Rheological detection method and system for viscosity of enema liquid medicine

Abstract

The invention relates to the technical field of medicine detection, in particular to a rheological detection method and system for viscosity of enema liquid medicine, and solves the technical problem that the measurement accuracy is influenced by interference of experimental instruments. The method comprises the following steps: acquiring a liquid level image of enema liquid medicine in a capillary viscometer in a flowing process through image acquisition equipment; performing morphological characteristic analysis on the liquid level image to obtain image characteristics of the enema liquid medicine, and evaluating a pollution degree value of the capillary viscometer based on the image characteristics; the image features comprise at least one of curvature features, symmetry features and smoothness features; and determining the viscosity measurement reliability of the capillary viscometer according to the pollution degree value.

Description

Technical Field

[0001] This invention relates to the field of drug detection technology, specifically to a rheological method and system for detecting the viscosity of an enema solution. Background Technology

[0002] As a pharmaceutical preparation that acts directly on the intestines, the viscosity of enema solutions is a key physical parameter affecting the drug's flowability and uniform distribution within the intestines. Flowability and uniform distribution directly determine the adhesion area and absorption efficiency of the drug on the intestinal mucosa, thus influencing clinical treatment efficacy. Therefore, accurate measurement of viscosity during the production quality control, finished product inspection, and research and development of enema solutions is a crucial technical step to ensure product quality meets pharmaceutical standards and guarantees stable clinical efficacy.

[0003] Currently, the mainstream method used in the industry to measure the viscosity of enema solutions mainly relies on laboratory instruments such as capillary viscometers. Capillary viscometers, based on the Hagen-Poiseuille law, calculate viscosity by measuring the flow time of the solution within a capillary. However, these instruments typically have stringent testing requirements for their internal structure. During use, issues such as fine particle residue and internal wear can occur, altering the normal flow state or mechanical response characteristics of the solution within the instrument. This leads to deviations in viscosity measurements, severely impacting the accuracy and repeatability of the data, and ultimately failing to provide a reliable basis for the quality control of enema solutions. Summary of the Invention

[0004] To address the technical problem of interference affecting the accuracy of measurements in experimental instruments, the present invention aims to provide a rheological method for detecting the viscosity of enema solutions. The specific technical solution adopted is as follows: Images of the liquid level of the enema solution during its flow in a capillary viscometer were acquired using an image acquisition device. Morphological feature analysis is performed on the liquid surface image to obtain the image features of the enema solution, and the degree of contamination of the capillary viscometer is evaluated based on the image features; the image features include at least one of curvature features, symmetry features, and smoothness features; The reliability of viscosity measurement by a capillary viscometer is determined based on the degree of contamination.

[0005] In one possible implementation, the liquid level image includes a first liquid level image of the enema solution in a uniform inner diameter region of the capillary viscometer and / or a second liquid level image of the enema solution in a non-uniform inner diameter region of the capillary viscometer.

[0006] In one possible implementation, the method includes: Morphological feature analysis was performed on the first liquid surface image to obtain the first image features of the enema solution in the uniform inner diameter region, and the regional contamination degree value of the uniform inner diameter region was evaluated based on the first image features; the first image features include the mean value of the standard deviation of curvature, the mean value of the symmetry index, and the mean value of the smoothness index; Morphological feature analysis was performed on the second liquid surface image to obtain the second image features of the enema solution in the non-uniform inner diameter region, and the regional contamination degree value of the non-uniform inner diameter region was evaluated based on the second image features; the second image features include the correlation coefficient between the rate of curvature change and the rate of pipe diameter change. The contamination level of the capillary viscometer is determined based on the regional contamination level values ​​of the uniform inner diameter region and the regional contamination level values ​​of the non-uniform inner diameter region.

[0007] In one possible implementation, the method includes: Based on the first liquid surface image, the uniform inner diameter region is divided into multiple partitions, and the first image feature data of the enema solution on each partition is extracted. The first image feature data includes the radius of curvature, offset distance, and edge fitting error of the liquid surface of the enema solution on the partition. The radius of curvature is the radius of the fitted circle obtained by fitting the liquid surface of the enema solution on the partition. The offset distance is the distance between the center of the fitted circle and the center of the uniform inner diameter region. The edge fitting error is used to characterize the deviation between the point on the edge of the liquid surface of the enema solution on the partition and the corresponding point on the edge of the fitted circle. The first image features of the enema solution in each partition of the uniform inner diameter region are determined based on the first image feature data of each partition. The degree of regional contamination in a region with a uniform inner diameter is evaluated based on the first image features corresponding to each partition.

[0008] In one possible implementation, the method includes: For each section of the uniform inner diameter region, the curvature is determined based on the radius of curvature of the enema solution on the section, and the mean value of the standard deviation of curvature is determined based on the curvature of the section. For each section of the uniform inner diameter region, the symmetry index corresponding to the section is calculated based on the offset distance of the enema solution on the liquid surface of the section, and the mean value of the symmetry index is determined based on the symmetry index corresponding to the section. For each partition of the uniform inner diameter region, the smoothness index corresponding to the partition is calculated based on the edge fitting error of the enema solution on the partition, and the mean smoothness index is determined based on the smoothness index corresponding to the partition.

[0009] In one possible implementation, the method includes: The regional contamination level of the uniform inner diameter region is determined based on the mean of the standard deviation of curvature, the mean of the symmetry index, and the mean of the smoothness index. The mean of the standard deviation of curvature is positively correlated with the regional contamination level of the uniform inner diameter region. The mean of the symmetry index and the mean of the smoothness index are negatively correlated with the regional contamination level of the uniform inner diameter region, respectively.

[0010] In one possible implementation, the method includes: Based on the second liquid surface image, second image feature data of the enema solution at multiple locations in the non-uniform inner diameter region are extracted; the second image feature data includes the rate of change of the capillary viscometer diameter at the corresponding location and the rate of change of the curvature of the enema solution surface at the corresponding location. The second image features of the enema solution in the non-uniform inner diameter region are determined based on the second image feature data from multiple locations; The degree of regional contamination in areas with non-uniform inner diameters is evaluated based on the second image features.

[0011] In one possible implementation, the method includes: Correlation analysis was performed on the rate of change of capillary viscometer diameter at multiple locations and the rate of change of curvature of the enema solution surface to determine the correlation coefficient between the rate of change of curvature and the rate of change of pipe diameter.

[0012] In one possible implementation, the method includes: The regional contamination level of the non-uniform inner diameter region is determined by the absolute value of the correlation coefficient between the rate of curvature change and the rate of pipe diameter change; the absolute value of the correlation coefficient is negatively correlated with the regional contamination level of the uniform inner diameter region.

[0013] This application provides a rheological detection system for the viscosity of an enema solution, including a capillary viscometer, an image acquisition device, and electronic equipment; A capillary viscometer is used to measure the viscosity of enema solutions; The image acquisition device is used to acquire images of the liquid level during the flow of enema solution in a capillary viscometer; An electronic device is used to acquire images of the liquid surface of an enema solution during its flow in a capillary viscometer via an image acquisition device; to perform morphological feature analysis on the liquid surface images to obtain image features of the enema solution, and to evaluate the degree of contamination of the capillary viscometer based on the image features; the image features include at least one of curvature features, symmetry features, and smoothness features; and to determine the viscosity measurement reliability of the capillary viscometer based on the degree of contamination value.

[0014] The present invention has the following beneficial effects: Based on the above technical solution, this application acquires images of the enema solution's surface during its flow in a capillary viscometer using an image acquisition device. This avoids subjective errors caused by manual observation of capillary contamination. Subsequently, feature analysis is performed on the acquired surface images, and the degree of contamination is assessed based on extracted image features such as curvature, symmetry, and smoothness, thereby accurately capturing minute contaminants that are difficult to distinguish with the naked eye. In this way, this application can determine measurement reliability based on the degree of contamination, effectively screen reliable detection data, avoid viscosity measurement deviations caused by equipment contamination, and significantly improve the accuracy and repeatability of enema solution viscosity detection. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system architecture diagram of a rheological detection system for the viscosity of an enema solution provided in one embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a capillary viscometer provided in one embodiment of the present invention; Figure 3 This is a schematic flowchart of a rheological method for detecting the viscosity of an enema solution according to an embodiment of the present invention. Figure 4 This is a schematic diagram showing the shape of the liquid surface of an enema solution in a capillary viscometer according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the non-uniform inner diameter region and the uniform inner diameter region in a capillary viscometer provided in an embodiment of the present invention. Figure 6 This is a schematic flowchart of another rheological method for detecting the viscosity of an enema solution provided in an embodiment of the present invention; Figure 7 This is a schematic flowchart of another rheological method for detecting the viscosity of an enema solution provided in an embodiment of the present invention; Figure 8 This is a schematic flowchart of another rheological method for detecting the viscosity of an enema solution provided in an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a rheological detection method and system for enema solution viscosity proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] To address the technical problem of interference affecting the accuracy of experimental instruments, this application provides a rheological detection method and system for enema solution viscosity. The method acquires images of the enema solution's surface as it flows through a capillary viscometer using an image acquisition device, avoiding subjective errors caused by manual observation of capillary contamination. Subsequently, feature analysis is performed on the acquired surface images, and the degree of contamination is assessed based on extracted image features such as curvature, symmetry, and smoothness, thereby accurately capturing minute contaminants that are difficult to distinguish with the naked eye. In this way, the reliability of the measurement can be determined based on the degree of contamination, effectively screening reliable detection data, avoiding viscosity measurement deviations caused by equipment contamination, and significantly improving the accuracy and repeatability of enema solution viscosity detection.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the rheological detection method and system for the viscosity of an enema solution provided by the present invention.

[0021] Figure 1 This is a schematic diagram of a rheological detection system for the viscosity of an enema solution provided in an embodiment of this application. Figure 1 As shown, the rheological detection system for the viscosity of the enema solution includes: a capillary viscometer 101, an image acquisition device 102, and an electronic device 103.

[0022] The capillary viscometer 101 is used to measure the viscosity of the enema solution. For example, as shown... Figure 2 As shown, the capillary viscometer 101 can be a U-shaped capillary viscometer made of transparent glass, whose inner diameter needs to be pre-calibrated and whose surface is smooth. To eliminate the influence of temperature on the viscosity of the drug solution, the capillary viscometer 101 can be equipped with a constant temperature water bath (temperature control accuracy of at least ±0.1℃). During use, the capillary of the capillary viscometer 101 is immersed in the water bath.

[0023] The image acquisition device 102 is used to acquire images of the liquid surface of the enema solution during its flow in a capillary viscometer. For example, the image acquisition device 102 may consist of a complementary metal-oxide-semiconductor (CMOS) camera, a macro lens, and an illumination module.

[0024] The CMOS camera is a high-resolution, high-frame-rate camera, ensuring it can capture subtle morphological changes in the liquid surface (usually crescent-shaped, hence also called the meniscus). The macro lens is used to magnify the image and clearly capture the details of the meniscus inside the capillary. The illumination module uses a diffuse light-emitting diode (LED) cold light source to provide uniform illumination from the side or back of the capillary, avoiding the blurring of the outline caused by specular reflection.

[0025] In some embodiments, the image acquisition device 102 can be fixed on a bracket, with the lens facing the liquid surface area of ​​the capillary (including areas with uniform inner diameter and areas with non-uniform inner diameter) to ensure a stable acquisition angle.

[0026] Electronic device 103 is used to acquire liquid surface images of enema solution during its flow in a capillary viscometer via an image acquisition device, perform morphological feature analysis on the liquid surface images to obtain image features of the enema solution, and evaluate the degree of contamination of the capillary viscometer based on the image features.

[0027] The image features include at least one of curvature features, symmetry features, and smoothness features; the reliability of the capillary viscometer's viscosity measurement is determined based on the degree of contamination.

[0028] For example, electronic device 103 may be a computer, industrial control computer or other device with data processing and storage functions, and is connected to image acquisition device via data cable or wireless link.

[0029] The rheological detection system for enema solution viscosity provided in this embodiment achieves automation and precision in enema solution viscosity detection through the coordinated operation of a capillary viscometer 101, an image acquisition device 102, and an electronic device 103. The capillary viscometer 101 provides a stable measurement medium, the image acquisition device 102 ensures the clarity and integrity of the liquid surface image, providing high-quality data for subsequent analysis, and the electronic device 103 integrates image analysis, contamination assessment, and reliability judgment, avoiding human error. The entire system has a simple structure and is easy to operate, meeting the needs of pharmaceutical companies, quality inspection agencies, and R&D units for rapid, accurate, and repeatable detection of enema solution viscosity, and is particularly suitable for quality control processes with high cleanliness requirements.

[0030] It should be noted that the various embodiments of this application can be referenced or learned from each other. For example, the same or similar steps, method embodiments, system embodiments and device embodiments can be referenced from each other without limitation.

[0031] Please see Figure 3 The diagram illustrates a rheological method for detecting the viscosity of an enema solution according to an embodiment of the present invention. The method includes the following steps: Step 301: Acquire images of the liquid surface of the enema solution during its flow in a capillary viscometer using an image acquisition device.

[0032] When the viscosity of the enema solution is measured using a capillary viscometer, the liquid surface of the enema solution appears as a crescent-shaped meniscus. For example, such as... Figure 4 As shown, a meniscus is a convex-concave liquid surface formed by the combined effects of surface tension and the wettability of the solid tube wall within a liquid, such as in a capillary tube. Its specific shape is determined by the contact angle between the liquid and the tube wall material, and is the result of mechanical equilibrium at the solid-liquid-gas three-phase interface.

[0033] For example, the image acquisition device can acquire images of the liquid surface of the enema solution during its flow in the capillary viscometer in real time at preset time intervals, thereby obtaining morphological image information of the liquid surface of the enema solution at various positions in the capillary viscometer for subsequent analysis.

[0034] Step 302: Perform morphological feature analysis on the liquid surface image to obtain the image features of the enema solution, and evaluate the contamination level of the capillary viscometer based on the image features.

[0035] The image features include at least one of curvature features, symmetry features, and smoothness features.

[0036] It should be noted that the curvature of the enema solution's surface is directly related to interfacial tension and pressure difference. In an ideally clean capillary, the meniscus should exhibit a stable, symmetrical, and smooth geometric shape. Any contamination will disrupt the original mechanical equilibrium, leading to changes in the contact angle, curvature distortion, or contour asymmetry. Therefore, this application can analyze the surface morphology by acquiring images of the enema solution's flow process in a capillary viscometer, and extracting image features such as curvature, symmetry, and smoothness characteristics, thereby achieving an objective and quantitative assessment of the surface state.

[0037] Curvature features are used to quantify the degree of curvature of a liquid surface. Essentially, they reflect the curvature of the liquid surface caused by the interaction between surface tension and the tube wall. When there is contamination in the capillary, it will change the interfacial tension balance between the liquid and the tube wall, causing abnormal fluctuations in the degree of curvature of the liquid surface. The more obvious the fluctuation, the more serious the contamination.

[0038] Symmetry is used to characterize the degree of symmetry of the liquid surface profile relative to the central axis of the capillary. It is a key parameter for measuring the regularity of the liquid surface shape. If the capillary wall is clean, the liquid surface will exhibit a regular shape with the central axis of the capillary as the line of symmetry under the action of uniform interfacial tension. When there is contamination on the capillary wall, the local interfacial tension imbalance will destroy this symmetry. The lower the degree of symmetry, the higher the degree of contamination.

[0039] Smoothness refers to the flatness and continuity of the liquid surface edge contour, used to characterize the regularity of the liquid surface morphology. In a clean capillary tube, the interaction between the liquid and the tube wall is uniform, resulting in a continuous and flat contour at the liquid surface edge. If there is residual contamination or adhering impurities on the tube wall, it will cause uneven local stress on the liquid surface, resulting in irregular protrusions, depressions, or breaks in the edge contour. The more uneven the contour, the worse the smoothness and the more severe the contamination.

[0040] This application can quantify the contamination level of a capillary viscometer based on at least one of the above image features.

[0041] Step 303: Determine the reliability of the capillary viscometer's viscosity measurement based on the degree of contamination.

[0042] It should be noted that contamination introduces systematic errors, causing measured values ​​to deviate from the true value. This application transforms the abstract amount of contamination into a specific reliability index by establishing a quantitative decay model between the degree of contamination and the reliability of viscosity measurement. This allows for objective and quantitative quality grading and risk control of the measurement results based on preset thresholds, ensuring the credibility of the output data. Viscosity measurement reliability is negatively correlated with the degree of contamination.

[0043] For example, the reliability of viscosity measurement satisfies the following formula: in, To ensure the reliability of viscosity measurements using capillary viscometers, This represents the contamination level value of the capillary viscometer. This is the attenuation coefficient, which is an empirical constant and can be calibrated experimentally.

[0044] In some embodiments, the reliability level of viscosity measurement can be classified by a threshold. For example, if the viscosity measurement reliability is ≥0.9, it indicates that the reliability of the measurement is high and the data can be used directly; if 0.7≤viscosity measurement reliability<0.9, it indicates that the measurement reliability is medium and needs to be judged in combination with other test results; if the viscosity measurement reliability<0.7, it indicates that the measurement reliability is insufficient and the capillary needs to be cleaned and retested.

[0045] Based on the above technical solution, this application acquires images of the enema solution's surface during its flow in a capillary viscometer using an image acquisition device. This avoids subjective errors caused by manual observation of capillary contamination. Subsequently, feature analysis is performed on the acquired surface images, and the degree of contamination is assessed based on extracted image features such as curvature, symmetry, and smoothness, thereby accurately capturing minute contaminants that are difficult to distinguish with the naked eye. In this way, this application can determine measurement reliability based on the degree of contamination, effectively screen reliable detection data, avoid viscosity measurement deviations caused by equipment contamination, and significantly improve the accuracy and repeatability of enema solution viscosity detection.

[0046] Furthermore, due to the complexity of the capillary viscometer structure, the morphological characteristics of the liquid surface in the tube wall of different shapes and structures are somewhat different. Therefore, this application can classify capillary viscometers according to their shape and structure, and perform feature analysis and contamination quantification respectively to obtain the final contamination level value of the capillary viscometer.

[0047] As one possible embodiment of this application, the liquid level image includes a first liquid level image of the enema solution in a uniform inner diameter region of the capillary viscometer and / or a second liquid level image of the enema solution in a non-uniform inner diameter region of the capillary viscometer.

[0048] In the capillary viscometer, the inner diameter of the tube wall in the uniform inner diameter region remains constant along the axial direction; while the inner diameter of the tube wall in the non-uniform inner diameter region changes along the axial direction.

[0049] It should be noted that, in this embodiment, the capillary viscosity plan can be divided into one or more non-uniform inner diameter regions and one or more uniform inner diameter regions. The first liquid surface image includes multiple frames of liquid surface images captured during the flow of the enema solution in each non-uniform inner diameter region, and the second liquid surface image includes multiple frames of liquid surface images captured during the flow of the enema solution in each uniform inner diameter region. For example, images can be acquired using an image acquisition device at a preset acquisition frequency (e.g., 10 frames / second), and the images in which the enema solution's liquid surface is located in a non-uniform inner diameter region can be classified as the first liquid surface image, while the images in which the enema solution's liquid surface is located in a uniform inner diameter region can be classified as the second liquid surface image.

[0050] For example, such as Figure 5 As shown, a capillary viscometer typically has a measuring ball and a reservoir ball. This part is a non-uniform inner diameter region, and the curvature will change to a certain extent, which will cause the uniformity of the capillary inner diameter to change. Other diameter regions are uniform inner diameter regions.

[0051] Judging the uniformity of the capillary inner diameter in the measurement area by visual inspection has a certain degree of subjective error. Therefore, this application can achieve the objectification and quantification of the measurement area division based on image processing technology, thus avoiding the error of manual judgment.

[0052] For example, this application can first acquire images of a blank capillary viscometer (without injected drug solution) to obtain a high-resolution grayscale image. (taking the axial direction of the capillary as) (Direction), the inner wall edge of the capillary is extracted using an edge detection operator (such as the Canny operator), resulting in a binarized capillary edge image. Along the capillary axis ( (Direction) Calculate the pixel distance of the inner wall edge line by line, and then convert it to the actual physical width using a pre-calibrated conversion coefficient between pixel width and actual width (e.g., 1 pixel corresponds to 0.01mm). .

[0053] For example, the actual physical width satisfies the following formula: in, For capillary viscometer in coordinate The corresponding actual physical width, This is the conversion factor between pixel distance and actual distance. For capillary viscometer in coordinate The corresponding pixel width.

[0054] This yields the functional mapping relationship between the capillary inner diameter and the axial position of the capillary viscometer. For functions Find the first derivative ,like This indicates that the inner diameter of the pipe wall at the current position remains constant along the axial direction, meaning the current position belongs to a region with a uniform inner diameter. If If the current position indicates that the inner diameter of the tube wall has changed along the axial direction, then the current position belongs to the non-uniform inner diameter region. In this way, the capillary viscosity plan can be divided into various uniform inner diameter regions and non-uniform inner diameter regions.

[0055] As one possible embodiment of this application, combined with Figure 3 ,like Figure 6 As shown, step 302 above can be achieved through the following steps: Step 601: Perform morphological feature analysis on the first liquid surface image to obtain the first image features of the enema solution in the uniform inner diameter region, and evaluate the regional contamination level value of the uniform inner diameter region based on the first image features.

[0056] The first image features include the mean of the standard deviation of curvature, the mean of the symmetry index, and the mean of the smoothness index.

[0057] The mean standard deviation of curvature refers to the average quantitative index of the degree of curvature fluctuation of the liquid surface in multiple frames of the first liquid surface image, reflecting the stability of curvature changes over time or position. When there is contamination in the capillary, uneven force on the liquid surface will cause frequent fluctuations in curvature. The larger the mean value, the more serious the contamination.

[0058] The mean symmetry index refers to the average value of the degree of symmetry of the liquid surface with respect to the central axis of the capillary tube in multiple frames of the first liquid surface image. It is a comprehensive parameter for measuring the regularity of the liquid surface shape. Under clean tube walls, the symmetry of the liquid surface is stable, and the contamination is less severe. After contamination, the local interfacial tension is unbalanced, the degree of symmetry decreases, and the smaller the mean value, the more severe the contamination.

[0059] The mean smoothness index refers to the average value of the smoothness and continuity of the liquid surface edge in multiple frames of the first liquid surface image, representing the overall level of edge morphology regularity; when the pipe wall is clean, the edge is subjected to uniform force to form a smooth contour, and the less contamination, the more severe the contamination; residual contamination will cause irregular protrusions or depressions on the edge, and the smaller the mean value, the more severe the contamination.

[0060] It should be noted that within the uniform inner diameter region of a capillary viscometer, the curvature, symmetry, and smoothness of the liquid surface directly reflect the interfacial tension balance of the solid-liquid-gas three-phase system. A clean capillary viscometer environment exhibits a highly stable, axisymmetric, and smooth liquid surface, with its radius of curvature and contact angle remaining constant. Any contaminant will non-uniformly alter the local interfacial energy, disrupting the original mechanical balance and leading to changes in contact angle, curvature distortion, loss of symmetry, or fluctuations in the profile.

[0061] Therefore, this application can quantitatively extract the first image features of the enema solution in a uniform inner diameter region through image processing technology, transforming the abstract pollution phenomenon into quantifiable feature parameters.

[0062] Step 602: Perform morphological feature analysis on the second liquid surface image to obtain the second image features of the enema solution in the non-uniform inner diameter region, and evaluate the regional contamination degree value of the non-uniform inner diameter region based on the second image features.

[0063] The second image feature includes the correlation coefficient between the rate of curvature change and the rate of pipe diameter change.

[0064] The rate of change of pipe diameter refers to how fast the pipe diameter changes with the axial position of the capillary in a non-uniform inner diameter region, reflecting the trend of pipe diameter distribution in that region; the pipe diameter in a non-uniform inner diameter region (such as a measuring ball) naturally changes, and this trend is stable under clean conditions.

[0065] The rate of curvature change refers to how quickly the curvature of the liquid surface changes with the axial position of the capillary within a non-uniform inner diameter region, reflecting the response of the degree of liquid surface curvature to changes in pipe diameter; under clean conditions, the curvature change will adjust synchronously with the natural changes in pipe diameter.

[0066] The correlation coefficient is an indicator that quantifies the degree of coordination between the rate of change of pipe diameter and the rate of change of curvature. Its value ranges from -1 to 1. The more coordinated the changes of the two (i.e., the closer the absolute value of the correlation coefficient is to 1), the more uniform the force exerted by the pipe wall on the liquid surface and the cleaner the capillary. Contamination will destroy this coordination, resulting in a decrease in the absolute value of the correlation coefficient and more serious contamination.

[0067] It should be noted that in an ideally clean capillary viscometer, for regions with non-uniform inner diameters, the change in liquid surface curvature follows the Yang-Laplace equation, and its rate of change is highly coordinated with the rate of change in the inner diameter of the tube; that is, the curvature adaptively responds to changes in geometric dimensions. If this region is contaminated, the contaminants will non-uniformly alter the local cross-sectional tension and contact angle, disrupting this inherent mechanical equilibrium and geometric correlation.

[0068] Therefore, this application can assess the degree of contamination by quantifying the correlation between the rate of change of liquid surface curvature and the rate of change of pipe diameter. The weaker the correlation, the more severe the interference of non-geometric factors (contamination) on the cross-sectional properties.

[0069] Step 603: Determine the contamination level of the capillary viscometer based on the regional contamination level values ​​of the uniform inner diameter region and the regional contamination level values ​​of the non-uniform inner diameter region.

[0070] For example, this application can calculate the overall contamination level of the capillary viscometer by weighted average or arithmetic average of the regional contamination level values ​​of the uniform inner diameter region and the regional contamination level values ​​of the non-uniform inner diameter region.

[0071] In one example, the contamination level of the capillary viscometer satisfies the following formula: in, This represents the contamination level value of the capillary viscometer. For a region with uniform inner diameter The regional pollution level value, Non-uniform inner diameter region The regional pollution level value.

[0072] Based on the above technical solution, this application further distinguishes between the uniform inner diameter region and the non-uniform inner diameter region of the capillary viscometer, and adopts differentiated image feature analysis methods for the structural characteristics of different regions: the uniform inner diameter region is quantified by the curvature fluctuation, symmetry, and smoothness mean of multiple frames of images, while the non-uniform inner diameter region is quantified by the synergy between the pipe diameter and curvature changes. This avoids the interference of structural differences between different regions on the contamination assessment, making the contamination degree calculation more accurate. At the same time, the overall contamination value is obtained by combining the contamination degree of the two regions, which further improves the objectivity of the measurement reliability judgment and provides a more comprehensive guarantee for the credibility of subsequent viscosity detection data.

[0073] As one possible embodiment of this application, combined with Figure 6 ,like Figure 7 As shown, step 601 above can be achieved through the following steps: Step 701: Based on the first liquid surface image, divide the uniform inner diameter region into multiple partitions and extract the first image feature data of the enema solution on each partition.

[0074] The first image feature data includes the radius of curvature, offset distance, and edge fitting error of the enema solution on the partition. The radius of curvature is the radius of the fitted circle obtained by fitting the enema solution on the partition. The offset distance is the distance between the center of the fitted circle and the center of the uniform inner diameter region. The edge fitting error is used to characterize the deviation between the point on the edge of the enema solution on the partition and the corresponding point on the edge of the fitted circle.

[0075] For example, this application can divide the uniform inner diameter region into multiple partitions of equal length along the axial direction (e.g., each partition is 1 mm long). For each partition, multiple frames of liquid surface images are acquired, and the first image feature data of each partition is extracted. That is to say, this application can extract multiple sets of first image feature data from multiple frames of liquid surface images acquired from a partition, and then determine the image features corresponding to each partition by calculating the mean, thereby improving data stability.

[0076] The radius of curvature can be obtained by fitting a circle to the edge of the meniscus of each partition, thus obtaining the radius of the fitted circle (i.e., the radius of curvature). The radius of curvature is the basic data for calculating curvature.

[0077] Offset distance is the distance from the center of the meniscus fitting circle to the central axis of the uniform inner diameter region (i.e., offset distance) measured with the central axis of the uniform inner diameter region as a reference. Offset distance is the basic data for evaluating symmetry.

[0078] Edge fitting error is calculated by uniformly extracting multiple feature points at the meniscus edge of each partition, calculating the distance from each feature point to the fitted circle, and using the distance from each feature point to the fitted circle as the basis for evaluating smoothness.

[0079] For example, taking a number of feature points of 20, the edge fitting error of the enema solution on the partition satisfies the following formula: in, The edge fitting error of the enema solution's liquid surface in the partition is considered. For feature points Distance to the fitted circle Step 702: Determine the first image features of the enema solution in each partition of the uniform inner diameter region based on the first image feature data of each partition.

[0080] In some embodiments, this application may calculate image features of corresponding dimensions based on the first image feature data described above.

[0081] Regarding curvature, this application can determine the curvature for each partition of a uniform inner diameter region based on the radius of curvature of the enema solution on the partition, and determine the mean value of the standard deviation of curvature based on the curvature corresponding to the partition.

[0082] For example, the curvature corresponding to each partition satisfies the following formula: in, The curvature corresponding to the partition. This represents the radius of curvature of the liquid surface in the corresponding partition.

[0083] It should be noted that a partition can correspond to multiple curvatures. For example, this application can acquire multiple first liquid surface images within the partition to obtain multiple curvatures corresponding to the partition, and then determine the mean of the standard deviation of curvature based on these multiple curvatures to ensure data stability.

[0084] Regarding symmetry, this application can calculate the symmetry index corresponding to each partition of the uniform inner diameter region based on the offset distance of the enema solution on the partition, and determine the mean value of the symmetry index based on the symmetry index corresponding to the partition.

[0085] For example, the symmetry index corresponding to the partition satisfies the following formula: in, The symmetry index corresponding to the partition. This represents the offset distance of the enema solution's liquid level in the designated area. This represents the radius of curvature of the liquid surface in the corresponding partition. It should be understood that when the calculated result of S is less than a preset minimum positive number, S is limited to this preset minimum positive number to avoid S being 0 or negative. The value of the preset minimum positive number is obtained empirically, for example, 0.0001.

[0086] Similarly, this application can acquire multiple first liquid surface images within the partition to obtain multiple symmetry indices corresponding to the partition, and then determine the mean value of the symmetry indices based on these multiple symmetry indices.

[0087] Regarding smoothness, this application can calculate the smoothness index corresponding to each partition of the uniform inner diameter region based on the edge fitting error of the enema solution on the partition, and determine the mean smoothness index based on the smoothness index corresponding to the partition.

[0088] For example, the smoothness index corresponding to the partition satisfies the following formula: in, The smoothness index corresponding to the partition. The edge fitting error of the enema solution on the partition is denoted as .

[0089] Similarly, this application can acquire multiple first liquid surface images within the partition to obtain multiple smoothness indices corresponding to the partition, and then determine the average smoothness index based on the multiple smoothness indices.

[0090] Step 703: Evaluate the regional contamination level of the uniform inner diameter region based on the first image features corresponding to each partition.

[0091] In one possible implementation, this application determines the regional contamination level of a uniform inner diameter region based on the mean of the standard deviation of curvature, the mean of the symmetry index, and the mean of the smoothness index.

[0092] Among them, the mean value of the standard deviation of curvature is positively correlated with the regional contamination level of the uniform inner diameter region. The mean values ​​of the symmetry index and smoothness index are negatively correlated with the regional contamination level of the uniform inner diameter region, respectively.

[0093] For example, this application can first determine the partition contamination level of the corresponding partition by the mean of the standard deviation of curvature, the mean of the symmetry index, and the mean of the smoothness index corresponding to each partition, and then determine the regional contamination level of the uniform inner diameter region based on the partition contamination level of each partition.

[0094] In one example, the partition contamination level value of a partition satisfies the following formula: in, For partitioning The pollution level value of the zone, For partitioning The corresponding mean of the standard deviation of curvature For partitioning The corresponding mean of the symmetry index, For partitioning The corresponding mean smoothness index. In this embodiment... This represents a normalization function, such as normalization using maximum and minimum values.

[0095] The regional pollution level of a uniform inner diameter region satisfies the following formula: in, For a region with uniform inner diameter The regional pollution level value, For partitioning The pollution level value of the zone, For a region with uniform inner diameter The number of partitions.

[0096] Based on the above technical solution, this application avoids the one-sidedness of single-location analysis by performing partitioned analysis of the uniform inner diameter region. At the same time, by extracting first image feature data such as radius of curvature, offset distance, and edge fitting error, and by calculating first image features (mean value of standard deviation of curvature, mean value of symmetry index, and mean value of smoothness index) based on the first image feature data, each step of the pollution degree assessment is supported by clear data, reducing subjective errors. In addition, by quantifying the correlation between each feature and the pollution degree, it is ensured that the regional pollution degree value of the uniform inner diameter region can accurately reflect the pollution status of the uniform inner diameter region, providing accurate basic data for subsequent comprehensive pollution degree calculation and measurement reliability judgment.

[0097] As one possible embodiment of this application, combined with Figure 6 ,like Figure 8 As shown, step 602 above can be achieved through the following steps: Step 801: Based on the second liquid surface image, extract the second image feature data of the enema solution at multiple locations in the non-uniform inner diameter region.

[0098] The second image feature data includes the rate of change of the capillary viscometer diameter at the corresponding location and the rate of change of curvature of the enema solution surface at the corresponding location. The rate of change of diameter characterizes the change in the diameter of the enema solution at the corresponding location during its flow within the non-uniform inner diameter region of the capillary viscometer. The rate of change of curvature characterizes the change in the curvature of the liquid surface at the corresponding location during its flow within the non-uniform inner diameter region of the capillary viscometer.

[0099] In an ideally clean capillary tube, for regions with non-uniform inner diameters, changes in the curvature of the liquid surface are typically highly correlated with the rate of change in tube diameter; that is, the curvature adaptively responds to changes in geometric dimensions. If this region becomes contaminated, the contaminants will non-uniformly alter the local cross-sectional tension and contact angle, disrupting this inherent mechanical equilibrium and geometric correlation.

[0100] Therefore, this application can assess the degree of contamination in a non-uniform inner diameter region by quantifying the correlation between the rate of change of liquid surface curvature and the rate of change of pipe diameter. The weaker the correlation, the more severe the interference of non-geometric factors (contamination) on the cross-sectional properties.

[0101] For example, this application can select multiple sampling positions (e.g., 20 positions) along the axial direction for a non-uniform inner diameter region (such as the measuring ball region of a capillary), and extract second image feature data based on the second liquid surface image of each position.

[0102] Regarding the rate of change of pipe diameter, this application can determine the functional relationship between the actual physical width and the axial coordinate at each position, and then perform differentiation to obtain the rate of change of pipe diameter. For details, please refer to the above embodiments, which will not be elaborated here.

[0103] Similarly, for curvature change rate data, this application can perform edge extraction and circular fitting on the liquid surface image at each sampling location to obtain the curvature at that location, and calculate the rate of curvature change with axial position as the basic data of curvature change rate.

[0104] Step 802: Determine the second image features of the enema solution in the non-uniform inner diameter region based on the second image feature data at multiple locations.

[0105] In one possible implementation, this application can perform correlation analysis based on the rate of change of capillary viscometer diameter at multiple locations and the rate of change of curvature of the enema solution surface to determine the correlation coefficient between the rate of change of curvature and the rate of change of pipe diameter.

[0106] For example, this application can evaluate the correlation coefficient between the rate of curvature change and the rate of pipe diameter change through covariance. For instance, the correlation coefficient between the rate of curvature change and the rate of pipe diameter change satisfies the following formula: in, This is the correlation coefficient between the rate of change of curvature and the rate of change of pipe diameter. For the rate of change of pipe diameter, The rate of change of curvature, This is the function for calculating the Pearson correlation coefficient. The range of values ​​is , The closer the correlation is to 1, the stronger the correlation.

[0107] Step 803: Evaluate the degree of regional contamination in the non-uniform inner diameter region based on the second image features.

[0108] In one possible implementation, the regional contamination level of the non-uniform inner diameter region is determined based on the absolute value of the correlation coefficient between the rate of curvature change and the rate of pipe diameter change.

[0109] Among them, the absolute value of the correlation coefficient is negatively correlated with the regional pollution level of the uniform inner diameter area.

[0110] For example, the regional contamination level of a non-uniform inner diameter region satisfies the following formula: in, Non-uniform inner diameter region The regional pollution level value, This is the absolute value of the correlation coefficient between the rate of curvature change and the rate of pipe diameter change.

[0111] Based on the above technical solution, this application addresses the characteristics of pipe diameter changes in non-uniform inner diameter regions by quantifying the degree of contamination through the correlation coefficient between the pipe diameter change rate and the curvature change rate. Under clean conditions, the two changes are highly synergistic and correlated; after contamination, the synergy is disrupted and the correlation decreases. This assessment method is consistent with the physical characteristics of non-uniform regions, making the assessment method more targeted and further improving the accuracy of contamination assessment in non-uniform inner diameter regions.

[0112] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for detecting the rheology of a viscosity of an enema liquid, characterized by, The method comprises: obtaining a liquid surface image of the enema liquid flowing in the capillary viscometer by an image acquisition device; performing morphological feature analysis on the liquid surface image to obtain image features of the enema liquid, and evaluating a pollution degree value of the capillary viscometer based on the image features; the image features include at least one of curvature features, symmetry features and smoothness features; determining the viscosity measurement reliability of the capillary viscometer according to the pollution degree value.

2. The method of claim 1, wherein the viscosity of the enema solution is measured by rheology. The liquid surface image includes a first liquid surface image of the enema liquid in a uniform inner diameter region of the capillary viscometer and / or a second liquid surface image of the enema liquid in a non-uniform inner diameter region of the capillary viscometer.

3. The method of claim 2, wherein the viscosity of the enema solution is measured by rheology. The morphological feature analysis on the liquid surface image to obtain the image features of the enema liquid, and the evaluation of the pollution degree value of the capillary viscometer based on the image features, comprises: performing morphological feature analysis on the first liquid surface image to obtain first image features of the enema liquid in the uniform inner diameter region, and evaluating a regional pollution degree value of the uniform inner diameter region based on the first image features; the first image features include curvature standard deviation size mean value, symmetry index mean value and smoothness index mean value; performing morphological feature analysis on the second liquid surface image to obtain second image features of the enema liquid in the non-uniform inner diameter region, and evaluating a regional pollution degree value of the non-uniform inner diameter region based on the second image features; the second image features include a correlation coefficient of curvature change rate and pipe diameter change rate; determining the pollution degree value of the capillary viscometer based on the regional pollution degree value of the uniform inner diameter region and the regional pollution degree value of the non-uniform inner diameter region.

4. The method of claim 3, wherein the viscosity of the enema solution is measured by rheology. The morphological feature analysis on the first liquid surface image to obtain the first image features of the enema liquid in the uniform inner diameter region, and the evaluation of the regional pollution degree value of the uniform inner diameter region based on the first image features, comprises: based on the first liquid surface image, dividing the uniform inner diameter region into multiple sub-regions, and extracting first image feature data of the enema liquid on each sub-region; the first image feature data includes the curvature radius, offset distance and edge fitting error of the liquid surface of the enema liquid on the sub-region; the curvature radius is the radius of the fitting circle obtained by fitting the liquid surface of the enema liquid on the sub-region; the offset distance is the distance between the center of the fitting circle and the center of the uniform inner diameter region; the edge fitting error is used to represent the deviation between the points on the edge of the liquid surface of the enema liquid on the sub-region and the corresponding points on the edge of the fitting circle; determining the first image features corresponding to each sub-region of the uniform inner diameter region according to the first image feature data of each sub-region; evaluating the regional pollution degree value of the uniform inner diameter region based on the first image features corresponding to each sub-region.

5. The method of claim 4, wherein the viscosity of the enema solution is measured by rheology. The determination of the first image features corresponding to each sub-region of the uniform inner diameter region according to the first image feature data of each sub-region, comprises: For each sub-area of the uniform inner diameter region, a curvature is determined according to a curvature radius of the liquid surface of the enema liquid on the sub-area, and a curvature standard deviation size mean value is determined according to the curvature corresponding to the sub-area; For each sub-area of the uniform inner diameter region, a symmetry index corresponding to the sub-area is calculated according to a displacement distance of the liquid surface of the enema liquid on the sub-area, and a symmetry index mean value is determined according to the symmetry index corresponding to the sub-area; For each sub-area of the uniform inner diameter region, a smoothness index corresponding to the sub-area is calculated according to an edge fitting error of the liquid surface of the enema liquid on the sub-area, and a smoothness index mean value is determined according to the smoothness index corresponding to the sub-area.

6. The method of claim 4, wherein the viscosity of the enema solution is measured by rheology. The evaluation of the regional pollution degree value of the uniform inner diameter region based on the first image feature corresponding to each sub-area includes: The regional pollution degree value of the uniform inner diameter region is determined according to the curvature standard deviation size mean value, the symmetry index mean value, and the smoothness index mean value; the curvature standard deviation size mean value is positively correlated with the regional pollution degree value of the uniform inner diameter region; the symmetry index mean value and the smoothness index mean value are negatively correlated with the regional pollution degree value of the uniform inner diameter region, respectively.

7. The method of claim 3, wherein the viscosity of the enema solution is measured by rheology. The morphological feature analysis of the second liquid surface image to obtain the second image feature of the enema liquid in the non-uniform inner diameter region, and the evaluation of the regional pollution degree value of the non-uniform inner diameter region based on the second image feature includes: Based on the second liquid surface image, second image feature data of the enema liquid in multiple positions of the non-uniform inner diameter region is extracted; the second image feature data includes a tube diameter change rate of the capillary viscometer at the corresponding position and a curvature change rate of the liquid surface of the enema liquid at the corresponding position; The second image feature of the enema liquid in the non-uniform inner diameter region is determined according to the second image feature data of the multiple positions; The regional pollution degree value of the non-uniform inner diameter region is evaluated based on the second image feature.

8. The method of claim 7, wherein the viscosity of the enema solution is measured by rheology. The determination of the second image feature of the enema liquid in the non-uniform inner diameter region according to the second image feature data of the multiple positions includes: Correlation analysis is performed according to the tube diameter change rate of the capillary viscometer and the curvature change rate of the liquid surface of the enema liquid at the multiple positions to determine a correlation coefficient of the curvature change rate and the tube diameter change rate.

9. The method of claim 7, wherein the viscosity of the enema solution is measured by rheology. The evaluation of the regional pollution degree value of the non-uniform inner diameter region based on the second image feature includes: The regional pollution degree value of the non-uniform inner diameter region is determined according to an absolute value of the correlation coefficient of the curvature change rate and the tube diameter change rate; the absolute value of the correlation coefficient is negatively correlated with the regional pollution degree value of the uniform inner diameter region.

10. A system for rheological detection of the viscosity of an enema liquid, characterized in that The rheological detection system includes a capillary viscometer, an image acquisition device, and an electronic device; The capillary viscometer is used to measure the viscosity of the enema liquid; The image acquisition device is used to acquire liquid surface images during the flow of the enema liquid in the capillary viscometer; The electronic device is configured to acquire a liquid surface image of the enema liquid flowing in the capillary viscometer through the image acquisition device; perform morphological feature analysis on the liquid surface image to obtain an image feature of the enema liquid, and evaluate a pollution degree value of the capillary viscometer based on the image feature; the image feature includes at least one of a curvature feature, a symmetry feature and a smoothness feature; and determine the viscosity measurement reliability of the capillary viscometer according to the pollution degree value.

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