A Dynamic Matching and Overall Layout Method for Sheet Metal Based on a Residual Material Feature Library

Abstract

This invention belongs to the field of sheet material matching technology, specifically a method for dynamic matching and overall arrangement of sheet materials based on a surplus material feature library. It includes extracting the geometric features of surplus materials and constructing a dynamic surplus material feature library based on historical usage records. The method involves obtaining a sheet material requirement list from the production order, retrieving a set of candidate surplus materials that meet dimensional tolerances and material requirements based on the dynamic surplus material feature library, prioritizing sheet material selection, and filtering suitable surplus materials from the candidate set to obtain the surplus material matching result. Based on the sheet material selection priority, the tasks to be cut are divided into multiple batches. The cutting type is determined according to the different cutting processes required for each batch, and a toolpath equipment allocation strategy is formulated based on the real-time cutting equipment status and workstation distribution. Finally, an overall sheet material arrangement plan is developed based on the surplus material matching result. This invention can significantly improve material utilization, reduce production costs, greatly improve matching and scheduling efficiency, shorten delivery cycles, and enhance the reliability and safety of production execution.

Description

Technical Field

[0001] This invention belongs to the field of sheet material matching technology, specifically a method for dynamic matching and overall arrangement of sheet materials based on a surplus material feature library. Background Technology

[0002] In the fields of steel structure manufacturing and large-scale machining, sheet metal cutting is one of the core processes. However, existing technologies for managing surplus material and arranging sheet metal still have significant shortcomings.

[0003] First, traditional waste material management methods are rudimentary, relying mainly on manual visual inspection or simple ledger records. Due to the complex environment of construction sites, the irregular shapes and haphazard stacking of waste materials, and the lack of digital extraction methods for their precise geometric characteristics, a large amount of usable waste material is treated as scrap steel due to missing information, resulting in a huge waste of resources. Second, plate matching efficiency is low. When faced with production orders, existing technologies often struggle to quickly retrieve candidate materials that meet the requirements of dimensional tolerances, material grades, and textures from massive amounts of waste material. The matching process is often based on a single dimension of size, ignoring multiple factors such as project priority, residual value of materials, and process adaptability, leading to frequent instances of using large materials for small purposes or using high-quality materials for inferior purposes. Third, cutting task allocation and equipment scheduling are unreasonable. Traditional production scheduling often separates cutting tasks from equipment status, failing to fully consider the real-time load of cutting equipment, tool wear, and workstation distribution, resulting in uneven equipment workload and increased idle strokes, severely restricting production efficiency.

[0004] Existing methods mostly perform nested layout under static conditions, lacking dynamic interference detection and collaborative optimization of cutting paths on the same scrap material for multiple batches of tasks. This easily leads to processing conflicts and makes it difficult to generate truly executable optimal layout schemes. Therefore, there is an urgent need for a dynamic matching and overall layout method for sheet metal based on a scrap material feature library. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention proposes a dynamic matching and overall arrangement method for sheet metal based on a surplus material feature library. This invention primarily addresses the problems of chaotic surplus material information management, low sheet metal matching efficiency, unreasonable cutting task allocation, and insufficient optimization of sheet metal arrangement schemes in traditional surplus material management.

[0006] The technical solution adopted by this invention to solve its technical problem is: the method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided by this invention, including: The system collects specification data of the boards to be processed and image data of leftover materials at the construction site in real time. It extracts the geometric features of the leftover materials through edge detection algorithms and builds a dynamic feature library of leftover materials by combining historical usage records.

[0007] Obtain the sheet material requirement list from the production order, retrieve the candidate surplus material set that meets the dimensional tolerance and material requirements based on the dynamic surplus material feature library, and sort the candidate surplus materials according to project priority, material value and process requirements to obtain the sheet material selection priority.

[0008] Based on the candidate surplus material set and the priority of plate selection, a multi-dimensional matching algorithm is used to screen suitable surplus materials and obtain surplus material matching results.

[0009] The cutting tasks are divided into multiple batches based on the priority of the selected sheet material. The cutting type is determined according to the different cutting processes required for the corresponding batch. The tool path equipment allocation strategy is formulated in combination with the real-time status of the cutting equipment and the distribution of workstations. The overall arrangement plan of the sheet material is formulated in combination with the matching results of the surplus material.

[0010] The method for dynamic matching and overall layout of sheet metal based on a scrap material feature library provided by this invention includes the following steps for extracting the geometric features of scrap materials: Multi-angle image capture of idle board scraps in the target area is used to obtain scrap material image data, and the length, width, thickness, material and tolerance grade information of the board to be processed are collected in real time as specification data.

[0011] The image data of the remaining material is processed by grayscale conversion, Gaussian filtering for noise reduction, brightness and contrast optimization, and morphological processing to obtain a clear image.

[0012] The Canny edge detection algorithm is used to identify the edges of the preprocessed image and extract the set of outer boundary pixels of the remaining material to form the material outline.

[0013] The material outline is fitted and geometrically corrected to obtain a corrected image. The outer dimension, actual effective area, minimum bounding dimension and outline coordinate point set are extracted from the corrected image as the geometric features of the remaining material.

[0014] The present invention provides a method for dynamic matching and overall arrangement of sheet metal based on a scrap material feature library. The steps for constructing the dynamic scrap material feature library include: The geometric features of the scrap material are organized one by one according to each scrap material to obtain a list of geometric features. The historical data of each scrap material is extracted and organized into a historical record of scrap material.

[0015] The geometric feature list and the surplus material history are standardized in terms of unit, field format, and numerical processing. A unique identifier is assigned to each piece of usable surplus material, and a basic library entry is created.

[0016] Based on the geometric characteristics of the scrap material and the historical usage records as weights, a comprehensive value scoring model is constructed to calculate the value score of each scrap material and sort the scrap materials according to the score.

[0017] Based on the geometric features of the scrap material and historical usage records, a unique adaptive tag is generated for each scrap material, a search index is built according to the usage frequency, and the status of the scrap material is updated in real time to obtain a dynamic scrap material feature library.

[0018] The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided by this invention includes the following steps for obtaining a candidate surplus material set: Extract the size, material, precision, and quantity parameters of the parts to be manufactured from the sheet metal requirement list as part requirement parameters, and calculate the actual size of the parts based on the reserved machining allowance as the requirement for a single type of part.

[0019] The requirements for single-type parts are categorized according to the principles of consistent material, consistent thickness, and similar size range, and identical requirements are merged to form a list of categorized requirements.

[0020] Based on the categorized requirements list, set hard and soft constraints as screening criteria for each group of requirements.

[0021] The system synchronizes real-time data from the dynamic surplus material feature library and quickly retrieves surplus materials that meet the screening criteria based on the search index, using them as a candidate surplus material set.

[0022] The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided by this invention includes the following steps for obtaining the selection priority of sheet metal: Extract project priority levels from production orders, set multi-level scoring criteria based on delivery dates, and determine the corresponding scores to obtain project priority scores.

[0023] Extract the effective area, comprehensive value, and idle priority of each piece of scrap material from the dynamic scrap material feature library, assign corresponding scores, and add them together to obtain the material value score.

[0024] By combining the process requirements of production orders and the process records of surplus materials in the dynamic surplus material feature library, process type, processing accuracy and processing loss are set as evaluation indicators to calculate the process adaptability score.

[0025] The allocation weights are set according to the current production scenario. The comprehensive score of each piece of leftover material is calculated by combining the project priority score, material value score and process adaptability score. The materials are sorted in descending order to obtain the priority of material selection.

[0026] The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided by this invention includes the following steps for obtaining surplus material matching results: The maximum number of nestings of the material to be cut on the candidate scrap and the material utilization rate are calculated as the geometric nesting matching degree.

[0027] The deviation between the material grade of the candidate scrap and the design requirements of the plate to be cut is calculated as the material consistency matching degree.

[0028] The time difference between the estimated availability of candidate surplus materials and the order delivery date is calculated as the time urgency matching degree.

[0029] An objective function is constructed based on geometric nesting matching degree, material consistency matching degree, and time urgency matching degree. The surplus material with the optimal objective function value is selected as the suitable surplus material, and the surplus material matching result is output.

[0030] The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided by this invention includes the following steps in dividing the cutting task into multiple batches according to the selection priority of the sheet metal: Based on the priority of material selection, tasks to be cut that have the same project source or the same material type are aggregated to obtain aggregated tasks.

[0031] Set the maximum processing time and maximum material consumption of a single batch as preset thresholds. When the aggregation task reaches the preset threshold, it will be cut off and a new batch will be generated.

[0032] This ensures that the tasks to be cut within the same batch are spatially continuous, reducing the idle travel of the cutting equipment and forming multiple orderly task batches.

[0033] The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided by this invention includes the following steps for determining the cutting type according to the different cutting processes required for the corresponding batch: Analyze the contour complexity, thickness specifications, and perforation density of the plates to be cut in each task batch.

[0034] If the contour complexity is lower than the preset complexity threshold and there are no internal perforations, it is determined to be a straight-line cutting type.

[0035] If the contour complexity exceeds the preset threshold or there is a complex curve, it is determined to be a curve cutting type.

[0036] If the thickness exceeds the standard threshold or requires special cross-sectional treatment, it is determined to be a plasma or laser precision cutting type.

[0037] The method for dynamic matching and overall arrangement of sheet metal based on a residual material feature library provided by this invention includes the following steps in formulating a toolpath equipment allocation strategy: Real-time acquisition of the operating status, current load, tool wear, and coordinates of all cutting equipment.

[0038] Based on the cutting type of the current task batch, select a set of available equipment with corresponding processing capabilities.

[0039] Calculate the travel distance and estimated waiting time for each device in the available equipment set to the storage area for the adapted surplus materials. Extract the specific storage area coordinates for the adapted surplus materials of the current task batch.

[0040] With the goal of minimizing the total processing cycle, task batches are allocated to the equipment with the shortest overall processing time, and cutting path sequences are planned to form a toolpath equipment allocation strategy.

[0041] The method for dynamic matching and overall layout of sheet metal based on a surplus material feature library provided by this invention includes the following steps in formulating an overall layout scheme for sheet metal: Spatial mapping is performed between the matching residual material location information in the residual material matching results and the cutting path sequence in the toolpath equipment allocation strategy.

[0042] Check if there is any interference or conflict in the cutting paths of different task batches on the same scrap plate. If there is a conflict, adjust the cutting order or fine-tune the path coordinates.

[0043] Generate a comprehensive layout plan for the sheet metal, including the scrap number, the coordinates of the cutting start point, the cutting trajectory instructions, and the equipment allocation information.

[0044] The beneficial effects of this invention are as follows: 1. This invention, through dynamic matching and optimized layout, can fully utilize the leftover materials generated during board processing, reducing waste and improving material utilization. Multi-dimensional matching algorithms and intelligent cutting task allocation methods can quickly and accurately match board requirements and leftover materials, rationally allocate cutting tasks, reduce equipment idle travel and waiting time, and improve production efficiency. Improving material utilization and production efficiency directly reduces production costs and decreases the need for new board material procurement, further lowering raw material costs. The construction and real-time updating of the dynamic leftover material feature library, along with the application of intelligent algorithms, enables intelligent management of the board production process, improving the precision of production management and the scientific nature of decision-making. By improving material utilization, production efficiency, and reducing production costs, enterprises can gain an advantage in market competition, improve product cost-effectiveness, and enhance their competitiveness. Attached Figure Description

[0045] The invention will now be further described with reference to the accompanying drawings.

[0046] Figure 1 This is a flowchart illustrating the method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided in this embodiment of the invention. Figure 2 This is a flowchart illustrating the process of constructing a dynamic surplus material feature library in the method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided in this embodiment of the invention. Figure 3 This is a flowchart illustrating the process of obtaining the matching results of surplus materials in the method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided in this embodiment of the invention. Detailed Implementation

[0047] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0048] like Figures 1 to 3 As shown, the method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library provided in this embodiment of the invention includes: The system collects specification data of the boards to be processed and image data of leftover materials at the construction site in real time. It extracts the geometric features of the leftover materials through edge detection algorithms and builds a dynamic feature library of leftover materials by combining historical usage records.

[0049] The steps for extracting the geometric features of the scrap material include: Multi-angle image capture of idle board scraps in the target area is used to obtain scrap material image data, and the length, width, thickness, material and tolerance grade information of the board to be processed are collected in real time as specification data.

[0050] The image data of the remaining material is processed by grayscale conversion, Gaussian filtering for noise reduction, brightness and contrast optimization, and morphological processing to obtain a clear image.

[0051] Grayscale processing: The acquired color scrap image is converted into a grayscale image to remove color information interference. The color value of each pixel is converted into a single grayscale value (0-255), simplifying the subsequent image processing calculations while retaining key information about the scrap outline.

[0052] Gaussian filtering for noise reduction: A Gaussian filter is used to filter grayscale images, eliminating random noise such as pixel interference caused by dust at construction sites and camera sensor noise. The principle is to use a Gaussian function to perform a weighted average of each pixel and its neighboring pixels, smoothing the image texture, preserving the complete edges of material outlines, and preventing noise from being misidentified as edges.

[0053] Brightness and Contrast Optimization: To address the differences in image brightness caused by uneven lighting, a histogram equalization algorithm is used to adjust the image's brightness and contrast, maximizing the grayscale difference between the scrap material outline and the background. By stretching the image's grayscale histogram, the dynamic range of grayscale values ​​is expanded, making the edges of the scrap material clearer and preventing edge blurring due to excessively low lighting or loss of edge details due to excessively high lighting.

[0054] Morphological processing: The image is further optimized using dilation and erosion operations. Dilation fills in small gaps in the material outline and connects broken edges. Erosion removes small burrs and noise points from the outline, resulting in a clear, non-redundant pre-processed image.

[0055] The Canny edge detection algorithm is used to identify the edges of the preprocessed image and extract the set of outer boundary pixels of the remaining material to form the material outline.

[0056] The steps of edge detection may include: Step 1: Calculate the gradient. The Sobel operator is used to perform convolution on the preprocessed grayscale image to calculate the gradient magnitude and gradient direction of each pixel. The larger the gradient magnitude, the more likely the pixel is to be an edge point, and the gradient direction indicates the direction of the edge.

[0057] Step 2: Non-maximum suppression. Non-maximum suppression is applied to the calculated gradient magnitude to remove false edge points. Specifically, for each pixel along the gradient direction, it is determined whether it is a local maximum in that direction. If not, its grayscale value is set to 0, retaining only the true edge points to make the edges finer and clearer.

[0058] Step 3: Dual-threshold segmentation. Two thresholds are set to segment the gradient image: pixels with a gradient magnitude greater than the higher threshold are directly identified as valid edge points. Pixels with a gradient magnitude less than the lower threshold are identified as background points and discarded. Pixels with gradient magnitudes between the two thresholds are identified as edge points if they are adjacent to valid edge points; otherwise, they are identified as background points. This step yields a continuous, closed edge contour for the remaining material.

[0059] The material outline is fitted and geometrically corrected to obtain a corrected image. The outer dimension, actual effective area, minimum bounding dimension and outline coordinate point set are extracted from the corrected image as the geometric features of the remaining material.

[0060] Contour Fitting: For the extracted edge contours of the scrap material, polygon fitting or minimum bounding rectangle fitting is used to simplify the contour structure. For rectangular scrap materials, minimum bounding rectangle fitting is used to accurately obtain the outer length and width of the scrap material. For irregularly shaped scrap materials, polygon fitting is used to retain the key contour features of the scrap material, eliminate minor fluctuations in the contour, and obtain a smooth fitted contour.

[0061] Geometric correction: Combining the parameters from the previous camera calibration, the pixel coordinates of the fitted contour are converted into actual physical dimensions. Specifically, the actual coordinates of each key point on the fitted contour are calculated through the pixel-to-physical-dimensional mapping relationship, eliminating perspective distortion caused by the camera shooting angle, such as the deviation in the size of the remaining material caused by tilted shooting, and ensuring that the extracted geometric parameters are consistent with the actual size of the remaining material.

[0062] The steps to construct a dynamic scrap feature library include: The geometric features of the scrap material are organized one by one according to each scrap material to obtain a list of geometric features. The historical data of each scrap material is extracted and organized into a historical record of scrap material.

[0063] Extract historical usage data for each piece of leftover material from the production management system, cutting equipment logs, order execution records, and inventory management ledger.

[0064] Specific content may include: ① Reuse basic records: historical reuse count, total historical matching count. ② Matching-related records: types of parts that were successfully matched in the past, reasons for matching failures. ③ Process adaptation records: historically adapted cutting processes, unsuitable process types, and losses during the cutting process. ④ Inventory and time records: first entry time, most recent usage time, cumulative idle days, and storage location. ⑤ Quality change records: historical quality level changes, and repair / handling records.

[0065] The geometric feature list and the surplus material history are standardized in terms of unit, field format, and numerical processing. A unique identifier is assigned to each piece of usable surplus material, and a basic library entry is created.

[0066] Unit standardization includes: Geometric characteristics: all dimensions are standardized to mm, areas are standardized to ㎡, and tolerances are standardized to ±X mm, avoiding the mixing of inches, centimeters, and millimeters, and eliminating calculation errors.

[0067] Time records: The entry time and usage time are uniformly formatted as YYYY-MM-DDHH:MM:SS, and the number of idle days is uniformly calculated as the current date minus the first entry date.

[0068] Field format standardization includes: classification coding: uniform coding of non-numerical data to facilitate system identification and retrieval, for example: outline shape: 01=rectangle, 02=irregular shape, 03=L-shaped, 04=with holes, 05=with gaps.

[0069] Quality grades: 01 = Grade A (no deformation), 02 = Grade B (slight deformation), 03 = Grade C (unusable).

[0070] Cutting processes: 01 = sawing, 02 = laser cutting, 03 = water jet cutting, 04 = plasma cutting.

[0071] Remaining material status: 01 = Idle (usable), 02 = Occupied (not cut after matching), 03 = Consumed (cutting completed), 04 = Scrapped (unusable).

[0072] Numerical processing includes: Historical matching success rate: Calculation method = number of historical reuses ÷ total number of historical matches. If the total number of historical matches is 0, the success rate is recorded as 0.5. The default is medium adaptation potential, and the result is rounded to 2 decimal places.

[0073] Idle priority: Calculated based on the cumulative number of idle days. The more idle days, the higher the priority value. For example, 10 days of idle time earns 10 points, and 30 days of idle time earns 30 points, which are used for subsequent priority matching and consumption.

[0074] Process compatibility: The compatibility is calculated based on the number of times the process has been adapted in the past. For example, if it is only adapted to laser cutting, the compatibility is recorded as 1.0. If it is adapted to both laser cutting and sawing, the compatibility is recorded as 0.8, which facilitates subsequent process matching.

[0075] Unique identifier allocation includes: Coding rules: Use the format of surplus material type + date + serial number to ensure uniqueness and non-repetition.

[0076] Identification Association: Associate the unique ID with the temporary on-site identifier and image of the surplus material to ensure that the items in the warehouse → surplus material on site → surplus material image correspond one by one, which is convenient for on-site verification.

[0077] Basic library entry creation: Basic information module: unique ID of surplus material, date of entry, storage location, and current status.

[0078] Geometric Feature Module: All standardized geometric features.

[0079] Historical usage module: All historical usage records after standardization and quantification.

[0080] Associated Information Module: Associated residual material image path, acquisition device number, and operator.

[0081] Based on the geometric characteristics of the scrap material and the historical usage records as weights, a comprehensive value scoring model is constructed to calculate the value score of each scrap material and sort the scrap materials according to the score.

[0082] Based on the geometric features of the scrap material and historical usage records, a unique adaptive tag is generated for each scrap material, a search index is built according to the usage frequency, and the status of the scrap material is updated in real time to obtain a dynamic scrap material feature library.

[0083] Overall value score = (effective area score × 0.4) + (historical matching success rate × 0.3) + (idle priority × 0.2) + (process compatibility × 0.1).

[0084] Explanation of scores for each item: ① Effective area score: graded according to the size of the effective area (e.g., ≥1㎡ = 10 points, 0.5~1㎡ = 8 points, 0.1~0.5㎡ = 5 points, <0.1㎡ = 2 points). ② Historical matching success rate score: directly calculated as the matching success rate × 10. ③ Idle priority score: directly calculated as the idle priority value. ④ Process adaptability score: directly calculated as the process adaptability × 10.

[0085] Based on geometric features and historical usage records, a unique adaptability tag is generated for each piece of scrap material for quick retrieval. For example: ① Size tag: Rectangular scrap material of 600~800mm, irregularly shaped scrap material of 1000mm and above. ② Process tag: Only compatible with laser cutting, also compatible with sawing + waterjet cutting. ③ Priority tag: High value (score ≥ 40 points), medium value (20~39 points), low value (< 20 points). ④ Preference tag: High-frequency adaptability, suitable for small rectangular parts and high-priority orders.

[0086] The search index includes: Dimension 1: Material + Thickness Index, grouped by material code + thickness.

[0087] Dimension 2: Size range index, grouped by length range + width range.

[0088] Dimension 3: Comprehensive value scoring index, grouped by high value / medium value / low value, to facilitate priority access to high-value surplus materials.

[0089] Dimension 4: Status Index, grouped by idle / occupied / consumed / scrapped, only retrieves remaining materials in the idle state to avoid invalid searches.

[0090] Dimension 5: Process Adaptation Index, grouped by cutting process code, to facilitate matching of leftover materials with corresponding process requirements.

[0091] The real-time update of the status of surplus materials is triggered by the following scenarios: Trigger scenario 1: The surplus material is successfully matched, and the system automatically changes the status of the surplus material from idle to occupied.

[0092] Triggering Scenario 2: When the remaining material is cut and used, the system automatically changes the status to consumed and updates the historical usage record.

[0093] Triggering Scenario 3: When leftover materials are deformed or damaged and manually determined to be scrap, the system automatically changes the status to scrap, removes them from the idle index, and archives them separately.

[0094] Triggering Scenario 4: When leftover material is cut a second time, the system automatically deletes the original leftover material entry, adds multiple small leftover material entries, and supplements geometric features and historical association records.

[0095] Obtain the sheet material requirement list from the production order, retrieve the candidate surplus material set that meets the dimensional tolerance and material requirements based on the dynamic surplus material feature library, and sort the candidate surplus materials according to project priority, material value and process requirements to obtain the sheet material selection priority.

[0096] The sheet material requirement list may include: basic order information: order number, delivery deadline, and project priority.

[0097] Part details: part name, part number, individual part dimensions, part quantity, and machining accuracy requirements.

[0098] Material and process requirements: Material of sheet metal required for parts, dimensional tolerance range, machining allowance, and surface quality requirements.

[0099] The steps to obtain the candidate surplus material set include: Extract the size, material, precision, and quantity parameters of the parts to be manufactured from the sheet metal requirement list as part requirement parameters, and calculate the actual size of the parts based on the reserved machining allowance as the requirement for a single type of part.

[0100] The actual length occupied by the part = the theoretical length of the part + 2 × machining allowance, with reserves on both the left and right sides to ensure no edge error during cutting.

[0101] The actual width occupied by the part = the theoretical width of the part + 2 × machining allowance, with reserves at the top and bottom.

[0102] The actual thickness occupied by the part equals the theoretical thickness of the part. There is no machining allowance for the thickness; it only needs to be consistent with the thickness of the surplus material.

[0103] The requirements for single-type parts are categorized according to the principles of consistent material, consistent thickness, and similar size range, and identical requirements are merged to form a list of categorized requirements.

[0104] Parts of the same material and thickness are grouped together, such as M01 multilayer board + 10mm thickness as a group.

[0105] Within the same group, parts with similar size ranges are further merged. For example, parts with an actual length of 500-600mm and a width of 300-400mm are merged and searched uniformly according to the largest actual size to avoid duplicate searches.

[0106] Based on the categorized requirements list, set hard and soft constraints as screening criteria for each group of requirements.

[0107] Hard constraints must be fully met; otherwise, the material will be rejected. These constraints include: Leftover material material code = Required material code; Leftover material thickness = Required thickness; Leftover material status = Idle; Leftover material quality grade ≥ Required quality grade.

[0108] Flexible constraints: satisfied within tolerance range, rejected if exceeding tolerance, including: The effective length of the surplus material is greater than or equal to the maximum actual length required minus the tolerance Δ, ensuring that the length of the surplus material is sufficient and does not exceed the tolerance range.

[0109] The effective length of the surplus material should be less than or equal to the maximum actual length required plus the tolerance Δ, to avoid excessive surplus material length and material waste.

[0110] The effective width of the surplus material is greater than or equal to the maximum actual width required minus the tolerance Δ.

[0111] The effective width of the surplus material is less than or equal to the maximum actual width required plus the tolerance Δ.

[0112] The effective area of ​​the surplus material is greater than or equal to the maximum actual area required, calculated as the actual length occupied multiplied by the actual width occupied, to ensure that the parts can be accommodated.

[0113] The system synchronizes real-time data from the dynamic surplus material feature library and quickly retrieves surplus materials that meet the screening criteria based on the search index, using them as a candidate surplus material set.

[0114] The steps to determine the priority of board selection include: Extract project priority levels from production orders, set multi-level scoring criteria based on delivery dates, and determine the corresponding scores to obtain project priority scores.

[0115] The scoring criteria can be divided into: Level 1 Priority (Urgent): Delivery period ≤ 3 days, requires priority guarantee, score 25-30 points.

[0116] Second-level priority (more urgent): Delivery time 4-7 days, score 20-24 points.

[0117] Level 3 Priority (Standard): Delivery time 8-15 days, score 15-19 points.

[0118] Level 4 Priority (Relaxed): Delivery time > 15 days, score 10-14 points.

[0119] Extract the effective area, comprehensive value, and idle priority of each piece of scrap material from the dynamic scrap material feature library, assign corresponding scores, and add them together to obtain the material value score.

[0120] Score for effective area of ​​surplus material: graded according to the size of the effective area. The larger the area, the higher the score. The larger the effective area, the more parts can be processed and the higher the value: Effective area ≥ 1.0㎡: 12-15 points.

[0121] 0.5-1.0㎡ (excluding 1.0㎡): 8-11 points.

[0122] 0.1-0.5㎡ (excluding 0.5㎡): 4-7 points.

[0123] <0.1㎡: 1-3 points.

[0124] Overall value score for scrap materials: The overall value score calculated in the dynamic scrap material feature library is directly adopted and converted to a 10-point scale according to the ratio. The score for this item is calculated as follows: (0-50 points) ÷ 5.

[0125] Priority score for idle surplus materials: The more days of idle materials, the higher the score. Long-term idle surplus materials are consumed first to avoid stockpiling: Idle days ≥ 30 days: 8-10 points.

[0126] 15-29 days: 5-7 points.

[0127] 7-14 days: 3-4 points.

[0128] <7 days: 1-2 points.

[0129] The total material value score = effective area score + comprehensive value conversion score + idle priority score.

[0130] By combining the process requirements of production orders and the process records of surplus materials in the dynamic surplus material feature library, process type, processing accuracy and processing loss are set as evaluation indicators to calculate the process adaptability score.

[0131] Process type compatibility score: This score determines whether the historical compatible process of the surplus material is consistent with the current project's process. Consistency results in a higher score, preventing processing failures due to process incompatibility. Full compatibility: 12-15 points. Basic compatibility: 8-11 points. Partial compatibility: 4-7 points. No compatibility: 0 points.

[0132] Machining accuracy matching score: Determine whether the dimensional tolerance and surface accuracy of the leftover material meet the machining accuracy requirements of the current project: Leftover material tolerance ≤ project required tolerance, and surface quality meets the standard (Grade A): 8-10 points.

[0133] Excess material tolerance ≤ project requirement tolerance, surface quality good (Grade B): 5-7 points.

[0134] The tolerance for surplus material is slightly larger than the project's required tolerance (but within the allowable range): 3-4 points.

[0135] The tolerance of the excess material exceeds the project's required tolerance: 0 points.

[0136] Processing Loss Fit Score: Based on historical processing loss records of surplus materials, the loss rate during processing of surplus materials is determined. The lower the loss, the higher the score. Historical loss rate ≤ 5%: 8-10 points.

[0137] Historical loss rate 6%-10%: 5-7 points.

[0138] Historical loss rate 11%-15%: 3-4 points.

[0139] Historical loss rate > 15%: 1-2 points.

[0140] Total score for process adaptability = score for process type adaptability + score for machining accuracy adaptability + score for machining loss adaptability.

[0141] The allocation weights are set according to the current production scenario. The comprehensive score of each piece of leftover material is calculated by combining the project priority score, material value score and process adaptability score. The materials are sorted in descending order to obtain the priority of material selection.

[0142] If multiple scrap materials have the same overall score, they will be sorted according to the following priority to determine the order of selection: First priority: Surplus materials with higher project priority score (A) will be prioritized for urgent projects.

[0143] Second priority: Leftover materials with higher process adaptability score (C) will be given priority to ensure processing feasibility.

[0144] Third priority: leftover materials with higher material value (B), while also taking material value into account.

[0145] Fourth priority: Excess materials that have been idle for longer periods should be consumed first.

[0146] Priority Classification: Based on the overall score, the surplus materials are divided into 3 priority levels to facilitate subsequent matching and scheduling: Level 1 Priority: Overall score ≥ 80 points, fully meeting the project, value, and process requirements.

[0147] Secondary priority: A comprehensive score of 60-79 points basically meets the requirements and serves as a supplement to the primary priority.

[0148] Level 3 Priority: Overall score < 60 points, low adaptability, only used when there is insufficient leftover material in Level 1 and Level 2 priorities.

[0149] Based on the candidate surplus material set and the priority of plate selection, a multi-dimensional matching algorithm is used to screen suitable surplus materials and obtain surplus material matching results.

[0150] The steps to obtain the matching results of the surplus materials include: The maximum number of nestings of the material to be cut on the candidate scrap and the material utilization rate are calculated as the geometric nesting matching degree.

[0151] Based on the actual length and width of the material to be cut, and the effective length and width of the leftover material, combined with the gaps to be reserved during cutting, calculate the maximum number of material pieces that can be neatly arranged in the length and width directions of the leftover material. Multiply the number that can be arranged in the length direction by the number that can be arranged in the width direction to obtain the maximum number of material pieces that can be nested on a single piece of leftover material. Multiply the maximum number of nested pieces by the area of ​​a single piece of material to be cut, and add the area occupied by the cutting gap to obtain the total area actually occupied on the leftover material. Divide the total occupied area by the effective area of ​​the leftover material to obtain the material utilization rate. Normalize the maximum number of nested pieces and convert it into a corresponding score. At the same time, directly convert the material utilization rate into a corresponding score. Add the maximum number of nested pieces score and the material utilization rate score according to a set ratio to obtain the geometric nesting matching degree.

[0152] The deviation between the material grade of the candidate scrap and the design requirements of the plate to be cut is calculated as the material consistency matching degree.

[0153] Read the design material grade required for the board to be cut, and the actual material grade of the candidate scrap. Calculate the absolute value of the grade difference between the two as the material deviation value. A matching score is given based on the magnitude of the deviation: a deviation of 0 indicates completely identical materials, and a perfect material consistency score.

[0154] The deviation is level 1, and the matching degree is appropriately reduced.

[0155] When the deviation reaches level 2 or above, the matching degree is significantly reduced.

[0156] If the deviation is too large or the material of the leftover material is lower than the minimum requirement, the material consistency matching degree is recorded as 0.

[0157] The time difference between the estimated availability of candidate surplus materials and the order delivery date is calculated as the time urgency matching degree.

[0158] Read the order delivery time, the estimated available time for surplus materials, and the total time required to complete the cutting and processing of this batch of boards. Subtract the available time for surplus materials from the delivery time, and then subtract the processing time to obtain the effective available time difference. Find the largest effective available time difference among all candidate surplus materials and use it as a benchmark for normalization. Convert the effective available time difference of each piece of surplus material into a fraction according to the ratio to obtain the time urgency matching degree. When the effective available time difference is negative, it indicates that processing cannot be completed on time, and the time urgency matching degree is recorded as 0.

[0159] An objective function is constructed based on geometric nesting matching degree, material consistency matching degree, and time urgency matching degree. The surplus material with the optimal objective function value is selected as the suitable surplus material, and the surplus material matching result is output.

[0160] Weight ratios are assigned to geometric nesting matching degree, material consistency matching degree, and time urgency matching degree, and the sum of the three weights equals 1.

[0161] Multiply the three matching scores of each piece of leftover material by their respective weights.

[0162] The three weighted results are summed to obtain the comprehensive objective function value of the surplus material.

[0163] For all remaining materials with a comprehensive objective function value greater than 0, sort them from highest to lowest function value.

[0164] The leftover material with the highest overall score and the highest ranking is selected as the optimal matching leftover material.

[0165] If multiple scrap materials have the same score, the following criteria will be compared in turn: geometric nesting matching degree, material consistency matching degree, and time urgency matching degree, with the one with the higher score taking priority.

[0166] Check if the maximum nesting quantity of the optimal surplus material meets the total number of boards to be cut. If not, continue to select the next surplus material to supplement until the requirement is met.

[0167] The results of the surplus material matching include: the selected matching surplus material number, location, size, material, available time, geometric nesting matching degree, material consistency matching degree, time urgency matching degree, comprehensive objective function value, maximum nesting quantity, material utilization rate, and whether new boards need to be added.

[0168] The cutting tasks are divided into multiple batches based on the priority of the selected sheet material. The cutting type is determined according to the different cutting processes required for the corresponding batch. The tool path equipment allocation strategy is formulated in combination with the real-time status of the cutting equipment and the distribution of workstations. The overall arrangement plan of the sheet material is formulated in combination with the matching results of the surplus material.

[0169] The steps for dividing the cutting task into multiple batches based on the priority of the selected sheet material include: Based on the priority of material selection, tasks to be cut that have the same project source or the same material type are aggregated to obtain aggregated tasks.

[0170] Set the maximum processing time and maximum material consumption of a single batch as preset thresholds. When the aggregation task reaches the preset threshold, it will be cut off and a new batch will be generated.

[0171] This ensures that the tasks to be cut within the same batch are spatially continuous, reducing the idle travel of the cutting equipment and forming multiple orderly task batches.

[0172] The steps for determining the cutting type based on the different cutting processes required for the corresponding batch include: Analyze the contour complexity, thickness specifications, and perforation density of the plates to be cut in each task batch.

[0173] Analyze the complexity of the outline: Extract the outline parameters of each piece of board to be cut in the batch of tasks, and count the type of outline lines, the number of curves, and whether there are irregular broken lines.

[0174] The contour parameters of each board are compared with a preset contour complexity threshold to determine the overall contour complexity of the batch. If all boards in the batch are below the threshold, the overall contour complexity of the batch is determined to be below the threshold. If any board exceeds the threshold, the overall contour complexity of the batch is determined to be above the threshold.

[0175] If the contour complexity of the boards within a batch is inconsistent, the contour complexity of the most complex board shall be used as the basis for determining the contour complexity of that batch.

[0176] Analyze thickness specifications: Extract the actual measured thickness of each piece of board to be cut in this batch of tasks, statistically analyze the thickness distribution range, and find the maximum thickness of the board in this batch.

[0177] The maximum thickness of this batch of boards is compared with the preset thickness standard threshold to determine whether the batch of boards exceeds the standard thickness. If the maximum thickness is less than or equal to the standard threshold, it is considered a normal thickness. If the maximum thickness is greater than the standard threshold, it is considered to exceed the standard thickness.

[0178] If there are boards of different thicknesses in a batch, the thickness of the thickest board will be used as the basis for determining the thickness specification of that batch, to ensure that the cutting type can be adapted to boards of all thicknesses.

[0179] Analyze perforation density: Extract perforation information for each board to be cut in this batch of tasks, count the number of perforations and the distribution of perforations in each board, and calculate the perforation density (number of perforations ÷ effective area of ​​board).

[0180] Determine if any board in the batch has internal perforations. If none of the boards in the batch have internal perforations, the batch is considered to have no internal perforations. If any board has an internal perforation, the batch is considered to have internal perforations. Record the perforation density to aid in determining the contour complexity.

[0181] If the board has internal perforations, regardless of the perforation density, it is considered to have internal perforations and is included in the subsequent cutting type determination criteria.

[0182] If the contour complexity is lower than the preset complexity threshold and there are no internal perforations, it is determined to be a straight-line cutting type.

[0183] If the contour complexity exceeds the preset threshold or there is a complex curve, it is determined to be a curve cutting type.

[0184] If the thickness exceeds the standard threshold or requires special cross-sectional treatment, it is determined to be a plasma or laser precision cutting type.

[0185] The steps for developing a toolpath allocation strategy include: Real-time acquisition of the operating status, current load, tool wear, and coordinates of all cutting equipment.

[0186] Based on the cutting type of the current task batch, select a set of available equipment with corresponding processing capabilities.

[0187] Equipment operating status: Real-time collection of the current operating status of each device, clearly distinguishing between four states: idle, busy, faulty, and under maintenance, to ensure that available devices can be quickly selected.

[0188] Current device load: Records the number of tasks currently being processed by each device, the progress of completed tasks, and the estimated remaining time to complete all current tasks, intuitively reflecting the device's busy level and providing a basis for subsequent estimated waiting time calculations.

[0189] Tool wear: The wear detection module built into the equipment reads the wear value of each tool in real time, and combined with the tool usage time, it determines whether the tool can meet the cutting requirements of the current batch of tasks.

[0190] Equipment workstation coordinates: Accurately record the specific workstation coordinates of each piece of equipment to ensure that the movement distance from the equipment to the waste material storage area can be accurately calculated later.

[0191] First, extract the marked cutting types of the current task batch, and at the same time clarify the core processing capability requirements corresponding to the cutting type. For example, straight cutting requires straight trajectory control capability, precision cutting requires high-precision processing capability, and curve cutting requires complex curve programming capability.

[0192] Determine the minimum usage standards for the cutting tools based on the cutting type to ensure that the tools can meet the cutting quality requirements.

[0193] Define available equipment selection rules based on the cutting type: Condition 1: The equipment has the processing capacity required for the current task batch cutting type.

[0194] Condition 2: The equipment meets the basic usage requirements.

[0195] Each device is checked individually. Devices that meet both of the above conditions are included in the set of available devices. Devices that do not meet either condition are excluded and not included in subsequent calculations.

[0196] The selected available equipment is categorized by equipment number, equipment type, and machining accuracy. The real-time load, tool wear, and workstation coordinates of each piece of equipment are labeled to form a set of available equipment.

[0197] Calculate the travel distance and estimated waiting time for each device in the available equipment set to the storage area for the adapted surplus materials. Extract the specific storage area coordinates for the adapted surplus materials of the current task batch.

[0198] Set the moving speed for different types of equipment, such as 5m / min for laser equipment and 3m / min for sawing equipment, and unify the unit of moving speed to ensure standardized time calculation.

[0199] Extract the estimated remaining time for each available device to complete the current task. If the device is idle, the estimated waiting time is 0. If the device is busy, the estimated waiting time is the remaining time for the device to complete the current task.

[0200] Based on the coordinates of the equipment's workstation and the coordinates of the waste material storage area, the distance calculation formula in the Cartesian coordinate system is used. The square root of the sum of the squares of the horizontal and vertical distances between the equipment's workstation coordinates and the waste material storage area coordinates is then used to calculate the straight-line movement distance from each piece of equipment to the waste material storage area.

[0201] Divide the moving distance of each piece of equipment by its moving speed to obtain the estimated moving time of the equipment from its current workstation to the waste material storage area.

[0202] The total time consumed by each piece of equipment = the time spent moving the equipment to the waste material storage area + the estimated waiting time of the equipment.

[0203] Calculate the total time consumed by each device in the available device set, mark the calculation results in the available device set, and sort them in advance from shortest to longest total time to facilitate subsequent task allocation.

[0204] With the goal of minimizing the total processing cycle, task batches are allocated to the equipment with the shortest overall processing time, and cutting path sequences are planned to form a toolpath equipment allocation strategy.

[0205] Task batch allocation rules include: Step 1: Check the overall time consumption of each device in the available device set, and prioritize the device with the shortest overall time consumption as the receiving device for the current task batch.

[0206] Step 2: If multiple devices have the same overall time consumption, further compare the current load and tool wear of the devices: prioritize the device with the lower current load and less tool wear.

[0207] Step 3: If the overall time consumption, current load, and tool wear are all the same, compare the processing accuracy of the equipment: prioritize the equipment with higher processing accuracy to ensure that the cutting quality meets the task requirements.

[0208] Step 4: After determining the equipment to be assigned, clarify that the equipment will undertake all the cutting tasks of the current batch. If the batch is large, the task can be split into multiple pieces of equipment with shorter overall processing time to ensure the shortest total processing cycle.

[0209] The steps for planning the cutting path sequence include: Step 1: Extract the information on the available leftover material, the outline of the board to be cut, and the cutting requirements for the current task batch. Combine this with the processing capabilities of the receiving equipment to determine the cutting sequence for all the boards to be cut.

[0210] Step 2: The core principles of planning the cutting path sequence, such as minimizing cutting time and reducing tool wear: Sort by scrap material placement location → material outline complexity: First cut materials closest to the equipment in the scrap material storage area to reduce equipment movement distance. Cut materials with simple outlines first, then cut materials with complex outlines to improve cutting efficiency.

[0211] Optimize trajectory connection: Connect the cutting paths of adjacent plates as much as possible to avoid frequent start-stop and back-and-forth movement of the tool and reduce unnecessary time consumption.

[0212] Consider tool wear: schedule cutting tasks with lower wear requirements earlier and tasks with higher wear requirements earlier, to ensure that the tool completes all tasks within its effective wear range.

[0213] Step 3: Clarify the specific details of the cutting path sequence, including the cutting order, cutting trajectory, and process parameters for each board, to ensure that the receiving equipment can efficiently execute the cutting task according to the sequence.

[0214] The steps for developing a comprehensive layout plan for the boards include: Spatial mapping is performed between the matching residual material location information in the residual material matching results and the cutting path sequence in the toolpath equipment allocation strategy.

[0215] A unified workshop coordinate system is defined. The relative coordinates of the sheet metal on the scrap material are added to the absolute coordinates of the scrap material itself within the workshop to obtain the absolute coordinates of the sheet metal within the workshop. The scrap material number, the absolute coordinates of the scrap material, the sheet metal number, the absolute starting coordinates of the sheet metal, and the cutting trajectory instructions are linked and organized to form a spatial mapping table. This clarifies which location each piece of scrap material needs to go to, which piece to find, and which part of the sheet metal to cut.

[0216] Check if there is any interference or conflict in the cutting paths of different task batches on the same scrap plate. If there is a conflict, adjust the cutting order or fine-tune the path coordinates.

[0217] If multiple machines are cutting on the same piece of scrap material simultaneously, the path spacing needs to be checked. If a single machine is cutting sequentially, the timing sequence needs to be checked.

[0218] The rules for implementing interference conflict detection include: Geometric position conflict detection: comparing the positions of plates from different task batches on the same piece of scrap material. If the cutting areas of two plates overlap, or the distance between them is less than the safe operating radius of the equipment, it is determined to be a positional interference conflict.

[0219] Cutting timing conflict detection: If different batches of toolpaths need to use the same area of ​​the remaining material at the same time, it is determined to be a timing conflict.

[0220] Trajectory command conflict detection: Detects whether there are logical conflicts in the cutting direction and start / stop sequence of different toolpaths, such as when one toolpath is cutting and another toolpath is cutting into the same area in the opposite direction.

[0221] The conflict adjustment mechanism includes: adjusting the cutting order: if there is only a timing conflict and no positional overlap, the cutting and starting order of the task batches will be adjusted to stagger the execution.

[0222] Fine-tuning path coordinates: If there is positional interference and overlap, without affecting the processing accuracy and structural strength of the board, the relative coordinates of one batch of boards are slightly translated. The adjustment range is usually controlled between ±1mm and ±5mm to eliminate path interference.

[0223] Reassigning equipment: If the conflict cannot be resolved through fine-tuning, the toolpath of one of the task batches will be assigned to other compatible spare parts or other equipment, and the space mapping will be re-performed.

[0224] Generate a comprehensive layout plan for the sheet metal, including the scrap number, the coordinates of the cutting start point, the cutting trajectory instructions, and the equipment allocation information.

[0225] Extract the unique identifier for each piece of equipment and the corresponding scrap material number to be processed by each equipment. Combine this with spatial mapping data to obtain the absolute coordinates of the cutting start point for each piece of material. Based on process requirements, generate standardized cutting trajectory instructions, including linear interpolation, circular interpolation, feed rate, and spindle start / stop commands. Define the equipment allocation information.

[0226] The overall layout plan for the boards includes: details of the layout of surplus materials: the number and location of each piece of surplus material, and the number and location of the boards to be cut placed on it.

[0227] Equipment toolpath details: each piece of equipment number, the amount of scrap material it is responsible for, the specific starting coordinates of the cutting, and the complete trajectory instructions.

[0228] Execution sequence: Start time and cutting order of all task batches.

[0229] In summary, the dynamic matching and overall arrangement method for sheet metal based on a surplus material feature library provided in this embodiment significantly improves the reuse rate of surplus materials through a precise surplus material feature library and multi-dimensional matching, reducing the procurement of new sheet metal and the generation of waste, thus directly reducing raw material costs. Automated retrieval and sorting replace manual searching and estimation, generating matching results in seconds; intelligent batching and scheduling reduce equipment waiting time and idle travel, significantly shortening the overall processing cycle from order to finished product. Non-standardized surplus materials are transformed into standardized digital assets, eliminating subjective errors from human experience. The comprehensive scoring model ensures that high-priority projects and orders with high-quality requirements receive the best resource guarantees. The dynamic scheduling strategy balances the load of each cutting device, avoiding situations where some devices are overloaded while others are idle; at the same time, allocating tasks according to tool wear and process requirements helps extend the service life of core equipment components. Through pre-interference detection and path planning, collision accidents and rework risks during on-site cutting are avoided, and the generated standardized CNC code ensures the consistency and traceability of the processing process.

[0230] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic matching and overall layout of sheet metal based on a surplus material feature library, characterized in that, include: The specification data of the boards to be processed and the image data of the leftover materials at the construction site are collected in real time. The geometric features of the leftover materials are extracted through edge detection algorithm and combined with historical usage records to build a dynamic leftover material feature library. Obtain the sheet material requirement list from the production order, retrieve the candidate surplus material set that meets the dimensional tolerance and material requirements based on the dynamic surplus material feature library, and sort the candidate surplus materials according to project priority, material value and process requirements to obtain the sheet material selection priority; Based on the candidate set of surplus materials and the selection priority of the board material, a multi-dimensional matching algorithm is used to filter suitable surplus materials to obtain the surplus material matching result. The cutting tasks are divided into multiple batches based on the priority of the selected sheet material. The cutting type is determined according to the different cutting processes required for the corresponding batch. A tool path equipment allocation strategy is formulated in combination with the real-time cutting equipment status and workstation distribution. A sheet material overall layout plan is formulated in combination with the surplus material matching results.

2. The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library according to claim 1, characterized in that: The steps for extracting the geometric features of the scrap material include: Multi-angle image captures are performed on the idle board scraps in the target area to obtain the scrap image data, and the length, width, thickness, material and tolerance grade information of the board to be processed are collected in real time as the specification data; The image data of the remaining material is subjected to grayscale processing, Gaussian filtering for noise reduction, brightness and contrast optimization, and morphological processing to obtain a clear image. The Canny edge detection algorithm is used to perform edge recognition on the preprocessed image, extract the set of outer boundary pixels of the remaining material, and form the material outline. The material outline is fitted and geometrically corrected to obtain a corrected image. The outer dimension, actual effective area, minimum bounding dimension, and outline coordinate point set are extracted from the corrected image as the geometric features of the remaining material.

3. The method for dynamic matching and overall layout of sheet metal based on a surplus material feature library according to claim 2, characterized in that: The steps for constructing the dynamic scrap feature library include: The geometric features of the scrap material are organized one by one according to each scrap material to obtain a list of geometric features. The historical data of each scrap material is extracted and organized into a scrap material history record. The geometric feature list and the surplus material history record are processed by unifying units, standardizing field formats and numerical values, assigning a unique identifier to each piece of usable surplus material, and creating basic library entries; Based on the geometric characteristics of the scrap material and the historical usage records as weights, a comprehensive value scoring model is constructed to calculate the value score of each scrap material and sort the scrap materials according to the score. Based on the geometric features of the scrap material and historical usage records, a unique adaptation tag is generated for each piece of scrap material, a retrieval index is constructed according to the usage frequency, and the status of the scrap material is updated in real time to obtain the dynamic scrap material feature library.

4. The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library according to claim 3, characterized in that: The steps for obtaining the candidate surplus material set include: Extract the size parameters, material parameters, accuracy parameters, and quantity parameters of the parts to be processed from the sheet metal requirement list as part requirement parameters, and calculate the actual size occupied by the parts in combination with the reserved processing allowance as the single type of part requirement; The single-type part requirements are categorized according to the principles of consistent material, consistent thickness, and similar size range, and identical requirements are merged to form a categorized requirement list; Based on the aforementioned list of categorized requirements, set hard and soft constraints as screening criteria for each group of requirements; Synchronize the real-time data of the dynamic scrap feature library, and quickly retrieve scrap materials that meet the screening criteria based on the retrieval index, as the candidate scrap material set.

5. The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library according to claim 1, characterized in that: The steps for obtaining the selection priority of the board material include: Extract project priority levels from production orders, set multi-level scoring criteria based on delivery dates, and determine the corresponding scores to obtain project priority scores; The effective area, comprehensive value, and idle priority of each piece of waste material are extracted from the dynamic waste material feature library, and corresponding scores are assigned. The scores are then added together to obtain the material value score. Based on the process requirements of the production order and the process records of the surplus material in the dynamic surplus material feature library, process type, processing accuracy and processing loss are set as evaluation indicators, and the process adaptability score is calculated. Based on the current production scenario, the allocation weights are set, and the comprehensive score of each piece of leftover material is calculated by combining the project priority score, the material value score, and the process adaptability score. The selection priority of the board material is obtained by sorting them in descending order.

6. The method for dynamic matching and overall layout of sheet metal based on a surplus material feature library according to claim 1, characterized in that: The steps to obtain the matching result of the surplus material include: Calculate the maximum number of nesting elements on the candidate scrap material and the material utilization rate of the plate to be cut as the geometric nesting matching degree; The deviation between the material grade of the candidate scrap and the design requirements of the plate to be cut is calculated as the material consistency matching degree. Calculate the time difference between the estimated availability of candidate surplus materials and the order delivery date as the time urgency matching degree; An objective function is constructed based on the geometric nesting matching degree, the material consistency matching degree, and the time urgency matching degree. The surplus material with the optimal objective function value is selected as the suitable surplus material, and the surplus material matching result is output.

7. The method for dynamic matching and overall layout of sheet metal based on a surplus material feature library according to claim 1, characterized in that: The step of dividing the cutting task into multiple batches based on the priority of the selected sheet material includes: Based on the priority order of the selected sheet material, the tasks to be cut that have the same project source or the same material type are aggregated to obtain aggregated tasks; Set the maximum processing time and maximum material consumption of a single batch as preset thresholds. When the aggregation task reaches the preset thresholds, it is cut off and a new batch is generated. This ensures that the tasks to be cut within the same batch are spatially continuous, reducing the idle travel of the cutting equipment and forming multiple orderly task batches.

8. The method for dynamic matching and overall layout of sheet metal based on a surplus material feature library according to claim 1, characterized in that: The steps for determining the cutting type based on the different cutting processes required for the corresponding batch include: Analyze the contour complexity, thickness specifications, and perforation density of the plates to be cut in each task batch; If the contour complexity is lower than a preset complexity threshold and there are no internal perforations, it is determined to be a straight-line cutting type; If the contour complexity exceeds the preset threshold or there is a complex curve, it is determined to be a curve cutting type. If the thickness exceeds the standard threshold or requires special cross-sectional treatment, it is determined to be a plasma or laser precision cutting type.

9. The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library according to claim 8, characterized in that: The steps for formulating the toolpath equipment allocation strategy include: Real-time acquisition of the operating status, current load, tool wear, and coordinates of all cutting equipment; Based on the cutting type of the current task batch, select a set of available equipment with corresponding processing capabilities; Calculate the movement distance and estimated waiting time of each device in the available device set to the storage area of ​​the adapted surplus material; extract the specific storage area coordinates of the adapted surplus material for the current task batch; With the goal of minimizing the total processing cycle, task batches are allocated to the equipment with the shortest overall processing time, and a cutting path sequence is planned to form the toolpath equipment allocation strategy.

10. The method for dynamic matching and overall arrangement of sheet metal based on a surplus material feature library according to claim 1, characterized in that: The steps for developing the overall layout plan for the aforementioned boards include: The matching residual material location information in the residual material matching result is spatially mapped to the cutting path sequence in the toolpath equipment allocation strategy; Detect whether there is any interference or conflict in the cutting paths of different task batches on the same scrap plate. If there is a conflict, adjust the cutting order or fine-tune the path coordinates. Generate a unified layout scheme for the sheet metal, which includes the scrap material number, the coordinates of the cutting start point, the cutting trajectory instructions, and the equipment allocation information.

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