Intelligent plate optimized layout system and method based on cloud computing

Abstract

The invention provides an intelligent plate optimized layout system and method based on cloud computing, and belongs to the technical field of plate processing, and the system comprises the steps that a user terminal uploads part information, plate information and layout parameters, and receives a layout result; the cloud server comprises a part processing module which analyzes part graphic data according to part information uploaded by a user; the layout algorithm execution module executes multi-thread NFP calculation based on the layout parameters, and a greedy algorithm generates an initial solution and iterative optimization; the stock layout task collaboration module is used for splitting the stock layout task into sub-tasks to be allocated to users; the database is used for storing part information, plate information, stock layout schemes and inter-user stock layout cooperation records; the layout result pushing module pushes the layout scheme and the cutting process to the bound cutting machine tool in real time; and the data open interface module is in butt joint with an enterprise, and obtains and exports statistical information of plate consumption. The plate utilization rate is increased, the production cost is reduced, and the stock layout efficiency and the cooperative work capacity are improved.

Description

Technical Field

[0001] This application belongs to the field of sheet metal processing technology, specifically relating to a cloud computing-based intelligent sheet metal optimization layout system and method. Background Technology

[0002] In the sheet metal processing industry, how to efficiently utilize sheet metal has always been a key concern. Traditional sheet metal layout methods typically rely on manual experience or stand-alone layout software, which has many limitations: First, manual nesting is inefficient and fails to maximize the utilization of materials, leading to significant material waste and increased production costs. Second, while stand-alone nesting software improves efficiency to some extent, it still suffers from inconvenient data management, inability to enable remote collaboration, and difficulty in fully utilizing computing resources. With increasing collaboration and specialization in manufacturing, remote collaboration is becoming more frequent, and cross-departmental, cross-professional, and cross-enterprise collaboration is becoming the norm. Traditional nesting methods can no longer meet the needs of modern enterprises.

[0003] Current patents use nested layout to improve the utilization rate of bridge parts, but this is limited to specific fields and does not solve the problem of multi-threaded computing efficiency. Other related patents use reinforcement learning methods to optimize global utilization, but the computational complexity is high and real-time collaborative optimization is not integrated.

[0004] Therefore, developing a cloud-based layout system that can improve the utilization rate of sheet materials, enable remote collaborative work, and make full use of computing resources is of great practical significance. Summary of the Invention

[0005] In a first aspect, embodiments of this application provide a cloud computing-based intelligent board material optimization layout system, including a user terminal and a cloud server; The user terminal is used to upload part information, sheet metal information and layout parameters, and to receive layout results; The cloud server, which communicates with the user terminal, includes: The parts processing module is used to parse the graphic data of the parts based on the part information uploaded by the user. The nesting algorithm execution module performs multi-threaded NFP calculations based on nesting parameters and part graphic data, generates a nesting scheme as an initial solution through a greedy algorithm, and iteratively optimizes it until the final nesting scheme is generated. The nesting task collaboration module is used to break down nesting tasks into sub-tasks, distribute sub-tasks among different users, and synchronize the overall nesting progress. The database is used to store part information, sheet metal information, layout plans, and layout collaboration records between users. The layout result push module is used to push the final layout plan and cutting process parameters to the bound cutting machine tool in real time; The data open interface module is used to connect to enterprise ERP and MES systems to obtain and export statistical information on board material consumption.

[0006] Furthermore, the parts processing module includes: The part drawing import unit is used to receive single or batch part drawings imported by users and extract part graphic data; The templated part drawing generation unit is used to obtain the size information of the ordered parts and fill it into a pre-built parametric template to generate part drawing data.

[0007] Furthermore, the nesting algorithm execution module includes: The NFP calculation unit is used to perform multi-threaded parallel calculation of the critical polygons (NFP) between the parts to be nested, and to construct the NFP matrix. ; The initial nesting unit generates the initial nesting scheme based on a greedy algorithm and single array constraints. ; The iterative optimization unit analyzes the local sparsity of the initial nesting scheme when the initial nesting method does not meet the preset conditions. Similarity matching degree between parts Dynamically adjust the layout order of parts and trigger rearrangement.

[0008] Furthermore, the sorting task collaboration module includes: The task allocation subunit is used to break down the layout task into subtasks and allocate the subtasks among users according to a preset method; The real-time synchronization submodule is used to obtain the layout progress of each user in real time and push it to each user terminal via the WebSocket protocol; The conflict detection subunit is used to lock editing permissions and generate a version snapshot when multiple users modify the same layout scheme.

[0009] Furthermore, the sampling result push module includes: The machine tool binding unit is used to bind a cutting machine tool using a dual authentication method of IP address and device ID. The process parameter embedding unit is used to add cutting process parameters and final layout scheme to the G code and push them to the bound cutting machine tool. The cutting process parameters include cutting speed and power compensation parameters. The nesting cancellation unit is used to respond to user cancellation commands, execute nesting plan withdrawal and re-push optimized version of nesting plan before cutting begins.

[0010] Secondly, embodiments of this application also provide a cloud computing-based intelligent board material optimization layout method, comprising the following steps: S1. Receive part information, sheet metal information, and layout parameters uploaded by the user terminal via the cloud server, and construct a parts set. and set of sorting parameters Extracting board material data ; S2. Based on the set of sorting parameters and parts collection The NFP matrix is ​​constructed by multi-threaded parallel computation of the critical polygons (NFP) between all parts. ,in Indicates parts and The critical polygon representing the relative positional relationships; S3. Assemble the parts The parts are sorted in descending order of area to obtain the parts sequence. ; S4. Based on the NFP matrix, a greedy algorithm is used to construct an initial layout scheme with the goal of maximizing the utilization rate of the board material. : Starting from one corner of the sheet metal, traverse the sequence of parts. Select the ones that satisfy the conditions in turn. The parts are placed in the designated positions, and the placement orientation of the parts is restricted by a single array constraint method. Obtain the initial layout scheme ,in For part coordinates, The rotation angle; S5. Determine the initial layout plan Sampling time or utilization rate Does the preset condition meet? like or Output the final layout scheme ; in, The preset maximum sorting time, This is the preset lower limit for utilization. Otherwise, proceed to step S6; S6. Analyze the initial layout plan Local sparsity and similarity between parts: The board material is divided into a grid, and a sparsity matrix is ​​constructed based on the grid. ; in, It is a part With parts sparsity, ; Construct a similarity matrix ; in, It is a part With parts similarity matching degree ; According to the coefficient matrix and similarity matrix Adjust the part layout order to generate a new part sequence. ,make Return to step S4.

[0011] Furthermore, in step S4, the lower left corner of the board is selected as the starting point; The single array constraint method is used to restrict the main axis direction of all parts to be parallel to the edge of the sheet metal.

[0012] Furthermore, the specific steps for multi-threaded parallel computation of the critical polygon NFP among all parts in S2 include: Assembly of parts Divide into m subsets ; For each subset Allocate a separate thread to compute the subset. The critical polygon NFP for all part pairs within the region; The critical polygon NFP results of each subset are merged and the critical polygon NFP of part pairs across subsets is calculated to generate a global NFP matrix. .

[0013] Furthermore, in step S4, the part similarity matching degree , For parts The projected area For parts The projected area For parts rotation angle, For parts rotation angle, , These are preset weighting coefficients, and , This is the sensitivity coefficient for angle differences.

[0014] Furthermore, in step S6, based on the coefficient matrix... and similarity matrix Adjust the part layout order to generate a new part sequence. The specific steps are as follows: Identifying sparsity The grid is used as the marked area, and a subset of parts within the marked area is extracted as the subset of free parts. ; This is a preset sparsity threshold; In the subset of spare parts Internal matching based on similarity Recombining in descending order yields a recombined sequence of parts. Then reassemble the parts sequence Insert the original part sequence to generate a new part sequence. .

[0015] As can be seen from the above technical solutions, this application has the following advantages: The cloud-based intelligent sheet metal layout optimization system and method provided in this application can improve sheet metal utilization and reduce enterprise production costs. Through multi-threaded parallel computing and advanced algorithms, it enhances layout efficiency and generates high-quality layout plans; it supports remote collaborative work, allowing team members to share layout data in real time, optimize layout tasks, and improve work efficiency; it accurately tracks the number of parts, avoiding over-cutting waste and optimizing production management processes; simultaneously, the layout results can be pushed to the cutting machine tool in real time, carrying cutting process parameters, eliminating the need for manual transmission and improving production timeliness and accuracy; it also provides rich open interfaces that can be integrated with enterprise ERP and MES systems to achieve data flow and sharing, breaking down information silos. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the cloud computing-based intelligent board material optimization layout system of the present invention.

[0018] Figure 2 This is a flowchart illustrating the cloud computing-based intelligent board material optimization layout method of the present invention. Detailed Implementation

[0019] The various embodiments of this disclosure will be described more fully in the following detailed description of the cloud-based intelligent board material optimization layout system. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0020] For example, in the field of sheet metal processing, improving sheet metal utilization is a key challenge. Traditional manual nesting is inefficient and wasteful. While stand-alone nesting software has improved, it still falls short in data management, collaborative work, and the utilization of computing resources. Existing patented technologies are either limited to specific scenarios or have high computational complexity, making it difficult to meet real-time collaborative needs. Therefore, there is an urgent need for an intelligent nesting system that can efficiently utilize sheet metal, support remote collaboration, and fully utilize cloud computing resources to drive industry progress.

[0021] To address the aforementioned issues, this embodiment provides a cloud-based intelligent board material optimization and layout system, which improves board material utilization, reduces material waste, enables remote collaborative layout, and enhances production efficiency and enterprise benefits.

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 The diagram shown is a schematic of a cloud-based intelligent board optimization layout system in a specific embodiment. The system includes a user terminal and a cloud server. The user terminal is used to upload part information, sheet metal information and layout parameters, and to receive layout results; It should be noted that the user terminal allows users to upload data such as part information, receive layout results, and achieve human-computer interaction. Cloud server, communicating and connecting with user terminals; It should be noted that cloud servers can centrally process nesting tasks, integrate resources, and improve computing efficiency and data management capabilities. Cloud servers include: The parts processing module is used to parse the graphic data of the parts based on the part information uploaded by the user. It should be noted that the parts processing module accurately parses the part graphic data and supports multiple parts information input methods, laying the foundation for subsequent layout. The nesting algorithm execution module performs multi-threaded NFP calculations based on nesting parameters and part graphic data, generates a nesting scheme as an initial solution through a greedy algorithm, and iteratively optimizes it until the final nesting scheme is generated. It should be noted that the nesting algorithm execution module can optimize the nesting scheme and improve the nesting quality and speed; The nesting task collaboration module is used to break down nesting tasks into sub-tasks, distribute sub-tasks among different users, and synchronize the overall nesting progress. It should be noted that the nesting task collaboration module can promote teamwork, realize the reasonable allocation of nesting tasks and the synchronization of progress, and improve work efficiency. The database is used to store part information, sheet metal information, layout plans, and layout collaboration records between users. It should be noted that the database can store various types of sorting-related data, ensuring data security and traceability, and supporting stable system operation; The layout result push module is used to push the final layout plan and cutting process parameters to the bound cutting machine tool in real time; It should be noted that the layout result push module can transmit the layout plan and cutting process parameters to the cutting machine tool in a timely manner, shortening the production preparation time. The data open interface module is used to connect with enterprise ERP and MES systems to obtain and export statistical information on board material consumption. It should be noted that the data open interface module can connect with enterprise ERP and MES systems, breaking down information silos and promoting production data sharing and collaboration.

[0024] This embodiment improves the utilization rate of sheet metal and reduces enterprise costs through cloud computing; enables remote collaborative work, breaks geographical limitations, and improves work efficiency; accurately tracks the number of parts, avoids material waste, and optimizes production management processes.

[0025] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another cloud computing-based intelligent board optimization layout system is provided, which includes a user terminal and a cloud server. The user terminal is used to upload part information, sheet metal information and layout parameters, and to receive layout results; The cloud server, which communicates with the user terminal, includes: The parts processing module is used to parse the graphic data of the parts based on the part information uploaded by the user. The parts processing module includes: The part drawing import unit is used to receive single or batch part drawings imported by users and extract part graphic data; The templated part drawing generation unit is used to obtain the size information of the ordered parts and fill it into a pre-built parametric template to generate part drawing data; It should be noted that the parts processing module has diverse parts format import functions, supporting not only batch import of common drawings but also intelligent extraction of drawing description information; at the same time, it supports specific drawing formats generated by other software, such as nc1 and ifc files generated by the steel structure detailing software Tekla, which can be directly read as parts for layout; in addition, it also supports automatically generating part graphics based on its own parametric templates and the size information of the order parts, greatly reducing the drawing pressure in the factory and reducing the probability of errors in manual drawing; The nesting algorithm execution module performs multi-threaded NFP calculations based on nesting parameters and part graphic data, generates a nesting scheme as an initial solution through a greedy algorithm, and iteratively optimizes it until the final nesting scheme is generated. The nesting algorithm execution module includes: The NFP calculation unit is used to perform multi-threaded parallel calculation of the critical polygons (NFP) between the parts to be nested, and to construct the NFP matrix. ; The initial nesting unit generates the initial nesting scheme based on a greedy algorithm and single array constraints. ; The iterative optimization unit analyzes the local sparsity of the initial nesting scheme when the initial nesting method does not meet the preset conditions. Similarity matching degree between parts Dynamically adjust the layout order of parts and trigger rearrangement; It should be noted that the nesting algorithm execution module provides a variety of automatic nesting parameter settings to meet the needs of most users in the market; it can set the shape of the leftover material in the nesting result, making the nesting result more neat and obtaining regular leftover material; it supports priority nesting of inner holes, making full use of inner hole waste and further improving the utilization rate of the material; during automatic nesting, it can automatically add compensation to the parts for nesting, avoiding inaccurate cutting dimensions of parts due to users forgetting to add compensation; at the same time, it supports the participation of combined parts in automatic nesting. The nesting task collaboration module is used to break down nesting tasks into sub-tasks, distribute sub-tasks among different users, and synchronize the overall nesting progress. The nesting task collaboration module includes: The task allocation subunit is used to break down the layout task into subtasks and allocate the subtasks among users according to a preset method; The real-time synchronization submodule is used to obtain the layout progress of each user in real time and push it to each user terminal via the WebSocket protocol; The conflict detection subunit is used to lock editing permissions and generate a version snapshot when multiple users modify the same layout scheme; It should be noted that for larger companies with multiple layout designers, the same company can be created as a single team; team members share team layout data, can clearly see the layout progress of their teammates, which facilitates layout collaboration and mutual optimization of layout tasks; team managers can understand the layout and production progress at any time, thereby reasonably arranging the production schedule. The database is used to store part information, sheet metal information, layout plans, and layout collaboration records between users. The layout result push module is used to push the final layout plan and cutting process parameters to the bound cutting machine tool in real time; The sorting result push module includes: The machine tool binding unit is used to bind a cutting machine tool using a dual authentication method of IP address and device ID. The process parameter embedding unit is used to add cutting process parameters and final layout scheme to the G code and push them to the bound cutting machine tool. The cutting process parameters include cutting speed and power compensation parameters. The nesting cancellation unit is used to respond to user cancellation commands, execute nesting plan withdrawal and re-push optimized version of nesting plan before cutting begins; It should be noted that the nesting results can be linked to the bound machine tool and pushed with one click; any machine tool connected to the network and equipped with a specific cutting system can be bound to a specific cloud nesting account. The nesting results generated by the cloud nesting can be pushed to the bound machine tool in real time, and can be withdrawn before cutting, modified and pushed again; the cutting process can also be carried along when pushing the nesting, and the machine tool can start processing directly after receiving the task. The data open interface module is used to connect with enterprise ERP and MES systems to obtain and export statistical information on board material consumption. The nesting system described in this application is an open and collaborative platform with abundant open interfaces. Third-party software can connect to the open platform to achieve efficient and seamless data flow between various process stages within the enterprise, solving the problem of data silos. Commonly used ERP and MES systems can obtain statistical information on the consumption of nested materials through the open platform. Cloud nesting can also serve as a unified and sole material cutting and nesting software for the enterprise. The nesting results can be exported as various G-code files and support dozens of common flame, plasma, and laser cutting equipment. The system in this application also includes a parts quantity tracking module, which can accurately count the number of parts and accurately track the total demand, the number of parts already scheduled, and the number of parts to be scheduled. When connected to a cutting machine tool, it can also count the number of parts already processed, effectively avoiding over-cutting and waste of sheet metal.

[0026] like Figure 2As shown, the following are embodiments of the cloud-based intelligent board material optimization layout method provided in this disclosure. This method and the cloud-based intelligent board material optimization layout system in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the cloud-based intelligent board material optimization layout method, please refer to the embodiments of the cloud-based intelligent board material optimization layout system.

[0027] The method includes the following steps: S1. Receive part information, sheet metal information, and layout parameters uploaded by the user terminal via the cloud server, and construct a parts set. and set of sorting parameters Extracting board material data ; It should be noted that by standardizing the data input process, accurate and complete initial data can be provided for subsequent nesting calculations. S2. Based on the set of sorting parameters and parts collection The NFP matrix is ​​constructed by multi-threaded parallel computation of the critical polygons (NFP) between all parts. ,in Indicates parts and The critical polygon representing the relative positional relationships; It should be noted that by using multi-threaded parallel computation of NFP, the preparatory work for nesting is accelerated and the system response speed is improved; S3. Assemble the parts The parts are sorted in descending order of area to obtain the parts sequence. ; It should be noted that properly arranging parts and optimizing the layout creates favorable conditions for generating a high-quality layout plan. S4. Based on the NFP matrix, a greedy algorithm is used to construct an initial layout scheme with the goal of maximizing the utilization rate of the board material. : Starting from one corner of the sheet metal, traverse the sequence of parts. Select the ones that satisfy the conditions in turn. The parts are placed in the designated positions, and the placement orientation of the parts is restricted by a single array constraint method. Obtain the initial layout scheme ,in For part coordinates, The rotation angle; It should be noted that a greedy algorithm combined with single array constraints is used to construct the initial layout scheme, which quickly obtains a feasible solution and improves the layout efficiency. S5. Determine the initial layout plan Sampling time or utilization rate Does the preset condition meet? like or Output the final layout scheme ; in, The preset maximum sorting time, This is the preset lower limit for utilization. Otherwise, proceed to step S6; It should be noted that determining whether the layout plan meets the preset conditions is crucial to ensuring that the final layout result meets production requirements and guarantees product quality. S6. Analyze the initial layout plan Local sparsity and similarity between parts: The board material is divided into a grid, and a sparsity matrix is ​​constructed based on the grid. ; in, It is a part With parts sparsity, ; Construct a similarity matrix ; in, It is a part With parts similarity matching degree ; According to the coefficient matrix and similarity matrix Adjust the part layout order to generate a new part sequence. ,make Return to step S4; It should be noted that by analyzing local sparsity and part similarity, adjusting the layout order, and further optimizing the layout scheme, the utilization rate of the sheet metal can be improved.

[0028] This embodiment uses cloud computing to optimize the layout of boards, improve board utilization, and reduce enterprise production costs. Through multi-threaded parallel computing and layout algorithms, it significantly improves layout efficiency and generates high-quality layout solutions.

[0029] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, another intelligent board material optimization layout method based on cloud computing is provided, which includes the following steps: S1. Receive part information, sheet metal information, and layout parameters uploaded by the user terminal via the cloud server, and construct a parts set. and set of sorting parameters Extracting board material data ; Specifically, the part information includes part graphic data, the sheet material information includes sheet material specifications (such as sheet material length, width and thickness), and the layout parameters include utilization rate threshold, time threshold and surplus material rules. S2. Based on the set of sorting parameters and parts collection The NFP matrix is ​​constructed by multi-threaded parallel computation of the critical polygons (NFP) between all parts. ,in Indicates parts and The critical polygon representing the relative positional relationships; The specific steps for multi-threaded parallel computation of the critical polygon NFP between all parts in S2 include: Assembly of parts Divide into m subsets ; For each subset Allocate a separate thread to compute the subset. The critical polygon NFP for all part pairs within the region; The critical polygon NFP results of each subset are merged and the critical polygon NFP of part pairs across subsets is calculated to generate a global NFP matrix. ; Specifically, the triangulation method is used to calculate the critical polygon NFP, and a multi-threaded divide-and-conquer strategy is employed to reduce the time complexity. Down to ; S3. Assemble the parts The parts are sorted in descending order of area to obtain the parts sequence. ; S4. Based on the NFP matrix, a greedy algorithm is used to construct an initial layout scheme with the goal of maximizing the utilization rate of the board material. : Starting from one corner of the sheet metal, traverse the sequence of parts. Select the ones that satisfy the conditions in turn. The parts are placed in the designated positions, and the placement orientation of the parts is restricted by a single array constraint method. In step S4, the lower left corner of the board is selected as the starting point; The single array constraint method is used to restrict the main axis direction of all parts to be parallel to the edge of the sheet metal. Obtain the initial layout scheme ,in For part coordinates, The rotation angle; Part similarity matching degree in step S4 , For parts The projected area For parts The projected area For parts rotation angle, For parts rotation angle, , These are preset weighting coefficients, and , The sensitivity coefficient for angular differences; S5. Determine the initial layout plan Sampling time or utilization rate Does the preset condition meet? like or Output the final layout scheme ; in, The preset maximum sorting time, This is the preset lower limit for utilization. Otherwise, proceed to step S6; For example, It can be set to 95%. Dynamically set according to the size of the board material (e.g.) , k =0.1 s / m 2); S6. Analyze the initial layout plan Local sparsity and similarity between parts: The board material is divided into a grid, and a sparsity matrix is ​​constructed based on the grid. ; in, It is a part With parts sparsity, ; Construct a similarity matrix ; in, It is a part With parts similarity matching degree ; According to the coefficient matrix and similarity matrix Adjust the part layout order to generate a new part sequence. ,make Return to step S4; In step S6, based on the coefficient matrix and similarity matrix Adjust the part layout order to generate a new part sequence. The specific steps are as follows: Identifying sparsity The grid is used as the marked area, and a subset of parts within the marked area is extracted as the subset of free parts. ; This is a preset sparsity threshold; In the subset of spare parts Internal matching based on similarity Recombining in descending order yields a recombined sequence of parts. Then reassemble the parts sequence Insert the original part sequence to generate a new part sequence. ; It also includes the following steps: S7. Final layout plan Push the data to the bound cutting machine and update the layout progress in the team collaboration interface simultaneously; S8. Export the board consumption data to the enterprise ERP or MES system through the open interface.

[0030] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0031] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cloud computing-based intelligent board material optimization layout system, characterized in that, This includes user terminals and cloud servers; The user terminal is used to upload part information, sheet metal information and layout parameters, and to receive layout results; The cloud server, which communicates with the user terminal, includes: The parts processing module is used to parse the graphic data of the parts based on the part information uploaded by the user. The nesting algorithm execution module performs multi-threaded NFP calculations based on nesting parameters and part graphic data, generates a nesting scheme as an initial solution through a greedy algorithm, and iteratively optimizes it until the final nesting scheme is generated. The nesting task collaboration module is used to break down nesting tasks into sub-tasks, distribute sub-tasks among different users, and synchronize the overall nesting progress. The database is used to store part information, sheet metal information, layout plans, and layout collaboration records between users. The layout result push module is used to push the final layout plan and cutting process parameters to the bound cutting machine tool in real time; The data open interface module is used to connect to enterprise ERP and MES systems to obtain and export statistical information on board material consumption.

2. The cloud computing-based intelligent board material optimization layout system according to claim 1, characterized in that, The parts processing module includes: The part drawing import unit is used to receive single or batch part drawings imported by users and extract part graphic data; The templated part drawing generation unit is used to obtain the size information of the ordered parts and fill it into a pre-built parametric template to generate part drawing data.

3. The cloud computing-based intelligent board material optimization layout system according to claim 1, characterized in that, The nesting algorithm execution module includes: The NFP calculation unit is used to perform multi-threaded parallel calculation of the critical polygons (NFP) between the parts to be nested, and to construct the NFP matrix. ; The initial nesting unit generates the initial nesting scheme based on a greedy algorithm and single array constraints. ; The iterative optimization unit analyzes the local sparsity of the initial nesting scheme when the initial nesting method does not meet the preset conditions. Similarity matching degree between parts Dynamically adjust the layout order of parts and trigger rearrangement.

4. The cloud computing-based intelligent board material optimization layout system according to claim 1, characterized in that, The nesting task collaboration module includes: The task allocation subunit is used to break down the layout task into subtasks and allocate the subtasks among users according to a preset method; The real-time synchronization submodule is used to obtain the layout progress of each user in real time and push it to each user terminal via the WebSocket protocol; The conflict detection subunit is used to lock editing permissions and generate a version snapshot when multiple users modify the same layout scheme.

5. The cloud computing-based intelligent board material optimization layout system according to claim 1, characterized in that, The sorting result push module includes: The machine tool binding unit is used to bind a cutting machine tool using a dual authentication method of IP address and device ID. The process parameter embedding unit is used to add cutting process parameters and final layout scheme to the G code and push them to the bound cutting machine tool. The cutting process parameters include cutting speed and power compensation parameters. The nesting cancellation unit is used to respond to user cancellation commands, execute nesting plan withdrawal and re-push optimized version of nesting plan before cutting begins.

6. A cloud computing-based intelligent board material optimization layout method, characterized in that, Includes the following steps: S1. Receive part information, sheet metal information, and layout parameters uploaded by the user terminal via the cloud server, and construct a parts set. and set of sorting parameters Extracting board material data ; S2. Based on the set of sorting parameters and parts collection The NFP matrix is ​​constructed by multi-threaded parallel computation of the critical polygons (NFP) between all parts. ,in Indicates parts and The critical polygon of relative positional relationships; S3. Assemble the parts The parts are sorted in descending order of area to obtain the parts sequence. ; S4. Based on the NFP matrix, a greedy algorithm is used to construct an initial layout scheme with the goal of maximizing the utilization rate of the board material. : Starting from one corner of the sheet metal, traverse the sequence of parts. Select the ones that satisfy the conditions in turn. The parts are placed in the designated positions, and the placement orientation of the parts is restricted by a single array constraint method. Obtain the initial layout scheme ,in For part coordinates, The rotation angle; S5. Determine the initial layout plan Sampling time or utilization rate Does the preset condition meet? like or Output the final layout scheme ; in, The preset maximum sorting time, This is the preset lower limit for utilization. Otherwise, proceed to step S6; S6. Analyze the initial layout plan Local sparsity and similarity between parts: The board material is divided into a grid, and a sparsity matrix is ​​constructed based on the grid. ; in, It is a part With parts sparsity, ; Construct a similarity matrix ; in, It is a part With parts similarity matching degree ; According to the coefficient matrix and similarity matrix Adjust the part layout order to generate a new part sequence. ,make Return to step S4.

7. The cloud computing-based intelligent board material optimization layout method according to claim 6, characterized in that, In step S4, the lower left corner of the board is selected as the starting point; The single array constraint method is used to restrict the main axis direction of all parts to be parallel to the edge of the sheet metal.

8. The intelligent board material optimization layout method based on cloud computing according to claim 6, characterized in that, The specific steps for multi-threaded parallel computation of the critical polygon NFP between all parts in S2 include: Assembly of parts Divide into m subsets ; For each subset Allocate a separate thread to compute the subset. The critical polygon NFP for all part pairs within the region; The critical polygon NFP results of each subset are merged and the critical polygon NFP of part pairs across subsets is calculated to generate a global NFP matrix. .

9. The intelligent board material optimization layout method based on cloud computing according to claim 6, characterized in that, Part similarity matching degree in step S4 , For parts The projected area For parts The projected area For parts rotation angle, For parts rotation angle, , These are preset weighting coefficients, and , This is the sensitivity coefficient for angle differences.

10. The intelligent board material optimization layout method based on cloud computing according to claim 6, characterized in that, In step S6, based on the coefficient matrix and similarity matrix Adjust the part layout order to generate a new part sequence. The specific steps are as follows: Identifying sparsity The grid is used as the marked area, and a subset of parts within the marked area is extracted as the subset of free parts. ; This is a preset sparsity threshold; In the subset of spare parts Internal matching degree Recombining in descending order yields a recombined sequence of parts. Then reassemble the parts sequence Insert the original part sequence to generate a new part sequence. .

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