Silicone sealant production control method and system based on digital twinning

Abstract

This invention provides a method and system for controlling the production of silicone sealant based on digital twins. The method includes: simulating the flow field evolution in a digital twin stirring model using impeller motion state data as motion boundary conditions, combined with static boundary conditions and initial flow field conditions, to obtain the flow state distribution relationship of the sealant within a physical stirring device; constructing a state evolution model of the sealant during the mixing process based on the flow state distribution relationship; predicting the stirring intervention strategy indicated when the sealant reaches the target mixing state based on the state evolution model; controlling the physical stirring device to perform operations based on the stirring intervention strategy; updating the motion boundary conditions based on the operating parameters of the physical stirring device after the operations; simulating the flow field evolution and state evolution prediction based on the updated motion boundary conditions; iteratively optimizing the stirring intervention strategy until the target mixing state is met, at which point the physical stirring device is stopped. This invention improves the product quality of silicone sealant during the production process.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for controlling the production of silicone sealant based on digital twins. Background Technology

[0002] The core of silicone sealant production lies in the thorough and uniform mixing of high-viscosity silicone base material and high specific surface area powder filler (such as fumed silica). Currently, the industry commonly uses a stirring control method based on a "time-speed" preset program. That is, based on historical process experience, the stirring process is divided into several stages, and each stage is set with a fixed speed and duration, which is automatically executed by PLC or timer.

[0003] To improve control precision, some existing improvement schemes introduce macroscopic physical quantities such as motor current, stirring torque, reactor temperature, or power consumption as criteria for judging the stirring endpoint, stopping stirring when these parameters reach a threshold. However, these indirect parameters lack a clear physical correlation with the actual dispersion state inside the adhesive. On the one hand, batch differences in fillers (such as particle size distribution, surface treatment degree, and moisture content) can change the rheological properties of the system, leading to vastly different actual dispersion effects under the same current or torque. On the other hand, the viscosity of silicone sealant under high shear exhibits a strong nonlinear and unsteady-state response, making judgments based on static thresholds prone to failure. Therefore, it is impossible to guarantee precise control of the stirring process during the production of silicone sealant, often resulting in filler agglomeration due to insufficient stirring (affecting product strength), thus compromising product quality during the production process. Summary of the Invention

[0004] This invention provides a method and system for controlling the production of silicone sealant based on digital twins. It aims to achieve adaptive and precise control of the stirring process during the production of silicone sealant, solving the technical problem of filler agglomeration caused by the inability of existing methods to adapt to batch differences and dynamic changes in materials, and improving the product quality of silicone sealant during the production process.

[0005] In a first aspect, the present invention provides a method for controlling the production of silicone sealant based on digital twins, which constructs a digital twin mixing model based on the structural dimension data of a physical mixing device and the physical property data of the silicone sealant; the digital twin mixing model indicates corresponding static boundary conditions and initial flow field conditions; The silicone sealant production control method includes: Using the blade motion state data of the physical stirring device as the motion boundary condition, and combining the static boundary condition and the initial flow field condition, the flow field evolution is simulated in the digital twin stirring model to obtain the flow state distribution relationship of the adhesive in the physical stirring device. Based on the flow state distribution relationship, a state evolution model of the adhesive solution during the mixing process is constructed, and based on the state evolution model, the stirring intervention strategy indicated when the adhesive solution reaches the target mixing state is predicted. Based on the stirring intervention strategy, the physical stirring device is controlled to perform corresponding operations, and the motion boundary conditions are updated based on the operating parameters of the physical stirring device after the operations are performed. Based on the updated motion boundary conditions, the flow field evolution and state evolution prediction are simulated, and the stirring intervention strategy is iteratively optimized until the target mixing state is met, at which point the physical stirring device is controlled to stop operating.

[0006] Secondly, the present invention also provides a silicone sealant production control system based on digital twins, for implementing the silicone sealant production control method based on digital twins as described in the first aspect; the system includes: The flow field evolution simulation module is used to simulate the flow field evolution in the digital twin mixing model by using the blade motion state data of the physical mixing device as the motion boundary condition, combined with the static boundary condition and the initial flow field condition, to obtain the flow state distribution relationship of the adhesive in the physical mixing device. The stirring intervention prediction module is used to construct a state evolution model of the adhesive during the mixing process based on the flow state distribution relationship, and to predict the stirring intervention strategy indicated when the adhesive reaches the target mixing state based on the state evolution model. The stirring intervention control module is used to control the physical stirring equipment to perform corresponding operations based on the stirring intervention strategy, and to update the motion boundary conditions based on the operating parameters of the physical stirring equipment after the operation is performed. The production optimization control module is used to simulate the flow field evolution and state evolution prediction based on the updated motion boundary conditions, iteratively optimize the stirring intervention strategy, and control the physical stirring equipment to stop operating when the target mixing state is met.

[0007] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the silicone sealant production control method based on digital twins as described above.

[0008] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the silicone sealant production control method based on digital twin as described above.

[0009] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the silicone sealant production control method based on digital twin as described above.

[0010] The silicone sealant production control method based on digital twin provided in this invention uses the impeller motion state data of a physical stirring device as the motion boundary condition, combined with static boundary conditions and initial flow field conditions. It simulates the flow field evolution in a digital twin stirring model constructed based on the structural dimensions of the physical stirring device and the physical properties of the silicone sealant, obtaining the distribution relationship of the sealant's flow state within the physical stirring device. This digital twin model achieves precise mapping of the sealant's flow state, breaking the limitations of existing technologies that rely solely on macroscopic indirect parameters, and directly obtaining the true internal flow state of the sealant. Based on the flow state distribution relationship, a state evolution model of the sealant mixing process is constructed. This model predicts the stirring intervention strategy indicated when the sealant reaches the target mixing state, linking the flow state with the mixing effect, thus realizing the transformation from "state monitoring" to "strategy prediction," avoiding the problem that existing fixed procedures or static thresholds cannot adapt to the dynamic changes of the sealant. This system controls physical mixing equipment to perform corresponding operations based on a stirring intervention strategy. The operational parameters of the physical mixing equipment after these operations are then used to update the motion boundary conditions, achieving a closed loop of "model prediction - physical execution - data feedback." This allows the digital twin model to accurately reflect the actual operating state of the physical equipment in real time, addressing the limitations of existing methods that cannot respond to batch-to-batch material differences or changes in adhesive rheological properties. Based on the updated motion boundary conditions, the system re-simulates and predicts the flow field evolution and state evolution, iteratively optimizing the stirring intervention strategy. Continuous iteration gradually corrects the stirring strategy, offsetting the impact of filler batch differences on the system's rheological properties. It avoids interference from the strong nonlinear and unsteady-state viscosity response of silicone sealant under high shear conditions, enabling dynamic adjustment of the stirring intervention strategy according to the actual dispersion state of the adhesive. This ensures that the stirring process always conforms to the actual mixing requirements of the adhesive, achieving adaptive and precise control of the stirring process during silicone sealant production. This solves the technical problem of filler agglomeration caused by the inability of existing methods to adapt to batch-to-batch material differences and dynamic changes, improving the product quality of silicone sealant during production. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of the silicone sealant production control method based on digital twin provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the silicone sealant production control system based on digital twin provided in an embodiment of the present invention; Figure 3 An embodiment diagram of the electronic device provided in this invention; Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0012] 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.

[0013] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0015] See Figure 1 , Figure 1 This is a flowchart illustrating the silicone sealant production control method based on digital twins provided by the present invention. In this embodiment, the execution entity of the silicone sealant production control method based on digital twins is the production control system. Therefore, the silicone sealant production control method based on digital twins includes: Step 10: Using the motion state data of the blades of the physical stirring device as the motion boundary condition, and combining the static boundary condition and the initial flow field condition, the flow field evolution is simulated in the digital twin stirring model to obtain the flow state distribution relationship of the adhesive in the physical stirring device.

[0016] Optionally, the production control system acquires the structural dimensional data of the physical mixing equipment and the physical property data of the silicone sealant. The structural dimensional data of the physical mixing equipment refers to the specific dimensional parameters of each component, including the inner diameter, height, and wall thickness of the mixing container; the number, length, width, and thickness of the impellers; the distance between the impellers and the bottom of the mixing container; and the installation position of the impellers on the mixing shaft. The physical property data of the silicone sealant refers to parameters reflecting the physical characteristics of the silicone sealant itself, including viscosity parameters, dynamic viscosity coefficient, and density parameters.

[0017] The production control system constructs a digital twin mixing model based on the structural dimension data of the physical mixing equipment and the physical property data of the silicone sealant. Therefore, the digital twin mixing model is a digital mapping of the physical mixing equipment and the mixing process of the silicone sealant, which can truly reflect the structural features of the physical mixing equipment and the physical properties of the silicone sealant. Optionally, the digital twin mixing model in this embodiment of the invention can be constructed based on 3ds Max, SolidWorks, or ContextCapture, which will not be elaborated here.

[0018] Optionally, the digital twin mixing model is pre-set with corresponding static boundary conditions and initial flow field conditions. The static boundary conditions include the geometric boundary of the inner wall of the mixing container and the no-slip boundary of the inner wall. The geometric boundary of the inner wall refers to the geometric contour boundary of the simulated inner wall of the mixing container in the digital twin mixing model, which is completely consistent with the geometric shape of the inner wall of the mixing container in the physical mixing equipment. The no-slip boundary of the inner wall refers to the boundary constraint condition that prevents relative sliding of the silicone sealant in contact with the inner wall of the mixing container during the mixing process. The initial flow field conditions include the initial zero-velocity field and the viscosity characteristics, dynamic viscosity coefficient, and density parameters of the silicone sealant. The initial zero-velocity field refers to the flow field state in the digital twin mixing model where the velocity in the region containing the silicone sealant is zero at the initial moment of mixing.

[0019] During the production of silicone sealant, the production control system acquires the movement status data of the blades of the physical mixing equipment. The blade movement status data refers to data that reflects the movement of the blades.

[0020] Optionally, the blade motion state data includes blade rotation angle data and blade speed data, wherein the blade rotation angle data refers to the rotation angle value at different times during the blade's rotation around the stirring shaft, and the blade speed data refers to the number of revolutions the blade makes around the stirring shaft per unit time.

[0021] The production control system uses the blade motion state data as the motion boundary condition, and simultaneously retrieves the preset static boundary condition and initial flow field condition from the digital twin mixing model. The motion boundary condition, static boundary condition, and initial flow field condition are input into the digital twin mixing model. The flow field evolution process of silicone sealant during the mixing process is simulated through the digital twin mixing model to obtain the flow state distribution relationship of the sealant in the physical mixing equipment, as shown in steps 101 to 104. The flow state distribution relationship can reflect the flow velocity and flow direction of the sealant at different positions and times in the physical mixing equipment.

[0022] Step 20: Construct a state evolution model of the adhesive during the mixing process based on the flow state distribution relationship, and predict the stirring intervention strategy indicated when the adhesive reaches the target mixing state based on the state evolution model.

[0023] Optionally, the production control system constructs a state evolution model of the adhesive liquid during the mixing process based on the flow state distribution relationship, combined with the physical property data of the silicone sealant and the structural characteristics of the physical stirring equipment, as shown in steps 201 to 204. The state evolution model is a mathematical model that can describe the change process of the adhesive liquid from the initial state to the mixed state, and can reflect the evolution law of the flow state and mixing uniformity over time during the mixing process of the adhesive liquid.

[0024] The target mixing state of the adhesive refers to the state at which the adhesive reaches preset quality requirements such as mixing uniformity and viscosity consistency. Mixing uniformity refers to the evenness of the distribution of each component in the adhesive, and viscosity consistency refers to the degree to which the viscosity value of the adhesive remains consistent at different locations. The production control system inputs the target mixing state into the state evolution model, which predicts the stirring intervention strategy required for the adhesive to reach the target mixing state, as shown in steps 205 to 208. This stirring intervention strategy enables the adhesive to gradually reach the target mixing state from its current mixing state.

[0025] Optionally, the stirring intervention strategy in the embodiments of the present invention includes at least the stirring speed of the impeller and the stirring duration, wherein the stirring speed refers to the target speed value that the impeller needs to run at, and the stirring duration refers to the length of time that needs to be continuously stirred at the current stirring speed.

[0026] Step 30: Control the physical stirring equipment to perform the corresponding operation based on the stirring intervention strategy, and update the motion boundary conditions based on the operating parameters of the physical stirring equipment after the operation is performed.

[0027] Optionally, the production control system sends corresponding control commands to the physical mixing equipment according to the mixing intervention strategy, controlling the physical mixing equipment to perform corresponding operations, ensuring that the operating status of the physical mixing equipment is consistent with the requirements of the mixing intervention strategy. During the execution of the corresponding operations by the physical mixing equipment, the production control system collects the operating parameters of the physical mixing equipment in real time after the operation through the angle sensor installed on the physical mixing equipment. The main parameter is the actual rotation angle of the blades. The angle sensor is specifically used to collect the actual angle value of the blades rotating around the mixing shaft, and uses it as the updated motion boundary condition.

[0028] In one embodiment, it is assumed that the stirring intervention strategy obtained in step 20 is as follows: the stirring speed of the paddle of the physical stirring equipment is set to 80 revolutions per minute, and the stirring duration is 25 minutes (i.e., stirring continuously at a speed of 80 revolutions per minute for 25 minutes). The production control system sends a control command to the physical stirring equipment, instructing it to continuously stir at a speed of 80 revolutions per minute for 25 minutes. During the execution of the above operation by the physical stirring equipment, the production control system collects the actual rotation angle of the paddle in real time through an angle sensor installed on the paddle shaft. After the physical stirring equipment has been running stably at a speed of 80 revolutions per minute for 10 minutes, the production control system collects the actual rotation angle of the paddle at this time as 14328 degrees (corresponding to 40.08 revolutions) and uses the actual rotation angle of the paddle of 14328 degrees as the updated motion boundary condition.

[0029] Step 40: Based on the updated motion boundary conditions, simulate the flow field evolution and state evolution prediction, iteratively optimize the stirring intervention strategy, and control the physical stirring equipment to stop operating after the target mixing state is met.

[0030] Optionally, the production control system uses a digital twin mixing model to re-simulate the flow field evolution of the adhesive within the physical mixing equipment based on updated motion boundary conditions, obtaining an updated distribution of the adhesive flow state. Subsequently, the updated flow state distribution is input into the state evolution model constructed in step 20. Based on this updated flow state distribution, the state evolution model re-predicts the mixing intervention strategy required for the adhesive to reach the target mixing state. This mixing intervention strategy only involves adjusting the blade rotation angle and does not involve any other parameters, completing one iterative optimization of the mixing intervention strategy.

[0031] The production control system compares the iteratively optimized stirring intervention strategy with the previous stirring intervention strategy, analyzes the differences between the two stirring intervention strategies, and at the same time uses the state evolution model to determine whether the current mixing state of the adhesive has reached the preset target mixing state. The judgment is based on whether the mixing uniformity of the adhesive has reached the preset threshold and whether the viscosity consistency meets the preset requirements. The preset threshold for mixing uniformity and the preset requirements for viscosity consistency are specific values ​​preset according to the product quality standards of silicone sealant.

[0032] If the production control system determines that the mixing state of the adhesive has not reached the target mixing state, steps 30 and 40 are repeated. This involves controlling the physical stirring equipment to perform corresponding operations based on the optimized stirring intervention strategy, collecting the operating parameters of the physical stirring equipment (i.e., the actual rotation angle of the blades), updating the motion boundary conditions, and then performing flow field evolution simulation and state evolution prediction based on the updated motion boundary conditions. The stirring intervention strategy is further iteratively optimized (only the blade rotation angle adjustment parameters are optimized) until the state evolution model determines that the mixing state of the adhesive has reached the preset target mixing state. When the state evolution model determines that the mixing state of the adhesive has reached the target mixing state, a stop command is immediately sent to the physical stirring equipment to stop the stirring operation, completing the mixing process of the silicone sealant.

[0033] Continuing with the embodiment based on step 30, the updated motion boundary conditions (actual blade rotation angle 14328 degrees), the static boundary conditions in the digital twin stirring model (geometric boundary of the inner wall of the stirring container, no slip boundary of the inner wall), and the current initial state of the flow field (based on the state of the adhesive flow field after the physical stirring equipment has been running for 10 minutes in step 30) are input into the digital twin stirring model to simulate the evolution of the adhesive flow field and obtain the updated distribution relationship of the adhesive flow state. The distribution relationship shows that the current mixing uniformity of the adhesive is 82%, while the preset target mixing uniformity threshold is 95%, the viscosity consistency deviation is 0.3 Pa·s, and the preset viscosity consistency deviation threshold is 0.1 Pa·s. At this time, the adhesive mixing state has not reached the target mixing state.

[0034] The production control system inputs the updated flow state distribution into the state evolution model to re-predict the stirring intervention strategy. The iteratively optimized stirring intervention strategy is as follows: adjust the blade rotation angle from the current 14328 degrees to 21600 degrees (corresponding to 60 revolutions), and continue stirring for 10 minutes after the adjustment. Following this iteratively optimized stirring intervention strategy, the production control system sends control commands to the physical stirring equipment, controlling it to adjust the blade rotation angle gradually from 14328 degrees to 21600 degrees, and then continue stirring for 10 minutes after the adjustment.

[0035] During the physical mixing equipment's operation, the production control system collects the actual rotation angle of the blades in real time through an angle sensor. When the blade rotation angle is adjusted to 21,600 degrees and mixing is continued for 8 minutes, the actual blade rotation angle is collected as 20,736 degrees (corresponding to 57.6 revolutions). This is used as a new and updated motion boundary condition and is input again into the digital twin mixing model for flow field evolution simulation. The mixing intervention strategy is then re-predicted through the state evolution model to complete the second iteration optimization.

[0036] Repeating the above iterative process, after three iterations of optimization, the production control system, through the state evolution model, detected that the mixing uniformity of the adhesive solution reached 96%, exceeding the preset 95% threshold, and the viscosity consistency deviation was 0.08 Pa·s, lower than the preset 0.1 Pa·s threshold, indicating that the adhesive solution had reached the target mixing state. At this point, the production control system immediately sent a stop command to the physical stirring equipment. Upon receiving the command, the physical stirring equipment stopped rotating its blades, completing the mixing process of the silicone sealant.

[0037] This invention, through simulation of flow field evolution and state evolution prediction, iteratively optimizes the stirring intervention strategy. Continuous iteration can gradually correct the stirring strategy, offsetting the influence of filler batch differences on the system's rheological properties, and avoiding the interference of strong nonlinear viscosity and unsteady response of silicone sealant under high shear on stirring control. It realizes dynamic adjustment of the stirring intervention strategy according to the actual dispersion state of the adhesive, ensuring that the stirring process always conforms to the actual mixing requirements of the adhesive. This achieves adaptive and precise control of the stirring process in the production of silicone sealant, solving the technical problem of filler agglomeration due to the inability to adapt to batch differences and dynamic changes in materials, and improving the product quality of silicone sealant in the production process.

[0038] Optionally, the processes of steps 101 to 104 include: Step 101: Based on the blade rotation angle data and the blade three-dimensional geometric contour data, combined with the inner wall geometric boundary, perform spatial position mapping analysis of the moving parts to obtain the instantaneous occupied space area of ​​the blade.

[0039] Optionally, the production control system acquires the three-dimensional geometric contour data of the blade. The three-dimensional geometric contour data of the blade refers to all data that can fully reflect the three-dimensional structural features of the blade, including the blade's length, width, thickness, surface curvature, and spatial coordinates of various parts of the blade.

[0040] The production control system analyzes the blade rotation angle data to determine the specific angle of the blade's rotation around the stirring shaft at the current moment, thus clarifying the blade's rotational posture in space.

[0041] Subsequently, the production control system correlates the parsed blade rotation angle data with the blade's three-dimensional geometric contour data, and performs spatial position mapping analysis of the moving parts by combining the spatial position constraints of the inner wall geometric boundary. Optionally, the specific analysis process of this embodiment of the invention is as follows: the three-dimensional geometric contour of the blade is adjusted in spatial posture according to the current rotation angle, the specific spatial range occupied by the blade in the mixing container at the current rotation angle is accurately calculated, the spatial coordinates of each part of the blade are determined, and the instantaneous occupied spatial area of ​​the blade is obtained. Therefore, the instantaneous occupied spatial area can accurately reflect the actual spatial position and occupied range of the blade in the mixing container at the current moment.

[0042] In one embodiment, the three-dimensional geometric profile data of the impeller is assumed to be: impeller length 50 cm, width 15 cm, thickness 3 cm, impeller surface curvature 15 degrees, and the initial spatial coordinates of the impeller with the center of the stirring shaft as the origin and the initial angle 0 degrees. The geometric boundary data of the inner wall are: inner diameter of the stirring container 100 cm, height 120 cm, and the spatial coordinate range of the inner wall is clearly defined. The current impeller rotation angle is 90 degrees. The production control system analyzes the 90-degree rotation angle data and adjusts the spatial posture of the impeller's three-dimensional geometric profile according to the 90-degree rotation angle. Combined with the spatial position constraints of the inner wall of the mixing container, the spatial coordinate range of the impeller within the mixing container is calculated. The specific calculation process is as follows: Horizontal range calculation: The inner diameter of the mixing container is 100 cm, meaning the distance from the center of the mixing shaft to the inner wall is 50 cm. The impeller length is 50 cm. When rotating to a 90-degree horizontal posture, a 5 cm safety gap is reserved to avoid collision between the impeller and the inner wall. Simultaneously, a reasonable gap is reserved between the impeller root and the mixing shaft mounting structure. Therefore, the starting point of the horizontal direction is 45 cm, and the ending point is 95 cm. Vertical range calculation: To avoid the impeller touching the bottom of the container, a 20 cm bottom gap is reserved. With an impeller length of 50 cm, the vertical range extends from 20 cm to 70 cm (20 cm + 50 cm), without exceeding the container's height of 120 cm, and sufficient top gap is reserved. Based on the above calculations, the instantaneous space occupied by the blade is determined as follows: with the center of the stirring shaft as the reference, the horizontal direction extends to a range of 45 cm to 95 cm, and the vertical direction covers a range of 20 cm to 70 cm. This area is the specific space occupied by the blade in the stirring container at the current moment.

[0043] Step 102: Based on the instantaneously occupied space region and viscosity characteristic parameters, perform near-wall boundary layer thickness analysis to obtain the near-wall boundary layer fluid domain surrounding the blade surface.

[0044] Optionally, the viscosity characteristic parameters of silicone sealant refer to parameters that can reflect the viscosity of silicone sealant as a function of shear rate, and can reflect the viscosity characteristics of silicone sealant under different flow conditions.

[0045] The production control system uses the instantaneously occupied space area of ​​the blade as a benchmark to perform near-wall boundary layer thickness analysis. The near-wall boundary layer refers to the thin fluid layer close to the blade surface that is affected by the blade motion and has a velocity gradient change. The near-wall boundary layer thickness refers to the distance that the fluid layer extends outward from the blade surface. Optionally, the analysis process in this embodiment of the invention is as follows: combining the viscosity characteristics of the silicone sealant, the fluid range affected by the blade motion is calculated according to the motion state of the blade surface, the specific thickness of the near-wall boundary layer is determined, and then the near-wall boundary layer fluid domain surrounding the blade surface is delineated. The near-wall boundary layer fluid domain refers to the space area occupied by the silicone sealant fluid surrounding the blade surface and within the near-wall boundary layer range.

[0046] Continuing with the embodiment based on step 101, the viscosity characteristic parameters of the silicone sealant (reflecting that the viscosity of the silicone sealant gradually decreases with increasing distance from the blade surface under the current operating conditions).

[0047] The production control system uses the blade surface as a reference and incorporates viscosity characteristics to calculate the near-wall boundary layer thickness. The specific calculation process is as follows: Based on the current viscosity characteristics of the silicone sealant and considering the shear effect of the blade motion on the surrounding sealant, the range of sealant affected by the blade motion is calculated, resulting in a near-wall boundary layer thickness of 2 cm. This thickness calculation comprehensively considers the interaction between the sealant viscosity and the blade motion, ensuring complete coverage of the sealant area directly affected by the blade motion. Specifically, the near-wall boundary layer extends 2 cm outward from the blade surface. Subsequently, the production control system uses the instantaneously occupied space area of ​​the blade as a reference and extends outward by 2 cm to delineate the complete near-wall boundary layer fluid domain. This fluid domain completely envelops the blade surface and maintains a uniform 2 cm distance from the instantaneously occupied space area of ​​the blade, accurately covering the sealant area directly affected by the blade motion.

[0048] Step 103: Based on the zero velocity field at the initial moment, perform boundary velocity analysis on the near-wall boundary layer fluid domain to obtain the first liquid velocity gradient field of the near-wall boundary layer fluid domain.

[0049] Optionally, the production control system performs boundary velocity analysis on the near-wall boundary layer fluid domain based on the initial zero-velocity field. This analysis involves combining the blade's motion state (reflected by blade rotation angle data) to analyze the velocity variation of the adhesive at different locations within the near-wall boundary layer fluid domain. Since the blade is in motion, it drives the adhesive on its surface to move. Initially, the adhesive is at zero velocity overall. Therefore, a velocity gradient gradually forms from the blade surface outwards within the near-wall boundary layer fluid domain. The adhesive velocity near the blade surface is close to the blade's motion velocity, while the velocity further away from the blade surface gradually decreases to zero. Through this boundary velocity analysis, the production control system accurately calculates the adhesive velocity variation at various locations within the near-wall boundary layer fluid domain, ultimately obtaining the first adhesive velocity gradient field. Therefore, the first adhesive velocity gradient field clearly reflects the variation and distribution of the adhesive velocity from the blade surface outwards within the near-wall boundary layer fluid domain.

[0050] Continuing with the embodiment based on step 102, the near-wall boundary layer fluid domain (a region surrounding the blade surface with a thickness of 2 cm) initially has a zero velocity field (at this time, the overall velocity of the adhesive is zero). The production control system, combined with the current rotation state of the blade (the motion trend at a 90-degree rotation angle), performs boundary velocity analysis on the near-wall boundary layer fluid domain. The specific calculation process is as follows: Based on the initial zero-velocity field and considering the shearing effect caused by the blade rotation, the change in adhesive velocity is calculated progressively from the blade surface outwards. The adhesive near the blade surface experiences the strongest driving force from the blade and has the highest velocity. As the distance from the blade surface increases, the driving force gradually weakens, and the adhesive velocity gradually decreases to zero. The calculations show that: in the region 0 to 0.5 cm from the blade surface, the adhesive velocity gradually decreases from 10 cm / s to 5 cm / s; in the region 0.5 to 1.5 cm, the adhesive velocity gradually decreases from 5 cm / s to 1 cm / s; and in the region 1.5 to 2 cm, the adhesive velocity gradually decreases from 1 cm / s to zero. Based on this velocity variation law, the production control system forms the first adhesive velocity gradient field in the near-wall boundary layer fluid domain.

[0051] Step 104: Based on the velocity gradient field of the first adhesive liquid combined with the slip-free boundary of the inner wall, the dynamic viscosity coefficient and density parameters, determine the flow state distribution relationship.

[0052] Optionally, the production control system determines the flow state distribution relationship based on the first adhesive velocity gradient field combined with the inner wall no-slip boundary, dynamic viscosity coefficient and density parameters, as in steps 1041 to 1044.

[0053] This invention achieves accurate analysis of the flow state of adhesive in a digital twin mixing model, breaking the limitation of relying on macroscopic indirect parameters. It can directly obtain the true flow state inside the adhesive, providing support for the construction of subsequent adhesive mixing state evolution models and the prediction of mixing intervention strategies, ensuring the accuracy of subsequent mixing control, and thus improving the product quality of silicone sealant in the production process.

[0054] Optionally, the process of steps 1041 to 1044 includes: Step 1041: Based on the velocity gradient field and density parameters of the first adhesive liquid, perform local momentum flux analysis to obtain the local momentum distribution of the near-wall boundary layer fluid domain.

[0055] Optionally, the production control system uses the first adhesive velocity gradient field as a basis and combines it with density parameters to perform local momentum flux analysis. Local momentum flux refers to the momentum passing through a unit area within the near-wall boundary layer fluid domain per unit time, reflecting the momentum distribution and transmission law of the adhesive within the near-wall boundary layer fluid domain. In the analysis process of this embodiment, the production control system calculates the local momentum flux at each spatial point within the near-wall boundary layer fluid domain based on the adhesive velocity (determined by the first adhesive velocity gradient field) and density parameters at that point. By summarizing the local momentum fluxes at all points, the local momentum distribution of the near-wall boundary layer fluid domain is obtained. This local momentum distribution can clearly reflect the magnitude and distribution characteristics of the momentum of the adhesive at different locations within the near-wall boundary layer fluid domain.

[0056] Continuing with the aforementioned embodiments, the first adhesive velocity gradient field (the near-wall boundary layer fluid domain thickness is known to be 2 cm, with different adhesive velocities in different regions) has a silicone sealant density parameter of 1.2 g / cm³. The production control system calculates the local momentum flux for each spatial point within the near-wall boundary layer fluid domain. The specific calculation process is as follows: Taking a point within the near-wall boundary layer fluid domain as an example, the adhesive velocity at this point is 5 cm / s, and the density is 1.2 g / cm³. The local momentum flux at this point is calculated by multiplying the velocity and density, i.e., 5 cm / s multiplied by 1.2 g / cm³, resulting in a local momentum flux of 6 g / cm² / s. Using the same calculation method, the local momentum flux at all points in the near-wall boundary layer fluid domain is calculated. For example, at a point near the blade surface with a velocity of 10 cm / s, the local momentum flux is 12 g / cm² / s; at a point near the outer side of the near-wall boundary layer with a velocity of 1 cm / s, the local momentum flux is 1.2 g / cm² / s; and at a point with zero velocity, the local momentum flux is zero.

[0057] Step 1042: Based on the local momentum distribution and dynamic viscosity coefficient, a viscous shear stress coupling analysis is performed to obtain the local viscous shear stress field between the near-wall boundary layer fluid domain and the external fluid domain. The external fluid domain is the fluid domain other than the near-wall boundary layer fluid domain.

[0058] Optionally, the dynamic viscosity coefficient refers to the ability of the silicone sealant to resist flow. It reflects the magnitude of the viscous resistance during the flow of the sealant and directly affects the shear stress transmission of the sealant.

[0059] The external fluid domain is the fluid domain other than the near-wall boundary layer fluid domain, that is, the area of ​​silicone sealant in the stirred container that is not covered by the near-wall boundary layer fluid domain. The sealant in this area is less directly affected by the movement of the blades, and its flow state is different from that of the near-wall boundary layer fluid domain.

[0060] The production control system performs viscous shear stress coupling analysis based on local momentum distribution and dynamic viscosity coefficient. Viscous shear stress refers to the interaction force generated between different velocity layers of the adhesive during flow due to viscosity. Coupling analysis combines the momentum transfer law reflected by the local momentum distribution with the viscous characteristics reflected by the dynamic viscosity coefficient to analyze the interaction between the near-wall boundary layer fluid domain and the external fluid domain. Through the above analysis, the production control system calculates the magnitude and direction of viscous shear stress at various points on the interface between the near-wall boundary layer fluid domain and the external fluid domain. The results are then summarized to obtain the local viscous shear stress field between the near-wall boundary layer fluid domain and the external fluid domain. This local viscous shear stress field reflects the momentum transfer intensity and interaction law between the two fluid domains.

[0061] Continuing with the embodiment based on step 1041, the local momentum distribution of the near-wall boundary layer fluid domain (e.g., exhibiting a gradual decrease from the impeller surface outwards), the dynamic viscosity coefficient of the silicone sealant is 0.8 Pa·s, clearly defining the external fluid domain as the adhesive region within the stirring vessel outside the near-wall boundary layer fluid domain (2 cm thick). A viscous shear stress coupling analysis is performed on the production control system; the specific calculation process is as follows: Based on the local momentum distribution, the momentum gradient at the interface between the near-wall boundary layer fluid domain and the external fluid domain is determined. Combined with the dynamic viscosity coefficient, the viscous shear stress at various points on the interface is calculated through the correlation between the momentum gradient and the dynamic viscosity coefficient. Specifically, the viscous shear stress at points on the interface closer to the blade, where the local momentum flux is higher, is 4 Pa. At points on the interface farther from the blade, where the local momentum flux is lower, the viscous shear stress is 0.8 Pa. By summing the viscous shear stresses at all points on the interface, the local viscous shear stress field between the near-wall boundary layer fluid domain and the external fluid domain is obtained.

[0062] Step 1043: Based on the local viscous shear stress field and the slip-free boundary of the inner wall, a global velocity field analysis is performed to obtain the second adhesive velocity gradient field of the external fluid domain.

[0063] Optionally, the production control system uses the local viscous shear stress field as a reference and combines the constraint requirements of the inner wall's no-slip boundary to perform a global velocity field analysis. This analysis mainly targets the external fluid domain and is based on the effect of the near-wall boundary layer fluid domain on the external fluid domain, as reflected by the local viscous shear stress field. Combined with the constraint of the inner wall's no-slip boundary, the system calculates the velocity variation law of the adhesive at each point in the external fluid domain.

[0064] Optionally, in the analysis process of this embodiment of the invention, the production control system determines the flow trend of the adhesive in the external fluid domain based on the distribution of the local viscous shear stress field. Combined with the constraint of the non-slip boundary of the inner wall (the adhesive velocity at the inner wall is zero), the adhesive velocity gradient at different positions in the external fluid domain is calculated step by step, and finally the second adhesive velocity gradient field of the external fluid domain is obtained. Therefore, the second adhesive velocity gradient field can clearly reflect the distribution and change law of adhesive velocity in the external fluid domain.

[0065] Continuing with the embodiment based on step 1042, a local viscous shear stress field is generated (the viscous shear stress at the contact interface gradually decreases from the blade projection area outwards), and there is no slip boundary on the inner wall (the adhesive velocity at the inner wall is zero). The production control system performs a global velocity field analysis, and the specific calculation process is as follows: Based on the local viscous shear stress field, it is determined that the adhesive in the external fluid domain is driven by the near-wall boundary layer fluid domain. At the contact interface, the adhesive flows under the action of viscous shear stress, and the velocity gradually decreases from 1 cm / s at the contact interface (consistent with the velocity on the outer side of the near-wall boundary layer) towards the direction away from the contact interface. Simultaneously, constrained by the non-slip boundary of the inner wall, the adhesive velocity near the inner wall of the stirring vessel decreases to zero. By progressively calculating the velocity changes at various points within the external fluid domain, a second adhesive velocity gradient field is obtained. This gradient field shows that within the external fluid domain, in the region near the near-wall boundary layer contact interface, the adhesive velocity gradually decreases from 1 cm / s to 0.2 cm / s. In the region near the inner wall of the stirring vessel, the adhesive velocity gradually decreases from 0.2 cm / s to zero.

[0066] Step 1044: Integrate the velocity gradient fields based on the first and second adhesive velocity gradient fields to obtain the flow state distribution relationship.

[0067] Optionally, the production control system integrates the first adhesive velocity gradient field and the second adhesive velocity gradient field to obtain the flow state distribution relationship.

[0068] The embodiments of the present invention can obtain the true distribution relationship of the adhesive flow state, which provides support for the construction of the subsequent adhesive mixing state evolution model and the prediction of stirring intervention strategy. This ensures that the subsequent stirring control can conform to the true flow state of the adhesive, improves the accuracy of stirring control, and thus improves the product quality of silicone sealant in the production process.

[0069] Optionally, the processes of steps 201 to 204 include: Step 201: Based on the flow state distribution relationship, perform shear rate distribution reconstruction analysis on the stirred container to obtain the local shear rate distribution at each location in the stirred container.

[0070] Optionally, the production control system performs a shear rate distribution reconstruction analysis of the mixing container based on the flow state distribution relationship. Shear rate refers to the ratio of the velocity difference between two adjacent fluid layers to the distance between them during the flow process. It reflects the shear intensity experienced by the adhesive and directly affects the dispersion effect of the components to be mixed and the mixing uniformity of the adhesive. During the reconstruction analysis, the production control system extracts the adhesive velocity data at each spatial point in the flow state distribution relationship. For each point, it calculates the velocity difference between it and its adjacent points. Combining this with the spatial distance between adjacent points, it calculates the local shear rate at that point using the ratio of the velocity difference to the spatial distance. The local shear rates at all spatial points within the mixing container are calculated and summarized one by one to complete the reconstruction of the shear rate distribution. Finally, the local shear rate distribution at each location within the mixing container is obtained, which clearly reflects the differences in the shear intensity experienced by the adhesive at different locations within the mixing container.

[0071] Continuing with the aforementioned embodiments, the flow state distribution relationship is as follows (it is known that the adhesive velocity in the near-wall boundary layer fluid domain gradually decreases from 10 cm / s at the impeller surface to 1 cm / s at the contact interface, and the adhesive velocity in the external fluid domain gradually decreases from 1 cm / s to zero velocity at the inner wall). The production control system performs a shear rate distribution reconstruction analysis, and the specific calculation process is as follows: Three typical points within the mixing vessel are selected for local shear rate calculation. The first point is located in the near-wall boundary layer fluid domain, 0.5 cm from the impeller surface, with an adhesive velocity of 5 cm / s. The adjacent point (0.1 cm away) has an adhesive velocity of 6 cm / s. By using the ratio of the velocity difference (6 cm / s minus 5 cm / s) to the spatial distance (0.1 cm), the local shear rate at this point is calculated to be 10 cm / s. The second point is located at the contact interface between the near-wall boundary layer and the external fluid domain, with an adhesive velocity of 1 cm / s. The adjacent point (0.1 cm away, located in the external fluid domain) has an adhesive velocity of 0.9 cm / s, and the local shear rate is calculated to be 1 cm / s. The third point is located in the outer fluid domain near the inner wall, with a liquid velocity of 0.2 cm / s. The adjacent point (0.1 cm away, near the inner wall) has a liquid velocity of 0.1 cm / s, and the calculated local shear rate is 1 cm / s. Following the same method, the local shear rates of all points within the stirring vessel are calculated one by one. The resulting distribution of local shear rates shows a pattern: higher local shear rates in the boundary layer fluid domain near the wall (1 to 10 cm / s), lower local shear rates in the outer fluid domain (0.5 to 1 cm / s), and local shear rates approaching zero near the inner wall.

[0072] Step 202: Based on the velocity gradient field of the first adhesive in the flow state distribution relationship and the spatial distribution coordinates of the components to be mixed in the silicone sealant in the stirring container, perform a tensile analysis of the mixing interface to obtain the area expansion rate of the components to be mixed and the adhesive at the contact interface.

[0073] Optionally, the production control system acquires the spatial distribution coordinates of the components to be mixed in the silicone sealant within the mixing container. The components to be mixed refer to various fillers, additives, and other substances that need to be mixed with the base adhesive during the silicone sealant production process. The spatial distribution coordinates refer to the specific spatial location coordinates of each particle or region of the components to be mixed within the mixing container, which can accurately reflect the initial distribution state of the components to be mixed in the adhesive.

[0074] The production control system performs a tensile analysis of the mixing interface based on the velocity gradient field of the first adhesive and the spatial distribution coordinates of the components to be mixed. The mixing interface refers to the contact interface between the components to be mixed and the base adhesive. The tensile analysis of the mixing interface analyzes the process and rate of stretching and expansion of the contact interface between the components to be mixed and the base adhesive under the action of adhesive flow. This rate can reflect the degree of contact and mixing progress between the components to be mixed and the base adhesive. During the analysis, the production control system determines the flow velocity and flow direction of the adhesive in the near-wall boundary layer fluid domain according to the velocity gradient field of the first adhesive. Combined with the spatial distribution coordinates of the components to be mixed, it tracks the dynamic changes of the contact interface between the components to be mixed and the base adhesive, calculates the increase in the area of ​​the contact interface per unit time, and obtains the area expansion rate of the components to be mixed and the adhesive at the contact interface. The larger the area expansion rate, the more sufficient the contact between the components to be mixed and the base adhesive, and the faster the mixing progress.

[0075] Continuing with the aforementioned embodiments, a first adhesive velocity gradient field (the adhesive velocity in the near-wall boundary layer fluid domain gradually decreases from 10 cm / s at the blade surface to 1 cm / s at the contact interface) is established. Simultaneously, the spatial distribution coordinates of the components to be mixed (assuming they are filler particles) are acquired. It is known that the total area of ​​the contact interface between the components to be mixed and the base adhesive at the initial moment is 100 square centimeters, and the components to be mixed are mainly distributed within the near-wall boundary layer fluid domain. The production control system performs a tensile analysis of the mixing interface. The specific calculation process is as follows: Based on the first adhesive velocity gradient field, the flow direction of the adhesive in the near-wall boundary layer fluid domain is determined to be along the blade rotation direction. The flow velocity drives the particles of the components to be mixed to move, causing tensile expansion of the contact interface. Tracking the dynamic changes of the contact interface, the total area of ​​the contact interface increases from 100 square centimeters to 150 square centimeters within 1 minute. Using the ratio of the increase in contact interface area per unit time (150 square centimeters minus 100 square centimeters) to time (1 minute), the area expansion rate of the components to be mixed and the adhesive at the contact interface is obtained as 50 square centimeters per minute.

[0076] Step 203: Based on the area expansion rate and the molecular diffusion coefficient of the silicone sealant raw material, the component concentration homogenization efficiency is analyzed to obtain the concentration difference attenuation index of the component to be mixed in the local area.

[0077] Optionally, the molecular diffusion coefficient of the silicone sealant raw material refers to the rate at which the molecules of the components to be mixed diffuse in the base adhesive solution. It can reflect the diffusion ability of the molecules of the components to be mixed in the adhesive solution and directly affect the homogenization rate of the component concentration.

[0078] The production control system analyzes the component concentration homogenization efficiency based on the area expansion rate and molecular diffusion coefficient. Component concentration homogenization efficiency refers to the speed at which the components to be mixed diffuse from high-concentration areas to low-concentration areas in the adhesive, gradually achieving uniform concentration. The concentration difference decay index refers to the degree and speed at which the concentration difference of the components to be mixed in a local area gradually decreases over time, quantitatively reflecting the progress of concentration homogenization. During the analysis, the production control system combines the area expansion rate (reflecting the degree of expansion of the contact interface; the larger the contact interface, the larger the diffusion area) and the molecular diffusion coefficient (reflecting the diffusion rate) to calculate the change in the concentration difference of the components to be mixed in a local area over time. This change is used to determine the concentration difference decay index; the smaller the index value, the more uniform the concentration of the components to be mixed in the local area, and the better the concentration homogenization effect.

[0079] Continuing with the embodiment based on step 202, the contact interface area expansion rate is 50 square centimeters per minute, and the molecular diffusion coefficient of the silicone sealant raw material is 0.01 square centimeters per second. The production control system performs component concentration homogenization efficiency analysis, and the specific calculation process is as follows: Based on the area expansion rate and molecular diffusion coefficient, the concentration diffusion rate of the component to be mixed in the local area is calculated. Combined with the initial concentration difference in the local area (assuming the initial concentration difference is 5 grams per cubic centimeter), the concentration difference in the local area is calculated to decrease from 5 grams per cubic centimeter to 3 grams per cubic centimeter within 1 minute. By the ratio of the difference between the initial concentration difference and the concentration difference after 1 minute (5 grams per cubic centimeter minus 3 grams per cubic centimeter) to time (1 minute), the concentration difference decay index of the component to be mixed in the local area is obtained as 2 grams per cubic centimeter per minute.

[0080] Step 204: Based on the local shear rate distribution and concentration difference decay index, construct a state evolution model of the adhesive during the mixing process.

[0081] Optionally, the production control system constructs a state evolution model of the adhesive solution during the mixing process based on the local shear rate distribution and concentration difference decay index, as specifically in steps 2041 to 2044.

[0082] The embodiments of this invention construct a state evolution model that can accurately describe the mixing and evolution process of the adhesive, providing support for subsequent prediction of the stirring intervention strategy when the adhesive reaches the target mixing state. This ensures that the prediction of the stirring intervention strategy can closely match the actual mixing state of the adhesive, achieving adaptive and precise control of the stirring process, thereby improving the product quality of silicone sealant in the production process.

[0083] Optionally, the process of steps 2041 to 2044 includes: Step 2041: Based on the local shear rate distribution and the structural strength parameters of the filler agglomerates in the silicone sealant, analyze the effectiveness of agglomerate breakage to obtain the critical particle size threshold of the filler agglomerates that can break under the current flow field.

[0084] Optionally, filler agglomerates refer to the aggregated structures formed by the aggregation of filler particles to be mixed in silicone sealant due to intermolecular forces, van der Waals forces, etc. The structural strength parameters of filler agglomerates refer to the minimum force required for the agglomerates to resist breakage, which can reflect the stability of the filler agglomerates and directly determine whether they can break under the shearing action of the flow field.

[0085] The production control system performs an effectiveness analysis of agglomeration breakup based on the local shear rate distribution and the structural strength parameters of the packing agglomerates. This analysis determines whether the shear force generated by the current flow field can overcome the structural strength of the packing agglomerates, thereby determining the critical size of the packing agglomerates that can break up.

[0086] Optionally, in the analysis process of this embodiment of the invention, the production control system calculates the local shear force generated by the flow field at different locations within the stirring container based on the local shear rate distribution. Then, it compares the local shear force at each location with the structural strength parameters of the packing agglomerates, filters out regions where the local shear force is greater than the structural strength of the packing agglomerates, and calculates the maximum particle size of the packing agglomerates that can be broken by shear force within this region. This maximum particle size is the critical particle size threshold for packing agglomerates that can be broken under the current flow field conditions. The critical particle size threshold clearly defines which sizes of packing agglomerates can be broken under the current flow field conditions.

[0087] Continuing with the aforementioned embodiments, the local shear rate distribution (local shear rate of 1 to 10 Pascals in the near-wall boundary layer fluid domain and 0.5 to 1 Pascals in the external fluid domain) and the structural strength parameters of the filler agglomerates in the silicone sealant (corresponding to a minimum shear force that can be resisted of 3 Pascals) are analyzed. The agglomerate breakup effectiveness analysis process is as follows: The local shear force at each location is calculated based on the local shear rate. Combined with the dynamic viscosity coefficient of the silicone sealant (0.8 Pascals), the local shear force is calculated by multiplying the local shear rate by the dynamic viscosity coefficient. Specifically, in the near-wall boundary layer region with a local shear rate of 10 Pascals, the local shear force is 8 Pascals. In the region with a local shear rate of 1 Pascal, the local shear force is 0.8 Pascals. In the external fluid domain, the local shear force is 0.8 Pascals. By comparing the local shear force in each region with the structural strength parameter of the packing agglomerates (3 Pascal), regions with local shear forces greater than 3 Pascals (only regions with local shear rates ≥ 3.75 s / s within the near-wall boundary layer) are selected. Then, the maximum particle size of the packing agglomerates that can be broken in this region is calculated, and the critical particle size threshold for the packing agglomerates that can be broken under the current flow field is found to be 50 micrometers. That is, packing agglomerates with a particle size greater than 50 micrometers can be broken by shear force in this flow field region, while packing agglomerates with a particle size less than or equal to 50 micrometers cannot be broken.

[0088] Step 2042: Based on the critical particle size threshold of the filler agglomerates and the particle size distribution data of the filler, perform powder dispersibility evolution analysis to obtain the decreasing trend curve of the average particle size of the filler with stirring time.

[0089] Optionally, the particle size distribution data of the filler refers to the distribution of the particle size and corresponding number or mass of all filler particles (including unagglomerated individual particles and agglomerates) in the silicone sealant, which can comprehensively reflect the initial particle size state of the filler.

[0090] The production control system performs powder dispersibility evolution analysis based on the critical particle size threshold of filler agglomerates and the particle size distribution data of the filler. Powder dispersibility refers to the degree of uniformity of filler dispersion in the adhesive solution. Powder dispersibility evolution analysis refers to analyzing the process and law of change in the average particle size of the filler as the filler agglomerates are broken and dispersed during stirring. In the analysis, based on the particle size distribution data of the filler, the number, mass, and particle size range of filler agglomerates with a particle size greater than the critical particle size threshold at the initial moment are statistically determined. Then, combined with the shear intensity of the current flow field (reflected by the local shear rate distribution), the particle size change of these breakable agglomerates after being broken is calculated at different stirring times. The particle size data of all filler particles at each time point are statistically analyzed, and the average particle size of the filler at each time point is calculated. By summarizing the average particle size data of the filler at different times, a decreasing trend curve of the average particle size of the filler with stirring time is obtained. This curve can clearly reflect the process of breakage and dispersion of filler agglomerates during stirring, and intuitively reflect the improvement of powder dispersibility.

[0091] Continuing with the embodiment based on step 2041, the critical particle size threshold for filler agglomerates is 50 micrometers. The particle size distribution data of the filler (initially, filler agglomerates with a particle size greater than 50 micrometers account for 30%, of which agglomerates with a particle size of 60 to 70 micrometers account for 15% and agglomerates with a particle size of 50 to 60 micrometers account for 15%. Filler particles with a particle size less than or equal to 50 micrometers account for 70%, with an average particle size of 30 micrometers) and the specific process of powder dispersion evolution analysis are as follows: The average particle size of the breakable agglomerates (particle size > 50 micrometers) at the initial moment was 65 micrometers. Considering the current shear intensity of the flow field, the agglomerate breakage under different stirring times was calculated: After 5 minutes of stirring, all agglomerates of 60 to 70 micrometers were broken into particles of 30 to 40 micrometers, and 50% of agglomerates of 50 to 60 micrometers were broken. At this point, the average particle size of the packing was calculated as (30% * (15% * 35 micrometers + 15% * 55 micrometers * 50% + 15% * 30 micrometers) + 70% * 30 micrometers) = 32 micrometers. After 10 minutes of stirring, all agglomerates with a particle size greater than 50 micrometers were broken into particles of 25 to 40 micrometers. At this point, the average particle size of the packing was calculated as (30% * 32.5 micrometers + 70% * 30 micrometers) = 30.75 micrometers. After 15 minutes of stirring, the agglomerate breakage stabilized, with the average particle size remaining around 30 micrometers. By summarizing the average particle size data at each time point, a curve showing the decreasing trend of the average particle size of the filler with stirring time was obtained. This curve shows a pattern of rapid decrease in average particle size in the first 10 minutes, followed by a stabilization after 10 minutes.

[0092] Step 2043: Based on the concentration difference decay index and the downward trend curve, a mixing coupling effect analysis is performed to obtain the uniformity index characterizing the adhesive in the current mixing state.

[0093] Optionally, the production control system performs a mixing coupling effect analysis based on the concentration difference decay index and the downward trend curve. The mixing coupling effect refers to the mutual influence and promotion between the breakup and dispersion of filler agglomerates (reflected by a decrease in average particle size) and the homogenization of the concentration of the components to be mixed (reflected by the concentration difference decay). This analysis integrates parameters from two dimensions to quantitatively characterize the current overall mixing state of the adhesive. During the analysis, the production control system combines the magnitude of the concentration difference decay index (reflecting the concentration homogenization rate) and the slope of the downward trend curve (reflecting the agglomerate breakup rate). Through the synergistic analysis of these two factors, a quantitative index that comprehensively reflects the uniformity of the adhesive mixing is obtained, namely, the uniformity index characterizing the adhesive in its current mixing state. The larger the uniformity index value, the higher the degree of uniformity of the adhesive mixing, reflecting both the dispersion effect of the filler and the homogenization effect of the concentration.

[0094] Continuing with the aforementioned embodiment, the concentration difference decay index is 2 g / cm³ / min, and the average particle size of the filler decreases with stirring time (average particle size 32 μm after 5 minutes of stirring, 30.75 μm after 10 minutes, and 30 μm after 15 minutes; the slope of the curve is 0.6 μm / min for the first 5 minutes, 0.25 μm / min from 5 to 10 minutes, and tends to 0 after 10 minutes). The production control system performs a mixed coupling effect analysis, and the specific calculation process is as follows: Combining the concentration difference decay index and the slope of the decreasing trend curve, a uniformity index is calculated using a synergistic quantification method. The larger the concentration difference decay index and the steeper the curve slope, the higher the uniformity index. The uniformity index is calculated as (2 g / cm³ / min * 0.6 μm / min) * 10 = 12 after 5 minutes of stirring. After 10 minutes of stirring, the concentration difference decay index drops to 1 g / cm³ / min, the curve slope is 0.25 μm / min, and the uniformity index is (1 * 0.25) * 10 = 2.5. After stirring for 15 minutes, the concentration difference decay index dropped to 0.3 g / cm³ / min, the slope of the curve approached 0.1, and the uniformity index was (0.3 * 0.1) * 10 = 0.3. This uniformity index clearly reflects that as the stirring time increases, the uniformity of the adhesive mixture first increases rapidly and then gradually stabilizes.

[0095] Step 2044: Based on the uniformity index and historical mixing uniformity trend, perform mixing process trend analysis to obtain the state evolution model of the adhesive during the mixing process.

[0096] Optionally, the historical mixing uniformity trend refers to the variation pattern formed by the sum of uniformity indices at different mixing times under the current mixing conditions, which can reflect the overall evolution characteristics of the adhesive mixing uniformity with mixing time. Based on the uniformity index and the historical mixing uniformity trend, the production control system performs mixing process trend analysis. This analysis process involves combining the current uniformity index with the historical mixing uniformity trend to predict the subsequent change pattern of the adhesive mixing uniformity with mixing time, clarifying the process and rate of the adhesive evolving from the current mixing state to the target mixing state. Through this analysis, the production control system integrates the uniformity index, filler dispersion progress, and concentration homogenization progress to construct a mathematical model that can completely describe the dynamic changes of the mixing uniformity, filler dispersion state, and concentration homogenization state of the adhesive over time during the mixing process—that is, the state evolution model of the adhesive during the mixing process.

[0097] The embodiments of this invention achieve accurate analysis and dynamic description of the adhesive mixing process, solving the problem of not being able to accurately capture the evolution law of the adhesive mixing state. This provides support for subsequent prediction of the stirring intervention strategy when the adhesive reaches the target mixing state, ensuring that the stirring intervention strategy can fit the actual mixing evolution process of the adhesive, improving the accuracy of stirring control, effectively avoiding product quality problems caused by filler agglomeration, and thus improving the product quality of silicone sealant in the production process.

[0098] Optionally, steps 205 to 208 include: Step 205: Based on the state evolution model and the target mixing uniformity threshold corresponding to the target mixing state during the silicone sealant production process, the stirring time is extrapolated and analyzed to obtain the stirring time when the silicone sealant reaches the target mixing state.

[0099] Optionally, the target mixing uniformity threshold corresponding to the target mixing state during the silicone sealant production process, wherein the target mixing uniformity threshold refers to the critical value of the uniformity index corresponding to the silicone sealant reaching the preset qualified mixing quality. This threshold is preset according to the product quality standard of silicone sealant and can quantitatively determine whether the adhesive has reached the target mixing state.

[0100] The production control system uses a state evolution model combined with a target mixing uniformity threshold to perform a stirring time prediction analysis. This analysis simulates the evolution process of the adhesive mixing state through the state evolution model and calculates the stirring time required for the adhesive uniformity index to reach the target mixing uniformity threshold.

[0101] Optionally, in the analysis process of this embodiment of the invention, the production control system uses a state evolution model to output the uniformity index corresponding to different stirring durations, compares the uniformity index corresponding to each duration with the target mixing uniformity threshold, and selects the stirring duration corresponding to the first time the uniformity index reaches or exceeds the target mixing uniformity threshold, which is the stirring duration when the silicone sealant reaches the target mixing state. This stirring duration can clearly define the continuous stirring time required for the adhesive to reach the target mixing state from the current mixing state under the current working conditions.

[0102] Continuing with the aforementioned embodiments, the state evolution model (which can output the uniformity index at any stirring time, and it is known that the uniformity index gradually decreases and tends to stabilize as the stirring time increases) sets the target mixing uniformity threshold to 1. The production control system performs a stirring time extrapolation analysis, and the specific calculation process is as follows: Using the state evolution model, the uniformity index corresponding to different stirring times is output: the uniformity index is 3.2 when stirring for 8 minutes, 2.5 when stirring for 10 minutes, 1.8 when stirring for 12 minutes, 1.1 when stirring for 14 minutes, 1 when stirring for 15 minutes, and 0.9 when stirring for 16 minutes. The uniformity index corresponding to each time is compared with the target mixing uniformity threshold (1), and the stirring time corresponding to the first time the uniformity index reaches the target threshold is selected as 15 minutes, that is, the stirring time when the silicone sealant reaches the target mixing state is 15 minutes. If it has been stirring for 5 minutes, it needs to continue stirring for another 10 minutes to reach the target mixing state.

[0103] Step 206: Analyze the energy input rate under the current operating conditions based on the stirring time and blade speed data to obtain the energy input value per unit time to maintain the current stirring state.

[0104] Optionally, the production control system analyzes the energy input rate under the current operating conditions based on the stirring time and impeller speed data. The energy input rate refers to the energy input to the adhesive per unit time during the stirring process, reflecting the energy supply during stirring and directly affecting the mixing efficiency and temperature rise of the adhesive. During the analysis, combining the total stirring energy demand corresponding to the stirring time and the energy input intensity corresponding to the impeller speed, the ratio of the total energy demand to the stirring time is used to calculate the energy input to the adhesive per unit time while maintaining the current stirring state (i.e., the current impeller speed). This value accurately quantifies the energy supply intensity under the current stirring conditions.

[0105] Continuing with the embodiment based on step 205, the stirring time is 15 minutes, and the current impeller speed is 80 revolutions per minute. The production control system performs an energy input rate analysis, and the specific calculation process is as follows: Based on the current impeller speed (80 revolutions per minute), the total energy input requirement of the stirring equipment under the current operating conditions is calculated. It is known that at the current speed, the energy input to the adhesive per minute of stirring is 1000 joules. Considering the stirring time of 15 minutes, the total energy input requirement is 15000 joules. The energy input per unit time is calculated by dividing the total energy input requirement by the stirring time, i.e., 15000 joules divided by 15 minutes, resulting in an energy input per unit time of 1000 joules per minute. This value represents the energy input to the adhesive per unit time while maintaining the current stirring speed of 80 revolutions per minute.

[0106] Step 207: Analyze the temperature rise trend of the adhesive solution based on the energy input value per unit time and the shear heat generation coefficient of the silicone sealant raw material to obtain the expected maximum temperature of the adhesive solution if it continues to run at the current speed until the end.

[0107] Optionally, the shear heat generation coefficient of silicone sealant raw materials refers to the amount of heat generated per unit energy input when the silicone sealant is subjected to shear action. It can reflect the ability of the sealant to generate heat due to shear action during the stirring process and directly determines the temperature rise rate of the sealant.

[0108] The production control system performs a temperature rise trend analysis of the adhesive solution based on the energy input value per unit time and the shear heat generation coefficient. This analysis calculates the highest temperature that the adhesive solution can reach when it continues to run at the current blade speed until the end of stirring (i.e., the stirring time obtained in step 205).

[0109] Optionally, in the analysis process of this embodiment of the invention, the production control system calculates the heat generated by the adhesive due to shearing action per unit time based on the energy input value per unit time and the shear heat generation coefficient. Then, combined with parameters such as the mass and specific heat capacity of the adhesive (which have been pre-selected), the system calculates the temperature rise rate of the adhesive per unit time. Subsequently, based on the remaining stirring time, the system calculates the total temperature rise of the adhesive during the remaining stirring time. Combined with the current actual temperature of the adhesive, the system finally obtains the expected maximum temperature of the adhesive when continuing to run at the current speed until the end. The expected maximum temperature of the adhesive can predict the upper limit of the temperature change of the adhesive during the stirring process.

[0110] Continuing with the embodiment based on step 206, the energy input per unit time is 1000 joules per minute, the shear heat generation coefficient of the silicone sealant raw material is 0.8 (i.e., for every 1000 joules of energy input, the adhesive generates 800 joules of heat), the current actual temperature of the adhesive is 25 degrees Celsius, the mass of the adhesive is 10 kg, the specific heat capacity of the adhesive is 2 joules per gram of degree Celsius, and the remaining stirring time is 10 minutes (currently stirring for 5 minutes, total stirring time 15 minutes). The specific process for analyzing the temperature rise trend of the adhesive is as follows: Calculate the heat generated by the adhesive per unit time, i.e., 1000 joules per minute multiplied by 0.8, resulting in 800 joules per minute. Then calculate the temperature rise rate of the adhesive per unit time, i.e., the heat generated per unit time divided by (adhesive mass * specific heat capacity), converted to units, 800 joules per minute divided by (10000 grams * 2 joules per gram of degree Celsius), resulting in 0.04 degrees Celsius per minute. The total temperature rise of the adhesive solution over the remaining 10 minutes is then calculated, which is 0.04 degrees Celsius per minute multiplied by 10 minutes, resulting in 0.4 degrees Celsius. Finally, considering the current adhesive solution temperature of 25 degrees Celsius, the expected maximum temperature of the adhesive solution is calculated to be 25.4 degrees Celsius. This means that if the current stirring speed of 80 revolutions per minute continues until the stirring ends, the maximum temperature of the adhesive solution will reach 25.4 degrees Celsius.

[0111] Step 208: Based on the expected maximum temperature of the adhesive and the critical temperature parameters of the thermal stability of the silicone sealant raw material, determine the stirring intervention strategy indicated when the adhesive reaches the target mixing state.

[0112] Optionally, the critical temperature parameter for thermal stability of the silicone sealant raw material refers to the highest temperature threshold at which the silicone sealant can maintain its own physical and chemical properties without deterioration or degradation. Exceeding this threshold will cause an irreversible decline in the sealant's performance, affecting product quality. The production control system determines the stirring intervention strategy indicated when the sealant reaches the target mixing state based on the expected maximum temperature of the sealant and the critical temperature parameter for thermal stability of the silicone sealant raw material, as described in steps 2081 to 2083.

[0113] The embodiments of this invention achieve accurate prediction of stirring intervention strategies, solving the problems of blind stirring strategies and inability to balance mixing quality and adhesive performance. It provides instruction support for the precise control of subsequent physical stirring equipment, realizing adaptive and precise control of the stirring process in the production of silicone sealant, thereby improving the product quality of silicone sealant in the production process.

[0114] Optionally, the process of steps 2081 to 2083 includes: Step 2081: Based on the expected maximum temperature of the adhesive and the critical temperature for thermal stability, perform a risk analysis of material thermal degradation to obtain the material safety margin index under the current stirring state.

[0115] Optionally, the critical temperature parameter for thermal stability of silicone sealant raw materials refers to the highest temperature threshold at which the silicone sealant can maintain its own physical and chemical properties without deterioration or degradation. Exceeding this threshold will cause irreversible degradation of the sealant's performance, affecting product quality. Material thermal degradation risk analysis refers to determining whether the sealant temperature will exceed the critical temperature for thermal stability under the current stirring state, thereby quantifying the risk of thermal degradation. The production control system performs material thermal degradation risk analysis based on the expected maximum temperature of the sealant and the critical temperature parameter for thermal stability. During the analysis, the production control system calculates the difference between the critical temperature parameter for thermal stability and the expected maximum temperature of the sealant. This difference directly reflects the safe margin of the sealant from thermal degradation; the larger the difference, the lower the risk of thermal degradation, and vice versa. Subsequently, the production control system compares this temperature difference with a preset safety difference standard. The ratio of the temperature difference to the safety difference standard is used to calculate the material safety margin index under the current stirring state. The value is greater than or equal to zero; the larger the index value, the better the thermal safety performance of the sealant and the lower the risk of thermal degradation. A value of zero or negative indicates that the expected maximum temperature of the adhesive has reached or exceeded the critical temperature for thermal stability, posing a risk of thermal degradation.

[0116] Continuing with the aforementioned embodiments, the expected maximum temperature of the adhesive is 25.4 degrees Celsius, the critical temperature parameter for the thermal stability of the silicone sealant raw material is 50 degrees Celsius, and the preset safety difference standard is 10 degrees Celsius.

[0117] The production control system performs a material thermal degradation risk analysis. The specific calculation process is as follows: The difference between the critical thermal stability temperature parameter and the expected maximum temperature of the adhesive solution is calculated, i.e., 50 degrees Celsius minus 25.4 degrees Celsius, resulting in a temperature difference of 24.6 degrees Celsius. Then, the ratio of this temperature difference to the preset safety margin standard is calculated, i.e., 24.6 degrees Celsius divided by 10 degrees Celsius, yielding a material safety margin index of 2.46 under the current stirring state. This index value is greater than 1, indicating that the current thermal safety performance of the adhesive solution is good, and there is no risk of thermal degradation.

[0118] Step 2082: Based on the material safety margin index and the motor characteristic curve of the physical mixing equipment, perform a boundary analysis of the equipment load capacity to obtain the maximum allowable mixing speed without causing motor overload.

[0119] Optionally, the motor characteristic curve of the physical mixing equipment refers to the curve showing the relationship between the motor output power, load torque, and mixing speed. It clearly reflects the motor's load capacity and output performance at different mixing speeds, directly determining the range of speeds within which the mixing equipment can operate stably. Equipment load capacity boundary analysis refers to determining the maximum mixing speed that the mixing equipment can achieve without overloading the motor and ensuring the thermal safety of the adhesive, based on the material safety margin index. The production control system performs equipment load capacity boundary analysis based on the material safety margin index and the motor characteristic curve of the physical mixing equipment. During the analysis, the production control system determines the upper limit of the speed corresponding to the maximum temperature rise that the adhesive can withstand, according to the material safety margin index. If the material safety margin index is high, it indicates sufficient thermal safety redundancy in the adhesive, and the mixing speed can be appropriately increased to improve mixing efficiency. If the material safety margin index is low, the mixing speed needs to be controlled to avoid excessive temperature rise. Subsequently, by combining the motor characteristic curve, the maximum stirring speed that the motor can output without overload (i.e., the motor output power and load torque do not exceed the rated value) is found. Combined with the upper limit of the speed corresponding to the thermal safety of the adhesive, the maximum allowable stirring speed is determined without causing motor overload, which satisfies the requirements for stable motor operation and ensures that the adhesive does not undergo thermal degradation.

[0120] Continuing with the embodiment based on step 2081, the material safety margin index is 2.46 (sufficient thermal safety redundancy). The motor characteristic curve of the physical mixing equipment shows that the rated output power of the motor is 10 kW. When the mixing speed is 100 rpm, the motor output power is 10 kW (reaching the rated value, about to be overloaded). When the mixing speed is 90 rpm, the motor output power is 8.5 kW (not overloaded). Combining the upper limit of the speed corresponding to the material safety margin index (after calculation, the maximum mixing speed that the adhesive can withstand is 110 rpm), the production control system performs a boundary analysis of the equipment load capacity. The specific calculation process is as follows: First, it is determined that the critical speed for motor overload is 100 rpm. Combining the upper limit of the speed corresponding to the thermal safety of the adhesive, 110 rpm, the smaller value of the two is taken as the maximum allowable mixing speed. That is, it is determined that the maximum allowable mixing speed without causing motor overload is 100 rpm. This speed meets the rated load requirements of the motor and will not cause the temperature rise of the adhesive to exceed the critical temperature for thermal stability.

[0121] Step 2083: The optimal stirring speed is three-quarters of the maximum stirring speed. Based on the optimal stirring speed and stirring time, a coordinated control scheme is constructed to obtain the stirring intervention strategy.

[0122] Optionally, the production control system uses three-quarters of the maximum stirring speed as the optimal stirring speed, and combines the optimal stirring speed and stirring time to construct a coordinated control scheme, thereby obtaining a stirring intervention strategy.

[0123] The embodiments of the present invention achieve precise determination of the stirring intervention strategy, ensuring that the stirring intervention strategy can not only make the adhesive reach the target mixing state, but also ensure stable operation of the equipment and good performance of the adhesive. This provides reliable instruction support for the precise control of the subsequent physical stirring equipment, realizes adaptive and precise control of the stirring process in the production of silicone sealant, and improves the product quality of silicone sealant in the production process.

[0124] Furthermore, the silicone sealant production control system based on digital twins provided by the present invention will be described below. The silicone sealant production control system based on digital twins described below can be referred to in correspondence with the silicone sealant production control method based on digital twins described above.

[0125] Optionally, refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of the silicone sealant production control system based on digital twin provided by the present invention. The silicone sealant production control system based on digital twin includes: The flow field evolution simulation module 210 is used to simulate the flow field evolution in a digital twin mixing model by using the blade motion state data of the physical mixing equipment as the motion boundary condition, combined with the static boundary condition and the initial flow field condition, so as to obtain the flow state distribution relationship of the adhesive in the physical mixing equipment. The stirring intervention prediction module 220 is used to construct a state evolution model of the adhesive during the mixing process based on the flow state distribution relationship, and predict the stirring intervention strategy indicated when the adhesive reaches the target mixing state based on the state evolution model. The stirring intervention control module 230 is used to control the physical stirring equipment to perform corresponding operations based on the stirring intervention strategy, and to update the motion boundary conditions based on the operating parameters of the physical stirring equipment after the operation is performed. The production optimization control module 240 is used to simulate the evolution of the flow field and predict the state evolution based on the updated motion boundary conditions, iteratively optimize the stirring intervention strategy, and control the physical stirring equipment to stop operating when the target mixing state is met.

[0126] This invention, through simulation of flow field evolution and state evolution prediction, iteratively optimizes the stirring intervention strategy. Continuous iteration can gradually correct the stirring strategy, offsetting the influence of filler batch differences on the system's rheological properties, and avoiding the interference of strong nonlinear viscosity and unsteady response of silicone sealant under high shear on stirring control. It realizes dynamic adjustment of the stirring intervention strategy according to the actual dispersion state of the adhesive, ensuring that the stirring process always conforms to the actual mixing requirements of the adhesive. This achieves adaptive and precise control of the stirring process in the production of silicone sealant, solving the technical problem of filler agglomeration due to the inability to adapt to batch differences and dynamic changes in materials, and improving the product quality of silicone sealant in the production process.

[0127] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it implements the processes of steps 10 to 40.

[0128] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it implements the processes of steps 10 to 40.

[0129] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the digital twin-based silicone sealant production control method provided by the above methods, which includes steps 10 to 40.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the production of silicone sealant based on digital twins, characterized in that, A digital twin mixing model is constructed based on the structural dimensions of the physical mixing equipment and the physical properties of the silicone sealant. The digital twin stirring model indicates the corresponding static boundary conditions and initial flow field conditions; The silicone sealant production control method includes: Using the blade motion state data of the physical stirring device as the motion boundary condition, and combining the static boundary condition and the initial flow field condition, the flow field evolution is simulated in the digital twin stirring model to obtain the flow state distribution relationship of the adhesive in the physical stirring device. Based on the flow state distribution relationship, a state evolution model of the adhesive solution during the mixing process is constructed, and based on the state evolution model, the stirring intervention strategy indicated when the adhesive solution reaches the target mixing state is predicted. Based on the stirring intervention strategy, the physical stirring device is controlled to perform corresponding operations, and the motion boundary conditions are updated based on the operating parameters of the physical stirring device after the operations are performed. Based on the updated motion boundary conditions, the flow field evolution and state evolution prediction are simulated, and the stirring intervention strategy is iteratively optimized until the target mixing state is met, at which point the physical stirring device is controlled to stop operating.

2. The method for controlling the production of silicone sealant based on digital twins according to claim 1, characterized in that, The blade motion state data includes blade rotation angle data; the static boundary conditions include the inner wall geometric boundary and the inner wall no-slip boundary of the stirring container; the initial flow field conditions include the initial zero velocity field and the viscosity characteristic parameters, dynamic viscosity coefficient and density parameters of the silicone sealant. Based on the aforementioned kinematic boundary conditions, combined with the static boundary conditions and the initial flow field conditions, the flow field evolution is simulated in the digital twin stirring model to obtain the flow state distribution relationship, including: Based on the blade rotation angle data and blade three-dimensional geometric contour data, combined with the inner wall geometric boundary, a spatial position mapping analysis of the moving parts is performed to obtain the instantaneous spatial area occupied by the blade. Based on the instantaneously occupied space region and the viscosity characteristic parameters, the near-wall boundary layer thickness is analyzed to obtain the near-wall boundary layer fluid domain surrounding the blade surface. Based on the initial zero velocity field, the boundary velocity analysis of the near-wall boundary layer fluid domain is performed to obtain the first adhesive velocity gradient field of the near-wall boundary layer fluid domain. The flow state distribution relationship is determined based on the first adhesive velocity gradient field combined with the inner wall no-slip boundary, the dynamic viscosity coefficient, and the density parameter.

3. The method for controlling the production of silicone sealant based on digital twins according to claim 2, characterized in that, The determination of the flow state distribution based on the first adhesive velocity gradient field combined with the inner wall no-slip boundary, the dynamic viscosity coefficient, and the density parameter includes: Based on the velocity gradient field of the first adhesive and the density parameter, a local momentum flux analysis is performed to obtain the local momentum distribution of the near-wall boundary layer fluid domain. Based on the local momentum distribution and the dynamic viscosity coefficient, a viscous shear stress coupling analysis is performed to obtain the local viscous shear stress field between the near-wall boundary layer fluid domain and the external fluid domain; the external fluid domain is the fluid domain other than the near-wall boundary layer fluid domain. Based on the local viscous shear stress field and the slip-free boundary of the inner wall, a global velocity field analysis is performed to obtain the second adhesive velocity gradient field of the external fluid domain. The flow state distribution relationship is obtained by integrating the velocity gradient fields of the first and second adhesive solutions.

4. The method for controlling the production of silicone sealant based on digital twins according to claim 1, characterized in that, The construction of the state evolution model of the adhesive during the mixing process based on the flow state distribution relationship includes: Based on the flow state distribution relationship, a shear rate distribution reconstruction analysis is performed on the stirred container to obtain the local shear rate distribution at each location within the stirred container. Based on the velocity gradient field of the first adhesive in the flow state distribution relationship and the spatial distribution coordinates of the components to be mixed in the silicone sealant in the stirring container, a tensile analysis of the mixing interface is performed to obtain the area expansion rate of the components to be mixed and the adhesive at the contact interface. Based on the area expansion rate and the molecular diffusion coefficient of the silicone sealant raw material, the component concentration homogenization efficiency was analyzed to obtain the concentration difference attenuation index of the component to be mixed in the local area. Based on the local shear rate distribution and the concentration difference decay index, a state evolution model of the adhesive solution during the mixing process is constructed.

5. The method for controlling the production of silicone sealant based on digital twins according to claim 4, characterized in that, The process of constructing a state evolution model of the adhesive solution during the mixing process based on the local shear rate distribution and the concentration difference decay index includes: Based on the local shear rate distribution and the structural strength parameters of the filler agglomerates in the silicone sealant, the effectiveness of agglomerate breakage is analyzed to obtain the critical particle size threshold of the filler agglomerates that can break under the current flow field. Based on the critical particle size threshold of the filler agglomerates and the particle size distribution data of the filler, the powder dispersity evolution analysis was performed to obtain the decreasing trend curve of the average particle size of the filler with stirring time. Based on the concentration difference decay index and the downward trend curve, a mixing coupling effect analysis is performed to obtain a uniformity index characterizing the adhesive solution in the current mixing state. Based on the uniformity index and historical mixing uniformity trends, a mixing process trend analysis is performed to obtain a state evolution model of the adhesive during the mixing process.

6. The method for controlling the production of silicone sealant based on digital twins according to any one of claims 1 to 5, characterized in that, The blade motion status data includes blade rotation speed data; The stirring intervention strategy indicated when the adhesive reaches the target mixing state based on the state evolution model includes: Based on the state evolution model and the target mixing uniformity threshold corresponding to the target mixing state during the production process of the silicone sealant, the stirring time is extrapolated and analyzed to obtain the stirring time when the silicone sealant reaches the target mixing state. Based on the stirring duration and the blade rotation speed data, the energy input rate under the current working condition is analyzed to obtain the energy input value per unit time for maintaining the current stirring state; Based on the energy input value per unit time and the shear heat generation coefficient of the silicone sealant raw material, the temperature rise trend of the adhesive solution is analyzed to obtain the expected maximum temperature of the adhesive solution if it continues to run at the current speed until the end. Based on the expected maximum temperature of the adhesive and the critical temperature parameters of the thermal stability of the silicone sealant raw material, a stirring intervention strategy is determined when the adhesive reaches the target mixing state.

7. The method for controlling the production of silicone sealant based on digital twins according to claim 6, characterized in that, The stirring intervention strategy determined based on the expected maximum temperature of the adhesive and the critical temperature parameter of the thermal stability of the silicone sealant raw material, when the adhesive reaches the target mixing state, includes: Based on the expected maximum temperature of the adhesive and the critical temperature parameter for thermal stability, a risk analysis of material thermal degradation is performed to obtain the material safety margin index under the current stirring state. Based on the material safety margin index and the motor characteristic curve of the physical mixing equipment, the equipment load capacity boundary analysis is performed to obtain the maximum allowable mixing speed without causing motor overload. The optimal stirring speed is defined as three-quarters of the maximum stirring speed. Based on the optimal stirring speed and the stirring duration, a coordinated control scheme is constructed to obtain the stirring intervention strategy.

8. A silicone sealant production control system based on digital twins, characterized in that, A method for implementing the silicone sealant production control system based on digital twins as described in any one of claims 1 to 7; the digital twin-based silicone sealant production control system includes: The flow field evolution simulation module is used to simulate the flow field evolution in the digital twin mixing model by using the blade motion state data of the physical mixing device as the motion boundary condition, combined with the static boundary condition and the initial flow field condition, to obtain the flow state distribution relationship of the adhesive in the physical mixing device. The stirring intervention prediction module is used to construct a state evolution model of the adhesive during the mixing process based on the flow state distribution relationship, and to predict the stirring intervention strategy indicated when the adhesive reaches the target mixing state based on the state evolution model. The stirring intervention control module is used to control the physical stirring equipment to perform corresponding operations based on the stirring intervention strategy, and to update the motion boundary conditions based on the operating parameters of the physical stirring equipment after the operation is performed. The production optimization control module is used to simulate the flow field evolution and state evolution prediction based on the updated motion boundary conditions, iteratively optimize the stirring intervention strategy, and control the physical stirring equipment to stop operating when the target mixing state is met.

9. An electronic device, comprising: Memory, used to store computer software programs; A processor for reading and executing the computer software program, characterized in that, when the processor executes the computer software program, it implements the silicone sealant production control method based on digital twin as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, wherein a computer software program is stored therein, characterized in that, When the computer software program is executed by the processor, it implements the silicone sealant production control method based on digital twin as described in any one of claims 1 to 7.

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