Pneumatic measuring system for detecting edge processing quality of spool of slide valve

Abstract

The invention discloses a pneumatic measurement system for slide valve core edge processing quality detection, and the system comprises a pneumatic measurement module which collects the pneumatic measurement data of the slide valve core edge, and transmits the data to a data processing and analysis module; the optical measurement module performs optical measurement on the size and the surface quality of the edge of the valve core of the slide valve and transmits optical measurement data to the data processing and analyzing module; the motion control module controls a pneumatic measuring head of the pneumatic measuring module to move along the axial direction of the spool of the slide valve so as to ensure that the pneumatic measuring head performs full-coverage scanning on the edge of the spool of the slide valve in the detection process; and fusing pneumatic measurement data, optical measurement data and motion control data, and carrying out defect identification and quality evaluation based on a deep learning algorithm. According to the technical scheme, pneumatic measurement and optical measurement are combined, high-precision detection of the edges of the valve element of the sliding valve is achieved, and the problem that the measurement precision is insufficient in a complex environment is solved by fusing multi-modal data.

Description

Technical Field

[0001] This invention relates to, but is not limited to, the field of spool valve core technology, specifically a pneumatic measurement system for detecting the machining quality of the spool valve core edges. Background Technology

[0002] The machining quality of the valve core edges directly affects the performance and lifespan of the hydraulic system. The precision and surface quality of the edges are key factors in ensuring valve core sealing and fluid control accuracy. During machining, the edges are susceptible to tool wear, vibration, and thermal deformation, leading to dimensional deviations and surface defects. Therefore, high-precision inspection and quality control of the edges are particularly important.

[0003] In existing technologies, a single measurement method, such as pneumatic or optical measurement, is typically used to detect the edge quality of a spool valve core. However, a single measurement method often fails to guarantee sufficient accuracy in complex processing environments. Pneumatic measurements are easily affected by changes in ambient temperature and humidity, while optical measurements may introduce errors due to specular reflection and light interference. These factors result in deficiencies in the measurement accuracy and reliability of existing technologies.

[0004] Furthermore, traditional testing methods largely rely on manual operation, which is inefficient and susceptible to human error. Manual testing is not only time-consuming and labor-intensive, but also highly subjective, making it difficult to guarantee consistency and accuracy. Current technologies lack automated and intelligent testing methods, failing to meet the demands of modern manufacturing for efficient and precise testing. Summary of the Invention

[0005] The purpose of this invention is to provide a pneumatic measurement system for inspecting the machining quality of the valve core edge of a slide valve, so as to solve the problems that traditional inspection methods rely heavily on manual operation, are inefficient and easily affected by human factors, and that traditional inspection methods, due to their use of a single measurement method, are difficult to guarantee the required inspection accuracy in complex machining environments.

[0006] The technical solution of this invention is as follows: This invention provides a pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve, comprising: The pneumatic measurement module is used to collect pneumatic measurement data of the valve core edge of the slide valve and transmit the collected pneumatic measurement data to the data processing and analysis module for pneumatic measurement. The optical measurement module and the pneumatic measurement module work together to form a multimodal detection chain. The optical measurement module is used to perform optical measurements on the dimensions and surface quality of the valve core edge of the slide valve, and transmits the optical measurement data to the data processing and analysis module to compensate for the pneumatic measurement results. The motion control module is communicatively connected to both the pneumatic measurement module and the optical measurement module. The motion control module is used to control the pneumatic probe of the pneumatic measurement module to move along the axial trajectory of the spool valve core, so as to ensure that the pneumatic probe performs a full-coverage scan of the spool valve core edge during the detection process. It is also used to perform closed-loop control of the pneumatic probe based on the position information of the pneumatic probe and the spool valve core monitored by the optical measurement module, so as to achieve high-precision motion adjustment of the pneumatic probe. The data processing and analysis module is communicatively connected to the pneumatic measurement module, the optical measurement module, and the motion control module, respectively. The data processing and analysis module is used to receive and fuse pneumatic measurement data, optical measurement data, and motion control data, perform defect identification and quality assessment based on deep learning algorithms, and provide defect detection results to the detection path optimization module and the manufacturing closed-loop optimization module. The detection path optimization module is communicatively connected to the data processing and analysis module and the motion control module, respectively. The detection path optimization module is used to optimize the detection path based on historical process data and transmit the optimized detection path parameters to the motion control module to adjust the movement trajectory of the pneumatic probe. The manufacturing closed-loop optimization module communicates with the data processing and analysis module to adjust the processing parameters based on the defect detection results from the data processing and analysis module, and transmits the optimized processing parameters to the processing equipment.

[0007] Optionally, in the pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve as described above, the pneumatic measurement module includes: a pneumatic probe, a pressure sensor, and an adaptive signal correction unit and a data transmission interface connected in sequence with the pressure sensor; The airflow outlet of the pneumatic probe faces the valve core edge, and is used to detect minute changes in airflow caused by changes in valve core edge size and structural mass by injecting airflow into the valve core edge area at a preset pressure. A barometric pressure sensor is used to acquire changes in airflow during measurement and convert these changes in airflow signal into a pneumatic signal. An adaptive signal correction unit is used to correct errors in pneumatic signals based on ambient temperature and humidity. The correction formula is as follows: ; in, The corrected airflow rate. and These are ambient temperature and ambient humidity, respectively. and These are reference temperature and reference humidity, respectively. and These are the correction coefficients obtained from the experimental calibration. A coefficient characterizing the effect of temperature changes on the flow signal. A coefficient characterizing the impact of humidity changes on the flow signal; The data transmission interface is used to transmit the aerodynamic signal corrected by the adaptive signal correction unit to the data processing and analysis module to achieve synchronous fusion with the optical measurement data.

[0008] Optionally, in the pneumatic measurement system for detecting the machining quality of the spool valve core as described above, the optical measurement module includes: a light source assembly, an optical sensor, an image processing unit, an optical compensation unit, and a data fusion interface; The light source assembly is used to provide uniform illumination conditions and reduce optical interference in the detection environment; An optical sensor is used to acquire surface topography images of the edge region of the spool valve core in order to measure the actual size data of the spool valve core edge. The image processing unit is used to perform edge enhancement and noise suppression processing on the raw images acquired by the optical sensor; The optical compensation unit is used to correct measurement errors caused by environmental changes and to perform calibration in conjunction with aerodynamic measurement data. The compensation calculation formula is as follows: ; in, The corrected measurement length. The length measured by the optical sensor. For ambient temperature, For reference temperature, This is the temperature compensation coefficient; The data fusion interface is used to ensure the timing consistency of optical and pneumatic measurement data through a time synchronization protocol, and to calculate the final dimension value by weighted fusion of the optical and pneumatic measurement data. ; in, Corrected dimensional values ​​for the pneumatic measurement module; The edge dimension value obtained by the optical measurement module; For fusion weighting coefficients.

[0009] Optionally, in the pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve as described above, the motion control module includes: a linear motor, a rotary platform, a trajectory planning unit, a servo drive unit, and an error compensation unit. Among them, the linear motor is used to drive the pneumatic probe to move along the axial direction of the spool valve core; A rotary platform is used to adjust the rotation angle of the spool valve core to meet the detection requirements of different edge directions; The trajectory planning unit is used to optimize the optimal detection path of the linear motor based on the detection process requirements and generate control commands; The servo drive unit uses a PID control algorithm to achieve precise positioning of the linear motor; The error compensation unit is used to monitor the position deviation of the linear motor in real time and improve the repeatability of the system through compensation strategies.

[0010] Optionally, in the pneumatic measurement system for inspecting the machining quality of the valve core edge of a slide valve as described above, the trajectory planning unit optimizes the detection path of the linear motor by minimizing the detection time, and the optimization formula for the detection path is: ; in, Total testing time. The total number of detection paths, The number of detection points on each path. For the first The first path The length of each detection segment For the pneumatic probe in the first The first path The motion speed of each detection segment This refers to the path switching time; The error compensation unit is specifically used to calculate the real-time position deviation of the linear motor. The compensation signal is calculated using a feedforward-feedback composite control strategy. ; in, This is the feedforward gain coefficient; This refers to the feedback integral gain coefficient. For time Position signal error at any given time. This is the integral of the error over time.

[0011] Optionally, in the pneumatic measurement system for detecting the machining quality of the valve core edge as described above, the data processing and analysis module includes: The data synchronization unit is used to time-align pneumatic measurement data, optical measurement data, and motion control data to ensure multimodal data synchronization. A multimodal feature extraction unit is used to construct data feature vectors and perform defect identification; The multimodal feature extraction unit has a built-in deep learning model for classifying and evaluating the quality of the extracted feature data. It uses a CNN-LSTM deep learning model to learn the spatiotemporal features in order to accurately identify the minute defects on the edge of the spool valve core and evaluate the quality level.

[0012] Optionally, in the pneumatic measurement system for inspecting the machining quality of the spool valve core as described above, the detection path optimization module includes: The historical error distribution path adjustment unit is used to adjust the path of the pneumatic probe based on historical error data. The real-time path adjustment unit is used to automatically increase the detection density of the edge region when the pneumatic measurement module detects a sudden change in pneumatic measurement data. The real-time path adjustment unit is also used to dynamically adjust the movement trajectory of the pneumatic probe according to the sudden changes in the pneumatic measurement data during real-time detection.

[0013] Optionally, in the pneumatic measurement system for inspecting the machining quality of the valve core edge as described above, the manufacturing closed-loop optimization module includes: The process parameter optimization unit is used to analyze the processing error of the edges based on the defect detection results obtained from the data processing and analysis module, and to adjust the processing parameters of the processing equipment. The parameter feedback unit is used to feed back the adjusted processing parameters to the processing equipment in order to adjust the compensation amount of the corresponding actuators in the processing equipment.

[0014] Optionally, in the pneumatic measurement system for detecting the machining quality of the valve core edge as described above, the process parameter optimization unit specifically employs an adaptive control algorithm to dynamically adjust the machining parameters, including: By establishing a mathematical model between processing parameters and defect detection results, the optimal adjustment amount of processing parameters can be calculated in real time; the mathematical model is expressed as: ; in, To control the adjustment amount of the output, This is the error value. Target value - Measured value; For proportional gain, and for integral gain, For differential gain, The integral of the error over time, Let be the rate of change of error with respect to time.

[0015] Optionally, in the pneumatic measurement system for inspecting the machining quality of the spool valve core as described above, The pneumatic measurement system is also used to automatically adapt to different sizes and types of spool valve cores and adjust the flow rate, scanning speed and detection threshold of the pneumatic probe by setting the measurement parameters within the system; wherein, the measurement parameters include: the size of the spool valve core, the axial movement step length of the pneumatic probe, the rotation step length of the spool valve core and the flow rate of the pneumatic probe.

[0016] Optionally, the pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve, as described above, further includes the following module: The data visualization module has the function of generating three-dimensional quality heat maps, which is used to display the distribution of processing quality and use color gradients to represent the degree of defects. The wireless data transmission module is used to transmit detection data to a remote server for cloud storage and analysis.

[0017] The beneficial effects of this invention are as follows: This invention provides a pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve, which has the following beneficial effects: First, the present invention adopts a technical solution that combines pneumatic measurement and optical measurement, which realizes high-precision detection of the valve core edge of the slide valve. By integrating multi-modal data, it achieves the technical effect of improving detection accuracy and reliability. Compared with the existing single measurement method technical solution, it solves the problem of insufficient measurement accuracy in complex environments.

[0018] Secondly, this invention achieves automated defect identification and quality assessment by introducing deep learning algorithms for data processing and analysis. It utilizes the CNN-LSTM structure to learn spatiotemporal features, which solves the shortcomings of traditional manual detection methods, such as low efficiency and large subjective errors.

[0019] Third, the detection path optimization module in this invention adjusts the path based on historical process data, achieving the technical effect of maximizing detection efficiency. By optimizing the detection path through the intelligent path planning unit, it solves the problem of poor adaptability under different working conditions compared with the fixed path technical solution in the prior art.

[0020] Fourth, the manufacturing closed-loop optimization module in this invention achieves dynamic optimization of processing quality by adjusting processing parameters in real time. It adopts a process parameter optimization unit and a defect feedback mechanism, which solves the problem of the inability to respond to processing errors in a timely manner compared with the existing technology of static parameter setting. Attached Figure Description

[0021] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of the present invention and do not constitute a limitation on the technical solutions of the present invention.

[0022] Figure 1 This is a schematic diagram of a pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve, provided in an embodiment of the present invention. Figure 2 for Figure 1 A schematic diagram of the pneumatic measurement module in the pneumatic measurement system provided in the embodiment shown; Figure 3 for Figure 1 A schematic diagram of the optical measurement module in the pneumatic measurement system provided in the embodiment shown; Figure 4 for Figure 1 The schematic diagram of the motion control module in the pneumatic measurement system provided in the embodiment shown is as follows; Figure 5 for Figure 1 The schematic diagram of the data processing and analysis module in the pneumatic measurement system provided in the embodiment shown is as follows; Figure 6 for Figure 1 The diagram shows the structure of the detection path optimization module in the pneumatic measurement system provided in the embodiment shown. Figure 7 for Figure 1 The illustrated embodiment provides a schematic diagram of the manufacturing closed-loop optimization module in the pneumatic measurement system. Figure 8 This is a schematic diagram illustrating the principle of flow-type pneumatic measurement. Figure 9 A schematic diagram of a pneumatic slit scanning measurement method; Figure 10 A schematic diagram showing the spatial morphological features of the working edge; Figure 11 This is a schematic diagram of edge fillet fitting. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see the appendix Figure 1 This is a schematic diagram of a pneumatic measurement system for detecting the machining quality of the edge of a slide valve core, provided by an embodiment of the present invention. The pneumatic measurement system for detecting the machining quality of the edge of a slide valve core provided by this embodiment of the present invention includes: The pneumatic measurement module is used to collect pneumatic measurement data of the valve core edge of the slide valve and transmit the collected pneumatic measurement data to the data processing and analysis module for pneumatic measurement. The optical measurement module and the pneumatic measurement module work together to form a multimodal detection chain. The optical measurement module is used to perform optical measurements on the dimensions and surface quality of the valve core edge of the slide valve, and transmits the optical measurement data to the data processing and analysis module to compensate for the pneumatic measurement results. The motion control module is communicatively connected to both the pneumatic measurement module and the optical measurement module. The motion control module is used to control the pneumatic probe of the pneumatic measurement module to move along the axial trajectory of the spool valve core, so as to ensure that the pneumatic probe performs a full-coverage scan of the spool valve core edge during the detection process. It is also used to perform closed-loop control of the pneumatic probe based on the position information of the pneumatic probe and the spool valve core monitored by the optical measurement module, so as to achieve high-precision motion adjustment of the pneumatic probe. The data processing and analysis module is communicatively connected to the pneumatic measurement module, the optical measurement module, and the motion control module, respectively. The data processing and analysis module is used to receive and fuse pneumatic measurement data, optical measurement data, and motion control data, perform defect identification and quality assessment based on deep learning algorithms, and provide defect detection results to the detection path optimization module and the manufacturing closed-loop optimization module. The detection path optimization module is communicatively connected to the data processing and analysis module and the motion control module, respectively. The detection path optimization module is used to optimize the detection path based on historical process data and transmit the optimized detection path parameters to the motion control module to adjust the movement trajectory of the pneumatic probe. The manufacturing closed-loop optimization module communicates with the data processing and analysis module to adjust the processing parameters based on the defect detection results from the data processing and analysis module, and transmits the optimized processing parameters to the processing equipment.

[0025] In this embodiment of the invention, the pneumatic measurement module uses a pneumatic probe to perform non-contact detection on the edge of the spool valve core. The airflow outlet of the pneumatic probe faces the edge, and when there are slight changes in height or shape on the edge surface, the airflow characteristics change accordingly. The airflow signal is collected by a pressure sensor and converted into a standardized electrical signal, i.e., a pneumatic signal. After filtering and stabilization processing, the pneumatic signal is further transmitted to the data processing and analysis module for feature extraction and defect identification.

[0026] In this embodiment of the invention, in order to improve measurement accuracy, the pneumatic measurement module is also equipped with an adaptive signal correction unit. This unit can compensate the pneumatic signal in real time according to changes in ambient temperature and humidity. The compensation mechanism of this unit is based on the temperature and humidity correction model obtained from experimental calibration, which ensures the stability of the airflow signal under different environmental conditions and reduces measurement errors caused by temperature drift and humidity effects.

[0027] In this embodiment of the invention, the optical measurement module and the pneumatic measurement module work together to provide high-precision measurements of edge dimensions and surface quality. The optical measurement module acquires the actual dimensional data of the valve core edge using a high-precision optical sensor and corrects measurement deviations caused by changes in temperature, humidity, and lighting conditions using an optical compensation algorithm. By fusing optical and pneumatic measurement data, the pneumatic measurement system can more comprehensively evaluate the edge processing quality and improve the accuracy of defect detection.

[0028] In this embodiment of the invention, the motion control module is responsible for the precise movement of the pneumatic probe to ensure full coverage scanning of the valve core edges. In one implementation, the motion control module includes a linear motor for smooth movement of the probe along the valve core axis, and a rotating platform for adjusting the rotation angle of the valve core to adapt to detection requirements in different edge directions. A path planning unit optimizes the detection path based on the edge error distribution, reducing redundant movements and improving detection efficiency.

[0029] In this embodiment of the invention, the data processing and analysis module integrates functions such as data synchronization, multimodal feature extraction, and deep learning algorithms. First, the module performs time alignment on aerodynamic measurement data, optical measurement data, and motion control data to ensure multimodal data synchronization. Then, it constructs data feature vectors through feature extraction and employs a CNN-LSTM deep learning model for defect identification and quality assessment. This model utilizes CNN to extract spatial features and combines them with LSTM to learn time-series information, enabling it to accurately identify minute edge defects and assess their quality level.

[0030] In this embodiment of the invention, to optimize the measurement path and improve detection efficiency, the detection path optimization module adjusts the path of the pneumatic probe based on historical error data. When the system detects a large error in a certain area of ​​the edge (i.e., when the airflow signal changes abruptly), the detection path optimization module increases the detection density in that area to ensure measurement accuracy. Simultaneously, during real-time detection, this module can also dynamically adjust the trajectory of the pneumatic probe according to sudden changes in the pneumatic measurement data, making the measurement path more intelligent.

[0031] In this embodiment of the invention, the manufacturing closed-loop optimization module achieves linked optimization of measurement results and the machining process. This module analyzes the machining error of the edges based on defect detection data and adjusts the process parameters of the machining equipment to reduce quality problems in subsequent processing. For example, when the pneumatic measurement system detects a large edge height error, this module can feed back the adjusted machining parameters to the machining equipment to adjust the tool compensation amount, thereby optimizing the cutting parameters and improving machining accuracy.

[0032] In practical applications, the pneumatic measurement system provided by this invention can adapt to spool valve cores of different specifications and materials. Users can automatically adapt to spool valve cores of different sizes and types and adjust the flow rate, scanning speed and detection threshold of the pneumatic probe by setting the measurement parameters in the system. The measurement parameters include: the size of the spool valve core, the axial movement step length of the pneumatic probe, the rotation step length of the spool valve core and the flow rate of the pneumatic probe. The system can be adapted to various manufacturing environments by setting the parameters.

[0033] Furthermore, the pneumatic measurement system also supports visualization image generation and wireless data transmission functions, specifically including corresponding modules to implement the above functions.

[0034] The data visualization module has the function of generating three-dimensional quality heat maps, which is used to display the distribution of processing quality and use color gradients to represent the degree of defects. The wireless data transmission module is used to transmit detection data to a remote server for cloud storage and analysis, providing long-term data support for process optimization.

[0035] The pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve provided in this embodiment of the invention has the following beneficial effects: 1. This invention adopts a technical solution that combines pneumatic measurement and optical measurement to achieve high-precision detection of the valve core edge of the slide valve. By fusing multimodal data, it achieves the technical effect of improving detection accuracy and reliability. Compared with the existing single measurement method technical solution, it solves the problem of insufficient measurement accuracy in complex environments.

[0036] 2. This invention achieves automated defect identification and quality assessment by introducing deep learning algorithms for data processing and analysis. It utilizes the CNN-LSTM structure to learn spatiotemporal features, which solves the shortcomings of traditional manual detection methods, such as low efficiency and large subjective errors.

[0037] 3. The detection path optimization module in this invention adjusts the path based on historical process data, achieving the technical effect of maximizing detection efficiency. By optimizing the detection path through the intelligent path planning unit, it solves the problem of poor adaptability under different working conditions compared with the fixed path technical solution in the prior art.

[0038] 4. The manufacturing closed-loop optimization module in this invention achieves dynamic optimization of processing quality by adjusting processing parameters in real time. It adopts a process parameter optimization unit and a defect feedback mechanism, which solves the problem of the inability to respond to processing errors in a timely manner compared with the existing technology of static parameter setting.

[0039] The following is a detailed description of the specific implementation schemes of each module in the pneumatic measurement system for detecting the machining quality of the valve core edge provided by the present invention.

[0040] Module 1: Pneumatic Measurement Module In the spool valve core edge machining quality inspection system, the pneumatic measurement module, as the front-end detection component, detects minute dimensional changes in the edge through airflow disturbance. This module 1 works in conjunction with the optical measurement module to form a multimodal detection chain, ensuring the complementarity and reliability of the measurement data. Furthermore, through an environmental parameter correction model, the system can eliminate the influence of temperature and humidity changes on gas density, improving detection stability.

[0041] Figure 2 for Figure 1 The illustrated embodiment provides a schematic diagram of the pneumatic measurement module in the pneumatic measurement system. (See diagram for reference.) Figure 2 As shown in this embodiment of the invention, the pneumatic measurement module includes a pneumatic probe, a pressure sensor, an adaptive signal correction unit, and a data transmission interface. The pneumatic probe injects a constant-pressure airflow into the edge region, detecting airflow disturbances caused by changes in edge dimensions. The pressure sensor collects the changes in airflow in real time and converts them into a processable pneumatic signal. The adaptive signal correction unit dynamically adjusts the pneumatic signal based on ambient temperature and humidity to reduce environmental interference. The data transmission interface transmits the corrected pneumatic signal to the data processing and analysis module, achieving synchronous fusion with optical measurement data.

[0042] In one possible implementation, the pneumatic probe is mounted on the testing platform via a fixed bracket, with its nozzle axis perpendicular to the edge of the spool valve core. During measurement, the pneumatic probe sprays airflow at a preset pressure. Changes in the edge gap cause airflow disturbance. The pressure sensor has a built-in micro-differential pressure detection element that captures the pressure fluctuations caused by the disturbance and generates a pneumatic signal. The pneumatic signal is transmitted to an adaptive signal correction unit, which combines real-time temperature and humidity data collected by external environmental sensors for error compensation.

[0043] Generally, gas density is significantly affected by temperature and humidity, which directly impacts measurement accuracy. In this embodiment, the correction coefficient is determined through experimental calibration. The measured real-time air flow rate, The corrected airflow rate. and These are ambient temperature and ambient humidity, respectively. and These are reference temperature and reference humidity, respectively. and The correction coefficients obtained from the experimental calibration are expressed by the following formula: ; in, A coefficient characterizing the effect of temperature changes on the flow signal. The coefficient characterizing the impact of humidity changes on the flow rate signal, with reference temperature. Compared with reference humidity Determined through calibration experiments under standard environmental conditions.

[0044] Specifically, the detection process includes the initialization stage, which sets the nozzle reference pressure of the pneumatic probe and calibrates the initial flow rate; the measurement stage, which starts the airflow injection and collects real-time flow data; the compensation stage, which calls the monitoring data of the external environmental sensors and calculates the compensated aerodynamic signal through the correction model; and the transmission stage, which encapsulates the data and sends it to the data processing and analysis module for fusion analysis with the optical measurement results.

[0045] In some embodiments, the pneumatic probe adopts a replaceable nozzle design, and the nozzle diameter is configured with different specifications according to the detection accuracy requirements. The air pressure sensor integrates a temperature self-compensation circuit to suppress the influence of the device's own temperature drift on the signal. The adaptive signal correction unit has a built-in non-volatile memory to store multiple sets of calibration coefficients to adapt to different gas media or detection scenarios.

[0046] This embodiment is applicable to mass production testing scenarios for high-precision mechanical components. The pneumatic measurement correction data obtained by the pneumatic measurement module can be fused with the contour scanning results of the optical module to output a sub-micron level edge dimension evaluation report. This multi-source data fusion strategy effectively improves the robustness and applicability of the pneumatic testing system.

[0047] Module 2: Optical Measurement Module In the spool valve core edge machining quality inspection system, the optical measurement module, as the core unit of the multimodal inspection chain, works in conjunction with the pneumatic measurement module. This module acquires the edge contour information of the spool valve core through optical imaging technology and fuses it with the pneumatic measurement data to achieve sub-micron level dimensional inspection. Through a complementary data verification mechanism, the system can eliminate the error accumulation of a single inspection method and ensure the reliability of the final inspection results.

[0048] Figure 3 for Figure 1 The illustrated embodiment provides a schematic diagram of the optical measurement module in the pneumatic measurement system. (See diagram for reference.) Figure 3 As shown, in this embodiment, the optical measurement module includes a light source assembly, an optical sensor, an image processing unit, an optical compensation unit, and a data fusion interface. The light source assembly provides uniform illumination conditions, reducing optical interference from the detection environment; the optical sensor is used to acquire surface topography images of the valve core edge region to measure the actual size data of the valve core edge; the image processing unit performs edge enhancement and noise suppression processing on the original image; the optical compensation unit corrects measurement errors caused by environmental changes and performs calibration by combining pneumatic measurement data. The data fusion interface performs weighted calculations on the processed optical measurement data and the pneumatic measurement data corrected by the pneumatic measurement module, outputting a comprehensive detection result.

[0049] In one possible implementation, the optical sensor employs a high-resolution industrial camera whose field of view covers the edge detection area of ​​the spool valve core. The light source assembly consists of a ring-shaped LED array, which provides uniform illumination through a diffuse reflector. The image processing unit incorporates an edge detection algorithm to extract pixel-level coordinate data of the edge contour. The data fusion interface uses a time synchronization protocol to ensure the temporal consistency between optical measurement data and pneumatic measurement data.

[0050] Generally, optical measurement errors originate from ambient light interference and image noise. This embodiment employs a dual compensation strategy: on the one hand, the light source component maintains a constant light intensity through a feedback adjustment mechanism; on the other hand, the image processing unit applies a wavelet denoising algorithm to eliminate high-frequency noise. The original image grayscale values, The processing procedure for the denoised image satisfies: ; in, These are the weighting coefficients calculated based on the Gaussian kernel function; The standard deviation parameter of the Gaussian kernel function; This represents the radius range of the filtering window; The horizontal and vertical offsets of the pixels within the filtering window; For the original image in coordinates The grayscale value at that location.

[0051] In one possible implementation, the formula for the optical compensation unit to perform compensation calculations is: ; in, The corrected measurement length. The length measured by the optical sensor. For ambient temperature, For reference temperature, This is the temperature compensation coefficient.

[0052] Specifically, the optical inspection process of module 2 includes the following steps: First, the camera's intrinsic and extrinsic parameters are determined through optical calibration to establish a mapping relationship between pixel coordinates and physical coordinates. Then, edge images are acquired and noise reduction is performed. Contour feature points are extracted and compensated. Finally, the feature point coordinates are transformed to the coordinate system of the aerodynamic measurement module to achieve data spatial alignment. The aligned data is then used to calculate the final size value through a weighted fusion model. ; in, Corrected dimensional values ​​for the pneumatic measurement module; The edge dimension value obtained by the optical measurement module; For fusion weighting coefficients.

[0053] In some preferred embodiments, the light source component can switch between multiple wavelength illumination modes. The image processing unit supports switching between various edge detection algorithms. The data fusion interface provides dynamic weight adjustment functionality, automatically optimizing based on signal confidence. The coefficient values ​​are calculated to satisfy: ; in, The signal-to-noise ratio of aerodynamic measurement data; This refers to the signal-to-noise ratio of optical measurement data.

[0054] In this embodiment, the data interaction between the optical measurement module and the pneumatic measurement module is achieved through shared memory. The real-time pneumatic signals of the pneumatic measurement module are written into a designated storage area, and the optical measurement module reads them and triggers fusion calculation. This design avoids bus communication delay and ensures the synchronization and timeliness of multimodal data.

[0055] Module 3: Motion Control Module In the spool valve core machining quality inspection system, the motion control module undertakes the driving and scheduling functions of the equipment execution unit, ensuring that the measuring device moves along a predetermined trajectory and maintaining the stability of the detection accuracy. This module receives position information from the pneumatic measurement module (the relative position of the pneumatic probe and the spool valve core, as well as the rotational position of the spool valve core), and combines this information with position information fed back from the real-time optical sensor to perform closed-loop control, achieving high-precision motion adjustment. Through adaptive trajectory planning and error compensation strategies, module 3 can meet the dynamic control requirements under different measurement environments and optimize detection efficiency.

[0056] Figure 4 for Figure 1 The illustrated embodiment provides a schematic diagram of the motion control module in the pneumatic measurement system. (See diagram for reference.) Figure 4 As shown, in this embodiment, the motion control module includes a linear motor, a rotary platform, a trajectory planning unit, a servo drive unit, and an error compensation unit. The linear motor drives the pneumatic probe to move axially along the spool of the valve core. The rotary platform adjusts the rotation angle of the spool to adapt to detection requirements in different edge directions. The trajectory planning unit calculates the optimal motion path of the linear motor (i.e., the pneumatic probe) based on the detection process requirements and generates control commands. The servo drive unit uses a PID control algorithm to achieve precise positioning of the linear motor (i.e., the pneumatic probe). The error compensation unit monitors the motion deviation of the linear motor (i.e., the pneumatic probe) in real time and improves the repeatability of the system through compensation strategies.

[0057] As an alternative, the trajectory planning unit uses a fifth-order polynomial interpolation algorithm to generate a smooth motion trajectory to reduce the impact during start-up and stopping. The servo drive unit supports PWM signal control to adjust the speed and position of the linear motor. The error compensation unit calculates the position error based on real-time feedback and dynamically adjusts the control parameters.

[0058] In one possible implementation, the trajectory planning unit optimizes the detection path of the linear motor based on the detection process requirements. The optimization objective is to minimize the detection time, and the optimization formula is as follows: ; in, Total testing time. The total number of detection paths, The number of detection points on each path. For the first The first path The length of each detection segment For the pneumatic probe in the first The first path The motion speed of each detection segment For path switching time Furthermore, the trajectory planning unit can adjust the sampling density based on the distribution of the detection points and calculate the movement speed of the actuator (linear motor).

[0059] ; in, Current speed of movement The maximum speed of motion; This represents the distance from the current location to the target measurement point. This is the speed adjustment coefficient. is Euler's constant.

[0060] Generally, motion control modules need to consider the dynamic response characteristics of the servo system. This embodiment calculates the real-time position deviation of the linear motor using an error compensation unit. The compensation signal is calculated using a feedforward-feedback composite control strategy. ; in, This is the feedforward gain coefficient; This refers to the feedback integral gain coefficient. For time Position signal error at any given time. This is the integral of the error over time.

[0061] Specifically, the error compensation unit combines feedback data from the optical sensor to calculate the deviation between the actual position and the target position, and dynamically adjusts the servo motor drive signal to improve the motion accuracy of the system.

[0062] In some embodiments, the motion control module supports EtherCAT or CANopen bus communication to achieve real-time data interaction with the host computer. The servo drive unit supports adaptive gain adjustment to optimize control performance under different operating conditions.

[0063] In this embodiment, the motion control module ensures stable movement of the detection device through high-precision trajectory planning and error compensation strategies, providing reliable mechanical support for subsequent data acquisition. This module is suitable for high-precision measurement scenarios and can improve the overall performance and response speed of the detection system.

[0064] Module 4: Data Processing and Analysis Module In the spool valve core edge machining quality inspection system, the data processing and analysis module is responsible for in-depth analysis and error compensation of the acquired signals. This module is closely integrated with the preceding modules to ensure the continuity of the data flow and the accuracy of the processing. Through real-time analysis of the measurement data and dynamic adjustment of the control data, module 4 can effectively improve the system's detection accuracy and stability.

[0065] Figure 5 for Figure 1 The illustrated embodiment provides a schematic diagram of the data processing and analysis module in the pneumatic measurement system. (See diagram for reference.) Figure 5 As shown, in this embodiment, module 4 includes a data synchronization unit and a multimodal feature extraction unit. The data synchronization unit is used to time-align pneumatic measurement data, optical measurement data, and motion control data to ensure multimodal data synchronization. The multimodal feature extraction unit is used to construct data feature vectors and perform defect identification. In specific implementation, the multimodal feature extraction unit has a built-in deep learning model for classifying and quality-evaluating the extracted feature data. A deep learning model with a CNN-LSTM structure is used to learn the spatiotemporal features to accurately identify minute defects on the edge of the spool valve core and perform quality level evaluation.

[0066] The following explains how to construct data feature vectors and perform defect identification.

[0067] (1) Measurement principle The principle of flow measurement is used to measure the radius of the edge, such as... Figure 8 The diagram shown is a schematic of the principle of flow-type pneumatic measurement.

[0068] Assuming the gas source pressure is After being stabilized by a pneumatic setter, it becomes If the pressure at the inlet of the cover plate is kept constant during the measurement process, then regardless of how the opening of the cover plate changes, the gas flow rate through the gap in the cover plate is: (1) In the formula, —Flow coefficient of the valve orifice; —The coefficient of air expansion; —The width of the narrow gap in the cover plate; —The average velocity of the gas; —The size of the opening in the narrow slot of the cover plate.

[0069] For an ideal gas, the average flow velocity is: (2) Therefore, when the pressure at the inlet is constant, the average air velocity through the narrow slit is constant, while the flow rate through the narrow slit is... It can be seen as being with They are directly proportional. Using flow rate measurement, the flow rate through the pre-throttle nozzle is: (3) because , It can be concluded that: (4) Substituting this into the equation yields: (5) In the formula, —Pressure loss at the throttling nozzle; —Pressure loss at the measuring nozzle.

[0070] As can be seen from Equation 5, when ΔP1 is much smaller than ΔP2, the flow rate through the flow meter is linearly related to the opening size of the measuring nozzle. Furthermore, the system is located at the small opening, precisely satisfying the condition that ΔP1 is much smaller than ΔP2. That is, the condition of a linear relationship between flow rate and opening size is achievable, and therefore, flow rate measurement can be used.

[0071] The valve core edge is measured using the above method. By moving the probe circumferentially, the morphology of the entire working edge is scanned. Due to manufacturing processes, the entire edge exhibits unevenness in the z-direction, which can be approximated by a curve. Furthermore, when the slit width is small, the change in the slit opening size can be approximated by a curve. The pressure inside the probe and the flow rate will change as gas flows through the narrow slit. The size of the opening changes with the pressure in the gas chamber or the flow rate through the throttle orifice. After scanning the entire edge, the obtained gas parameters are converted into the size of the side opening, thus obtaining a set of data. By calculating the range This allows us to determine the magnitude of the edge runout. For example... Figure 9 The diagram shown is a schematic of a pneumatic slit scanning measurement method.

[0072] In obtaining Then, using this set of data, the perpendicularity of the working edge is described. Treating the spatial distribution of the working edge as a circle, and taking the center of the working edge as the origin, the spatial coordinates of each measurement point on the working edge can be obtained. For example... Figure 10 The figure shows a schematic diagram of the spatial morphological features of the working edge; the dashed line represents the shape of the ideal edge, and the solid line represents the fit to the real situation.

[0073] Obtain the actual coordinates of the spatial point Next, a plane fitting is needed to obtain the fitting plane for the edges. For example... Figure 3-4 As shown, the plane is translated along the normal direction, and the positions corresponding to the maximum and minimum jump values ​​are respectively... and Substituting the values, we can obtain two planes that enclose the entire edge. Calculate the distance between the two enclosing planes. Using distance This is used to indicate the perpendicularity of the working edge.

[0074] Assume the equation of the plane is Then the normal vector of the plane is The angle between the plane normal vector and the axis. have: (6) Note that the fitting points used are divided by In addition, the remaining coordinate values ​​are all accurate values, so when fitting the plane, only the coordinates need to be adjusted. The residual distance in the direction is analyzed.

[0075] For the obtained spatial point coordinates In this regard, the distance from each point to the fitting plane is: (7) Therefore, the distance from each fitting point to the ideal fitting plane Sum of squares of the distances for: (8) like If the minimum is: (9) Right now: (10) Converting to matrix form yields: (11) By solving the matrix, we can obtain Therefore, the angle value for the perpendicularity of the edge is: (12) The measurement principle of edge fillet is as follows: After calibration, the size of the slit opening of the pneumatic probe within a certain range can be reflected by the gas parameters in the measuring chamber. When the slit opening is large, the influence of the edge fillet is negligible, and the gas parameters are not significantly different from the ideal state. However, when the slit opening is small, the gas parameters exhibit a non-linear relationship due to the presence of the edge fillet. Therefore, starting from where the gas parameter is 0, the slit opening is gradually increased. Data collection stops when the slit opening reaches a certain size and the changes in gas parameters show a linear correlation between the values ​​at several consecutive sampling points. The theoretical zero point is determined by linearly fitting the curve at the larger opening. The actual zero point is obtained by linearly fitting the curve at the smaller opening. The difference between the theoretical and actual zero points is... That is, the size of the rounded corners at the narrow slit. ,like Figure 11 The image shows a schematic diagram of edge fillet fitting.

[0076] The fillet size of the working edge at different circumferential positions is obtained by narrow slit scanning. (i=1,2,3,……,n). Calculating its geometric mean, the average fillet radius of the edges can be obtained as follows: (13) Based on the above calculation method, the characteristics of defect identification are reflected by the angle value of the edge perpendicularity and the average radius of the edge.

[0077] In this embodiment, the data processing and analysis module adopts a multi-layered data processing architecture. First, the signal undergoes preliminary noise reduction processing through a filter. Generally, an adaptive filtering algorithm is used to eliminate the influence of environmental noise. Alternatively, a Kalman filter can be used for smoothing dynamic signals to improve signal stability.

[0078] Specifically, Module 4 corrects signal deviations through an error compensation mechanism. The core of error compensation lies in the combination of feedforward and feedback control. Feedforward control, based on a predictive model, adjusts system parameters in advance to cope with anticipated errors. Feedback control, on the other hand, dynamically adjusts compensation parameters by monitoring signal deviations in real time.

[0079] In one possible implementation, the compensation signal The calculation formula is: ; in, Indicates the compensation signal. For time Position signal error at any given time. This is the feedforward gain coefficient. For feedback integral gain coefficient, The integral representing the error.

[0080] In some embodiments, module 4 also introduces an adaptive gain adjustment mechanism. Specifically, the system dynamically adjusts the gain based on changes in the real-time error. and The value is adjusted to adapt to different operating conditions. For example, when high-frequency noise is detected, the system will increase... To enhance the effect of feedforward control; and when low-frequency drift is significant, to improve To enhance the ability of feedback control.

[0081] Furthermore, this data processing and analysis module also has the function of handling nonlinear errors. By using a piecewise linearization method, the error interval is divided into several subdomains, and compensation parameters are configured for each subdomain to avoid deviations caused by the global linearity assumption. Generally, this method can effectively improve the robustness and accuracy of the system.

[0082] Module 5: Detection Path Optimization Module The detection path optimization module plays a crucial role in the entire system, responsible for planning and optimizing the motion path of the pneumatic measurement system to improve detection efficiency and accuracy. This module 5 is closely integrated with both the data processing and analysis module and the motion control module, ensuring seamless integration of path planning and data processing. Through the application of intelligent algorithms, module 5 can dynamically adjust the path to adapt to different detection requirements and environmental changes.

[0083] Figure 6 for Figure 1 The illustrated embodiment provides a schematic diagram of the detection path optimization module in the pneumatic measurement system. (See diagram for reference.) Figure 6As shown, the detection path optimization module in this embodiment includes a historical error distribution path adjustment unit and a real-time path adjustment unit. The historical error distribution path adjustment unit adjusts the path of the pneumatic probe based on historical error data; the real-time path adjustment unit functions as follows: on the one hand, when the pneumatic measurement module detects a sharp edge region where the pneumatic measurement data changes abruptly, it automatically increases the detection density of that edge region; on the other hand, during real-time detection, it dynamically adjusts the movement trajectory of the pneumatic probe according to the abrupt changes in the pneumatic measurement data.

[0084] In this embodiment, the detection path optimization module adopts a path planning strategy based on genetic algorithms. Generally, genetic algorithms optimize path selection step by step by simulating the natural selection process. As an alternative, particle swarm optimization algorithm can also be used for path optimization to improve computational efficiency.

[0085] Specifically, the goal of path optimization is to minimize detection time and path length while ensuring detection accuracy. The path optimization problem can be expressed as: ; in, Indicates the first in the path The length of the segment This represents the total number of road segments.

[0086] In one possible implementation, the path optimization module operates through the following steps: First, it initializes the path population by randomly generating several initial paths. Then, it evaluates the merits of each path based on a fitness function, which considers factors such as path length, detection time, and accuracy. Next, through selection, crossover, and mutation operations, it generates a new path population, and after multiple iterations, finally obtains the optimal path.

[0087] In some embodiments, the path optimization module also incorporates a dynamic adjustment mechanism. Specifically, the system dynamically adjusts path planning parameters based on real-time detection data. For example, when an obstacle is detected, the system automatically adjusts the path to avoid it; when the detection environment changes, the system recalculates the optimal path.

[0088] In a preferred embodiment, module 5 also has the ability to perform multi-objective optimization. By introducing a multi-objective optimization algorithm, module 5 can optimize multiple objectives simultaneously, such as path length, detection time, and energy consumption. Generally, this method can effectively improve the overall performance of the system.

[0089] Module 6: Manufacturing Closed-Loop Optimization Module The manufacturing closed-loop optimization module plays a crucial role in the quality inspection system for the machining of spool valve core edges. Through close collaboration with the data processing and analysis module, this module adjusts machining parameters based on defect detection results, thereby achieving dynamic optimization of the machining process. The introduction of this module ensures that the system can form a closed-loop feedback between inspection and machining, improving machining accuracy and efficiency.

[0090] Figure 7 for Figure 1 The illustrated embodiment provides a schematic diagram of the manufacturing closed-loop optimization module in the pneumatic measurement system. (See diagram for reference.) Figure 7 As shown, in this embodiment, the manufacturing closed-loop optimization module achieves closed-loop control through the following process. First, the process parameter optimization unit receives defect detection results from the data processing and analysis module. These results include information such as edge size deviations and surface quality defects. Based on these defect detection results, the process parameter optimization unit calculates the processing parameters that need to be adjusted in the processing equipment. Subsequently, the defect feedback unit feeds back the adjusted processing parameters to the processing equipment, and the process parameter optimization unit adjusts the compensation amount of the corresponding actuators in the processing equipment.

[0091] Alternatively, module 6 can employ an adaptive control algorithm to dynamically adjust processing parameters. Specifically, the module establishes a mathematical model between processing parameters and defect detection results to calculate the optimal adjustment amount of processing parameters in real time. This mathematical model can be expressed as: ; in, To control the adjustment amount of the output, The error value represents the difference between the "current measured value" and the "target value" (i.e., Target value - Measured value); The proportional gain means that the larger the error, the faster the response, and the more aggressively it can be adjusted. The integral gain is used to adjust the accumulation of error over time, thereby eliminating long-term deviations. The differential gain is used to predict the trend of error changes, allowing the system to react in advance and enhance stability. This is the integral of the error over time, used to accumulate small errors and gradually correct them. This is the rate of change of error with respect to time, used to suppress oscillations when errors suddenly change.

[0092] In one possible implementation, the module can also optimize parameters by incorporating historical process data. By analyzing historical data, module 6 can identify recurring errors in the processing and compensate for them in advance. For example, when a repeatable error is detected under a specific process condition, module 6 will automatically adjust the processing parameters to eliminate the error.

[0093] In some embodiments, module 6 also introduces machine learning algorithms to improve the intelligence level of parameter adjustment. Specifically, the module trains a neural network model to learn the complex relationship between processing parameters and detection results, thereby achieving more accurate parameter adjustment.

[0094] It should be noted that the pneumatic measurement system provided by the present invention is also used to automatically adapt to different sizes and types of spool valve cores by setting the measurement parameters within the system; the measurement parameters include: the size of the spool valve core, the axial movement step length of the pneumatic probe, the rotation step length of the spool valve core, and the flow rate of the pneumatic probe.

[0095] While the embodiments disclosed in this invention are as described above, they are merely illustrative of the embodiments to facilitate understanding of the invention and are not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in the form and details of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A pneumatic measurement system for inspecting the machining quality of the valve core edge of a slide valve, characterized in that, include: The pneumatic measurement module is used to collect pneumatic measurement data of the valve core edge of the slide valve and transmit the collected pneumatic measurement data to the data processing and analysis module for pneumatic measurement. The optical measurement module and the pneumatic measurement module work together to form a multimodal detection chain. The optical measurement module is used to perform optical measurements on the dimensions and surface quality of the valve core edge of the slide valve, and transmits the optical measurement data to the data processing and analysis module to compensate for the pneumatic measurement results. The motion control module is communicatively connected to both the pneumatic measurement module and the optical measurement module. The motion control module is used to control the pneumatic probe of the pneumatic measurement module to move along the axial trajectory of the spool valve core, so as to ensure that the pneumatic probe performs a full-coverage scan of the spool valve core edge during the detection process. It is also used to perform closed-loop control of the pneumatic probe based on the position information of the pneumatic probe and the spool valve core monitored by the optical measurement module, so as to achieve high-precision motion adjustment of the pneumatic probe. The data processing and analysis module is communicatively connected to the pneumatic measurement module, the optical measurement module, and the motion control module, respectively. The data processing and analysis module is used to receive and fuse pneumatic measurement data, optical measurement data, and motion control data, perform defect identification and quality assessment based on deep learning algorithms, and provide defect detection results to the detection path optimization module and the manufacturing closed-loop optimization module. The detection path optimization module is communicatively connected to both the data processing and analysis module and the motion control module. The detection path optimization module is used to optimize the detection path based on historical process data and transmit the optimized detection path parameters to the motion control module to adjust the motion trajectory of the pneumatic probe. The manufacturing closed-loop optimization module communicates with the data processing and analysis module to adjust the processing parameters based on the defect detection results from the data processing and analysis module, and transmits the optimized processing parameters to the processing equipment.

2. The pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 1, characterized in that, The pneumatic measurement module includes: a pneumatic probe, a pressure sensor, and an adaptive signal correction unit and a data transmission interface connected in sequence with the pressure sensor; The airflow outlet of the pneumatic probe faces the valve core edge, and is used to detect minute changes in airflow caused by changes in valve core edge size and structural mass by injecting airflow into the valve core edge area at a preset pressure. A barometric pressure sensor is used to acquire changes in airflow during measurement and convert these changes in airflow signal into a pneumatic signal. An adaptive signal correction unit is used to correct errors in pneumatic signals based on ambient temperature and humidity. The correction formula is as follows: ； in, The corrected airflow rate. and These are ambient temperature and ambient humidity, respectively. and These are reference temperature and reference humidity, respectively. and These are the correction coefficients obtained from the experimental calibration. A coefficient characterizing the effect of temperature changes on the flow signal. A coefficient characterizing the impact of humidity changes on the flow signal; The data transmission interface is used to transmit the aerodynamic signal corrected by the adaptive signal correction unit to the data processing and analysis module to achieve synchronous fusion with the optical measurement data.

3. The pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 1, characterized in that, The optical measurement module includes: a light source assembly, an optical sensor, an image processing unit, an optical compensation unit, and a data fusion interface; The light source assembly is used to provide uniform illumination conditions and reduce optical interference in the detection environment; An optical sensor is used to acquire surface topography images of the edge region of the spool valve core in order to measure the actual size data of the spool valve core edge. The image processing unit is used to perform edge enhancement and noise suppression processing on the raw images acquired by the optical sensor; The optical compensation unit is used to correct measurement errors caused by environmental changes and to perform calibration in conjunction with aerodynamic measurement data. The compensation calculation formula is as follows: ； in, The corrected measurement length. The length measured by the optical sensor. For ambient temperature, For reference temperature, This is the temperature compensation coefficient; The data fusion interface is used to ensure the timing consistency of optical and pneumatic measurement data through a time synchronization protocol, and to calculate the final dimension value by weighted fusion of the optical and pneumatic measurement data. ； in, Corrected dimensional values ​​for the pneumatic measurement module; The edge dimension value obtained by the optical measurement module; For fusion weighting coefficients.

4. The pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 1, characterized in that, The motion control module includes: a linear motor, a rotary platform, a trajectory planning unit, a servo drive unit, and an error compensation unit; Among them, the linear motor is used to drive the pneumatic probe to move along the axial direction of the spool valve core; A rotary platform is used to adjust the rotation angle of the spool valve core to meet the detection requirements of different edge directions; The trajectory planning unit is used to optimize the optimal detection path of the linear motor based on the detection process requirements and generate control commands; The servo drive unit uses a PID control algorithm to achieve precise positioning of the linear motor; The error compensation unit is used to monitor the position deviation of the linear motor in real time and improve the repeatability of the system through compensation strategies.

5. A pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 4, characterized in that, The trajectory planning unit optimizes the detection path of the linear motor by minimizing the detection time, and the optimization formula for the detection path is as follows: ； in, Total testing time. The total number of detection paths, The number of detection points on each path. For the first The first path The length of each detection segment For the pneumatic probe in the first The first path The motion speed of each detection segment This refers to the path switching time; The error compensation unit is specifically used to calculate the real-time position deviation of the linear motor. A feedforward-feedback composite control strategy is used for compensation, and the formula for calculating the compensation signal is as follows: ； in, This is the feedforward gain coefficient; This refers to the feedback integral gain coefficient. For time Position signal error at any given time. This is the integral of the error over time.

6. The pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 1, characterized in that, The data processing and analysis module includes: The data synchronization unit is used to time-align pneumatic measurement data, optical measurement data, and motion control data to ensure multimodal data synchronization. A multimodal feature extraction unit is used to construct data feature vectors and perform defect identification; The multimodal feature extraction unit has a built-in deep learning model for classifying and evaluating the quality of the extracted feature data. It uses a CNN-LSTM deep learning model to learn the spatiotemporal features in order to accurately identify the minute defects on the edge of the spool valve core and evaluate the quality level.

7. A pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 1, characterized in that, The detection path optimization module includes: The historical error distribution path adjustment unit is used to adjust the path of the pneumatic probe based on historical error data. The real-time path adjustment unit is used to automatically increase the detection density of the edge region when the pneumatic measurement module detects a sudden change in pneumatic measurement data. The real-time path adjustment unit is also used to dynamically adjust the movement trajectory of the pneumatic probe according to the sudden changes in the pneumatic measurement data during real-time detection.

8. A pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to claim 1, characterized in that, The manufacturing closed-loop optimization module includes: The process parameter optimization unit is used to analyze the processing error of the edges based on the defect detection results obtained from the data processing and analysis module, and to adjust the processing parameters of the processing equipment. The parameter feedback unit is used to feed back the adjusted processing parameters to the processing equipment in order to adjust the compensation amount of the corresponding actuator in the processing equipment; Specifically, the process parameter optimization unit employs an adaptive control algorithm to dynamically adjust the processing parameters, including: By establishing a mathematical model between processing parameters and defect detection results, the optimal adjustment amount of processing parameters can be calculated in real time; the mathematical model is expressed as: ； in, To control the adjustment amount of the output, This is the error value. Target value - Measured value; For proportional gain, and for integral gain, For differential gain, The integral of the error over time, Let be the rate of change of the error with respect to time.

9. A pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to any one of claims 1 to 8, characterized in that, The pneumatic measurement system is also used to automatically adapt to different sizes and types of spool valve cores and adjust the flow rate, scanning speed and detection threshold of the pneumatic probe by setting the measurement parameters within the system; wherein, the measurement parameters include: the size of the spool valve core, the axial movement step length of the pneumatic probe, the rotation step length of the spool valve core and the flow rate of the pneumatic probe.

10. A pneumatic measurement system for detecting the machining quality of the valve core edge of a slide valve according to any one of claims 1 to 8, characterized in that, It also includes the following modules: The data visualization module has the function of generating three-dimensional quality heat maps, which is used to display the distribution of processing quality and use color gradients to represent the degree of defects. The wireless data transmission module is used to transmit detection data to a remote server for cloud storage and analysis.

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