Method and system for high-throughput mixing of multiple asphalt particles and performance characterization

Abstract

The invention discloses a method and a system for high-flux mixing of multiple asphalt particles and performance characterization. The method comprises the following steps: S1, transmitting ultrasonic waves to a reflection platform through an ultrasonic device; s2, selecting an optimal suspension point location according to the emission range of the ultrasonic device; s3, the mechanical arm moves the micro needle head to the optimal suspension point position of the reflection platform; s4, extruding particles by the micro needle head, heating the particles to a preset temperature by utilizing temperature control equipment, and shooting the particles by a camera; and S5, acting on the particles through the reflection platform device and the ultrasonic device, and carrying out characterization analysis on the particles. According to the invention, non-contact uniform mixing of various materials in the asphalt high-flux experiment is realized, such as liquid-liquid mixing, solid-liquid mixing, mixing of various materials and heating mixing.

Description

Technical Field

[0001] The present invention relates to the field of high-throughput mixing of multiple asphalt particles, and in particular to a method and system for high-throughput mixing of multiple asphalt particles and performance characterization. Background Art

[0002] Characterization of asphalt performance is a key step in testing its quality, stability and applicability. In highway construction and maintenance, asphalt is an important paving material, and its performance directly affects the service life, driving comfort and safety of the road surface. With the continuous advancement of science and technology, in addition to conventional asphalt, some modified asphalt prepared by the fusion of nanomaterials, metal elements, modifiers, etc., as well as recycled and reused waste asphalt are also increasing. Its physical property tests, such as softening point, needle penetration, ductility, and dynamic shear rheological properties (DSR) of asphalt, all require experimental characterization through a variety of instruments and test methods. However, with the improvement of people's environmental awareness, the environmental performance of asphalt has also received more and more attention. For example, a large number of sample production and repeated characterization experiments may cause unnecessary energy waste and cost increase in the process. Therefore, there is an urgent need for a high-throughput experimental device to evaluate the various properties of materials.

[0003] High-throughput characterization experiments refer to the use of computer-controlled automation to effectively reduce operational errors, quickly screen and characterize a large number of samples, save sample resources, and reduce experimental costs. However, this method is still subject to some limitations, such as material problems, technology integration, contact control and other operational issues. In particular, there is currently no good solution for high-throughput detection of asphalt materials. With the requirements for high-throughput control and the diversification of detection materials, existing technologies cannot meet the needs, and there is a lack of a high-precision and high-efficiency control strategy based on automated high-throughput mixing of multiple asphalt particles and performance characterization.

[0004] The existing technology has the following limitations:

[0005] (1) Material limitations: Currently, high-throughput experiments cannot cover all materials and environmental conditions. For example, when using droplet microfluidic sorting (DMFS) technology, the chip is likely to be clogged due to excessive material viscosity.

[0006] (2) Material mixing: When mixing multiple materials, it is difficult to ensure that all materials are mixed evenly due to the inconsistent properties of the mixture, and the process of mixing materials may take a lot of time, especially when the mixing ratio and degree of mixing need to be precisely controlled.

[0007] (3) Cost and resource consumption: Although high-throughput experiments have advantages in efficiency, their cost and resource consumption are also relatively high. In particular, a large amount of investment may be required in terms of material preparation, equipment maintenance, and data processing.

[0008] (4) Complexity of technology integration: Currently, high-throughput technology is only used in some simple mechanical tests. It is a technical challenge to achieve comprehensive performance characterization and automated testing with a long read time. How to effectively integrate the performance characterization technology of asphalt materials and optimize the experimental process is a problem that needs to be solved.

[0009] (5) In addition, the current use of ultrasonic levitation technology for droplet control can only perform simple movement manipulation and cannot meet performance testing requirements. Summary of the invention

[0010] In order to solve the above problems, the present invention proposes a method and system for realizing automated high-throughput mixing of multiple asphalt particles and performance characterization. The method and system include the following steps:

[0011] S1: transmitting ultrasonic waves to the reflection platform through an ultrasonic device;

[0012] S2: Select the best suspension point according to the transmission range of the ultrasonic device;

[0013] S3: The robotic arm moves the micro needle to the optimal suspension point of the reflection platform;

[0014] S4: Micro needles extrude microparticles, heat the microparticles to a preset temperature using a temperature control device, and photograph the microparticles using a camera;

[0015] S5: The particles are subjected to characterization and analysis by using a reflection platform device and an ultrasonic device.

[0016] Furthermore, in step S3, the control method of the robotic arm is as follows:

[0017] u(t)=Kp*e(t)+Ki*∫e(t)dt+Kd*de(t) / dt

[0018] Where u(t) is the control input, e(t) is the difference between the current position and the target position, and Kp, Ki, and Kd are the adjusted PID control parameters.

[0019] Furthermore, in step S4, the control method of micro needle extrusion of microparticles is as follows:

[0020] Q=(πPd^4) / (128ηL)

[0021] Where Q is the flow rate, P is the pressure difference, d is the pipe diameter, η is the viscosity of the fluid, and L is the length of the pipe.

[0022] Furthermore, in step S4, the camera takes an image of the particles for preprocessing, including:

[0023] S41: Use Gaussian filtering to remove noise and enhance contrast to improve image quality. The formula is:

[0024]

[0025] Among them, G(x,y) is the concentration data after filtering, x and y represent the spatial coordinates, and σ is the standard deviation, which affects the filtering effect;

[0026] S42: Perform threshold segmentation and edge detection, further optimize image quality through corrosion and expansion, separate particles from background for feature extraction, cover the entire image with structural elements, and only when the particle image is completely within the foreground pixel, set the center pixel to 1 or retain its value, otherwise set it to 0 or change its value to the background value:

[0027] B(x,y)=minall(u,v)∈SA(x+u,y+v)

[0028] B=open(A,S)=dilate(erode(A,S),S), B=close(A,S)=erode(dilate(A,S),S)

[0029] Among them, A is the original image, B is the image after corrosion, S is the structural element, (x, y) is the position coordinate in the image, and in the feature extraction process, the center coordinate or boundary coordinate of each feature is recorded;

[0030] S43: Obtaining the initial information of the particles, including shape, outline, volume characteristics, and position coordinates of each pixel block in the image,

[0031] (xc,yc)=centroid(B); (xb,yb)=boundary(B)

[0032] Among them, (xc, yc) represents the centroid coordinates of the object, (xb, yb) represents the boundary coordinates of the object,

[0033] The area is: Area = ∑(x,y)∈B1; the perimeter is: in, represents pixels on the object boundary; the concentration is: C = waveDenoise (C'), where waveDenoise represents the wavelet transform denoising algorithm; the shape descriptor is:

[0034] Furthermore, in step S5, the actions of the reflection platform device include stationary, tilting, and rotating. The particles accordingly perform stationary, translational, and rotating actions; and the ultrasonic device causes the particles to be squeezed, deformed, or exploded by ultrasonic waves.

[0035] Furthermore, in step S2, the optimal suspension point is determined by the following formula:

[0036]

[0037] Among them, fadjust is the adjusted ultrasonic frequency, f0 is the basic frequency, Δf is the frequency adjustment range, d is the current distance between the micro needle and the ultrasonic transmitter, d0 is the initial distance, and dmax is the maximum adjustment distance.

[0038] Furthermore, in step S5, the controller model is deployed to the controller of the system, and the controller model is trained and applied, and cooperated with a high-speed camera to implement various high-throughput experimental tests and record the characterization data of the particles.

[0039] Furthermore, a machine learning algorithm is used to train the controller model to control different modes or different types of particles, and a recognition model is established in conjunction with a high-speed camera. After the image recognition model is trained, the collected particle pictures are input into the droplet image recognition model to identify the initial position, outline, and size of the particles in the image to monitor: the number, position, suspension, and motion state of different particles; the swelling behavior of different particle mixtures; the stretching length, aspect ratio, rotation change, and collapse time of different particles or mixed particles; monitor and follow the dynamic changes of the particles, establish a model and compare it with the database, and then realize automated high-throughput mixing of multiple asphalt particles and performance characterization.

[0040] The present invention also provides a system for high-throughput mixing of multiple asphalt particles and performance characterization method, comprising:

[0041] A reflective platform for carrying asphalt particles;

[0042] A robotic arm, the execution end of which is connected to a micro needle;

[0043] Microneedles, used to release or absorb particles;

[0044] The ultrasonic device is arranged above the reflecting platform and is used to generate acoustic radiation force on the particles to suspend or deform them, and has adjustable frequency and power output functions;

[0045] Temperature control equipment, including a concentrated heating device, with heating and cooling modules, integrated around the reflective platform;

[0046] A camera for photographing particles;

[0047] The controller has a built-in data analysis module and a model training application module. It coordinates and optimizes the interaction and operation between components according to preset experimental parameters or user-defined instructions, and adjusts the motion trajectory of the reflection platform and the robotic arm, as well as the frequency and power output of the ultrasonic device.

[0048] Furthermore, the surface layer of the reflection platform is a surface layer of a super-hydrophobic nanostructure, and the surface layer is a smooth or concave-convex particle structure.

[0049] The lower end of the surface layer is connected to six push rods for pushing the surface layer to move.

[0050] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) Realize non-contact uniform mixing of multiple materials in high-throughput asphalt experiments, such as liquid-liquid mixing, solid-liquid mixing, mixing of multiple materials, and heating mixing;

[0052] (2) Save materials, effectively integrate asphalt material performance characterization technology and optimize experimental processes, and reduce investment in equipment maintenance and data processing;

[0053] (3) Achieve technology integration and effectively conduct long-term reading and measurement to achieve efficient closed-loop integration and performance characterization of asphalt materials;

[0054] (4) Utilize analysis modules and machine learning to achieve high-throughput intelligent detection and data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of a method for high-throughput mixing of multiple asphalt particles and performance characterization according to an embodiment of the present invention.

[0056] Figure 2 This is a structural diagram of the high-throughput mixing of multiple asphalt particles and performance characterization system of the present invention.

[0057] Figure 3 This is a diagram of particle suspension mixing.

[0058] Figure 4 Figure 1. Heating and mixing diagram for microparticle suspension.

[0059] Figure 5 Schematic diagram of particle suspension stretching.

[0060] Figure 6 Schematic diagram of particle suspension fracture.

[0061] Figure 7 Schematic diagram of the changes in particle suspension mixing fatigue detection. DETAILED DESCRIPTION

[0062] The present invention is further described below by specific embodiments:

[0063] In the present invention, the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. In the description, the directions or positional relationships indicated by "upper", "lower", "left", "right", "front", and "back" are based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention, and are not intended to indicate or imply that the device referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the scope of protection of the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B / and or C, which can indicate: A or B or C exists alone, A and B / C exist at the same time, and A and B and C exist at the same time. The character " / " generally indicates that the front and back associated objects are in an "or" relationship.

[0064] See also Figure 1 The embodiment of the present invention provides a method for realizing automated high-throughput mixing of multiple asphalt particles and performance characterization, which comprises the following steps:

[0065] 1) Transmit ultrasonic waves to the reflection platform through an ultrasonic device.

[0066] The ultrasonic system starts to transmit ultrasonic waves to the reflection platform, while the reflection platform device is kept in a horizontal and static state, and the ultrasonic wave sensor is used to feed back to the controller model.

[0067] The surface of the reflection platform is provided with two types of smooth and concave-convex particles, which can be selected according to the experimental needs. There is a six-axis parallel drive device at the bottom of the reflection platform to drive the surface to perform corresponding actions, and connect the controller for control.

[0068] In this step, the ultrasonic system is set vertically above the reflection platform to generate acoustic radiation force on the particles to suspend or deform them. It has adjustable frequency and power output functions and can accurately control the intensity and frequency of ultrasound according to different asphalt particle characteristics and experimental requirements. It is connected to the controller through a feedback mechanism to start the ultrasonic system emission function and ensure that the bottom reflection platform device is maintained in a horizontal and static position. The feedback information is transmitted to the controller model through the acoustic sensor. When the controller data is stable, it indicates the stable operation of the system. 2) Select the best suspension point according to the emission range of the ultrasonic device.

[0069] Specifically, the best suspension point is selected according to the emission range of the ultrasonic device displayed by the sensor.

[0070] In this step, in order to ensure that the ultrasonic suspension device can operate stably and efficiently, when the micro needle moves to the optimal suspension point, the ultrasonic system adjusts its transmission frequency and power output according to the precise position of the micro needle through a real-time feedback mechanism to ensure stable suspension of the particles. The relationship between the ultrasonic wave and the position of the micro needle is adjusted by the following formula:

[0071]

[0072] Among them, fadjust is the adjusted ultrasonic frequency, f0 is the basic frequency, Δf is the frequency adjustment range, d is the current distance between the micro needle and the ultrasonic transmitter, d0 is the initial distance, and dmax is the maximum adjustment distance.

[0073] 3) The robotic arm moves the micro needle to the optimal suspension point on the reflection platform.

[0074] In this step, the mechanical arm has the ability to extend and move, including position, speed and direction control, and is provided with a gripper at the front end and connected to a controller. The gripper is connected to the micro needle, which has a micro pump and valve system, as well as multiple output ports and pipelines, and is equipped with a propeller, an inhaler and a sensor array. It can accurately capture and measure the flow rate of asphalt particles, absorb and release particles, and has an adaptive adjustment mechanism. It dynamically adjusts the control parameters according to the changes in experimental conditions to ensure that the optimal contact distance with the asphalt particles can be maintained in different mixed materials, and is connected to the controller through a guide rail.

[0075] The details include:

[0076] u(t)=Kp*e(t)+Ki*∫e(t)dt+Kd*de(t) / dt

[0077] Where u(t) is the control input (e.g. torque or voltage at a joint), e(t) is the difference between the current position and the target position, and Kp, Ki, and Kd are controller parameters.

[0078] 4) Micro needles extrude microparticles, heat the microparticles to a preset temperature using a temperature control device, and photograph the microparticles using a camera.

[0079] The microparticles in the microneedle are slowly squeezed out, heated to the preset temperature using a temperature control device, and the initial information of the particles is obtained through analysis and processing with a high-speed camera.

[0080] Specifically, it includes the following: First, control the micro needle to accurately release the number of particles to ensure that the entire process follows the predetermined trajectory:

[0081] Q = (πPd^4) / (128ηL) (2)

[0082] Q is the flow rate, P is the pressure difference, d is the pipe diameter, η is the viscosity of the fluid, and L is the length of the pipe.

[0083] The high-speed camera is located on one side of the entire system, equipped with a high-resolution lens and high-speed capture capability, capable of distinguishing the coordinate position of the particles, and recording in real time the dynamic behavior of the asphalt particles during the mixing process, such as the suspension, movement, rotation, aggregation and deformation, explosion, contour change, etc. of the particles, and is connected to the controller through a high-speed data transmission interface to provide intuitive and detailed video data. The temperature control equipment includes a focusing heating device with a heating and heat dissipation module, which is integrated around the reflective platform and adopts high-efficiency heat exchange technology. It can quickly and evenly heat the material to meet the temperature requirements of different asphalt mixing processes, and is connected to the controller through a sensor.

[0084] In this step, the image information of the particles can be processed to obtain initial information, which specifically includes the following:

[0085] By using the temperature control device to heat to a preset temperature, the preheating conditions of particle A and / or particle B and / or particle C are achieved, and the high-speed camera device automatically adjusts the imaging, captures the suspended image of the particles and performs preprocessing, including using Gaussian filtering to remove noise and enhance contrast to improve image quality. Then, threshold segmentation and edge detection are performed, and the image quality is further optimized by corrosion, expansion, etc., and the particles are separated from the background for feature extraction. Finally, the initial information of the particles is obtained, including characteristics such as concentration, shape, contour, volume size, and the position coordinates of each pixel block in the image.

[0086] Secondly, the suspended image of the captured particles is preprocessed by the automatic imaging adjustment of the high-speed camera device, including the use of Gaussian filtering for denoising and contrast enhancement to improve the image quality. The formula is:

[0087]

[0088] Among them, G(x,y) is the concentration data after filtering, x and y represent the spatial coordinates, and σ is the standard deviation, which affects the filtering effect. Then threshold segmentation and edge detection are performed, and the image quality is further optimized by corrosion, expansion, etc., and the particles are separated from the background for feature extraction. By covering the entire image with a structural element (usually a 3x3 or 5x5 matrix), the center pixel is set to 1 (or retains its value) only when the particle image is completely within the foreground pixel, otherwise it is set to 0 (or its value is changed to the background value):

[0089] B(x,y)=minall(u,v)∈SA(x+u,y+v) (4)

[0090] B=open(A,S)=dilate(erode(A,S),S), B=close(A,S)=erode(dilate(A,S),S) (5)

[0091] Where A is the original image, B is the image after corrosion, S is the structural element, and (x, y) is the position coordinate in the image. In the feature extraction process, the center coordinate or boundary coordinate of each feature (such as a particle) can be recorded. Finally, the initial information of the particle is obtained, including features such as shape, contour, volume size, and the position coordinate of each pixel block in the image.

[0092] (xc,yc)=centroid(B); (xb,yb)=boundary(B) (6)

[0093] Among them, (xc, yc) represents the coordinates of the object's center of mass, and (xb, yb) represents the coordinates of the object's boundary. The area is: Area = ∑(x, y)∈B1; the perimeter is: in, represents pixels on the object boundary; the concentration is: C = waveDenoise (C'), where waveDenoise represents the wavelet transform denoising algorithm; the shape descriptor (circularity) is:

[0094]

[0095] 5) The particles are acted on by a reflection platform device and an ultrasonic device to characterize and analyze them.

[0096] Specifically including: adjusting the state and action of the reflection platform device to make the particles still, translate, and rotate. The reflection platform device can use the bottom rotating shaft to simulate the actions of various series mechanisms, and the operation actions can be switched at any time, and the actions include stillness, tilting, and rotation. According to the synergistic effect of the ultrasonic device described in step 1 and the reflection platform device, the particles can quickly reach a preset still state or tilted state, realizing the still suspension, extrusion deformation, rotation, and twisting of the particles.

[0097] The ultrasonic system is controlled to generate acoustic radiation force on the particles, causing them to be squeezed, deformed or even exploded.

[0098] This step achieves squeezing, deformation and even bursting of the droplets by controlling the voltage of the ultrasonic device and the height between the ultrasonic device and the reflective platform device at the bottom. When the voltage of the ultrasonic device is sufficiently higher than a certain threshold, it can increase the acoustic radiation force on the surface of the reflective platform device, and the particles are under pressure. When the acoustic radiation force is large enough or when the platform device moves up and down, the particles are squeezed in a stretched state, and the aspect ratio increases; when the platform device rotates, the particles are in a rotational motion state, and a variety of suspended particles can be evenly mixed without other interference; when the platform device moves left and right, in conjunction with the reflective platform device panel with concave-convex particles, the suspended particles can be repeatedly twisted, causing them to be squeezed, deformed and even burst.

[0099] 7) Deploy the controller model to the controller of the overall system, train and apply the controller model, and cooperate with high-speed cameras to conduct a variety of high-throughput experimental tests to achieve efficient closed loops and record data;

[0100] The controller model is deployed in the controller of the overall system, and cooperates with a high-speed camera to perform multiple high-throughput experimental tests to achieve efficient closed-loop, and record the characterization data of the particles. The high-speed camera calculates and analyzes the various performance characteristics of the particles according to the degree of deformation of the particles, and the controller of each component can perform model training and application in the controller of the overall system. Based on this, the present invention also proposes a system for realizing automated high-throughput mixing of multiple asphalt particles and performance characterization, see Figure 2 , including ultrasonic device, temperature control equipment, camera, reflection platform, robotic arm, micro needle and controller.

[0101] The reflective platform has two types of surfaces: smooth and concave-convex particles. The bottom has a six-axis parallel drive device connected to the controller.

[0102] The robotic arm has the ability to extend and move, with a gripper at the front end connected to the controller;

[0103] The micro needle has multiple output ports and pipelines, equipped with a propeller, an inhaler and a sensor array, which can accurately capture and measure the flow rate of asphalt particles. It has an adaptive adjustment mechanism to ensure that the optimal contact distance with the asphalt particles is maintained in different mixed materials. It is connected to the controller through a guide rail.

[0104] The ultrasonic system is installed vertically above the reflection platform to generate acoustic radiation force on the particles to suspend or deform them. It has adjustable frequency and power output functions and can accurately control the intensity and frequency of ultrasonic waves according to different asphalt particle characteristics and experimental requirements. It is connected to the controller through a feedback mechanism.

[0105] Temperature control equipment, including a concentrated heating device, with heating and cooling modules, integrated around the reflective platform, using high-efficiency heat exchange technology, can quickly and evenly adjust the temperature of the material surface to meet the temperature requirements of different asphalt mixing processes, and connected to the controller through sensors;

[0106] The high-speed camera, located on one side of the entire system, is equipped with a high-resolution lens and high-speed capture capability. It can distinguish the coordinate positions of the particles and record the dynamic behavior of the asphalt particles in the mixing process in real time, including but not limited to the suspension, rotation, aggregation and deformation of the particles, contour changes, etc. It is connected to the controller through a high-speed data transmission interface to provide intuitive and detailed video data.

[0107] The controller, with built-in data analysis modules and model training applications, coordinates and optimizes the interaction and operation between components according to preset experimental parameters or user-defined instructions, adjusts the motion trajectory of the six-axis parallel drive device and the robotic arm, as well as the frequency and power output of the ultrasonic system, to ensure high precision and repeatability of the entire experimental process.

[0108] The controller can also perform intelligent data processing. Through machine learning, it can deeply analyze the flow rate, suspension state, deformation degree and mixing efficiency of asphalt particles. It can automatically analyze the mixing efficiency, particle size distribution, surface morphology and various performance characterizations of asphalt particles, and provide intuitive and detailed experimental reports and performance characterization results.

[0109] Preferably, the surface layer of the reflective platform is a surface layer of a super-hydrophobic nanostructure, the surface layer is a smooth or concave-convex particle structure, and six push rods are simultaneously working at the lower end of the surface layer, and the center load of the table is ≥200N;

[0110] Preferably, the working radius of the mechanical arm is ≥1500mm; the load is ≥10kg, and the repeatability is ±0.01mm;

[0111] Preferably, the diameter of the micro needle is ≥0.23 mm and ≤3 mm, and the advancement accuracy is ±0.01 mm;

[0112] Preferably, the ultrasonic device uses a plurality of ultrasonic needles or ultrasonic array needles, with a transmission range of 380mm (L) * 380mm (W) * 200mm (H), a transmission frequency of 10-22KHz, and a particle volume range of 5nL-500μL controlled by the ultrasonic device;

[0113] Preferably, the temperature control device is a focusing heating device with a working distance of ≤10mm, a focus point of ≥5mm, an adjustable focal length of 30mm to 40mm, and a temperature of ≤800°C;

[0114] Preferably, the camera is a high-speed camera with a frame rate of ≥100,000 frames per second, a working distance between the high-speed camera and the particles of ≥50 mm, and a field of view of ≥5000*200 mm, and low-noise imaging.

[0115] Preferably, the controller has a built-in data analysis module and a model training application module, which coordinates and optimizes the interaction and operation between the components according to preset experimental parameters or user-defined instructions, and adjusts the motion trajectory of the reflection platform and the robotic arm, as well as the frequency and power output of the ultrasonic device.

[0116] In this step, machine learning algorithms can be used to train the controller model to control different modes or different types of particles, and a recognition model can be established with a high-speed camera. After the image recognition model is trained, the collected particle images are input into the droplet image recognition model to identify the initial position, outline, and size of the particles in the image. The following can be monitored: ① The number, position, suspension, and motion state of different particles; ② The swelling behavior of different particle mixtures; ③ The stretching length, aspect ratio, rotation change, and collapse time of different particles or mixed particles; ④ Try to monitor and follow the dynamic changes of the particles, and establish a model and database for comparison. This will achieve automated high-throughput mixing of multiple asphalt particles and performance characterization.

[0117] 8) Use micro needles to suck away particles.

[0118] The specific examples are as follows:

[0119] Embodiment 1: The lower end reflection platform is kept in a horizontal and stationary state, and the ultrasonic device is made to emit sound waves at a uniform speed through a controller, so that the asphalt droplet particles in the micro needle head are slowly squeezed to a preset suspension point, and the characteristics of the particles are obtained through camera analysis and processing, including shape, outline, volume and other characteristics, as well as the position coordinates of each pixel block in the image. At the same time, the concentration of asphalt droplet particles is detected according to the ultrasonic sensor analyzing the reflected sound wave data.

[0120] Example 2: Under the premise of not affecting the physical and chemical properties, different colorants are used to color the particles to be mixed, the lower end reflection platform is kept in a horizontal and static state, and the ultrasonic device is controlled by a controller to emit sound waves at a uniform speed to slowly extrude the asphalt particles or epoxy material particles and / or regeneration agent particles in the micro needles to a preset suspension point. Without other intervention, the fusion characteristics of the particles are observed by a camera, including fusion, separation phenomena, fusion time, and the position coordinates of each pixel block in the image, to evaluate the compatibility of the mixed materials.

[0121] Embodiment 3: The lower end reflection platform is kept in a horizontal and static state, and the liquid, powder or solid micro-particles to be mixed by the micro needle are squeezed to a preset suspension point through the controller, such as epoxy resin, modifier, fiber, etc., and heated to a preset temperature by a temperature control device to melt the solid particles. The controller slowly squeezes the asphalt droplets in the micro needle to a preset suspension point, and cooperates with the controller to control the left and right rotation of the lower end reflection platform to achieve full mixing of multiple different materials. After a period of mixing, the change amount of the mixed material is obtained through camera analysis and processing, and the swelling change of the mixed material is obtained through intelligent data processing.

[0122] Embodiment 4: The lower end reflection platform is kept in a horizontal and static state, and the asphalt droplet particles in the micro needle are slowly squeezed out to a preset suspension point by a controller, and heated to a preset temperature by a temperature control device, and the initial deformation and contour information of the particles, as well as the position coordinates of each pixel block in the image are obtained by camera analysis and processing. At the same time, the ultrasonic device is controlled by the controller to adjust the sound wave to a preset size, and the controller is used to control the rising or falling state of the lower end reflection platform, so that the asphalt droplet particles are deformed at a preset amplitude, and the length-width ratio change of the asphalt droplet particles is obtained by camera image processing technology, and recorded in real time over a period of time to analyze the viscosity of the asphalt droplet particles.

[0123] Example 5: The lower end reflection platform is kept in a horizontal and static state, and the ultrasonic device is controlled to emit sound waves at a uniform speed through the controller. The asphalt droplet particles in the micro needle are slowly squeezed out to a preset suspension point through the controller. At the same time, the lower end reflection platform is used to make the asphalt droplet particles deform or even crack under different amplitudes. The camera image processing technology obtains the deformation amount of the asphalt droplet particles and records it in real time and feeds it back to the control system to analyze its tensile strength and fracture performance.

[0124] Example 6: Replace the surface of the lower reflection platform with a panel with bumps and depressions, maintain a horizontal static state, squeeze the liquid in the micro needle to a preset suspension point through the controller, obtain the initial shape and outline of the particles, and the position coordinates of each pixel block in the image through camera analysis and processing, maintain the preset temperature using a temperature control device, cooperate with the controller to control the lower reflection platform to rotate and repeatedly bend, and analyze the fatigue of mixed or single particles.

Claims

1. A method for high-throughput mixing of multiple asphalt particles and performance characterization, characterized in that: Includes steps: S1: transmitting ultrasonic waves to the reflection platform through an ultrasonic device; S2: Select the best suspension point according to the transmission range of the ultrasonic device; S3: The robotic arm moves the micro needle to the optimal suspension point of the reflection platform; S4: Micro needles extrude microparticles, heat the microparticles to a preset temperature using a temperature control device, and photograph the microparticles using a camera; S5: The particles are subjected to characterization and analysis by using a reflection platform device and an ultrasonic device.

2. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 1, characterized in that: Step S3 In the example, the control method of the robot arm is as follows: u(t)=Kp*e(t)+Ki*∫e(t)dt+Kd*de(t) / dt Where u(t) is the control input, e(t) is the difference between the current position and the target position, and Kp, Ki, and Kd are the adjusted PID control parameters.

3. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 1, characterized in that: Step S4 In the process, the control method of microneedle extrusion of microparticles is as follows: Q=(πPd^4) / (128ηL) Where Q is the flow rate, P is the pressure difference, d is the pipe diameter, η is the viscosity of the fluid, and L is the length of the pipe.

4. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 1, characterized in that: Step S4 In the process, the camera takes images of particles and performs preprocessing, including: S41: Use Gaussian filtering to remove noise and enhance contrast to improve image quality. The formula is: Among them, G(x,y) is the concentration data after filtering, x and y represent the spatial coordinates, and σ is the standard deviation, which affects the filtering effect; S42: Perform threshold segmentation and edge detection, further optimize image quality through corrosion and expansion, separate particles from background for feature extraction, cover the entire image with structural elements, and only when the particle image is completely within the foreground pixel, set the center pixel to 1 or retain its value, otherwise set it to 0 or change its value to the background value: B(x,y)=minall(u,v)∈SA(x+u,y+v) B=open(A,S)=dilate(erode(A,S),S), B=close(A,S)=erode(dilate(A,S),S) Where A is the original image, B is the image after corrosion, S is the structural element, (x, y) is the position coordinate in the image, and in the feature extraction process, the center coordinate or boundary coordinate of each feature is recorded; S43: obtaining the initial information of the particles, including shape, outline, volume characteristics, and position coordinates of each pixel block in the image, (xc,yc)=centroid(B); (xb,yb)=boundary(B) Among them, (xc, yc) represents the centroid coordinates of the object, (xb, yb) represents the boundary coordinates of the object, The area is: Area = ∑(x,y)∈B1; the perimeter is: in, Represents pixels on the object boundary; The concentration is: C = waveDenoise (C'), where waveDenoise represents the wavelet transform denoising algorithm; the shape descriptor is:

5. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 1, characterized in that: In step S5, the actions of the reflection platform device include stationary, tilting, and rotating. The particles perform stationary, translational, and rotating actions accordingly. The ultrasonic device causes the particles to be squeezed, deformed, or exploded by ultrasonic waves.

6. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 1, characterized in that: In step S2, the optimal suspension point is determined by the following formula: Among them, fadjust is the adjusted ultrasonic frequency, f0 is the basic frequency, Δf is the frequency adjustment range, d is the current distance between the micro needle and the ultrasonic transmitter, d0 is the initial distance, and dmax is the maximum adjustment distance.

7. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 1, characterized in that: In step S5, the controller model is deployed to the controller of the system, and the controller model is trained and applied, and cooperated with a high-speed camera to implement various high-throughput experimental tests and record the characterization data of the particles.

8. The method for high-throughput mixing of multiple asphalt particles and characterization of properties according to claim 7, characterized in that: Using machine learning algorithms, the controller model is trained to control different modes or different types of particles, and a recognition model is established in conjunction with a high-speed camera. After the image recognition model is trained, the collected particle images are input into the droplet image recognition model to identify the initial position, outline, and size of the particles in the image to monitor: the number, position, suspension, and motion state of different particles; the swelling behavior of different particle mixtures; the stretching length, aspect ratio, rotation change, and collapse time of different particles or mixed particles; Monitor and follow the dynamic changes of particles, establish models and database comparison, and then realize automated high-throughput mixing of multiple asphalt particles and performance characterization.

9. A system for high-throughput mixing of multiple asphalt particles and performance characterization method as described in any one of claims 1 to 8, characterized in that: include: A reflective platform for carrying asphalt particles; A robotic arm, the execution end of which is connected to a micro needle; Microneedles, used to release or absorb particles; The ultrasonic device is arranged above the reflecting platform and is used to generate acoustic radiation force on the particles to suspend or deform them, and has adjustable frequency and power output functions; Temperature control equipment, including a concentrated heating device, with heating and cooling modules, integrated around the reflective platform; A camera for photographing particles; The controller has a built-in data analysis module and a model training application module. It coordinates and optimizes the interaction and operation between components according to preset experimental parameters or user-defined instructions, and adjusts the motion trajectory of the reflection platform and the robotic arm, as well as the frequency and power output of the ultrasonic device.

10. The system according to claim 8, characterized in that The surface layer of the reflection platform is a surface layer of a super-hydrophobic nanostructure, the surface layer is a smooth or concave-convex particle structure, and the lower end of the surface layer is connected to six push rods for pushing the surface layer to move.

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