Intelligent feeding and discharging integrated control system for plastics

Abstract

The invention discloses an intelligent feeding and discharging integrated control system for plastics, and relates to the technical field of plastic feeding and discharging, and the system comprises a material identification and positioning module which is used for obtaining multi-dimensional characteristic parameter information of plastic materials through a feeding and discharging robot; the control plan analysis module is used for determining target feeding and discharging control parameters and determining feeding and discharging motion path information based on the plastic material multi-dimensional characteristic parameter information and plastic material control database analysis; the feeding and discharging execution module is used for executing feeding and discharging control based on the target feeding and discharging control parameters and the feeding and discharging motion path information and obtaining feeding and discharging execution parameter information; and the feedback optimization analysis module is used for carrying out abnormal optimization on the loading and unloading execution parameter information and determining loading and unloading execution optimization parameters to carry out loading and unloading integrated control. The technical effects that efficient and automatic feeding and discharging of the materials on the plastic production line are achieved, the control precision and the control efficiency are improved, and then the product quality and the production safety are improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of plastic loading and unloading technology, and more particularly to an intelligent integrated loading and unloading control system for plastics. Background Technology

[0002] In current plastic production processes, loading and unloading operations are generally carried out manually or using simple mechanical equipment. However, existing technologies are not only inefficient but also susceptible to human factors, leading to unstable product quality and difficulty in ensuring production safety. With the continuous development of intelligent manufacturing technology, the application of robotics and intelligent control systems in manufacturing is becoming increasingly widespread, providing possibilities for the automation upgrade of plastic production lines. Summary of the Invention

[0003] This application provides an intelligent integrated loading and unloading control system for plastics, which solves the technical problems of low accuracy and efficiency in the loading and unloading control of plastics in the prior art, resulting in unstable product quality. It achieves the technical effect of realizing efficient and automated loading and unloading of materials on plastic production lines, improving control accuracy and efficiency, and thus improving product quality and production safety.

[0004] In view of the above problems, the present invention provides an intelligent integrated control system for loading and unloading plastics.

[0005] This application provides an intelligent integrated loading and unloading control system for plastics. The system includes: a material identification and positioning module for acquiring a loading and unloading robot, which integrates a vision sensor, a laser sensor, a locator, and a weight sensor. The loading and unloading robot identifies and positions plastic materials on the target production line, acquiring multi-dimensional characteristic parameter information of the plastic materials; and a control planning and analysis module for collecting and acquiring a plastic material control database. Based on the multi-dimensional characteristic parameter information of the plastic materials and the plastic material control database, the module performs a traversal analysis to determine the target loading and unloading control parameters, and simultaneously monitors and acquires production line characteristic parameter information, and adjusts the production line characteristics accordingly. The system performs path planning based on parameter information and multi-dimensional characteristic parameters of the plastic material to determine the loading and unloading movement path information. The loading and unloading execution module, through the loading and unloading robot, performs loading and unloading control on the plastic material based on the target loading and unloading control parameters and the loading and unloading movement path information, and monitors the execution process in real time to obtain loading and unloading execution parameter information. The feedback optimization analysis module identifies anomalies in the loading and unloading execution parameter information, obtains abnormal loading and unloading characteristic parameters, performs optimization analysis based on these abnormal loading and unloading characteristic parameters, determines optimized loading and unloading execution parameters, and performs integrated loading and unloading control through these optimized parameters.

[0006] In a possible implementation, obtaining multidimensional characteristic parameter information of the plastic material further includes: capturing images of the plastic material using the vision sensor to obtain visual image information of the plastic material, and simultaneously acquiring laser point cloud data information of the plastic material using the laser sensor; spatially registering and fusing the visual image information of the plastic material and the laser point cloud data information to generate a visual model of the plastic material; extracting multidimensional parameters from the visual model of the plastic material to obtain plastic appearance feature parameter information, which includes shape, size, and orientation; identifying and sensing the plastic appearance feature parameter information using the locator and weight sensor to obtain plastic positioning information and plastic weight parameters; and determining the multidimensional characteristic parameter information of the plastic material based on the plastic appearance feature parameter information, the plastic positioning information, and the plastic weight parameters.

[0007] In a possible implementation, generating a visualization model of plastic materials further includes: performing edge feature recognition on the visual image information of the plastic material to determine the edge contour information of the plastic material; filtering and denoising the laser point cloud data information using the edge contour information of the plastic material to obtain usable plastic point cloud data information; extracting key feature points from the visual image information of the plastic material based on the edge contour information of the plastic material to obtain a set of key feature points of the plastic material; performing feature point mapping extraction on the usable plastic point cloud data information according to the set of key feature points of the plastic material to obtain a set of key feature points of the plastic material point cloud; and performing data registration and spatial alignment fusion on the set of key feature points of the plastic material and the set of key feature points of the plastic material point cloud to generate the visualization model of the plastic material.

[0008] In a possible implementation, determining the target loading and unloading control parameters further includes: obtaining multi-dimensional characteristic parameter information of historical plastic materials and corresponding loading and unloading control parameters based on the plastic material control database; performing feature segmentation and integration on the plastic material control database based on the multi-dimensional characteristic parameter information to obtain a plastic calibration control database; performing matching analysis and measurement between the multi-dimensional characteristic parameter information of the plastic materials and the plastic calibration control database to obtain a plastic characteristic matching degree set; and optimizing the plastic calibration control database based on the plastic characteristic matching degree set to determine the target loading and unloading control parameters.

[0009] In a possible implementation, determining the target loading and unloading control parameters further includes: optimizing the plastic calibration control database based on the plastic characteristic matching degree set to determine matching loading and unloading control parameters; performing regression fitting with the multidimensional feature parameter information as independent variables and the loading and unloading control parameters as dependent variables in sequence to generate a set of loading and unloading feature parameter regression curves; associating and fusing the regression curves in the set of loading and unloading feature parameter regression curves to generate a loading and unloading feature parameter association model; and calculating and optimizing the matching loading and unloading control parameters based on the loading and unloading feature parameter association model to determine the target loading and unloading control parameters.

[0010] In a possible implementation, determining the loading and unloading movement path information further includes: generating a loading and unloading production line map by performing graphical representation based on the production line characteristic parameter information; marking obstacles on the loading and unloading production line map to determine loading and unloading constraint information; obtaining a loading and unloading optimization objective; evaluating and fitting the loading and unloading optimization objective to generate a loading and unloading fitness function; performing path searches within the loading and unloading production line map based on the loading and unloading constraint information using a list of path planning algorithms to obtain a set of planned loading and unloading paths; and comparing and optimizing the set of planned loading and unloading paths using the loading and unloading fitness function to determine the loading and unloading movement path information.

[0011] In a possible implementation, generating the loading / unloading production line map further includes: performing object identification and marking on the production line feature parameter information to obtain a set of production line distribution objects, and abstracting the set of production line distribution objects as a set of graph model nodes; performing path attribute identification on the set of graph model nodes according to the production line feature parameter information to obtain a set of graph model connecting edges; and generating the loading / unloading production line map based on the set of graph model nodes and the set of graph model connecting edges.

[0012] In a possible implementation, determining the material handling execution optimization parameters further includes: performing emergency strategy optimization analysis based on the abnormal material handling characteristic parameters to obtain multiple material handling optimization measures strategy parameter schemes; constructing a material handling effect simulation module, and performing simulation optimization on the multiple material handling optimization measures strategy parameter schemes based on the material handling effect simulation module to determine the material handling execution optimization parameters.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages: This technical solution employs a loading / unloading robot to identify and locate plastic materials on the target production line. It acquires multi-dimensional characteristic parameters of the plastic materials and analyzes them against a plastic material control database to determine target loading / unloading control parameters. Simultaneously, it monitors and acquires production line characteristic parameters, performs path planning based on these parameters, and determines the loading / unloading movement path. The loading / unloading robot then executes loading / unloading control based on these parameters and path information, with real-time monitoring of the process. Anomalies are identified in the execution parameters, and abnormal characteristic parameters are used for optimization analysis to determine optimized loading / unloading parameters for integrated control. This achieves highly efficient and automated loading / unloading on the plastic production line, improving control accuracy and efficiency, and ultimately enhancing product quality and production safety.

[0014] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the intelligent integrated loading and unloading control system for plastics used in this application. Figure 2 This is a flowchart illustrating the process of acquiring multi-dimensional characteristic parameters of plastic materials in the intelligent integrated control system for loading and unloading plastics used in this application.

[0016] Explanation of reference numerals in the attached diagram: Material identification and positioning module 11, control planning and analysis module 12, loading and unloading execution module 13, feedback optimization and analysis module 14. Detailed Implementation

[0017] This application provides an intelligent integrated loading and unloading control system for plastics, which solves the technical problems of low accuracy and efficiency in the loading and unloading control of plastics in the prior art, resulting in unstable product quality. It achieves the technical effect of realizing efficient and automated loading and unloading of materials on plastic production lines, improving control accuracy and efficiency, and thus improving product quality and production safety.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] Example 1, as Figure 1As shown, this application provides an intelligent integrated loading and unloading control system for plastics, the system comprising: The material identification and positioning module 11 is used to acquire the loading and unloading robot. The loading and unloading robot integrates a vision sensor, a laser sensor, a locator, and a weight sensor. The loading and unloading robot identifies and positions the plastic materials on the target production line and acquires multi-dimensional characteristic parameter information of the plastic materials.

[0020] like Figure 2 As shown, furthermore, the step of obtaining multidimensional characteristic parameter information of plastic materials in this application also includes: The visual sensor captures images of the plastic material to obtain visual image information, while the laser sensor collects laser point cloud data of the plastic material. The visual image information and the laser point cloud data are spatially registered and fused to generate a visual model of the plastic material. Multidimensional parameters are extracted from the visual model to obtain plastic appearance feature parameters, including shape, size, and orientation. The locator and weight sensor are used to identify and perceive these appearance feature parameters to obtain plastic positioning information and plastic weight parameters. Based on the appearance feature parameters, positioning information, and weight parameters, multidimensional characteristic parameters of the plastic material are determined.

[0021] Furthermore, the step of generating the visualization model of the plastic material in this application also includes: Edge feature recognition is performed on the visual image information of the plastic material to determine the edge contour information of the plastic material. The laser point cloud data information is then filtered and denoised using the edge contour information of the plastic material to obtain usable plastic point cloud data information. Based on the edge contour information of the plastic material, key feature points are extracted from the visual image information of the plastic material to obtain a set of key feature points of the plastic material. Feature point mapping is then performed on the usable plastic point cloud data information according to the set of key feature points of the plastic material to obtain a set of key feature points of the plastic material point cloud. The set of key feature points of the plastic material and the set of key feature points of the plastic material point cloud are then registered and spatially aligned and fused to generate the visualization model of the plastic material.

[0022] Specifically, to achieve efficient and automated material loading and unloading on the plastic production line, a material identification and positioning module 11 is used to acquire a loading and unloading robot. This module is a functional module for omnidirectional feature recognition and positioning of the plastic materials. The loading and unloading robot can perform loading and unloading operations on plastic materials, improving the accuracy of loading and unloading. The robot integrates multiple sensors, including vision sensors, laser sensors, locators, and weight sensors. The loading and unloading robot identifies and positions the plastic materials on the target production line. First, the vision sensor captures images of the plastic materials. The vision sensor can preferably be an industrial camera, which acquires the corresponding visual image information of the plastic materials. Simultaneously, the laser sensor emits laser pulses to acquire laser point cloud data information of the plastic materials.

[0023] Spatial registration and fusion are performed on the visual image information of the plastic material and the laser point cloud data. First, edge feature recognition is performed on the visual image information of the plastic material using edge detection operators such as Canny and Sobel. The results of the edge detection algorithm determine the edge contour information of the plastic material, which describes the shape boundary of the plastic material in the two-dimensional image. The laser sensor may be affected by environmental noise, equipment errors, and other factors when acquiring point cloud data, generating unnecessary noise points. Therefore, the laser point cloud data is filtered and denoised using the edge contour information of the plastic material. By comparing and matching with the contour information, point cloud data that clearly does not belong to the plastic material contour can be removed, resulting in more accurate usable plastic point cloud data. Based on the edge contour information of the plastic material, key feature points are extracted from the visual image information of the plastic material to obtain a corresponding set of key feature points. These feature points are usually important nodes describing the shape and properties of the plastic material, such as corners and intersections. According to the position of the set of key feature points in the visual image, corresponding feature point mapping and extraction are performed on the usable plastic point cloud data to obtain the corresponding set of key feature points for the plastic material point cloud. The key feature point set of the plastic material and the key feature point set of the plastic material point cloud are registered. The optimal transformation parameters (such as rotation and translation) between the two datasets are found through data registration, ensuring they are aligned in the same coordinate system. After data registration, the visual image information and laser point cloud data are spatially aligned and fused. A 3D modeling technique is then used to generate a visual model of the plastic material that integrates the image and point cloud information, accurately reflecting the shape, size, and surface features of the plastic material.

[0024] Multidimensional parameters are extracted from the visualized model of the plastic material to obtain corresponding plastic appearance feature parameters, including shape, size, and distribution direction. The locator and weight sensor identify and perceive these plastic appearance feature parameters to obtain corresponding plastic positioning and weight parameters. Based on the plastic appearance feature parameters, positioning information, and weight parameters, multidimensional characteristic parameters of the plastic material are comprehensively determined. This enables comprehensive feature monitoring of the plastic material, providing a basis for subsequent loading and unloading control, thereby improving the accuracy and efficiency of plastic material control.

[0025] The control planning and analysis module 12 is used to collect and acquire the plastic material control database, perform traversal analysis based on the multi-dimensional characteristic parameter information of the plastic material and the plastic material control database, determine the target loading and unloading control parameters, and simultaneously monitor and acquire the production line characteristic parameter information. The module then performs path planning based on the production line characteristic parameter information and the multi-dimensional characteristic parameter information of the plastic material to determine the loading and unloading movement path information.

[0026] Furthermore, the step of determining the target loading and unloading control parameters in this application also includes: Based on the plastic material control database, multidimensional characteristic parameter information of historical plastic materials and corresponding loading and unloading control parameters are obtained; based on the multidimensional characteristic parameter information, the plastic material control database is segmented and integrated to obtain a plastic calibration control database; the multidimensional characteristic parameter information of plastic materials is matched and analyzed with the plastic calibration control database to obtain a plastic characteristic matching degree set; based on the plastic characteristic matching degree set, the plastic calibration control database is optimized and adjusted to determine the target loading and unloading control parameters.

[0027] Furthermore, the step of determining the target loading and unloading control parameters in this application also includes: Based on the plastic property matching degree set, the plastic calibration control database is optimized to determine matching loading and unloading control parameters; the multidimensional feature parameter information is used as independent variables, and the loading and unloading control parameters are used as dependent variables in sequence for regression fitting to generate a set of loading and unloading feature parameter regression curves; the regression curves in the set of loading and unloading feature parameter regression curves are correlated and fused to generate a loading and unloading feature parameter correlation model; based on the loading and unloading feature parameter correlation model, the matching loading and unloading control parameters are calculated and optimized to determine the target loading and unloading control parameters.

[0028] Specifically, the control planning and analysis module 12 collects and acquires a plastic material control database. This module is a functional module used for precise analysis of the loading and unloading control parameters and control paths of plastic materials. The plastic material control database contains historical loading and unloading control parameters for various types of plastic materials. A comprehensive analysis is performed based on the multi-dimensional characteristic parameter information of the plastic materials and the plastic material control database. The specific analysis process is as follows: First, based on the plastic material control database, multi-dimensional characteristic parameter information of historical plastic materials and corresponding loading and unloading control parameters such as loading and unloading speed, angle, direction, and force are obtained. Then, based on the multi-dimensional characteristic parameter information, the plastic material control database is feature-divided and integrated, grouping data with the same multi-dimensional characteristic parameter information type into one category to obtain the integrated plastic calibration control database. A similarity algorithm is used to match and analyze the multi-dimensional characteristic parameter information of the plastic materials with the plastic calibration control database, obtaining the similarity calculation results between the control data of each type of plastic in the plastic calibration control database and the multi-dimensional characteristic parameter information of the plastic materials. The similarity calculation results are then used as a plastic characteristic matching degree set.

[0029] The plastic calibration control database is optimized based on the plastic property matching degree set. First, the database is optimized using the plastic property matching degree set to determine the parameter with the highest matching degree as the matching loading and unloading control parameter. Then, the multidimensional feature parameter information is used as the independent variable, and each type of control parameter in the matching loading and unloading control parameters is used as the dependent variable for regression fitting, generating a corresponding set of loading and unloading feature parameter regression curves. These curves describe the functional relationship between different multidimensional feature parameters and each loading and unloading control parameter. The regression curves in the loading and unloading feature parameter regression curve set are correlated and fused using neural networks or similar methods to generate a comprehensive loading and unloading feature parameter correlation model. This model is used to analyze the loading and unloading control parameters based on the multidimensional feature parameters. Since the multidimensional feature parameter information corresponding to the matching loading and unloading control parameters may not be completely matched, resulting in feature deviation, the feature deviation of the matching loading and unloading control parameters is calculated and optimized based on the loading and unloading feature parameter correlation model. The optimized values ​​of the loading and unloading control parameters are output, thereby determining the optimized target loading and unloading control parameters considering the feature deviation.

[0030] Simultaneously, the production line control system monitors and acquires characteristic parameter information of the production line. This characteristic parameter information includes production line status parameters, such as equipment operating status, distribution, dimensional parameters, production speed, and production line load. Multiple path planning algorithms are used to plan paths based on the production line characteristic parameter information and the multi-dimensional characteristic parameters of the plastic material, determining the optimal loading and unloading motion path. This achieves optimized analysis of plastic loading and unloading control parameters while ensuring the safe and efficient movement path of the robot, thereby improving the efficiency of plastic material loading and unloading and product quality.

[0031] Furthermore, the step of determining the loading and unloading movement path information in this application also includes: Based on the production line characteristic parameter information, a graph representation is performed to generate a loading / unloading production line map. Obstacles are marked on the loading / unloading production line map to determine loading / unloading constraint information. The loading / unloading optimization objective is obtained, evaluated, and fitted to generate a loading / unloading fitness function. Based on the loading / unloading constraint information, a path search is performed within the loading / unloading production line map using a list of path planning algorithms to obtain a set of planned loading / unloading paths. The loading / unloading fitness function is then used to compare and optimize the set of planned loading / unloading paths to determine the loading / unloading movement path information.

[0032] Furthermore, the step of generating the loading and unloading production line map in this application also includes: The production line feature parameter information is used to identify and mark objects to obtain a set of production line distributed objects. The set of production line distributed objects is then abstracted as a set of graph model nodes. The graph model node set is then marked with path attributes according to the production line feature parameter information to obtain a set of graph model connecting edges. A topological graph representation is then performed based on the graph model node set and the graph model connecting edge set to generate a map of the loading and unloading production line.

[0033] Specifically, the planning of the loading and unloading movement path for plastic materials is as follows: Based on the production line characteristic parameter information, a graph representation is created to achieve rapid path visualization and planning. First, object recognition and labeling of the production line characteristic parameter information is performed through image recognition. This information includes the location, size, type, and function of equipment, resulting in a corresponding set of production line distributed objects, including equipment and plastic materials. Then, each object in the production line distributed object set is abstracted as a node in a graph model, and node attributes are identified based on node location, size, and other information, thus obtaining a graph model node set. According to the node distribution distance, material flow path, and path complexity of the production line characteristic parameter information, path attribute identification is performed between the nodes in the graph model node set, obtaining a set of graph model connection edges between each node. Based on the graph model node set and the graph model connection edge set, a topology graph representation is created. In the topology graph, nodes represent various objects on the production line, and connection edges represent the path relationships between objects, generating a corresponding loading and unloading production line map to more intuitively display the layout and path relationships of the production line.

[0034] Obstacles are marked on the loading / unloading production line map. These obstacles may include fixed machinery, transport tracks, safety zones, etc., which limit the movement range of the loading / unloading robots and are identified as loading / unloading constraints. Loading / unloading optimization objectives are obtained, typically including factors such as efficiency, cost, and safety, for example, minimizing loading / unloading time, minimizing energy consumption, or maximizing production line throughput. Weights are assigned to each objective in the loading / unloading optimization objectives according to the loading / unloading requirements of the plastic materials. Then, a multivariate evaluation fitting is performed on the loading / unloading optimization objectives based on the weight allocation results to generate a loading / unloading fitness function. This fitness function is used to evaluate the merits of different paths; a higher fitness indicates a better path performance. A list of path planning algorithms is compiled based on the optimization objectives. This list includes various algorithms such as A* algorithm, Dijkstra's algorithm, genetic algorithm, and particle swarm optimization algorithm. Based on the loading / unloading constraints, path searches are performed within the loading / unloading production line map according to each path planning algorithm in the list, obtaining a set of optimized loading / unloading paths for each algorithm. Then, the loading / unloading fitness function is used to evaluate and compare the set of planned loading / unloading paths to find the best path. The path with the highest fitness is then selected as the loading / unloading path information to maximize the optimization goal while satisfying all constraints. By using a multi-path planning algorithm to optimize loading / unloading paths, the rationality and accuracy of path planning are improved, thereby increasing loading / unloading production efficiency and product quality.

[0035] The loading and unloading execution module 13 is used to perform loading and unloading control on the plastic material by the loading and unloading robot based on the target loading and unloading control parameters and the loading and unloading motion path information, and to monitor the execution process in real time and obtain loading and unloading execution parameter information.

[0036] Specifically, the loading / unloading execution module 13 is used to control the loading / unloading robot to perform loading / unloading control operations on the plastic material based on the target loading / unloading control parameters and the loading / unloading motion path information, and to monitor the execution process in real time to obtain loading / unloading execution parameter information. The loading / unloading execution parameter information is loading / unloading status feedback parameters, including loading / unloading position, speed, and robot control status, etc., to provide optimization basis for subsequent loading / unloading execution parameters, so as to improve the accuracy and timeliness of loading / unloading control.

[0037] The feedback optimization analysis module 14 is used to identify anomalies in the loading and unloading execution parameter information, obtain abnormal loading and unloading feature parameters, perform optimization analysis based on the abnormal loading and unloading feature parameters, determine the loading and unloading execution optimization parameters, and perform integrated loading and unloading control through the loading and unloading execution optimization parameters.

[0038] Furthermore, the step of determining the optimization parameters for loading and unloading also includes: Based on the abnormal material loading and unloading characteristic parameters, an emergency strategy optimization analysis is performed to obtain multiple material loading and unloading optimization measures strategy parameter schemes; a material loading and unloading effect simulation module is constructed, and the multiple material loading and unloading optimization measures strategy parameter schemes are simulated and optimized based on the material loading and unloading effect simulation module to determine the material loading and unloading execution optimization parameters.

[0039] Specifically, the feedback optimization analysis module 14 is used to train historical data through machine learning to obtain an anomaly identification model. This model then identifies anomalies in the loading and unloading execution parameters, yielding corresponding loading and unloading anomaly characteristic parameters. These parameters include the type and severity of the anomaly, such as material misalignment, force control issues, and robot malfunctions. Emergency strategy optimization analysis is performed on these anomaly characteristic parameters using historical data of the plastic loading and unloading strategy to match targeted emergency strategies. Through this matching optimization analysis, multiple loading and unloading optimization measures and strategy parameter schemes are obtained, including adjusting loading and unloading speeds, optimizing loading and unloading paths, and improving force control algorithms.

[0040] A material handling effect simulation module is constructed using simulation software based on historical material handling data. This module can simulate and predict the actual material handling control state. Within the simulation module, parameters and conditions similar to the actual production environment are set. Multiple material handling optimization strategies and parameter schemes are simulated based on this module, and the material handling effects under different schemes, such as efficiency, accuracy, and energy consumption, are compared. Based on the simulation results, the optimal material handling execution optimization parameters are determined, and the selected scheme is further simulated, verified, and fine-tuned to ensure its effectiveness in actual production. Integrated material handling control is achieved through these optimized execution parameters, enabling continuous improvement and optimization of material handling control parameters, increasing control accuracy and efficiency, and ultimately improving product quality and production safety.

[0041] In summary, the intelligent integrated loading and unloading control system for plastics provided in this application has the following technical advantages: This technical solution employs a loading / unloading robot to identify and locate plastic materials on the target production line. It acquires multi-dimensional characteristic parameters of the plastic materials and analyzes them against a plastic material control database to determine target loading / unloading control parameters. Simultaneously, it monitors and acquires production line characteristic parameters, performs path planning based on these parameters, and determines the loading / unloading movement path. The loading / unloading robot then executes loading / unloading control based on these parameters and path information, with real-time monitoring of the process. Anomalies are identified in the execution parameters, and abnormal characteristic parameters are used for optimization analysis to determine optimized loading / unloading parameters for integrated control. This achieves highly efficient and automated loading / unloading on the plastic production line, improving control accuracy and efficiency, and ultimately enhancing product quality and production safety.

[0042] This specification and accompanying drawings are merely illustrative examples of this application, but the scope of protection of this application is not limited thereto. It should be noted that any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An intelligent integrated loading and unloading control system for plastics, characterized in that, The system includes: The material identification and positioning module is used to acquire the loading and unloading robot, which integrates a vision sensor, a laser sensor, a locator, and a weight sensor. The loading and unloading robot identifies and positions the plastic materials on the target production line and acquires multi-dimensional characteristic parameter information of the plastic materials. The control planning and analysis module is used to collect and acquire the plastic material control database, perform traversal analysis based on the multi-dimensional characteristic parameter information of the plastic material and the plastic material control database, determine the target loading and unloading control parameters, and simultaneously monitor and acquire the production line characteristic parameter information. The module then performs path planning based on the production line characteristic parameter information and the multi-dimensional characteristic parameter information of the plastic material to determine the loading and unloading movement path information. The loading and unloading execution module is used to control the loading and unloading of the plastic material by the loading and unloading robot based on the target loading and unloading control parameters and the loading and unloading motion path information, and to monitor the execution process in real time and obtain the loading and unloading execution parameter information. The feedback optimization analysis module is used to identify anomalies in the loading and unloading execution parameter information, obtain abnormal loading and unloading characteristic parameters, perform optimization analysis based on the abnormal loading and unloading characteristic parameters, determine the optimized loading and unloading execution parameters, and perform integrated loading and unloading control through the optimized loading and unloading execution parameters.

2. The intelligent integrated loading and unloading control system for plastics as described in claim 1, characterized in that, The acquisition of multidimensional characteristic parameter information of plastic materials includes: The visual sensor is used to capture images of the plastic material to obtain visual image information of the plastic material, and the laser sensor is used to collect laser point cloud data information of the plastic material. Spatial registration and fusion of the visual image information of the plastic material and the laser point cloud data information are performed to generate a visual model of the plastic material. Multidimensional parameter extraction is performed on the plastic material visualization model to obtain plastic appearance feature parameter information, which includes shape, size and orientation. The locator and weight sensor are used to identify and perceive the plastic's appearance feature parameters to obtain the plastic's positioning information and weight parameters. Based on the plastic appearance feature parameters, the plastic positioning information, and the plastic weight parameters, the multidimensional characteristic parameters of the plastic material are determined.

3. The intelligent integrated loading and unloading control system for plastics as described in claim 2, characterized in that, The generation of the plastic material visualization model includes: Edge feature recognition is performed on the visual image information of the plastic material to determine the edge contour information of the plastic material, and the laser point cloud data information is filtered and denoised using the edge contour information of the plastic material to obtain usable plastic point cloud data information. Based on the edge contour information of the plastic material, key feature points are extracted from the visual image information of the plastic material to obtain a set of key feature points of the plastic material. Based on the set of key feature points of the plastic material, feature point mapping and extraction are performed on the available plastic point cloud data information to obtain the set of key feature points of the plastic material point cloud. The key feature point set of the plastic material and the key feature point set of the plastic material point cloud are registered and spatially aligned and fused to generate the visualization model of the plastic material.

4. The intelligent integrated loading and unloading control system for plastics as described in claim 1, characterized in that, The determination of the target loading and unloading control parameters includes: Based on the plastic material control database, obtain multi-dimensional characteristic parameter information of historical plastic materials and corresponding loading and unloading control parameters; Based on the multidimensional feature parameter information, the plastic material control database is segmented and integrated to obtain a plastic calibration control database. The multidimensional characteristic parameter information of the plastic material is matched and analyzed with the plastic calibration and control database to obtain a set of plastic characteristic matching degrees. The plastic calibration control database is optimized and tuned based on the plastic property matching degree set to determine the target loading and unloading control parameters.

5. The intelligent integrated loading and unloading control system for plastics as described in claim 4, characterized in that, Determining the target loading and unloading control parameters includes: Based on the plastic property matching degree set, the plastic calibration control database is optimized to determine the matching loading and unloading control parameters; Using the multidimensional feature parameter information as independent variables and the loading and unloading control parameters as dependent variables in turn, regression fitting is performed to generate a set of loading and unloading feature parameter regression curves. The regression curves in the set of regression curves for loading and unloading characteristic parameters are correlated and fused to generate a correlation model for loading and unloading characteristic parameters. The matching loading and unloading control parameters are calculated and optimized based on the loading and unloading feature parameter association model to determine the target loading and unloading control parameters.

6. The intelligent integrated loading and unloading control system for plastics as described in claim 1, characterized in that, The determination of the loading and unloading movement path information includes: Based on the characteristic parameter information of the production line, a graphical representation is performed to generate a loading and unloading production line map. Obstacles are marked on the loading and unloading production line map, and loading and unloading constraint information is determined. Obtain the material loading and unloading optimization target, evaluate and fit the material loading and unloading optimization target, and generate the material loading and unloading fitness function; Based on the loading and unloading constraint information, the path planning algorithm list is used to perform path searches within the loading and unloading production line map to obtain a set of loading and unloading planning paths. The loading and unloading fitness function is used to compare and optimize the set of planned loading and unloading paths to determine the loading and unloading movement path information.

7. The intelligent integrated loading and unloading control system for plastics as described in claim 6, characterized in that, The generation of the loading and unloading production line map includes: The characteristic parameter information of the production line is marked by object recognition to obtain a set of production line distributed objects, and the set of production line distributed objects is abstracted as a set of graph model nodes. The graph model node set is identified by path attribute according to the production line characteristic parameter information to obtain the graph model connection edge set. A topological graph representation is generated based on the set of nodes and the set of edges of the graph model, and a map of the loading and unloading production line is generated.

8. The intelligent integrated loading and unloading control system for plastics as described in claim 1, characterized in that, The determination of the optimization parameters for loading and unloading includes: Based on the abnormal material loading and unloading characteristic parameters, an emergency strategy optimization analysis was performed to obtain multiple material loading and unloading optimization measures and strategy parameter schemes. A simulation module for loading and unloading effects is constructed. Based on the simulation module, the multiple loading and unloading optimization measures, strategies, and parameter schemes are simulated and optimized to determine the loading and unloading execution optimization parameters.

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