Visual inspection-based back-suction valve operation test method and apparatus

Abstract

The application relates to the technical field of data analysis, in particular to a back-suction valve operation test method and device based on visual detection. The application is used for judging the adaptability of a valve in a use stage by comprehensively analyzing the influence of valve abnormalities on fluid flow and the damage of fluid flow to the valve, replacing manual experience judgment by machine vision and multi-sensor automatic detection, and realizing rapid, accurate and standardized back-suction valve operation state evaluation.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to a visual inspection-based method and equipment for testing the operation of a backflow valve. Background Technology

[0002] The back suction valve (also known as a suction valve or shut-off valve) is a key component in the processing of polymer materials such as injection molding, extrusion, and coating. It is installed at the front end of the nozzle of the screw or plunger. Its core function is to create a negative pressure in the flow channel instantaneously after the injection or extrusion action is completed through the rapid reverse movement of the valve core (back suction), thereby precisely cutting off the melt or fluid and preventing product defects (such as drooling, stringing, and dripping) caused by material sagging or dripping. It is crucial for ensuring product molding quality, saving raw materials, and maintaining a clean working environment. Currently, the assessment and maintenance of the back suction valve's working status mainly relies on traditional manual experience-based judgment methods, specifically: Operators or maintenance personnel typically make qualitative judgments after the equipment is shut down by listening (judging whether the sound of the back suction action is crisp), looking (observing whether there is continuous leakage or material buildup at the nozzle), and touching (sensing abnormal nozzle temperature or vibration). This method relies heavily on personal experience, lacks standardized and quantifiable criteria, and may lead to discrepancies in judgments among different personnel, resulting in misjudgments or omissions. Furthermore, this is a reactive approach and cannot provide early warnings before a malfunction occurs.

[0003] During the operation of production equipment, key performance parameters such as the movement state of the valve core inside the back suction valve, the fit of the sealing surface, and the timing and force of the back suction action are in a closed, high-temperature, and high-pressure black box environment, making it difficult to achieve real-time, online direct measurement using traditional methods. Equipment control systems can typically only provide valve core drive signals (such as pneumatic or hydraulic on / off signals), but cannot verify whether the valve core accurately and completely executes the command, let alone assess the degradation of its dynamic performance.

[0004] Existing maintenance strategies are primarily reactive repairs or preventative maintenance based on fixed cycles. The former leads to unplanned downtime and production losses, while the latter can result in over- or under-maintenance because it is not based on the actual health status of the backflow valve. The entire industry generally lacks predictive maintenance capabilities based on real-time performance data, making it impossible to identify signs of performance degradation and intervene early. Summary of the Invention

[0005] To overcome the defects and shortcomings of existing technologies, the technical solution of this application judges the suitability of valves for use during the service stage by comprehensively analyzing the impact of valve anomalies on fluid flow and the damage caused by fluid flow to valves. By using machine vision and multi-sensor automated detection, it replaces manual experience judgment and achieves rapid, accurate and standardized evaluation of the operating status of back suction valves.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a visual inspection-based method for testing the operation of a backflow valve, comprising the following specific steps: Step 1: Use the machine vision module to acquire images of the back suction valve during operation, the basic conditions of the fluid, and the parameters during the flow process. Step 2: Based on the images and parameters during the operation of the back suction valve, perform anomaly analysis of the back suction valve. Combine the results of the back suction valve anomaly analysis with the fluid demand to analyze the impact of valve anomalies on fluid flow. Step 3: Analyze the damage to the valve caused by fluid flow based on the basic fluid conditions and the results of the abnormal operation analysis of the back suction valve; Step 4: Based on the analysis results of the impact of valve abnormalities on fluid flow and the damage caused by fluid flow to the valve, determine the suitability of the valve for use during the service phase. Step 5: Output the valve test results based on the valve's adaptability assessment results during the usage phase.

[0007] In one implementation of this application, acquiring the image data, basic fluid conditions, and flow parameters during the operation of the back suction valve includes the following specific steps: The first step is to use a high-speed industrial camera and microscope to capture a continuous sequence of images of the critical areas of the back suction valve during the opening and closing process under backlight or a specific wavelength light source. Simultaneously, the pressure sensor and flow meter data at the valve inlet and outlet, as well as the position signal of the valve stem displacement sensor, are collected to ensure that all data timestamps are synchronized. The second step is to collect basic physical property parameters of the test fluid, such as type (e.g., water, oil, specific chemical medium), temperature, viscosity, and density, through the fluid property recording table. The third step is to collect flow fluctuation curves, pressure pulsation frequency and amplitude, and bubble or particulate matter content in the fluid in real time during the flow process; and store the collected data in the corresponding storage modules.

[0008] In one implementation of this application, the abnormal operation analysis of the back suction valve includes the following specific steps: The first step is to acquire the actual displacement-time curve and velocity of the valve core through image sequences; to acquire images of the sealing surface at the instant of closure and the bubble situation in the fluid; and to acquire the changes in fluid flow rate. The second step is to obtain the displacement anomaly value by dividing the average distance of the actual displacement relative to the displacement corresponding to the command by the safety distance, and to obtain the motion speed anomaly value by obtaining the standard deviation of the motion speed relative to the safety range. The motion anomaly is obtained by weighted summing the displacement anomaly value and the motion speed anomaly value. The third step is to obtain an image of the sealing surface at the moment of closure and perform sealing analysis on the sealing surface. Divide the volume of the deformation position of the sealing surface by the overall volume of the seal to obtain the deformation anomaly. The movement anomaly and the deformation anomaly are weighted and summed to obtain the back suction valve operation anomaly.

[0009] In one implementation of this application, the impact of the valve malfunction on fluid flow includes the following specific aspects: The first step is to obtain the flow fluctuation during the valve holding process, and to obtain the standard deviation of the fluid flow process as the flow fluctuation anomaly. The fluid crosstalk anomaly is obtained by multiplying the back suction valve operation anomaly by the operation anomaly influence coefficient. The fluid crosstalk anomaly is used to quantify the risk of particulate matter generation or external contaminant intrusion caused by seal failure or material wear. It is obtained by quantifying the content of particulate matter generation or external contaminants in the fluid. Preferably, the fluid crosstalk anomaly is obtained by dividing the rate of change of particulate matter generation or external contaminant content per unit time by the safe rate. The operation anomaly influence coefficient is obtained by averaging historical data. The second step is to obtain the abnormal impact of valve anomalies on fluid flow by weighted summation of the obtained abnormal flow fluctuations and abnormal fluid crosstalk.

[0010] In one implementation of this application, the damage analysis of the valve caused by the fluid flow includes the following specific aspects: The first step is to qualitatively assess the erosion risk of the valve core and sealing surface by combining the particulate matter content, hardness, and fluid velocity in the fluid. The specific content of erosion risk is as follows: obtain the particulate matter content and hardness of the fluid, and the fluid velocity; obtain the ratio of the particulate matter content and hardness to the corresponding safety value to obtain the anomaly of the corresponding parameter; set the square of the ratio of the fluid velocity to the safety velocity as the velocity anomaly; and multiply the weighted sum of the anomalies of the corresponding parameters by the velocity anomaly to obtain the erosion anomaly. The second step is to obtain the gas content, pressure pulsation amplitude and frequency, and fluid temperature of the fluid to assess cavitation anomalies. The specific content of cavitation anomaly is as follows: obtain the amplitude anomaly by dividing the average pressure pulsation amplitude of the fluid by the safe amplitude; obtain the frequency anomaly by dividing the average pressure pulsation frequency by the safe frequency; obtain the pulsation anomaly by multiplying the amplitude anomaly by the frequency anomaly; obtain the gas content anomaly by dividing the gas content by the safe content; obtain the temperature anomaly by dividing the fluid temperature by the safe temperature; and obtain the cavitation anomaly by weighted summing of the pulsation anomaly, gas content anomaly, and temperature anomaly. The third step involves analyzing the damage to the valve caused by fluid flow by combining the erosion risk, cavitation anomaly, and deformation anomaly of the valve core and sealing surface. This includes the following specific steps: obtaining the erosion risk and cavitation anomaly of the fluid on the valve core and sealing surface, and weighted summing them to obtain the corrosion impact anomaly; multiplying the deformation anomaly by the protection impact coefficient to obtain the deformation impact anomaly; and multiplying the corrosion impact anomaly by the deformation impact anomaly to obtain the damage analysis results of fluid flow on the valve. The protection impact coefficient is obtained by averaging the reduction in protection performance caused by historical deformation anomalies, and is used to characterize the reduction in protection performance caused by deformation anomalies.

[0011] In one implementation of this application, the valve compatibility determination during the usage phase includes the following specific steps: Obtain the damage analysis results of fluid flow on the valve and the abnormal impact of valve abnormality on fluid flow. Sum the damage analysis results of fluid flow on the valve with the value 1, and then multiply it with the abnormal impact of valve abnormality on fluid flow to obtain the valve use adaptation abnormality.

[0012] In one implementation of this application, the output of the valve test results includes the following specific steps: The system compares the detected valve mismatch with the set mismatch threshold. If the valve mismatch is greater than or equal to the set threshold, it indicates that the back suction valve is not compatible with the fluid and needs to be replaced, and a replacement reminder is issued. If the valve mismatch is less than the set threshold, it indicates that the back suction valve is compatible with the fluid and does not need to be replaced, and a matching reminder is issued.

[0013] Secondly, this application also provides a visual inspection-based test device for the operation of a backflow valve, including the following specific modules: Data acquisition module, data analysis module, and replacement early warning module; The data acquisition module acquires images, basic fluid conditions, and flow parameters during the operation of the back suction valve through the machine vision module. The data analysis module performs anomaly analysis on the back suction valve based on the image data and flow parameters during its operation. It then analyzes the impact of valve anomalies on fluid flow by combining the results of the back suction valve anomaly analysis with fluid demand. Next, it performs damage analysis on the valve caused by fluid flow based on the basic fluid conditions and the results of the back suction valve anomaly analysis. Finally, it assesses the valve's suitability for use during the operational phase based on the results of the analysis of the impact of valve anomalies on fluid flow and the damage caused by fluid flow to the valve. The replacement early warning module outputs valve test results based on the valve's compatibility judgment during the usage phase.

[0014] Thirdly, this application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a visual inspection-based backflow valve operation test method and device by calling the computer program stored in the memory.

[0015] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a visual inspection-based method and apparatus for testing the operation of a backflow valve.

[0016] Compared with the prior art, this application has the following advantages: By comprehensively analyzing the impact of valve anomalies on fluid flow and the damage caused by fluid flow to the valve, the suitability of the valve for use during the operational phase is determined. Machine vision and multi-sensor automated detection replace manual experience judgment, enabling rapid, accurate, and standardized assessment of the operating status of back suction valves. Attached Figure Description

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure of the method in this application; Figure 2 This is a schematic diagram of the damage analysis process of the valve caused by fluid flow in the method of this application; Figure 3 This is a system schematic diagram of the device in this application. Detailed Implementation

[0018] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0019] Example Please see Figures 1 to 2 , Figure 1 This is a schematic diagram of the overall process of the visual inspection-based backflow valve operation test method provided in the embodiments of this application, which specifically includes the following steps: Step 1: Use the machine vision module to acquire images of the back suction valve during operation, the basic conditions of the fluid, and the parameters during the flow process. In this embodiment, acquiring the image data, basic fluid conditions, and flow parameters during the operation of the back suction valve includes the following specific steps: The first step involves using a high-speed industrial camera and microscope to capture a continuous sequence of images of key areas of the backflow valve (valve core, sealing surface, flow channel outlet) during the opening and closing process under backlight or a specific wavelength light source. Simultaneously, pressure sensor and flow meter data at the valve inlet and outlet, as well as position signals from the valve stem displacement sensor, are acquired, ensuring that all data timestamps are synchronized. This step achieves multi-dimensional, high-precision, and spatiotemporally synchronized objective data acquisition of the backflow valve's operating status. The combination of a high-speed industrial camera and microscope can capture millisecond-level high-speed movement details of the valve core and the microscopic sealing surface state, overcoming the limitations and subjectivity of human observation. The simultaneous acquisition of physical signals such as pressure, flow, and displacement, and their time alignment with the image data, provides a data foundation for subsequently constructing a precise causal relationship model of action-state-result. This is a prerequisite for achieving quantitative, online monitoring. The second step involves collecting basic physical property parameters of the test fluid, such as type (e.g., water, oil, specific chemical media), temperature, viscosity, and density, through a fluid property recorder. This step establishes a fluid benchmark for evaluation, ensuring the relevance and accuracy of the analysis results. Different fluids (e.g., high-viscosity melts and low-viscosity solvents) have vastly different effects on valve wear mechanisms, sealing requirements, and cavitation risks. The third step involves real-time acquisition of flow rate fluctuation curves, pressure pulsation frequency and amplitude, and the content of bubbles or particles in the fluid during the flow process. The acquired data of each type is stored in its corresponding storage module. This step achieves the capture of dynamic process characteristics and structured data storage. Flow rate and pressure fluctuation curves, as well as fluid purity (bubbles, particles), are important indicators reflecting the internal flow stability, sealing immediacy, and component health of valves. Real-time acquisition of these dynamic parameters can detect transient anomalies and trend degradation. Step 2: Based on the images and parameters during the operation of the back suction valve, perform anomaly analysis of the back suction valve. Combine the results of the back suction valve anomaly analysis with the fluid demand to analyze the impact of valve anomalies on fluid flow. In this embodiment, the abnormal operation analysis of the back suction valve includes the following specific steps: The first step involves acquiring the actual displacement-time curve and velocity of the valve core through image sequences; obtaining images of the sealing surface at the instant of closure and the presence of air bubbles in the fluid; and simultaneously acquiring changes in fluid flow rate. This step transforms the raw images and flow data into primary features that can be directly used for performance evaluation. Extracting the valve core's kinematic curves (displacement and velocity) from the image sequences is crucial for evaluating the accuracy, speed, and stability of its actions, directly corresponding to the efficiency and mechanical wear of the drive system. Analyzing the images of the sealing instant and the air bubbles in the fluid provides a direct means of diagnosing the tightness of the seal. Combining this with changes in flow rate allows for cross-verification of the effectiveness of the actions (e.g., whether the flow rate immediately returns to zero after backflow). The second step involves dividing the average distance of the actual displacement relative to the commanded displacement by the safety distance to obtain the displacement anomaly value, and obtaining the standard deviation of the movement speed relative to the safety range to obtain the movement speed anomaly value. The movement anomaly is obtained by weighted summation of the displacement anomaly value and the movement speed anomaly value. This step quantifies and scores the valve core's motion performance. By calculating the displacement anomaly value (reflecting positioning accuracy) and the movement speed anomaly value (reflecting action response and smoothness), the continuous motion curve is transformed into two key performance scalars. The weighted summation of the movement anomaly value enables a single-index evaluation of complex motion states, facilitating threshold setting and trend comparison. The parameter values ​​and their advantages are as follows: the safety distance is typically taken as a percentage of the valve core's design stroke (e.g., ±2%), or calculated based on historical normal data; the safety range is the allowable fluctuation band of the standard speed. This method transforms engineering tolerances into calculable indicators, making the judgment criteria objective and consistent. The third step involves acquiring images of the sealing surface at the moment of closure for sealing surface analysis. The volume of the deformation location on the sealing surface is divided by the overall sealing volume to obtain the deformation anomaly. The movement anomaly and the deformation anomaly are weighted and summed to obtain the backflow valve operation anomaly. This quantifies the health status of the sealing surface. Deformation anomalies (such as the sealing ring extrusion deformation rate and wear indentation volume ratio) are calculated through image analysis, directly assessing the physical state of the sealing material and the fit quality of the sealing pair. The weighted summation of movement anomalies and deformation anomalies into the backflow valve operation anomaly provides a comprehensive health index reflecting the valve's mechanical action and sealing performance. Parameter selection method and benefits: The weighting coefficients can be determined by expert experience or regression analysis of historical fault data based on the importance of movement and deformation to the overall valve function. This allows the evaluation model to flexibly adapt to different types of valves (such as anti-drip valves that prioritize speed and high-pressure valves that prioritize sealing), where the volume of the deformation location represents the volume of material lost due to wear (indentation volume). The effects of valve malfunctions on fluid flow include the following specific aspects: The first step involves acquiring the flow fluctuation during valve holding. The standard deviation of the fluid flow is defined as the flow fluctuation anomaly. The fluid crosstalk anomaly is obtained by multiplying the backflow valve's operational anomaly by the operational anomaly influence coefficient. This fluid crosstalk anomaly quantifies the risk of particulate matter generation or external contaminant intrusion due to seal failure or material wear. It is obtained by quantifying the content of particulate matter generation or external contaminants in the fluid, preferably by dividing the rate of change of particulate matter generation or external contaminant content per unit time by a safe rate. The operational anomaly influence coefficient is obtained by averaging historical data. This assesses the direct contamination risk of valve anomalies to the process fluid. Flow fluctuation anomalies characterize the decrease in flow control accuracy caused by valve misclosure or vibration. Fluid crosstalk anomalies specifically quantify the risk of internal or external leakage due to seal failure, and the resulting particulate matter (such as wear debris) contamination. The operational anomaly influence coefficient establishes a correlation model between valve anomalies and fluid contamination consequences. Parameter selection method and benefits: The operational anomaly influence coefficient is obtained by averaging the correlation analysis (such as linear regression) between historical operational anomaly values ​​and the observed rate of change of particulate matter content. This upgrades the impact assessment from a qualitative correlation to a semi-quantitative prediction, making it more persuasive. The second step involves weighted summation of the obtained flow fluctuation anomalies and fluid crosstalk anomalies to determine the impact of valve anomalies on fluid flow. This comprehensive summation considers both the impact of valve anomalies on fluid control accuracy and purity, outputting a total flow impact anomaly value. This indicator is directly related to the quality of the final product (dimensional fluctuations, impurity content). Weighted summation provides a unified measurement scale, facilitating horizontal comparisons after adjusting the focus in different application scenarios (high accuracy requirements or high cleanliness requirements). Step 3: Analyze the damage to the valve caused by fluid flow based on the basic fluid conditions and the results of the abnormal operation analysis of the back suction valve; In this embodiment, the damage analysis of the valve caused by fluid flow includes the following specific details: The first step is to qualitatively assess the erosion risk of the valve core and sealing surface by combining the particulate matter content, hardness, and fluid velocity in the fluid. The specific content of erosion risk assessment includes: obtaining the particulate matter content, hardness, and fluid velocity in the fluid; obtaining the ratios of particulate matter content and hardness to their corresponding safety values ​​to identify anomalies in these parameters; setting the square of the ratio of fluid velocity to the safe velocity as the velocity anomaly; and multiplying the weighted sum of the corresponding parameter anomalies by the velocity anomaly to obtain the erosion anomaly. This step assesses the wear (erosion) potential of the valve material from a fluid perspective. By quantifying the impact of particulate matter (content, hardness) and fluid kinetic energy (velocity square relationship), erosion risk is transformed from empirical judgment into a calculable erosion anomaly index. Parameter selection methods and benefits: Safety values ​​(such as safe content, safe hardness, and safe velocity) are typically determined based on tolerance manual data or accelerated life test data for valve materials (such as hardened steel and ceramics). The ratio of actual parameters to safety values ​​is considered anomaly, allowing parameters of different dimensions (content, hardness, velocity) to be normalized and calculated together. This method is scientific and practical. The safety distance is determined as follows: Within multiple historical periods when the valve condition is considered normal, the deviation between the actual displacement and the commanded displacement of the valve core is collected. The average value μ and standard deviation σ of all deviation values ​​are calculated. The safety distance is set as μ + nσ (or μ - nσ, determined according to the direction of deviation), where n is a coefficient set based on engineering experience, usually 2 or 3. For safety values ​​based on manuals or tests: the safety velocity (for erosion) refers to the velocity at which the erosion wear rate of the valve's critical materials (such as the hardened layer on the valve core surface) is below a certain acceptable threshold (e.g., 0.01 mm per year). This velocity value is obtained by consulting the erosion rate-flow velocity relationship chart of the material in a fluid environment, or by extrapolating accelerated life test data conducted at this flow rate. The second step is to obtain the gas content, pressure pulsation amplitude and frequency, and fluid temperature of the fluid to assess cavitation anomalies. The specific content of cavitation anomaly is as follows: The amplitude anomaly is obtained by dividing the average pressure pulsation amplitude of the fluid by the safe amplitude; the frequency anomaly is obtained by dividing the average pressure pulsation frequency by the safe frequency; the pulsation anomaly is obtained by multiplying the amplitude anomaly by the frequency anomaly; the gas content anomaly is obtained by dividing the gas content by the safe content; the temperature anomaly is obtained by dividing the fluid temperature by the safe temperature; and the cavitation anomaly is obtained by weighted summation of the pulsation anomaly, gas content anomaly, and temperature anomaly. This step assesses another common fluid damage mechanism—cavitation. Cavitation is caused by localized low pressure leading to the formation and collapse of bubbles, and its destructive power is enormous. This method integrates three key factors inducing cavitation: gas content (gas nuclei), pressure pulsation (generating low-pressure areas), and temperature (affecting saturated vapor pressure). By calculating the pulsation anomaly, gas content anomaly, and temperature anomaly, and weighting and combining them into the cavitation anomaly, a quantitative assessment of cavitation risk is achieved. Parameter selection method and benefits: Thresholds such as safe amplitude and safe frequency can be determined through simulation or experiment based on fluid medium characteristics (vaporization pressure) and valve flow channel design. The combination of multiplication or weighted summation reflects the possible synergistic aggravation effect among various factors (such as high-frequency pulsation being more likely to induce cavitation in gas-bearing fluids). The third step involves analyzing the damage to the valve caused by fluid flow, considering the erosion risk, cavitation anomalies, and deformation anomalies affecting the valve core and sealing surface. This includes the following: obtaining the erosion risk and cavitation anomalies of the fluid affecting the valve core and sealing surface through weighted summation to obtain the corrosion impact anomaly; multiplying the deformation anomaly by the protection impact coefficient to obtain the deformation impact anomaly; and multiplying the corrosion impact anomaly and deformation impact anomaly to obtain the damage analysis results of fluid flow affecting the valve. The protection impact coefficient is obtained by averaging the historical reduction in protection performance caused by deformation anomalies, used to characterize the reduction in protection performance caused by deformation anomalies. The combined impact of external fluid erosion (erosion, cavitation) and valve structural degradation (deformation leading to decreased protection performance) on valve lifespan is comprehensively considered. Multiplying the corrosion impact anomaly and deformation impact anomaly is crucial, based on the engineering principle of the weakest link effect or risk coupling—when the valve itself has already suffered deformation damage, its resistance to fluid corrosion decreases sharply, and the total risk increases non-linearly. Parameter selection method and benefits: The protection influence coefficient is obtained by averaging the relationship between anomaly values ​​in historical data and the actual observed decline in corrosion resistance (such as changes in wear rate under the same erosion conditions). This data-driven coefficient calibration makes the model more reflective of the true degradation law of a specific valve under actual working conditions. The protection influence coefficient can be the difference in material wear rate or the increase in sealing leakage rate between valves with and without deformation anomalies under the same erosion test conditions. Step 4: Based on the analysis results of the impact of valve abnormalities on fluid flow and the damage caused by fluid flow to the valve, determine the suitability of the valve for use during the service phase. In this embodiment, the valve compatibility determination during the usage phase includes the following specific steps: The system obtains the damage analysis results of fluid flow on the valve and the abnormal impact of valve anomalies on fluid flow. The sum of the damage analysis results and a numerical value of 1 is multiplied by the abnormal impact of valve anomalies on fluid flow to obtain the valve's compatibility anomaly. A formula is used to comprehensively evaluate the negative impact of the valve on the fluid and the risk of damage to the valve, thereby determining whether the current matching is sustainable and economical. The formula is: (Damage Result + 1) * Flow Influence Anomaly: Damage Result + 1: Ensures this term is a multiplier of at least 1. When the damage risk is very low (close to 0), the multiplier is slightly greater than 1, and the overall result mainly depends on the flow influence anomaly, i.e., the focus is on whether the valve meets the process quality requirements. The multiplicative relationship reflects the amplification effect of two-way mismatch. Even if the valve's impact on flow is slight (small flow influence anomaly), if the fluid causes significant damage to the valve (large damage result), replacement or adjustment is still the economical choice in the long run (because the valve life is extremely short, and maintenance costs surge). The reverse is also true. This avoids the limitations of one-way thinking and supports decision-making based on the entire life cycle cost. Step 5: Output the valve test results based on the valve's adaptability assessment results during the usage phase; In this embodiment, the output of the valve test results includes the following specific steps: The process compares the acquired valve mismatch information with the set mismatch threshold. If the mismatch is greater than or equal to the threshold, it indicates that the backflow valve is not compatible with the fluid and needs to be replaced, triggering a replacement alert. If the mismatch is less than the threshold, it indicates that the backflow valve is compatible with the fluid and does not need to be replaced, triggering a matching alert. This step transforms continuous quantitative analysis results into clear, actionable binary decisions (replace / keep) and triggers corresponding alerts. The initial threshold can be determined by statistical analysis (such as ROC curve analysis) of mismatch anomalies from historical successful and failed cases. In this embodiment, the preferred method for determining the weights and thresholds is as follows: obtain the image data, basic fluid conditions, and flow parameters during the historical back suction valve operation process, and simultaneously obtain the judgment result of whether the historical back suction valve is operating normally in the next cycle. Normal operation means that the valve core accurately executes the command with a deviation rate of less than 3%. The parameters are imported into this embodiment to calculate the adaptation anomaly, and the adaptation anomaly calculation results and judgment results are imported into MATLAB fitting software for linear fitting to output the value that meets the maximum judgment accuracy.

[0020] In this embodiment, it should be noted that averaging refers to performing linear regression analysis on these paired data (Xi, Yi), and using the slope of the regression line as the operational anomaly impact coefficient. For example, the operational anomaly impact coefficient is obtained through the following steps: collecting multiple sets of historical test data, each set including an operational anomaly value Xi of the back suction valve and the corresponding measured unit-time particulate matter content change rate value Yi; performing univariate linear regression analysis on all data pairs (Xi, Yi), and using the slope k in the resulting regression equation Y=k*X+b as the operational anomaly impact coefficient; The advantages of this embodiment are: by comprehensively analyzing the impact of valve anomalies on fluid flow and the damage caused by fluid flow to the valve, the suitability of the valve for use during the service phase is determined; by using machine vision and multi-sensor automated detection, manual experience judgment is replaced, and a fast, accurate and standardized assessment of the backflow valve's operating status is achieved.

[0021] like Figure 3 As shown in the embodiments of this application, a vision-based back-suction valve operation testing device is also provided. The device adopts a compact, integrated design. The body is made of corrosion-resistant, high-cleanliness 304 stainless steel with a baked enamel finish. This design is suitable for the clean environment of semiconductor laboratories and can resist the potential corrosion of high-purity chemicals. The overall shape is simple and neat, facilitating placement and maintenance. The device is divided into three main functional areas from top to bottom: the operation and control area, the test chamber area, and the pipeline and power area. Each area is rationally laid out and clearly divided, ensuring both ease of operation and avoiding mutual interference between different functional modules, ensuring a stable and orderly testing process. The operation and control area is located at the top of the device and integrates a 7-inch high-definition touch screen, an emergency stop button, a power switch, and function buttons, serving as the core of the device's control. The test chamber area is located in the middle of the device and adopts a fully enclosed transparent structure, allowing for direct observation of the valve's operating status and fluid flow during the test. The chamber contains a built-in constant temperature control component and a universal clamping fixture, adaptable to different specifications of PFA electric / pneumatic back-suction valves. The pipeline and power area is located at the bottom of the device and includes: Data acquisition module, data analysis module, and module replacement early warning module; The data acquisition module uses machine vision to acquire images of the back suction valve during operation, the basic conditions of the fluid, and the parameters during the flow process. The data analysis module analyzes abnormal operation of the back suction valve based on images and parameters during its operation. It then combines these abnormal operation analysis results with fluid demand to analyze the impact of valve abnormalities on fluid flow. Next, it analyzes the damage to the valve caused by fluid flow based on basic fluid conditions and the results of the back suction valve abnormality analysis. Finally, it assesses the valve's suitability for use during the operational phase based on the analysis results of the valve's impact on fluid flow and the damage caused by fluid flow. Replace the early warning module and output valve test results based on the valve's compatibility assessment during the usage phase. The system includes visual inspection and acquisition: it can capture real-time details of the electric / pneumatic backflow valve's operation and fluid status, intelligently identify anomalies such as bubbles and leaks, and synchronously transmit visual signals to assist in accurately judging test conditions, improving testing convenience and accuracy; it is also placed on a vibration-isolated platform for enhanced stability. The system boasts a shooting rate ≥300fps, shooting resolution ≥1280*720p, shooting angle ≥40°, and exposure time ≤27μs; image visual processing: it automatically processes the captured photos and outputs saved photos and flow rate values. The camera height can be adjusted to capture the entire glass tube.

[0022] The parameters and steps for each unit module to achieve the corresponding functions in the visual inspection-based backflow valve operation test method and equipment described above can be referred to the parameters and steps in the embodiments of the visual inspection-based backflow valve operation test method and equipment described above, and will not be repeated here.

[0023] Embodiments of this application also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a backflow valve operation test method based on vision detection, which can be loaded and executed by the processor as provided in the above embodiments.

[0024] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the vision-based suction valve operation testing method provided in the above embodiments, etc. The data storage area may store data involved in the vision-based suction valve operation testing method provided in the above embodiments, etc.

[0025] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit this.

[0026] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0027] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for a visual inspection-based backflow valve operation test method.

[0028] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0029] The term includes, or any other variation thereof, is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0030] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A visual inspection-based method for testing the operation of a backflow valve, characterized in that, Includes the following specific modules: Step 1: Use the machine vision module to acquire images of the back suction valve during operation, the basic conditions of the fluid, and the parameters during the flow process. Step 2: Based on the images and parameters during the operation of the back suction valve, perform anomaly analysis of the back suction valve. Combine the results of the back suction valve anomaly analysis with the fluid demand to analyze the impact of valve anomalies on fluid flow. Step 3: Analyze the damage to the valve caused by fluid flow based on the basic fluid conditions and the results of the abnormal operation analysis of the back suction valve; Step 4: Based on the analysis results of the impact of valve abnormalities on fluid flow and the damage caused by fluid flow to the valve, determine the suitability of the valve for use during the service phase. Step 5: Output the valve test results based on the valve's adaptability assessment results during the usage phase.

2. The visual inspection-based backflow valve operation test method according to claim 1, characterized in that, The process of acquiring images, basic fluid conditions, and flow parameters during the operation of the back suction valve includes the following specific steps: The first step is to use a high-speed industrial camera and microscope to capture a continuous sequence of images of the critical areas of the back suction valve during the opening and closing process under backlight or a specific wavelength light source. Simultaneously, the pressure sensor and flow meter data at the valve inlet and outlet, as well as the position signal of the valve stem displacement sensor, are collected to ensure that all data timestamps are synchronized. The second step is to collect the basic physical property parameters of the test fluid using the fluid property recording table. The third step is to collect flow fluctuation curves, pressure pulsation frequency and amplitude, and bubble or particulate matter content in the fluid in real time during the flow process; and store the collected data in the corresponding storage modules.

3. The visual inspection-based backflow valve operation test method according to claim 1, characterized in that, The abnormal operation analysis of the back suction valve includes the following specific steps: The first step is to acquire the actual displacement-time curve and velocity of the valve core through image sequences; to acquire images of the sealing surface at the instant of closure and the bubble situation in the fluid; and to acquire the changes in fluid flow rate. The second step is to obtain the displacement anomaly value by dividing the average distance of the actual displacement relative to the displacement corresponding to the command by the safety distance, and to obtain the motion speed anomaly value by obtaining the standard deviation of the motion speed relative to the safety range. The motion anomaly is obtained by weighted summing the displacement anomaly value and the motion speed anomaly value. The third step is to obtain an image of the sealing surface at the moment of closure and perform sealing analysis on the sealing surface. Divide the volume of the deformation position of the sealing surface by the overall volume of the seal to obtain the deformation anomaly. The movement anomaly and the deformation anomaly are weighted and summed to obtain the back suction valve operation anomaly.

4. The visual inspection-based test method for the operation of a backflow valve according to claim 3, characterized in that, The impact of valve malfunction on fluid flow includes the following specific aspects: The first step is to obtain the flow fluctuation during the valve holding process, and set the standard deviation of the fluid flow process as the flow fluctuation anomaly. The fluid crosstalk anomaly is obtained by multiplying the back suction valve operation anomaly by the operation anomaly influence coefficient. The fluid crosstalk anomaly is used to quantify the risk of particulate matter generation or external contaminant intrusion caused by seal failure or material wear. It is obtained by quantifying the content of particulate matter generation or external contaminants in the fluid. The second step is to obtain the abnormal impact of valve anomalies on fluid flow by weighted summation of the obtained abnormal flow fluctuations and abnormal fluid crosstalk.

5. The visual inspection-based test method for the operation of a backflow valve according to claim 1, characterized in that, The damage analysis of the valve caused by the fluid flow includes the following specific contents: The first step is to qualitatively assess the erosion risk of the valve core and sealing surface by combining the particulate matter content, hardness, and fluid velocity in the fluid. The specific content of erosion risk is as follows: obtain the particulate matter content and hardness of the fluid, and the fluid velocity; obtain the ratio of the particulate matter content and hardness to the corresponding safety value to obtain the anomaly of the corresponding parameter; set the square of the ratio of the fluid velocity to the safety velocity as the velocity anomaly; and multiply the weighted sum of the anomalies of the corresponding parameters by the velocity anomaly to obtain the erosion anomaly. The second step is to obtain the gas content, pressure pulsation amplitude and frequency, and fluid temperature of the fluid to assess cavitation anomalies. The specific content of cavitation anomaly is as follows: obtain the amplitude anomaly by dividing the average pressure pulsation amplitude of the fluid by the safe amplitude; obtain the frequency anomaly by dividing the average pressure pulsation frequency by the safe frequency; obtain the pulsation anomaly by multiplying the amplitude anomaly by the frequency anomaly; obtain the gas content anomaly by dividing the gas content by the safe content; obtain the temperature anomaly by dividing the fluid temperature by the safe temperature; and obtain the cavitation anomaly by weighted summing of the pulsation anomaly, gas content anomaly, and temperature anomaly. The third step involves analyzing the damage to the valve caused by fluid flow, considering the erosion risk, cavitation anomalies, and deformation anomalies of the valve core and sealing surface. This includes the following specific steps: obtaining the erosion risk and cavitation anomalies of the fluid on the valve core and sealing surface, weighted summing to obtain the corrosion impact anomaly; multiplying the deformation anomaly by the protection impact coefficient to obtain the deformation impact anomaly; and multiplying the corrosion impact anomaly by the deformation impact anomaly to obtain the damage analysis results of fluid flow on the valve.

6. The visual inspection-based test method for the operation of a backflow valve according to claim 1, characterized in that, The valve compatibility assessment during the usage phase includes the following specific steps: Obtain the damage analysis results of fluid flow on the valve and the abnormal impact of valve abnormality on fluid flow. Sum the damage analysis results of fluid flow on the valve with the value 1, and then multiply it with the abnormal impact of valve abnormality on fluid flow to obtain the valve use adaptation abnormality.

7. The visual inspection-based test method for the operation of a backflow valve according to claim 1, characterized in that, The output of the valve test results includes the following specific steps: The system compares the detected valve mismatch with the set mismatch threshold. If the valve mismatch is greater than or equal to the set threshold, it indicates that the back suction valve is not compatible with the fluid and needs to be replaced, and a replacement reminder is issued. If the valve mismatch is less than the set threshold, it indicates that the back suction valve is compatible with the fluid and does not need to be replaced, and a matching reminder is issued.

8. A visual inspection-based backflow valve operation testing device, used to implement the visual inspection-based backflow valve operation testing method according to any one of claims 1-7, characterized in that, Includes the following specific modules: Data acquisition module, data analysis module, and replacement early warning module; The data acquisition module acquires images, basic fluid conditions, and flow parameters during the operation of the back suction valve through the machine vision module. The data analysis module performs anomaly analysis on the back suction valve based on the image data and flow parameters during its operation. It then analyzes the impact of valve anomalies on fluid flow by combining the results of the back suction valve anomaly analysis with fluid demand. Next, it performs damage analysis on the valve caused by fluid flow based on the basic fluid conditions and the results of the back suction valve anomaly analysis. Finally, it assesses the valve's suitability for use during the operational phase based on the results of the analysis of the impact of valve anomalies on fluid flow and the damage caused by fluid flow to the valve. The replacement early warning module outputs valve test results based on the valve's compatibility judgment during the usage phase.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the visual inspection-based backflow valve operation test method as described in any one of claims 1-7 by calling the computer program stored in the memory.

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