phases, this interface appears as a distinct grayscale transition band in the microscopic image. The outer contour boundary obtained after edge detection is a set of discrete edge pixels distributed around the droplet's outer contour. However, due to noise and local grayscale inhomogeneity, these edge pixels are not strictly located on an ideal circle. The inner phase interface is also extracted using an edge detection algorithm. The inner phase interface corresponds to the interface between the microcapsule core and the outer shell. Since the inner and outer phase fluids typically have different optical properties, this interface appears as a second grayscale transition band located inside the droplet in the microscopic image. Detecting the inner phase interface is generally more difficult than detecting the outer contour boundary because the grayscale contrast of the inner phase interface may be weaker and it is easily interfered with by reflected light from the outer contour boundary. To improve the detection accuracy of the internal phase interface, local contrast enhancement can be performed on the internal region of the droplet before edge detection, or a multi-scale edge detection method can be used for comprehensive judgment.

[0074] The actual outer circle is fitted using a circular fitting algorithm, which fits the discrete pixels of the outer contour boundary into a standard circle. The input to the circular fitting algorithm is the set of coordinates of the edge pixels, and the output is the coordinates of the circle center and the radius of the circle. Commonly used circular fitting methods include least squares circular fitting and Hough circle transform. Least squares circular fitting determines the circle parameters by minimizing the sum of the squares of the distances from the edge points to the fitted circle, while Hough circle transform identifies parameter combinations whose edge point distribution conforms to circular characteristics through a parameter space voting mechanism. The fitted circle is defined as the actual outer circle, and the diameter of the actual outer circle is the actual total particle size. The actual total particle size is calculated by converting twice the radius of the fitted circle using a pixel size calibration coefficient. The pixel size calibration coefficient is obtained by taking an image of a standard ruler of known size under the same optical configuration, and calculating the ratio of the actual length of the ruler to the number of pixels it occupies in the image. This ratio is the actual physical size corresponding to each pixel. The fitting process for the actual inner circle is similar to that for the actual outer circle. Discrete pixels at the inner phase interface are fitted to a standard circle, and the resulting circle is defined as the actual inner circle. The diameter of the actual inner circle is the actual core diameter. The actual outer circle and the actual inner circle together form a real-time concentric circle group. This group shares the droplet's centroid coordinates as its common center. The actual outer circle represents the actual outer contour of the current droplet, and the actual inner circle represents the actual core-shell interface. Simplifying the complex physical morphology of the microcapsule droplet into a geometric representation of a real-time concentric circle group is based on the highly axisymmetric physical characteristic of microcapsule droplets under steady-state generation conditions. The laminar flow field within the microfluidic chip channel subjects the droplet to uniform shear force during generation. Driven by surface tension, the droplet tends to form a spherical shape, and its projection on the observation plane is close to a perfect circle. Using a concentric circle model instead of a more complex elliptical or irregular shape model significantly reduces the computational complexity of subsequent geometric comparisons while preserving key information about the core-shell structure. If the droplet shape deviates significantly from a circle, it indicates that the flow field is in an unstable state. The system should first adjust the flow velocity parameters to restore the flow field to stability before fine-tuning.

[0075] Step S22: Geometrically superimpose the real-time concentric circle group and the ideal concentric ring geometric model with the centroid coordinate as the origin. Generate an outer diameter deviation ring based on the regional differences between the actual outer circle and the ideal outer circle, and generate a structural deviation ring based on the differences between the actual shell region and the ideal shell region. The actual shell region is the annular region between the actual outer circle and the actual inner circle, and the ideal shell region is the annular region between the ideal outer circle and the ideal inner circle. The deviation annular domain is jointly formed by the outer diameter deviation ring and the structural deviation ring.

[0076] Specifically, the geometric superposition is performed in a virtual coordinate system with the droplet's centroid coordinates as its origin. This origin ensures that the real-time concentric circle group and the ideal concentric ring geometric model are aligned with a common geometric center. During the geometric superposition process, the actual outer circle of the real-time concentric circle group overlaps with the ideal outer circle of the ideal concentric ring geometric model, and the actual inner circle of the real-time concentric circle group overlaps with the ideal inner circle of the ideal concentric ring geometric model, forming a geometric superposition diagram containing four concentric circles. Because there is usually a deviation between the actual droplet's geometric parameters and the target parameters, the actual outer circle and the ideal outer circle do not completely coincide, and the actual inner circle and the ideal inner circle also do not completely coincide. These non-coincident areas constitute the geometric difference regions characterizing manufacturing deviations.

[0077] The outer diameter deviation ring is formed by the region between the actual outer circle and the ideal outer circle, and its geometry depends on the relative size of the actual total particle size and the target total particle size. When the actual total particle size is larger than the target total particle size, the radius of the actual outer circle is larger than the radius of the ideal outer circle, and the circumference of the actual outer circle lies outside the circumference of the ideal outer circle. A ring-shaped region is formed between the two circles, defined as the positive expansion region. The existence of the positive expansion region indicates that the current droplet size exceeds the target specification, the fluid shear force is weaker than the expansion force, and the system needs to enhance the shear compression vector or weaken the core expansion vector to compress the droplet size back to the target range. When the actual total particle size is smaller than the target total particle size, the radius of the actual outer circle is smaller than the radius of the ideal outer circle, and the circumference of the actual outer circle lies inside the circumference of the ideal outer circle. The ring-shaped region formed between the two circles is defined as the negative contraction region. The existence of the negative contraction region indicates that the current droplet size is smaller than the target specification, the fluid shear force is stronger than the expansion force, and the system needs to weaken the shear compression vector or enhance the core expansion vector to expand the droplet size back to the target range. The radial width of the outer diameter deviation ring is equal to half the absolute value of the difference between the actual total droplet size and the target total droplet size. This width value directly reflects the degree to which the overall size of the droplet deviates from the target.

[0078] The actual shell region is defined as the annular region between the actual outer circle and the actual inner circle, corresponding to the outer shell portion of the current droplet. The ideal shell region is defined as the annular region between the ideal outer circle and the ideal inner circle, corresponding to the outer shell portion of the target droplet. The structural deviation loop is generated by comparing the differences between the actual shell region and the ideal shell region. Since the radii of the actual and ideal outer circles may differ, directly comparing the two shell regions would be affected by the overall scale difference; therefore, a comparison of structural proportions is required in a normalized coordinate system. The ideal concentric ring geometric model is scaled proportionally so that the ideal outer circle coincides with the actual outer circle. The scaled ideal inner circle is defined as the normalized ideal inner circle, and its radius is equal to the actual total particle size multiplied by the target core diameter ratio and then divided by two. The structural deviation loop is defined as the annular region between the normalized ideal inner circle and the actual inner circle. The actual core diameter ratio is defined as the actual core diameter divided by the actual total particle size, and the target core diameter ratio is defined as the target core diameter divided by the target total particle size. When the actual core diameter ratio is less than the target core diameter ratio, the radius of the actual inner circle is smaller than the radius of the normalized ideal inner circle, and the actual inner circle is located inside the normalized ideal inner circle. This indicates that the proportion of the actual core diameter to the actual total particle size is low, meaning the shell is relatively thick relative to the overall size. The structural deviation ring corresponding to this state is defined as the shell redundancy domain. When the actual core diameter ratio is greater than the target core diameter ratio, the radius of the actual inner circle is greater than the radius of the normalized ideal inner circle, and the actual inner circle is located outside the normalized ideal inner circle. This indicates that the proportion of the actual core diameter to the actual total particle size is high, meaning the shell is relatively thin relative to the overall size. The structural deviation ring corresponding to this state is defined as the shell deficiency domain. The radial width of the structural deviation ring is equal to the absolute value of the difference between the actual inner circle radius and the normalized ideal inner circle radius. This width value is proportional to the deviation between the actual core diameter ratio and the target core diameter ratio. The deviation ring domain is composed of the outer diameter deviation ring and the structural deviation ring. The outer diameter deviation ring reflects the deviation of the overall droplet size from the target size, while the structural deviation ring reflects the deviation of the internal structure ratio of the droplet from the target ratio. The geometric visualization of the two types of deviation rings is intuitive: the existence of the outer diameter deviation ring can be directly determined by observing whether the actual outer circle coincides with the ideal outer circle; the properties of the structural deviation ring can be directly determined by observing the "expansion and contraction" state of the actual shell region relative to the ideal shell region. This geometrically visualized deviation representation transforms the abstract fluid dynamics parameter deviation into a concrete difference in graphic area, facilitating the subsequent step S23 to quantify it into a numerical deviation that can participate in mathematical calculations. The construction of the deviation ring domain maps the unobservable three-phase fluid dynamics imbalance state in the microfluidic system into a visualized geometric difference. The outer diameter deviation ring reflects the degree of imbalance between the shear compression vector and the overall expansion force, while the structural deviation ring reflects the relative degree of imbalance between the core expansion vector and the shell support vector. The independent construction of the two types of deviation rings lays the geometric foundation for the hierarchical quantification in the subsequent step S23.Traditional microfluidic control methods typically focus only on droplet size, neglecting the monitoring of the internal structure proportions of the droplet. This results in the failure to detect defective products that meet the external dimensional standards but have internal structural imbalances in a timely manner. The deviation annular domain, by simultaneously presenting outer diameter deviation rings and structural deviation rings, uniformly represents the overall dimensional deviations and internal structural deviations of the droplet within the same geometric framework. This allows subsequent control strategies to simultaneously address the correction needs of both types of deviations.

[0079] Step S23: Measure the actual outer diameter as the actual total particle size, measure the actual inner diameter as the actual core diameter, and calculate the actual core diameter ratio based on the ratio of the actual core diameter to the actual total particle size; calculate the difference between the actual total particle size and the target total particle size as the overall scale deviation, and calculate the difference between the actual core diameter ratio and the target core diameter ratio as the structural proportion deviation. The overall scale deviation and the structural proportion deviation together constitute the geometric deformation characteristic component.

[0080] Specifically, the extraction of geometric deformation feature components is achieved through quantitative hierarchical measurement of the deviation annular domain. The outer diameter deviation ring, as the first layer of deviation characterization, has a radial width that directly corresponds to the absolute deviation between the actual total particle size and the target total particle size. The structural deviation ring, as the second layer of deviation characterization, has a radial width that reflects the relative deviation between the actual core diameter ratio and the target core diameter ratio. The hierarchical decomposition process transforms the two-dimensional annular geometric difference into two independent one-dimensional deviation components, enabling subsequent regulation to adopt corresponding compensation strategies for deviations at different levels. The measurement of the actual total particle size is based on the actual outer circle parameters obtained by circular fitting in step S21. The diameter value of the actual outer circle, after conversion using pixel size calibration coefficients, becomes the physical dimension value of the actual total particle size. The measurement of the actual core diameter is based on the actual inner circle parameters obtained by circular fitting in step S21. The diameter value of the actual inner circle, after conversion using the same pixel size calibration coefficients, becomes the physical dimension value of the actual core diameter. Consistency in pixel size calibration coefficients ensures the dimensional comparability between the actual total particle size and the actual kernel diameter, avoiding distortion in kernel diameter ratio calculation due to calibration errors.

[0081] The actual nucleus-to-diameter ratio is calculated as a ratio, specifically by dividing the actual nucleus diameter by the actual total droplet diameter. The actual nucleus-to-diameter ratio is a dimensionless value, ranging from 0 to 1, representing the relative proportion of the droplet nucleus within the overall size: a ratio closer to 1 indicates a larger nucleus proportion and a relatively thinner shell; a ratio closer to 0 indicates a smaller nucleus proportion and a relatively thicker shell. The reason for using the nucleus-to-diameter ratio instead of directly using the absolute value of the shell thickness is that the absolute value of the shell thickness changes with the overall droplet size, making it difficult to independently reflect the proportional relationship of the internal structure. The nucleus-to-diameter ratio, as a dimensionless proportional parameter, can independently represent the relative state of the internal structure even with changes in the overall droplet size, allowing for separate evaluation of whether the "overall size meets the standard" and whether the "internal structure meets the standard." The overall scale deviation is calculated using a subtraction method, specifically by subtracting the target total droplet diameter from the actual total droplet diameter, with the result being a signed numerical value. The sign of the overall scale deviation carries information about the direction of the deviation: a positive value indicates that the actual total particle size is larger than the target total particle size, meaning the current droplet size is too large; a negative value indicates that the actual total particle size is smaller than the target total particle size, meaning the current droplet size is too small; and a zero value indicates that the actual total particle size is equal to the target total particle size, meaning the current droplet size meets the target. The absolute value of the overall scale deviation carries information about the magnitude of the deviation; the larger the absolute value, the further the size deviates from the target. The physical meaning of the overall scale deviation lies in reflecting the degree of imbalance between the shear compression vector and the overall expansion force: when the overall scale deviation is positive, it indicates that the expansion force is stronger than the shear compression force, and the droplet is expanded; when the overall scale deviation is negative, it indicates that the shear compression force is stronger than the expansion force, and the droplet is compressed.

[0082] The calculation of structural proportion deviation also employs a subtraction method. Specifically, the calculation process involves subtracting the target core diameter ratio from the actual core diameter ratio, resulting in a signed numerical value. The sign of the structural proportion deviation carries information about the direction of deviation: a positive value indicates that the actual core diameter ratio is greater than the target core diameter ratio, meaning the current droplet core proportion is high and the shell is thin; a negative value indicates that the actual core diameter ratio is less than the target core diameter ratio, meaning the current droplet core proportion is low and the shell is thick; a zero value indicates that the actual core diameter ratio equals the target core diameter ratio, meaning the current droplet internal structure meets the standard. The physical meaning of the structural proportion deviation lies in reflecting the relative imbalance between the core expansion vector and the shell support vector: when the structural proportion deviation is positive, it indicates that the core expansion vector is stronger relative to the shell support vector, resulting in an enlarged core and a relatively thinner shell; when the structural proportion deviation is negative, it indicates that the shell support vector is stronger relative to the core expansion vector, resulting in a thicker shell and a lower relative core proportion.

[0083] The geometric deformation feature components are composed of the overall scale deviation and the structural proportion deviation, represented as a two-dimensional vector. The first component of the vector is the overall scale deviation, and the second component is the structural proportion deviation. The introduction of the geometric deformation feature components achieves a precise transformation from the visualized deviation annular domain in step S22 to a numerical vector that can participate in matrix operations. Although the deviation annular domain can intuitively present the geometric distribution of manufacturing deviations, it is itself a graphical object and cannot be directly used as input to the control algorithm. The geometric deformation feature components quantify the graphical deviation into vector components with clear numerical values ​​and signs, enabling the inverse coupling deconstruction matrix in step S31 to perform matrix operations on it and calculate the flow rate adjustment amount required to eliminate the deviation.

[0084] Decomposing the geometric deformation characteristic components into two independent components—overall size deviation and structural proportion deviation—is based on the two-dimensional nature of microcapsule droplet quality evaluation. The qualification of a microcapsule droplet requires simultaneously satisfying two conditions: the overall size and the internal structural proportions must be within the target range. These two conditions are independent; droplets may exhibit states of "meeting size standards but structural misalignment" or "meeting structural standards but size deviation." Single-dimensional deviation detection cannot comprehensively reflect the droplet's quality status. The two-dimensional structure of the geometric deformation characteristic components strictly corresponds to the output dimension of the force-shape coupling response matrix: the first row of the force-shape coupling response matrix describes the influence of flow velocity on the total particle size, corresponding to the overall size deviation; the second row describes the influence of flow velocity on the core diameter ratio, corresponding to the structural proportion deviation. This dimensional correspondence ensures the mathematical self-consistency of the inverse coupling deconstruction matrix operation in step S31. The force-shape coupling response matrix established in step S12 describes the positive mapping relationship from the change in flow velocity to the change in geometric parameters. The geometric deformation feature components extracted in step S23 correspond precisely to the output dimension of the force-shape coupling response matrix. Step S31 achieves the reverse mapping from geometric parameter deviation to flow velocity adjustment by obtaining the inverse matrix of the force-shape coupling response matrix. If the dimension or definition of the deviation output in step S23 does not match the output dimension of the force-shape coupling response matrix, the matrix operation in step S31 will result in a dimension error, and the collaborative control strategy will fail to execute. The definition of the geometric deformation feature components strictly follows the output structure of the force-shape coupling response matrix, ensuring a smooth mathematical path from deviation detection to collaborative compensation.

[0085] Step S20 completes the real-time perception mapping from physical morphology to digital features through image sequence acquisition, real-time concentric circle fitting, and deviation annular domain decomposition. First, machine vision is used to capture transient fluid morphology and fit it into a real-time concentric circle group. A visualized deviation annular domain is generated by geometrically superimposing this onto an ideal concentric annular geometric model, intuitively presenting the dual physical states of "outer diameter expansion and contraction" and "structural misalignment." Second, the actual core diameter ratio is introduced to decouple the abstract graphic difference quantity into an overall scale deviation quantity and a structural proportion deviation quantity, constructing geometric deformation feature components that strictly correspond to the output dimension of the force-shape coupling response matrix. This process not only overcomes the time lag and the one-sidedness of single-index evaluation in traditional offline sampling but also transforms unmeasurable fluid dynamics errors into calculable vector data, establishing a precise numerical feedback foundation for the closed-loop collaborative control in the subsequent step S30.

[0086] Step S30: Calculate the cooperative flow velocity adjustment vector based on the geometric deformation characteristic components and the force-shape coupling response matrix, form a composite fluid correction field based on the cooperative flow velocity adjustment vector, and iteratively execute the deviation loop ablation cycle to achieve microcapsule geometric convergence.

[0087] Specifically, step S30 utilizes the force-shape coupling response matrix for inverse calculation, transforming the detected geometric deviation into a three-phase flow velocity correction command capable of eliminating the deviation. Through closed-loop iteration, the geometry of the microcapsule droplets gradually converges to the target state. In the microfluidic system, the flow velocities of the inner phase, outer phase, and mobile phase have a cross-influence on the total particle size and core diameter ratio of the microcapsule droplets. This multi-input, multi-output coupling characteristic renders traditional single-variable feedback control strategies ineffective: when the operator discovers that the microcapsule shell is too thick and attempts to correct it by reducing the outer phase flow velocity, the reduction not only thins the shell but also alters the pressure distribution within the channel, causing a change in the effective shear force of the mobile phase on the droplets and triggering an unexpected shift in the total particle size; when the operator continues to adjust the mobile phase flow velocity to correct the particle size shift, it in turn interferes with the shell thickness, forming a repeated cycle of correction and deviation, causing the system to remain in an oscillating state for an extended period and unable to converge to a steady state. Step S30 introduces an inverse coupling deconstruction matrix to achieve a one-time coordinated calculation from geometric deviation to flow rate regulation. Within a single control cycle, it simultaneously provides the correction values ​​for the three-phase flow rates, enabling the three-phase pumps to operate synchronously and form a composite fluid correction field. This correction field simultaneously considers the coordinated compensation requirements for total particle size and core diameter ratio, avoiding parameter competition and oscillation problems caused by single-variable regulation, and achieving synchronous convergence of multiple objectives.

[0088] Further, step S30 includes:

[0089] Step S31: Perform a generalized inverse operation on the force-shape coupling response matrix to obtain the inverse coupling deconstruction matrix, construct the geometric deformation characteristic components as deviation vectors, perform matrix operations on the inverse coupling deconstruction matrix and the deviation vectors, and introduce an anti-oscillation gain coefficient to calculate the coordinated flow velocity regulation vector containing the inner phase flow velocity regulation amount, the outer phase flow velocity regulation amount and the mobile phase flow velocity regulation amount.

[0090] Specifically, the construction of the inverse coupling deconstruction matrix is ​​based on the force-shape coupling response matrix established in step S12. The force-shape coupling response matrix is ​​a 2x3 matrix. The number of rows corresponds to the geometric parameter output dimension, including the total particle size change and the core diameter ratio change. The number of columns corresponds to the velocity input dimension, including the internal phase velocity change, the external phase velocity change, and the mobile phase velocity change. This matrix describes the positive mapping relationship from velocity changes to geometric parameter changes. The purpose of solving the inverse coupling deconstruction matrix is ​​to establish a negative mapping relationship from geometric parameter deviations to velocity regulation, enabling the control system to directly calculate the velocity regulation of each phase required to eliminate the detected geometric deviation. Since the force-shape coupling response matrix is ​​a non-square matrix, its number of rows and columns are not equal, so standard matrix inversion cannot be directly applied. The generalized inverse operation uses the Moore-Ponrose pseudo-inverse method to process the force-shape coupling response matrix. The Moore-Ponrose pseudo-inverse is a mathematical tool that extends the concept of matrix inversion to non-square matrices; for any matrix, there exists a unique pseudo-inverse matrix. The pseudo-inverse of the force-form coupling response matrix is ​​defined as the inverse coupling deconstruction matrix. The inverse coupling deconstruction matrix has a dimension of 3 rows and 2 columns, and its dimension is the transpose of the force-form coupling response matrix. The formula for calculating the inverse coupling deconstruction matrix follows the standard definition of the Moore-Ponros pseudo-inverse: Let the force-form coupling response matrix be J, first calculate J and its transpose matrix. product The product is a 2x2 square matrix; then, the inverse of this matrix is ​​obtained. Finally, calculate the transpose matrix of J. and the inverse square The product of, i.e., the inverse coupling deconstruction matrix In this formula, the transpose of the force-shape coupling response matrix is ​​a 3x2 matrix, and the product of the force-shape coupling response matrix and its transpose is a 2x2 square matrix. The inverse of this square matrix is ​​also a 2x2 square matrix. Multiplying the 3x2 matrix by the 2x2 square matrix yields a 3x2 inverse coupling deconstruction matrix. The technical reason for using the Moore-Ponrose pseudo-inverse method instead of other generalized inverse methods is that the force-shape coupling response matrix describes an underdetermined system where the input dimension (three-phase velocity) is larger than the output dimension (two geometric parameters). There are infinitely many sets of velocity adjustments that can produce the same geometric parameter changes. The Moore-Ponrose pseudo-inverse selects the solution with the smallest norm among all feasible solutions, i.e., the solution with the smallest sum of velocity adjustments for each phase. This characteristic minimizes the disturbance to the system while meeting the geometric correction requirements, which is beneficial for maintaining the stability of the microfluidic system.

[0091] The elements in the first row and first column of the inverse coupling deconstruction matrix represent the contribution weight of the global scale deviation to the internal phase velocity regulation, and the elements in the first row and second column represent the contribution weight of the structural proportion deviation to the internal phase velocity regulation. Together, they determine the regulation direction and magnitude of the internal phase pump. The elements in the second row and first column of the inverse coupling deconstruction matrix represent the contribution weight of the global scale deviation to the external phase velocity regulation, and the elements in the second row and second column represent the contribution weight of the structural proportion deviation to the external phase velocity regulation. Together, they determine the regulation direction and magnitude of the external phase pump. The elements in the third row and first column of the inverse coupling deconstruction matrix represent the contribution weight of the global scale deviation to the mobile phase velocity regulation, and the elements in the third row and second column represent the contribution weight of the structural proportion deviation to the mobile phase velocity regulation. Together, they determine the regulation direction and magnitude of the mobile phase pump. For example, when the overall size deviation is positive, meaning the droplet size is too large, while the structural proportion deviation is zero, meaning the core-to-diameter ratio meets the standard, the control system needs to maintain the core-to-diameter ratio unchanged while compressing the droplet size. The inverse coupling deconstruction matrix automatically calculates the three-phase flow rate synergistic adjustment amount that can achieve "adjusting only the droplet size while keeping the core-to-diameter ratio unchanged" through the weighting ratio of the elements in its first column. This adjustment amount does not only adjust the flow rate of the mobile phase, but also adjusts the flow rates of the inner phase, outer phase, and mobile phase in a precise proportion at the same time. The synergistic effect of the three-phase adjustment amount makes the core and shell shrink proportionally during the radial compression of the droplet, thereby maintaining the core-to-diameter ratio unchanged.

[0092] The construction of the deviation vector is based on the geometric deformation feature components output in step S23. These components include two parts: the overall scale deviation and the structural proportion deviation. The overall scale deviation equals the actual total particle size minus the target total particle size, and the structural proportion deviation equals the actual core diameter ratio minus the target core diameter ratio. The deviation vector is defined as a two-dimensional column vector with the overall scale deviation as the first component and the structural proportion deviation as the second component. The construction of the deviation vector integrates two independent scalar deviation values ​​into a single vector form, allowing it to directly participate in matrix operations. The dimension of the deviation vector strictly matches the number of columns in the inverse coupling deconstruction matrix, ensuring the mathematical validity of matrix multiplication: multiplying the three-row, two-column inverse coupling deconstruction matrix by the two-row, one-column deviation vector yields a three-row, one-column cooperative flow rate regulation vector. The sign of each component in the deviation vector carries the deviation direction information. A positive overall scale deviation indicates that the droplet is too large, and a negative deviation indicates that the droplet is too small. A positive structural proportion deviation indicates that the shell is too thin, and a negative deviation indicates that the shell is too thick. The sign information is automatically transmitted to the cooperative flow velocity adjustment vector in the matrix operation, so that the adjustment direction and the deviation direction form a negative feedback correspondence.

[0093] The calculation of the coordinated flow velocity regulation vector employs matrix multiplication. After multiplying the inverse coupling deconstruction matrix by the deviation vector, a negative sign is introduced to achieve reverse compensation control: when the deviation vector indicates a larger droplet size, the coordinated flow velocity regulation vector must reduce the droplet size; when the deviation vector indicates a thicker shell, the coordinated flow velocity regulation vector must thin the shell. The introduction of the negative sign transforms positive deviation into reverse regulation, conforming to the basic principle of closed-loop negative feedback control. The complete formula for calculating the coordinated flow velocity regulation vector is: the coordinated flow velocity regulation vector Δv equals the negative inverse coupling deconstruction matrix multiplied by the deviation vector e, and then multiplied by the anti-oscillation gain coefficient k, i.e., Δv = -J. + ×e×k. The coordinated flow rate regulation vector is a three-dimensional column vector. The first component is the internal phase flow rate regulation amount, the second component is the external phase flow rate regulation amount, and the third component is the mobile phase flow rate regulation amount. The three regulation amounts are not calculated independently, but are determined collaboratively through the same matrix operation. This collaborative determination characteristic makes the regulation actions of the three-phase pump mathematically related and physically coordinated, avoiding mutual interference caused by coupling effects when each phase is regulated independently.

[0094] The introduction of the anti-oscillation gain coefficient is based on considerations of control system stability. In a closed-loop control system, if the adjustment gain is too large, the system's response to deviations will be too aggressive, potentially leading to overshoot, where the adjustment exceeds the actual demand, causing the deviation to overshoot. If overshoot occurs repeatedly, the system will fall into a state of continuous oscillation, with droplet parameters fluctuating around the target value without stability. The anti-oscillation gain coefficient, as a positive scalar between zero and one, scales the cooperative flow rate adjustment vector, limiting the adjustment range of each iteration to a safe range. The closer the anti-oscillation gain coefficient is to one, the faster the adjustment response but the smaller the stability margin; the closer the value is to zero, the slower the adjustment response but the higher the stability. The specific value of the anti-oscillation gain coefficient needs to be determined based on the dynamic response characteristics of the microfluidic system. The determination method is as follows: during the system debugging phase, start with a small gain value and gradually increase it, observe the convergence process of the deviation loop ablation cycle, record the convergence speed and whether oscillation occurs at different gain values, and select the gain value with the fastest convergence speed without oscillation as the anti-oscillation gain coefficient. For example, for high-viscosity fluid systems with significant response lag, the anti-oscillation gain coefficient can be set to 0.3 to 0.5; for low-viscosity fluid systems with sensitive response, the anti-oscillation gain coefficient can be set to 0.6 to 0.8. The anti-oscillation gain coefficient acts multiplicatively on the matrix operation result in the calculation formula of the coordinated flow velocity adjustment vector, scaling the three flow velocity adjustment quantities proportionally to maintain the proportional relationship between the three-phase adjustment quantities, ensuring that the correctness of the coordinated compensation direction is not affected by the gain scaling.

[0095] Step S31 directly collaborates with step S12. The force-shape coupling response matrix established in step S12 quantifies the cross-influence of three-phase flow velocity on two geometric parameters. This forward mapping relationship is the mathematical premise for the reverse decoupling in step S31. Without the matrix construction in step S12, step S31 would not have an input object for generalized inverse operation, and the solution of the inverse coupling deconstruction matrix would be impossible. Step S31 also directly collaborates with step S23. The geometric deformation characteristic components output by step S23 provide the numerical content of the deviation vector. Without the deviation quantization in step S23, step S31 would lose the operation object for matrix operation, and the calculation of the coordinated flow velocity adjustment vector would lack input data. The introduction of the inverse coupling deconstruction matrix enables the control system to deconstruct multivariable coupling relationships, a capability that traditional single-variable feedback control lacks. Traditional control methods typically employ a single adjustment strategy of reducing the external phase velocity when shell thickness is detected. This strategy ignores the collateral effect of external phase velocity changes on the total particle size, leading to unexpected deviations in the total particle size while shell thickness is being corrected. Step S31 calculates the coordinated adjustment amount of the three-phase velocity in one step through the inverse coupling deconstruction matrix. This adjustment amount pre-compensates for the collateral effect on the total particle size while correcting the shell thickness, enabling both geometric parameters to converge to the target value simultaneously and avoiding the parameter competition phenomenon of "one increasing while the other decreases".

[0096] Step S32: The inner phase flow rate regulation amount in the coordinated flow rate regulation vector is converted into an inner phase pump control command, the outer phase flow rate regulation amount is converted into an outer phase pump control command, the mobile phase flow rate regulation amount is converted into a mobile phase pump control command, the inner phase pump control command is sent to the inner phase pump, the outer phase pump control command is sent to the outer phase pump, and the mobile phase pump control command is sent to the mobile phase pump. The three-phase pumps execute the control commands synchronously, forming a composite fluid correction field in the microfluidic chip channel.

[0097] Specifically, the three regulation quantities in the coordinated flow rate regulation vector are all in the form of flow rate increments, with units of volumetric flow rate, such as microliters per minute. These need to be converted into control commands recognizable by the pump body to drive the physical equipment. The process of converting the internal phase flow rate regulation quantity into an internal phase pump control command involves two levels: at the numerical level, the internal phase flow rate regulation quantity is superimposed on the current operating flow rate value of the internal phase pump to obtain the target flow rate setpoint of the internal phase pump; at the command level, the target flow rate setpoint is encapsulated according to the communication protocol format of the internal phase pump to generate a digital command that can be parsed by the internal phase pump controller. The process of converting the external phase flow rate regulation quantity into an external phase pump control command and the mobile phase flow rate regulation quantity into a mobile phase pump control command is the same as the generation process of the internal phase pump control command, only the operating objects are replaced by the external phase pump and the mobile phase pump, respectively. All three-phase pump control commands are generated by the central control module. The central control module is connected to the three-phase pump bodies in the flow supply module via a digital communication bus, which supports synchronous command transmission. The three-phase pump control commands are transmitted using a synchronous timing mechanism. The central control module simultaneously sends the control commands for the inner phase pump, the outer phase pump, and the flowing phase pump to the corresponding pump controllers, ensuring that the three-phase pumps can synchronously initiate adjustment actions upon receiving the commands. The purpose of synchronous execution of the control commands by the three-phase pumps is that the three-phase fluids within the microfluidic chip channel are in a state of dynamic interaction. Individual changes in the velocity of any phase will instantly affect the pressure distribution and flow field structure within the channel. If there is a time difference in the three-phase adjustment, the phase that is adjusted first will change the flow field state independently before the phase that is adjusted later has responded, resulting in unexpected and drastic fluctuations in droplet morphology during transient processes. Synchronous execution ensures that the three-phase velocity changes occur simultaneously, and the pressure distribution and flow field structure within the channel are synchronously reconstructed according to the preset proportional relationship of the coordinated velocity adjustment vector, avoiding transient interference caused by asynchronous adjustment.

[0098] After the three-phase pump body synchronously executes the adjustment command, the three-phase flow velocities within the microfluidic chip channel all change. The change in the inner phase flow velocity alters the magnitude of the core expansion vector, the change in the outer phase flow velocity alters the magnitude of the shell support vector, and the change in the flowing phase flow velocity alters the magnitude of the shear compression vector. The synchronous change in the magnitudes of the three force field vectors causes a complete reconstruction of the multiphase hydrodynamic state within the channel, forming a fluid action field different from that before adjustment. This fluid action field is defined as the composite fluid correction field. The "composite" characteristic of the composite fluid correction field is reflected in the fact that it is not generated by a single-phase flow velocity change, but by a precise proportional synergistic change in the three-phase flow velocities calculated according to the inverse coupling deconstruction matrix. The superposition effect of the three-phase action can accurately match the correction requirements of geometric deviations. The influence of the composite fluid correction field on droplet formation follows the mapping relationship described by the force-shape coupling response matrix in step S12: the changes in the three-phase flow velocities contained in the composite fluid correction field, after being mapped by the force-shape coupling response matrix, produce geometric parameter changes that are opposite in direction to the deviation vector, and whose amplitudes, after being scaled by the anti-oscillation gain coefficient, are proportional to the magnitude of the deviation vector. The composite fluid correction field acts on the droplet generation region, forcing the shape of subsequently generated droplets to change. The direction of the change is to move closer to the target shape, and the magnitude of the change is controlled by the anti-oscillation gain coefficient.

[0099] Step S33: Under the action of the composite fluid correction field, acquire the droplet image sequence of the newly generated droplets, and repeat steps S21 to S32 to form a deviation loop ablation cycle. In each deviation loop ablation cycle, calculate the weighted energy function of the geometric deformation characteristic component. When the calculated value of the weighted energy function is less than the preset convergence threshold, it is determined to be in steady state, and the current internal phase flow velocity, external phase flow velocity and mobile phase flow velocity are locked.

[0100] Specifically, after the composite fluid correction field is applied to the microfluidic chip channel, the hydrodynamic state of the droplet generation region changes, and the morphology of the subsequently generated droplets differs from that before the application of the composite fluid correction field. The microscopic imaging component continuously acquires droplet image sequences after the formation of the composite fluid correction field; the newly acquired image sequences contain morphological information of the newly generated droplets under the action of the composite fluid correction field. The construction of the deviation ring ablation cycle is achieved by repeatedly executing steps S21 to S32. Step S21 extracts the centroid coordinates, outer contour boundary, and inner phase interface from the new droplet image sequence and fits them to obtain a new real-time concentric circle group; Step S22 geometrically superimposes the new real-time concentric circle group with the ideal concentric ring geometric model to generate a new deviation ring domain; Step S23 performs hierarchical decomposition on the new deviation ring domain to obtain new geometric deformation feature components; Step S31 calculates a new cooperative flow rate adjustment vector based on the new geometric deformation feature components and the inverse coupling deconstruction matrix; Step S32 converts the new cooperative flow rate adjustment vector into a pump control command and executes it synchronously to form a new composite fluid correction field. The above sequence of steps constitutes a complete deviation loop ablation cycle. After each cycle, the geometric deformation characteristic components of the droplet decrease towards zero, and the area of ​​the deviation loop domain tends to shrink. This gradual decrease is figuratively described as the "ablation" of the deviation loop. The iterative execution of the deviation loop ablation cycle causes the droplet's geometric parameters to gradually approach the target value, achieving a gradual convergence from the initial deviation state to the steady-state target state.

[0101] The weighted energy function is calculated in each deviation loop ablation cycle and is used to quantify the proximity of the current geometric deformation eigencomponent to the zero vector. The weighted energy function E is defined as the square of the global scale deviation. Multiply by the scale weighting factor Square of the structural proportion deviation Multiply by structural weight coefficient The sum of The reason this function uses a squared form is that the squaring operation eliminates the sign information of the deviation, making positive and negative deviations contribute equally to the energy function, reflecting that the severity of the "deviation from the target" state is independent of the direction of deviation; the squaring operation also makes the contribution of larger deviations to the energy function more significant, prompting the control system to prioritize the elimination of larger deviations. Scale weighting coefficient and structural weight coefficients The introduction of this is used to balance the contributions of the two types of biases to the energy function. Due to the overall scale bias... The dimension of this is a unit of length, such as μm, representing structural proportion deviation. Since these are dimensionless values, their numerical scales differ significantly. Direct addition would cause the energy function to be dominated by the larger value. (Scale weighting coefficient) The dimension of is the negative square of length, such as μm.-2 Structural weight coefficient The energy function after weighting is a dimensionless number. It becomes a dimensionless scalar. The method for determining the weighting coefficients is as follows: first, determine the baseline allowable range of the overall scale deviation based on the application scenario. and the baseline allowable range of structural proportion deviation ,set up This ensures that the contribution to the energy function is 1 when any deviation reaches its allowable boundary; if it is necessary to adjust the relative importance of the two types of deviations, one can adjust the... or Multiply by an importance weighting factor. For example, the allowable deviation for the target total particle size is... μm, the allowable deviation of the target kernel diameter ratio is Application scenarios, setting , For drug sustained-release microcapsule applications requiring higher uniformity in shell thickness than in particle size, the following can be considered: Multiplying by an importance weighting factor of 2 makes the contribution of the core diameter ratio deviation to the energy function more significant, prompting the control system to prioritize ensuring the accuracy of the shell thickness.

[0102] The convergence threshold is set based on the application accuracy requirements of the microcapsule droplets. The convergence threshold is a positive scalar; when the calculated value of the weighted energy function is less than the convergence threshold, the system is considered to have reached a steady state. The convergence threshold defines the numerical boundary of "sufficiently close to the target": a weighted energy function less than the convergence threshold indicates that both the overall scale deviation and the structural proportion deviation have been controlled within acceptable ranges, and the accuracy improvement from further iterations is no longer significant; the droplet quality can be considered to have met the target requirements. The convergence threshold is determined as follows: based on the allowable deviation ranges of the target total particle size and the target core diameter ratio, calculate the weighted energy function value when the overall scale deviation equals the upper limit of the allowable deviation and the structural proportion deviation equals the upper limit of the allowable deviation. Use this value or a certain proportion thereof as the convergence threshold. For example, if the allowable deviation of the target total particle size is ±5 μm and the allowable deviation of the target core diameter ratio is ±0.02, substituting the overall scale deviation of 5 μm and the structural proportion deviation of 0.02 into the weighted energy function calculation, the resulting value is the reference value for the convergence threshold.

[0103] Once the system determines that a steady state has been reached, it performs a flow rate locking operation. The specific implementation of flow rate locking is as follows: the central control module stops executing the iterative logic of the deviation loop ablation cycle, stores the current values ​​of the inner phase flow rate, outer phase flow rate, and mobile phase flow rate as steady-state flow rate parameters, and sends a hold command to the three-phase pump to maintain the current flow rate. After flow rate locking, the composite fluid correction field within the microfluidic chip channel stabilizes into a mechanical equilibrium state matching the target geometry. Subsequent droplets maintain the target shape under this equilibrium state, achieving steady-state mass production of microcapsule droplets. Flow rate locking is not permanent. While locked, the system continues to perform droplet image acquisition and geometric deformation feature component calculation. If environmental disturbances, such as temperature drift causing changes in fluid viscosity, cause the weighted energy function to exceed the convergence threshold again, the system automatically unlocks and re-enters the deviation loop ablation cycle to correct the newly generated deviation. After correction, the flow rate is locked again. This mechanism ensures that the system can maintain the stability of droplet mass under external disturbance conditions.

[0104] Steps S21 to S32 constitute a single iteration of the deviation loop ablation cycle. Step S33 defines the termination condition and convergence criterion of the iteration, transforming the deviation loop ablation cycle from an infinite loop to a goal-oriented finite iteration. The introduction of the weighted energy function compresses the two-dimensional geometric deformation characteristic components into a one-dimensional scalar index, simplifying the convergence criterion from a multi-condition logic of "both components satisfying the condition" to a single-condition logic of "a single scalar satisfying the threshold." This simplifies the criterion logic while retaining the ability to distinguish the importance of the two types of deviations through the weighting coefficients. The iterative mechanism of the deviation loop ablation cycle enables the control system to asymptotically approach the target. Each iteration reduces the deviation, and after multiple iterations, the deviation converges below the threshold. This asymptotic convergence characteristic ensures the stability of the control process and avoids trial and error. Figure 1 The step-by-step operation eliminates the risk of over-adjustment. Flow rate locking minimizes control system overhead during steady-state operation. The energy consumption and wear of the pump body operating at a constant flow rate are lower than in continuous adjustment mode, extending equipment lifespan and reducing operating costs.

[0105] Step S30 completes the closed-loop control link from geometric deviation perception to physical field collaborative reconstruction by constructing an inverse coupling deconstruction matrix, calculating the collaborative flow rate adjustment vector, and iteratively executing the deviation loop ablation loop. First, the inverse coupling deconstruction matrix is ​​solved using generalized inverse operation, which maps the multidimensional coupling deviation that cannot be handled by single-variable feedback back to the collaborative flow rate adjustment vector, and calculates the synchronous correction command of the three-phase pump body in one go. Second, by introducing an anti-oscillation gain coefficient to drive the three-phase fluid to construct a composite fluid correction field, the total particle size and core diameter ratio are decoupled and compensated synchronously in physical space, fundamentally eliminating the parameter runaway oscillation of "correction-deviation-recorrection". Finally, a weighted energy function is used as a unified convergence criterion for two-dimensional deviation, combined with a flow rate locking mechanism, to ensure that the system converges to a steady state and maintains optimal operation within a finite number of iterations, realizing steady-state mass production with both the external size and internal structure of the microcapsules meeting the standards.

[0106] Example 2:

[0107] This embodiment, based on Embodiment 1, provides a microfluidic droplet formation detection and control system, such as... Figure 6 As shown, it includes:

[0108] Preset flow rate generation module: used to obtain the target parameters of microcapsules, construct an ideal concentric ring geometric model, establish a force-shape coupling response matrix, solve the preset flow rate triplet based on the ideal concentric ring geometric model and the force-shape coupling response matrix, and generate initial droplets based on the preset flow rate triplet;

[0109] Deformation feature extraction module: used to acquire droplet image sequence of initial droplet, fit real-time concentric circle group according to droplet image sequence, geometrically superimpose real-time concentric circle group with ideal concentric ring geometric model to generate deviation ring domain, and perform hierarchical decomposition of deviation ring domain to obtain geometric deformation feature components.

[0110] The collaborative flow rate control module is used to calculate the collaborative flow rate adjustment vector based on the geometric deformation characteristic components and the force-shape coupling response matrix, form a composite fluid correction field based on the collaborative flow rate adjustment vector, and iteratively execute the deviation loop ablation cycle to achieve the geometric convergence of the microcapsule.

[0111] Furthermore, in the preset flow velocity generation module, the method for constructing the ideal concentric ring geometric model includes:

[0112] The target kernel diameter is calculated based on the total target particle size and the target shell thickness. An ideal outer circle is constructed with the total target particle size as the diameter, and an ideal inner circle is constructed with the target kernel diameter as the diameter. The ideal outer circle and the ideal inner circle together form an ideal concentric ring geometric model.

[0113] The method for solving the preset velocity triplet includes:

[0114] Based on the target total particle size, the initial value of the mobile phase velocity is determined; based on the determined initial value of the mobile phase velocity, a reference velocity triplet is constructed; and a geometric target deviation vector is constructed based on the reference velocity triplet, the target total particle size, and the target core diameter ratio.

[0115] Based on a defined initial flow velocity of the mobile phase, the target total particle size and target core diameter ratio in the ideal concentric ring geometric model are used as constraints. The geometric target deviation vector is mapped to a flow velocity adjustment vector using the generalized inverse matrix of the force-shape coupling response matrix. The flow velocity adjustment vector is then superimposed on the reference flow velocity triplet to obtain a preset flow velocity triplet containing the initial values ​​of the inner phase flow velocity, the outer phase flow velocity, and the initial values ​​of the mobile phase flow velocity.

[0116] Furthermore, in the deformation feature extraction module, the method for generating the deviation annular domain includes:

[0117] An outer diameter deviation ring is generated based on the regional difference between the actual outer circle and the ideal outer circle, and a structural deviation ring is generated based on the difference between the actual shell region and the ideal shell region. The actual shell region is the annular region between the actual outer circle and the actual inner circle, and the ideal shell region is the annular region between the ideal outer circle and the ideal inner circle. The deviation annular domain is jointly formed by the outer diameter deviation ring and the structural deviation ring.

[0118] Furthermore, in the collaborative flow velocity control module, the calculation method for the collaborative flow velocity adjustment vector includes:

[0119] A generalized inverse operation is performed on the force-shape coupling response matrix to obtain the inverse coupling deconstruction matrix. The geometric deformation characteristic components are constructed as deviation vectors. The inverse coupling deconstruction matrix and the deviation vector are subjected to matrix operation, and an anti-oscillation gain coefficient is introduced to calculate the coordinated flow velocity regulation vector, which includes the internal phase flow velocity regulation, the external phase flow velocity regulation, and the mobile phase flow velocity regulation.

[0120] The methods and systems of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this application are not limited to the order specifically described above, unless otherwise specifically stated.

[0121] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0122] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A microfluidic droplet formation detection and regulation method, characterized in that, The method includes: The target parameters of the microcapsules are obtained, including the target total particle size and the target shell thickness. The target core diameter is calculated based on the target total particle size and the target shell thickness. An ideal outer circle is constructed with the target total particle size as the diameter, and an ideal inner circle is constructed with the target core diameter as the diameter. The ideal outer circle and the ideal inner circle together form an ideal concentric ring geometric model. A ternary force field vector space is defined, which includes the core expansion vector generated by the inner phase flow velocity, the shell support vector generated by the outer phase flow velocity, and the shear compression vector generated by the mobile phase flow velocity. The partial derivative relationships of the inner phase flow velocity, outer phase flow velocity, and mobile phase flow velocity with respect to the total particle size and the core diameter ratio are established. A force-shape coupling response matrix is ​​constructed. The preset flow velocity triplet is solved based on the ideal concentric ring geometric model and the force-shape coupling response matrix. The initial droplet is generated based on the preset flow velocity triplet. The droplet image sequence of the initial droplet is acquired, and a real-time concentric circle group is fitted according to the droplet image sequence. The real-time concentric circle group is geometrically superimposed with the ideal concentric ring geometric model to generate a deviation ring domain. The deviation ring domain is decomposed in layers to obtain the geometric deformation feature components. The coordinated flow velocity adjustment vector is calculated based on the geometric deformation characteristic components and the force-shape coupling response matrix. A composite fluid correction field is formed based on the coordinated flow velocity adjustment vector. The deviation loop ablation cycle is iteratively executed to achieve microcapsule geometric convergence. This includes acquiring droplet image sequences of newly generated droplets under the action of the composite fluid correction field, refitting a real-time concentric circle group based on the droplet image sequence of the newly generated droplets, geometrically superimposing the refitted real-time concentric circle group with the ideal concentric ring geometric model to generate a new deviation ring domain, performing hierarchical decomposition on the new deviation ring domain to obtain new geometric deformation characteristic components, recalculating the coordinated flow velocity adjustment vector based on the new geometric deformation characteristic components and the force-shape coupling response matrix, and forming a new composite fluid correction field based on the recalculated coordinated flow velocity adjustment vector. The above process constitutes a complete deviation loop ablation cycle. In each deviation loop ablation cycle, the weighted energy function of the geometric deformation characteristic components is calculated. When the calculated value of the weighted energy function is less than the preset convergence threshold, it is determined to be in a steady state, and the current internal phase flow velocity, external phase flow velocity, and mobile phase flow velocity are locked to achieve microcapsule geometric convergence.

2. The microfluidic droplet forming detection and regulation method of claim 1, wherein, The method for solving the preset velocity triplet includes: Based on the target total particle size, the initial value of the mobile phase velocity is determined; based on the determined initial value of the mobile phase velocity, a reference velocity triplet is constructed; and a geometric target deviation vector is constructed based on the reference velocity triplet, the target total particle size, and the target core diameter ratio. Based on a defined initial flow velocity of the mobile phase, the target total particle size and target core diameter ratio in the ideal concentric ring geometric model are used as constraints. The geometric target deviation vector is mapped to a flow velocity adjustment vector using the generalized inverse matrix of the force-shape coupling response matrix. The flow velocity adjustment vector is then superimposed on the reference flow velocity triplet to obtain a preset flow velocity triplet containing the initial values ​​of the inner phase flow velocity, the outer phase flow velocity, and the initial values ​​of the mobile phase flow velocity.

3. The microfluidic droplet forming detection and regulation method of claim 2, wherein, The method for fitting a real-time set of concentric circles includes: The centroid coordinates, outer contour boundary, and inner phase interface of a single droplet are extracted from the droplet image sequence. The outer contour boundary is fitted to the actual outer circle, and the inner phase interface is fitted to the actual inner circle. The actual outer circle and the actual inner circle together form a real-time concentric circle group.

4. The microfluidic droplet forming detection and regulation method of claim 3, wherein, The method for generating the deviation annular domain includes: An outer diameter deviation ring is generated based on the regional difference between the actual outer circle and the ideal outer circle, and a structural deviation ring is generated based on the difference between the actual shell region and the ideal shell region. The actual shell region is the annular region between the actual outer circle and the actual inner circle, and the ideal shell region is the annular region between the ideal outer circle and the ideal inner circle. The deviation annular domain is jointly formed by the outer diameter deviation ring and the structural deviation ring.

5. The microfluidic droplet forming detection and regulation method of claim 4, wherein, The method for generating the geometric deformation feature components includes: The actual outer diameter is measured as the actual total particle size, and the actual inner diameter is measured as the actual core diameter. The actual core diameter ratio is calculated based on the ratio of the actual core diameter to the actual total particle size. The difference between the actual total particle size and the target total particle size is calculated as the overall scale deviation, and the difference between the actual core diameter ratio and the target core diameter ratio is calculated as the structural proportion deviation. The overall scale deviation and the structural proportion deviation together constitute the geometric deformation characteristic component.

6. The microfluidic droplet forming detection and regulation method of claim 5, wherein, The calculation method for the coordinated flow velocity adjustment vector includes: A generalized inverse operation is performed on the force-shape coupling response matrix to obtain the inverse coupling deconstruction matrix. The geometric deformation characteristic components are constructed as deviation vectors. The inverse coupling deconstruction matrix and the deviation vector are subjected to matrix operation, and an anti-oscillation gain coefficient is introduced to calculate the coordinated flow velocity regulation vector, which includes the internal phase flow velocity regulation, the external phase flow velocity regulation, and the mobile phase flow velocity regulation.

7. The microfluidic droplet formation detection and control method according to claim 6, characterized in that, The method for generating the composite fluid correction field includes: The internal phase velocity regulation amount in the coordinated velocity regulation vector is converted into an internal phase pump control command, the external phase velocity regulation amount is converted into an external phase pump control command, and the mobile phase velocity regulation amount is converted into a mobile phase pump control command. The internal phase pump control command is sent to the internal phase pump, the external phase pump control command is sent to the external phase pump, and the mobile phase pump control command is sent to the mobile phase pump. The three-phase pumps execute the control commands synchronously, forming a composite fluid correction field within the microfluidic chip channel.

8. A microfluidic droplet formation detection and control system, used to implement the microfluidic droplet formation detection and control method according to any one of claims 1-7, characterized in that, The system includes: Preset flow rate generation module: used to obtain the target parameters of microcapsules, construct an ideal concentric ring geometric model, establish a force-shape coupling response matrix, solve the preset flow rate triplet based on the ideal concentric ring geometric model and the force-shape coupling response matrix, and generate initial droplets based on the preset flow rate triplet; Deformation feature extraction module: used to acquire droplet image sequence of initial droplet, fit real-time concentric circle group according to droplet image sequence, geometrically superimpose real-time concentric circle group with ideal concentric ring geometric model to generate deviation ring domain, and perform hierarchical decomposition of deviation ring domain to obtain geometric deformation feature components. The collaborative flow rate control module is used to calculate the collaborative flow rate adjustment vector based on the geometric deformation characteristic components and the force-shape coupling response matrix, form a composite fluid correction field based on the collaborative flow rate adjustment vector, and iteratively execute the deviation loop ablation cycle to achieve the geometric convergence of the microcapsule.

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