Automatic production process control method and system based on design model analysis

Abstract

The invention provides an automatic production process control method and system based on design model analysis, and the method comprises the steps: obtaining a building block design model corresponding to a sorting order number from a manufacturing execution system, and carrying out the model analysis to generate a material list; according to the materials in the material list, task allocation is carried out on a vibration disc in a manufacturing execution system, and a sorting task plan corresponding to the sorting order number is obtained; associating the empty material boxes in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releasing the empty material boxes to a production line according to the association relationship; and carrying out sorting tracking on the empty material boxes put into the production line according to the sorting task plans until the sorting task plans corresponding to the sorting order numbers are completed. By combining design model analysis and automatic control, dynamic planning of sorting tasks and efficient management of the production line are achieved, diversified production requirements can be flexibly met, and the intelligent level and the production efficiency of the automatic sorting production line are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to an automated production process control method and system based on design model analysis. Background Art

[0002] As a highly efficient automated feeding equipment, the vibration plate is widely used in various production lines. Through vibration, the vibration plate can arrange the materials in an orderly state and remove the stacked or incorrectly positioned materials, thereby achieving stable feeding operations. In the traditional mass production line of standardized products, the vibration plate is usually used in conjunction with conveyor belts and other devices, and the discharge quantity of each material is set to a fixed value. For example, on the automatic sorting line of standard building block sets, multiple vibration plates are responsible for putting a preset number of building blocks into the circulating material box to complete the sorting task of fixed orders. This control method is suitable for the production of single batches of standardized products and can effectively improve the efficiency of the production line.

[0003] However, as the market demand for diversified and personalized products increases, the traditional fixed discharging method is unable to cope with the needs of various types of small batches or even personalized products that are different for each order. Since the required quantity of each material is not fixed, the existing system lacks flexibility and cannot meet the changing production requirements. At the same time, sorting task planning, material box management and production line scheduling still rely on manual or preset rules, with limited automation, which easily leads to material waste, reduced efficiency and extended production cycle. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problems that the existing automatic sorting production line cannot flexibly adapt to the production needs of various types of small batches or personalized products, resulting in inflexible sorting task planning, fixed vibration plate control and low production line scheduling efficiency; A first aspect of the present invention provides an automated production process control method based on design model analysis, the automated production process control method based on design model analysis comprising: Obtaining a sorting order number, obtaining a building block design model corresponding to the sorting order number from a manufacturing execution system, and performing model analysis on the building block design model to obtain a material list; Assigning tasks to the vibration plates in the manufacturing execution system according to the materials in the material list to obtain a sorting task plan corresponding to the sorting order number; Associating the empty material box in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releasing the empty material box to the production line according to the association relationship; According to the sorting task plan, the empty material boxes put into the production line are sorted and tracked until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the obtaining of the sorting order number, obtaining a building block design model corresponding to the sorting order number from a manufacturing execution system, and performing model analysis on the building block design model to obtain a material list includes: Obtaining a sorting order number, obtaining a building block design model corresponding to the sorting order number from a manufacturing execution system, and performing a three-dimensional structural decomposition on the building block design model to obtain spatial position relationship data of building block components in the building block design model; Performing topological sorting processing on the building block components according to the spatial position relationship data to obtain a building block assembly sequence list, and performing parameter extraction processing on each building block component in the building block assembly sequence list to obtain a corresponding component feature set; A preset standard building block library is queried according to the component feature set to obtain a standard building block model corresponding to each building block component, and the standard building block models are classified and counted to obtain a standardized material list.

[0006] Optionally, in a second implementation of the first aspect of the present invention, performing three-dimensional structural decomposition on the building block design model to obtain spatial position relationship data of building block components in the building block design model includes: voxelize the building block design model to obtain a three-dimensional voxel matrix composed of a plurality of voxel units; Performing connected domain analysis on the three-dimensional voxel matrix to obtain a plurality of independent connected domains, wherein each connected domain represents a building block component; Perform boundary extraction processing on each connected domain to obtain a three-dimensional boundary point set of each building block component, and calculate the minimum bounding box of each building block component based on the three-dimensional boundary point set to obtain the spatial position and size information of each building block component; Relative coordinate transformation is performed on the spatial position and size information of all building block components to obtain the relative spatial position relationship between the building block components, and the spatial position, size information and the relative spatial position relationship are used as the spatial position relationship data of the building block components in the building block design model.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the assigning tasks to the vibration plates in the manufacturing execution system according to the materials in the material list to obtain a sorting task plan corresponding to the sorting order number includes: Matching the materials in the material list with the preset types of materials that can be automatically sorted to obtain a subset of materials that can be automatically sorted; According to the preset material constraint rules, the materials in the automatically sortable material subset are combined and optimized to obtain the optimal box sorting solution; The vibration plate is allocated and processed according to the optimal box sorting scheme and the status information of the vibration plate in the manufacturing execution system to obtain a sorting task plan corresponding to the sorting order number, wherein the sorting task plan includes a material discharging sub-task list of the vibration plate.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the combining and optimizing the materials in the automatically sortable material subset according to the preset material constraint rules to obtain the optimal box sorting solution includes: Performing feature extraction processing on the materials in the automatically sortable material subset to obtain a material feature vector set; Constructing a material compatibility graph according to the material feature vector set and preset material constraint rules, performing community detection processing on the material compatibility graph to obtain initial material grouping; The initial material grouping is optimized by using a preset genetic optimization algorithm to obtain an optimal box division scheme.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, constructing a material compatibility graph according to the material feature vector set and preset material constraint rules, and performing community detection processing on the material compatibility graph to obtain initial material grouping includes: Performing similarity calculation processing on materials according to the material feature vector set to obtain a material similarity matrix; The material similarity matrix and the preset material constraint rules are integrated to obtain a material compatibility weight matrix; Constructing a weighted undirected graph according to the material compatibility weight matrix, and performing edge pruning on the weighted undirected graph to obtain a material compatibility graph; Applying a multi-scale modularity maximization algorithm to the material compatibility graph to perform community detection processing to obtain a hierarchical community structure, wherein the communities in the hierarchical community structure represent highly compatible material groups; The hierarchical community structure is adaptively merged according to a preset community merging threshold to obtain an initial material grouping.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the step of sorting and tracking the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed, and outputting the sorted material boxes from the production line includes: According to the sorting task plan, the empty material boxes put into the production line are sorted and tracked. When the empty material boxes arrive at the corresponding position of the vibration plate, the numbers of the empty material boxes and the sorting task plan are checked to confirm the correctness of the sorting task plan and the material box sequence of the empty material boxes; When it is confirmed that the sorting task plan and the material box sequence of the empty material box are wrong, the missing material boxes in the sorting tracking process are analyzed and an alarm signal of the missing material box is generated; When it is confirmed that the sorting task plan and the material box sequence of the empty material box are correct, the vibration plate is controlled to put a specified amount of material into the current empty material box according to the sorting task plan; Control the material boxes to flow forward along the production line until all the vibration plates have been sorted, and then output the sorted material boxes from the production line.

[0011] A second aspect of the present invention provides an automated production process control system based on design model analysis, the automated production process control system based on design model analysis comprising: A model analysis module, used to obtain a sorting order number, obtain a building block design model corresponding to the sorting order number from a manufacturing execution system, and perform model analysis on the building block design model to obtain a material list; A task allocation module, used to allocate tasks to the vibration plates in the manufacturing execution system according to the materials in the material list, and obtain a sorting task plan corresponding to the sorting order number; An association module, used for associating the empty material box in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releasing the empty material box to the production line according to the association relationship; The sorting tracking module is used to sort and track the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line.

[0012] The above-mentioned automated production process control method and system based on design model analysis obtains the building block design model corresponding to the sorting order number from the manufacturing execution system, performs model analysis to generate a material list; assigns tasks to the vibration plate in the manufacturing execution system according to the materials in the material list, and obtains a sorting task plan corresponding to the sorting order number; associates the empty material boxes in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releases the empty material boxes to the production line according to the association relationship; sorts and tracks the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed. The present invention realizes dynamic planning of sorting tasks and efficient management of production lines by combining design model analysis with automated control, can flexibly adapt to diversified production needs, and significantly improves the intelligence level and production efficiency of automatic sorting production lines.

[0013] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of a first embodiment of an automated production process control method based on design model analysis in an embodiment of the present invention; Figure 2 It is a schematic diagram of an embodiment of an automated production process control system based on design model analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0018] To facilitate understanding of this embodiment, firstly, an automated production process control method based on design model analysis disclosed in an embodiment of the present invention is described in detail. Figure 1 As shown, the method comprises the following steps: 101. Obtain the sorting order number, obtain the building block design model corresponding to the sorting order number from the manufacturing execution system, and perform model analysis on the building block design model to obtain a material list; In one embodiment of the present invention, the obtaining of the sorting order number, obtaining the building block design model corresponding to the sorting order number from the manufacturing execution system, and performing model analysis on the building block design model to obtain a material list includes: obtaining the sorting order number, obtaining the building block design model corresponding to the sorting order number from the manufacturing execution system, and performing three-dimensional structural decomposition on the building block design model to obtain spatial position relationship data of building block components in the building block design model; performing topological sorting processing on the building block components according to the spatial position relationship data to obtain a building block assembly sequence list, and performing parameter extraction processing on each building block component in the building block assembly sequence list to obtain a corresponding component feature set; querying a preset standard building block library according to the component feature set to obtain a standard building block model corresponding to each building block component, and classifying and counting the standard building block models to obtain a standardized material list.

[0019] Specifically, we start by obtaining the sorting order number, which can be done by manual user input or automatic selection from the order queue. After obtaining the order number, the system will connect to the manufacturing execution system (MES) through a preset API interface and use the order number as a query parameter to retrieve the corresponding building block design model data from the MES database. This building block design model is usually stored in the form of a three-dimensional CAD file, which contains the complete structural information of the entire building block product. After obtaining the building block design model, the system will decompose its three-dimensional structure. This process involves complex computer graphics algorithms. First, the system will convert the three-dimensional model into a voxel representation, that is, discretize the continuous three-dimensional space into a series of small cubes. Then, the connected domain analysis algorithm is used to identify interconnected voxel sets, each of which represents an independent building block component. For each identified component, the system will calculate its center coordinates, orientation, and size information in three-dimensional space. By comparing the relative positions and orientations between different components, the system can build a detailed spatial position relationship data structure, which not only contains the position information of each component, but also includes the connection relationship and relative orientation between components.

[0020] Specifically, the system will use the spatial position relationship data obtained in the previous step to perform topological sorting on the building block components. Topological sorting is a graph theory algorithm that is used to arrange the nodes in a directed acyclic graph into a linear sequence so that all directed edges point from the front to the back in the sequence. In this scenario, each building block component is regarded as a node in the graph, and the dependencies between components (for example, one component must be installed before another component) are regarded as directed edges. The system first builds a dependency graph, and then uses depth-first search or Kahn algorithm for topological sorting to obtain a building block assembly sequence list that conforms to the assembly logic. This list ensures that in the actual assembly process, each component can be installed after the components it depends on are installed. At the same time, the system will perform parameter extraction processing on each building block component in the building block assembly sequence list. This process involves analyzing the geometric features, material properties, and functional characteristics of each component. The system calculates the geometric parameters of the component, such as volume, surface area, and center of gravity position, identifies the material type of the component (such as plastic type, color, etc.), and determines its functional characteristics (such as connectors, decorative parts, etc.) based on the role of the component in the overall structure. These extracted parameters constitute the feature set of each component and provide the basis for subsequent standardized matching.

[0021] Specifically, after obtaining the component feature set, the system queries the preset standard building block library and maps each custom component to the closest standard building block model. The standard building block library is a pre-established database that contains detailed information on all available standard building block parts. The matching process uses a multi-dimensional feature comparison algorithm, considering similarities in multiple aspects such as geometry, size, and function. The system calculates the similarity score with all building blocks in the standard library for each custom component and selects the standard building block with the highest score as the matching result. If there is no completely matching standard building block, the system selects the closest model and records the difference information for subsequent processing. After the matching is completed, the system classifies and counts the obtained standard building block models. The classification process is based on a predefined building block category system, such as classification by shape (bricks, plates, shafts, etc.) or function (structural parts, connectors, decorative parts, etc.). The counting process counts the number of each standard building block model used in the entire design. Finally, the system integrates all the information to generate a standardized material list. This list contains the model, quantity, classification information, and any special requirements (such as specific colors or materials) of each standard building block.

[0022] Furthermore, the three-dimensional structural decomposition of the building block design model to obtain the spatial position relationship data of the building block components in the building block design model includes: voxelizing the building block design model to obtain a three-dimensional voxel matrix composed of multiple voxel units; performing connected domain analysis on the three-dimensional voxel matrix to obtain multiple independent connected domains, wherein each connected domain represents a building block component; performing boundary extraction on each connected domain to obtain a three-dimensional boundary point set of each building block component, and performing minimum bounding box calculation on each building block component based on the three-dimensional boundary point set to obtain spatial position and size information of each building block component; performing relative coordinate transformation on the spatial position and size information of all building block components to obtain the relative spatial position relationship between the building block components, and using the spatial position, size information and the relative spatial position relationship as the spatial position relationship data of the building block components in the building block design model.

[0023] Specifically, voxelization is the process of converting a continuous three-dimensional model into a discrete voxel representation. In specific implementation, the system first determines an appropriate voxel size, which needs to balance accuracy and computational efficiency. Then, the system divides the entire design space into equal-sized cubic grids. For each point in the model, the system checks whether it falls within a voxel. If it falls within a voxel, the voxel is marked as "real", otherwise it is marked as "empty". This process is completed by traversing all vertices and faces of the model. For complex surfaces, the system also needs to perform surface sampling to ensure that the surface is correctly voxelized. Finally, the entire building block design model is converted into a three-dimensional Boolean matrix, in which each element represents a voxel, a value of 1 indicates that the voxel is occupied, and a value of 0 indicates that the voxel is empty. This voxelized representation simplifies subsequent geometric analysis, making operations such as connectivity analysis and boundary extraction more direct and efficient.

[0024] Specifically, the system performs a connected domain analysis on the obtained three-dimensional voxel matrix. The purpose of connected domain analysis is to identify and separate independent components in the model. This process uses a three-dimensional connectivity algorithm, usually a depth-first search (DFS) or a breadth-first search (BFS). The algorithm starts with an occupied voxel and checks the voxels adjacent to its six faces. If the adjacent voxel is also occupied, it is marked as the same connected domain and the search continues. This process is performed recursively or iteratively until no more adjacent occupied voxels can be found. The algorithm then moves to the next unprocessed occupied voxel and starts a new connected domain search. Each connected domain is assigned a unique identifier. In a complex building block model, there may be components connected by small connection points, and the system needs to decide whether to regard these components as independent connected domains based on predefined connection rules. Ultimately, each connected domain represents an independent building block component, and the system generates a mapping that associates each voxel with the connected domain (i.e., building block component) to which it belongs. The result of this step is a labeled three-dimensional voxel matrix, in which the value of each non-zero element represents the identifier of the building block component to which it belongs.

[0025] Specifically, the system performs boundary extraction processing on each identified connected domain. The purpose of boundary extraction is to determine the exact shape and position of each building block component. When implemented, the system traverses all voxels in each connected domain. For each voxel, check whether the voxels adjacent to its six faces belong to the same connected domain. If at least one adjacent voxel does not belong to the same connected domain (that is, it is empty or belongs to another component), the current voxel is marked as a boundary voxel. The coordinate set of all boundary voxels constitutes the three-dimensional boundary point set of the building block component. Then, the system uses these boundary point sets to calculate the minimum bounding box of each building block component. The minimum bounding box calculation usually uses the rotating caliper algorithm or the principal component analysis (PCA) method. The system first calculates the covariance matrix of the boundary point set, and then performs eigenvalue decomposition on the matrix to obtain three main directions. These main directions define the orientation of the minimum bounding box. Then, the system finds the maximum and minimum coordinates of the boundary points in these three directions to determine the size and position of the bounding box. The coordinates of the center point of the minimum bounding box are used as the position of the building block component, and the three dimensions of the bounding box represent the size of the component.

[0026] Specifically, the system performs relative coordinate transformation on the spatial position and size information of all building block components. The purpose of this step is to establish the relative spatial relationship between building block components, which is crucial to understanding the structure of the entire building block model. First, the system selects a reference point, usually the geometric center of the entire model or the center of a specific component. Then, the position of each building block component is transformed and expressed as an offset relative to this reference point. This process involves simple vector subtraction operations. In addition to the position, the system also needs to consider the orientation of the component. This is usually achieved by calculating the angle between the main axis of each component and the axis of the global coordinate system. For each pair of building block components, the system calculates the vector between their center points, which represents both the relative position and the implicit direction information. At the same time, the system also calculates the shortest distance between the surfaces of the component bounding boxes, which helps to determine whether the components are directly connected. All of this information - including the absolute position and size of each component, the relative position relationship, the orientation difference, and the surface distance - together constitutes the complete spatial position relationship data. This data is organized into a structured data set that contains the identifier of each component, its spatial properties, and a description of its relationship with all other components.

[0027] 102. Assign tasks to the vibration plates in the manufacturing execution system according to the materials in the material list, and obtain a sorting task plan corresponding to the sorting order number; In one embodiment of the present invention, the task assignment for the vibration plate in the manufacturing execution system according to the materials in the material list to obtain the sorting task plan corresponding to the sorting order number includes: matching the materials in the material list with preset types of materials that can be automatically sorted to obtain a subset of materials that can be automatically sorted; combining and optimizing the materials in the subset of materials that can be automatically sorted according to preset material constraint rules to obtain an optimal box sorting scheme; assigning the vibration plate according to the optimal box sorting scheme and the status information of the vibration plate in the manufacturing execution system to obtain the sorting task plan corresponding to the sorting order number, wherein the sorting task plan includes a list of material discharging subtasks of the vibration plate.

[0028] Specifically, the materials in the material list are first matched with the preset types of materials that can be automatically sorted. The system first obtains a complete material list from the manufacturing execution system, which contains all the building blocks required for the order. At the same time, the system maintains a preset database of materials that can be sorted automatically, which stores all material types and their characteristics that can be sorted by automated equipment (such as vibration plates). The matching process uses a multidimensional feature comparison algorithm, considering multiple attributes of the material such as geometry, size, and weight. The system compares each item in the material list with the entries in the database of materials that can be sorted automatically one by one, and calculates the similarity score. If the similarity exceeds the predetermined threshold, the material is marked as automatically sortable. In addition, the system also needs to consider the special requirements of the material, such as fragility, surface treatment, etc., which may affect whether the material is suitable for automatic sorting. Through this matching process, the system screens out all materials that can be sorted by automated equipment to form a subset of materials that can be sorted automatically. This subset not only contains a list of materials that can be sorted automatically, but also includes the detailed attributes and sorting requirements of each material, providing basic data for subsequent box sorting optimization and task allocation.

[0029] Specifically, the system performs combinatorial optimization on the materials in the automatically sortable material subset according to the preset material constraint rules. Material constraint rules are a series of predefined conditions used to determine which materials can be placed in the same sorting box. These rules include material compatibility (for example, some materials cannot be in direct contact), weight restrictions, volume restrictions, etc. The system first constructs a material compatibility matrix to represent the degree of compatibility between each pair of materials. Then, a heuristic algorithm (such as simulated annealing or genetic algorithm) is used to generate multiple candidate box separation schemes. Each candidate scheme is a way of assigning materials to sorting boxes. The optimization process considers multiple objectives, including minimizing the number of sorting boxes, maximizing the space utilization of each sorting box, minimizing the contact probability of incompatible materials, etc. The system scores each candidate scheme, and the scoring criteria include the degree of satisfaction of all constraint rules and the overall efficiency index. After multiple iterations and optimizations, the system finally selects the scheme with the highest score as the optimal box separation scheme. This optimal scheme not only meets all material constraint rules, but also achieves the best balance in terms of efficiency and resource utilization. The optimal sorting solution includes a detailed list of materials for each sorting box and the ideal arrangement of the materials in the sorting box.

[0030] Specifically, the system allocates and processes the vibration trays according to the optimal box sorting scheme and the status information of the vibration trays in the manufacturing execution system. The vibration tray status information includes the current working status, remaining capacity, and compatible material type of each vibration tray. The system first matches each material in the optimal box sorting scheme with the available vibration trays. The matching process considers multiple factors, such as the material adaptability, current load, and position of the vibration tray. For each sorting box, the system generates a series of vibration tray discharge instructions, specifying the type and quantity of materials that each vibration tray needs to discharge. During the distribution process, the system also needs to consider the balance of the overall production line to avoid some vibration trays being overloaded while others are idle. In addition, the system also needs to consider the movement path of the sorting box on the production line to ensure that the discharge order of the vibration tray matches the arrival order of the sorting box. Through this distribution process, the system generates a detailed sorting task plan, which includes a list of discharge subtasks for each vibration tray. Each subtask specifies when the vibration tray needs to discharge to which sorting box, and the type and quantity of materials to be discharged.

[0031] Furthermore, the combination optimization processing of the materials in the automatically sortable material subset according to the preset material constraint rules to obtain the optimal box division scheme includes: performing feature extraction processing on the materials in the automatically sortable material subset to obtain a material feature vector set; constructing a material compatibility graph according to the material feature vector set and the preset material constraint rules, performing community detection processing on the material compatibility graph to obtain initial material grouping; and performing group optimization processing on the initial material grouping through a preset genetic optimization algorithm to obtain the optimal box division scheme.

[0032] Specifically, the materials in the subset of materials that can be automatically sorted are first subjected to feature extraction. Feature extraction is the process of converting various properties of materials into numerical representations, which is crucial for subsequent compatibility analysis and optimization. The system first establishes a comprehensive feature set, including geometric features (such as length, width, height, volume), physical features (such as weight, material, surface roughness), functional features (such as connection method, load-bearing capacity) and other related attributes (such as color, production batch) of the material. For each material, the system uses specialized algorithms and sensor data to quantify these features. For example, geometric features can be obtained through 3D scanning and image processing technology, physical features can be measured by weight sensors and material analyzers, and functional features may need to be determined in combination with design specifications and experimental data. All these features are organized into a multidimensional vector, with each dimension corresponding to a specific attribute. In order to ensure the comparability between different features, the system also standardizes these feature values, usually using Z-score or Min-Max scaling methods. Finally, the system obtains a set of material feature vectors, where each vector represents a material and each element of the vector is the standardized value of the material on a specific attribute. This material feature vector set provides a mathematical basis for subsequent compatibility analysis, making comparison and clustering between materials possible.

[0033] Specifically, the system constructs a material compatibility graph based on a set of material feature vectors and preset material constraint rules, and performs community detection on the graph. The process of constructing a material compatibility graph first involves calculating the compatibility scores between materials. The system uses the material feature vectors and preset material constraint rules to evaluate the compatibility between each pair of materials. Material constraint rules can include weight restrictions, volume restrictions, material compatibility, etc. For example, two materials with similar materials may have higher compatibility, while materials with large volume differences may have lower compatibility. The system converts these rules into mathematical functions, operates on the feature vectors of each pair of materials, and obtains a compatibility score between 0 and 1. Using these scores, the system constructs a weighted undirected graph in which nodes represent materials and edge weights represent compatibility scores between materials. Next, the system performs community detection on this compatibility graph. The purpose of community detection is to find highly interconnected groups of nodes in the graph, which represent sets of materials that are highly compatible with each other. Common community detection algorithms include Louvain method, label propagation algorithm, or spectral clustering. These algorithms divide communities by optimizing modularity or other community quality indicators. The result of community detection is a series of material groups, and the materials within each group have high compatibility with each other. These groups constitute the initial material grouping, which provides a good starting point for subsequent optimization. The initial material grouping not only takes into account the intrinsic characteristics of the materials, but also reflects the preset constraint rules, so it is feasible for practical operation.

[0034] Specifically, the system optimizes the initial material grouping by using a preset genetic optimization algorithm. Genetic algorithm is an optimization method that simulates the natural evolution process and is particularly suitable for dealing with complex combinatorial optimization problems. In this scenario, each chromosome represents a box division scheme, and the gene sequence corresponds to the distribution of materials. The initial population is generated based on the initial material grouping obtained in the previous step, which ensures that the initial solution has a certain quality. The fitness function design is the key, which needs to comprehensively consider multiple objectives, such as minimizing the number of sorting boxes, maximizing space utilization, and minimizing the contact probability of incompatible materials. The system also needs to design appropriate gene crossover and mutation operations to generate new candidate solutions. The crossover operation can exchange part of the groupings in the two parent solutions, while the mutation operation may randomly change the grouping of a certain material. In each generation of evolution, the system evaluates the fitness of all candidate solutions, selects the best individuals to retain in the next generation, and introduces a certain degree of randomness to maintain population diversity. This process is iterative until the preset termination condition is reached, such as reaching the maximum number of iterations or the fitness improvement is lower than the threshold. Finally, the system selects the individual with the highest fitness from the last generation of population as the optimal box division scheme. This solution not only meets all the preset constraints, but also achieves the best balance between multiple optimization objectives. The optimal sorting solution contains a detailed list of materials for each sorting box and the ideal arrangement of materials in the sorting box.

[0035] Furthermore, the material compatibility graph is constructed according to the material feature vector set and the preset material constraint rules, and the material compatibility graph is subjected to community detection processing to obtain the initial material grouping, including: performing similarity calculation processing on the materials according to the material feature vector set to obtain a material similarity matrix; fusing the material similarity matrix and the preset material constraint rules to obtain a material compatibility weight matrix; constructing a weighted undirected graph according to the material compatibility weight matrix, and performing edge pruning processing on the weighted undirected graph to obtain a material compatibility graph; applying a multi-scale modularity maximization algorithm to perform community detection processing on the material compatibility graph to obtain a hierarchical community structure, wherein the communities in the hierarchical community structure represent highly compatible material groups; and adaptively merging the hierarchical community structure according to a preset community merging threshold to obtain the initial material grouping.

[0036] Specifically, first, the similarity between materials is calculated based on the material feature vector set. The system uses the material feature vector set obtained in the previous step to calculate the similarity of each pair of materials. Commonly used similarity calculation methods include Euclidean distance, cosine similarity, or Mahalanobis distance. The selection of an appropriate similarity measurement method depends on the nature and distribution of the features. For example, for high-dimensional and sparse feature vectors, cosine similarity is usually more suitable. The system traverses all possible material pairs, calculates the similarity between them, and fills the results into an n×n matrix, where n is the total number of materials. This matrix is ​​the material similarity matrix, and each of its elements (i, j) represents the similarity between material i and material j. The matrix is ​​symmetric because the similarity between materials i and j is the same as the similarity between j and i. The diagonal elements are usually set to 1, indicating that the material is completely similar to itself. This similarity matrix provides the basis for subsequent compatibility analysis, which reflects the degree of proximity between materials in the feature space.

[0037] Specifically, the system fuses the material similarity matrix and the preset material constraint rules. The purpose of this step is to combine the purely feature-based similarity with the actual operational constraints to obtain a more practical compatibility measure. The preset material constraint rules include weight restrictions, volume restrictions, material compatibility, etc. The system quantifies these rules into numerical weights or penalty factors. For example, if the sum of the weights of two materials exceeds the preset limit, the system will reduce their compatibility scores. The fusion process usually adopts the method of weighted summation or multiplication combination. For each pair of materials, the system calculates a comprehensive compatibility score that takes into account both the feature similarity and the degree of satisfaction of the constraint rules. The result is a new n×n matrix, called the material compatibility weight matrix. Each element of this matrix represents the comprehensive compatibility score of the corresponding material pair after considering all constraints. The compatibility weight matrix can better reflect the combination suitability of materials in the actual sorting process than the simple similarity matrix.

[0038] Specifically, the system constructs a weighted undirected graph based on the material compatibility weight matrix and performs edge pruning on the graph. In this graph, each node represents a material, and the weight of the edge corresponds to the score in the compatibility weight matrix. After the complete graph is constructed, the system performs edge pruning to remove weak compatibility connections and highlight strong compatibility relationships. Pruning usually uses a threshold method, that is, removing all edges with weights below a certain threshold. The selection of the threshold requires a trade-off between the connectivity and sparsity of the graph, and the optimal threshold can be determined by statistical analysis or cross-validation. The graph after edge pruning is the final material compatibility graph. This graph retains the most important compatibility relationships, simplifies the subsequent community detection process, and also reduces the impact of noise. The system applies the multi-scale modularity maximization algorithm to the material compatibility graph for community detection. Multi-scale modularity maximization is an efficient community detection method that can identify community structures at different scales. The algorithm first calculates the modularity gain between each pair of nodes at the finest granularity, and then iteratively merges the node pairs that can maximize the modularity. This process produces a hierarchical community structure, gradually merging from the finest granularity of individual materials to larger groups. At each level, the algorithm calculates the modularity of the current partition and records the composition of the community. This hierarchical structure allows the clustering of materials to be observed at different granularities, providing a basis for subsequent flexible grouping.

[0039] Specifically, the system adaptively merges the hierarchical community structure according to the preset community merge threshold. The purpose of this step is to convert the hierarchical structure into a practical and usable material grouping. The community merge threshold defines when to stop the merging process, which can be based on modularity gain, community size, or other custom criteria. The system starts from the finest granularity and gradually traverses the hierarchy upward, stopping when it encounters a level that meets the threshold conditions. In this process, the system also considers actual operational constraints, such as the maximum sorting box capacity, to adjust the merging decision. The final initial material grouping is a series of material groups, and the materials in each group are highly compatible.

[0040] 103. Associate the empty material box in the manufacturing execution system with the sorting task plan to obtain an association relationship, and release the empty material box to the production line according to the association relationship; In one embodiment of the present invention, the system first obtains information about available empty boxes from the box management module of the manufacturing execution system, including the box's unique identifier, size, status, etc. At the same time, the system also obtains the sorting task plans to be executed from the task scheduling module, which contain detailed information about each task, such as the type, quantity, priority, etc. of the materials to be sorted. The association process uses an intelligent matching algorithm to consider multiple factors to determine the best box-task pairing. The algorithm first checks whether the physical properties of the box meet the task requirements, such as whether the size is sufficient to accommodate the predetermined number of materials. Then, the algorithm considers the priority and expected execution time of the task, and tries to ensure that high-priority tasks can be assigned to the appropriate box in a timely manner. In addition, the algorithm also needs to consider the balance of the overall production line to avoid the situation where some boxes are overused while others are idle. During the matching process, the system uses a dynamic programming method to optimize the overall matching effect and ensure maximum resource utilization. Once the best box-task pairing is determined, the system will create a corresponding association record in the database. This association record contains the box's unique identifier, the task ID assigned to it, the expected start and end time, and the bill of materials required for the task. At the same time, the system updates the status of the box and the task, marking the box as "assigned" and the task as "ready to execute". The establishment of this association not only ensures that each task has a corresponding box, but also provides a clear execution guide for the subsequent production process. Next, the system starts the release process of the empty box based on the established association. The release process first involves the necessary preparation of the box. The system sends instructions to the box preparation station to instruct the staff or automated equipment to clean, inspect and label the specified box. The label usually contains the task ID, target material information, etc., which is convenient for subsequent identification and tracking. After preparation, the system updates the box status to "ready". Then, the system decides the specific time to release the box based on the current status of the production line and the priority of the task. This decision-making process takes into account multiple factors, including the number of boxes already on the production line, the load of each station, and the urgency of the task associated with the box. The system uses a real-time scheduling algorithm to optimize the release time to ensure the continuous and efficient operation of the production line. When the conditions are met, the system sends a release instruction to the control device at the box entrance. The release command triggers the mechanical device at the box entrance, which physically pushes the prepared box onto the conveyor belt of the production line. At the same time, the system updates the box status to "entered the line" and starts tracking the movement of the box on the production line. The system uses a network of sensors distributed throughout the production line to monitor the position and status of the box in real time. This real-time tracking not only ensures that the box moves along the predetermined path, but also provides a mechanism for timely detection and response to possible abnormal situations (such as jamming, misfeeding, etc.).

[0041] 104. According to the sorting task plan, the empty material boxes put into the production line are sorted and tracked until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line.

[0042] In one embodiment of the present invention, the empty material boxes put into the production line are sorted and tracked according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line, including: sorting and tracking the empty material boxes put into the production line according to the sorting task plan, when the empty material box reaches the corresponding position of the vibration plate, checking the number of the empty material box and the sorting task plan to confirm the correctness of the sorting task plan and the material box sequence of the empty material box; when it is confirmed that the sorting task plan and the material box sequence of the empty material box are wrong, analyzing the missing material boxes in the sorting and tracking process, and generating an alarm signal for the missing material box; when it is confirmed that the sorting task plan and the material box sequence of the empty material box are correct, controlling the vibration plate to put a specified amount of material into the current empty material box according to the sorting task plan; controlling the material box to flow forward along the production line until all vibration plates are sorted, and outputting the sorted material box from the production line.

[0043] Specifically, the empty boxes put into the production line are first sorted and tracked according to the sorting task plan. The system uses a network of sensors distributed throughout the production line to monitor the position and status of the boxes in real time. Each box is equipped with a unique identification tag, such as an RFID or QR code, which enables the system to accurately track the movement of each box. When the box enters the production line, the system begins to record its movement trajectory and compares this information with the predetermined sorting task plan in real time. This continuous tracking not only ensures that the box moves along the predetermined path, but also provides a basis for the timely detection of abnormal situations. When the empty box arrives at the corresponding position of the vibration plate, the system will check the number of the empty box and the sorting task plan. This verification process is a key step to ensure the accuracy of the entire sorting process. The system first confirms that the box has arrived at the designated vibration plate position through a position sensor, and then reads the unique identifier of the box. Next, the system queries the currently active sorting task plan to check whether the box is the next box that should arrive at the vibration plate according to the plan. This inspection process not only verifies the identity of the box, but also ensures that the order in which the boxes arrive is consistent with the sorting plan. This double verification mechanism effectively prevents sorting errors that may be caused by incorrect box order or mixed identification.

[0044] Specifically, if the system finds that the sorting task plan does not match the order of the empty boxes during the verification process, the error handling process will be triggered. First, the system will immediately suspend the operation of the current vibration plate to prevent the wrong material from being put in. Then, the system will analyze the historical data during the sorting tracking process to try to identify possible missing boxes. This analysis process involves backtracking the movement trajectory of the box to check whether any box has been accidentally removed on the way or failed to enter the production line correctly. Once the missing box is identified, the system will generate an alarm signal containing detailed information about the missing box. This alarm signal will not only be displayed on the console, but also sent to relevant personnel through preset communication channels (such as SMS and email). At the same time, the system will start a predefined exception handling process, which may include suspending the production line in the relevant area, issuing sound and light alarms, etc., to attract the attention of the operator and take necessary corrective measures.

[0045] When the sorting task plan and the order of the empty material box are confirmed to be correct, the system will control the vibration plate to put the specified amount of materials into the current empty material box according to the sorting task plan. This process first involves the precise adjustment of the vibration plate control system. The system will adjust the vibration frequency and amplitude of the vibration plate according to the current task requirements to ensure that the materials can be transported out at the appropriate speed and posture. At the same time, the system will activate the counting device matched with the vibration plate, such as a photoelectric sensor or a weight sensor, to accurately measure the amount of materials put in. When it is detected that the amount of materials put in reaches the specified value, the system will immediately stop the output of the vibration plate. The entire feeding process is under real-time monitoring. If any abnormality is found, such as material jamming or counting errors, the system will immediately interrupt the operation and issue an alarm. After completing the feeding of a vibration plate, the system will control the material box to flow forward along the production line until the sorting tasks of all vibration plates are completed. During the movement of the material box, the system continues to track its position and repeats the aforementioned verification and feeding process at each vibration plate position. The system will also update the completion status of each material box in real time and record the type and quantity of materials that have been put in. When the box has completed all the scheduled sorting tasks, the system will mark it as "sorting completed". Finally, the system controls the conveyor belt to transport the sorted box to the exit of the production line. At the exit, the system will perform a final verification to confirm that the contents of the box completely match the sorting task plan. If the verification passes, the box will be output to the designated collection area; if any mismatch is found, the system will direct the box to the exception handling area for further inspection.

[0046] In this embodiment, the building block design model corresponding to the sorting order number is obtained from the manufacturing execution system, and the model analysis is performed to generate a material list; the vibration plate in the manufacturing execution system is assigned tasks according to the materials in the material list to obtain a sorting task plan corresponding to the sorting order number; the empty material boxes in the manufacturing execution system are associated with the sorting task plan to obtain an association relationship, and the empty material boxes are released to the production line according to the association relationship; the empty material boxes put into the production line are sorted and tracked according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed. The present invention realizes dynamic planning of sorting tasks and efficient management of production lines by combining design model analysis with automated control, can flexibly adapt to diversified production needs, and significantly improve the intelligence level and production efficiency of automatic sorting production lines.

[0047] The above describes the automated production process control method based on design model analysis in the embodiment of the present invention. The following describes the automated production process control system based on design model analysis in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an automated production process control system based on design model analysis includes: The model analysis module 201 is used to obtain a sorting order number, obtain a building block design model corresponding to the sorting order number from a manufacturing execution system, and perform model analysis on the building block design model to obtain a material list; A task allocation module 202 is used to allocate tasks to the vibration plates in the manufacturing execution system according to the materials in the material list, and obtain a sorting task plan corresponding to the sorting order number; An association module 203 is used to associate the empty material box in the manufacturing execution system with the sorting task plan to obtain an association relationship, and release the empty material box to the production line according to the association relationship; The sorting tracking module 204 is used to sort and track the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line.

[0048] In an embodiment of the present invention, the automated production process control system based on design model analysis runs the automated production process control method based on design model analysis, and the automated production process control system based on design model analysis obtains the building block design model corresponding to the sorting order number from the manufacturing execution system, performs model analysis to generate a material list; assigns tasks to the vibration disk in the manufacturing execution system according to the materials in the material list, and obtains a sorting task plan corresponding to the sorting order number; associates the empty material boxes in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releases the empty material boxes to the production line according to the association relationship; sorts and tracks the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed. The present invention realizes dynamic planning of sorting tasks and efficient management of production lines by combining design model analysis with automated control, can flexibly adapt to diversified production needs, and significantly improves the intelligence level and production efficiency of automatic sorting production lines.

[0049] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0050] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0051] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automated production process control method based on design model analysis, characterized in that: The automated production process control method based on design model analysis includes: Obtaining a sorting order number, obtaining a building block design model corresponding to the sorting order number from a manufacturing execution system, and performing model analysis on the building block design model to obtain a material list; Assigning tasks to the vibration plates in the manufacturing execution system according to the materials in the material list to obtain a sorting task plan corresponding to the sorting order number; Associating the empty material box in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releasing the empty material box to the production line according to the association relationship; According to the sorting task plan, the empty material boxes put into the production line are sorted and tracked until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line.

2. The automated production process control method based on design model analysis according to claim 1, characterized in that: The obtaining of the sorting order number, obtaining the building block design model corresponding to the sorting order number from the manufacturing execution system, and performing model analysis on the building block design model to obtain the material list includes: Obtaining a sorting order number, obtaining a building block design model corresponding to the sorting order number from a manufacturing execution system, and performing a three-dimensional structural decomposition on the building block design model to obtain spatial position relationship data of building block components in the building block design model; Performing topological sorting processing on the building block components according to the spatial position relationship data to obtain a building block assembly sequence list, and performing parameter extraction processing on each building block component in the building block assembly sequence list to obtain a corresponding component feature set; A preset standard building block library is queried according to the component feature set to obtain a standard building block model corresponding to each building block component, and the standard building block models are classified and counted to obtain a standardized material list.

3. The automated production process control method based on design model analysis according to claim 2 is characterized in that: The three-dimensional structural decomposition of the building block design model to obtain spatial position relationship data of building block components in the building block design model includes: voxelize the building block design model to obtain a three-dimensional voxel matrix composed of a plurality of voxel units; Performing connected domain analysis on the three-dimensional voxel matrix to obtain a plurality of independent connected domains, wherein each connected domain represents a building block component; Perform boundary extraction processing on each connected domain to obtain a three-dimensional boundary point set of each building block component, and calculate the minimum bounding box of each building block component based on the three-dimensional boundary point set to obtain the spatial position and size information of each building block component; Relative coordinate transformation is performed on the spatial position and size information of all building block components to obtain the relative spatial position relationship between the building block components, and the spatial position, size information and the relative spatial position relationship are used as the spatial position relationship data of the building block components in the building block design model.

4. The automated production process control method based on design model analysis according to claim 1, characterized in that: The step of allocating tasks to the vibration plates in the manufacturing execution system according to the materials in the material list to obtain a sorting task plan corresponding to the sorting order number includes: Matching the materials in the material list with the preset types of materials that can be automatically sorted to obtain a subset of materials that can be automatically sorted; According to the preset material constraint rules, the materials in the automatically sortable material subset are combined and optimized to obtain the optimal box sorting solution; The vibration plate is allocated and processed according to the optimal box sorting scheme and the status information of the vibration plate in the manufacturing execution system to obtain a sorting task plan corresponding to the sorting order number, wherein the sorting task plan includes a material discharging sub-task list of the vibration plate.

5. The automated production process control method based on design model analysis according to claim 4 is characterized in that: The combining and optimizing the materials in the automatically sortable material subset according to the preset material constraint rules to obtain the optimal box sorting solution includes: Performing feature extraction processing on the materials in the automatically sortable material subset to obtain a material feature vector set; Constructing a material compatibility graph according to the material feature vector set and preset material constraint rules, performing community detection processing on the material compatibility graph to obtain initial material grouping; The initial material grouping is optimized by using a preset genetic optimization algorithm to obtain an optimal box division scheme.

6. The automated production process control method based on design model analysis according to claim 5, characterized in that: The step of constructing a material compatibility graph according to the material feature vector set and the preset material constraint rules, and performing community detection processing on the material compatibility graph to obtain initial material grouping includes: Performing similarity calculation processing on materials according to the material feature vector set to obtain a material similarity matrix; The material similarity matrix and the preset material constraint rules are integrated to obtain a material compatibility weight matrix; Constructing a weighted undirected graph according to the material compatibility weight matrix, and performing edge pruning on the weighted undirected graph to obtain a material compatibility graph; Applying a multi-scale modularity maximization algorithm to the material compatibility graph to perform community detection processing to obtain a hierarchical community structure, wherein the communities in the hierarchical community structure represent highly compatible material groups; The hierarchical community structure is adaptively merged according to a preset community merging threshold to obtain an initial material grouping.

7. The automated production process control method based on design model analysis according to claim 1, characterized in that: The step of sorting and tracking the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed, and outputting the sorted material boxes from the production line includes: According to the sorting task plan, the empty material boxes put into the production line are sorted and tracked. When the empty material boxes arrive at the corresponding position of the vibration plate, the numbers of the empty material boxes and the sorting task plan are checked to confirm the correctness of the sorting task plan and the material box sequence of the empty material boxes; When it is confirmed that the sorting task plan and the material box sequence of the empty material box are wrong, the missing material boxes in the sorting tracking process are analyzed and an alarm signal of the missing material box is generated; When it is confirmed that the sorting task plan and the material box sequence of the empty material box are correct, the vibration plate is controlled to put a specified amount of material into the current empty material box according to the sorting task plan; Control the material boxes to flow forward along the production line until all the vibration plates have been sorted, and then output the sorted material boxes from the production line.

8. An automated production process control system based on design model analysis, characterized in that: The automated production process control system based on design model analysis includes: A model analysis module, used to obtain a sorting order number, obtain a building block design model corresponding to the sorting order number from a manufacturing execution system, and perform model analysis on the building block design model to obtain a material list; A task allocation module, used to allocate tasks to the vibration plates in the manufacturing execution system according to the materials in the material list, and obtain a sorting task plan corresponding to the sorting order number; An association module, used for associating the empty material box in the manufacturing execution system with the sorting task plan to obtain an association relationship, and releasing the empty material box to the production line according to the association relationship; The sorting tracking module is used to sort and track the empty material boxes put into the production line according to the sorting task plan until the sorting task plan corresponding to the sorting order number is completed, and the sorted material boxes are output from the production line.

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