Micro-liquid dynamic liquid level high-precision reconstruction system and method

Abstract

The invention discloses a high-precision reconstruction method for a dynamic liquid level of a trace liquid. The method comprises the following steps: synchronously acquiring a binocular image sequence of the dynamic liquid level of the trace liquid to be measured; based on the binocular image sequence, obtaining an initial three-dimensional point cloud of the dynamic liquid level through stereoscopic vision three-dimensional reconstruction; constructing a fluid mechanics constraint model of the micro-liquid based on the action of surface tension and gravity; performing iterative optimization processing on the initial three-dimensional point cloud by taking a fluid mechanics constraint model as a constraint condition to obtain an optimized dynamic liquid level three-dimensional model; based on the optimized dynamic liquid level three-dimensional model, the liquid level height and the volume capacity of the micro liquid are calculated. According to the method, visual three-dimensional reconstruction and fluid mechanics constraints are fused, the liquid level form better conforms to the physical reality through iterative optimization, the measurement precision, response speed and anti-jamming capability of the micro-liquid in the dynamic process are remarkably improved, and the method is suitable for the precise metering requirements of the fields of biomedicine, chemical analysis and the like for micro-upgrading liquid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-liquid detection, in particular to a micro-liquid dynamic liquid surface high-precision reconstruction system and method. BACKGROUND

[0002] In biomedical detection, chemical analysis and microfluidic chip applications, it is often necessary to accurately measure the liquid level and volume of micro-liquid below 0.2 milliliters. Traditional methods such as weighing method, optical transmission method and capacitance sensing method, although each has its application, but in the face of dynamic changes in liquid surface, there are generally problems such as insufficient precision, response lag or weak anti-interference ability. For example, the optical method is easily disturbed by reflection and impurities, and the capacitance method is sensitive to medium changes, which is difficult to adapt to real-time dynamic scenarios.

[0003] In recent years, three-dimensional vision reconstruction technology provides a new way for object shape capture, but when applied to micro-liquid surface reconstruction, it still faces challenges such as weak liquid surface features, large noise interference and slow reconstruction speed. In addition, micro-liquid is significantly affected by surface tension, and its liquid surface shape is constrained by fluid mechanics laws, so it is difficult to accurately reflect its true physical state by relying only on visual reconstruction, resulting in a deviation between the reconstruction result and the actual situation.

[0004] Therefore, the prior art lacks a dynamic liquid surface reconstruction method that takes into account both visual information and fluid mechanics constraints, making it difficult to achieve high-precision, real-time monitoring of micro-liquid in a dynamic process. It is necessary to propose a method that combines three-dimensional reconstruction and physical models to improve the accuracy and reliability of dynamic liquid surface reconstruction. SUMMARY

[0005] In view of the above technical problems in the related art, the present application proposes a micro-liquid dynamic liquid surface high-precision reconstruction system and method, which can overcome the above shortcomings of the prior art.

[0006] To achieve the above technical purpose, the technical solution of the present application is as follows: A micro-liquid dynamic liquid surface high-precision reconstruction method; The micro-liquid dynamic liquid surface high-precision reconstruction method comprises the following steps: S1. Synchronously acquiring a binocular image sequence of a dynamic liquid surface of a micro-liquid to be measured; S2. Based on the binocular image sequence, an initial three-dimensional point cloud of the dynamic liquid surface is obtained by three-dimensional reconstruction of stereo vision; S3. A fluid mechanics constraint model of the micro-liquid based on surface tension and gravity is constructed; S4. The initial three-dimensional point cloud is iteratively optimized with the fluid mechanics constraint model as a constraint condition to obtain an optimized dynamic liquid surface three-dimensional model; S5. Calculate the liquid level height and volume capacity of the micro-liquid based on the optimized dynamic liquid surface three-dimensional model.

[0007] Further, before step S1, there is also a step of calibrating the binocular vision system used for image acquisition, which adopts Zhang Zhengyou calibration method to obtain the internal parameters of the camera and the external parameters between the binocular cameras. Step S2 specifically includes: S21. Preprocess the acquired binocular image sequence, which includes denoising, image enhancement and distortion correction; S22. Based on the calibration parameters, use SIFT algorithm or ORB algorithm to perform stereo matching on the preprocessed images to generate a disparity map; S23. Calculate and generate the initial three-dimensional point cloud according to the disparity map and the calibration parameters.

[0008] Further, in step S3, the fluid mechanics constraint model is constructed based on the Young-Laplace equation to describe the static form constraint of the liquid surface, and further combined with the simplified form of the Navier-Stokes equation to describe the dynamic change constraint of the liquid surface.

[0009] Further, in step S4, the particle swarm optimization algorithm is used for the iterative optimization processing, and the termination condition of the iterative optimization processing is that the optimization error is less than a preset threshold.

[0010] Further, in step S5, the integral method is used to calculate the volume capacity of the micro-liquid, and the liquid level height is calculated based on the coordinates of the liquid surface vertex in the optimized dynamic liquid surface three-dimensional model.

[0011] According to another aspect of the present application, a high-precision micro-liquid dynamic liquid surface reconstruction system is provided. The high-precision micro-liquid dynamic liquid surface reconstruction system includes: An image acquisition module including at least two cameras and a light source constituting a binocular vision system for synchronously acquiring an image sequence of a dynamic liquid surface of a micro-liquid to be measured; A three-dimensional reconstruction module for obtaining an initial three-dimensional point cloud of the dynamic liquid surface through stereo vision three-dimensional reconstruction based on the image sequence; A fluid mechanics constraint module for constructing a fluid mechanics constraint model of the micro-liquid based on surface tension and gravity; A liquid surface optimization reconstruction module for performing iterative optimization processing on the initial three-dimensional point cloud with the fluid mechanics constraint model as a constraint condition to obtain an optimized dynamic liquid surface three-dimensional model; A volume calculation module for calculating the liquid level height and volume capacity of the micro-liquid based on the optimized dynamic liquid surface three-dimensional model.

[0012] Further, a calibration module is further included for calibrating the binocular vision system to obtain internal parameters of the cameras and external parameters between the binocular cameras; The three-dimensional reconstruction module comprises: A preprocessing unit is configured to perform denoising, image enhancement and distortion correction on the collected binocular image sequence; A stereo matching unit is configured to perform stereo matching on the preprocessed images based on the calibration parameters and generate a disparity map using a SIFT algorithm or an ORB algorithm; A point cloud generation unit is configured to calculate and generate the initial three-dimensional point cloud according to the disparity map and the calibration parameters.

[0013] Further, in the image acquisition module, the camera is an industrial camera with a resolution of not less than 5 million pixels and a frame rate of not less than 30 fps, and is equipped with a macro lens with a magnification of not less than 10 times; the light source is a ring-shaped uniform light source.

[0014] Further, the fluid mechanics constraint module constructs a model based on the Young-Laplace equation, and further combines a simplified form of the Navier-Stokes equation.

[0015] Further, the liquid surface optimization reconstruction module uses a particle swarm optimization algorithm for iterative optimization processing; and the volume calculation module uses an integral method to calculate the volume capacity.

[0016] The beneficial effects of the present application are: by fusing binocular vision three-dimensional reconstruction and fluid mechanics constraint model, iteratively optimizing and reconstructing the dynamic liquid surface, so that the liquid surface morphology is more consistent with the real physical behavior, significantly improving the measurement accuracy and dynamic response capability of the liquid level and volume, and effectively suppressing external interference such as reflection and vibration, thereby realizing high stability and high adaptability monitoring of the dynamic process of micro-liquid, and meeting the precision requirements of micro-liter liquid metering in the fields of biomedicine and chemical analysis. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is a schematic diagram of the overall structure of the micro-liquid dynamic liquid surface high-precision reconstruction system according to the embodiments of the present application; Figure 2is a flow chart of steps of the micro-liquid dynamic liquid surface high-precision reconstruction method according to an embodiment of the present application; Figure 3 is a schematic diagram of binocular vision system calibration according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0020] As shown in Figure 2 the micro-liquid dynamic liquid surface high-precision reconstruction method according to an embodiment of the present application comprises the following steps: S1. synchronously collecting a binocular image sequence of a dynamic liquid surface of a micro-liquid to be measured; S2. based on the binocular image sequence, obtaining an initial three-dimensional point cloud of the dynamic liquid surface through three-dimensional reconstruction of stereovision; S3. constructing a fluid mechanics constraint model of the micro-liquid based on surface tension and gravity; S4. taking the fluid mechanics constraint model as a constraint condition, performing iterative optimization processing on the initial three-dimensional point cloud to obtain an optimized dynamic liquid surface three-dimensional model; S5. based on the optimized dynamic liquid surface three-dimensional model, calculating a liquid level height and a volume capacity of the micro-liquid.

[0021] The micro-liquid dynamic liquid surface high-precision reconstruction method according to an embodiment of the present application, in a specific embodiment, further comprises a step of calibrating a binocular vision system used for collecting images before step S1, and the calibration adopts Zhang Zhengyou calibration method to obtain internal parameters of a camera and external parameters between binocular cameras; Step S2 specifically comprises: S21. pre-processing the collected binocular image sequence, and the pre-processing comprises denoising, image enhancement and distortion correction; S22. based on the calibration parameters, performing stereomatching on the pre-processed images by using a SIFT algorithm or an ORB algorithm to generate a disparity map; S23. calculating and generating the initial three-dimensional point cloud according to the disparity map and the calibration parameters.

[0022] According to an embodiment of the present invention, in a specific embodiment, in step S3, the fluid dynamic constraint model is constructed based on the Young-Laplace equation to describe the static morphological constraints of the liquid surface, and further combined with the simplified form of the Navier-Stokes equation to describe the dynamic change constraints of the liquid surface.

[0023] According to an embodiment of the present invention, in a specific embodiment, in step S4, a particle swarm optimization algorithm is used to perform the iterative optimization process, and the termination condition of the iterative optimization process is that the optimization error is less than a preset threshold.

[0024] According to an embodiment of the present invention, in a specific embodiment, in step S5, the volume of the micro-liquid is calculated using an integral method, and the liquid level height is calculated based on the coordinates of the liquid surface vertex in the optimized dynamic liquid surface three-dimensional model.

[0025] Secondly, according to Figure 1 and Figure 3 As shown, the high-precision micro-liquid dynamic liquid level reconstruction system according to an embodiment of the present invention includes: The image acquisition module includes at least two cameras and a light source constituting a binocular vision system for synchronously acquiring image sequences of the dynamic liquid surface of the trace liquid to be measured. A 3D reconstruction module is used to obtain the initial 3D point cloud of the dynamic liquid surface through stereo vision 3D reconstruction based on the image sequence; The fluid dynamics constraint module is used to construct a fluid dynamics constraint model of the micro-liquid based on the effects of surface tension and gravity. The liquid surface optimization and reconstruction module is used to iteratively optimize the initial three-dimensional point cloud using the fluid dynamics constraint model as a constraint condition to obtain an optimized dynamic three-dimensional liquid surface model. The volume calculation module is used to calculate the liquid level height and volume capacity of the trace liquid based on the optimized dynamic liquid surface three-dimensional model.

[0026] The high-precision reconstruction system for dynamic liquid level of micro-liquid according to an embodiment of the present invention further includes a calibration module in a specific embodiment, which is used to calibrate the binocular vision system to obtain the intrinsic parameters of the camera and the extrinsic parameters between the binocular cameras. The three-dimensional reconstruction module includes: The preprocessing unit is used to perform denoising, image enhancement, and distortion correction on the acquired binocular image sequence; The stereo matching unit is used to perform stereo matching on the preprocessed image based on the SIFT or ORB algorithm using calibration parameters and generate a disparity map. a point cloud generation unit configured to generate the initial three-dimensional point cloud according to the parallax map and the calibration parameters.

[0027] According to the micro-liquid dynamic liquid surface high-precision reconstruction system provided by the embodiment of the present application, in a specific embodiment, the camera in the image acquisition module is an industrial camera with a resolution of not less than 5 million pixels and a frame rate of not less than 30 fps, and is equipped with a macro lens with a magnification of not less than 10 times; and the light source is a ring-shaped uniform light source.

[0028] According to the micro-liquid dynamic liquid surface high-precision reconstruction system provided by the embodiment of the present application, in a specific embodiment, the model constructed by the fluid mechanics constraint module is based on the Young-Laplace equation, and further combined with a simplified form of the Navier-Stokes equation.

[0029] According to the micro-liquid dynamic liquid surface high-precision reconstruction system provided by the embodiment of the present application, in a specific embodiment, the liquid surface optimization reconstruction module adopts a particle swarm optimization algorithm for iterative optimization processing; and the volume calculation module adopts an integral method to calculate the volume capacity.

[0030] In order to facilitate the understanding of the above technical solutions of the present application, the above technical solutions of the present application will be described in detail through specific implementation details and principles.

[0031] In specific use, according to the micro-liquid dynamic liquid surface high-precision reconstruction method and system provided by the present application, the specific implementation steps of the method are as follows: Step one: system calibration Place the checkerboard calibration board in the detection area, and control the binocular camera to shoot a total of 20 calibration board images from different angles and positions. Process these images using the Zhang Zhengyou calibration algorithm in the OpenCV library to obtain the camera intrinsic parameters and extrinsic parameters. The obtained intrinsic parameters include: focal length f_x=2500 pixels, f_y=2500 pixels, principal point coordinates u_0=1250 pixels, v_0=1000 pixels, distortion coefficients k1=-0.01, k2=0.005. The extrinsic parameters include: rotation matrix R=[[0.9998,-0.012,0.015],[0.012,0.9999,-0.008],[-0.015,0.008,0.9998]], translation vector T=[-50.2mm,0.5mm,150mm]. The calibration parameters are stored in the system database for subsequent calling.

[0032] Step two: dynamic image acquisition A quartz glass capillary with an inner diameter of 1 mm and a height of 50 mm is used as a container, which contains 0.2 milliliters of deionized water, and is placed in the center of the detection area. The annular LED light source is started, and the binocular camera is controlled to synchronously capture dynamic liquid surface image sequences during the liquid injection process at a frame rate of 50 frames per second. The acquisition time is set to 10 seconds to ensure that the complete process of liquid surface change is captured.

[0033] Step three: image preprocessing and initial three-dimensional point cloud generation The acquired binocular image sequences are preprocessed. First, median filtering with a window size of 3x3 is used for denoising. Subsequently, the histogram equalization algorithm is used to enhance the image contrast, and the image is corrected for distortion. Then, based on the calibration parameters obtained in step one, the improved ORB feature matching algorithm is used for stereo matching of the preprocessed left and right views, and the disparity map of the liquid surface area is calculated. Finally, according to the camera geometric model and the calibration parameters, the disparity map is converted into an initial three-dimensional point cloud, with a density of about 1000 points per square millimeter.

[0034] Step four: construction of fluid mechanics constraint model A fluid mechanics constraint model is constructed based on the physical properties of the measured liquid, the geometric parameters of the container, and the environmental conditions. In this embodiment, the surface tension coefficient γ of deionized water at 25°C is 72.0 mN / m, the density ρ is 1000 kg / m³, the gravitational acceleration g is 9.8 m / s², and the capillary radius r is 0.5 mm. First, based on the Young-Laplace equation ΔP = 2γ / r, where ΔP is the pressure difference inside and outside the liquid surface, a constraint equation is constructed to describe the static shape of the liquid surface. Further, combined with the simplified form of the Navier-Stokes equation, a constraint equation is constructed to describe the dynamic change process of the liquid surface, thus forming a complete fluid mechanics constraint model.

[0035] Step five: optimization and reconstruction of three-dimensional liquid surface model The initial three-dimensional point cloud generated in step three is input into the optimization module. The fluid mechanics constraint model established in step four is used as the physical constraint condition, and the particle swarm optimization algorithm is used to iteratively optimize the initial point cloud. In this embodiment, the number of particle swarms is set to 50, the maximum number of iterations is set to 100, and the optimization error threshold is set to 0.001 millimeters. The optimization process aims to minimize the difference between the initial point cloud data and the predicted shape of the fluid mechanics model, and automatically remove abnormal points that do not meet the physical constraints. After optimization, the abnormal point removal rate is about 3%, and the overall error of the high-precision dynamic liquid surface three-dimensional model is less than 0.001 millimeters.

[0036] Step six: calculation of liquid level height and volume The optimized liquid surface three-dimensional model obtained in step five and the pre-input three-dimensional model of the capillary container of the system are called. The tetrahedron volume integration method is used to calculate the liquid volume, and the integration step is set to 0.01 millimeters. The calculated liquid volume in this embodiment is 0.2005 milliliters. At the same time, the vertical coordinates of the highest point and the lowest point of the liquid surface are extracted from the optimized liquid surface three-dimensional model, and the liquid level height is calculated to be 50.9 millimeters. It is evaluated that the volume measurement error of this embodiment is less than 0.002 milliliters. The calculation results can be displayed and stored in real time.

[0037] The micro-liquid dynamic liquid surface high-precision reconstruction system disclosed by the application mainly includes an image acquisition module, a calibration module, a three-dimensional reconstruction module, a fluid mechanics constraint module, a liquid surface optimization reconstruction module, and a volume calculation module. These modules cooperate with each other at the hardware and software levels to realize the high-precision reconstruction and measurement of the dynamic liquid surface.

[0038] The image acquisition module is used to obtain the original image data of the dynamic liquid surface. In a specific embodiment, the module includes at least two high-resolution industrial cameras, a matching micro-lens, and a dedicated light source system. To meet the needs of capturing the details of the micro-liquid surface, the selected camera has a resolution of not less than 5 million pixels and a frame rate of not less than 30 frames per second. The magnification of the micro-lens is not less than 10 times to ensure sufficient imaging resolution. The light source is preferably a ring-shaped uniform light source arranged around the measured container to reduce the high light interference caused by the mirror reflection of the liquid surface. The two cameras are symmetrically arranged on both sides of the micro-liquid container to form a standard binocular stereo vision system, which can synchronously acquire image sequences of the dynamic liquid surface change process.

[0039] The calibration module is used to calibrate the above-mentioned binocular vision system to obtain accurate camera parameters. In a specific implementation, the Zhang Zhengyou calibration method is used for camera intrinsic parameter calibration to obtain internal parameters including focal length, principal point coordinates, and distortion coefficients. At the same time, by shooting a specific pattern of a checkerboard calibration board, the relative position relationship between the two cameras, i.e., the external parameters, including the rotation matrix and the translation vector, is calculated. These calibration parameters are the mathematical basis for subsequent accurate three-dimensional reconstruction.

[0040] The three-dimensional reconstruction module is responsible for converting the two-dimensional image sequence into the three-dimensional geometric information of the liquid surface. The module first pre-processes the images collected by the binocular camera, including denoising, contrast enhancement, and distortion correction based on the calibration parameters. Then, the liquid surface regions in the left and right views are matched through a stereo matching algorithm to calculate the disparity map. Finally, the disparity map is converted into the initial three-dimensional point cloud describing the preliminary shape of the liquid surface in combination with the camera internal and external parameters provided by the calibration module.

[0041] The fluid mechanics constraint module is used to establish a physical model describing the behavior of the micro-liquid liquid surface. The module comprehensively considers the surface tension of the liquid, gravity and the boundary conditions of the container wall, and constructs a constraint equation of the static form of the liquid surface based on the Young-Laplace equation. At the same time, in order to describe the dynamic change of the liquid surface in the process of injection, extraction and the like, the module further combines a simplified form of the liquid dynamic motion equation, that is, the Navier-Stokes equation, to form a complete fluid mechanics constraint model, which provides a physically reasonable constraint for the liquid surface form.

[0042] The core function of the liquid surface optimization reconstruction module is to fuse visual observation data and physical prior knowledge. The module receives the initial three-dimensional point cloud from the three-dimensional reconstruction module, and takes the mechanical constraint conditions output by the fluid mechanics constraint module as the optimization target, and uses an iterative optimization algorithm to optimize the initial point cloud. By iteratively minimizing the error between the initial point cloud data and the predicted form of the physical model, abnormal data points that do not conform to the laws of fluid mechanics are removed, and finally a high-precision dynamic liquid surface three-dimensional model that conforms to both visual observation and physical constraints is output.

[0043] The volume calculation module is used for quantitative calculation according to the reconstructed liquid surface model. The module calls the high-precision liquid surface three-dimensional model generated by the liquid surface optimization reconstruction module, and combines the pre-stored three-dimensional model of the container to calculate the volume of the closed space below the liquid surface by using a numerical integration method, thereby obtaining the accurate capacity of the liquid. At the same time, the module can extract the highest and lowest point coordinates from the liquid surface three-dimensional model, calculate and output the liquid level height data in real time.

[0044] In summary, by means of the above technical solutions of the present application, through the fusion of binocular vision three-dimensional reconstruction and fluid mechanics constraint model, the dynamic liquid surface is iteratively optimized and reconstructed, so that the liquid surface form is more consistent with the real physical behavior, and the measurement accuracy and dynamic response capability of the liquid level and volume are significantly improved, while effectively suppressing external interference such as reflection and vibration, thereby realizing high stability and high adaptability monitoring of the dynamic process of micro-liquid, and meeting the precision requirements of micro-liter liquid metering in the fields of biomedicine and chemical analysis.

[0045] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for high-precision reconstruction of dynamic liquid levels in trace amounts of liquid, characterized in that, Includes the following steps: S1. Simultaneously acquire binocular image sequences of the dynamic liquid surface of the trace liquid to be measured; S2. Based on the binocular image sequence, obtain the initial three-dimensional point cloud of the dynamic liquid surface through stereo vision three-dimensional reconstruction; S3. Construct a hydrodynamic constraint model for the micro-liquid based on the effects of surface tension and gravity; S4. Using the fluid dynamics constraint model as a constraint condition, the initial three-dimensional point cloud is iteratively optimized to obtain the optimized dynamic liquid surface three-dimensional model; S5. Based on the optimized dynamic liquid surface three-dimensional model, calculate the liquid level height and volume capacity of the trace liquid.

2. The method for high-precision reconstruction of dynamic liquid surface in trace liquids according to claim 1, characterized in that, Before step S1, the system further includes a step of calibrating the binocular vision system used to acquire images. The calibration adopts the Zhang Zhengyou calibration method to obtain the intrinsic parameters of the camera and the extrinsic parameters between the binocular cameras. Step S2 specifically includes: S21. The acquired binocular image sequence is preprocessed, including denoising, image enhancement and distortion correction; S22. Based on the calibration parameters, use the SIFT algorithm or ORB algorithm to perform stereo matching on the preprocessed image to generate a disparity map; S23. Calculate and generate the initial three-dimensional point cloud based on the disparity map and calibration parameters.

3. The method for high-precision reconstruction of dynamic liquid surface in trace liquids according to claim 1, characterized in that, In step S3, the fluid dynamics constraint model is constructed based on the Young-Laplace equation to describe the static morphological constraints of the liquid surface, and further combined with the simplified form of the Navier-Stokes equation to describe the dynamic change constraints of the liquid surface.

4. The method for high-precision reconstruction of dynamic liquid surface in trace liquids according to claim 1, characterized in that, In step S4, the particle swarm optimization algorithm is used to perform the iterative optimization process, and the termination condition of the iterative optimization process is that the optimization error is less than a preset threshold.

5. The method for high-precision reconstruction of dynamic liquid surface in trace liquids according to claim 1, characterized in that, In step S5, the volumetric capacity of the trace liquid is calculated using the integral method, and the liquid level height is calculated based on the coordinates of the liquid surface vertex in the optimized dynamic liquid surface three-dimensional model.

6. A high-precision system for reconstructing the dynamic liquid level of a micro-liquid, characterized in that, The system for implementing the method as described in any one of claims 1 to 5 comprises: The image acquisition module includes at least two cameras and a light source constituting a binocular vision system for synchronously acquiring image sequences of the dynamic liquid surface of the trace liquid to be measured. A 3D reconstruction module is used to obtain the initial 3D point cloud of the dynamic liquid surface through stereo vision 3D reconstruction based on the image sequence; The fluid dynamics constraint module is used to construct a fluid dynamics constraint model of the micro-liquid based on the effects of surface tension and gravity. The liquid surface optimization and reconstruction module is used to iteratively optimize the initial three-dimensional point cloud using the fluid dynamics constraint model as a constraint condition to obtain an optimized dynamic three-dimensional liquid surface model. The volume calculation module is used to calculate the liquid level height and volume capacity of the trace liquid based on the optimized dynamic liquid surface three-dimensional model.

7. A high-precision reconstruction system for dynamic liquid level of micro-liquids according to claim 6, characterized in that, It also includes a calibration module for calibrating the binocular vision system to obtain the intrinsic parameters of the camera and the extrinsic parameters between the binocular cameras; The three-dimensional reconstruction module includes: The preprocessing unit is used to perform denoising, image enhancement, and distortion correction on the acquired binocular image sequence; The stereo matching unit is used to perform stereo matching on the preprocessed image based on the SIFT or ORB algorithm using calibration parameters and generate a disparity map. The point cloud generation unit is used to calculate and generate the initial three-dimensional point cloud based on the disparity map and calibration parameters.

8. A high-precision reconstruction system for dynamic liquid levels in trace liquids according to claim 6, characterized in that, In the image acquisition module, the camera is an industrial camera with a resolution of no less than 5 million pixels and a frame rate of no less than 30fps, and is equipped with a macro lens with a magnification of no less than 10x; the light source is a ring-shaped uniform light source.

9. A high-precision reconstruction system for dynamic liquid level of micro-liquids according to claim 6, characterized in that, The model constructed by the fluid dynamics constraint module is based on the Young-Laplace equations and further incorporates a simplified form of the Navier-Stokes equations.

10. A high-precision reconstruction system for dynamic liquid levels in trace liquids according to claim 6, characterized in that, The liquid level optimization and reconstruction module uses a particle swarm optimization algorithm for iterative optimization; the volume calculation module uses an integral method to calculate the volume capacity.

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