Adaptive control method and system for portable repair clamping device

Abstract

The application discloses a self-adaptive regulation and control method and system of a portable maintenance clamping device, and relates to the technical field of industrial maintenance. The method comprises the following steps: receiving a maintenance task through a control terminal and constructing a three-dimensional maintenance visual diagram of a part to determine the freedom space of the maintenance clamping device; positioning multiple maintenance points based on the three-dimensional visual diagram and optimizing clamping parameters in combination with the freedom space to determine optimal clamping parameters and rotation guidance information; and controlling the clamping device to clamp through the optimal clamping parameters while feeding back the visual rotation guidance information to the control terminal to provide accurate clamping and rotation operation guidance. The application solves the technical problems of poor adaptability, insufficient clamping precision and stability of existing maintenance clamping devices when clamping parts of different shapes and materials, and achieves the technical effect of optimizing clamping parameters through self-adaptive regulation and control to realize high-precision and stable clamping control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial maintenance, in particular to an adaptive control method and system of a portable maintenance clamping device. BACKGROUND

[0002] In the field of modern industrial maintenance, with the continuous improvement of equipment complexity and precision, traditional maintenance clamping tools gradually expose many limitations. For example, existing clamping devices can usually only adapt to parts of specific shapes and sizes, and the clamping effect for complex shapes or irregular parts is not good, which can easily cause damage to the parts or unstable clamping. In addition, the operation process of traditional clamping tools is relatively cumbersome, and the operator needs to manually adjust the clamping force and clamping angle, which not only increases the labor intensity, but also reduces the maintenance efficiency. SUMMARY

[0003] The present application provides an adaptive control method and system of a portable maintenance clamping device, which is used to solve the technical problems of poor adaptability, insufficient clamping precision and stability of existing maintenance clamping devices when clamping parts of different shapes and materials.

[0004] The first aspect of this application provides an adaptive control method for a portable maintenance clamping device. The method includes: connecting a control terminal of the maintenance clamping device to receive a target maintenance clamping task and constructing a three-dimensional maintenance view of the part to be repaired; determining the degree-of-freedom space of the maintenance clamping device; locating multiple maintenance points based on the three-dimensional maintenance view, and, in conjunction with the degree-of-freedom space of the maintenance clamping device, performing clamping parameter optimization under a rotational scenario of the maintenance points, determining optimal clamping parameters and visible rotation guidance information for the maintenance points, including: collecting a historical clamping point sample dataset based on the part material and three-dimensional structure of the part to be repaired; constructing a first clamping point optimization space using other part regions besides the multiple maintenance points; based on the location of the multiple maintenance points and the degree-of-freedom space of the maintenance clamping device, performing clamping point optimization within the first clamping point optimization space based on the historical clamping point sample dataset, generating a first clamping point optimization result, the first clamping point optimization result including the clamping point position and the rotation parameters of the corresponding gripper and the gripper and base assembly; determining whether the first clamping point optimization result is empty; if not, clamping each clamping point based on the first clamping point optimization result. Optimize the holding force to generate the optimal clamping parameters; based on the clamping point position, the corresponding gripper and the rotation parameters of the base assembly, and combined with the 3D maintenance view, perform a visual modeling of the maintenance point rotation to generate the maintenance point visual rotation guidance information; if the first clamping point optimization result is empty, classify the multiple maintenance points into two categories according to distance deviation to generate the first group of maintenance points' clamping point optimization space and the second group of maintenance points' clamping point optimization space; based on the first group of maintenance points' clamping point optimization space and the second group of maintenance points' clamping point optimization space, perform phased clamping point and... The clamping force is optimized to construct the optimal clamping parameters and visual rotation guidance information for the first and second stages. Based on the optimal clamping parameters and visual rotation guidance information for the first and second stages, phased clamping control and visual guidance are performed. The optimal clamping parameters are used to control the maintenance clamping device to clamp the part to be repaired, and the visual rotation guidance information for the maintenance point is transmitted back to the control terminal for visual guidance of the rotation of the maintenance clamping device.

[0005] A second aspect of this application provides an adaptive control system for a portable repair clamping device. The system includes: a 3D repair view construction module, which connects to the control terminal of the repair clamping device and receives a target repair clamping task to construct a 3D repair view of the part to be repaired; a degree-of-freedom space determination module, which determines the degree-of-freedom space of the repair clamping device; and a clamping parameter optimization module, which locates multiple repair points based on the 3D repair view and, in conjunction with the degree-of-freedom space of the repair clamping device, performs clamping under a rotating repair point scenario. Parameter optimization, determining optimal clamping parameters and visible rotation guidance information for repair points, includes: collecting a historical clamping point sample dataset based on the part material and 3D structure of the part to be repaired; constructing a first clamping point optimization space using other part areas besides the multiple repair points; based on the positioning of the multiple repair points and the degree of freedom space of the repair clamping device, performing clamping point optimization within the first clamping point optimization space based on the historical clamping point sample dataset, generating a first clamping point optimization result, which includes the clamping point position and the corresponding rotation parameters of the gripper and the base assembly; determining whether the first clamping point optimization result is satisfactory. If the first clamping point optimization result is empty, the clamping force of each clamping point is optimized based on the first clamping point optimization result to generate the optimal clamping parameters; based on the clamping point position, the corresponding gripper and gripper, and the rotation parameters of the base assembly, the rotation of the maintenance point is visualized and modeled in conjunction with the three-dimensional maintenance view to generate the maintenance point visual rotation guidance information; if the first clamping point optimization result is empty, the multiple maintenance points are classified into two categories according to the distance deviation to generate the clamping point optimization space of the first group of maintenance points and the clamping point optimization space of the second group of maintenance points; based on the clamping point optimization space of the first group of maintenance points and the clamping point optimization space of the second group of maintenance points, the process is carried out in stages. The clamping point and clamping force are optimized to construct the optimal clamping parameters and maintenance point visual rotation guidance information for the first and second stages. Based on the optimal clamping parameters and maintenance point visual rotation guidance information for the first and second stages, phased clamping control and visual guidance are performed. A clamping control module is used to control the maintenance clamping device to clamp the part to be repaired with the optimal clamping parameters, and to send the maintenance point visual rotation guidance information back to the control terminal for visual guidance of the rotation of the maintenance clamping device.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The adaptive control method and system for a portable maintenance clamping device provided in this application relate to the field of industrial maintenance technology. It receives maintenance tasks through a control terminal and constructs a three-dimensional maintenance view, determines the degree of freedom of the clamping device, locates maintenance points, and optimizes clamping parameters. Based on the optimal clamping parameters, it controls the clamping device to perform clamping, while simultaneously transmitting visual rotation guidance information back to the control terminal, providing precise clamping and rotation operation guidance. This solves the technical problems of poor adaptability, insufficient clamping accuracy, and instability in existing maintenance clamping devices when clamping parts of different shapes and materials. It achieves high-precision and stable clamping control through adaptive adjustment and optimization of clamping parameters, improving the operational efficiency and accuracy of complex maintenance tasks. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic flowchart of the adaptive control method for the portable maintenance clamping device provided in the embodiments of this application;

[0010] Figure 2 This is a schematic diagram of the adaptive control system structure of the portable maintenance clamping device provided in the embodiments of this application.

[0011] Explanation of reference numerals in the attached diagram: 11. 3D maintenance view construction module; 12. Degree of freedom space determination module; 13. Clamping parameter optimization module; 14. Clamping control module. Detailed Implementation

[0012] This application provides an adaptive control method and system for a portable maintenance clamping device, which solves the technical problems of poor adaptability, insufficient clamping accuracy and stability of existing maintenance clamping devices when clamping parts of different shapes and materials.

[0013] 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 them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides an adaptive control method for a portable maintenance clamping device, the method comprising:

[0016] P10: The control terminal connected to the maintenance clamping device receives the target maintenance clamping task and constructs a three-dimensional maintenance view of the part to be maintained.

[0017] Furthermore, step P10 in this embodiment of the application also includes:

[0018] P11: Analyze the target maintenance clamping task and determine the preset maintenance plan for the part to be maintained; P12: Perform 3D point cloud modeling on the part to be maintained to generate a 3D model of the part; P13: Extract the maintenance points and maintenance plan from the preset maintenance plan, mark them on the 3D model of the part, and generate the 3D maintenance view.

[0019] It should be understood that by receiving and processing the target maintenance clamping task through the control terminal, a three-dimensional maintenance view of the part to be maintained is generated to support subsequent clamping parameter optimization and precise execution of maintenance operations.

[0020] First, the control terminal connects to the maintenance clamping device and receives the target maintenance clamping task. This task includes basic information about the part to be repaired and the maintenance requirements. Task information can be obtained through user input or device transmission, and typically includes the part type, maintenance objectives, and related technical requirements. Upon receiving the task, the control terminal needs to construct a 3D maintenance view of the part through calculation and data processing. This view not only provides maintenance personnel with an intuitive maintenance perspective but also supports subsequent clamping control and optimization algorithms.

[0021] Subsequently, the target repair clamping task is analyzed to extract key information, including the model of the part to be repaired, the type of fault, and the repair requirements. Based on this information, the system calls upon a pre-stored repair knowledge base to match a preset repair plan relevant to the current task. This repair plan details the required operating steps, tool selection, and specific locations of repair points, providing guidance for subsequent 3D modeling and visualization marking.

[0022] After determining the pre-defined repair plan, the 3D point cloud modeling process is initiated. High-precision 3D scanning equipment is used to perform a comprehensive scan of the part to be repaired, acquiring point cloud data of its surface. The point cloud data consists of a large number of discrete 3D coordinate points, accurately reflecting the part's geometry and dimensions. Subsequently, the system employs advanced point cloud processing algorithms to perform filtering, noise reduction, and feature extraction on the acquired point cloud data. For example, filtering algorithms remove noise points, preserving the true surface features of the part; feature extraction algorithms identify key geometric features of the part, such as edges and holes. Based on the processed point cloud data, the system further constructs a 3D model of the part. This model not only possesses high-precision geometry but also supports subsequent visualization and analysis.

[0023] Furthermore, based on the generated 3D model of the part, relevant information about repair points and repair plans is extracted from the pre-set repair schemes. Repair points refer to the specific locations where operations are required during the repair process, while the repair plan details the operational steps and methods to be performed at each repair point. Using deep learning technology, especially object detection algorithms (such as the YOLO series algorithms), repair points are automatically identified and located on the 3D model of the part. These algorithms, through pre-trained models, can quickly and accurately identify the locations of repair points and mark them on the 3D model. Simultaneously, the operational steps from the repair plan are appended to the corresponding repair point markers in text or graphic form, generating a complete 3D repair view. This view not only intuitively displays the location of the repair points but also provides detailed repair guidance information, enabling repair personnel to clearly understand the specific operational requirements for each repair point.

[0024] These steps automate the entire process from receiving a maintenance task to generating a 3D maintenance visualization. This process not only improves the efficiency of maintenance preparation but also reduces the difficulty of maintenance operations and enhances maintenance quality and reliability through precise 3D visualization guidance.

[0025] P20: Determine the degree-of-freedom space of the maintenance clamping device. The maintenance clamping device includes a jaw assembly and a base assembly. The degree-of-freedom space of the maintenance clamping device includes the degree-of-freedom space of each jaw in the jaw assembly and the degree-of-freedom space of the base assembly.

[0026] Specifically, the control terminal needs to determine the degrees of freedom of the maintenance clamping device in order to perform appropriate operation and control during clamping tasks. The maintenance clamping device mainly consists of a gripper assembly and a base assembly, and the degrees of freedom of these two parts play a crucial role in the clamping process.

[0027] First, the degree of freedom (DOF) of a gripper assembly refers to the range of movement or rotation each gripper can make within a given space. This DDF is limited by the movement DDF of the robotic arm to which the gripper is connected. The DDF of the robotic arm is typically determined by its structure and design; common robotic arms can rotate and extend in multiple directions. Therefore, the DDF of the gripper includes not only its movement in three directions (forward, backward, left, right, and up / down) but also its ability to rotate around multiple axes. These DDFs determine whether the gripper can accurately locate the various repair points of the part to be repaired and whether it can flexibly adjust the gripping angle and direction during the gripping process. In practical applications, the DDF of the gripper assembly can be determined through kinematic modeling and analysis. For example, the DH parameter method can be used to model the links and joints of the gripper, optimizing the parameters of each link to determine the DDF of the gripper.

[0028] Secondly, the degree of freedom of the base assembly is typically manifested as horizontal rotation. In this design, the base assembly can generally rotate 360° in the horizontal plane, allowing for omnidirectional adjustments of the maintenance clamping device and ensuring that the grippers can clamp the parts to be maintained from different angles. The degree of freedom of the base provides a wider range of support and adjustment capabilities for the gripper assembly, enabling the clamping device to adapt to different maintenance environments and task requirements.

[0029] Therefore, when determining the degree of freedom (DOF) of the maintenance gripping device, it is first necessary to analyze the respective range of motion and interaction between the gripper assembly and the base assembly. Based on the design of the robotic arm and the structure of the gripper assembly, combined with the 360° rotation capability of the base, the control terminal can accurately calculate the entire DDF of the gripping device and optimize the gripping parameters and path accordingly, ensuring precise and efficient operation during maintenance. Determining this DDF is the foundation for subsequent gripping parameter optimization and maintenance task execution.

[0030] P30: Based on the three-dimensional maintenance view, locate multiple maintenance points, and combine the degree of freedom space of the maintenance clamping device to perform clamping parameter optimization under the maintenance point transformation and rotation scenario, and determine the optimal clamping parameters and maintenance point visual rotation guidance information.

[0031] Furthermore, step P30 in this embodiment of the application also includes:

[0032] P31: Collect a historical clamping point sample dataset based on the part material and 3D structure of the part to be repaired; P32: Construct a first clamping point optimization space using other part areas besides the multiple repair points; P33: Based on the positioning of the multiple repair points and the degree of freedom space of the repair clamping device, perform clamping point optimization within the first clamping point optimization space based on the historical clamping point sample dataset, generating a first clamping point optimization result, which includes the clamping point position and the rotation parameters of the corresponding gripper and the base assembly; P34: Determine whether the first clamping point optimization result is empty; P35: If not, optimize the clamping force of each clamping point based on the first clamping point optimization result, generating the optimal clamping parameters; P36: Based on the clamping point position, the rotation parameters of the corresponding gripper and the base assembly, and combined with the 3D repair view, perform a visual modeling of the repair point rotation, generating the repair point visual rotation guidance information.

[0033] Optionally, the maintenance point can be located based on the 3D maintenance view, and combined with the degree of freedom space of the maintenance clamping device, the clamping parameters can be optimized under the rotational scenario of the maintenance point transformation to determine the optimal clamping parameters and the visual rotational guidance information of the maintenance point, thereby achieving efficient and accurate maintenance task execution.

[0034] First, historical repair records similar to the material and three-dimensional structure of the part to be repaired are extracted from the repair database. These records contain information such as the location of clamping points, the magnitude of clamping force, the clamping posture, and the repair results during previous repairs. Successful repair cases are selected using data mining algorithms, and clamping point sample data is extracted from them. This sample data will serve as a reference for subsequent clamping point selection. For example, for metal parts, clamping points typically need to avoid stress concentration areas, and the clamping force needs to be controlled within a certain range to prevent part deformation.

[0035] Next, based on the 3D maintenance visualization, the locations and extents of multiple maintenance points are defined. In the 3D model of the part, the maintenance point and a certain area around it are marked as a non-clamping area to avoid interference from clamping operations. The remaining part area is defined as the preferred space for the first clamping point. For example, if the maintenance point is located at the edge of the part, the preferred space for the first clamping point can be set in the middle of the part or other non-critical areas.

[0036] Furthermore, by combining the degrees of freedom space of the maintenance clamping device, the range of motion and attitude adjustment capability of the gripper assembly and the base assembly are analyzed. Based on the location of the maintenance point, within the first clamping point optimization space, clamping points are optimized using a historical clamping point sample dataset. For example, the first clamping point optimization space is meshed to generate multiple candidate clamping points. For each candidate clamping point, its clamping stability, clamping force distribution, and relative positional relationship with the maintenance point are evaluated using historical sample data. Through optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms), the optimal combination of clamping points is selected under the premise of satisfying the degrees of freedom space constraints of the maintenance clamping device. The optimization results of the first clamping point include the position of the clamping point, the corresponding gripper rotation parameters (such as the opening and closing angle and rotation angle of the gripper), and the rotation parameters of the base assembly (such as the horizontal rotation angle of the base).

[0037] Next, check if the first clamping point optimization result is empty. If the first clamping point optimization result is empty, it means that no suitable combination of clamping points was found within the first clamping point optimization space. In this case, it is necessary to re-evaluate the range of the first clamping point optimization space or adjust the parameters of the optimization algorithm, and then perform clamping point optimization again.

[0038] If the first clamping point optimization result is not empty, then the clamping force of each clamping point is further optimized. For example, based on the mechanical properties of the part material and the position of the clamping point, a mechanical model is established between the clamping force and the part deformation. With clamping stability as the primary objective, while also considering the stress safety of the part, the optimal clamping force magnitude and distribution at each clamping point are determined through methods such as finite element analysis or experimental data fitting. The optimized clamping force parameters are combined with the clamping point position, jaw rotation parameters, and base rotation parameters from the first clamping point optimization result to generate the final optimal clamping parameters.

[0039] Finally, the clamping point position, jaw rotation parameters, and base rotation parameters are input into the 3D maintenance visualization to create a visual model of the maintenance point rotation. For example, the clamping point position and clamping posture are highlighted or marked with different colors in the 3D maintenance visualization. Based on the rotation parameters of the jaws and base, the motion trajectory and posture changes of the maintenance clamping device during the maintenance process are simulated and dynamically displayed in the 3D model. Visual rotation guidance information for the maintenance point is generated, including key parameters such as the rotation sequence, rotation angle, and rotation speed of the clamping point. This information is presented in the form of animations, text descriptions, or graphic annotations, providing maintenance personnel with intuitive operational guidance.

[0040] Through the above steps, this application embodiment achieves full-process optimization from clamping point selection to clamping force optimization, and then to visual guidance for rotation of maintenance points, which can effectively improve the accuracy and efficiency of maintenance clamping operations and ensure the smooth progress of maintenance tasks.

[0041] Furthermore, step P33 in this embodiment of the application also includes:

[0042] P33-1: Construct a virtual clamping model based on the degree-of-freedom space of the maintenance clamping device; P33-2: Filter multiple sets of clamping point sample data from the historical clamping point sample dataset, where all clamping points fall within the first clamping point optimization space, and call the clamping virtual model to perform rotation simulation, determine a set of clamping point sample data where the rotation of the maintenance clamping device causes any maintenance point to fall within the preset maintenance position range, and generate the first clamping point distribution position; P33-3: Generate the first clamping point optimization result based on the first clamping point distribution position.

[0043] In one possible embodiment of this application, a more accurate calculation and verification method is provided for clamping point optimization to ensure that the clamping device can operate efficiently and accurately in different maintenance tasks.

[0044] First, based on the structural parameters and degrees of freedom of the maintenance clamping device, a virtual model of the clamping device is constructed using computer-aided design (CAD) software or a virtual simulation platform. This model should include the precise geometry, kinematic parameters, and dynamic characteristics of the gripper and base components. The virtual model can simulate the motion behavior of the clamping device in actual operation, including the opening and closing, rotation of the grippers, and horizontal rotation of the base. By setting motion constraints on the model, it is ensured that its range of motion and attitude adjustment capabilities are consistent with the actual maintenance clamping device.

[0045] Next, multiple sets of clamping point sample data were selected from the historical clamping point sample dataset, all of which fell within the first clamping point optimal space. These sample data included information such as clamping point positions, clamping force magnitudes, and clamping postures under different maintenance scenarios. The first clamping point optimal space was determined based on the shape of the part being repaired, the distribution of maintenance points, and the degrees of freedom of the clamping device. By selecting the sample data, it was ensured that only clamping point data that met the actual maintenance requirements were used. After selecting the qualified clamping point data, a rotational simulation was performed using the clamping virtual model. The rotational simulation verified whether the maintenance clamping device could accurately align the maintenance points with the preset maintenance positions at different angles. The simulation results showed which clamping points could meet the precise alignment requirements of the maintenance points, thus generating the first clamping point distribution positions. These positions marked the potential positions of the optimal clamping points, ensuring that the clamping device could accurately locate each maintenance point during actual maintenance.

[0046] Furthermore, based on the distribution of the first clamping points, optimization results for the first clamping points are generated. These results include the position of each clamping point, the rotation parameters of the gripper and base assembly, and whether the clamping points meet the requirements of the preset maintenance position. These parameters are obtained through operations on the virtual clamping model during rotation simulation, ensuring that the maintenance clamping device can accurately adjust the maintenance points to the preset maintenance position range during actual operation. The optimization results of the first clamping points provide fundamental data support for subsequent clamping force optimization and the generation of visual rotation guidance information for maintenance points.

[0047] This process not only considers the freedom space of the maintenance clamping device and historical clamping point sample data, but also verifies the feasibility of clamping point combinations through virtual simulation technology, ensuring the flexibility and adaptability of clamping operations and providing strong technical support for the efficient completion of complex maintenance tasks.

[0048] Furthermore, step P34 in this embodiment of the application also includes:

[0049] P34-1: If the first clamping point optimization result is empty, the multiple maintenance points are classified into two categories according to the distance deviation, generating the clamping point optimization space of the first group of maintenance points and the clamping point optimization space of the second group of maintenance points; P34-2: Based on the clamping point optimization space of the first group of maintenance points and the clamping point optimization space of the second group of maintenance points, the clamping point and clamping force are optimized in stages, and the optimal clamping parameters and maintenance point visual rotation guidance information of the first stage and the optimal clamping parameters and maintenance point visual rotation guidance information of the second stage are constructed; P34-3: Based on the optimal clamping parameters and maintenance point visual rotation guidance information of the first stage and the optimal clamping parameters and maintenance point visual rotation guidance information of the second stage, the clamping control and visual guidance are performed in stages.

[0050] It should be understood that when the result of the first clamping point optimization is empty, that is, no suitable combination of clamping points can be found in the initial first clamping point optimization space, the multiple maintenance points are further classified into two categories.

[0051] Specifically, the distance deviation between each repair point and the geometric center of the part to be repaired is calculated. This deviation is obtained by measuring the straight-line distance from the repair point to the geometric center of the part and comparing it with the average distance from all repair points to the center. Based on the magnitude of the distance deviation, the repair points are divided into two groups: the first group consists of repair points with smaller distance deviations, typically located in the central region or a relatively compact area of ​​the part; the second group consists of repair points with larger distance deviations, typically located in the edge region or a relatively dispersed area of ​​the part. Based on the locations of these two groups of repair points, the area of ​​the part other than the first group of repair points is defined as the preferred clamping point space for the first group of repair points, and the area of ​​the part other than the second group of repair points is defined as the preferred clamping point space for the second group of repair points. This method ensures that the clamping operations of the two groups of repair points do not interfere with each other, while fully utilizing the available space of the part and providing a reasonable spatial range for subsequent clamping point selection.

[0052] Next, based on the optimal clamping point space of the first and second groups of maintenance points, the clamping points and clamping forces are optimized in stages. In the first stage, for the optimal clamping point space of the first group of maintenance points, the operation process of step P33 is repeated, and the clamping points are optimized using the historical clamping point sample dataset to generate the clamping point optimization results for the first stage. This result includes the position of the clamping point and the rotation parameters of the corresponding gripper and base assembly. Based on the clamping point optimization results of the first stage, the clamping force of each clamping point is further optimized to generate the optimal clamping parameters for the first stage. At the same time, combined with the 3D maintenance view, the rotation of the maintenance points is visualized and modeled to generate the visual rotation guidance information for the maintenance points in the first stage. In the second stage, for the optimal clamping point space of the second group of maintenance points, the operation of step P33 is repeated to optimize the clamping points and generate the clamping point optimization results for the second stage. Based on this result, the clamping force is optimized to generate the optimal clamping parameters for the second stage, and combined with the 3D maintenance view, the visual rotation guidance information for the maintenance points in the second stage is generated. By optimizing the clamping points and clamping forces in stages, the optimal clamping scheme can be found for different maintenance points in different areas, ensuring the stability and reliability of maintenance operations.

[0053] Finally, based on the optimal clamping parameters of the first and second stages and the visual rotation guidance information of the repair points, phased clamping control and visual guidance are implemented. In the first stage, the maintenance clamping device is controlled to clamp the first group of repair points according to the optimal clamping parameters of the first stage. Simultaneously, the visual rotation guidance information of the repair points in the first stage is displayed on the control terminal, providing intuitive operational guidance to the operator and ensuring that the operator can accurately complete the clamping and repair operations of the first group of repair points. After completing the clamping operation in the first stage, the second stage begins. Based on the optimal clamping parameters of the second stage, the maintenance clamping device is controlled to clamp the second group of repair points, and the visual rotation guidance information of the repair points in the second stage is displayed on the control terminal, guiding the operator to complete the repair tasks of the second group of repair points. Through phased clamping control and visual guidance, it can be ensured that the maintenance clamping device can be precisely controlled according to the optimal clamping parameters in different stages of operation. At the same time, the operator can clearly understand the rotation path of the repair points and the clamping posture adjustment suggestions through the visual guidance information, thereby improving the efficiency and accuracy of the repair operation.

[0054] Through these phased optimizations and controls, the system can efficiently and accurately handle complex maintenance tasks, ensuring that each maintenance point receives optimal clamping and rotation support, thereby improving maintenance efficiency and the quality of parts repair.

[0055] Furthermore, step P34-2 of the embodiments of this application also includes:

[0056] P34-21: In the clamping point optimization space of the first group of maintenance points, based on the historical clamping point sample dataset, clamping point optimization is performed for the first group of maintenance points to generate a first group of preferred clamping points; P34-22: In the clamping point optimization space of the second group of maintenance points, based on the historical clamping point sample dataset, clamping point optimization is performed for the second group of maintenance points to generate a second group of preferred clamping points; P34-23: Based on the first group of preferred clamping points, clamping force optimization and maintenance point rotation visualization modeling are performed to generate the optimal clamping parameters and maintenance point visual rotation guidance information for the first stage; P34-24: Based on the second group of preferred clamping points, clamping force optimization and maintenance point rotation visualization modeling are performed to generate the optimal clamping parameters and maintenance point visual rotation guidance information for the second stage.

[0057] Specifically, when the first clamping point optimization result is empty, it indicates that no combination of clamping points meeting the conditions could be found within the initial first clamping point optimization space. At this time, the system will classify multiple maintenance points into two categories according to their distance deviation, generating clamping point optimization spaces for the first group of maintenance points and the second group of maintenance points respectively. Subsequently, the system will perform phased optimization of clamping points and clamping forces for these two spaces.

[0058] First, within the optimal clamping point space for the first group of repair points, clamping point optimization is performed based on a historical clamping point sample dataset. This historical dataset contains information such as the successful clamping point locations, clamping force magnitudes, and clamping postures from previous similar repair tasks. Using data mining algorithms, clamping point samples matching the part material and structural characteristics of the first group of repair points are selected from the sample dataset. Subsequently, optimization algorithms (such as genetic algorithms or particle swarm optimization) are used to evaluate and optimize the selected clamping point samples within the optimal clamping point space for the first group of repair points. Factors such as clamping stability, clamping force distribution, and relative positional relationship with the repair points are comprehensively considered to ultimately generate the first group of optimal clamping points. The positional information of these clamping points will serve as the basis for subsequent clamping force optimization.

[0059] Next, within the optimal clamping point space for the second set of repair points, clamping point optimization is performed based on the historical clamping point sample dataset. Clamping point samples matching the second set of repair points are selected from the sample dataset, and optimization algorithms are used to evaluate and optimize them within the optimal clamping point space for the second set of repair points. During optimization, the system considers the special locations and requirements of the second set of repair points; for example, repair points in edge areas may require more careful clamping methods to avoid part deformation. Finally, a second set of optimal clamping points is generated, providing key positional information for the clamping operation in the second stage.

[0060] Next, based on the first set of preferred clamping points, the clamping force is optimized. First, a mechanical model is established between the clamping force and the deformation of the part, based on the mechanical properties of the part material and the positions of the first set of preferred clamping points. Using methods such as finite element analysis or experimental data fitting, the optimal clamping force magnitude and distribution at each clamping point are determined to ensure the stability of the clamping operation and the safety of the part. Subsequently, a visual model of the rotation of the maintenance points is created using a 3D maintenance view. In the 3D model, the positions and clamping postures of the first set of preferred clamping points are highlighted or marked with different colors, and the movement trajectory and posture changes of the maintenance clamping device during the maintenance process are simulated. Finally, the optimal clamping parameters for the first stage and visual rotation guidance information for the maintenance points are generated, including key parameters such as the rotation sequence, rotation angle, and rotation speed of the clamping points, presented in the form of animation, text descriptions, or graphic annotations to provide intuitive operational guidance for operators.

[0061] Finally, based on the second set of preferred clamping points, the clamping force is optimized. Similar to the first stage, the system establishes a mechanical model based on the mechanical properties of the part material and the positions of the second set of preferred clamping points. The optimal clamping force magnitude and distribution for each clamping point are determined through methods such as finite element analysis or experimental data fitting. Subsequently, a visual model of the maintenance point rotation is created using a 3D maintenance view. In the 3D model, the positions and clamping postures of the second set of preferred clamping points are marked, and the motion trajectory and posture changes of the maintenance clamping device are simulated. Ultimately, the optimal clamping parameters for the second stage and visual rotation guidance information for the maintenance points are generated, presented in the form of animation, text descriptions, or graphic annotations, providing clear guidance for operators.

[0062] By refining these steps, the system can optimize the clamping parameters and rotation paths of the two sets of repair points separately, ensuring that each repair point receives optimal clamping force and rotational support, thereby improving repair efficiency and the quality of part repair. This phased optimization strategy effectively addresses varying levels of difficulty in repair tasks, ensuring accurate and stable clamping control at each repair point.

[0063] Furthermore, step P35 in this embodiment of the application also includes:

[0064] P35-1: Determine the material and structural information of the part to be repaired and analyze the clamping force-damage relationship at each clamping point; P35-2: Based on the clamping force-damage relationship, construct the clamping force range of each clamping point that satisfies the preset damage threshold; P35-3: Randomly generate a first set of clamping parameters in each clamping force range and evaluate the clamping stability to generate a first clamping stability fitness; P35-4: If the first clamping stability fitness satisfies the preset fitness, generate the optimal clamping parameters using the first set of clamping parameters.

[0065] Optionally, the clamping force can be optimized and evaluated based on the relationship between clamping force and part damage, thereby ensuring that each clamping point can achieve stable clamping without causing damage to the part.

[0066] First, based on the material and structural information of the part to be repaired, the clamping force-damage relationship at each clamping point is analyzed. The material of the part determines its response characteristics to force during clamping, while the structure determines the shape, size, and force distribution at different clamping points. Through detailed analysis of this information, the system can identify the mechanical influences that each clamping point may experience during actual repair, thereby predicting the relationship between clamping force and part damage. This relationship describes the degree of damage that may occur to the part under different clamping forces. For example, for metal parts, excessive clamping force may cause surface scratches or structural deformation; while for plastic parts, excessive clamping force may cause permanent deformation or cracking of the material. Through material mechanical property testing or finite element analysis, a quantitative relationship model between clamping force and part damage can be established, providing a basis for subsequently constructing the clamping force range.

[0067] Next, based on the aforementioned clamping force-damage relationship, a clamping force range for each clamping point that satisfies a preset damage threshold is constructed. The preset damage threshold refers to the maximum allowable degree of damage during maintenance operations, typically set according to the part's usage requirements and quality standards. For example, for high-precision parts, the damage threshold may be lower to ensure that the part's performance is not affected. By analyzing the clamping force-damage relationship model, the upper and lower limits of the clamping force at each clamping point are determined, ensuring that the clamping force applied within this range will not cause damage to the part exceeding the preset threshold.

[0068] Furthermore, within the defined clamping force range, a first set of clamping parameters is randomly generated. These parameters include the clamping force at each clamping point, the opening and closing angle of the jaws, the rotation angle of the jaws, and the rotation angle of the base. Subsequently, the clamping stability of the first set of clamping parameters is evaluated. The clamping stability evaluation can be performed by simulating the clamping operation process to check whether the part can be firmly clamped under the current clamping parameters without slippage, loosening, or instability. The evaluation results are expressed in the form of clamping stability fitness, which is a quantitative index reflecting the degree of stability of the clamping operation. The higher the fitness value, the better the stability of the clamping parameters.

[0069] If the first clamping stability fitness meets the preset fitness requirement, it means that the current first set of clamping parameters can achieve stable clamping operation without damaging the part. At this time, the first set of clamping parameters is used as the optimal clamping parameters for subsequent clamping control. If the first clamping stability fitness does not meet the preset fitness requirement, new clamping parameters need to be generated and evaluated until an optimal clamping parameter that meets the preset fitness is found.

[0070] Through the above steps, this embodiment of the application fully considers the limitations of the clamping force imposed by the material and structural characteristics of the part during the clamping force optimization process, ensuring that the clamping operation is both stable and does not damage the part. This process utilizes a clamping force-damage relationship model and clamping stability assessment technology, and through multiple iterations of optimization, finally determines the optimal clamping parameters, providing scientific and reasonable parameter support for maintenance clamping operations.

[0071] Furthermore, the embodiments of this application also include steps P35-4a:

[0072] If the first clamping stability fitness does not meet the preset fitness, a second set of clamping parameters that is not exactly the same as the first set of clamping parameters will be generated within each clamping force range and clamping stability will be evaluated. This process will be repeated multiple times until the optimal clamping parameters that meet the preset fitness are generated.

[0073] In one possible embodiment of this application, when it is determined that the first clamping stability adaptability does not meet the preset adaptability requirement, it indicates that the current first set of clamping parameters cannot achieve stable clamping operation while ensuring that the part is not damaged. At this time, the system will continue to generate a second set of clamping parameters that are not exactly the same as the first set of clamping parameters within the clamping force range of each clamping point.

[0074] First, if the first set of clamping stability fitness does not meet the preset fitness criteria, new clamping parameters are generated within each clamping force range. These newly generated clamping parameters will differ from the previous set (i.e., the first set of clamping parameters), avoiding repeated evaluation of the same parameter combinations. By randomly generating new clamping parameters within the existing clamping force range, more possible combinations can be explored, increasing the chance of finding the optimal clamping parameters.

[0075] Next, the clamping stability of each newly generated clamping parameter set is evaluated. The evaluation process is the same as before, simulating the clamping process and determining whether the generated clamping parameters can provide sufficient stability to ensure that the part is not damaged during clamping. The evaluation results will generate the clamping stability fitness of that set of clamping parameters. If the fitness value meets the preset standard, the system will select that set of clamping parameters as the new optimal clamping parameters.

[0076] This process involves multiple iterations until the optimal clamping parameters that meet the preset fitness requirements are found. In each iteration, the system generates new clamping parameters and performs a stability evaluation, thus gradually approaching the optimal solution. Through this iterative optimization process, the clamping parameters can be precisely adjusted, ultimately ensuring that all repair points receive the best clamping force while avoiding any damage to the parts. This significantly improves the reliability and accuracy of clamping control, thereby guaranteeing the safety and efficiency of the repair process.

[0077] P40: Control the maintenance clamping device to clamp the part to be maintained using the optimal clamping parameters, and send the visual rotation guidance information of the maintenance point back to the control terminal for visual guidance of the rotation of the maintenance clamping device.

[0078] Specifically, the optimal clamping parameters are used to control the maintenance clamping device to clamp the parts to be maintained, and the visual rotation guidance information of the maintenance point is transmitted back to the control terminal to realize the visual guidance of the rotation of the maintenance clamping device.

[0079] First, the optimal clamping parameters, determined beforehand, are used to control the maintenance clamping device to precisely clamp the part to be repaired. These optimal clamping parameters include the opening and closing angle of the jaws, the clamping force, and the rotation angle of the jaws and the base. These parameters are derived through a series of optimization processes to ensure the stability and safety of the clamping operation while avoiding damage to the part. Based on these parameters, the maintenance clamping device adjusts its own posture and clamping force to accurately grasp the part to be repaired and fix it in the appropriate position for subsequent maintenance operations.

[0080] Simultaneously, visual rotation guidance information for the maintenance point is transmitted back to the control terminal. This guidance information, generated based on a 3D maintenance visualization, includes key parameters such as the rotation path, rotation angle, and rotation speed of the maintenance point, as well as dynamic attitude adjustment suggestions for the clamping device during the maintenance process. Through visualization, operators can intuitively see the positional changes of the maintenance point and the rotation process of the clamping device on the control terminal, thereby better understanding and executing maintenance operations. This visual guidance not only improves maintenance efficiency but also reduces operational difficulty, enabling maintenance personnel to complete complex maintenance tasks more accurately and ensuring maintenance quality and reliability.

[0081] In summary, the embodiments of this application have at least the following technical effects:

[0082] This application connects to a control terminal, receives maintenance tasks, and constructs a 3D view of the part to be repaired, thus determining the degrees of freedom of the clamping device. Based on the 3D view, multiple maintenance points are located, and clamping parameters are optimized using the degrees of freedom space to determine the optimal clamping parameters and visual rotation guidance information for each maintenance point. The optimal parameters are then used to control the clamping device for precise clamping, and the visual rotation guidance information is transmitted back to the control terminal, providing visual guidance throughout the maintenance process.

[0083] This technology achieves the technical effect of optimizing clamping parameters through adaptive adjustment, realizing high-precision and stable clamping control, and improving the operational efficiency and accuracy of complex maintenance tasks.

[0084] Example 2, based on the same inventive concept as the adaptive control method of the portable maintenance clamping device in the foregoing examples, such as... Figure 2 As shown, this application provides an adaptive control system for a portable maintenance clamping device. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0085] The three-dimensional repair view construction module 11 is used to connect to the control terminal of the repair clamping device and receive the target repair clamping task to construct a three-dimensional repair view of the part to be repaired.

[0086] The degree-of-freedom space determination module 12 is used to determine the degree-of-freedom space of the maintenance clamping device.

[0087] The clamping parameter optimization module 13 is used to locate multiple maintenance points based on the three-dimensional maintenance view, and combine the degree of freedom space of the maintenance clamping device to perform clamping parameter optimization under the maintenance point transformation and rotation scenario, and determine the optimal clamping parameters and maintenance point visual rotation guidance information.

[0088] The clamping control module 14 is used to control the maintenance clamping device to clamp the part to be repaired with the optimal clamping parameters, and to send the visual rotation guidance information of the maintenance point back to the control terminal for visual guidance of the rotation of the maintenance clamping device.

[0089] Furthermore, the three-dimensional maintenance visualization construction module 11 is also used to perform the following steps:

[0090] The target maintenance clamping task is analyzed to determine the preset maintenance plan for the part to be maintained; a 3D point cloud model is performed on the part to be maintained to generate a 3D model of the part; maintenance points and maintenance plans in the preset maintenance plan are extracted, marked on the 3D model of the part, and the 3D maintenance view is generated.

[0091] Furthermore, the degree-of-freedom space determination module 12 is also used to perform the following steps:

[0092] The maintenance clamping device includes a jaw assembly and a base assembly. The degree of freedom of the maintenance clamping device includes the degree of freedom of each jaw in the jaw assembly and the degree of freedom of the base assembly.

[0093] Furthermore, the clamping parameter optimization module 13 is also used to perform the following steps:

[0094] A historical clamping point sample dataset is collected based on the part material and 3D structure of the part to be repaired; a first clamping point optimization space is constructed using other part areas besides the multiple repair points; based on the positioning of the multiple repair points and the degree of freedom space of the repair clamping device, clamping points are optimized within the first clamping point optimization space based on the historical clamping point sample dataset, generating a first clamping point optimization result, which includes the clamping point position and the rotation parameters of the corresponding gripper and the base assembly; it is determined whether the first clamping point optimization result is empty; if not, the clamping force of each clamping point is optimized based on the first clamping point optimization result, generating the optimal clamping parameters; based on the clamping point position, the rotation parameters of the corresponding gripper and the base assembly, and combined with the 3D repair view, a visual model of the rotation of the repair point is performed, generating the visual rotation guidance information of the repair point.

[0095] Furthermore, the clamping parameter optimization module 13 is also used to perform the following steps:

[0096] If the first clamping point optimization result is empty, the multiple maintenance points are classified into two groups according to their distance deviation, generating a clamping point optimization space for the first group of maintenance points and a clamping point optimization space for the second group of maintenance points. Based on the clamping point optimization spaces of the first group of maintenance points and the second group of maintenance points, the clamping points and clamping forces are optimized in stages, constructing the optimal clamping parameters and visual rotation guidance information for the maintenance points in the first and second stages. Based on the optimal clamping parameters and visual rotation guidance information for the maintenance points in the first and second stages, staged clamping control and visual guidance are performed.

[0097] Furthermore, the clamping parameter optimization module 13 is also used to perform the following steps:

[0098] In the clamping point optimization space of the first group of maintenance points, based on the historical clamping point sample dataset, clamping point optimization is performed for the first group of maintenance points to generate a first set of preferred clamping points; in the clamping point optimization space of the second group of maintenance points, based on the historical clamping point sample dataset, clamping point optimization is performed for the second group of maintenance points to generate a second set of preferred clamping points; based on the first set of preferred clamping points, clamping force optimization and maintenance point rotation visualization modeling are performed to generate the optimal clamping parameters and maintenance point visual rotation guidance information for the first stage; based on the second set of preferred clamping points, clamping force optimization and maintenance point rotation visualization modeling are performed to generate the optimal clamping parameters and maintenance point visual rotation guidance information for the second stage.

[0099] Furthermore, the clamping parameter optimization module 13 is also used to perform the following steps:

[0100] The material and structural information of the part to be repaired are determined, and the clamping force-damage relationship at each clamping point is analyzed. Based on the clamping force-damage relationship, a clamping force range for each clamping point that satisfies a preset damage threshold is constructed. A first set of clamping parameters is randomly generated within each clamping force range, and clamping stability is evaluated to generate a first clamping stability fitness. If the first clamping stability fitness satisfies the preset fitness, the optimal clamping parameters are generated using the first set of clamping parameters.

[0101] Furthermore, the clamping parameter optimization module 13 is also used to perform the following steps:

[0102] If the first clamping stability fitness does not meet the preset fitness, a second set of clamping parameters that is not exactly the same as the first set of clamping parameters will be generated within each clamping force range and clamping stability will be evaluated. This process will be repeated multiple times until the optimal clamping parameters that meet the preset fitness are generated.

[0103] Furthermore, the clamping parameter optimization module 13 is also used to perform the following steps:

[0104] A virtual clamping model is constructed based on the degree-of-freedom space of the maintenance clamping device; multiple sets of clamping point sample data are selected from the historical clamping point sample dataset, in which all clamping points fall within the first clamping point preferred space, and the clamping virtual model is called to perform rotation simulation to determine a set of clamping point sample data in which the rotation of the maintenance clamping device makes any maintenance point fall within the preset maintenance position range, and the first clamping point distribution position is generated; the first clamping point optimization result is generated based on the first clamping point distribution position.

[0105] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0106] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0107] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. An adaptive regulation method for portable repair clamping devices, characterized in that, The method comprises the following steps: a control terminal connected to a maintenance clamping device receives a three-dimensional maintenance visual view of a part to be maintained for a target maintenance clamping task; determine the degree of freedom space of the maintenance clamping device; According to the three-dimensional maintenance visual view, a plurality of maintenance points are located, and the maintenance point transformation and clamping parameter optimization in the rotating scene are performed in combination with the degree of freedom space of the maintenance clamping device to determine the optimal clamping parameter and the visual rotation guidance information of the maintenance point, comprising: Based on the part material and three-dimensional structure of the part to be maintained, a historical clamping point sample data set is collected; A first clamping point preferred space is constructed based on other part areas other than the plurality of maintenance points; Based on the positioning of the plurality of maintenance points and the degree of freedom space of the maintenance clamping device, the clamping point optimization is performed in the first clamping point preferred space based on the historical clamping point sample data set to generate a first clamping point optimization result, which includes the clamping point position and the corresponding rotating parameters of the clamping jaw and the base assembly; Determine whether the first clamping point optimization result is empty; If not, the clamping force optimization is performed for each clamping point based on the first clamping point optimization result to generate the optimal clamping parameter; Based on the clamping point position, the rotating parameters of the corresponding clamping jaw and base assembly, and the three-dimensional maintenance visual view, the visual modeling of the maintenance point rotation is performed to generate the visual rotation guidance information of the maintenance point. If the first clamping point optimization result is empty, the plurality of maintenance points are classified according to the distance deviation to generate the clamping point preferred space of the first group of maintenance points and the clamping point preferred space of the second group of maintenance points; Based on the clamping point preferred space of the first group of maintenance points and the clamping point preferred space of the second group of maintenance points, the clamping point and clamping force optimization are performed in stages to construct the optimal clamping parameter and the visual rotation guidance information of the maintenance point in the first stage and the optimal clamping parameter and the visual rotation guidance information of the maintenance point in the second stage; Based on the optimal clamping parameter and the visual rotation guidance information of the maintenance point in the first stage and the optimal clamping parameter and the visual rotation guidance information of the maintenance point in the second stage, the clamping control and visual guidance are performed in stages; The maintenance clamping device is controlled by the optimal clamping parameter to clamp the part to be maintained, and the visual rotation guidance information of the maintenance point is fed back to the control terminal for visual guidance of the rotation of the maintenance clamping device.

2. The self-regulating method of claim 1, wherein, Receiving a target maintenance clamping task to construct a three-dimensional maintenance visual view of a part to be maintained, comprising: Analyzing the target maintenance clamping task to determine a preset maintenance scheme for the part to be maintained; Three-dimensional point cloud modeling is performed on the part to be maintained to generate a part three-dimensional model; Extracting the maintenance points and maintenance scheme in the preset maintenance scheme, marking the part three-dimensional model to generate the three-dimensional maintenance visual view.

3. The self-regulating method of claim 1, wherein, The maintenance clamping device comprises a clamping jaw assembly and a base assembly, and the degree of freedom space of the maintenance clamping device comprises the degree of freedom space of each clamping jaw in the clamping jaw assembly and the degree of freedom space of the base assembly.

4. The self-regulating method of claim 1, wherein, Constructing the optimal clamping parameter and the visual rotation guidance information of the maintenance point in the first stage and the optimal clamping parameter and the visual rotation guidance information of the maintenance point in the second stage, comprising: In the clamping point preferred space of the first group of repair points, clamping point optimization is performed for the first group of repair points based on the historical clamping point sample data set, and a first group of preferred clamping points is generated; In the clamping point preferred space of the second group of repair points, clamping point optimization is performed for the second group of repair points based on the historical clamping point sample data set, and a second group of preferred clamping points is generated; Based on the first group of preferred clamping points, clamping force optimization and visual modeling of repair point rotation are performed to generate optimal clamping parameters and visual rotation guidance information of the repair point in the first stage; Based on the second group of preferred clamping points, clamping force optimization and visual modeling of repair point rotation are performed to generate optimal clamping parameters and visual rotation guidance information of the repair point in the second stage.

5. The self-regulating method of adapting portable repair clamping devices of claim 1, wherein, Based on the first clamping point optimization result, clamping force optimization is performed on each clamping point to generate the optimal clamping parameters, including: Determine the part material information and part structure information of the part to be repaired to analyze the clamping force-damage relationship of each clamping point; Based on the clamping force-damage relationship, the clamping force range of each clamping point that meets the preset damage threshold is constructed; A first group of clamping parameters is randomly generated in the clamping force range and clamping stability evaluation is performed to generate a first clamping stability fitness; If the first clamping stability fitness meets the preset fitness, the optimal clamping parameters are generated with the first group of clamping parameters.

6. The self-regulating method of adapting a portable repair clamping device of claim 5, wherein, If the first clamping stability fitness does not meet the preset fitness, a second group of clamping parameters that is not completely the same as the first group of clamping parameters is continuously generated in the clamping force range and clamping stability evaluation is performed, and so on, iterated multiple times, until the optimal clamping parameters that meet the preset fitness are generated.

7. The self-regulating method of adapting a portable repair clamping device of claim 1, wherein, Based on the positioning of the plurality of repair points and the degree of freedom space of the repair clamping device, clamping point optimization is performed in the first clamping point preferred space based on the historical clamping point sample data set to generate a first clamping point optimization result, including: Construct a clamping virtual model based on the degree of freedom space of the repair clamping device; In the historical clamping point sample data set, multiple groups of clamping point sample data are screened in which all clamping points fall into the first clamping point preferred space, and the clamping virtual model is called to perform rotation simulation to determine a group of clamping point sample data in which any repair point falls into a preset repair position range through rotation of the repair clamping device, and a first clamping point distribution position is generated. The first clamping point optimization result is generated based on the first clamping point distribution position.

8. An adaptive control system for a portable repair clamping device, characterized by The system comprises: A three-dimensional repair visual view construction module, which is connected to the control terminal of the repair clamping device, receives a target repair clamping task, and constructs a three-dimensional repair visual view of the part to be repaired; A degree of freedom space determination module, which is used to determine the degree of freedom space of the repair clamping device; The clamping parameter optimization module is configured to locate a plurality of maintenance points according to the three-dimensional maintenance view, and perform clamping parameter optimization in a maintenance point transformation and rotation scene in combination with a degree of freedom space of the maintenance clamping device, to determine optimal clamping parameters and maintenance point visual rotation guidance information, and includes the following steps: Collecting historical clamping point sample data sets based on part materials and three-dimensional structures of the parts to be maintained; Constructing a first clamping point optimization space from other part regions other than the plurality of maintenance points; Based on the positioning of the plurality of maintenance points and the degree of freedom space of the maintenance clamping device, performing clamping point optimization in the first clamping point optimization space based on the historical clamping point sample data sets to generate a first clamping point optimization result, the first clamping point optimization result including clamping point positions and corresponding rotation parameters of clamping jaws and base assemblies; Judging whether the first clamping point optimization result is empty; If not, performing clamping force optimization for each clamping point based on the first clamping point optimization result to generate the optimal clamping parameters; Based on the clamping point positions, the corresponding rotation parameters of the clamping jaws and the base assemblies, and the three-dimensional maintenance view, performing visual modeling of maintenance point rotation to generate the maintenance point visual rotation guidance information; If the first clamping point optimization result is empty, performing two classification on the plurality of maintenance points according to distance deviation to generate a clamping point optimization space of a first group of maintenance points and a clamping point optimization space of a second group of maintenance points; Based on the clamping point optimization space of the first group of maintenance points and the clamping point optimization space of the second group of maintenance points, performing staged clamping point and clamping force optimization in sequence to construct optimal clamping parameters and maintenance point visual rotation guidance information of a first stage and optimal clamping parameters and maintenance point visual rotation guidance information of a second stage; Performing staged clamping control and visual guidance based on the optimal clamping parameters and maintenance point visual rotation guidance information of the first stage and the optimal clamping parameters and maintenance point visual rotation guidance information of the second stage; The clamping control module is configured to control the maintenance clamping device to clamp the parts to be maintained according to the optimal clamping parameters, and to return the maintenance point visual rotation guidance information to the control terminal for visual guidance of maintenance clamping device rotation.

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