A method and system for intelligent control of aluminum profile inspection fixture

Abstract

This application relates to the field of industrial automation testing technology, specifically providing an intelligent control method and system for aluminum profile testing fixtures. The method includes the following steps: S1, acquiring real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected; S2, acquiring a preset target pressure distribution model corresponding to the aluminum profile; S3, comparing the real-time pressure distribution characteristics with the preset target pressure distribution model to identify local abnormal areas; S4, generating local pressure adjustment commands for the local abnormal areas based on the direction and value of the pressure deviation; S5, controlling multiple adjustment units distributed on the contact interface to act independently according to the local pressure adjustment commands to change the physical displacement or deformation of the clamping actuator in the corresponding local area, and then returning to S1. This method can effectively solve the problems of uneven clamping force distribution, minor profile slippage, and surface damage caused by surface deposits.

Description

Technical Field

[0001] This application relates to the field of industrial automation testing technology, and more specifically, to an intelligent control method and system for an aluminum profile testing fixture. Background Technology

[0002] In modern aluminum profile production workshops, ensuring product qualification rates and quality inspection is a crucial step. Typically, an automated quality inspection station is located at the end of the production line. Its function is to comprehensively inspect the dimensions, straightness, and surface quality of extruded and pre-processed aluminum profiles. The inspection fixture is one of the core pieces of equipment in this station. When an aluminum profile arrives at the inspection position via the conveyor belt, the grippers of the inspection fixture quickly move, firmly holding the profile from both sides or top and bottom, keeping it stationary during the inspection process. This allows high-precision optical sensors or laser scanning equipment to accurately capture its shape and surface information. To accommodate the production of aluminum profiles of different specifications and models, this inspection fixture usually has intelligent control capabilities. Its control system pre-stores information on various aluminum profile models, along with the optimal clamping force, clamping position, and other process parameters corresponding to these models. When a new profile enters the testing station, the system immediately identifies its specific model by reading the identification code on the profile or the accompanying fixture. It then retrieves the corresponding parameters from the database and instructs the fixture to perform precise clamping actions. This method works well under ideal working conditions, effectively ensuring the stability and accuracy of the testing. It also avoids damage to the profile surface due to excessive clamping force, or measurement errors caused by the profile shaking during testing due to insufficient clamping force.

[0003] However, in actual production environments, the situation is far more complex than the ideal model. With continuous optimization of production processes and the need for cost control, some aluminum profiles undergo simple surface cleaning after extrusion and initial cooling. However, due to the fast production pace or limitations of the cleaning process, trace amounts of release agent, coolant, or cutting fluid sometimes remain on the profile surface. These residues are usually oily or form a thin film after drying, making them difficult to detect with the naked eye. Simultaneously, in the workshop environment, especially near cutting and grinding processes, fine metal dust or fibrous particles are suspended in the air. During profile transportation, these particles adhere to the surface of profiles with residues, mixing with oily substances to form a microscopic, uneven adhesion layer.

[0004] When aluminum profiles with an adhesive layer enter the inspection fixture, the contact between the fixture's jaws and the profile surface is no longer an ideal metal-to-metal contact. The jaws are typically made of a high-friction material and designed with specific textures to increase gripping force. However, when an adhesive layer is present, it significantly alters the frictional characteristics between the jaws and the profile, reducing the effective coefficient of friction and making the contact surface uneven. At this point, while the preset clamping force called by the intelligent control system based on the profile model may be correct macroscopically, the pressure distribution between the jaws and the profile may become extremely uneven at the microscopic level. In areas with a thicker or more oily adhesive layer, the jaws may not provide sufficient friction to completely secure the profile, resulting in minute slippage or vibrations that are difficult to detect with the naked eye during inspection. In other areas, to maintain overall clamping stability, the jaws may generate excessively high pressure locally to compensate for insufficient friction.

[0005] Such minute slippage or vibration can introduce significant measurement errors for systems relying on high-precision optical sensors or laser scanning equipment for dimensional, straightness, and surface defect detection. For example, during profile cross-sectional contour scanning, slight displacement of the profile can cause the scanning trajectory to deviate, resulting in inaccurate dimensional data. This could lead to the misclassification of qualified products as unqualified, or the misclassification of defective products as qualified, causing them to flow into subsequent processes and resulting in greater losses. A more direct and serious consequence is that in areas of excessively high local pressure, the grippers may leave permanent indentations on the profile surface. If the profile experiences slight slippage during clamping, the relative movement between the grippers and the profile surface, combined with the abrasive effect of adhering substances, can easily create visible scratches on the profile surface. These indentations and scratches may not be immediately detected during inspection, but they will become exceptionally prominent in subsequent surface treatment processes such as anodizing, spraying, or electrophoresis due to uneven penetration of the surface treatment agent or differences in coating thickness, ultimately leading to the product being judged as defective or scrapped, resulting in waste of materials and time.

[0006] Existing intelligent control systems rely on profile type and preset parameters for clamping force control. While they can monitor whether the clamping force reaches the set value, they cannot detect the actual friction between the grippers and the profile surface, the uniformity of micro-pressure distribution, or detect minor slippage or surface damage during clamping. Therefore, the control system's operation logs show that all operations perfectly follow the preset program, with no alarms or anomalies recorded. However, actual quality problems persist and are difficult to trace. This makes it difficult for production managers to accurately determine the root cause of finished product scrap, increasing the difficulty and time cost of troubleshooting and severely impacting production efficiency and product quality stability.

[0007] There is currently no effective technical solution to the above problems. Summary of the Invention

[0008] The purpose of this application is to provide an intelligent control method and system for aluminum profile inspection fixtures, which can effectively solve the problems of uneven clamping force distribution, minor slippage of profiles, and surface damage caused by surface deposits.

[0009] In a first aspect, this application provides an intelligent control method for an aluminum profile inspection fixture, which includes the following steps: S1. Obtain the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected; S2. Obtain the identification information of the aluminum profile to be inspected, and obtain the preset target pressure distribution model corresponding to the aluminum profile based on the identification information; the preset target pressure distribution model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state. S3. Compare the real-time pressure distribution characteristics with the preset target pressure distribution model to identify local abnormal areas where the pressure deviation exceeds the preset threshold. S4. Generate a local pressure adjustment command for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. S5. Control the independent operation of multiple adjustment units distributed on the contact interface according to the local pressure adjustment command, so as to change the physical displacement or deformation of the clamping actuator in the corresponding local area, realize the dynamic reconstruction of the force field of the contact interface, and then return to step S1.

[0010] Secondly, this application also provides an intelligent control system for an aluminum profile inspection fixture, which includes: The first acquisition module is used to acquire the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected. The second acquisition module is used to acquire the identification information of the aluminum profile to be inspected, and to acquire the preset target pressure distribution model corresponding to the aluminum profile based on the identification information; the preset target pressure distribution model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state; The comparison module is used to compare the real-time pressure distribution characteristics with the preset target pressure distribution model in order to identify local abnormal areas where the pressure deviation exceeds the preset threshold. The generation module is used to generate local pressure adjustment commands for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. The adjustment module is used to control multiple adjustment units distributed on the contact interface to act independently according to the local pressure adjustment command, so as to change the physical displacement or deformation of the clamping actuator in the corresponding local area, realize the dynamic reconstruction of the force field of the contact interface, and then trigger the first acquisition module.

[0011] As can be seen from the above, the intelligent control method and system for aluminum profile inspection fixture provided in this application obtains the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected, compares it with the preset target pressure distribution model, identifies local abnormal areas, and then achieves dynamic reconstruction of the force field by controlling the independent action of multiple adjustment units. This effectively solves the problems of uneven clamping force distribution, micro-slippage of the profile, and surface damage caused by surface deposits in the prior art. It has the advantages of being able to sense the pressure distribution state of the clamping interface in real time, effectively avoiding surface damage of the profile and micro-slippage during the inspection process by dynamically reconstructing the force field, thereby significantly improving the inspection accuracy and product quality stability. Attached Figure Description

[0012] Figure 1 A flowchart illustrating an intelligent control method for an aluminum profile inspection fixture provided in this application embodiment.

[0013] Figure 2 This is a schematic diagram of the structure of an intelligent control system for an aluminum profile inspection fixture provided in an embodiment of this application.

[0014] Reference numerals in the attached figures: 1. First acquisition module; 2. Second acquisition module; 3. Comparison module; 4. Generation module; 5. Adjustment module. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0016] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0017] Firstly, such as Figure 1 As shown, this application provides an intelligent control method for an aluminum profile inspection fixture, which includes the following steps: S1. Obtain the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected; S2. Obtain the identification information of the aluminum profile to be inspected, and obtain the preset target pressure distribution model corresponding to the aluminum profile based on the identification information; the preset target pressure distribution model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state. S3. Compare the real-time pressure distribution characteristics with the preset target pressure distribution model to identify local abnormal areas where the pressure deviation exceeds the preset threshold. S4. Generate a local pressure adjustment command for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. S5. Control the independent operation of multiple adjustment units distributed on the contact interface according to the local pressure adjustment command, so as to change the physical displacement or deformation of the clamping actuator in the corresponding local area, realize the dynamic reconstruction of the force field of the contact interface, and then return to step S1.

[0018] To facilitate understanding, some key terms in this embodiment are explained below. The clamping actuator in this embodiment refers to the component in the aluminum profile inspection fixture that directly contacts the aluminum profile to be inspected and applies clamping force. It typically includes grippers and pads on the gripper surface, its function being to securely fix the aluminum profile and ensure that the profile does not shift or shake during inspection. The real-time pressure distribution characteristic in this embodiment refers to the magnitude and distribution of pressure experienced by various local areas at a certain moment on the contact interface between the clamping actuator and the aluminum profile to be inspected. This characteristic is collected in real time by a sensor network and presented in digital form, such as forming a pressure distribution map, reflecting the detailed distribution of the force field on the contact surface. The identification information of the aluminum profile to be inspected in this embodiment refers to data used to uniquely identify the model, specifications, and other attributes of the aluminum profile to be inspected. This information can be obtained through QR codes, RFID tags, etc., and is the basis for the system to retrieve corresponding preset parameters and models. The preset target pressure distribution model in this embodiment is a mathematical or data model for the ideal pressure range that each local area of ​​the contact interface between the clamping actuator and the profile should reach under a stable and non-destructive clamping state, specifically for a particular type of aluminum profile. This model is pre-established through experiments and simulations and serves as a benchmark for real-time pressure adjustment. The local abnormal area in this embodiment refers to a local area of ​​the contact interface where, after comparing the real-time pressure distribution characteristics with the preset target pressure distribution model, the pressure deviation (the difference between the real-time pressure in the real-time pressure distribution characteristics and its corresponding preset pressure range) exceeds a preset threshold. These areas may have problems with excessively high or low pressure and require targeted adjustment. The local pressure adjustment command in this embodiment is a command generated by the control system based on the direction (excessively high or low) and magnitude of the pressure deviation in the local abnormal area, used to guide the action of the adjustment unit. This command aims to precisely adjust the clamping force in the local area to restore it to the preset target pressure range. The adjustment unit in this embodiment refers to a miniature actuator distributed at the contact interface between the clamping actuator and the aluminum profile under inspection, capable of independent action to change the physical displacement or deformation of the corresponding local area. By adjusting the actions of the unit, the force field at the contact interface can be dynamically reconstructed, thereby precisely controlling the local pressure.

[0019] This application proposes an intelligent control method for aluminum profile inspection fixtures, which aims to solve the problems of uneven clamping force distribution, minor profile slippage, measurement errors, and local indentation or scratch damage caused by the inability to sense the micro-pressure distribution at the contact interface when there are adhering substances on the surface of aluminum profiles by traditional fixtures.

[0020] This method first acquires the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile under test. In one implementation, multiple independent pressure sensors can be placed on the surface of the clamping jaws to collect pressure data at the contact interface in real time. For example, multiple piezoresistive sensors can be evenly distributed on the jaw surface. When the jaws clamp the aluminum profile, the pressure applied to the profile surface changes the resistance value of these piezoresistive materials. By measuring the resistance change and converting it into a corresponding local pressure value, a real-time pressure distribution map is formed. Another implementation method uses a capacitive sensor array, which reflects the local pressure by measuring the capacitance change in different areas.

[0021] Next, the method acquires the identification information of the aluminum profile to be inspected and obtains the corresponding preset target pressure distribution model based on the identification information. In one implementation, the identification information can be obtained by scanning the QR code on the aluminum profile or reading the RFID tag. For example, when the aluminum profile enters the inspection area, an industrial-grade fixed barcode reader can read the identification code on the profile, or an RFID reader can read the RFID tag on the accompanying tooling plate, thereby accurately identifying the specific model of the current aluminum profile. Once the model is identified, the system immediately retrieves the preset target pressure distribution model corresponding to that model from its internally stored parameter database. This model is obtained through multiple experiments on the model of the profile under completely clean and non-depositing conditions, combined with finite element analysis simulation, defining the pressure range that each tiny area on the contact surface of the gripper should withstand under the premise of achieving stable clamping without damaging the profile. For example, for an H-shaped aluminum profile, its preset target pressure distribution model may show that the flange part of the H-shaped profile needs relatively high pressure to ensure stability, while the web part needs lower pressure to avoid deformation.

[0022] Then, the method compares the real-time pressure distribution characteristics with a preset target pressure distribution model to identify local abnormal areas where the pressure deviation exceeds a preset threshold. In one implementation, the system continuously compares the real-time pressure distribution data obtained from the pressure sensor network with the preset target pressure distribution model retrieved for the current profile model point by point. The comparison process can be achieved by calculating the difference between the actual pressure value at each sensor point and the preset pressure range corresponding to the local area to which the sensor belongs. The system sets an allowable pressure deviation threshold; if the difference value at a point exceeds this threshold, the system marks it as a local abnormal area. For example, if the actual pressure in a certain area is too low, it may be due to insufficient friction caused by microscopic adhesions such as oily residues or a mixture of metal dust and fiber particles, requiring increased pressure to enhance stability. If the actual pressure in a certain area is too high, there is a risk of damaging the profile surface, requiring a reduction in pressure to avoid indentations or scratches.

[0023] Subsequently, the method generates local pressure adjustment commands for the local anomaly areas based on the direction and value of the pressure deviation. In one implementation, if the actual pressure in a local anomaly area is identified as being lower than a preset pressure range, the system generates a command to increase the pressure in that area, specifying the required increase in pressure. Conversely, if the actual pressure in a local anomaly area is identified as being higher than the preset pressure range, the system generates a command to decrease the pressure in that area, specifying the required decrease in pressure. These commands are precise and directly respond to the nature and degree of the deviation.

[0024] Finally, the method controls multiple adjustment units distributed on the contact interface to operate independently according to local pressure adjustment commands, thereby changing the physical displacement or deformation of the clamping actuator in the corresponding local area, realizing the dynamic reconstruction of the force field of the contact interface, and then returning to step S1. In one implementation, multiple micro-drive units, such as micro piezoelectric actuators, are integrated inside the gripper pad. Each actuator corresponds to a pressure sensor area on the gripper pad. Each piezoelectric actuator receives voltage commands from the intelligent control system through an independent drive circuit. When the system identifies that the pressure in a certain area is insufficient, it sends a command to the piezoelectric actuator in the corresponding area to increase its drive voltage, causing the piezoelectric actuator to extend slightly, thereby slightly increasing the clamping pressure on the profile in that area. Conversely, when the pressure in a certain area is too high, the system reduces the drive voltage of the corresponding piezoelectric actuator, causing it to contract slightly, thereby appropriately reducing the pressure in that area. This process continues until the difference between the actual pressure distribution and the preset target pressure distribution model converges to within a preset threshold in all areas.

[0025] The following example will provide a more detailed explanation of the above technical solution: Imagine an H-shaped aluminum profile on an aluminum profile production line. After initial cleaning, due to the rapid production pace, trace amounts of release agent and metal dust may remain on some areas of its surface. When this H-shaped profile is conveyed to the inspection station and clamped by an inspection fixture, a traditional fixture applies a preset overall clamping force based on its model. However, due to the presence of surface deposits, the actual pressure distribution at the interface between the clamping jaws and the profile becomes uneven. For example, in areas with thicker deposits, the friction between the clamping jaws and the profile is significantly reduced, causing the actual pressure in that area to be lower than the pressure required for a stable clamping. In other areas, to maintain overall clamping stability, the clamping jaws may generate excessively high pressure locally. This uneven pressure distribution can cause slight slippage of the profile during inspection, introducing measurement errors, and even causing indentations or scratches on the profile surface in areas of localized high pressure.

[0026] To address this issue, this application proposes an intelligent control method. First, when the H-shaped aluminum profile enters the detection fixture, the real-time pressure distribution characteristics of the interface between the clamping actuator and the profile are acquired. Specifically, a network of microarray pressure sensors embedded within the gripper pads collects pressure distribution data at the contact interface in real time. These sensors can be piezoresistive thin-film sensors, for example, thin films made of conductive rubber or piezoresistive materials, cut into 2mm x 2mm microsquares and arranged in a 10x20 array with a 2.5mm spacing, covering the entire contact area of ​​the gripper pads. Each microsquare is connected to an independent electrode lead, which is connected to an analog-to-digital converter via a multiplexer. When the grippers begin to clamp the aluminum profile, the pressure applied to the profile surface changes the resistance value of these piezoresistive materials. The microcontroller measures the resistance change of each sensor and converts it into a corresponding local pressure value. These local pressure values ​​are collected in real time and form a digital "pressure distribution map" in the control system, clearly showing the detailed distribution of the clamping force on the contact surface.

[0027] Simultaneously, the system acquires the identification information of the H-shaped aluminum profile to be inspected. For example, it reads the identification code on the profile using a QR code scanner, or reads the RFID tag on the accompanying tooling plate using an RFID reader, thereby accurately identifying the specific model of the current aluminum profile. Once the model is identified, the system immediately retrieves the preset target pressure distribution model corresponding to that model of H-shaped aluminum profile from its internally stored parameter database. This model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state. For example, for H-shaped aluminum profiles, the preset target pressure distribution model may show that the flange portion of the H-shaped profile requires relatively higher pressure to ensure stability, while the web portion requires lower pressure to avoid deformation.

[0028] Next, the intelligent control system compares the real-time pressure distribution characteristics with a preset target pressure distribution model. The system continuously compares the real-time pressure distribution data obtained from the microarray pressure sensor network with the preset target pressure distribution model retrieved for the current H-shaped aluminum profile model, point by point. This comparison is achieved by calculating the difference between the actual pressure value at each sensor point and the preset pressure range. The system sets an allowable pressure deviation threshold. If the difference at a point exceeds this threshold, the system marks it as a local anomaly area. For example, in areas with thicker deposits on the surface of the H-shaped aluminum profile, the real-time pressure may be lower than the preset target pressure range, and this area will be identified as a local anomaly area with excessively low pressure; conversely, in other areas, if the real-time pressure is too high, it will be identified as a local anomaly area with excessively high pressure.

[0029] For these identified localized anomaly areas, the system generates targeted local pressure adjustment commands based on the direction and value of their pressure deviation. For example, if the actual pressure in a localized anomaly area is too low, the system will generate a command to increase the pressure in that area, specifying the required increase. Conversely, if the actual pressure in a localized anomaly area is too high, the system will generate a command to decrease the pressure in that area, specifying the required decrease.

[0030] Finally, the system controls multiple adjustment units distributed across the contact interface to operate independently based on these local pressure adjustment commands. Multiple miniature piezoelectric actuators are integrated within the gripper pad, each corresponding to one or more pressure sensor areas on the gripper pad. Each piezoelectric actuator receives voltage commands from the intelligent control system via an independent drive circuit. When the system detects insufficient pressure in a certain area, it sends a command to the corresponding piezoelectric actuator to increase its drive voltage, causing the actuator to slightly elongate, thereby slightly increasing the clamping pressure on the profile in that area. Conversely, when the pressure in a certain area is too high, the system decreases the drive voltage of the corresponding piezoelectric actuator, causing it to slightly contract, thereby appropriately reducing the pressure in that area. In this way, the physical displacement or deformation of the clamping actuator in the corresponding local area is changed, achieving dynamic reconstruction of the force field at the contact interface. This process of pressure sensing, comparative analysis, and local force field reconstruction is not completed all at once, but is a high-speed, continuous closed-loop control process. The system continuously receives the latest data and sends update commands hundreds of times per second until the difference between the actual pressure distribution and the preset target pressure model converges to within the preset threshold in all areas, thereby ensuring that the H-shaped aluminum profile is always in a stable and undamaged clamping state throughout the entire detection process.

[0031] The aforementioned technical solution, by introducing a closed-loop feedback control mechanism, achieves a shift from macroscopic overall clamping to dynamic reconstruction of microscopic local pressure, effectively solving the problem of uneven pressure distribution at the contact interface caused by surface deposits. Compared to traditional systems that rely solely on preset parameters for overall clamping force control, this application can sense the actual microscopic pressure distribution between the gripper and the profile surface in real time and make precise and dynamic adjustments for local abnormal areas. Traditional systems cannot detect in real time whether the profile is experiencing minor slippage or surface damage, while this application, through precise control of local pressure, can effectively compensate for the decrease in friction or excessive local pressure caused by deposits, ensuring clamping stability while avoiding damage to the profile surface. This localized active adjustment method significantly improves the reliability of clamping and the integrity of the profile surface, avoiding misjudgments due to measurement errors and scrap due to surface damage, thereby improving production efficiency and product quality stability.

[0032] In some preferred embodiments, the clamping actuator includes a jaw and a pad disposed on the surface of the jaw, and step S1 includes: S11. Real-time acquisition of pressure distribution data at the contact interface through a network of microarray pressure sensors embedded inside the liner.

[0033] The gripper is the component that applies clamping force in the clamping actuator. Its shape and size are usually designed according to the geometric characteristics of the aluminum profile to be inspected. The pad is a flexible material layer placed on the surface of the gripper in direct contact with the aluminum profile to be inspected. Its main function is to buffer the clamping force and ensure the clamping actuator fits the aluminum profile, protecting the surface of the aluminum profile from damage, and providing a mounting carrier for the pressure sensor. The pad material can be polyurethane, silicone rubber, or special composite materials that have a certain degree of elasticity, wear resistance, and are easy to process. A microarray pressure sensor network is a sensor system integrated from multiple miniature pressure sensors arranged in a specific array. It can perform refined and spatial real-time sensing of the pressure distribution on the contact interface. Real-time acquisition of pressure distribution data at the contact interface refers to acquiring the pressure values ​​of various local areas on the contact interface continuously or at high frequency through the aforementioned microarray pressure sensor network, and converting them into electrical or digital signals that can be processed by the control system. This process typically involves sensor signal conditioning, multiplexing, analog-to-digital conversion, and data transmission (all of which are existing technologies) to ensure the time synchronization and accuracy of the data.

[0034] When the clamping actuator grips the aluminum profile, the grippers contact the profile through pads on their surfaces. A network of microarray pressure sensors within the pads divides the entire contact interface into multiple independent local regions and collects pressure data for each region in real time. This pressure data is transmitted to the control system, forming a detailed real-time pressure distribution characteristic map. This real-time pressure distribution characteristic map serves as the basis for comparison with a preset target pressure distribution model in subsequent steps, enabling the system to overcome the limitations of traditional control methods that can only sense the overall clamping force, achieving refined and spatial perception of the pressure distribution at the contact interface. This sensing method can capture local pressure anomalies caused by surface deposits or uneven contact, thus providing accurate raw data for subsequent identification of abnormal local areas and generation of targeted adjustment commands. In this way, the method can provide reliable and precise data support for obtaining the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile under test. This enables the effective implementation of subsequent pressure deviation identification, local pressure adjustment command generation, and independent action of the adjustment unit, ultimately achieving dynamic reconstruction of the force field at the contact interface and ensuring stable and non-destructive clamping of the aluminum profile during the testing process.

[0035] In some preferred embodiments, the adjustment unit is a miniature piezoelectric actuator embedded inside the liner, and the miniature piezoelectric actuator is spatially positioned corresponding to the microarray pressure sensor network.

[0036] Embedding refers to integrating or encapsulating an adjustment unit (such as a micro piezoelectric actuator) within the padding of a clamping actuator. This arrangement allows the adjustment unit to fit tightly against the contact interface and act directly on the clamping surface, thereby achieving precise and direct control of localized pressure. Furthermore, the embedded design helps protect the adjustment unit from wear and contamination from the external environment and maintains the compactness and stability of the overall clamping actuator structure. A micro piezoelectric actuator is a miniature actuator that operates using the inverse piezoelectric effect of piezoelectric materials. When a voltage is applied, the piezoelectric material undergoes minute deformation, producing precise physical displacement. These actuators offer advantages such as fast response speed, high positioning accuracy, small size, and relatively low power consumption, making them ideal for applications requiring precise, localized force control. Their working principle is based on the mechanical strain generated by piezoelectric ceramics and other materials under the influence of an electric field, thereby achieving micron- or even nanometer-level displacement output. Spatial correspondence refers to the one-to-one or regional correspondence between the physical position of the micro piezoelectric actuator inside the liner and a specific sensor or sensor area in the micro array pressure sensor network. This correspondence is the basis for achieving closed-loop control and precise local adjustment. It ensures that when a pressure sensor in a certain local area detects an anomaly, the micro piezoelectric actuator located in or adjacent to that area can respond and adjust precisely, avoiding interference with non-abnormal areas, thereby achieving highly localized force field reconstruction.

[0037] This solution embeds a miniature piezoelectric actuator as an adjustment unit within the pad of the clamping actuator, establishing a precise spatial correspondence between it and a microarray pressure sensor network. This creates a highly integrated and rapidly responsive local force field adjustment system. When the microarray pressure sensor network acquires real-time pressure distribution data at the contact interface between the clamping actuator and the aluminum profile under test, the system identifies local anomaly areas where pressure deviations exceed preset thresholds. For these anomaly areas, the system generates corresponding local pressure adjustment commands. The miniature piezoelectric actuator, embedded within the pad and corresponding to the anomaly area, receives and executes these commands. Utilizing the deformation properties of its piezoelectric material, the miniature piezoelectric actuator directly alters the physical displacement or deformation of the aluminum profile in its localized area through precise micro-elongation or contraction. This direct and localized physical action allows the force field at the contact interface to be dynamically and precisely reconstructed. For example, when the sensor detects that the pressure in a certain local area is too low, the corresponding miniature piezoelectric actuator will slightly extend, increasing the clamping force in that area; conversely, when the pressure is too high, the actuator will slightly contract, reducing the clamping force in that area. This spatial correspondence and coordinated operation between the sensor and the actuator ensures that pressure regulation commands can be accurately translated into actual physical actions, thereby eliminating local pressure unevenness caused by surface deposits on the profile at the microscopic level. This effectively prevents minor slippage of the profile during the detection process and avoids surface damage caused by excessive local pressure. In this way, this solution tightly integrates pressure sensing with local adjustment, achieving closed-loop, intelligent control of the clamping process, significantly improving the stability and non-destructive nature of clamping.

[0038] In some preferred embodiments, step S3 includes: S31. For each local area on the contact interface, obtain multiple real-time pressure data points of the local area within the preset time window based on multiple real-time pressure distribution characteristics collected within the preset time window, and then obtain the stable pressure value of the local area based on all real-time pressure data points. S32. For each local area, compare the stable pressure value corresponding to the local area with the preset pressure range of the local area in the preset target pressure distribution model to obtain the pressure deviation of the local area. S33. For each local area, analyze whether the following conditions are met simultaneously: the pressure deviation corresponding to the local area persists within a preset time window; the pressure deviation corresponding to the local area is consistent with the pressure deviation of its adjacent local areas; and the absolute value of the pressure deviation corresponding to the local area is greater than a preset threshold. If so, the pressure anomaly in the local area is considered to be caused by the surface deposits of the profile, and the local area is marked as a local anomaly area. If not, the pressure anomaly in the local area is considered to be caused by a transient disturbance, and the local area is not marked as a local anomaly area.

[0039] A preset time window refers to a continuous time period pre-set by the system during pressure data acquisition and analysis. Its purpose is to provide a time-dimensional data accumulation and observation range to capture the dynamic characteristics of pressure changes and distinguish between instantaneous disturbances and persistent anomalies. This time window can be set according to factors such as the production line cycle time, the physical characteristics of the profiles, and the sensor response speed; for example, it can be set to 0.1 seconds to 1 second. Multiple real-time pressure data points refer to the pressure measurements continuously collected at different times for each local area on the contact interface within the preset time window, through a pressure sensor network. These data points reflect the pressure change trajectory of that local area over a period of time. Acquiring these data points is the foundation for time series analysis and can be achieved using fixed-frequency sampling (e.g., sampling once every millisecond) or event-triggered sampling. A stable pressure value is a value obtained by statistically processing multiple real-time pressure data points collected within the preset time window, representing the average or trend pressure of that local area over a period of time. Its function is to eliminate the influence of instantaneous noise and fluctuations, obtaining a more representative pressure state. Methods for obtaining stable pressure values ​​may include, but are not limited to, calculating the arithmetic mean and median of all data points.

[0040] In this embodiment, the pressure deviation refers to the difference between the stable pressure value of a local area and the preset pressure range of that local area in the preset target pressure distribution model. Its function is to quantify the degree of deviation between the actual clamping state and the ideal state. The pressure deviation can be the difference between the stable pressure value and the center value of the preset pressure range, or the amount by which the stable pressure value exceeds the upper or lower limit of the preset pressure range.

[0041] The persistence of pressure deviation within a preset time window refers to the continuous existence of pressure deviation in a certain local area within a set time period, rather than a brief, instantaneous fluctuation. Its purpose is to eliminate false alarms caused by occasional interferences (such as mechanical vibration or airflow changes), ensuring that the identified pressure anomalies are stable and require attention. Whether the pressure deviation persists within the preset time window can be determined by statistically analyzing whether the number of sampling points with pressure deviations exceeding a threshold within the preset time window reaches a certain proportion, or by calculating whether the duration of the pressure deviation exceeds a preset duration threshold. Consistency of pressure deviations in adjacent local areas refers to spatially close local areas exhibiting similar directions and / or magnitudes of pressure deviations. Its purpose is to utilize the continuity of physical contact to further confirm the authenticity of the anomaly and eliminate individual sensor malfunctions or isolated minor disturbances. For example, if the pressure in one local area is too low, and surrounding local areas also generally show a trend of low pressure, then consistency is considered. Consistency can be determined by calculating the Euclidean distance between the pressure deviations of adjacent areas. The preset threshold is a critical value set when determining whether a pressure deviation constitutes an anomaly. Its function is to distinguish between normal fluctuations and significant anomalies that require attention. Only when the absolute value of the pressure deviation exceeds this preset threshold will it be considered a potential anomaly.

[0042] This solution effectively improves the accuracy of anomaly identification by introducing a comprehensive analysis mechanism that integrates time and spatial dimensions. First, by collecting multiple pressure data points and calculating stable pressure values ​​within a preset time window, random noise caused by transient disturbances is smoothed out, ensuring that the pressure data used for subsequent comparisons reflects the true steady-state contact between the profile and the fixture. Second, by comparing the stable pressure value with a preset pressure range, the pressure deviation in local areas can be accurately quantified, providing an accurate numerical basis for subsequent logical judgments. Finally, a rigorous anomaly screening logic is constructed by setting three judgment conditions: persistence, spatial consistency, and absolute value threshold. Specifically, the pressure deviation must persist within the time window to eliminate transient interference signals; the pressure deviations in adjacent areas must be consistent, utilizing the continuity of physical contact to further filter out local sensor malfunctions or isolated transient disturbances; and the absolute value of the pressure deviation must exceed a preset threshold to ensure that only anomalies reaching a certain level are processed. This multi-dimensional logical judgment can accurately distinguish between real pressure anomalies caused by deposits on the profile surface and instantaneous disturbances caused by environmental factors, thereby avoiding malfunctions and significantly enhancing the robustness of the fixture control system. Compared with the basic solution that relies solely on pressure data at a single moment for comparison, this method is more intelligent and reliable in identifying pressure anomalies, effectively reducing false positives and false negatives, and thus improving the stability and efficiency of the entire intelligent control method for aluminum profile inspection fixtures.

[0043] As a specific implementation method, in the intelligent control method for aluminum profile inspection fixtures, step S3 can be implemented as follows: After the clamping actuator clamps the aluminum profile to be inspected, the system will start a preset time window, for example, set to 0.5 seconds. Within this 0.5 seconds, the microarray pressure sensor network embedded in the pads on the surface of the grippers continuously collects real-time pressure data of each local area on the contact interface at a frequency of 100 times per second. For each local area, for example, the area corresponding to a sensor unit with a size of 2 mm x 2 mm, 50 real-time pressure data points will be collected within 0.5 seconds. Subsequently, the system can calculate the arithmetic mean of these 50 data points to obtain the stable pressure value of the local area. For example, if the pressure data points of a certain local area within 0.5 seconds are [100 kPa, 102 kPa, 98 kPa, ..., 101 kPa], then its stable pressure value may be calculated as 100.5 kPa. Next, the system compares the stable pressure value of the local area (e.g., 100.5 kPa) with the preset pressure range corresponding to that local area in the preset target pressure distribution model. Assume the preset pressure range for this local area is [95 kPa, 105 kPa]. If the stable pressure value is within this range, the pressure deviation is 0; if the stable pressure value exceeds this range, for example, a stable pressure value of 90 kPa, the pressure deviation is -5 kPa, or a stable pressure value of 110 kPa, the pressure deviation is +5 kPa. Finally, the system makes a comprehensive judgment for each local area. For example, for a certain local area with a pressure deviation of -5 kPa, the system will first check whether the -5 kPa pressure deviation persists within a preset time window of 0.5 seconds by analyzing whether at least 80% of the sampling points show pressures below 95 kPa. Secondly, the system analyzes whether the pressure deviation of the local area (-5 kPa) is consistent with the pressure deviations of its eight adjacent local areas by examining whether the pressure deviations of adjacent areas are also generally negative and of similar magnitude (e.g., between -3 kPa and -7 kPa). Finally, the system determines whether the absolute value of the pressure deviation (5 kPa) is greater than a preset threshold, for example, a threshold set to 3 kPa. Only when all three conditions—persistence, spatial consistency, and absolute value greater than the preset threshold—are met simultaneously will the system determine that the pressure anomaly in the local area is caused by deposits on the profile surface and mark it as a local anomaly area, thereby triggering subsequent local pressure adjustment. If any of these conditions are not met, for example, if the pressure deviation exists but is only a momentary fluctuation, or if the pressure in its adjacent areas is normal, the anomaly will be considered a momentary disturbance and will not be marked or adjusted.

[0044] Through the above technical solution, this application, when identifying local abnormal areas at the contact interface between the clamping actuator and the aluminum profile under inspection, no longer relies solely on pressure data at a single moment, but introduces a comprehensive analysis of time and space dimensions. Specifically, by collecting multiple real-time pressure data points within a preset time window and calculating stable pressure values, random noise caused by common instantaneous disturbances in the production environment can be effectively filtered out, ensuring that the analyzed pressure data more accurately reflects the clamping state. Simultaneously, by judging the persistence of pressure deviation within the preset time window and its consistency with adjacent local areas, combined with whether the absolute value of the pressure deviation exceeds a preset threshold, a more rigorous and intelligent abnormality identification logic is constructed. This multi-verification mechanism can accurately distinguish between real, continuous pressure anomalies caused by surface deposits on the profile and transient, isolated instantaneous disturbances caused by environmental factors, thereby preventing the system from misjudging normal instantaneous pressure fluctuations as abnormal areas requiring adjustment, significantly reducing unnecessary clamping actions. Therefore, the solution of this application effectively improves the stability and reliability of the intelligent control method for aluminum profile inspection clamps, reduces the probability of misadjustment, and thus improves the accuracy of inspection and production efficiency.

[0045] In some preferred embodiments, step S33 includes: S331. For each local area, perform time series analysis on the pressure deviation of the local area within a preset time window to obtain the continuous evolution characteristics of the pressure deviation of the local area. S332. For each local area, perform spatial distribution evolution analysis on the pressure deviation of the local area and its adjacent local areas to obtain the consistency characteristics of the spatial evolution of pressure deviation in that local area. S333. For each local area, determine the duration judgment threshold and consistency judgment threshold based on the continuous evolution characteristics of the pressure deviation and the spatial evolution consistency characteristics of the pressure deviation in that local area. S334. For each local area, analyze whether the following conditions are met simultaneously: the duration of the pressure deviation corresponding to the local area within the preset time window is greater than or equal to the duration judgment threshold; the consistency between the pressure deviation corresponding to the local area and the pressure deviation of its adjacent local areas is greater than or equal to the consistency judgment threshold; and the absolute value of the pressure deviation corresponding to the local area is greater than the preset threshold. If so, the pressure anomaly in the local area is considered to be caused by the surface deposits of the profile, and the local area is marked as a local anomaly area. If not, the pressure anomaly in the local area is considered to be caused by the transient disturbance, and the local area is not marked as a local anomaly area.

[0046] Step S331 aims to capture the dynamic patterns of pressure deviation over time, such as whether it continuously increases, decreases, remains stable, or exhibits periodic fluctuations. Specifically, statistical methods such as moving averages and exponential smoothing can be used to process the pressure deviation data within a preset time window, extracting features such as mean, variance, and rate of change. Furthermore, signal processing techniques such as Fourier transform or wavelet analysis can be used to analyze the time series data of pressure deviation and identify its periodicity, trend, or abrupt changes. Through these analyses, the system can quantify the persistence and evolution trend of pressure anomalies (persistent evolution characteristics of pressure deviation).

[0047] Step S332 is used to assess the spatial correlation of pressure anomalies and their spatial diffusion, contraction, or movement patterns over time. For example, spatial consistency can be quantified by calculating indicators such as the correlation coefficient, Euclidean distance, or Manhattan distance between the pressure deviations of a local region and its neighboring local regions. Alternatively, morphological operations in image processing, such as dilation, erosion, or connected component analysis, can be used to identify changes in the shape, size, and location of pressure anomaly regions, thereby obtaining spatial evolution consistency characteristics.

[0048] The core of step S333 lies in achieving adaptive adjustment of the judgment criteria. For example, a mapping table can be established for combinations of persistent evolution features and spatial evolution consistency features, and their corresponding duration judgment thresholds and consistency judgment thresholds. This mapping table stores the duration judgment thresholds and consistency judgment thresholds corresponding to different persistent evolution features and spatial evolution consistency features. After obtaining the persistent evolution features and spatial evolution consistency features, this embodiment extracts the duration judgment thresholds and consistency judgment thresholds corresponding to the obtained persistent evolution features and spatial evolution consistency features from the mapping table.

[0049] Step S334 achieves accurate identification of the cause of pressure anomalies by comprehensively considering the above three dynamically adjusted judgment conditions. This can be achieved by performing an AND logical operation on the three judgment conditions using logic gate circuits or conditional judgment statements in software. Only when all conditions are met is the local area marked as a local anomaly area caused by the adhering substances on the profile surface.

[0050] This application's solution achieves accurate identification of the causes of pressure anomalies by introducing multi-dimensional evolution consistency analysis. Specifically, the system first performs time-series analysis on the pressure deviation of each local area within a preset time window to capture its continuous evolution characteristics. This allows the system to perceive whether the pressure anomaly caused by the deposit is increasing, decreasing, stabilizing, or fluctuating, providing a quantitative basis for subsequent judgment. Simultaneously, the system also performs spatial distribution evolution analysis on the pressure deviation of each local area and its adjacent local areas to obtain the spatial evolution consistency characteristics of the pressure deviation in that local area. This helps the system understand the spatial influence range and morphological changes of the deposit on the contact surface, such as the diffusion of oily substances or the movement of particles, thereby supporting the dynamic adjustment of subsequent spatial consistency judgment criteria.

[0051] Based on these dynamically acquired persistent evolution characteristics and spatial consistency characteristics, the system can adaptively determine the duration and consistency thresholds for each local region. This means that the duration and spatial correlation (spatial consistency) criteria required for anomaly detection are no longer static fixed values, but are adjusted according to the actual dynamic changes in pressure deviation. For example, if the pressure deviation increases rapidly and shows a diffusion trend, the system can appropriately shorten the time required for persistence detection and expand the scope of spatial consistency detection to improve real-time response to rapidly changing attachments. Conversely, if the pressure deviation changes slowly and shows isolated points, the system can extend the detection time to improve robustness and adjust the detection logic to adapt to discretely distributed anomalies.

[0052] Ultimately, the system comprehensively considers whether the duration of the pressure deviation corresponding to the local area within a preset time window is greater than or equal to a dynamically determined duration threshold, whether the consistency between the pressure deviation of the local area and the pressure deviation of its adjacent local areas is greater than or equal to a dynamically determined consistency threshold, and whether the absolute value of the pressure deviation corresponding to the local area is greater than a preset threshold. Only when all three conditions are met simultaneously is the pressure anomaly in the local area identified as being caused by surface deposits on the profile, and it is marked as a local anomaly area. This multi-dimensional, adaptive judgment mechanism can effectively distinguish between substantial pressure anomalies caused by surface deposits on the profile and transient disturbances, avoiding ineffective adjustments to transient disturbances. In this way, based on the aforementioned intelligent control method for aluminum profile inspection fixtures, this solution significantly improves the real-time performance and adaptability of anomaly area identification, ensuring the effectiveness and specificity of subsequent local pressure adjustment commands, thereby more accurately achieving dynamic reconstruction of the contact interface force field, and further guaranteeing the stable clamping and non-destructive nature of the aluminum profile during the inspection process.

[0053] The following is a concrete example. Suppose that during the clamping of an aluminum profile, the microarray pressure sensor network detects that the pressure deviation in a certain local area on the gripper pad is consistently lower than the preset target pressure. The system will initiate a time-series analysis of the pressure deviation in that local area. For example, within a preset 5-second time window, the pressure deviation in this local area gradually decreases from -5 kPa to -18 kPa, and the rate of decrease shows an accelerating trend. Through time-series analysis, the system can identify this continuous evolution characteristic of "continuously decreasing and accelerating" pressure deviation. Simultaneously, the system will also perform a spatial distribution evolution analysis of the pressure deviation in this local area and its eight adjacent local areas. For example, within the aforementioned 5-second time window, not only does the pressure in the central local area continuously decrease, but the pressure in its four adjacent local areas also successively decreases, and the pressure deviations in these adjacent areas show a high correlation with the pressure deviation in the central area, forming a gradually expanding low-pressure region. Through spatial distribution evolution analysis, the system can identify this consistent spatial evolution characteristic of "pressure decreasing in the central area and spreading to the surrounding areas" in the pressure deviation.

[0054] Based on the above analysis results, the system will dynamically adjust the judgment thresholds. For example, if the system detects a rapid decrease and outward diffusion of the pressure deviation, it can determine that this may be a rapidly evolving deposit (such as liquid diffusion). Therefore, the duration judgment threshold can be adjusted from the default 2 seconds to 1.5 seconds, and the consistency judgment threshold can be adjusted from the default 0.7 (correlation coefficient) to 0.85 to improve the sensitivity to such rapidly spreading anomalies.

[0055] Finally, the system will make a comprehensive judgment based on these dynamically adjusted thresholds. If the pressure deviation in a local area persists for 1.8 seconds (greater than the adjusted 1.5 seconds) within a preset time window, its consistency with the pressure deviation of adjacent areas is 0.88 (greater than the adjusted 0.85), and its absolute pressure deviation is 1.8 kPa (greater than the preset 10 kPa threshold), then the system will determine that the pressure anomaly in this local area is caused by substances adhering to the profile surface and mark it as a local anomaly area. Conversely, if the pressure deviation only occurs momentarily, or is limited to a single local area and has no obvious connection with the surrounding areas, it will be considered a transient disturbance and will not be marked as an anomaly.

[0056] Through the above technical solution, this application effectively solves the problem of delayed or misidentification caused by the dynamic changes of deposits when identifying pressure anomalies caused by surface deposits on profiles using traditional methods. By performing time-series analysis on pressure deviation, the system can capture the continuous evolution characteristics of pressure anomalies, thereby perceiving whether the pressure changes caused by deposits are increasing, decreasing, or stabilizing, providing a dynamic basis for subsequent judgment. Simultaneously, through spatial distribution evolution analysis, the system can understand the spatial influence range and morphological changes of deposits on the contact surface, avoiding misjudgments caused by the spatial dynamic changes of deposits. Based on this, the system adaptively determines the duration judgment threshold and consistency judgment threshold according to these dynamic evolution characteristics, so that the judgment criteria are no longer static fixed values, but are adjusted according to the actual dynamic changes in pressure deviation, significantly improving the real-time performance and adaptability of anomaly area identification. Finally, by comprehensively considering the three dimensions of duration, spatial consistency, and absolute value of pressure deviation, the system can accurately distinguish substantial pressure anomalies caused by surface deposits on profiles, thereby avoiding ineffective adjustment of transient disturbances. This enables the clamp control system to more accurately identify the local areas that need adjustment and generate more targeted local pressure adjustment commands. This ensures that changes in the physical displacement or deformation of the clamping actuator in the corresponding local area can achieve dynamic reconstruction of the force field at the contact interface, effectively preventing profile damage or detection errors caused by uneven clamping force, and significantly improving the accuracy of aluminum profile inspection and the stability of product quality.

[0057] In some preferred embodiments, step S332 includes: A1. For each local area, identify the first pressure abnormality area and the second pressure abnormality area corresponding to each time node of the preset time window; the first pressure abnormality area is the area in the current local area where the pressure deviation exceeds the preset threshold, and the second pressure abnormality area is the area in the local area adjacent to the current local area where the pressure deviation exceeds the preset threshold. A2. Obtain the first spatial features corresponding to each time node of the first pressure anomaly region within a preset time window, and obtain the second spatial features corresponding to each time node of the second pressure anomaly region within the preset time window; both the first and second spatial features include the location, size, and shape of the pressure anomaly region. A3. Obtain the trend of change of the first spatial features based on all first spatial features, and obtain the trend of change of the second spatial features based on all second spatial features; A4. Obtain the similarity of the change trends based on the change trends of the first spatial feature and the change trends of the second spatial feature, and use the similarity of the change trends as the consistency feature of the spatial evolution of pressure deviation.

[0058] In step A1, identifying the first and second pressure anomaly regions aims to distinguish between the pressure anomalies within the current local area itself and those in its surrounding areas. The first pressure anomaly region refers to a sub-region within the current local area whose pressure deviation exceeds a preset threshold, indicating a significant pressure anomaly within that local area. The second pressure anomaly region refers to a sub-region adjacent to the current local area whose pressure deviation also exceeds the preset threshold, which helps assess the spatial diffusion or correlation of the pressure anomaly. This distinction allows for a more detailed analysis of the locality and proximity effects of the pressure anomaly.

[0059] In step A2, obtaining the first and second spatial features is to quantitatively describe the geometric characteristics of these pressure anomaly regions. Both the first and second spatial features can include the location, size, and shape of the pressure anomaly region. For example, the location can be represented by the centroid coordinates or bounding box coordinates of the anomaly region; the size can be represented by the area, perimeter, or aspect ratio of the anomaly region; and the shape can be represented by a shape factor, moment invariant, or contour descriptor. Obtaining these spatial features makes it possible to quantitatively analyze the dynamic changes of the pressure anomaly regions.

[0060] In step A3, the changing trends of the first spatial features are obtained based on all first spatial features, and the changing trends of the second spatial features are obtained based on all second spatial features. This means that within a preset time window, the spatial features of the first and second pressure anomaly regions identified at each time point are analyzed sequentially. For example, the movement trajectory of the centroid of the anomaly region, the increase or decrease in area, and the evolution of shape can be tracked. By performing trend analysis on these sequential data, it can be revealed whether the pressure anomaly is stable, spreading, contracting, or moving in space.

[0061] Finally, in step A4, the similarity of the changing trends is obtained based on the changing trends of the first and second spatial features, and this similarity is used as a consistency feature of the spatial evolution of pressure deviation. The similarity of the changing trends can be calculated using various methods, such as correlation coefficients or Euclidean distance algorithms, to measure the degree of similarity between the changing trends of the first and second spatial features. A high similarity indicates a high degree of consistency in the spatial evolution of the pressure anomaly in the current local area with that in its adjacent areas. This strongly suggests that the pressure anomaly is caused by a relatively stable factor with a certain spatial range (such as surface deposits on the profile), rather than an instantaneous, random disturbance.

[0062] This application's solution extends pressure anomaly monitoring from a single local area to neighborhood correlation analysis by introducing spatial evolution consistency analysis, thereby effectively improving the accuracy of anomaly source determination. By identifying the pressure anomaly distribution of the current local area and adjacent areas over time, the spatial linkage effect generated by the attachment during the clamping process can be captured. Specifically, by acquiring the spatial characteristics such as the location, size, and shape of the first and second pressure anomaly areas at different time points, a dynamic trajectory of the anomaly area's evolution over time can be constructed. Based on this, by comparing the changing trends of the first and second spatial characteristics and calculating their similarity, the degree of spatial co-evolution of local pressure anomalies can be quantitatively assessed. This feature extraction method based on spatial evolution consistency can effectively filter out isolated and irregular pressure fluctuations caused by instantaneous disturbances, ensuring that only when the pressure anomaly exhibits an evolution pattern highly consistent with the surrounding area is it determined to be caused by surface attachments. This provides a reliable decision-making basis for subsequent pressure reconstruction and significantly enhances the system's anti-interference capability and identification accuracy under complex working conditions.

[0063] As a specific implementation, assuming that when clamping and inspecting an aluminum profile, the microarray pressure sensor network inside the pad of the clamping actuator collects pressure distribution data of the contact interface in real time, with each abnormal area corresponding to multiple pressure sensors. Within a preset time window, the system detects that the pressure deviation exceeds a preset threshold in a certain local area and its adjacent areas.

[0064] In step A1, at each time point, the system identifies the sub-region in the current local area where the pressure deviation exceeds a preset threshold as the first pressure anomaly region, and identifies the sub-region in its adjacent local area where the pressure deviation exceeds the preset threshold as the second pressure anomaly region. The system repeats this identification process at each sampling time point (e.g., every 1 millisecond) within a preset time window (e.g., 100 milliseconds). For example, at time t1, the first pressure anomaly region might be a circular region, and the second pressure anomaly region might be an elliptical region adjacent to it.

[0065] In step A2, the system acquires the spatial features of these abnormal regions. For example, for the first pressure anomaly region at time t1, its spatial features may include centroid coordinates (x1, y1), area A1, and shape factor S1; for the second pressure anomaly region, its spatial features may include centroid coordinates (x'1, y'1), area A'1, and shape factor S'1. At subsequent times t2, t3...tn, the system continuously acquires the spatial features corresponding to these anomaly regions. For each pressure anomaly region, its location can be represented by the average coordinates (centroid) of the pressure sensors contained within the region, its size can be represented by the number of pressure sensors contained within the region, and its shape can be approximated by calculating the aspect ratio of the minimum bounding rectangle of the region or the ratio of the perimeter to the area of ​​its outline.

[0066] In step A3, the system analyzes the changing trends of the first and second pressure anomaly regions based on these time-series spatial feature data. For example, it performs linear fitting on the centroid coordinate sequence to obtain its velocity and direction vectors; it performs differencing on the size value sequence to obtain its growth or contraction rate; and it performs differencing on the shape sequence to obtain its growth or contraction rate. Similarly, the system performs similar processing on all second spatial feature data to obtain the changing trends of the second spatial features. If the centroid of the first pressure anomaly region hardly moves within a preset time window, and its area and shape remain relatively stable, it indicates that its changing trend is stable. Similarly, if the second pressure anomaly region also exhibits a similarly stable changing trend...

[0067] Finally, in step A4, the system calculates the similarity between the first spatial feature change trend and the second spatial feature change trend. For example, if both trends are represented as vectors (such as a direction of movement vector or a rate of change of size vector), their similarity can be quantified by calculating the cosine similarity between these two vectors. If the similarity value (e.g., close to 1 indicates high similarity) exceeds a certain preset similarity threshold, the two anomalous regions are considered to have a high degree of consistency in spatial evolution, and this similarity value is used as a consistency feature of the spatial evolution of pressure deviation. This consistency feature will serve as an important basis for determining whether the pressure anomaly is caused by deposits on the profile surface.

[0068] Through the above technical solution, this application can significantly improve the accuracy of determining the source of pressure anomalies in the intelligent control method for aluminum profile inspection fixtures. Traditional methods, when faced with complex and ever-changing production environments, struggle to effectively distinguish between persistent pressure anomalies caused by deposits on the profile surface and random pressure fluctuations caused by environmental noise or transient disturbances. This application, by introducing spatial evolution consistency analysis, extends the monitoring of pressure anomalies in a single local area to neighborhood correlation analysis, thereby capturing the spatial linkage effect generated by deposits during clamping. Specifically, by continuously identifying the first and second pressure anomaly areas within a preset time window and accurately acquiring their spatial characteristics such as position, size, and shape at each time node, this application can construct a dynamic trajectory of the anomaly area's evolution over time. Based on this, by comparing the changing trends of the current local area with those of adjacent local areas and calculating their trend similarity, the degree of spatial co-evolution of local pressure anomaly areas can be quantitatively assessed. This feature extraction method based on spatial evolution consistency can effectively filter out isolated and irregular pressure fluctuations caused by transient disturbances, ensuring that only when the pressure anomaly exhibits an evolution pattern highly consistent with the surrounding area is it determined to be caused by surface deposits. Therefore, the solution in this application provides a more reliable decision-making basis for subsequent pressure reconstruction, significantly enhancing the system's anti-interference capability and recognition accuracy under complex working conditions. This enables the fixture to more accurately identify the actual pressure unevenness caused by surface deposits on the profile, thereby avoiding unnecessary adjustments to transient and harmless disturbances, reducing misjudgments and misoperations, improving the stability and reliability of the clamping process, ultimately ensuring the accuracy of aluminum profile detection, and effectively avoiding profile damage caused by improper clamping.

[0069] In some preferred embodiments, step S4 includes: S41. Generate initial adjustment commands for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. S42. Obtain structural characteristic information and damage sensitivity information corresponding to local abnormal regions; S43. Based on structural characteristic information and damage sensitivity information, adjust the initial adjustment command to obtain a local pressure adjustment command for the local abnormal area.

[0070] When generating initial adjustment commands for localized abnormal areas, the commands can be proportionally generated based on the magnitude of the pressure deviation. For example, the larger the pressure deviation, the stronger the initial adjustment command, and the direction directly corresponds to pressure increase or decrease. Alternatively, a mapping table between pressure deviation and initial adjustment commands can be pre-established, allowing the system to retrieve the corresponding command based on the real-time pressure deviation.

[0071] When acquiring structural characteristic information and damage sensitivity information corresponding to local abnormal areas, the structural characteristics (e.g., wall thickness, cross-sectional shape, material strength grade) and damage sensitivity (e.g., surface hardness, scratch resistance grade) information of the aluminum profile can be retrieved from the preset profile database based on the aluminum profile identification information in different local areas.

[0072] When adjusting the initial adjustment command based on structural characteristic information and damage sensitivity information, safety thresholds can be set. Specifically, the maximum safe bearing pressure upper limit and the minimum stable clamping pressure lower limit for the local area are determined based on the structural characteristic information; the maximum non-scratching pressure upper limit is determined based on the damage sensitivity information. Then, the initial adjustment command is limited to these safety threshold ranges.

[0073] This solution ensures non-destructive clamping by incorporating consideration of the physical properties of the aluminum profile itself during the generation of local pressure adjustment commands. Specifically, in the intelligent control method for aluminum profile inspection fixtures, the system first acquires real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile under inspection through a micro-array pressure sensor network, and compares this with a preset target pressure distribution model to identify local abnormal areas where pressure deviations exceed preset thresholds. Based on this, the solution further generates a preliminary adjustment command according to the direction and value of the pressure deviation in the identified local abnormal areas, aiming to correct pressure unevenness. However, to avoid damaging the aluminum profile while correcting pressure, the system further acquires structural characteristic information and damage sensitivity information of the local abnormal area. This information reflects the profile's load-bearing capacity and surface tolerance in this area. Subsequently, the system uses this structural characteristic information and damage sensitivity information to fine-tune the previously generated initial adjustment command. This adjustment mechanism ensures that the final output local pressure adjustment command not only effectively eliminates pressure deviation but also controls the clamping pressure within the safe tolerance range of the aluminum profile, avoiding structural deformation or surface scratches caused by overpressure. In this way, the solution of this application elevates simple pressure deviation compensation to intelligent adjustment that also considers the physical safety of the profile. This allows for the subsequent dynamic reconstruction of the contact interface force field by independently changing the physical displacement or deformation of the clamping actuator in the corresponding local area through the adjustment unit, ensuring a stable and non-destructive clamping of the aluminum profile at all times.

[0074] The following is a concrete example. Suppose that during aluminum profile inspection, the system detects that the real-time pressure in a certain local area is lower than the preset target pressure, and the pressure deviation is significant. In this case, the system will generate an initial pressure increase command based on the direction (requiring increased pressure) and value of this pressure deviation, for example, requesting an increase of 50N in local pressure. Simultaneously, the system will retrieve the structural characteristics of the abnormal local area from the database based on the aluminum profile's identification information. For example, the area has a thin wall thickness, a material strength grade of T5, and damage sensitivity information, such as low surface hardness and susceptibility to scratches. Based on this information, the system will calculate the maximum safe bearing pressure limit for this local area as 40N, the maximum scratch-free pressure limit as 35N, and the minimum stable clamping pressure limit as 20N. Since the initial adjustment command requires an increase of 50N in pressure, this exceeds the maximum scratch-free pressure limit of 35N and the maximum safe bearing pressure limit of 40N. Therefore, the system will adjust the initial adjustment command according to these safety thresholds. Specifically, the system will adjust the final local pressure adjustment command to increase the pressure by 35N to ensure that the pressure deviation is eliminated without causing scratches on the surface of the aluminum profile.

[0075] Through the above technical solution, this application effectively solves the damage problem caused by the failure to consider the physical properties of aluminum profiles in traditional methods. In the intelligent control method for aluminum profile inspection fixtures, by introducing and applying information on the structural characteristics and damage sensitivity of local abnormal areas, the generated local pressure adjustment commands can fully consider the load-bearing capacity and surface tolerance of the aluminum profiles. This avoids structural damage or surface scratches to the profiles due to over- or improper adjustment when correcting pressure deviations, thus ensuring the non-destructive nature of the clamping process. This solution significantly improves the safety and reliability of clamping while achieving dynamic reconstruction of the force field at the contact interface, effectively reducing product scrap rates and ensuring the inspection quality of aluminum profiles.

[0076] In some preferred embodiments, step S43 includes: S431. Based on the structural characteristics of the local abnormal area, determine the maximum safe pressure limit of the local abnormal area. S432. Based on the damage sensitivity information of the local abnormal area, determine the upper limit of the maximum scratch-free pressure of the local abnormal area; S433. Based on the structural characteristics of the local abnormal area and the preset target pressure distribution model, determine the minimum stable clamping pressure lower limit of the local abnormal area; the minimum stable clamping pressure lower limit is less than the maximum safe bearing pressure lower limit and the maximum non-scratching pressure upper limit; S434. If the maximum safe pressure limit is greater than or equal to the maximum scratch-free pressure limit, the final acceptable pressure range for this local abnormal area is generated based on the maximum scratch-free pressure limit and the minimum stable clamping pressure limit. S435. If the maximum safe pressure limit is less than the maximum pressure limit without scratches, the final acceptable pressure range for this local abnormal area is generated based on the maximum safe pressure limit and the minimum stable clamping pressure limit. S436. Analyze whether the pressure corresponding to the initial adjustment command is within the final acceptable pressure range. If so, treat the initial adjustment command as a local pressure adjustment command for the local abnormal area. If not, proceed to step S437. S437. If the pressure corresponding to the initial adjustment command is greater than the upper limit of the final acceptable pressure range, then the pressure corresponding to the initial adjustment command is adjusted to the upper limit of the final acceptable pressure range to obtain a local pressure adjustment command for the local abnormal area; if the pressure corresponding to the initial adjustment command is less than the lower limit of the final acceptable pressure range, then the pressure corresponding to the initial adjustment command is adjusted to the lower limit of the final acceptable pressure range to obtain a local pressure adjustment command for the local abnormal area.

[0077] When determining the maximum safe bearing capacity of local abnormal areas, this upper limit is designed to ensure that local areas of the aluminum profile will not undergo permanent plastic deformation or structural damage due to excessive pressure during clamping. This upper limit can be calculated based on the material mechanical properties (such as yield strength and tensile strength) and geometry (such as wall thickness and section modulus) of the aluminum profile through finite element analysis (FEA), or empirical data can be obtained by conducting destructive and non-destructive tests (such as ultrasonic testing and eddy current testing) on ​​similar aluminum profiles.

[0078] When determining the maximum scratch-free pressure limit for localized abnormal areas, this limit is designed to prevent clamping forces from leaving visible indentations or scratches on the aluminum profile surface, thereby ensuring product surface quality. This limit can be comprehensively evaluated based on the aluminum profile's surface hardness (e.g., Vickers hardness, Rockwell hardness), surface roughness, and the material properties and coefficient of friction of the clamping actuators (e.g., grippers and pads). For example, it can be determined by microscopic observation and scratch testing of the profile surface under controlled conditions at different pressures, or by referring to industry standards and empirical data.

[0079] When determining the minimum minimum clamping pressure limit for local anomaly areas, this limit is designed to ensure that the aluminum profile is securely fixed in the local area during clamping, preventing slippage or vibration, thereby ensuring the accuracy and stability of the inspection. This limit can be calculated based on the structural characteristics of the local anomaly area (such as the profile's cross-sectional shape, the type and thickness of surface deposits) and a pre-defined target pressure distribution model (which defines the ideal pressure range required for secure clamping). For example, it can be calculated based on tribological principles, combining the friction coefficient between the profile and the clamping pad with the profile's own weight, to determine the minimum normal force required, or it can be determined through dynamic friction testing and vibration analysis.

[0080] This solution achieves refined closed-loop control of the clamping force adjustment process by constructing a dynamic pressure constraint range. First, the maximum safe bearing pressure upper limit is determined using structural characteristic information of local abnormal areas to ensure that the clamping force does not cause structural deformation of the profile. Simultaneously, damage sensitivity information is used to determine the maximum scratch-free pressure upper limit, setting a pressure threshold from the perspective of surface quality protection. By logically comparing the minimum stable clamping pressure lower limit with the above two upper limits, the most stringent constraint condition can be automatically selected as the boundary of the final acceptable pressure range, thereby minimizing the risk of surface damage while ensuring that the profile does not slip. At the execution level, the initial adjustment command is compared with this dynamic range in real time. If the command exceeds the range, it is forcibly corrected to the boundary value of the range. This forced correction mechanism effectively avoids overpressure or underpressure problems caused by deviations in the adjustment command, ensuring that the clamping force is always within a reasonable range that provides stable clamping without causing damage, achieving adaptive optimization and safety protection of the clamping force field. This approach, combined with the aforementioned technique of generating initial adjustment commands based on the direction and value of pressure deviation in local abnormal areas, ensures that the physical limits and stability requirements of the profile are considered from the outset when the adjustment commands are generated. This avoids secondary damage or clamping instability that may result from blind adjustment, thereby significantly improving the robustness and reliability of the entire intelligent control method.

[0081] The following is a concrete example to illustrate this. Suppose that in the intelligent control method for aluminum profile inspection fixtures, by comparing real-time pressure distribution characteristics with a preset target pressure distribution model, a positive pressure deviation is identified in a localized abnormal area on the contact interface, indicating that the pressure in that area is too high and needs to be reduced. Based on the direction and value of this pressure deviation, the system generates an initial adjustment command, instructing the pressure in that localized area to be reduced by 50N. At this point, the system further executes the following steps to optimize this adjustment command: First, based on the structural characteristics of the localized abnormal area (e.g., the area is a thin-walled portion of the profile, made of 6063-T5 aluminum alloy), the system queries a pre-built database of structural characteristics and safe pressure limits to determine its maximum safe pressure limit as 120N. Second, based on the damage sensitivity information of the localized abnormal area (e.g., the area is a decorative surface of the profile, highly sensitive to scratches), the system queries a pre-built database of damage sensitivity and scratch-free pressure limits to determine its maximum scratch-free pressure limit as 100N. Next, combining the structural characteristics of this local anomaly area with the preset target pressure distribution model, the system determines its minimum stable clamping pressure lower limit to be 60N. Since the maximum safe bearing pressure upper limit of 120N is greater than the maximum scratch-free pressure upper limit of 100N, the system will select the maximum scratch-free pressure upper limit as the upper limit of the final acceptable pressure range, i.e., the final acceptable pressure range is [60N, 100N]. At this time, the system analyzes whether the pressure corresponding to the initial adjustment command (assuming the current actual pressure is 130N) is within the final acceptable pressure range [60N, 100N]. Since 130N is outside this range, the system adjusts the pressure corresponding to the initial adjustment command to 100N.

[0082] Through the above technical solution, this application effectively addresses the problem in intelligent control methods for aluminum profile inspection fixtures that lack quantitative constraints on the upper and lower limits of clamping force when adjustments are made solely based on structural characteristics and damage sensitivity. This solution dynamically constructs a "safe and stable" pressure operating range and intelligently corrects initial adjustment commands, ensuring that the clamping force remains within a reasonable range that effectively secures the profile, prevents slippage, and maximizes protection of the profile surface from damage. This significantly improves the accuracy and reliability of clamping control, avoids profile indentations or scratches caused by overpressure, and inspection errors caused by underpressure, thereby improving product quality pass rate and production efficiency.

[0083] In some preferred embodiments, the preset threshold is determined based on the structural characteristics of the contact interface. These structural characteristics may include, but are not limited to, physical parameters such as the material properties, geometry, surface roughness, elastic modulus, and stiffness of the grippers and pads of the clamping actuator, as well as the material properties and surface morphology of the aluminum profile to be inspected. By determining the preset threshold based on these structural characteristics, it can be ensured that the threshold accurately reflects the normal pressure fluctuation range that each local area of ​​the contact interface can withstand under a stable and non-destructive clamping condition. For example, for a contact interface with high stiffness, its pressure fluctuation range may be small, so the preset threshold can be set relatively low; while for a contact interface with greater elasticity, its pressure fluctuation range may be large, so the preset threshold can be set correspondingly higher.

[0084] This solution combines the determination of a preset threshold with the structural characteristics of the contact interface, making the pressure deviation identification process more accurate and intelligent. Traditionally, preset thresholds may use fixed or empirical values. This can lead to unreasonable threshold settings when dealing with aluminum profiles or fixtures with different structural characteristics, thus affecting the accuracy of abnormal area identification. For example, when the threshold is too high, some subtle but potentially damaging pressure anomalies may be overlooked; when the threshold is too low, normal pressure fluctuations may be misjudged as abnormal, leading to unnecessary adjustments. By dynamically or pre-determining the preset threshold based on the structural characteristics of the contact interface, it is ensured that the threshold matches the actual physical interaction characteristics, thereby improving the reliability of identifying local abnormal areas in step S3.

[0085] The following example illustrates this concept. The clamping actuator includes grippers and pads on the gripper surfaces. The system can pre-store a structural feature database containing structural feature information for each local area on the pads. For example, one area might be labeled a "high-elasticity area," and another a "high-friction texture area." When the system compares the real-time pressure of a "high-elasticity area" with the target pressure, it retrieves or calculates a specific preset threshold, such as T_elasticity, based on the "high-elasticity" structural features of that area from the database. This T_elasticity may be relatively small to ensure sensitive detection of even minor pressure changes in that area, preventing potential damage due to excessive elastic deformation. When comparing a "high-friction texture area," the system determines another preset threshold, such as T_friction, based on its "high-friction texture" structural features. This T_friction may be relatively large because high-friction texture areas are typically designed to withstand greater shear forces and have a higher tolerance for pressure fluctuations. In this way, the system can tailor the criteria for judging pressure abnormalities for each local area on the contact interface, thereby more accurately identifying the local abnormal areas that need adjustment and generating more targeted local pressure adjustment commands.

[0086] Through the above technical solution, the preset threshold is no longer a uniform fixed value, but is dynamically adjusted according to the structural characteristics of the contact interface. This enables the effective differentiation between normal pressure fluctuations caused by structural characteristics and abnormal pressure deviations caused by surface deposits or instantaneous disturbances when identifying local abnormal areas. For example, for structurally fragile or vulnerable areas, a stricter threshold can be set to detect potential overpressure or underpressure risks earlier, avoiding damage to the aluminum profile; while for structurally robust areas, a more lenient threshold can be set to reduce unnecessary adjustments and improve system operating efficiency. This refined identification of abnormal areas significantly improves the robustness and accuracy of pressure anomaly identification, effectively avoiding the misjudgment or missed judgment problems that may occur under traditional uniform threshold settings. Based on this, combined with the above-mentioned overall method of obtaining real-time pressure distribution characteristics, obtaining a preset target pressure distribution model, comparing and identifying local abnormal areas, generating local pressure adjustment commands, and controlling the independent action of the adjustment unit, this solution can achieve dynamic reconstruction of the force field of the clamping actuator at the contact interface, ensuring stable clamping while maximizing the protection of the aluminum profile surface from damage, thereby improving the accuracy of detection and product qualification rate.

[0087] Secondly, such as Figure 2 As shown, this application also provides an intelligent control system for aluminum profile inspection fixtures, which includes: The first acquisition module 1 is used to acquire the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected; The second acquisition module 2 is used to acquire the identification information of the aluminum profile to be inspected, and to acquire the preset target pressure distribution model corresponding to the aluminum profile based on the identification information; the preset target pressure distribution model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state. The comparison module 3 is used to compare the real-time pressure distribution characteristics with the preset target pressure distribution model in order to identify local abnormal areas where the pressure deviation exceeds the preset threshold. Generation module 4 is used to generate local pressure adjustment commands for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. The adjustment module 5 is used to control multiple adjustment units distributed on the contact interface to act independently according to the local pressure adjustment command, so as to change the physical displacement or deformation of the clamping actuator in the corresponding local area, realize the dynamic reconstruction of the force field of the contact interface, and then trigger the first acquisition module 1.

[0088] The intelligent control system for aluminum profile inspection fixtures provided in this application includes a first acquisition module 1, a second acquisition module 2, a comparison module 3, a generation module 4, and an adjustment module 5. The intelligent control system for aluminum profile inspection fixtures provided in this embodiment is used to execute the steps in the intelligent control method for aluminum profile inspection fixtures provided in the first aspect above. The principle of the intelligent control system for aluminum profile inspection fixtures provided in this embodiment is the same as the principle of the intelligent control method for aluminum profile inspection fixtures provided in the first aspect above, and will not be repeated here.

[0089] As can be seen from the above, the intelligent control method and system for aluminum profile inspection fixture provided in this application obtains the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected, compares it with the preset target pressure distribution model, identifies local abnormal areas, and then achieves dynamic reconstruction of the force field by controlling the independent action of multiple adjustment units. This effectively solves the problems of uneven clamping force distribution, micro-slippage of the profile, and surface damage caused by surface deposits in the prior art. It has the advantages of being able to sense the pressure distribution state of the clamping interface in real time, effectively avoiding surface damage of the profile and micro-slippage during the inspection process by dynamically reconstructing the force field, thereby significantly improving the inspection accuracy and product quality stability.

[0090] In the embodiments provided in this application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the above units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another robot, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0091] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0092] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0093] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent control of an aluminum profile inspection fixture, characterized in that, The intelligent control method for the aluminum profile inspection fixture includes the following steps: S1. Obtain the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected; S2. Obtain the identification information of the aluminum profile to be inspected, and obtain the preset target pressure distribution model corresponding to the aluminum profile based on the identification information; the preset target pressure distribution model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state. S3. Compare the real-time pressure distribution characteristics with the preset target pressure distribution model to identify local abnormal areas where the pressure deviation exceeds the preset threshold. S4. Generate a local pressure adjustment command for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. S5. Control the multiple adjustment units distributed on the contact interface to operate independently according to the local pressure adjustment command, so as to change the physical displacement or deformation of the clamping actuator in the corresponding local area, realize the dynamic reconstruction of the force field of the contact interface, and then return to step S1.

2. The intelligent control method for aluminum profile inspection fixture according to claim 1, characterized in that, The clamping actuator includes a jaw and a pad disposed on the surface of the jaw. Step S1 includes: S11. Pressure distribution data of the contact interface is acquired in real time by a network of microarray pressure sensors embedded inside the liner.

3. The intelligent control method for aluminum profile inspection fixture according to claim 2, characterized in that, The adjustment unit is a miniature piezoelectric actuator embedded inside the liner, and the miniature piezoelectric actuator is spatially positioned corresponding to the microarray pressure sensor network.

4. The intelligent control method for aluminum profile inspection fixture according to claim 1, characterized in that, Step S3 includes: S31. For each local area on the contact interface, obtain multiple real-time pressure data points of the local area within the preset time window based on multiple real-time pressure distribution characteristics collected within the preset time window, and then obtain the stable pressure value of the local area based on all the real-time pressure data points. S32. For each local region, the stable pressure value corresponding to the local region is compared with the preset pressure range of the local region in the preset target pressure distribution model to obtain the pressure deviation of the local region. S33. For each local area, analyze whether the following conditions are met simultaneously: the pressure deviation corresponding to the local area persists within the preset time window; the pressure deviation corresponding to the local area and the pressure deviation of its adjacent local areas are consistent; and the absolute value of the pressure deviation corresponding to the local area is greater than a preset threshold. If so, the pressure anomaly in the local area is considered to be caused by the surface deposits of the profile, and the local area is marked as a local anomaly area. If not, the pressure anomaly in the local area is considered to be caused by a transient disturbance, and the local area is not marked as a local anomaly area.

5. The intelligent control method for aluminum profile inspection fixture according to claim 4, characterized in that, Step S33 includes: S331. For each local region, perform time series analysis on the pressure deviation of the local region within the preset time window to obtain the continuous evolution characteristics of the pressure deviation of the local region. S332. For each of the local regions, perform spatial distribution evolution analysis on the pressure deviation of the local region and its adjacent local regions to obtain the spatial evolution consistency characteristics of the pressure deviation of the local region. S333. For each local region, determine the duration judgment threshold and the consistency judgment threshold based on the continuous evolution characteristics of the pressure deviation and the spatial evolution consistency characteristics of the pressure deviation in that local region. S334. For each local area, analyze whether the following conditions are met simultaneously: the duration of the pressure deviation corresponding to the local area within the preset time window is greater than or equal to the duration judgment threshold; the consistency between the pressure deviation corresponding to the local area and the pressure deviation of its adjacent local areas is greater than or equal to the consistency judgment threshold; and the absolute value of the pressure deviation corresponding to the local area is greater than the preset threshold. If so, the pressure anomaly in the local area is considered to be caused by the surface deposits of the profile, and the local area is marked as a local anomaly area. If not, the pressure anomaly in the local area is considered to be caused by a transient disturbance, and the local area is not marked as a local anomaly area.

6. The intelligent control method for aluminum profile inspection fixture according to claim 5, characterized in that, Step S332 includes: A1. For each of the local regions, identify the first pressure abnormality region and the second pressure abnormality region corresponding to each time node of the preset time window; the first pressure abnormality region is the region in the current local region where the pressure deviation exceeds a preset threshold, and the second pressure abnormality region is the region in the local region adjacent to the current local region where the pressure deviation exceeds a preset threshold. A2. Obtain the first spatial features corresponding to each time node of the first pressure anomaly region within the preset time window, and obtain the second spatial features corresponding to each time node of the second pressure anomaly region within the preset time window; both the first spatial features and the second spatial features include the location, size, and shape of the pressure anomaly region. A3. Obtain the change trend of the first spatial features based on all the first spatial features, and obtain the change trend of the second spatial features based on all the second spatial features; A4. Obtain the similarity of change trends based on the first spatial feature change trend and the second spatial feature change trend, and use the similarity of change trends as the consistency feature of spatial evolution of pressure deviation.

7. The intelligent control method for aluminum profile inspection fixture according to claim 1, characterized in that, Step S4 includes: S41. Generate an initial adjustment command for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. S42. Obtain the structural characteristic information and damage sensitivity information corresponding to the local abnormal region; S43. Based on the structural characteristic information and damage sensitivity information, adjust the initial adjustment command to obtain a local pressure adjustment command for the local abnormal area.

8. The intelligent control method for aluminum profile inspection fixture according to claim 7, characterized in that, Step S43 includes: S431. Based on the structural characteristics of the local abnormal region, determine the maximum safe pressure bearing capacity of the local abnormal region. S432. Based on the damage sensitivity information of the local abnormal area, determine the maximum scratch-free pressure limit of the local abnormal area; S433. Based on the structural characteristics of the local abnormal region and the preset target pressure distribution model, determine the minimum stable clamping pressure lower limit of the local abnormal region; the minimum stable clamping pressure lower limit is less than the maximum safe bearing pressure lower limit and the maximum scratch-free pressure upper limit; S434. If the maximum safe pressure limit is greater than or equal to the maximum scratch-free pressure limit, then the final acceptable pressure range of the local abnormal area is generated based on the maximum scratch-free pressure limit and the minimum stable clamping pressure limit. S435. If the maximum safe bearing pressure limit is less than the maximum scratch-free pressure limit, then the final acceptable pressure range of the local abnormal area is generated based on the maximum safe bearing pressure limit and the minimum stable clamping pressure limit. S436. Analyze whether the pressure corresponding to the initial adjustment command is within the final acceptable pressure range. If yes, then use the initial adjustment command as a local pressure adjustment command for the local abnormal area. If no, then proceed to step S437. S437. If the pressure corresponding to the initial adjustment command is greater than the upper limit of the final acceptable pressure range, then the pressure corresponding to the initial adjustment command is adjusted to the upper limit of the final acceptable pressure range to obtain a local pressure adjustment command for the local abnormal area; if the pressure corresponding to the initial adjustment command is less than the lower limit of the final acceptable pressure range, then the pressure corresponding to the initial adjustment command is adjusted to the lower limit of the final acceptable pressure range to obtain a local pressure adjustment command for the local abnormal area.

9. The intelligent control method for aluminum profile inspection fixture according to claim 1, characterized in that, The preset threshold is determined based on the structural features of the contact interface.

10. An intelligent control system for an aluminum profile inspection fixture, characterized in that, The intelligent control system for the aluminum profile inspection fixture is used to execute the steps in the intelligent control method for the aluminum profile inspection fixture according to claim 1, and the intelligent control system for the aluminum profile inspection fixture includes: The first acquisition module is used to acquire the real-time pressure distribution characteristics of the contact interface between the clamping actuator and the aluminum profile to be inspected. The second acquisition module is used to acquire the identification information of the aluminum profile to be inspected, and to acquire the preset target pressure distribution model corresponding to the aluminum profile based on the identification information; the preset target pressure distribution model defines the preset pressure range of each local area of ​​the contact interface under a stable and non-destructive clamping state. The comparison module is used to compare the real-time pressure distribution features with the preset target pressure distribution model to identify local abnormal areas where the pressure deviation exceeds a preset threshold. The generation module is used to generate a local pressure adjustment command for the local abnormal area based on the direction and value of the pressure deviation in the local abnormal area. The adjustment module is used to control multiple adjustment units distributed on the contact interface to operate independently according to the local pressure adjustment command, so as to change the physical displacement or deformation of the clamping actuator in the corresponding local area, realize the dynamic reconstruction of the force field of the contact interface, and then trigger the first acquisition module.

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