Efficient processing control method, device and equipment based on cloud platform and storage medium

Abstract

The invention relates to the technical field of workpiece machining control, in particular to an efficient machining control method and device based on a cloud platform, equipment and a storage medium. The method comprises the following steps: acquiring a workpiece machining multidirectional monitoring image and workpiece machining parameters; carrying out total image brightness balance calculation on the workpiece processing multidirectional monitoring image, and carrying out high-frequency filtering detail optimization so as to construct a detail optimization workpiece image; performing three-dimensional geometric structure analysis on the detail optimized workpiece image, performing three-dimensional point cloud modeling, and constructing a workpiece fine point cloud structure model; obtaining multi-stage machining procedure data based on the workpiece machining parameters; global process flow reconstruction is carried out on the multi-stage machining process data, and a reconstruction machining process series is constructed; and performing dynamic process mapping on the workpiece fine point cloud structure model based on the reconstruction processing process series, and constructing a dynamic workpiece processing twin model. According to the invention, efficient and accurate workpiece processing control is realized.

Description

Technical Field

[0001] The present invention relates to the field of workpiece machining control technology, and in particular to an efficient machining control method, device, equipment and storage medium based on a cloud platform. Background Art

[0002] With the continuous improvement of industrial automation and intelligent manufacturing, various CNC equipment and industrial robots are widely used in production, processing, assembly and other links, playing a key role in improving manufacturing efficiency and product quality. Take CNC machining technology as an example. As one of the core processes of the manufacturing industry, it has long been responsible for high-precision and high-efficiency workpiece processing tasks. The optimization of its processing quality and production efficiency directly affects the operating efficiency of the entire manufacturing system.

[0003] However, in actual production, CNC machining processes are easily affected by various factors, such as machine tool wear, changes in material properties, and fluctuations in ambient temperature and humidity, which may cause deviations in workpiece machining quality or even serious machining defects. Traditional machining control methods often rely on experienced operators to perform manual intervention and adjustments, which are inefficient and difficult to achieve adaptive and precise control. With the continuous improvement of the intelligence and automation of industrial production, there is an urgent need for an efficient workpiece machining control method based on intelligent technology to meet the modern manufacturing industry's ever-increasing demand for product quality and production efficiency. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an efficient processing control method, device, equipment and storage medium based on a cloud platform to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides an efficient processing control method for a cloud platform, comprising the following steps: Step S1: obtaining a multi-directional monitoring image of workpiece processing and workpiece processing parameters; performing full-image brightness balance calculation on the multi-directional monitoring image of workpiece processing, and performing high-frequency filtering detail optimization, thereby constructing a detail-optimized workpiece image; Step S2: Performing 3D geometric structure analysis on the detail optimized workpiece image, and performing 3D point cloud modeling to construct a fine point cloud structure model of the workpiece; Step S3: obtaining multi-stage processing process data based on the workpiece processing parameters; reconstructing the global process flow of the multi-stage processing process data to construct a reconstructed processing process series; Step S4: dynamically mapping the workpiece fine point cloud structure model based on the reconstructed processing process series to construct a dynamic workpiece processing twin model; Step S5: performing fine-tuning definition of multiple groups of parameters based on the workpiece processing parameters, thereby obtaining multiple fine-tuning processing parameters; performing dynamic processing path planning based on the multiple fine-tuning processing parameters, and generating multiple processing planning paths; Step S6: Make an optimal parameter adjustment decision for the dynamic workpiece machining twin model based on multiple machining planning paths, so as to obtain an optimal parameter adjustment strategy; and execute the workpiece machining automation control operation based on the optimal parameter adjustment strategy.

[0006] The present invention also provides an efficient processing control device based on a cloud platform, comprising: Image enhancement module, which obtains multi-directional monitoring images and processing parameters of workpieces; performs full-image brightness balance calculation on the multi-directional monitoring images of workpieces, and performs high-frequency filtering detail optimization to construct detail-optimized workpiece images; The 3D structure module performs 3D geometric structure analysis on the detail-optimized workpiece image, performs 3D point cloud modeling, and constructs a fine point cloud structure model of the workpiece; The process dependency mining module obtains multi-stage processing process data based on the workpiece processing parameters; reconstructs the global process flow of the multi-stage processing process data and constructs a reconstructed processing process series; The dynamic process mapping module dynamically maps the workpiece fine point cloud structure model based on the reconstructed processing process series and builds a dynamic workpiece processing twin model; Iterative simulation module, based on the workpiece processing parameters, multiple groups of parameter fine-tuning definitions are performed to obtain multiple fine-tuning processing parameters; dynamic processing path planning is performed based on multiple fine-tuning processing parameters to generate multiple processing planning paths; The optimal parameter adjustment module makes optimal parameter adjustment decisions for the dynamic workpiece machining twin model based on multiple machining planning paths, thereby obtaining the optimal parameter adjustment strategy; based on the optimal parameter adjustment strategy, the workpiece machining automation control operation is executed.

[0007] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any of the above-mentioned efficient processing control methods based on a cloud platform are implemented.

[0008] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned efficient processing control methods based on a cloud platform are implemented.

[0009] The cloud-based efficient processing control method, device, equipment and storage medium provided by the present invention have the following beneficial effects: the overall image brightness balance calculation and high-frequency filtering detail optimization help to improve the image quality, making the workpiece surface details clearer, constructing detail-optimized workpiece images help to accurately analyze the features and defects of the workpiece surface, and provide a more accurate data basis for subsequent steps, three-dimensional geometric structure analysis and point cloud modeling help to obtain the geometric information of the workpiece, and provide an accurate reference for subsequent processing procedures, and constructing a fine point cloud structure model of the workpiece can provide accurate three-dimensional model data for subsequent processes, thereby improving the processing accuracy and efficiency, and global process flow reconstruction helps to optimize the processing flow, improve processing efficiency and quality, and construct a reconstructed processing The process series can make the machining process more orderly and controllable, reduce potential errors and waste, dynamic process mapping helps to effectively match the process with the workpiece structure, improve machining accuracy and efficiency, and build a dynamic workpiece machining twin model to achieve virtual simulation, help optimize the machining process and reduce risks in actual machining. Multiple sets of parameter fine-tuning definitions can optimize machining parameters and improve machining quality and efficiency. Dynamic machining path planning can generate a variety of machining paths based on different parameter settings, providing more options for selecting the optimal path. The optimal parameter adjustment strategy can ensure the best effect of the machining process, improve machining quality and efficiency, and perform workpiece machining automation control operations to reduce human intervention and improve the level of automation and consistency in production. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of the steps of a cloud platform-based efficient processing control method of the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0012] The present application example provides an efficient processing control method, device, equipment and storage medium based on a cloud platform. The execution subjects of the efficient processing control method, device, equipment and storage medium based on a cloud platform include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload equipment, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of a cloud platform-based efficient processing control method of the present invention. In this example, the steps of the cloud platform-based efficient processing control method include: Step S1: obtaining a multi-directional monitoring image of workpiece processing and workpiece processing parameters; performing full-image brightness balance calculation on the multi-directional monitoring image of workpiece processing, and performing high-frequency filtering detail optimization, thereby constructing a detail-optimized workpiece image; In this embodiment, a multi-angle camera or other image acquisition device is used to obtain multi-directional monitoring images during the workpiece processing process. At the same time, the workpiece processing parameters, such as processing speed, processing depth, processing time, etc., are recorded. The brightness histogram of each image is analyzed to determine the brightness distribution of the image. A brightness equalization algorithm, such as histogram equalization, adaptive histogram equalization, etc., is used to perform brightness equalization processing on the image. A workpiece image after brightness equalization is generated to make the brightness distribution of the image more uniform and improve the contrast and clarity of the image. A suitable image high-frequency filter is selected, such as a Laplace operator, a Gaussian filter, etc., to enhance the detail information of the image. The image after brightness equalization is subjected to high-frequency filtering processing to enhance the edge, texture and other detail information of the image. A workpiece image after detail optimization is generated to make the details of the image clearer, which is conducive to subsequent image analysis and recognition.

[0014] Step S2: Performing 3D geometric structure analysis on the detail optimized workpiece image, and performing 3D point cloud modeling to construct a fine point cloud structure model of the workpiece; In this embodiment, the multi-directional monitoring images are registered, and images at different angles are spliced ​​into a complete workpiece image. Image feature extraction algorithms, such as edge detection and corner detection, are used to extract feature points in the workpiece image. Three-dimensional geometric structure reconstruction algorithms, such as stereo vision and structured light scanning, are used to reconstruct the three-dimensional geometric structure of the workpiece based on the extracted feature points. Based on the reconstructed three-dimensional geometric structure, a point cloud model of the workpiece is generated, where each point represents a point on the surface of the workpiece. The generated point cloud model is optimized, for example, by removing noise points, filling missing points, etc., to improve the quality of the point cloud model. A fine point cloud structure model of the workpiece is constructed, which includes all point cloud data on the workpiece surface and the coordinate information of each point.

[0015] Step S3: obtaining multi-stage processing process data based on the workpiece processing parameters; reconstructing the global process flow of the multi-stage processing process data to construct a reconstructed processing process series; In this embodiment, the workpiece processing parameters, such as processing speed, processing depth, processing path, etc., are analyzed to identify different processing stages. According to different processing stages, corresponding processing procedure data are extracted, such as processing time, processing path, processing tools, etc. of each stage. Multi-stage processing procedure data is generated, and each stage corresponds to a set of process data as input data for subsequent steps. Multi-stage processing procedure data is analyzed to identify the processing objectives and processing sequence of each stage. According to the processing objectives and processing sequence, the process flow is optimized, for example, adjusting the processing sequence, merging repeated processes, etc. A reconstructed processing procedure series is constructed, which includes the optimized processing procedure sequence and detailed data of each process, as input data for subsequent steps.

[0016] Step S4: dynamically mapping the workpiece fine point cloud structure model based on the reconstructed processing process series to construct a dynamic workpiece processing twin model; In this embodiment, according to the processing objectives and processing paths of each process in the reconstructed processing process series, process mapping rules are defined, for example, each process is mapped to a specific area in the point cloud model, or each process is mapped to a specific feature point in the point cloud model. According to the defined process mapping rules, each process in the reconstructed processing process series is dynamically mapped to the fine point cloud structure model of the workpiece to realize the association between the process and the point cloud model. Generate dynamic process mapping data, record the point cloud model area or feature point information corresponding to each process, and use it as input data for subsequent steps. Select a suitable dynamic workpiece processing twin model framework, such as a framework based on a physical model, a framework based on machine learning, etc. According to the reconstructed processing process series, the fine point cloud structure model of the workpiece and the dynamic process mapping data, set the parameters of the twin model, such as processing speed, processing depth, processing path, etc. Use training data to train the twin model, for example, using historical processing data, simulation data, etc. Constructing a dynamic workpiece processing twin model can simulate the workpiece processing process and predict the processing results according to different processing parameters.

[0017] Step S5: performing fine-tuning definition of multiple groups of parameters based on the workpiece processing parameters, thereby obtaining multiple fine-tuning processing parameters; performing dynamic processing path planning based on the multiple fine-tuning processing parameters, and generating multiple processing planning paths; In this embodiment, according to the actual situation of the workpiece processing parameters, the fine-tuning range of each parameter is determined. For example, the fine-tuning range of the processing speed can be ±10%, and the fine-tuning range of the processing depth can be ±0.1mm. According to the fine-tuning range of each parameter, multiple parameter combinations are generated. For example, 10 groups of parameter combinations can be generated, and each group of parameter combinations corresponds to a fine-tuning processing parameter. Generate multiple fine-tuning processing parameters, each fine-tuning processing parameter corresponds to a parameter combination, as input data for subsequent steps. Select a suitable dynamic processing path planning algorithm, such as an algorithm based on geometric constraints, an algorithm based on optimization objectives, etc. According to each fine-tuning processing parameter, set the parameters of the path planning algorithm, such as processing speed, processing depth, processing path, etc. Use the dynamic path planning algorithm to generate multiple processing planning paths according to each fine-tuning processing parameter, and each processing planning path corresponds to a fine-tuning processing parameter as input data for subsequent steps.

[0018] Step S6: Make an optimal parameter adjustment decision for the dynamic workpiece machining twin model based on multiple machining planning paths, so as to obtain an optimal parameter adjustment strategy; and execute the workpiece machining automation control operation based on the optimal parameter adjustment strategy.

[0019] In this embodiment, a dynamic workpiece processing twin model is used to simulate each processing planning path and predict the processing results of each path, such as processing time, processing accuracy, processing cost, etc. According to the simulation results of the twin model, the parameter adjustment strategy corresponding to each processing planning path is evaluated. For example, it can be evaluated according to indicators such as processing time, processing accuracy, and processing cost. According to the evaluation results, the optimal parameter adjustment strategy is selected. For example, it can be selected according to comprehensive indicators or according to specific indicators. According to the optimal parameter adjustment strategy, parameter adjustment instructions are generated, for example, adjusting parameters such as processing speed, processing depth, and processing path. The parameter adjustment instructions are sent to an automated control system, such as a CNC machine tool, a robot, etc., to perform workpiece processing operations. The processing process is monitored in real time, for example, parameters such as processing speed, processing depth, and processing path are monitored to ensure that the processing process meets expectations.

[0020] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Acquire multi-directional monitoring images of workpiece processing and workpiece processing parameters; Step S12: performing full-image brightness balance calculation on the multi-directional monitoring image of workpiece processing, thereby obtaining the average brightness of the image; Step S13: performing brightness distribution enhancement processing on the workpiece processing monitoring image based on the image brightness average value to generate a brightness enhanced workpiece image; Step S14: Performing contour visual recognition on the brightness enhanced workpiece image to extract the workpiece contour line; Step S15: Perform high-frequency filtering detail optimization based on the workpiece contour line, thereby constructing a detail-optimized workpiece image.

[0021] In this embodiment, during the workpiece processing, multiple cameras are used to monitor and photograph the workpiece in all directions, and various parameters of the workpiece processing equipment, such as processing speed, feed rate, current, etc., are collected at the same time. The global brightness distribution of each monitoring image is analyzed, and the brightness average of each image is calculated as a reference for subsequent brightness enhancement. According to the calculated brightness average, the original monitoring image is globally adjusted in brightness, and methods such as histogram equalization or gamma correction are used to improve the overall brightness and contrast of the image, and a workpiece image with brightness enhancement is generated, which provides a good visual basis for subsequent contour recognition. Computer vision technologies such as edge detection and contour extraction are used to extract the contour line of the workpiece from the brightness enhanced image to obtain accurate contour information of the workpiece shape. The extracted workpiece contour line is smoothed to remove high-frequency noise and burrs. Edge-preserving filtering and other methods are used to optimize the detail quality of the workpiece image to generate a workpiece image with optimized details. In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: marking the workpiece key points of the detail-optimized workpiece image and extracting multiple workpiece feature points; Step S22: performing multi-directional visual difference analysis on multiple workpiece feature points to obtain multi-directional parallax feature data of the image; Step S23: performing a three-dimensional geometric structure analysis on the detail-optimized workpiece image based on the multi-directional parallax feature data of the image, thereby generating three-dimensional geometric structure data of the workpiece; Step S24: performing 3D point cloud modeling on the 3D geometric structure data of the workpiece to generate an initial structure model of the workpiece; Step S25: locally iteratively refine the initial structure model of the workpiece to construct a fine point cloud structure model of the workpiece.

[0022] In this embodiment, an image processing algorithm is used, for example, SIFT feature point detection algorithm, ORB Feature point detection algorithm, etc., mark key points of detail optimized workpiece images, identify feature points of workpieces, extract marked key points, and record their coordinate information as input data for subsequent steps, match workpiece images acquired from different angles, for example, use feature point matching algorithm to find the correspondence between corresponding feature points in different images, calculate the disparity of corresponding feature points in different images according to the matching results, obtain multi-directional disparity feature data of the image, use 3D reconstruction algorithm, for example, stereo vision reconstruction algorithm, multi-view geometric reconstruction algorithm, etc., perform 3D geometric structure analysis on the workpiece according to the multi-directional disparity feature data of the image, generate 3D geometric structure data of the workpiece, for example, point cloud data, mesh model, etc., use point cloud modeling algorithm, for example, Poisson reconstruction algorithm, triangulation meshing algorithm, etc., perform point cloud modeling on the 3D geometric structure data of the workpiece, generate an initial structure model of the workpiece, generate the initial structure model of the workpiece as input data for subsequent steps, use local iterative refinement algorithm, for example, refinement algorithm based on surface fitting, refinement algorithm based on geometric constraints, etc., perform local iterative refinement on the initial structure model of the workpiece to improve the accuracy and details of the model. Generate a detailed point cloud structure model of the workpiece for subsequent workpiece quality assessment and defect detection.

[0023] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing time-series variation analysis on the workpiece processing parameters to generate time-series variation processing parameters; Step S32: performing phased process identification on the time-series varying processing parameters, thereby obtaining multi-stage processing process data; Step S33: perform process dependency deep mining on multi-stage processing process data to generate dependency relationships between stage processes; Step S34: performing process operation characteristic analysis on the multi-stage processing process data to obtain the operation characteristic parameters of each stage process; Step S35: Based on the dependency relationship between the stage processes, the operating characteristic parameters of each stage process are globally reconstructed to construct a reconstructed processing process series.

[0024] In this embodiment, a time series analysis method, such as a moving average method, an exponential smoothing method, etc., is used to perform a time series change analysis on the collected parameter data to identify the trend of the parameter change over time. According to the time series analysis results, a time series change processing parameter is generated, such as a curve of the change of the processing speed over time, a curve of the change of the processing depth over time, etc. A process identification algorithm, such as cluster analysis, pattern recognition, etc., is used to analyze the time series change processing parameters and identify different processing stages. According to the process identification results, multi-stage processing process data is generated, such as the processing time, processing speed, processing depth, etc. of each stage. A dependency mining algorithm, such as an association rule mining algorithm, a causal reasoning algorithm, etc., is used to analyze the multi-stage processing process data and mine the dependency relationship between processes in different stages. According to the dependency mining results, the dependency relationship between the stage processes is generated, such as the processing speed of stage 1 affects the processing depth of stage 2, etc. A feature analysis method, such as statistical analysis, machine learning, etc., is used to analyze the process data of each stage and extract the operation characteristic parameters of the process of that stage, such as the average value of the processing speed, the standard deviation of the processing depth, etc. Generate the operating characteristic parameters of each stage process as input data for subsequent steps. Use process reconstruction algorithms, such as constraint-based process reconstruction algorithms and optimization-based process reconstruction algorithms, to perform global process flow reconstruction on the operating characteristic parameters of each stage process according to the dependencies between stage processes, so as to optimize the process flow and improve processing efficiency. Generate a series of reconstructed processing procedures for subsequent workpiece processing optimization and control.

[0025] In this embodiment, step S4 includes the following steps: Step S41: performing dynamic process positioning on the workpiece fine point cloud structure model based on the reconstructed processing process series, thereby obtaining point cloud processing positioning points; Step S42: performing a processing shape change analysis on the point cloud processing positioning points, thereby obtaining positioning point shape change data; Step S43: performing position and posture change analysis on the positioning point morphology change data, thereby obtaining the positioning point position and posture change data; Step S44: Dynamically map the workpiece processing steps to the fine point cloud structure model based on the positioning point position and posture change data, and construct a dynamic workpiece processing twin model.

[0026] In this embodiment, the reconstructed processing process series is mapped to the fine point cloud structure model of the workpiece, and the area corresponding to each process on the point cloud model is determined. According to the dynamic changes of the process, such as processing speed, processing depth, etc., the position of the process on the point cloud model is dynamically adjusted to determine the point cloud processing positioning point. Generate the point cloud processing positioning point as input data for subsequent steps. Use a morphological analysis algorithm, such as a morphological analysis algorithm based on geometric features, a morphological analysis algorithm based on deep learning, etc., to perform morphological change analysis on the point cloud processing positioning point. According to the morphological analysis results, generate positioning point morphological change data, such as change information such as the shape, size, and position of the point cloud. Use a posture change analysis algorithm, such as a posture change analysis algorithm based on feature point matching, a posture change analysis algorithm based on an inertial measurement unit, etc., to perform position and posture change analysis on the positioning point morphological change data. According to the posture change analysis results, generate positioning point position and posture change data, such as change information such as the position, direction, and rotation of the point cloud. Use dynamic mapping algorithms, such as those based on geometric transformation and physical simulation, to dynamically map the workpiece fine point cloud structure model to the process according to the position and posture change data of the positioning points. Build a dynamic workpiece processing twin model that can reflect the dynamic changes in the workpiece processing process in real time, and is used to simulate and predict the processing process, and to optimize and control it.

[0027] In this embodiment, the specific steps of step S5 are: Step S51: performing multiple groups of parameter fine-tuning definitions based on the workpiece processing parameters, thereby obtaining multiple fine-tuning processing parameters; Step S52: performing iterative parameter adjustment simulation on the dynamic workpiece machining twin model based on multiple fine-tuning machining parameters, thereby obtaining iterative machining simulation data; Step S53: Perform dynamic processing path planning on the iterative processing simulation data to generate multiple processing planning paths.

[0028] In this embodiment, according to the actual situation of the workpiece processing parameters, the fine-tuning range of each parameter is determined, for example, the fine-tuning range of the processing speed is ±5%, the fine-tuning range of the processing depth is ±0.1mm, etc. According to the parameter range, multiple groups of parameter combinations are defined. For example, 10 different parameter combinations can be defined, and each parameter combination corresponds to a different processing speed and processing depth. Generate multiple fine-tuning processing parameters, each parameter combination corresponds to a fine-tuning processing parameter, as input data for subsequent steps. Use simulation algorithms, such as algorithms based on physical simulation, algorithms based on machine learning, etc., to perform iterative parameter adjustment simulation on the dynamic workpiece processing twin model. Each fine-tuning processing parameter is applied to the dynamic workpiece processing twin model, simulated and calculated, and the simulation results are observed, such as processing time, processing quality, processing efficiency, etc. Iterative processing simulation data is generated, and the simulation results corresponding to each fine-tuning processing parameter are recorded as input data for subsequent steps. Use path planning algorithms, such as path planning algorithms based on geometric constraints, path planning algorithms based on optimization algorithms, etc., to perform dynamic processing path planning on iterative processing simulation data. According to the iterative processing simulation data, such as processing time, processing quality, etc., the best processing parameter combination is selected, and the processing path is planned according to the parameter combination. Multiple processing planning paths are generated, each path corresponds to a different processing parameter combination, which is used for subsequent processing process selection and optimization.

[0029] In this embodiment, the specific steps of step S6 are: Step S61: performing a comprehensive evaluation of machining simulation on the dynamic workpiece machining twin model based on multiple machining planning paths, thereby obtaining multiple groups of planning path evaluation values; Step S62: extracting the optimal planned path based on multiple groups of planned path evaluation values; Step S63: making an optimal parameter adjustment decision for the optimal planned path, thereby obtaining an optimal parameter adjustment strategy; Step S64: Upload to the cloud platform based on the optimal parameter adjustment strategy to perform workpiece processing automation control operations.

[0030] In this embodiment, a simulation evaluation algorithm is used, for example, an evaluation algorithm based on a simulation model, an evaluation algorithm based on machine learning, etc., to simulate the dynamic workpiece processing twin model, and a comprehensive evaluation is performed on each processing planning path. Appropriate evaluation indicators are selected, such as processing time, processing quality, processing efficiency, cost, etc., to evaluate each processing planning path. Multiple groups of planning path evaluation values ​​are generated, and each planning path corresponds to a group of evaluation values ​​as input data for subsequent steps. Multiple groups of planning path evaluation values ​​are compared, for example, the path with the shortest processing time, the best processing quality, the highest processing efficiency, and the lowest cost is selected. According to the evaluation value comparison results, the optimal planning path is extracted as input data for subsequent steps. According to the optimal planning path, the optimal parameter adjustment strategy is determined, for example, the parameters such as the processing speed, processing depth, and processing path are adjusted to optimize the processing process. The optimal parameter adjustment strategy is generated as input data for subsequent steps. The optimal parameter adjustment strategy is applied to the workpiece processing automation control cloud platform, such as CNC machine tools, robots, etc., to control the processing process. The workpiece processing automation control operation is performed, and the processing process is controlled according to the optimal parameter adjustment strategy to achieve the best processing effect.

[0031] In this embodiment, the present invention further provides an efficient processing control device based on a cloud platform, comprising: Image enhancement module, which obtains multi-directional monitoring images and processing parameters of workpieces; performs full-image brightness balance calculation on the multi-directional monitoring images of workpieces, and performs high-frequency filtering detail optimization to construct detail-optimized workpiece images; The 3D structure module performs 3D geometric structure analysis on the detail-optimized workpiece image, performs 3D point cloud modeling, and constructs a fine point cloud structure model of the workpiece; The process dependency mining module obtains multi-stage processing process data based on the workpiece processing parameters; reconstructs the global process flow of the multi-stage processing process data and constructs a reconstructed processing process series; The dynamic process mapping module dynamically maps the workpiece fine point cloud structure model based on the reconstructed processing process series and builds a dynamic workpiece processing twin model; Iterative simulation module, based on the workpiece processing parameters, multiple groups of parameter fine-tuning definitions are performed to obtain multiple fine-tuning processing parameters; dynamic processing path planning is performed based on multiple fine-tuning processing parameters to generate multiple processing planning paths; The optimal parameter adjustment module makes optimal parameter adjustment decisions for the dynamic workpiece machining twin model based on multiple machining planning paths, thereby obtaining the optimal parameter adjustment strategy; based on the optimal parameter adjustment strategy, the workpiece machining automation control operation is executed.

[0032] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of efficient processing control based on a cloud platform as described above are implemented.

[0033] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of efficient processing control based on a cloud platform as described above are implemented.

[0034] Those skilled in the art clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0035] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it is stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or partly or all or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media for storing program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks. Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0036] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An efficient processing control method based on a cloud platform, characterized in that: The following steps are involved: Step S1: obtaining a multi-directional monitoring image of workpiece processing and workpiece processing parameters; performing full-image brightness balance calculation on the multi-directional monitoring image of workpiece processing, and performing high-frequency filtering detail optimization, thereby constructing a detail-optimized workpiece image; Step S2: Performing 3D geometric structure analysis on the detail optimized workpiece image, and performing 3D point cloud modeling to construct a fine point cloud structure model of the workpiece; Step S3: obtaining multi-stage processing process data based on the workpiece processing parameters; reconstructing the global process flow of the multi-stage processing process data to construct a reconstructed processing process series; Step S4: dynamically mapping the workpiece fine point cloud structure model based on the reconstructed processing process series to construct a dynamic workpiece processing twin model; Step S5: performing fine-tuning definition of multiple groups of parameters based on the workpiece processing parameters, thereby obtaining multiple fine-tuning processing parameters; performing dynamic processing path planning based on the multiple fine-tuning processing parameters, and generating multiple processing planning paths; Step S6: Make an optimal parameter adjustment decision for the dynamic workpiece machining twin model based on multiple machining planning paths, so as to obtain an optimal parameter adjustment strategy; and execute the workpiece machining automation control operation based on the optimal parameter adjustment strategy.

2. The efficient processing control method based on cloud platform according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: Acquire multi-directional monitoring images of workpiece processing and workpiece processing parameters; Step S12: performing full-image brightness balance calculation on the multi-directional monitoring image of workpiece processing, thereby obtaining the average brightness of the image; Step S13: performing brightness distribution enhancement processing on the workpiece processing monitoring image based on the image brightness average value to generate a brightness enhanced workpiece image; Step S14: Performing contour visual recognition on the brightness enhanced workpiece image to extract the workpiece contour line; Step S15: Perform high-frequency filtering detail optimization based on the workpiece contour line, thereby constructing a detail-optimized workpiece image.

3. The efficient processing control method based on cloud platform according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: marking the workpiece key points of the detail-optimized workpiece image and extracting multiple workpiece feature points; Step S22: performing multi-directional visual difference analysis on multiple workpiece feature points to obtain multi-directional parallax feature data of the image; Step S23: performing a three-dimensional geometric structure analysis on the detail-optimized workpiece image based on the multi-directional parallax feature data of the image, thereby generating three-dimensional geometric structure data of the workpiece; Step S24: performing 3D point cloud modeling on the 3D geometric structure data of the workpiece to generate an initial structure model of the workpiece; Step S25: locally iteratively refine the initial structure model of the workpiece to construct a fine point cloud structure model of the workpiece.

4. The efficient processing control method based on cloud platform according to claim 1 is characterized in that: The specific steps of step S3 are: Step S31: performing time-series variation analysis on the workpiece processing parameters to generate time-series variation processing parameters; Step S32: performing phased process identification on the time-series varying processing parameters, thereby obtaining multi-stage processing process data; Step S33: perform process dependency deep mining on multi-stage processing process data to generate dependency relationships between stage processes; Step S34: performing process operation characteristic analysis on the multi-stage processing process data to obtain the operation characteristic parameters of each stage process; Step S35: Based on the dependency relationship between the stage processes, the operating characteristic parameters of each stage process are globally reconstructed to construct a reconstructed processing process series.

5. The efficient processing control method based on cloud platform according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: performing dynamic process positioning on the workpiece fine point cloud structure model based on the reconstructed processing process series, thereby obtaining point cloud processing positioning points; Step S42: performing a processing shape change analysis on the point cloud processing positioning points, thereby obtaining positioning point shape change data; Step S43: performing position and posture change analysis on the positioning point morphology change data, thereby obtaining the positioning point position and posture change data; Step S44: Dynamically map the workpiece processing steps to the fine point cloud structure model based on the positioning point position and posture change data, and construct a dynamic workpiece processing twin model.

6. The efficient processing control method based on cloud platform according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: performing multiple groups of parameter fine-tuning definitions based on the workpiece processing parameters, thereby obtaining multiple fine-tuning processing parameters; Step S52: performing iterative parameter adjustment simulation on the dynamic workpiece machining twin model based on multiple fine-tuning machining parameters, thereby obtaining iterative machining simulation data; Step S53: Perform dynamic processing path planning on the iterative processing simulation data to generate multiple processing planning paths.

7. The efficient processing control method based on cloud platform according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: performing a comprehensive evaluation of machining simulation on the dynamic workpiece machining twin model based on multiple machining planning paths, thereby obtaining multiple groups of planning path evaluation values; Step S62: extracting the optimal planned path based on multiple groups of planned path evaluation values; Step S63: making an optimal parameter adjustment decision for the optimal planned path, thereby obtaining an optimal parameter adjustment strategy; Step S64: Upload to the cloud platform based on the optimal parameter adjustment strategy to perform workpiece processing automation control operations.

8. An efficient processing control device based on a cloud platform, characterized in that: include: Image enhancement module, which obtains multi-directional monitoring images and processing parameters of workpieces; performs full-image brightness balance calculation on the multi-directional monitoring images of workpieces, and performs high-frequency filtering detail optimization to construct detail-optimized workpiece images; The 3D structure module performs 3D geometric structure analysis on the detail-optimized workpiece image, performs 3D point cloud modeling, and constructs a fine point cloud structure model of the workpiece; The process dependency mining module obtains multi-stage processing process data based on the workpiece processing parameters; reconstructs the global process flow of the multi-stage processing process data and constructs a reconstructed processing process series; The dynamic process mapping module dynamically maps the workpiece fine point cloud structure model based on the reconstructed processing process series and builds a dynamic workpiece processing twin model; Iterative simulation module, which performs multiple parameter fine-tuning definitions based on workpiece processing parameters, thereby obtaining multiple fine-tuning processing parameters; Perform dynamic machining path planning based on multiple fine-tuning machining parameters to generate multiple machining planning paths; The optimal parameter adjustment module makes optimal parameter adjustment decisions for the dynamic workpiece machining twin model based on multiple machining planning paths, thereby obtaining the optimal parameter adjustment strategy; based on the optimal parameter adjustment strategy, the workpiece machining automation control operation is executed.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the efficient processing control method based on a cloud platform described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the efficient processing control method based on a cloud platform described in any one of claims 1 to 7 are implemented.

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