A pipeline polishing device and method for hydraulic engineering

Abstract

This invention discloses a pipe grinding device and method for hydraulic engineering, belonging to the technical field of hydraulic engineering construction equipment. By real-time monitoring of multi-dimensional parameters such as grinding head linear velocity, tool vibration, cable voltage, trajectory parameters, surface temperature difference, substrate hardness, weld morphology, surface cleanliness, and acoustic emission signals, the device calculates the tool efficiency coefficient, process regularity coefficient, working condition constraint coefficient, and process state compliance. Based on this, a dynamic pressure model is constructed to adjust the pressure of the tool on the pipe surface in real-time using a closed-loop system. This device is used to execute the above method. This invention realizes the transformation of the grinding process from "experience-driven" to "data-driven," enabling it to adapt to complex working conditions and significantly improving the uniformity, stability, and efficiency of grinding quality.

Description

Technical Field

[0001] This invention belongs to the technical field of water conservancy engineering construction equipment, and particularly relates to a pipe grinding equipment and method for water conservancy engineering. Background Technology

[0002] After welding and long-term operation, the weld residue, rust, and dirt on the inner and outer walls of water conservancy pipelines (especially large-diameter water transmission pipes and pressure steel pipes) need to be ground to meet the requirements of fluid performance, corrosion prevention, and subsequent testing. Currently, grinding operations in this field are gradually transitioning from purely manual operations to mechanization and automation, but the refined and intelligent control of grinding quality remains a challenge for the industry.

[0003] In existing technologies, automated grinding equipment primarily focuses on improving mechanical structures. For example, Chinese patent CN218696864U discloses a pipe end-face grinding device that adjusts the grinding discs on a slider via a screw to adapt to the synchronous grinding of pipe end faces of different sizes. Another Chinese patent, CN111761494A, discloses a self-propelled rotary rust removal device for hydraulic engineering pipelines, which aims to improve the efficiency and power stability of rust removal operations through an adaptive clamping and moving structure and a rotary grinding structure. Furthermore, for the automated treatment of pipe inner walls, Chinese patent CN221696266U provides a flexible device for grinding the inner walls of steel pipes, which drives steel brushes for long-distance grinding through multiple assembled clamping units and linear modules.

[0004] However, the aforementioned existing technologies still have significant limitations: their control logic largely relies on preset fixed programs or simple mechanical adjustments, failing to cope with dynamic interference factors such as tool wear, changes in pipeline morphology, and fluctuations in ambient temperature and humidity during the grinding process. Specifically, existing equipment generally lacks multi-dimensional real-time perception and intelligent decision-making capabilities for the grinding process, resulting in process parameters (such as grinding pressure) being unable to adaptively adjust according to the actual tool condition, process execution precision, and on-site working conditions. This makes grinding quality heavily dependent on operator experience, easily leading to under-grinding or over-grinding that damages the base material, poor quality consistency, and insufficient adaptability to the complex and variable on-site environments commonly encountered in water conservancy projects.

[0005] Therefore, there is an urgent need in this field for a pipeline grinding method and equipment that can perceive the status of the entire grinding process in real time and make intelligent decisions and dynamic controls based on the fusion of multi-source information, so as to fundamentally improve the automation and intelligence level of grinding operations and the controllability of process quality. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a pipe grinding device and method for water conservancy projects, which solves the aforementioned problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for grinding pipes used in water conservancy projects, comprising:

[0008] Based on the linear speed of the grinding head, the vibration value of the grinding tool, and the cable voltage, the tool efficiency coefficient is determined.

[0009] Based on trajectory overlap rate, intersection angle deviation, and grinding to cooling time ratio, the process regularity coefficient was determined.

[0010] Based on the initial dew point temperature difference, the surface hardness of the substrate, and the geometric cross-sectional area of ​​the weld reinforcement, the working condition constraint coefficient is determined.

[0011] Based on the grayscale contrast of surface cleanliness and the main frequency of acoustic emission signal under the tool efficiency coefficient and process regularity coefficient, the compliance of process status is confirmed.

[0012] Based on the process state compliance, operating condition constraint coefficient, and the current tool pressure on the surface, the pressure of the target tool on the surface is confirmed.

[0013] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0014] A further technical solution: The method for confirming the pressure of the target tool on the surface is as follows:

[0015] Based on the deviation of the process state compliance degree from the target process state compliance degree, and using the working condition constraint coefficient as the deviation adjustment factor, the current tool pressure is nonlinearly adjusted to output the target tool pressure.

[0016] A further technical solution: The method for confirming the compliance of the process status is as follows:

[0017] Obtain the grayscale contrast and acoustic emission signal frequency of the surface cleanliness;

[0018] The contrast ratio of the grayscale values ​​of surface cleanliness is obtained by performing maximum-min normalization.

[0019] The acoustic emission frequency deviation index is obtained by processing the absolute difference between the main frequency and the optimal frequency of the acoustic emission signal and then comparing it with the optimal frequency.

[0020] The tool efficiency coefficient and the process regularity coefficient are fused to generate the process state coefficient, which is positively correlated with both tool efficiency and process regularity.

[0021] Based on the process state coefficient, contrast index, and acoustic emission frequency deviation index, the process state compliance is confirmed. The process state compliance is positively correlated with the process state coefficient and negatively correlated with the sum of the contrast index and frequency deviation index. The process state coefficient is constrained within a preset range, and the larger its value, the better the overall polishing state.

[0022] Further technical solution: The method for confirming the operating condition constraint coefficient is as follows:

[0023] Obtain the geometric cross-sectional area of ​​the initial dew point temperature difference, substrate surface hardness, and weld reinforcement;

[0024] The dew point temperature difference index is obtained by comparing the initial dew point temperature difference with the safe temperature difference value.

[0025] The surface hardness of the substrate is normalized to a maximum and minimum to obtain the substrate hardness index.

[0026] The weld area index is obtained by comparing the geometric cross-sectional area of ​​the weld reinforcement with the reference area value.

[0027] Based on the dew point temperature difference index, the substrate hardness index, and the weld area index, the working condition constraint coefficient is determined.

[0028] The working condition constraint coefficient is obtained by weighted summation of the dew point temperature difference index, the substrate hardness index, and the weld area index. The dew point temperature difference index and the weld area index need to be mapped by a monotonic nonlinear function before fusion, so that the working condition constraint coefficient increases with the increase of the severity of the working conditions.

[0029] Further technical solution: The method for determining the process regularity coefficient is as follows:

[0030] Obtain trajectory overlap rate, intersection angle deviation, and grinding to cooling time ratio;

[0031] The trajectory overlap rate is processed by maximum-min normalization to obtain the trajectory overlap rate index.

[0032] After performing maximum-min normalization on the cross angle deviation, take its complement to obtain the cross angle deviation index;

[0033] The absolute difference between the grinding and cooling time ratio and the optimal time ratio is compared with the optimal time ratio to obtain the time ratio deviation index.

[0034] Based on the trajectory overlap rate index, the intersection angle deviation index, and the time ratio deviation index, the process regularity coefficient is determined.

[0035] The process regularity coefficient is obtained by linearly weighting and summing the overlap rate index and the angle deviation index, and then adding it to a monotonically decreasing function of the time ratio deviation index. The value of the coefficient increases with the improvement of the process execution standardization.

[0036] Further technical solution: The method for confirming the tool's efficiency coefficient is as follows:

[0037] Obtain the linear speed of the grinding head, the vibration value of the grinding tool, and the voltage drop of the cable;

[0038] The grinding head linear velocity, grinding tool vibration value, and cable voltage are normalized and compared with safety thresholds to obtain the speed index, vibration index, and voltage index. The vibration index is calculated to ensure that its value decreases as vibration intensifies.

[0039] Based on the linear velocity index, vibration index, and voltage index, the tool efficiency coefficient is determined.

[0040] The tool efficiency coefficient is obtained by weighted geometric average of linear velocity index, vibration index and voltage index, and the tool efficiency coefficient increases with the improvement of the overall performance of the tool.

[0041] A pipe grinding device for water conservancy projects, which adopts the above-mentioned pipe grinding method for water conservancy projects.

[0042] This invention provides a pipe grinding device and method for water conservancy projects, which has the following advantages compared with the prior art:

[0043] 1. This invention constructs a multi-level evaluation model that includes tool efficiency, process regularity, working condition constraints, and process state compliance, transforming discrete process parameters into quantifiable decision indicators to drive dynamic adjustment of grinding pressure, thereby changing the process from open-loop execution to closed-loop optimization.

[0044] 2. This invention dynamically adjusts parameters based on real-time data, effectively overcoming interference caused by tool wear, substrate differences, and environmental changes, reducing reliance on subjective human experience, and ensuring uniform and reliable pipe grinding results under different working conditions and in different batches.

[0045] 3. This invention comprehensively considers multiple constraints such as voltage stability, vibration state, surface temperature difference, and weld morphology, enabling the grinding system to automatically identify and adapt to the changing working environment at water conservancy engineering sites, thereby improving the robustness and applicability of the equipment.

[0046] 4. By monitoring and providing feedback on tool performance and process regularity in real time, this invention can optimize the process rhythm while ensuring quality, prevent rework or equipment damage caused by poor tool condition or process deviation, and improve overall work efficiency and safety. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0050] Please see Figure 1 According to one embodiment of the present invention, a method for grinding pipes used in water conservancy projects includes:

[0051] Based on the linear speed of the grinding head, the vibration value (vibration acceleration) of the grinding tool, and the cable voltage, the tool efficiency coefficient is determined;

[0052] Based on trajectory overlap rate, intersection angle deviation, and grinding to cooling time ratio, the process regularity coefficient was determined.

[0053] Based on the initial dew point temperature difference (the absolute difference between the metal surface temperature and the ambient dew point temperature), the surface hardness of the substrate, and the geometric cross-sectional area of ​​the weld reinforcement, the operating condition constraint coefficient is determined.

[0054] Based on the grayscale contrast of surface cleanliness under tool efficiency coefficient and process regularity coefficient (the smaller the better) and the main frequency of acoustic emission signal, confirm the compliance of process status;

[0055] Based on the process state compliance, operating condition constraint coefficient, and the current tool pressure on the surface, the pressure of the target tool on the surface is confirmed.

[0056] The following example will provide a more detailed explanation of the above technical solution:

[0057] Suppose an automated grinding operation is being performed on the inner wall of a pipeline in a water conservancy project. Initially, the grinding equipment is set to operate at a preset pressure. As the grinding operation progresses, the grinding tools may wear out, and uneven hardness or variations in weld seam height may occur inside the pipeline. Simultaneously, ambient temperature and humidity may fluctuate.

[0058] First, the system continuously monitors the linear speed of the grinding head, the vibration value of the grinding tool, and the cable voltage. For example, when the linear speed of the grinding head decreases slightly due to increased resistance, while the vibration value of the grinding tool begins to rise and the cable voltage also fluctuates slightly, these data are collected and input into the system. Based on this real-time data, the system confirms the current tool efficiency coefficient. For instance, through comprehensive analysis of the parameters, the system determines that the tool efficiency coefficient has decreased, indicating that the grinding tool may be experiencing wear or performance degradation.

[0059] Simultaneously, the system also monitors the trajectory overlap rate, intersection angle deviation, and grinding-to-cooling time ratio in real time. For example, the vision system detects a slight decrease in the overlap rate of the grinding trajectories, an increase in the intersection angle deviation, and a change in the grinding-to-cooling time ratio due to decreased grinding efficiency. Based on this information, the system confirms the current process regularity coefficient. For instance, if the system determines that the process regularity coefficient has decreased, it indicates that the execution standardization of the grinding operation has been affected.

[0060] In addition, the system also senses the working conditions at the work site. For example, an infrared thermometer detects a decrease in the difference between the pipe surface temperature and the ambient dew point temperature, indicating a risk of condensation; a portable hardness tester measures a substrate surface hardness higher than the average in a specific area; and a laser scanner detects a large geometric cross-sectional area of ​​weld reinforcement in a certain section. Based on this data, the system confirms the current working condition constraint coefficient. For example, if the system determines that the working condition constraint coefficient is high, it indicates that the current working environment or material properties impose strong limitations on the grinding process.

[0061] Next, the system combines the confirmed tool efficiency coefficient and process regularity coefficient with the real-time acquired grayscale contrast of surface cleanliness and the dominant frequency of the acoustic emission signal to confirm the compliance of the process state. For example, when both the tool efficiency coefficient and the process regularity coefficient decrease, the system further analyzes whether the grayscale contrast of surface cleanliness increases (cleanliness decreases) and the dominant frequency of the acoustic emission signal fluctuates abnormally. The system will then comprehensively determine that the compliance between the current polishing process state and the target state is low. This indicates that the polishing quality may be declining and adjustments are needed.

[0062] Finally, the system uses the currently confirmed process status compliance, operating condition constraint coefficient, and the current tool pressure on the surface measured by the force sensor as input. For example, if the process status compliance is low, the operating condition constraint coefficient is high, and the current tool pressure on the surface is a preset value, the system will dynamically determine a target tool pressure on the surface based on this comprehensive information. For instance, to compensate for a decrease in tool performance and improve cleanliness, the system may calculate a target pressure slightly higher than the current pressure, but considering higher operating condition constraints (such as condensation risk or increased hardness), the system will avoid excessive pressure to prevent damage to the base material or exacerbation of condensation. In this way, the system can intelligently adjust the grinding pressure to adapt to dynamically changing grinding environments and tool conditions, thereby ensuring consistent grinding quality.

[0063] Based on the above examples, the method provided in this embodiment demonstrates a significant technical contribution. Existing grinding equipment often relies on preset fixed programs or simple mechanical adjustments for control logic, which cannot effectively cope with dynamic interference factors such as tool wear, changes in pipeline morphology, and fluctuations in ambient temperature and humidity during the grinding process. For example, in the above examples, if existing technology is used, when the performance of the grinding tool deteriorates, the process execution deviates, or the operating conditions worsen, the equipment will continue to operate at a preset pressure, potentially leading to insufficient grinding, over-grinding that damages the base material, or inconsistent grinding quality.

[0064] In contrast, the method in this embodiment achieves refined and intelligent control of the grinding process by introducing real-time perception and fusion of multi-dimensional parameters. Specifically, by monitoring parameters such as the linear speed of the grinding head, the vibration value of the grinding tool, and the cable voltage, the effectiveness of the tool can be evaluated in real time, rather than relying solely on the tool's running time or preset lifespan. Simultaneously, the perception of trajectory overlap rate, intersection angle deviation, and the ratio of grinding to cooling time allows for a quantitative assessment of the standardization of process execution, avoiding the limitations of relying solely on operator experience. Furthermore, consideration of the initial dew point temperature difference, substrate surface hardness, and the geometric cross-sectional area of ​​the weld reinforcement allows the system to fully understand and respond to complex working conditions at the work site, rather than ignoring dynamic changes in the environment and materials.

[0065] More importantly, the method in this embodiment combines the aforementioned coefficients (tool efficiency coefficient, process regularity coefficient, and working condition constraint coefficient) with indicators that directly reflect grinding quality, such as the grayscale contrast of surface cleanliness and the dominant frequency of acoustic emission signals, to confirm the compliance of the process status. This multi-source information fusion decision-making mechanism enables the system to comprehensively and accurately judge the real-time quality status of the grinding process. Finally, based on the compliance of the process status, the working condition constraint coefficient, and the current tool pressure on the surface, the pressure of the target tool on the surface is dynamically confirmed, achieving adaptive adjustment of grinding parameters. This dynamic control capability allows the grinding equipment to make intelligent decisions and adjustments based on the actual tool status, process execution accuracy, and on-site working conditions, thereby overcoming the problems of existing technologies where grinding quality heavily relies on operator experience, is prone to under-grinding or over-grinding, has poor quality consistency, and is insufficiently adaptable to complex environments. The method in this embodiment fundamentally improves the automation, intelligence, and process quality control of hydraulic engineering pipeline grinding operations.

[0066] Preferably, the method for confirming the pressure of the target tool on the surface is as follows: based on the deviation of the process state compliance degree from the target process state compliance degree, and using the working condition constraint coefficient as the deviation adjustment factor, the current tool pressure is nonlinearly adjusted to output the target tool pressure;

[0067] Specifically, the pressure exerted by the target tool on the surface is determined using a dynamic pressure model, which is expressed as follows:

[0068]

[0069] in, This indicates the pressure exerted by the target tool on the surface. This indicates the pressure exerted by the tool on the surface. Indicates the operating condition constraint coefficient. Indicates the degree of compliance of the process status. This indicates the degree of conformity of the target process state.

[0070] Among them, the This represents the pressure exerted by the target tool on the surface. It is the ideal force that the grinding tool should apply to the pipe surface under current operating conditions. This pressure value serves as the target value for the control system to adjust the grinding pressure, guiding the grinding operation. For example, the actual pressure of the grinding tool can be monitored in real time using a force sensor and compared with... Comparisons are made to form a closed-loop control, or, based on the drive mechanism (such as a cylinder, hydraulic cylinder, or servo motor), [the control is determined]. Adjust the distance or force between the tool and the pipe surface. This represents the pressure exerted by the tool on the surface, which is the actual force applied by the grinding tool to the pipe surface. This pressure value serves as one of the inputs to the dynamic pressure model, reflecting the current grinding state. For example, it can be measured in real time by pressure sensors, force sensors, or load cells installed on the grinding tool or its support structure, or indirectly calculated by monitoring the pressure or current of the actuator (such as a cylinder or hydraulic cylinder) driving the grinding tool. This represents the working condition constraint coefficient, which reflects a comprehensive index of the current grinding environment and substrate conditions. In the dynamic pressure model, As a regulating factor, it is used to quantify the impact of working conditions on grinding pressure; for example, more careful pressure adjustments may be needed under harsh working conditions. This indicates the degree of conformity to the process status, and is a comprehensive indicator reflecting the quality and effect of the current grinding process. In the dynamic pressure model, Used to evaluate the real-time performance of the polishing process, when Deviation At that time, the model will adjust the pressure to correct the deviation. This represents the degree of conformity to the target process state. It is a preset, desired ideal degree of conformity to the grinding process state. This target value serves as a reference benchmark in the dynamic pressure model, and the model's goal is to make the actual grinding process conform to the target state. Approaching The target value can be preset by the operator or the system based on the quality requirements, material properties and process standards of pipe grinding, or it can be determined by analyzing historical grinding data to determine the process state compliance under the best grinding effect as the target value.

[0071] The solution in this application achieves adaptive adjustment of the pressure exerted by the target tool on the surface by introducing a dynamic pressure model. This dynamic pressure model... The pressure of the current tool on the surface Operating condition constraint coefficient Target process status conformity and real-time process status compliance As input, the pressure of the new target tool on the surface is calculated. Specifically, when the real-time monitored process status conforms to the standard... The process state is lower than the preset target state. When this occurs, it indicates that the current polishing effect has not met expectations. When the value is positive, the model calculates a correction factor greater than 1 through the hyperbolic tangent function tanh, thus adjusting the calculated pressure of the target tool on the surface. Greater than the current pressure This instructs the system to increase the polishing pressure. Conversely, when... Higher than This indicates that the polishing effect may be too good or there is a risk of excessive wear. If the value is negative, the model will calculate a correction factor less than 1, making... Less than The system indicates that the grinding pressure should be reduced. It is worth noting the operating condition constraint coefficient. This plays a crucial regulatory role in this model. When At higher values ​​(e.g., under harsh conditions such as high substrate hardness, complex weld reinforcement, or large ambient dew point temperature differences), the model becomes more sensitive to deviations in process state conformity. A larger absolute value results in a larger range of pressure adjustments, ensuring that grinding pressure can be adjusted more quickly and accurately under complex or challenging conditions to avoid potential grinding defects. In this way, the dynamic pressure model can intelligently adjust the grinding pressure based on real-time feedback from the grinding process and objective limitations of the working conditions, keeping the grinding operation in an optimized state. This effectively overcomes the limitations of traditional static pressure verification methods and significantly improves the adaptability and control accuracy of grinding.

[0072] The following is a specific example. In a weld grinding operation on a hydraulic engineering pipeline, the grinding equipment is equipped with a pressure sensor to measure the pressure of the tool on the surface in real time. It also integrates an image recognition module and an acoustic sensor to acquire the grayscale contrast of surface cleanliness and the main frequency of acoustic emission signals in real time, and confirms the compliance of process status through a built-in calculation module. In addition, the equipment also obtains information such as the initial dew point temperature difference and substrate surface hardness through environmental sensors and material databases to confirm the operating condition constraint coefficient. Before the grinding process began, the operators set a target process compliance level based on the pipe material and grinding requirements. During the polishing process, assume the initial pressure of the tool on the surface. The value is 100N. As the grinding process progresses, the system monitors in real time the decrease in the cleanliness of the pipe surface and the abnormality of the main frequency of the acoustic emission signal. The calculated process state compliance is... The value is 0.6, while the preset target process state compliance rate is... The value is 0.8. Meanwhile, due to the high ambient humidity, the calculated operating condition constraint coefficient is... The value is 0.7. At this point, the dynamic pressure model substitutes these real-time data into the formula. Perform the calculation: = 113.9 N. The calculation results show that the pressure of the target tool on the surface is 113.9 N. The pressure should be adjusted to approximately 113.9 N. Upon receiving this target pressure value, the grinding equipment's control system will immediately drive the actuator (e.g., a grinding head controlled by a servo motor) to increase the force applied to the pipe surface, raising it from 100 N to 113.9 N. In this way, the grinding pressure can be dynamically and precisely adjusted based on real-time feedback of the grinding effect and operating conditions, ensuring the stability and efficiency of the grinding process.

[0073] Through the above technical solution, this application effectively solves the problem of static pressure confirmation in traditional grinding methods, which prevents real-time adaptation to changes in working conditions. This dynamic pressure model intelligently calculates and adjusts the pressure of the target tool on the surface based on real-time acquired data such as the current tool pressure on the surface, working condition constraint coefficients, process state compliance, and preset target process state compliance. This makes the pressure application during the grinding process no longer fixed or simply preset, but dynamically adaptively adjusted according to the pipe surface condition, grinding tool condition, and environmental factors. Especially when the working condition constraint coefficient is high (i.e., harsh working conditions), the model is more sensitive to deviations in process state compliance, enabling timely and accurate pressure adjustment. This avoids insufficient grinding or excessive wear caused by improper pressure, significantly improving the stability and consistency of grinding quality, extending the service life of grinding tools, and enhancing the overall efficiency and intelligence of the grinding operation.

[0074] Preferably, the method for confirming the compliance of the process status is as follows:

[0075] Obtain the grayscale contrast and acoustic emission signal frequency of the surface cleanliness;

[0076] The contrast ratio of the grayscale values ​​of surface cleanliness is obtained by performing maximum-min normalization.

[0077] The acoustic emission frequency deviation index is obtained by processing the absolute difference between the main frequency and the optimal frequency of the acoustic emission signal and then comparing it with the optimal frequency.

[0078] Based on the tool efficiency coefficient and the process regularity coefficient, the process state coefficient is determined. Specifically, the tool efficiency coefficient and the process regularity coefficient are fused to generate the process state coefficient. The process state coefficient is positively correlated with both tool efficiency and process regularity.

[0079] Specifically, the process state coefficients can be determined through a process state model, which is expressed as follows:

[0080]

[0081] in, Represents the process state coefficient. Indicates the tool effectiveness coefficient. Indicates the process regularity coefficient;

[0082] Based on the process state coefficient, contrast index, and acoustic emission frequency deviation index, the process state compliance is confirmed. The process state compliance is positively correlated with the process state coefficient and negatively correlated with the sum of the contrast index and frequency deviation index. The process state coefficient is constrained within a preset range, and the larger its value, the better the overall polishing state.

[0083] Specifically, the process state compliance can be confirmed through a process state compliance model, which is expressed as follows:

[0084]

[0085] in, Indicates the degree of compliance of the process status. Represents the process state coefficient. Indicates the contrast ratio. The acoustic emission frequency deviation index is represented by the following. Furthermore, the higher the value, the better the overall polishing condition.

[0086] Among these parameters, the grayscale contrast of surface cleanliness reflects the amount of residue on the surface after polishing or the uniformity of surface roughness. This is typically obtained through image processing techniques, such as using a CCD camera to acquire surface images and analyze their grayscale distribution. The dominant frequency of the acoustic emission signal characterizes the dynamic characteristics of the interaction between abrasive grains and the workpiece material during polishing, such as the cutting state of the abrasive grains, wear conditions, or the presence of abnormal vibrations. This is usually obtained through acoustic emission sensors mounted on the polishing tool or workpiece for acquisition and spectral analysis. The acoustic emission frequency deviation index is obtained by processing the absolute difference between the dominant frequency and the optimal frequency, and then comparing it to the optimal frequency. This step quantifies the degree to which the dominant frequency deviates from the ideal polishing state. The optimal frequency is the acoustic emission signal dominant frequency value corresponding to the best polishing effect, determined empirically or experimentally. By calculating the absolute difference between the current dominant frequency and the optimal frequency, the deviation amount can be obtained. Ratioing this deviation to the optimal frequency yields a dimensionless deviation index. The larger the index, the more severe the deviation from the optimal polishing state. Based on the tool efficiency coefficient and process regularity coefficient, the process state coefficient is determined, which can be confirmed through a process state model, expressed as follows: This step aims to comprehensively evaluate the impact of polishing tool performance and the standardization of polishing procedures on the polishing process. Tool efficiency coefficient. It reflects the performance of the polishing tool itself, such as the degree of wear and working efficiency; process regularity coefficient. This reflects whether the grinding operation conforms to the preset process specifications, such as trajectory, speed, and time. By multiplying these two coefficients and taking the square root, a comprehensive process state coefficient can be obtained. This model directly links the quality of tools and processes to the process state, providing a fundamental and macroscopic assessment for subsequent process state compliance calculations. Based on the process state coefficient, contrast index, and acoustic emission frequency deviation index, process state compliance is confirmed using the process state compliance model, which is expressed as follows: This step is crucial in ultimately determining the compliance of the polishing process. (Process compliance) It is a comprehensive indicator used to quantify the degree of matching between the current polishing state and the ideal state. This model incorporates the macroscopic process state coefficients calculated earlier. Contrast index, reflecting surface cleanliness And the acoustic emission frequency deviation index, which reflects the dynamic characteristics of grinding. Organic integration. Among them, The term represents the process state coefficient. The degree of inadequacy, when The closer to 1 (the better the tools and processes). The smaller, the better The smaller the impact; conversely, when The smaller, The larger, the better The greater the impact. and This directly reflects the polishing quality and dynamic stability. Through the form of an exponential function, it makes... The value is in the range of (0, 1], and the larger the value, the better the overall polishing condition. It can more sensitively reflect the small changes in the polishing condition and provide an accurate basis for subsequent pressure adjustment.

[0087] The following is a specific example to illustrate this. As a concrete implementation method, the following approach can be used to confirm the compliance of the process status during the grinding of water conservancy engineering pipelines. First, a real-time image of the grinding area is acquired using an industrial camera installed near the grinding tool. Image processing software is then used to analyze the grayscale distribution of the image, calculating the grayscale contrast of the surface cleanliness. Simultaneously, a piezoelectric acoustic emission sensor is installed on the grinding head or pipe surface to collect acoustic emission signals during the grinding process in real time. The dominant frequency of the acoustic emission signal is extracted using digital signal processing methods such as Fourier transform. After obtaining the grayscale contrast, it can be normalized to a maximum-minimum value. For example, if the minimum grayscale contrast in historical data is 0.1, the maximum is 0.9, and the current measured value is 0.5, then the contrast index can be calculated as (0.5 - 0.1) / (0.9 - 0.1) = 0.5. For the dominant frequency of the acoustic emission signal, it is assumed that the optimal frequency for the grinding state, determined through previous experiments, is 150kHz. If the currently measured main frequency is 165kHz, then the acoustic emission frequency deviation index can be calculated as |165 - 150| / 150 = 0.1. When confirming the process state coefficient, the tool efficiency coefficient can be evaluated based on parameters such as the wear level of the grinding tool and the motor power. For example, it can be set to 0.8. Simultaneously, the process regularity coefficient is evaluated based on parameters such as the overlap rate and cross-angle deviation of the grinding trajectory. For example, set it to 0.9. Then, through the process state model... Calculate the process state coefficients ,Right now = 0.8485. Finally, the calculated contrast index... (e.g., 0.5), acoustic emission frequency deviation index (e.g., 0.1) and process state coefficient (e.g., 0.8485) Substitute into the process state compliance model Then the process state conformity =0.913. The value (0.913) reflects the overall state of the current polishing process; the closer the value is to 1, the better the polishing condition. The system can use this value to... The pressure of the grinding tool on the surface is adjusted by combining the working condition constraint coefficient and the degree of conformity with the target process state through a dynamic pressure model.

[0088] Through the above technical solution, this application provides a more refined and comprehensive method for confirming the compliance of the grinding process status. This method not only considers the grinding result (surface cleanliness) and the dynamic characteristics of the grinding process (acoustic emission signal frequency), but also incorporates a macroscopic assessment of the performance of the grinding tools and the standardization of the process. This multi-dimensional, hierarchical evaluation system makes the confirmation of process status compliance more accurate and sensitive, capable of capturing subtle changes in the grinding status in real time. Compared to evaluation methods that rely on only a single or a few indicators, this method can more comprehensively reflect the actual situation of the grinding operation, effectively avoiding pressure adjustment lag or over-adjustment due to inaccurate evaluation. Therefore, when this accurately confirmed process status compliance is input into the dynamic pressure model, it can significantly improve the accuracy and response speed of the target tool to surface pressure adjustment, thereby ensuring that the grinding process is always in the optimal working range, effectively improving grinding quality, reducing rework rate, and extending tool life, ultimately achieving intelligent and efficient grinding operations for hydraulic engineering pipelines.

[0089] Preferably, the method for determining the operating condition constraint coefficient is as follows:

[0090] Obtain the initial dew point temperature difference (measure the absolute difference between the metal surface temperature and the ambient dew point temperature), the surface hardness of the substrate, and the geometric cross-sectional area of ​​the weld reinforcement.

[0091] The dew point temperature difference index is obtained by comparing the initial dew point temperature difference with the safe temperature difference value.

[0092] The surface hardness of the substrate is normalized to a maximum and minimum to obtain the substrate hardness index.

[0093] The weld area index is obtained by comparing the geometric cross-sectional area of ​​the weld reinforcement with the reference area value.

[0094] Based on the dew point temperature difference index, the substrate hardness index, and the weld area index, the working condition constraint coefficient is determined. Specifically, the working condition constraint coefficient is obtained by weighted summation of the dew point temperature difference index, the substrate hardness index, and the weld area index. The dew point temperature difference index and the weld area index need to be mapped by a monotonic nonlinear function before fusion, so that the working condition constraint coefficient increases with the increase of the severity of the working conditions.

[0095] Specifically, the operating condition constraint coefficients can be determined through the operating condition constraint model, which is expressed as follows:

[0096]

[0097] in, Indicates the operating condition constraint coefficient. Indicates the dew point temperature difference index. Indicates the hardness index of the substrate. Indicates the weld area index. Represents the weight coefficient and The Furthermore, the larger the value, the more stringent the working conditions and the stronger the constraints.

[0098] The initial dew point temperature difference refers to the absolute difference between the metal surface temperature and the ambient dew point temperature. Its magnitude directly reflects the likelihood of condensation on the metal surface and is a key indicator for assessing the impact of moisture during grinding. This temperature difference can be calculated by measuring the metal surface temperature with a non-contact infrared thermometer and combining it with the ambient dew point temperature obtained from an ambient temperature and humidity sensor, or it can be directly measured using a dew point meter. The surface hardness of the substrate is an important parameter for measuring the pipe material's resistance to plastic deformation and directly affects the wear of grinding tools and grinding efficiency. This hardness can be measured using a portable Leeb hardness tester or by non-destructive testing using an ultrasonic hardness tester. The geometric cross-sectional area of ​​the weld reinforcement characterizes the volume of the weld protrusion and is an important basis for grinding removal and grinding path planning. This area can be obtained by scanning and calculating the weld with a laser profilometer or by analyzing the three-dimensional morphological data of the weld obtained using a structured light 3D scanner. The initial dew point temperature difference is compared with a preset safety value to obtain the dew point temperature difference index. This step aims to standardize the actual measured temperature difference value to quantify its potential risk to the grinding operation. The safety value can be preset based on factors such as pipe material characteristics, grinding process requirements, and ambient humidity. For example, it can be set to 3°C or 5°C; values ​​below this are considered to have a high risk of condensation. The ratio processing visually reflects the deviation of the current temperature difference from the safety threshold. The surface hardness of the substrate is normalized to a maximum-minimum value to obtain the substrate hardness index. This processing method converts hardness values ​​of different dimensions into a unified dimensionless range (e.g., 0 to 1), facilitating comprehensive calculations with other parameters. The maximum and minimum hardness values ​​can be determined based on the typical hardness range of the pipe material being ground or industry standards. For example, for a specific steel, the maximum hardness can be set to 100 HRB, and the minimum hardness can be set to 60 HRB. The geometric cross-sectional area of ​​the weld reinforcement is compared with a preset reference area value to obtain the weld area index. This step quantifies the size and complexity of the weld, thereby assessing the difficulty of grinding. The reference area value can be determined based on common weld sizes, grinding efficiency requirements, or empirical values; for example, it can be set to 10 mm² or 12 mm². Based on the dew point temperature difference index, substrate hardness index, and weld area index obtained above, the working condition constraint coefficients are confirmed through a working condition constraint model, which is expressed as follows: This model uses a combination of exponential functions, hyperbolic tangent functions, and weighting coefficients to nonlinearly weight and fuse various independent operating condition indices. For example, the exponential function... This allows the influence of the dew point temperature difference index to increase rapidly on the operating condition constraint coefficient when the dew point temperature difference index is small (i.e., close to the dew point, with a high risk of condensation), reflecting the nonlinear aggravation effect of condensation risk; hyperbolic tangent function This can be used to process the weld area index, allowing it to influence the constraint coefficient within a certain range, and tending to saturate when the area is too large, reflecting the nonlinear change in grinding difficulty as the weld area increases. Weighting coefficient This can be determined based on expert experience, the analytic hierarchy process (AHP), or orthogonal experimental design to reflect the relative importance of different working conditions on the overall grinding operation's constraint level. The final working condition constraint coefficients are then obtained. The value range is [0,1], and the larger the value, the more stringent the working conditions and the stronger the constraints on the grinding process.

[0099] The following is a concrete example. Suppose that when grinding the weld seam of a stainless steel pipe used in a water conservancy project, it is necessary to determine the current operating condition constraint coefficients. First, relevant data is acquired using sensors and measuring equipment. For example, an infrared thermometer measures the surface temperature of the pipe weld seam to be 28℃, while a temperature and humidity sensor measures the ambient dew point temperature to be 25℃; therefore, the initial dew point temperature difference is 3℃. A portable Leeb hardness tester measures the surface hardness of the substrate to be 85 HRB. A laser profilometer scans the weld seam, and the geometric cross-sectional area of ​​the weld reinforcement is calculated to be 18 mm². Next, these raw data are indexed. Assuming a preset temperature difference safety value of 2℃, the dew point temperature difference index is... The value is 3℃ / 2℃ = 1.5. Assuming the maximum-minimum normalized range of substrate hardness is 60 HRB to 100 HRB, then the substrate hardness index... The value is (85 - 60) / (100 - 60) = 25 / 40 = 0.625. Assuming the reference area is 15 mm², the weld area index is... The result is 18mm² / 15mm² = 1.2. Finally, these exponents are substituted into the working condition constraint model for calculation. Assume weighting coefficients... , , The values ​​are 0.4, 0.3, and 0.3 respectively (the specific values ​​can be determined through expert experience, analytic hierarchy process (AHP), or orthogonal experimental design). Therefore, the working condition constraint coefficients are... = 0.5269. Through the above calculations, the current working condition constraint coefficient is approximately 0.5269. This value will serve as an important input parameter for subsequent dynamic pressure model verification of the target tool's surface pressure, guiding the grinding system to make precise pressure adjustments based on the current actual working conditions.

[0100] In the aforementioned pipe grinding methods for hydraulic engineering, although methods have been proposed to determine the pressure of the target tool on the surface based on process state compliance, working condition constraint coefficients, and the current tool pressure on the surface, the lack of refined and quantitative confirmation of the working condition constraint coefficients may lead to delayed or inaccurate responses of the grinding system to environmental and material changes, thereby affecting grinding quality and efficiency. The working condition constraint coefficient confirmation method proposed in this application can comprehensively and accurately assess the challenges posed by the external environment and substrate characteristics faced by the grinding operation. Specifically, by obtaining the initial dew point temperature difference, substrate surface hardness, and geometric cross-sectional area of ​​the weld reinforcement, and converting them into standardized dew point temperature difference indices, substrate hardness indices, and weld area indices, and then performing comprehensive calculations through a working condition constraint model, an accurate working condition constraint coefficient reflecting the severity of the current operating conditions can be obtained. The introduction of this coefficient allows subsequent adjustments to the target tool pressure on the surface to fully consider the actual operating environment and workpiece condition, avoiding instability in grinding results due to environmental changes or workpiece differences. For example, in humid environments, the dew point temperature difference index affects the operating condition constraint coefficient, prompting the system to adjust pressure to avoid surface oxidation or corrosion. For substrates with higher hardness or welds with larger geometric cross-sectional areas, the operating condition constraint coefficient will increase accordingly, guiding the system to apply more appropriate pressure to ensure effective material removal without damaging the substrate. Therefore, the solution in this application significantly improves the adaptability, accuracy, and reliability of pipe grinding methods for hydraulic engineering, ensuring high-quality grinding results even under complex and variable operating conditions, and effectively extending tool life.

[0101] Preferably, the process regularity coefficient is determined as follows:

[0102] Obtain trajectory overlap rate, intersection angle deviation, and grinding to cooling time ratio;

[0103] The trajectory overlap rate is processed by maximum-min normalization to obtain the trajectory overlap rate index.

[0104] After performing maximum-min normalization on the cross angle deviation, take its complement to obtain the cross angle deviation index;

[0105] The absolute difference between the grinding and cooling time ratio and the optimal time ratio is compared with the optimal time ratio to obtain the time ratio deviation index.

[0106] Based on the trajectory overlap rate index, intersection angle deviation index, and time ratio deviation index, the process regularity coefficient is determined. Specifically, the process regularity coefficient is obtained by linearly weighting the overlap rate index and the angle deviation index, and then adding it to a monotonically decreasing function of the time ratio deviation index. The value of the process regularity coefficient increases with the improvement of the process execution standardization.

[0107] Specifically, the process regularization coefficient can be determined through a process regularization model, which is expressed as follows:

[0108]

[0109] in, Represents the process regularity coefficient. Indicates the trajectory overlap rate index. Indicates the cross angle deviation index. Indicates the time-to-deviation index. Indicates the weighted index and The Furthermore, the higher the value, the more standardized the process execution.

[0110] Among them, trajectory overlap rate refers to the degree of overlap between adjacent grinding paths, reflecting the uniformity and integrity of grinding coverage. Excessively high or low overlap rates can affect grinding efficiency and surface quality. Cross angle deviation refers to the angular difference between the grinding tool's direction of movement and the preset or ideal direction when moving on the pipe surface. This parameter is directly related to the regularity of the grinding texture and the processing effect on features such as welds. The grinding-to-cooling time ratio reflects the rhythm of the grinding operation and the thermal management strategy. A reasonable ratio helps control the temperature of the grinding area, reduce thermal damage, and extend the grinding head's life. These parameters can be obtained through visual sensors, inertial measurement units (IMUs), or process log analysis. Max-min normalization is performed on the trajectory overlap rate to obtain the trajectory overlap rate index. The purpose is to transform the original trajectory overlap rate data into a uniform, dimensionless range, such as the [0, 1] interval. After performing maximum-minimum normalization on the cross angle deviation, the complement is taken to obtain the cross angle deviation index. This index converts the negative indicator of cross angle deviation (larger deviation indicates poorer regularity) into a positive indicator (larger index indicates better regularity). The absolute difference between the grinding / cooling time ratio and the optimal time ratio is compared to the optimal time ratio to obtain the time ratio deviation index. This index quantifies the degree to which the actual grinding / cooling time ratio deviates from the ideal state. The optimal time ratio is a pre-set ideal value based on grinding head characteristics, material properties, and grinding requirements. By calculating the relative deviation between the actual value and the optimal value, an index reflecting the efficiency and rationality of time management can be obtained. Based on the trajectory overlap rate index, cross angle deviation index, and time ratio deviation index, the process regularity coefficient is determined. This coefficient can be confirmed through a process regularity model, which is expressed as follows: The model is a comprehensive mathematical expression used to weight and combine the three processed indices to obtain a single coefficient that can comprehensively reflect the regularity of the polishing process. These represent weighting indices, which determine the relative importance of each index in the final process regularity coefficient. Their sum is 1, ensuring the rationality of the coefficients. These indices can be assigned values ​​based on expert experience or determined through the analytic hierarchy process (AHP) or orthogonal experimental design. Index Term The design ensures that the effect of the time ratio deviation index on the process regularity coefficient is non-linear. When the time ratio deviation is small, the impact on the process regularity coefficient is small, while when the deviation is large, the impact is more significant. This aligns with the precision requirements for time management in actual grinding processes. This process regularity coefficient... The value ranges from [0, 1], and the larger the value, the more standardized the process execution.

[0111] As a specific implementation method, the process regularity coefficient can be determined in the pipeline grinding process of hydraulic engineering using the following approach. First, the grinding trajectory is monitored in real time using a vision sensor or laser ranging module installed on the grinding tool. Combined with data from the motion control system, the trajectory overlap rate of adjacent grinding paths is calculated. For example, after the grinding head completes one revolution, the lateral distance between the starting position of the next revolution and the ending position of the previous revolution is compared with the width of the grinding head to obtain the trajectory overlap rate. Simultaneously, the attitude information of the grinding tool relative to the pipe surface can be acquired in real time using an inertial measurement unit (IMU) or angle sensor integrated on the grinding tool, thereby calculating the actual cross angle deviation. In addition, the control system of the grinding equipment records the actual working time (grinding time) and non-working time (cooling time) of the grinding tool, thereby calculating the grinding-to-cooling-time ratio. After acquiring this raw data, the central processing unit of the grinding equipment (e.g., a high-performance industrial controller or embedded system) performs subsequent calculations. For the trajectory overlap rate, assuming its theoretical range is 0.5 to 0.9, it is mapped to the [0, 1] interval using the maximum-minimum normalization formula to obtain the trajectory overlap rate index. For the intersection angle deviation, assuming its theoretical range is 0 to 10 degrees, the maximum-minimum normalization process is also performed, and then the normalized value is subtracted from 1 to obtain the intersection angle deviation index. For the grinding and cooling time ratio, assuming the optimal time ratio is 3:1, i.e., 3, if the actual time ratio is 2.5, the absolute difference between it and the optimal time ratio (0.5) is calculated, and then divided by the optimal time ratio (3) to obtain the time ratio deviation index. Finally, the calculated trajectory overlap rate index, intersection angle deviation index, and time ratio deviation index are input into the process regularization model. For example, the preset weight index It is 0.4. It is 0.3. The value is 0.3. For example, if the trajectory overlap rate index is 0.8, the intersection angle deviation index is 0.9, and the time ratio deviation index is 0.1, then the calculated process regularity coefficient is 0.3. It will be a value between 0 and 1, which will then be used to confirm the compliance of the subsequent process status, thereby affecting the adjustment of the surface pressure by the target tool.

[0112] Through the above technical solution, this application provides a comprehensive and quantitative method for confirming the process regularity coefficient. This method acquires three key parameters—trajectory overlap rate, intersection angle deviation, and the ratio of grinding to cooling time—and standardizes them, thereby objectively and accurately assessing the standardization of the grinding process. In particular, by normalizing the intersection angle deviation and taking its complement, and by processing the grinding to cooling time ratio using a deviation ratio, all indicators can reflect the quality of the process in a uniform positive manner. Furthermore, a process regularity model integrates these standardized indicators into a single process regularity coefficient, which comprehensively reflects the uniformity of the grinding path, the accuracy of the grinding direction, and the rationality of the grinding rhythm. This precise confirmation of the process regularity coefficient significantly improves the accuracy of process state compliance assessment in the aforementioned pipeline grinding method for hydraulic engineering, thereby enabling more precise and intelligent adjustment of the target tool's pressure on the surface. This helps avoid problems such as decreased grinding quality, premature wear of the grinding head, or damage to the pipeline surface caused by non-standard process execution, thus effectively improving the stability and reliability of grinding operations and ensuring that the pipeline grinding quality meets engineering requirements.

[0113] Preferably, the method for determining the tool efficiency coefficient is as follows:

[0114] Obtain the linear speed of the grinding head, the vibration value (vibration acceleration) of the grinding tool, and the voltage drop of the cable;

[0115] The grinding head linear velocity, grinding tool vibration value, and cable voltage are normalized and compared with safety thresholds to obtain the velocity index, vibration index, and voltage index. The vibration index is calculated to ensure that its value decreases as vibration intensifies. Specifically:

[0116] The linear speed of the grinding head is compared with the maximum allowable linear speed of the equipment to obtain the linear speed index.

[0117] Import the vibration value of the grinding tool into the formula. The vibration index is obtained from the data, where, This indicates the vibration value of the grinding tool. This indicates the vibration threshold that affects surface quality;

[0118] Import the cable voltage into the formula The voltage index is obtained from the data, where... Indicates cable voltage. Indicates the rated voltage. Indicates the minimum voltage required for the tool to function properly;

[0119] The tool efficiency coefficient is determined based on the linear velocity index, vibration index, and voltage index. The tool efficiency coefficient is obtained by weighted geometric average of the linear velocity index, vibration index, and voltage index, and the tool efficiency coefficient increases with the improvement of the overall tool performance.

[0120] Specifically, the tool effectiveness coefficient can be determined through a tool effectiveness model, which is expressed as follows:

[0121]

[0122] in, Indicates the tool effectiveness coefficient. Indicates the linear velocity index. Indicates the vibration index. Indicates the voltage index. Indicates the weighted index and The Furthermore, the higher the value, the better the tool's performance.

[0123] The linear speed of the grinding head refers to the speed at which the grinding head moves across the pipe surface, monitored in real time by sensors or encoders. This speed directly affects grinding efficiency and surface roughness. The linear speed can be calculated by measuring the grinding head's rotational speed using an encoder or speed sensor mounted on the grinding head drive mechanism, combined with the grinding head diameter. Alternatively, a laser tachometer can be used to directly measure the linear speed of the grinding head surface. Obtaining the grinding tool's vibration value (vibration acceleration) aims to assess the tool's stability during operation. Excessive vibration can lead to uneven grinding or tool damage. This can be achieved by installing an acceleration sensor on the grinding tool to monitor its vibration acceleration in real time, or by using an acoustic sensor to monitor the noise generated by the tool, indirectly reflecting the vibration situation. Cable voltage reflects the power loss supplied to the grinding tool's motor, indirectly indicating the tool's load and operating status. This can be achieved by measuring the voltage at both ends of the cable or at the tool's motor input.

[0124] The ratio of the grinding head's linear speed to the maximum permissible linear speed of the equipment is used to standardize the actual linear speed to between 0 and 1, facilitating subsequent model calculations and assessing whether the current speed is within a safe range. This process involves setting a preset maximum permissible linear speed value, typically provided by the equipment manufacturer or determined empirically. The linear speed exponent is then obtained by dividing the real-time measured grinding head linear speed by this maximum value. The vibration value of the grinding tool is then incorporated into the formula. The vibration index is obtained by converting vibration values ​​into an index that reflects the negative impact of vibration on tool performance. When the vibration value is below a threshold, the index tends to its maximum value, indicating a small vibration impact. When the vibration value exceeds the threshold, the index decreases, indicating an increased vibration impact. This process first determines a vibration threshold that affects surface quality. This threshold can be set through experimentation, experience, or industry standards, and then the vibration value of the grinding tool acquired in real time will be used. Substitute the values ​​into the formula for calculation. Import the cable voltage into the formula. The voltage index is obtained by converting the cable voltage into an index that reflects the positive contribution of voltage to tool performance. When the voltage is close to the rated voltage, the index tends to 1, indicating good power supply; when the voltage is close to the minimum operating voltage, the index tends to 0, indicating insufficient power supply. This method involves presetting the rated voltage. and the minimum voltage to maintain normal operation of the tool Then, the cable voltage is measured in real time. Then substitute the values ​​into the formula to calculate.

[0125] The tool effectiveness coefficient is determined based on the linear velocity index, vibration index, and voltage index through a tool effectiveness model. By comprehensively considering three key factors—linear velocity, vibration, and voltage—the overall effectiveness of the tool is quantitatively calculated. This model, through exponential form and weighting coefficients, flexibly reflects the relative importance of different factors to tool effectiveness. The verification method involves using the previously calculated linear velocity exponent. Vibration index and voltage index Substitute into the tool effectiveness model, where the weight index It can be determined through orthogonal experimental design, expert experience, or analytic hierarchy process. Calculation results. This is the tool effectiveness coefficient, which ranges from 0 to 1. The higher the value, the better the tool effectiveness.

[0126] The following is a concrete example. In an automated grinding system for pipelines used in hydraulic engineering, the efficiency of the grinding tools needs to be confirmed in real time to accurately control the grinding process. The system first obtains the linear velocity of the grinding head through an encoder installed on the grinding head motor, for example, measured as 25 m / s. Simultaneously, an accelerometer installed on the grinding tool measures the vibration value of the grinding tool. The value is 0.05g. Furthermore, the voltage sensor monitors the cable voltage at the input terminal of the grinding motor. The voltage is 370V. To convert this raw data into an exponent, the system presets the maximum permissible linear velocity for equipment safety to be 30 m / s; therefore, the linear velocity exponent is... = 25 / 30 = 0.833. Preset vibration threshold affecting surface quality. If it is 0.1g, then the vibration index is... = 0. Preset rated voltage 380V, the minimum voltage required to maintain normal operation of the tool. If it is 340V, then the voltage index = 0.75. Finally, these indices are substituted into the tool effectiveness model, assuming a weighted index. =0.4, =0.3, =0.3, then the tool effectiveness coefficient = 0. This result indicates that although the linear velocity and voltage are acceptable, the vibration index is 0 (due to the design of the formula, the index drops rapidly to 0 when the vibration value exceeds the threshold), resulting in a tool efficiency coefficient of 0, suggesting that the tool may have a serious problem and needs to be checked or adjusted immediately.

[0127] Through the above technical solution, this application can systematically obtain the linear speed of the grinding head, the vibration value of the grinding tool, and the voltage drop of the cable, and convert them into standardized linear speed index, vibration index, and voltage index. Then, the tool effectiveness coefficient can be accurately determined through a tool effectiveness model. This quantitative and objective evaluation method avoids the uncertainty brought about by traditional experience-based judgment, making the evaluation of tool effectiveness more accurate and reliable. This provides a solid foundation for subsequent confirmation of process state compliance and the pressure of the target tool on the surface based on the tool effectiveness coefficient, thereby ensuring the stability and consistency of the grinding process, effectively improving the grinding quality of pipelines used in water conservancy projects, and helping to extend the service life of grinding tools.

[0128] A pipe grinding device for water conservancy projects, which adopts the above-mentioned pipe grinding method for water conservancy projects.

[0129] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0130] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for grinding pipes used in water conservancy projects, characterized in that, include: Based on the linear speed of the grinding head, the vibration value of the grinding tool, and the cable voltage, the tool efficiency coefficient is determined. Based on trajectory overlap rate, intersection angle deviation, and grinding to cooling time ratio, the process regularity coefficient was determined. Based on the initial dew point temperature difference, the surface hardness of the substrate, and the geometric cross-sectional area of ​​the weld reinforcement, the working condition constraint coefficient is determined. Based on the grayscale contrast of surface cleanliness and the main frequency of acoustic emission signal under the tool efficiency coefficient and process regularity coefficient, the compliance of process status is confirmed. Based on the process state compliance, operating condition constraint coefficient, and the current tool pressure on the surface, the pressure of the target tool on the surface is confirmed.

2. The method for grinding pipes used in water conservancy projects according to claim 1, characterized in that, The method for confirming the pressure of the target tool on the surface is as follows: Based on the deviation of the process state compliance degree from the target process state compliance degree, and using the working condition constraint coefficient as the deviation adjustment factor, the current tool pressure is nonlinearly adjusted to output the target tool pressure.

3. The method for grinding pipes used in water conservancy projects according to claim 2, characterized in that, The method for confirming the compliance of the process status is as follows: Obtain the grayscale contrast and acoustic emission signal frequency of the surface cleanliness; The contrast ratio of the grayscale values ​​of surface cleanliness is obtained by performing maximum-min normalization. The acoustic emission frequency deviation index is obtained by processing the absolute difference between the main frequency and the optimal frequency of the acoustic emission signal and then comparing it with the optimal frequency. The tool efficiency coefficient and the process regularity coefficient are fused to generate the process state coefficient, which is positively correlated with both tool efficiency and process regularity. Based on the process state coefficient, contrast index, and acoustic emission frequency deviation index, the process state compliance is confirmed. The process state compliance is positively correlated with the process state coefficient and negatively correlated with the sum of the contrast index and frequency deviation index. The process state coefficient is constrained within a preset range, and the larger its value, the better the overall polishing state.

4. The method for grinding pipes used in water conservancy projects according to claim 2, characterized in that, The method for determining the operating condition constraint coefficient is as follows: Obtain the geometric cross-sectional area of ​​the initial dew point temperature difference, substrate surface hardness, and weld reinforcement; The dew point temperature difference index is obtained by comparing the initial dew point temperature difference with the safe temperature difference value. The surface hardness of the substrate is normalized to a maximum and minimum to obtain the substrate hardness index. The weld area index is obtained by comparing the geometric cross-sectional area of ​​the weld reinforcement with the reference area value. Based on the dew point temperature difference index, the substrate hardness index, and the weld area index, the working condition constraint coefficient is determined. The working condition constraint coefficient is obtained by weighted summation of the dew point temperature difference index, the substrate hardness index, and the weld area index. The dew point temperature difference index and the weld area index need to be mapped by a monotonic nonlinear function before fusion, so that the working condition constraint coefficient increases with the increase of the severity of the working conditions.

5. The method for grinding pipes used in water conservancy projects according to claim 3, characterized in that, The method for determining the process regularity coefficient is as follows: Obtain trajectory overlap rate, intersection angle deviation, and grinding to cooling time ratio; The trajectory overlap rate is processed by maximum-min normalization to obtain the trajectory overlap rate index. After performing maximum-min normalization on the cross angle deviation, take its complement to obtain the cross angle deviation index; The absolute difference between the grinding and cooling time ratio and the optimal time ratio is compared with the optimal time ratio to obtain the time ratio deviation index. Based on the trajectory overlap rate index, the intersection angle deviation index, and the time ratio deviation index, the process regularity coefficient is determined. The process regularity coefficient is obtained by linearly weighting and summing the overlap rate index and the angle deviation index, and then adding it to a monotonically decreasing function of the time ratio deviation index. The value of the coefficient increases with the improvement of the process execution standardization.

6. The method for grinding pipes used in water conservancy projects according to claim 3, characterized in that, The method for determining the tool's effectiveness coefficient is as follows: Obtain the linear speed of the grinding head, the vibration value of the grinding tool, and the voltage drop of the cable; The grinding head linear velocity, grinding tool vibration value, and cable voltage are normalized and compared with safety thresholds to obtain the speed index, vibration index, and voltage index. The vibration index is calculated to ensure that its value decreases as vibration intensifies. Based on the linear velocity index, vibration index, and voltage index, the tool efficiency coefficient is determined. The tool efficiency coefficient is obtained by weighted geometric average of linear velocity index, vibration index and voltage index, and the tool efficiency coefficient increases with the improvement of the overall performance of the tool.

7. A pipe grinding device for water conservancy projects, characterized in that, The method for grinding pipes used in water conservancy projects as described in any one of claims 1-6 is adopted.

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