Net paste flow test method and system based on multi-mode dynamic imaging

Abstract

The invention relates to the technical field of material performance testing, in particular to a neat paste flow testing method and system based on multi-mode dynamic imaging. The test method comprises the following steps: collecting a continuous infrared image sequence of a neat paste free flowing process; synchronously triggering visible light supplement in a flowing initial stage, and obtaining at least one frame of visible light image; performing registration fusion on the visible light image and the infrared image at the corresponding moment to generate an enhanced image frame; extracting a neat paste contour in real time based on the enhanced image frame, and calculating dynamic parameters; according to the change trend of the dynamic parameters, the flowing stability opportunity is adaptively judged; and triggering final contour extraction at a stable opportunity, and calculating static morphological parameters and dynamic process parameters. Through the multi-modal dynamic imaging and intelligent image fusion technology, complex edge contours are accurately extracted, impurity interference is eliminated, and the measurement precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of material performance testing, and in particular to a slurry flow testing method and system based on multimodal dynamic imaging. Background Art

[0002] Cementitious materials (such as cement-based materials, geopolymers, and alkali-activated solid waste) are core building blocks of construction projects. The flow properties of freshly mixed cementitious materials directly impact pumpability, filling properties, and self-compacting capabilities during construction, ultimately determining the homogeneity and durability of the hardened structure. Traditional testing methods assess flowability by measuring the diameter of the spread formed when the cementitious material is freely spread on a horizontal surface. This method is simple to operate but has limited accuracy, making it difficult to meet the high-standard testing requirements for new cementitious materials.

[0003] The slurry fluidity test device includes a test platform, a mold, and a measuring tool. The standard consistency slurry is filled into the mold and smoothed. The mold is quickly lifted to allow the slurry to spread freely. After the flow stabilizes, the maximum expansion diameter of the paving body and its vertical diameter are measured manually or by infrared imaging. Finally, the average value is taken to represent the fluidity.

[0004] The above method has the following technical problems: First, the edges of the slurry paving body are often blurred and irregular due to segregation, water seepage or bubble interference. It is difficult to accurately identify the true outline by manual visual inspection or single infrared imaging, resulting in diameter measurement errors. At the same time, relying solely on the final static diameter cannot capture the dynamic parameters during the flow process and cannot fully reflect the rheological properties of the material.

[0005] Secondly, relying on a preset fixed time or the operator's experience to judge the "stable state" of the pure slurry can easily lead to premature (unstable) or too late (already shrunk) measurements due to differences in the thixotropic properties of the materials, introducing systematic errors.

[0006] In addition, only the average diameter is output, which cannot characterize the morphological characteristics of the paving body, such as roundness, symmetry, and edge stability, while these parameters are directly related to the segregation tendency and homogeneity of the material.

[0007] Finally, the automation level is low and the operation is cumbersome. The entire process relies on manual leveling of the platform, placement of the slurry, and visual readings, which is inefficient and prone to human error. Summary of the Invention

[0008] In view of the deficiencies of the prior art, the purpose of the embodiments of the present application is to provide a net slurry flow test method and system based on multi-modal dynamic imaging, which can accurately extract complex edge contours and exclude impurity interference through multi-modal dynamic imaging and intelligent image fusion technology, and improve measurement accuracy; avoid subjective errors by adaptively determining stable timing based on real-time dynamic parameters (area change rate, centroid displacement); output multi-dimensional parameters (dynamic rheological indicators + advanced morphological parameters) to comprehensively represent material performance; eliminate the influence of manual operation by integrating active horizontal adjustment and automatic measurement process, and ensure efficient and reliable testing.

[0009] To achieve the above object, the embodiments of the present application provide the following technical solutions: A net slurry flow test method based on multi-modal dynamic imaging, comprising: collecting a continuous infrared image sequence of a net slurry free-flowing process; synchronously triggering visible light fill light in the initial stage of flow to obtain at least one visible light image; registering and fusing the visible light image and the infrared image at the corresponding time to generate an enhanced image frame; extracting the net slurry contour in real time based on the enhanced image frame and calculating dynamic parameters; adaptively determining the flow stable timing according to the change trend of the dynamic parameters; triggering the final contour extraction at the stable timing to calculate the static morphological parameters and dynamic process parameters.

[0010] Optionally, the dynamic parameters include at least one of the equivalent diameter change rate and the contour centroid displacement.

[0011] Optionally, the adaptive determination of the flow stable timing comprises: starting timing when the equivalent diameter change rate is first lower than an initial threshold value; continuously monitoring whether the equivalent diameter change rate is lower than a dynamically reduced threshold value and whether the centroid displacement is less than a preset displacement threshold value during the timing; if both conditions are met continuously for a preset duration, it is determined that the stable state is reached.

[0012] Optionally, the dynamically reduced threshold value is 5%-15% of the historical maximum change rate.

[0013] Optionally, the generation of the enhanced image frame comprises: assigning a higher weight to the edge region of the registered visible light image and infrared image, and assigning a higher weight to the non-edge region of the infrared image; outputting a fused image based on the weighted result, and marking the impurity region identified in the visible light image.

[0014] Optionally, the final contour extraction adopts an active contour model, and sets a repulsive force field based on the marked impurity region to constrain the contour convergence direction.

[0015] Optionally, the static shape parameters include at least three of the maximum diameter, the vertical diameter, the average diameter, the minimum circumscribed circle diameter, the maximum inscribed circle diameter, the roundness, and the eccentricity.

[0016] Optionally, the dynamic process parameters include at least one of the initial diffusion speed, the time to reach stability, and the time required for the equivalent diameter to grow to a predetermined value of the final value.

[0017] The embodiment of the present application also provides a system for the multi-modal dynamic imaging-based clean slurry flow test method as described above, comprising: a test platform for carrying the clean slurry; an active horizontal adjustment system including an inclination sensor and a driving unit for maintaining the platform horizontal in real time; an infrared imaging module arranged above the platform for collecting an infrared image sequence of the clean slurry flow; a visible light supplementing module arranged below the platform for triggering visible light imaging in the initial stage of the flow; and a central control unit connected to the active horizontal adjustment system, the infrared imaging module, and the visible light supplementing module.

[0018] Optionally, the driving unit includes at least three servo motors distributed on the bottom surface of the test platform to form a three-point linkage leveling structure.

[0019] The one or more technical solutions provided in the embodiment of the present application have at least the following technical effects or advantages: 1. The test method of the present application collects a continuous infrared image sequence to record the whole flow process, triggers visible light supplementing in the initial stage, captures key frames by using the high-resolution characteristic of visible light on edge details, generates enhanced image frames by registering and fusing the infrared and visible light images to overcome the defects of single imaging caused by segregation and bubble interference, extracts the contour based on the enhanced frames and calculates dynamic parameters to provide a quantitative basis for adaptive determination, determines the stable timing according to the change trend of the dynamic parameters to avoid subjective errors caused by relying on fixed time, and finally extracts a high-precision contour at the stable timing and synchronously outputs the static shape parameters and the dynamic process parameters. The method solves the precision and information dimension problems of the traditional clean slurry flow test by multi-modal dynamic imaging, and each step forms a closed loop: dynamic monitoring drives timing determination, timing determination guarantees the accuracy of the final measurement, and multi-modal fusion supports whole process analysis.

[0020] 2. The multi-modal image fusion effectively solves the interference of complex edges (bleeding, segregation) and internal impurities (bubbles, particles) on the imaging quality, provides a clear and accurate data source for contour extraction, and accurately excludes the interference of non-main region in the final analysis stage by using the active contour combined with impurity prior information, ensures that the extracted contour truly reflects the shape of the clean slurry main body, and reduces the measurement deviation caused by impurities. The adaptive stability determination is based on the behavior of the material itself to accurately capture the measurement timing.

[0021] 3. Through whole-process dynamic monitoring, key dynamic parameters characterizing the rheological properties of the material are obtained. These parameters directly reflect the thixotropy, yield stress and plastic viscosity of the neat paste, providing data support for material research and formulation optimization that traditional static tests cannot provide. A variety of advanced morphological parameters are introduced, which comprehensively describe the overall morphological characteristics of the neat paste paving body (such as expansion uniformity, symmetry, edge stability, segregation tendency), which can more comprehensively evaluate the homogeneity, stability and working performance of the material, far beyond the limitations of a single average value.

[0022] 4. Non-fixed time mode is adopted, and the stable point is intelligently judged based on the real-time flow behavior (change rate, centroid displacement) and historical observation (maximum change rate) of the material itself, automatically adapting to various complex cementitious materials from rapid setting to slow setting, from high flow to low flow. Machine learning and advanced image processing techniques are used to process complex contours and impurity interference, improving analysis accuracy and robustness.

[0023] The advantages of the additional aspects of the application will be given in the following description, some of which will become apparent from the following description, or will be understood by those skilled in the art through practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0025] Figure 1 is a schematic diagram of the test method provided by embodiment 1 of the present application; Figure 2 is a front view of the test system provided by embodiment 2 of the present application; Figure 3 is a side view of the test system provided by embodiment 2 of the present application; Figure 4 is a top view of the test system provided by embodiment 2 of the present application; In the figure: 1, infrared imaging module; 2, test platform; 3, central control unit; 4, visible light supplement module; 5, driving unit; 6, inclination sensor; DETAILED DESCRIPTION It should be noted that the following detailed description is illustrative and is intended to further illustrate the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0026] Example 1 like Figure 1 As shown, this embodiment proposes a net pulp flow testing method based on multimodal dynamic imaging, including: collecting a continuous infrared image sequence of the net pulp free flow process; synchronously triggering visible light fill illumination in the initial stage of flow to obtain at least one frame of visible light image; aligning and fusing the visible light image with the infrared image at the corresponding moment to generate an enhanced image frame; extracting the net pulp contour in real time based on the enhanced image frame, and calculating the dynamic parameters; adaptively determining the flow stabilization timing according to the changing trend of the dynamic parameters; triggering the final contour extraction at the stable timing, and calculating the static morphological parameters and dynamic process parameters.

[0027] The acquisition of infrared image sequences can fully record the dynamic process of the clean slurry from flow to stability, and the synchronously acquired visible light images provide high-detail edge information for subsequent fusion; image registration and fusion combine the advantages of strong infrared penetration and clear visible light details to improve the accuracy of contour extraction and solve the problem that single imaging is difficult to capture complex edges; dynamic parameters are calculated based on the contour and the stable state is adaptively determined, avoiding the mechanical dependence on fixed time. By real-time monitoring of the area change rate and center of mass displacement, the accuracy of stability judgment is ensured and the problem of inaccurate measurement timing is solved; the static and dynamic parameters finally extracted characterize the clean slurry performance from multiple dimensions of morphology and process, making up for the defect of single dimension of results of traditional methods.

[0028] The dynamic parameters include at least one of the equivalent diameter change rate and the profile centroid displacement. The equivalent diameter change rate, calculated through the area derivative, reflects the flow rate decay and is used to capture the thixotropic recovery process. The profile centroid displacement monitors the overall deflection of the paving mass and identifies asymmetric flow caused by platform micro-tilt or material segregation. These two parameters work together to quantify the transition state of the slurry from flowing to stable, replacing traditional manual observation. This provides a key basis for adaptive stability determination, ensuring that final measurements are taken when the slurry flow is truly stable, thereby improving the reliability of the measurement results.

[0029] The adaptive determination of flow stabilization timing includes: The timing starts when the equivalent diameter change rate is first lower than the initial threshold value; the equivalent diameter change rate is continuously monitored during the timing whether it is lower than the dynamically reduced threshold value, and the centroid displacement is continuously monitored whether it is less than the preset displacement threshold value; if both are continuously satisfied for a preset duration, it is determined that the stable state is reached.

[0030] The timing starts when the equivalent diameter change rate is first lower than the initial threshold value, which is based on the observation of the rapid diffusion stage of the initial stage of the net slurry flow, and the introduction of the initial threshold value effectively filters the short-term fluctuations in the flow process. The equivalent diameter change rate is continuously monitored during the timing whether it is lower than the dynamically reduced threshold value, and the centroid displacement is continuously monitored whether it is less than the preset displacement threshold value, and through the double monitoring mechanism, the rigor and accuracy of the stable determination are ensured. If both are continuously satisfied for a preset duration, it is determined that the stable state is reached. This determination logic based on time and parameter double conditions effectively avoids misjudgment caused by accidental factors and ensures the accuracy of the final measurement opportunity.

[0031] The dynamically reduced threshold value is 5%-15% of the historical maximum change rate, which reflects the maximum diffusion rate of the net slurry at the initial stage of the flow. Dynamically adjusting the threshold value based on this can adapt to the flow characteristics of different net slurry materials. The 5%-15% ratio range ensures the sensitivity of the determination while effectively reducing the risk of misjudgment, ensuring the robustness and adaptability of the stable determination algorithm.

[0032] The generated enhanced image frame includes: giving the edge region of the registered visible light image and infrared image a higher weight of visible light, and giving the non-edge region a higher weight of infrared; outputting a fusion image based on the weighting result, and marking the impurity region identified in the visible light image.

[0033] The edge region of the registered visible light image and infrared image is given a higher weight of visible light, which enhances the contour clarity by using the high spatial resolution of visible light, and the non-edge region is given a higher weight of infrared, which takes advantage of the infrared penetration to reduce the interference of surface bubbles. This weight distribution strategy fully takes advantage of the advantages of visible light imaging in edge detail capture and infrared imaging in main contour presentation. Based on the weighting result, a fusion image is output, and the impurity region identified in the visible light image is marked, which provides a more accurate and detailed data basis for subsequent contour extraction, effectively improving the accuracy of the final measurement result.

[0034] The final contour extraction adopts an active contour model, and sets a repulsive force field based on the marked impurity region to constrain the contour convergence direction.

[0035] The active contour model uses the gradient information of the image and the set energy function to drive the contour line to converge to the real edge of the net paste, ensuring high precision of the contour extraction. The introduction of the repulsive force field of the impurity area effectively avoids the diffusion of the contour line to the impurity area, thereby accurately excluding the interference of the non-main body area and ensuring that the extracted contour truly reflects the shape of the net paste main body, reducing the measurement deviation caused by impurities.

[0036] The static shape parameters include at least three of the maximum diameter, the vertical diameter, the average diameter, the minimum circumscribed circle diameter, the maximum inscribed circle diameter, the roundness, and the eccentricity.

[0037] Among them, the basic parameters: maximum diameter, vertical diameter, average diameter meet the standard flowability evaluation. Advanced parameters: minimum circumscribed circle diameter, reflecting the uniformity of the spreading body expansion; maximum inscribed circle diameter, representing edge stability (the smaller the value, the more irregular the edge); roundness, evaluating overall symmetry (the roundness of the segregated material is <0.9); eccentricity, quantifying the degree of shape elongation (the eccentricity of the net paste with poor pumpability is >0.7).

[0038] These parameters comprehensively describe the overall shape characteristics of the net paste spreading body from different dimensions, such as expansion uniformity, symmetry, edge stability, and segregation tendency. By calculating these parameters, the homogeneity, stability, and working performance of the material can be more comprehensively evaluated, providing more abundant data support for material research and quality control.

[0039] The dynamic process parameters include at least one of the initial diffusion speed, the time to reach stability, and the time required for the equivalent diameter to grow to a predetermined value of the final value.

[0040] The initial diffusion speed is the average growth rate of the equivalent diameter within 0-2 seconds, reflecting the initial flowability of the net paste after the mold is released, and is a key indicator for evaluating the pumpability and filling ability of the material. The time to reach stability is the time from the start of flow to meet the stability criterion, reflecting the time required for the net paste to reach a stable state from the start of flow, which is closely related to the thixotropy and yield stress of the material. The T95 time is the time for the equivalent diameter to reach 95% of the final value, further refining the time characteristics of the net paste flow process and providing more accurate time dimension data for studying the rheological behavior of the material.

[0041] Specific net paste flow test method process: Step 1: Start the test and collect dynamic image sequence 1.1 The user places the net paste and starts the test, and the central control unit controls the active horizontal adjustment system to complete the platform fine leveling.

[0042] 1.2 The moment the mold is lifted, the central control unit immediately commands the infrared industrial camera to start continuous acquisition of infrared image sequences at a preset initial frame rate (e.g. 30 frames / second). The dynamic changes from the beginning of the flow to the final stability of the net slurry are recorded, providing a data basis for subsequent time series analysis.

[0043] 1.3 Within the first 1-2 seconds after the mold is lifted, a short synchronous trigger of a visible light band fill light (e.g. LED white light) is performed to obtain a high-contrast visible light image (denoted as visible light frame).

[0044] A high-resolution characteristic of visible light is used to obtain an auxiliary frame. The purpose is to assist in identifying difficult-to-distinguish edge details and impurity areas in the subsequent processing of infrared images, improving the overall contour extraction accuracy. This operation is short and does not affect the main infrared imaging.

[0045] Step 2: Multimodal image fusion and real-time edge detection 2.1 The central processing unit pre-processes each newly arrived infrared image (e.g. denoising, contrast enhancement).

[0046] 2.2 If there is a visible light frame, perform fusion: 2.2.1 Image registration is performed between the visible light frame and the most matching infrared frame at the current time (usually the first or second frame after the mold is lifted) (spatial alignment is ensured based on feature point matching).

[0047] 2.2.2 Apply weighted average image fusion algorithm: The corresponding pixel gray values of the registered visible light frame and infrared frame are weighted and calculated. In the edge area (detected by Sobel operator, etc.), the visible light frame is given a higher weight (e.g. 0.7) to retain its high spatial details. In the non-edge area, the infrared frame is given a higher weight (e.g. 0.7) to maintain its advantage of good main body penetration. Through pre-experiment comparison, the edge signal-to-noise ratio is improved by 40% when the weight is 0.7. An enhanced image frame is generated.

[0048] 2.2.3 In the enhanced image frame, identify and mark the suspected impurity areas (bubbles, segregation particles) determined by visible light information.

[0049] 2.3 If there is no visible light frame, mainly rely on the pre-processed infrared frame.

[0050] 2.4 In the enhanced image frame (or pre-processed infrared frame), apply a real-time edge detection algorithm (e.g. Canny operator) to preliminarily extract the net slurry contour of the current frame.

[0051] By weighted average fusion, the edge definition is targeted to be improved and impurities are identified, which significantly improves the accuracy and robustness of the contour extraction, overcomes the limitations of single infrared imaging under complex edge and impurity interference, and provides a more reliable data source for subsequent dynamic analysis and final measurement. Real-time edge detection provides contour basic data for each frame for calculating dynamic parameters.

[0052] Step 3: Dynamic parameter calculation and adaptive stability determination 3.1 Based on the contour extracted in step 2, the key dynamic parameters are calculated: Equivalent diameter: D eq 2 =S / π, where the area S is the net paste paving area calculated by the contour.

[0053] Area change rate: R area =|current frame area-previous frame area| / (Δt), Δt is the time interval between adjacent two frame image acquisition.

[0054] Contour centroid displacement: calculate the displacement amount Δ 质心 of the contour centroid coordinate of the current frame relative to the centroid of the previous frame.

[0055] 3.2 Adaptive stability determination algorithm: 3.2.1 Real-time monitoring of R area and Δ 质心 .

[0056] 3.2.2 Set an initial threshold (e.g. 0.05 / s). When R area first falls below the initial threshold, record the time t s .

[0057] 3.2.3 Start a duration timer stability timer.

[0058] 3.2.4 After t>=t s , continuously check: Subsequent R area must continue to be lower than a dynamically decreasing dynamic threshold: dynamic threshold=α*maximum historical change rate. Where α is a preset proportion coefficient, through the test data fitting of 100 different net paste samples, the stability determination accuracy is >95% when α is 0.05-0.15; the maximum historical change rate is the maximum R area value observed within 0 to t s period. At the same time, Δ 质心 must be less than a preset small displacement threshold (e.g. 0.5mm / frame).

[0059] 3.2.5 If the above conditions are continuously met for a preset stable duration retention time (e.g. 0.5 seconds), it is determined that the net paste flow has reached a stable state.

[0060] 3.2.6 During the monitoring process, the tilt sensor data is received in real time. If the platform levelness exceeds the preset tolerance range (levelness tolerance ≤ 0.1°), the stability determination process is paused or reset.

[0061] 3.3 When the stability condition is met, a command is issued to the imaging system to collect one or more frames of high-resolution / high-quality infrared images for final analysis, which are recorded as the final infrared frames. At the same time, the trigger time t is recorded. f .

[0062] By quantifying the slurry flow process, a basis is provided for determining the stable state, ultimately outputting valuable dynamic indicators. Furthermore, the calculated dynamic parameters and the observed historical maximum values ​​are used to determine when the slurry has truly stabilized, rather than relying on a fixed time. This improves the accuracy of triggering the final measurement timing and adapts to materials with different flow characteristics.

[0063] Step 4: Final shape intelligent contour analysis and multi-parameter extraction 4.1 The central processing unit receives the final infrared frame, which is the image triggered at the stable moment.

[0064] 4.2 Apply advanced contour extraction: 4.2.1 If an impurity region labeling map has been generated in step 2.2.3, input it as prior knowledge.

[0065] 4.2.2 Apply the improved active contour model (such as Gradient Vector FlowSnake) on the final infrared frame: Initialize a contour (the contour of the last frame before the stable determination in step 3 can be used).

[0066] The model uses the gradient information of the image and the set energy function to drive the contour line to converge to the true edge of the net slurry.

[0067] During the iteration process, the impurity region marking map (if any) is used to set a repulsive force field to prevent the contour line from spreading into the marked impurity region.

[0068] 4.2.3 Output the final contour of the pure pulp body that is accurate and free of impurity interference.

[0069] 4.3 Calculation of static morphological parameters: Basic parameters: Get the global maximum diameter D of the final contour max , and D max Diameter D in the vertical direction perp , calculate the average diameter D avg =(D max +Dperp ) / 2.

[0070] Advanced morphological parameters: Minimum circumscribed circle diameter (D mincirc ): diameter of the smallest circle containing the entire contour; Maximum inscribed circle diameter (D mincirc ): diameter of the largest circle that can be accommodated within the contour.

[0071] Circularity (Circularity): Circularity = 4πS / (L 2 ), the closer the value to 1, the more circular, where S is the contour area and L is the total length of the closed path after connecting the contour boundary pixel points.

[0072] Eccentricity (Eccentricity): calculated based on the ratio of major and minor axes of the ellipse fitted to the contour, reflecting the degree of shape elongation.

[0073] 4.4 Extraction of dynamic process parameters: Initial expansion rate (Initial speed): calculate the average growth rate (mm / s) of the equivalent diameter in the first few seconds (e.g. 0-2 seconds) after the mold is lifted.

[0074] Steady time (Steady time): Steady time = t f .

[0075] T95 time (T95 time): find the time when the equivalent diameter first reaches 0.95 of the final equivalent diameter (calculated from the final contour area) from the recorded equivalent diameter time series data.

[0076] Through the prior information of impurities and active contour model by multi-modal fusion, overcome the possible edge blur and impurity interference in the final image, obtain the accurate and pure net paste main contour.

[0077] Not only output standard parameters, but also provide rich advanced morphological parameters and key dynamic parameters, these parameters from different dimensions depth characterization of net paste flow performance and morphological characteristics, far beyond the single average value of prior art.

[0078] Example 2 This embodiment proposes a system for implementing the net paste flow test method based on multi-modal dynamic imaging described in Example 1, which adopts an active horizontal control system, a non-contact optical measurement system, and combines image processing and dynamic monitoring algorithms to realize automatic control and accurate data extraction of the test process, significantly improve the test precision and equipment stability, and provide a new solution for the research and development and quality control of cementitious materials.

[0079] The clean slurry flow test system includes a test platform 2, an active horizontal adjustment system, a non-contact optical measurement system (infrared imaging module 1, visible light supplement module 4), and a central control unit 3, etc. The test platform 2 is used to carry the clean slurry and is the main measurement carrier. The active horizontal adjustment system includes an inclination sensor 6 and a drive unit 5, which can monitor and adjust the platform level in real time, ensure the accurate control of the test reference, and effectively eliminate the influence of platform inclination on the measurement results. The infrared imaging module 1 is arranged above the platform and is used to collect infrared image sequences of the clean slurry flow, realizing non-contact dynamic monitoring of the clean slurry flow process. The visible light supplement module 4 is arranged below the platform and triggers visible light imaging in the initial stage of flow, providing high-contrast visible light images for subsequent image fusion. The central control unit 3 connects and coordinates the work of each module, realizing the automatic control and data processing of the entire test process.

[0080] The drive unit 5 includes at least three servo motors distributed on the bottom surface of the test platform 2, forming a three-point linkage leveling structure. Based on the principle of geometric stability, the three-point linkage leveling structure can quickly and accurately adjust the platform level through the coordinated action of the at least three servo motors. The servo motors have high precision and fast response characteristics, ensuring the real-time horizontal adjustment capability of the platform during the test process, and providing a stable reference platform for the clean slurry flow test.

[0081] Specifically, as shown in Figure 2 、 Figure 3 、 Figure 4 1. Test platform 2: The test platform 2 is the basic structure of the entire device and is used to carry the clean slurry to be tested. The platform surface is made of transparent material (such as glass or plastic that transmits light in a specific waveband), allowing light to pass through for optical measurement. The test platform 2 is designed to freely spread the clean slurry and maintain a horizontal state through the active horizontal adjustment system, ensuring that there is no inclination interference during the test process.

[0082] 2. Active horizontal adjustment system: This system accurately adjusts and maintains the horizontal plane of the test platform 2, ensuring that the platform remains horizontal during the entire test process. Thus, errors introduced due to platform inclination are eliminated, ensuring the reliability and accuracy of the test data. The system includes: Attitude monitoring module: equipped with a high-precision inclination sensor 6 to monitor the inclination angle of the platform in real time; Attitude execution module: composed of multiple precision drive units 5 (servo motors, stepper motors, or piezoelectric ceramic drivers), responsible for adjusting the horizontal state of the platform according to the control signal.

[0083] ​Both work together through the central control unit 3 to form a closed-loop feedback control system. This system effectively solves the problem of instability in precision caused by manual adjustment in the prior art, ensuring accurate control of the test reference.

[0084] 3. Non-contact optical measurement system: The system collects two-dimensional morphological data of the spread net paste, including: Visible light fill-in module 4: arranged below the test platform 2, using an infrared light source for backlight illumination Infrared imaging module 1: arranged above the test platform 2, using an infrared industrial camera to take high-resolution photos of the spread net paste The combination of infrared light source and industrial camera is used for optical measurement based on the principle of backlight illumination. Through intelligent image processing algorithm, the central control unit 3 can automatically extract the contour of the net paste and calculate the geometric parameters representing the fluidity. Through algorithm optimization and image enhancement technology, the robustness of edge recognition is improved, and the influence of bubbles, isolates and other interference on the measurement accuracy is reduced.

[0085] 4. Central control unit 3 Responsible for coordinating the work of each subsystem and data processing, including: image processing unit extracts the outer contour of the net paste from the image data obtained from the imaging module; geometric analysis unit calculates the geometric parameters of the net paste and calculates the fluidity value; dynamic analysis unit extracts the time parameters in the spreading process to provide comprehensive rheological performance data.

[0086] The embodiment provides a full-automatic, accurate and reliable net paste fluidity test system, which has reasonable structure design and full-automatic operation process. The operator only needs to place the net paste to be tested on the test platform 2 and constrain the initial shape through the mold, and the system can automatically adjust the platform level, release the mold constraint and start automatic measurement. In terms of data processing, the central control unit 3 is built-in image processing and geometric analysis algorithm, which automatically extracts and calculates the geometric parameters required for net paste fluidity, greatly improving the efficiency and accuracy of the test process and reducing the error of human operation.

[0087] Through the close cooperation of each core component, the system realizes high-precision and full-automatic net paste fluidity test, which can significantly improve the precision and automation level of the existing test technology, overcome the problems of inaccurate test reference, large measurement error and complex operation in the prior art, not only suitable for precise research in laboratory, but also widely used in quality control in industrial field, has high market application value and broad popularization prospect.

[0088] The above describes the specific embodiments of the present application in combination with the drawings, but is not a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A method for testing slurry flow based on multimodal dynamic imaging, characterized in that: include: Collect continuous infrared image sequences of the free-flowing process of the clean pulp; During the initial stage of the flow, the visible light fill light is synchronously triggered to obtain at least one frame of visible light image; Registering and fusing the visible light image with the infrared image at the corresponding moment to generate an enhanced image frame; Extracting the net pulp profile in real time based on the enhanced image frame and calculating dynamic parameters; Adaptively determine the flow stabilization timing based on the changing trend of dynamic parameters; The final contour extraction is triggered at a stable time, and the static morphological parameters and dynamic process parameters are calculated.

2. The method for testing slurry flow based on multimodal dynamic imaging according to claim 1, wherein: The dynamic parameters include at least one of an equivalent diameter change rate and a profile centroid displacement.

3. The method for testing slurry flow based on multimodal dynamic imaging according to claim 1, wherein: The adaptive determination of flow stabilization timing includes: The timing starts when the equivalent diameter change rate falls below the initial threshold for the first time; During the timing period, the rate of change of the equivalent diameter is continuously monitored to see whether it is lower than a dynamically decreasing threshold value, and the displacement of the center of mass is monitored to see whether it is lower than a preset displacement threshold value; If both conditions continuously meet the preset duration, it is determined that a stable state is reached.

4. The method for testing the slurry flow based on multimodal dynamic imaging according to claim 3, wherein: The threshold value for dynamic reduction is 5%-15% of the historical maximum change rate.

5. The method for testing slurry flow based on multimodal dynamic imaging according to claim 1, wherein: Generating the enhanced image frame comprises: The edge areas of the registered visible light image and infrared image are given a higher weight for visible light, and the non-edge areas are given a higher weight for infrared. The fused image is output based on the weighted results, and the impurity areas identified in the visible light image are marked.

6. The method for testing slurry flow based on multimodal dynamic imaging according to claim 1, wherein: The final contour extraction adopts an active contour model and sets a repulsive force field based on the marked impurity region to constrain the contour convergence direction.

7. The method for testing slurry flow based on multimodal dynamic imaging according to claim 1, wherein: The static morphological parameters include at least three of the maximum diameter, perpendicular diameter, average diameter, minimum circumscribed circle diameter, maximum inscribed circle diameter, roundness and eccentricity.

8. The method for testing slurry flow based on multimodal dynamic imaging according to claim 1, wherein: The dynamic process parameter includes at least one of an initial diffusion rate, a time to reach stability, and a time required for the equivalent diameter to grow to a predetermined final value.

9. A system for the slurry flow testing method based on multimodal dynamic imaging according to any one of claims 1 to 8, characterized in that: include: Testing platform, used to carry the clean slurry; Active leveling system, including tilt sensors and drive units, used to maintain the platform level in real time; The infrared imaging module is installed above the platform and is used to collect infrared image sequences of the slurry flow; The visible light fill light module is set under the platform and is used to trigger visible light imaging at the initial stage of flow; The central control unit is connected to the active level adjustment system, the infrared imaging module and the visible light fill light module.

10. The system according to claim 9, wherein: The driving unit includes at least three servo motors distributed on the bottom surface of the test platform to form a three-point linkage leveling structure.

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