Material utilization rate optimization layout and order combination method in copper bar machining process

Abstract

The invention relates to the technical field of copper bar machining, and discloses a material utilization rate optimized layout and order merging method in the copper bar machining process, and the method comprises the steps: obtaining a machining order set and cutting equipment state parameters, performing feature preprocessing on the part to calculate an effective occupied length; mapping the length to a discrete interval to generate an effective virtual filling degree spectrum, and screening orders by using a comprehensive utility function and a seed adsorption strategy to construct a to-be-processed pool; extracting topological characteristics of a synthetic spectrum of a to-be-treated pond, and adaptively matching a layout solver according to the topological characteristics; and a cutting scheme is generated through a solver, and a machining instruction is generated after pre-execution verification is passed. According to the method, the order combination structure is optimized through the spectrum complementation strategy, and the utilization rate of the copper bar material is remarkably improved while the feasibility of the cutting scheme is ensured in combination with the self-adaptive solution and the physical constraint pre-mapping.

Description

Technical Field

[0001] This invention relates to the field of copper busbar processing technology, specifically to a method for optimizing material utilization and merging orders during copper busbar processing. Background Technology

[0002] Copper busbars are key conductive components in electrical equipment, and their raw material costs are high. Improving material utilization during the cutting and processing process is crucial for controlling production costs. In industrial production, nesting techniques are typically used to arrange parts of different lengths on fixed-length raw materials to reduce waste and scrap. To improve the economies of scale in nesting calculations, production management systems usually need to consolidate scattered copper busbar processing orders to create a batch production task pool.

[0003] Existing order merging and layout technologies primarily aggregate and group orders based on management attributes such as material specifications, delivery deadlines, or customer levels. After determining the pool of tasks to be processed, the system typically calls a single layout algorithm (such as linear programming or fixed heuristic rules) to generate a cutting scheme, and then sends the calculated geometric layout data to the CNC equipment to perform physical cutting.

[0004] However, existing processing methods have several shortcomings in practical applications. First, order merging based solely on management attributes ignores the complementarity of part length specifications in geometric distribution, resulting in some production batches failing to achieve high utilization rates due to mismatched length distribution characteristics at the time of generation. Second, existing technologies typically employ fixed calculation processes for all batches, making it difficult to dynamically adjust the optimization steps based on the discrete or centralized characteristics of order combinations, and struggling to strike a balance between computational efficiency and global optimization capabilities. Furthermore, traditional nesting calculations are mostly based on ideal geometric dimensions, failing to effectively quantify physical constraints such as kerf loss and equipment clamping avoidance, often leading to theoretically high utilization schemes being difficult to implement effectively in actual processing due to physical interference or dimensional deviations.

[0005] Therefore, this invention proposes a method for optimizing material utilization and merging orders during copper busbar processing to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method for optimizing material utilization and merging orders during copper busbar processing. This method solves the problems in existing technologies, such as the lack of consideration for the geometric complementarity of parts in the order merging process, the difficulty of adaptively adjusting the single layout calculation method according to task characteristics, and the failure of the layout calculation process to cover equipment physical constraints, which leads to low material utilization and poor feasibility of processing schemes.

[0007] To achieve the above objectives, the present invention provides a method for optimizing material utilization and merging orders during copper busbar processing, comprising the following steps: Obtain the copper busbar processing order set and the status parameters of the cutting equipment in the workshop; Based on the status parameters of the cutting equipment, the parts included in the copper busbar processing order set are preprocessed to calculate the effective occupied length of the parts; The effective occupied length is mapped to a discrete interval of the standard raw material length to generate an effective virtual fill degree spectrum for each order; Using a comprehensive utility function and a seed adsorption strategy, orders are iteratively filtered from the copper busbar processing order set to construct a pool of orders to be processed; Extract the synthetic spectrum topology features corresponding to the order pool to be processed, and adaptively match the sorting solver according to the synthetic spectrum topology features; The nesting solver is used to generate a cutting scheme for the pool of orders to be processed, and the cutting scheme is pre-executed and verified. After the pre-execution verification is passed, a processing instruction is generated.

[0008] Preferably, the step of performing feature preprocessing on the parts included in the copper busbar processing order set based on the cutting equipment status parameters and calculating the effective occupied length of the parts includes: The digital geometric description file of the part is parsed to identify process features, and the physical distance from the edge boundary of the process feature to the nearest end face of the part is calculated as the feature distance; The minimum processable length of the cutting equipment in the status parameters is obtained as the safety clamping threshold, and the feature distance is compared with the safety clamping threshold to determine the feature safety margin. The real-time kerf width of the cutting equipment is obtained from the status parameters of the cutting equipment. The effective occupied length of the part is calculated by linearly summing the physical geometric length of the part, the characteristic safety margin, and the real-time kerf width of the equipment.

[0009] Preferably, the step of mapping the effective occupied length to a discrete interval of the standard raw material length to generate an effective virtual fill degree spectrum for each order includes: The standard raw material length is divided into multiple equally spaced discrete intervals according to a preset discretization resolution; Calculate the index of the discrete interval to which the effective occupied length of the part belongs, and accumulate the effective occupied length into the components of the corresponding original length distribution vector; The original length distribution vector is normalized using the L1 norm criterion to generate the effective virtual fill degree spectrum in the form of a probability density function.

[0010] Preferably, the step of iteratively filtering orders from the copper busbar processing order set to construct a pool of orders to be processed using a comprehensive utility function and a seed adsorption strategy includes: The dynamic urgency index of each order in the copper busbar processing order set is calculated based on the order priority weight and the order's dwell time in the buffer. The order with the highest dynamic urgency index is selected as the seed order and moved into the newly created pool of pending orders; The effective virtual fill spectrum corresponding to the seed order is assigned to the mixed fill spectrum of the order pool to be processed, and the sum of the effective occupied lengths of all parts contained in the seed order is assigned to the cumulative effective occupied length of the order pool to be processed.

[0011] Preferably, the step of iteratively filtering orders from the copper busbar processing order set to construct a pool of orders to be processed using a comprehensive utility function and a seed adsorption strategy further includes: Calculate the spectral complementarity index between the effective virtual fill degree spectrum of candidate orders in the remaining order set and the mixed fill degree spectrum of the order pool to be processed; The comprehensive utility value of the candidate order is calculated based on the weighted sum of the spectral complementarity index and the normalized dynamic urgency index. The candidate order with the largest comprehensive utility value is selected as the target order and moved into the pending order pool. The mixed fill degree spectrum and the cumulative effective occupied length of the pending order pool are updated based on the characteristics of the target order.

[0012] Preferably, the step of extracting the synthetic spectrum topological features corresponding to the order pool to be processed includes: The mixed fill degree spectrum of the order pool to be processed is determined as the composite spectrum; Calculate the spectral entropy value feature of the synthesized spectrum, which is used to quantify the discrete disorder of the part length requirements in the order pool to be processed; Calculate the kurtosis feature of the synthesized spectrum. The kurtosis feature is used to quantify the central tendency of the dominant length specifications in the order pool to be processed. The kurtosis feature is the maximum value among the vector components of the synthesized spectrum.

[0013] Preferably, the step of adaptively matching the sorting solver based on the topological features of the synthesized spectrum includes: A complexity discrimination function is constructed by calculating the difference between the weighted normalized spectral entropy feature and the weighted spectral kurtosis feature. The value of the complexity discrimination function is compared with the preset complexity switching threshold; If the value is greater than the complexity switching threshold, the metaheuristic iterative solver is selected as the sorting solver. If the value is less than or equal to the complexity switching threshold, the constructive heuristic solver is selected as the nesting solver.

[0014] Preferably, the step of generating a cutting plan for the order pool to be processed using the nesting solver and performing pre-execution verification on the cutting plan includes: The current availability status of the cutting equipment is determined in the status parameters. If the current availability status indicates that the equipment is normal, the nesting solver is invoked. Using all parts in the order pool to be processed as the nesting objects and the standard raw material length as the constraint boundary, the nesting solver is run to output the cutting scheme; Calculate the actual material utilization rate of the cutting scheme, which is the ratio of the sum of the effective occupied lengths of all parts in the order pool to the total length of the standard raw materials used in the cutting scheme; The actual material utilization rate is compared with a preset utilization rate qualification threshold.

[0015] Preferably, the step of generating processing instructions after the pre-execution verification passes includes: If the actual material utilization rate is greater than or equal to the qualified utilization rate threshold, the pre-execution verification is deemed to have passed. The geometric coordinate data in the cutting scheme is analyzed, and the machining instructions, including tool positioning instructions, linear cutting feed instructions and auxiliary control instructions, are generated according to the motion control logic of the cutting equipment. The processing instructions are sent to the copper busbar cutting equipment to drive the copper busbar cutting equipment to perform physical cutting operations.

[0016] Preferably, the method further includes performing the following steps when the pre-execution verification fails: If the actual material utilization rate is less than the qualified utilization rate threshold, calculate the outlier contribution value of each order in the order pool to be processed; The order with the largest outlier contribution value is removed from the pool of pending orders, and the nesting solver is called again for the updated pool of pending orders.

[0017] This invention provides a method for optimizing material utilization and merging orders during copper busbar processing. It offers the following advantages: 1. This invention constructs an effective virtual fill degree spectrum, transforming the discrete part length requirements in an order into standardized geometric distribution characteristics. It then utilizes a seed adsorption strategy combining spectral complementarity and dynamic urgency for order screening. This mechanism, while meeting production delivery timeliness requirements, prioritizes aggregating orders with complementary length distributions into the same processing pool. This solves the problem of traditional order merging focusing only on quantity and neglecting geometric matching, laying a structural foundation for improving copper busbar material utilization from the outset.

[0018] 2. This invention extracts the topological features of the synthetic spectrum of the order pool to be processed, quantifies the discrete disorder and central tendency of the part length distribution, and constructs a complexity discriminant function based on this to adaptively match the nesting solver. It overcomes the performance instability of a single algorithm when facing different nesting scenarios. For simple, centralized tasks, a constructive solver is used to ensure speed, while for complex, discrete tasks, a metaheuristic solver is used to ensure quality, achieving a dynamic balance between nesting computation efficiency and global optimization capability.

[0019] 3. This invention calculates the effective occupied length of parts using equipment status parameters, pre-quantifies the kerf loss and clamping avoidance requirements during the physical cutting process, and maps them to the layout calculation. Simultaneously, it performs pre-execution verification and outlier order rollback operations based on actual utilization feedback. This approach eliminates dimensional deviations between theoretical layout design and actual physical processing, and automatically identifies and removes abnormal orders that lead to decreased utilization, thus ensuring that the generated cutting scheme is both physically feasible and strictly meets the economic requirements for material cost control during production. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for optimizing material utilization and merging orders during copper busbar processing according to the present invention. Figure 2 This is a flowchart of the adaptive matching process of the sorting solver based on synthetic spectrum features of the present invention; Figure 3 This is a system architecture diagram of material utilization optimization layout and order merging in the copper busbar processing process according to the present invention.

[0021] Among them, 100 is the optimized computing server; 200 is the production management system; 300 is the CNC cutting equipment; and 400 is the industrial communication network. Detailed Implementation

[0022] The technical solutions in 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] See attached document Figure 3 This invention provides a material utilization optimization layout and order merging system for copper busbar processing. The system is applied in an industrial copper busbar processing workshop environment and mainly includes: an optimization calculation server 100, a production management system 200, a CNC cutting equipment 300, and an industrial communication network 400.

[0024] The optimization computing server 100 establishes communication connections with the production management system 200 and the CNC cutting equipment 300 respectively through the industrial communication network 400. The industrial communication network 400 can use industrial Ethernet, fieldbus, or industrial wireless network protocols to achieve bidirectional data transmission. The optimization computing server 100 is configured to execute the method of the present invention, and at the physical layer includes at least one processor and a memory. The memory stores computer program instructions, and the processor executes the computer program instructions to implement the following method for optimizing material utilization and merging orders during copper busbar processing.

[0025] The production management system 200 is used to generate and manage the original copper busbar processing orders, and the CNC cutting equipment 300 is the hardware terminal that performs the physical cutting action. It is equipped with sensor and controller interfaces for feedback of equipment status parameters.

[0026] See attached document Figure 1 This invention provides a method for optimizing material utilization and merging orders during copper busbar processing, comprising the following steps: S10, obtain the copper busbar processing order set and the status parameters of the cutting equipment in the workshop; S20, perform feature preprocessing on the parts included in the copper busbar processing order set according to the status parameters of the cutting equipment, and calculate the effective occupied length of the parts; S30 maps the effective occupied length to a discrete interval of the standard raw material length, generating an effective virtual fill degree spectrum for each order; S40 utilizes a comprehensive utility function and a seed adsorption strategy to iteratively filter orders from the copper busbar processing order set to construct a pool of orders to be processed; S50: Extract the topological features of the synthetic spectrum corresponding to the order pool to be processed, and adaptively match the sorting solver according to the topological features of the synthetic spectrum. S60 uses the nesting solver to generate cutting schemes for the order pool to be processed, and performs pre-execution verification on the cutting schemes. After the pre-execution verification passes, it generates processing instructions.

[0027] To further clarify the technical details of the method and system in this embodiment, the specific implementation, principles and technical content of the above system and method will be described in detail below.

[0028] See attached document Figure 1 and Figure 3 When implementing material utilization optimization layout and order merging methods during copper busbar processing, the system first establishes a connection with external information sources through a data interaction interface to complete the initialization of basic data. This process mainly involves acquiring and parsing the copper busbar processing order set, as well as real-time acquisition and mapping of the status parameters of the cutting equipment in the workshop.

[0029] To obtain the copper busbar processing order set, the system communicates with the production management system 200 to read the list of production tasks to be executed. The system then parses the read unstructured or semi-structured production task data into a standardized mathematical model to construct the copper busbar processing order set. This copper busbar processing order set contains A single pending processing order is represented as: ; in, This represents the total number of orders in the set; Indicates the first A single order object ( ).

[0030] For each order object in the copper busbar processing order set The optimized computing server 100 further analyzes its internal structure, dividing it into sets of parts. With order metadata. Parts collection Defined as: ; in, Indicates order The number of different types of parts included; Indicates the first A type of part object. The optimized computing server 100 extracts values ​​from the original order details line and assigns them to the part object. The attributes, including physical geometric length and demand quantity .

[0031] For order arrival time in order metadata Order priority weight The optimized computing server 100 uses the following logic for extraction: It reads the order creation timestamp or last modification timestamp from the header information of the original order and directly assigns it to... Read the "Customer Level" or "Urgency Level" field from the original order, and convert this discrete field into a numerical order priority weight using a pre-defined mapping table. A pre-defined mapping table stores the correspondence between non-numerical business level identifiers and numerical weight coefficients (for example, mapping "urgent" to a weight value of 10.0, "normal" to 5.0, and "regular" to 1.0), thereby achieving the quantitative transformation of qualitative data. For the above field reading and data transformation operations, those skilled in the art can utilize the data interface provided by the production management system 200 and conventional data cleaning scripts.

[0032] Simultaneously, the optimization calculation server 100 connects to the controller interface of the CNC cutting equipment 300 via the industrial communication network 400 to obtain the status parameters of the target cutting equipment specified in the current production task. The optimization calculation server 100 reads the sensor feedback or register values ​​of the target equipment. The cutting equipment status parameters include: Real-time saw kerf width of equipment The real-time kerf width of this device characterizes the length of material loss caused by the physical cutting process. The system determines this width by directly reading the tool parameters configured in the device controller or by obtaining feedback values ​​from recent processing data. This parameter directly participates in the calculation of the effective length occupied by subsequent parts and is a physical correction item affecting the accuracy of material utilization calculation.

[0033] Minimum processable length of equipment The minimum processable length of this equipment represents the minimum material length required for the clamping mechanism to stably hold and complete the feeding action. In subsequent feature preprocessing steps, this parameter serves as a benchmark value for determining whether a part requires process avoidance and is defined as the safe clamping threshold. The system will assign the read device limit parameters to... ,Right now .

[0034] Current availability status of the device The current availability status of this device represents its operational capability at the current moment. It is obtained by polling the device's status register in real time.

[0035] See attached document Figure 1 and Figure 3After obtaining the copper busbar processing order set and the status parameters of the cutting equipment, the optimization calculation server 100 executes step S20, that is, to perform feature preprocessing on the parts contained in the copper busbar processing order set according to the status parameters of the cutting equipment, and to calculate the effective occupied length of the parts.

[0036] Optimize the computing server to traverse the copper busbar processing order set 100 times. Each order and its constituent parts For each part The optimized computing server 100 calls the computer-aided design (CAD) parsing engine to read the digital geometric description file of the part. The parsing engine scans the geometric feature data of the part, identifies the solid primitives defined in the part coordinate system, and detects whether there are specific process features at the end of the part. Process features include punching features (defined by circular or polygonal closed contours) and bending features (defined by bending lines or bending area markings). For each identified process feature, the system calculates the physical distance from the edge boundary of the feature to the nearest end face of the part (i.e., the cutting reference plane) and defines this distance as the feature distance. For the parsing of CAD files and extraction of geometric features, those skilled in the art can use existing computer graphics algorithm libraries (such as OpenCASCADE), which are well-known technologies in the field and will not be elaborated here.

[0037] In obtaining feature distance Subsequently, the optimized computing server 100 utilizes feature security margins. To quantify process avoidance requirements, the system retrieves the cutting equipment status parameters obtained in step S10 and extracts the minimum processable length of the equipment as the safety clamping threshold. This safety clamping threshold characterizes the minimum interference-free clamping area length required by the clamping mechanism of the cutting equipment to ensure machining accuracy and safety. The system will calculate the characteristic distance. With safety clamping threshold The comparison is performed, and the feature safety margin is calculated based on the following logic. : ; When detected When this occurs, it indicates that the process feature falls into the clamping interference zone of the equipment. If the layout is done according to the original length, the clamps may damage the feature area or fail to clamp it securely. Therefore, a compensation difference is needed as a margin. When this is detected... When the condition is met, it indicates that the process feature is outside the safe clamping area and there is no risk of interference; therefore, the margin is set to zero.

[0038] After determining the characteristic safety margin, the optimization calculation server 100, in conjunction with the physical dimensions of the part and the loss parameters of the equipment, calculates the actual length consumed by the part on the raw material bar, i.e., the effective occupied length. This calculation process takes the physical geometric length of the part as an example. The real-time saw kerf width obtained in step S10 and the characteristic safety margin calculated above Perform linear accumulation. The calculation formula is as follows: ; in, It is the physical geometric length; The real-time kerf width of the equipment; This represents a characteristic safety margin. Through this calculation, the system maps all non-geometric constraints related to equipment capabilities to... Within this single scalar, the generated effective occupied length is associated and stored in the part. In the data structure.

[0039] See attached document Figure 1 and Figure 3 After calculating the effective occupied length of the part, the optimization calculation server 100 executes step S30, which maps the effective occupied length to a discrete interval of the standard raw material length, and generates an effective virtual fill degree spectrum for each order.

[0040] The optimized computing server 100 reads the preset standard raw material length from the memory. Standard raw material length This refers to the fixed length (e.g., 6000 mm) of the raw copper busbars used for subsequent cutting and processing in the workshop warehouse. This value is determined by purchasing specifications or inventory management data and serves as the upper limit of the physical boundary for layout optimization. To construct the discrete interval, the system defines a discretization resolution. Discretization resolution It is a preset length step value (e.g., 50 mm or 100 mm) used to determine the fineness of dividing a continuous length field into several statistical units. The setting of this value needs to be balanced between computational accuracy and computational complexity.

[0041] The system is based on the standard raw material length With discretization resolution Divide the continuous length field into There are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ... The calculation formula is: ; in, This represents the floor function. The resulting set of discrete intervals covers the range from 0 to... The complete length range, the first intervals ( The length range represented by ) is .

[0042] For copper busbar processing order collection Each order Optimize computing server 100 to initialize a length of The all-zero vector as the original length distribution vector The system then iterates through the orders. All parts included Extract the effective occupancy length calculated in step S20. and the quantity required For each part, the system calculates its corresponding discrete interval index. : ; in, This indicates a floor operation. If the calculated index value is greater than... Then it will be corrected to After determining the index, the system adds the total effective length contribution of the part to the corresponding vector component, i.e., updates the index. The value: ; Execute the above accumulation operation until all orders have been traversed. All parts in the original length distribution vector at this time The absolute quantities of material requirements for this order at different length scales were recorded.

[0043] To eliminate the dimensional inconsistencies caused by differences in total demand among different orders and to accurately characterize the geometric distribution features of orders, the computation server 100 was optimized to handle the original length distribution vectors. Normalization is performed to generate an effective virtual fill spectrum. The effective virtual fill degree spectrum is defined as a vector in the form of a probability density function. The normalization calculation uses the L1 norm criterion, and the calculation formula is as follows: ; in, Indicating the first in the effective virtual fill spectrum The values ​​of each component, the denominator term Representative orders Total effective demand length. Generated effective virtual fill spectrum. It can intuitively reflect whether the order tends to consume long materials, short materials, or is evenly distributed.

[0044] See attached document Figure 1 and Figure 3 After generating the effective virtual fill spectrum for each order, the optimization computing server 100 executes step S40, which uses a comprehensive utility function and a seed adsorption strategy to iteratively filter orders from the copper busbar processing order set to build a pool of orders to be processed.

[0045] The optimized computing server 100 performs the selection of seed orders, a process that employs a seed agglomeration strategy. The seed agglomeration strategy is a heuristic construction method whose key logic is to first identify a core object with the highest processing priority (i.e., the seed order), and then aggregate objects from the remaining objects that are geometrically complementary to the core object while also considering time urgency.

[0046] To determine the seed order, the computation server was optimized to traverse the copper busbar processing order set 100 times. For each of the unprocessed orders, calculate the total number of orders. Dynamic urgency index The calculation formula is as follows: ; in, As order priority weight; This refers to the order arrival time; The current system time; This represents the duration an order remains in the buffer. and For the preset weighting coefficients (e.g., take...) , ) Optimize computing server selection 100 The order with the largest value is used as the seed order. And move it to the newly created pool of pending orders. .

[0047] Simultaneously, the system optimizes the initialization status parameters of the pending order pool on computing server 100. The system also optimizes the effective virtual fill degree spectrum corresponding to the seed orders. The mixed fill spectrum assigned to the order pool to be processed ,Right now This assignment operation serves to establish the initial baseline for iterative calculations. Mixed filling degree spectrum. This refers to the length distribution state when only seed orders are included. This refers to the seed order generated in step S30. The corresponding effective virtual fill spectrum.

[0048] In addition, the system calculates seed orders. The sum of the effective occupied lengths of all parts in the process is denoted as . (That is, the cumulative effective occupied length of all parts contained in the seed order), and assign this value to the cumulative effective occupied length of the order pool to be processed. ,Right now This step establishes the initial weight state for iterative calculations. The cumulative effective occupied length refers to the current pool of orders awaiting processing. The scalar sum of the effective occupied lengths of all parts contained in all orders already accepted (including seed orders and subsequent orders).

[0049] After the seed orders are determined, the optimization computing server 100 enters the iterative filtering phase, selecting from the remaining order set. Find the most suitable candidate order to be added to the current pending order pool. For each candidate order The optimized computing server 100 calculates its comprehensive utility value based on the comprehensive utility function. The comprehensive utility function is a mathematical model used to evaluate the degree of matching between candidate orders and the current order pool. This mathematical model is composed of a weighted average of spectral complementarity and urgency indices.

[0050] Specifically, the optimized computing server 100 first calculates the candidate orders. Effective virtual fill spectrum Mixed fill spectrum with the current pending order pool Spectral complementarity index The calculation formula is as follows: ; in, for In the Component values ​​of a discrete interval; for In the The component values ​​of each discrete interval. This index uses the complementary form of cosine similarity to characterize geometric orthogonality; the larger the value, the less overlap there is in the length distribution, and the stronger the complementarity.

[0051] Subsequently, the optimized computing server 100, combined with the urgency index (i.e., the dynamic urgency index), uses a comprehensive utility function to calculate the comprehensive utility value. : ; in, For candidate orders The dynamic urgency index is calculated using the same logic as the seed order selection process mentioned above. This is the maximum value of the urgency index in the current set of remaining orders, used for normalization. and The preset adjustment weight factor refers to the numerical coefficient used to balance the weights of different optimization objectives (e.g., , ),in Used to adjust the degree of influence of spectral complementarity on decision-making. This is used to adjust the degree of influence of production urgency on decision-making; the sum of the two is usually set to 1.

[0052] Optimize computing servers to filter out those with the highest overall utility value. Candidate orders And mark that specific candidate order as the target order. Then remove it from the remaining order set Transferred to the pending order pool In the middle. Optimize computing server 100 according to target orders. Feature update of the mixed fill spectrum of the pending order pool The update logic uses a weighted average method based on the total effective length: ; in, The sum of the effective occupied lengths of all parts in the pending order pool before the update (i.e., the cumulative workload of existing orders in the pool) is used as the weight of the original spectral features. For target orders The total effective occupied length is used as the weight of the newly added spectral features; The mixed filling degree spectrum before the update; The effective virtual fill spectrum for the target order.

[0053] After the spectrum update is completed, the optimization computing server 100 updates the cumulative effective occupied length of the pending order pool. That is, execution This is to reflect the expansion of the order pool and to provide a new weighting benchmark for the next iteration.

[0054] The optimized computing server 100 repeatedly executes the steps described above: calculating the overall utility value, selecting the target order, moving it to the pending order pool, and updating the mixed fill degree spectrum, until a preset termination condition is met. The termination condition is: the cumulative effective occupied length in the pending order pool. Exceeding the preset batch capacity threshold Or the number of orders in the pending order pool has reached the preset limit. Preset batch capacity threshold This refers to the maximum allowed total length of material to be processed in a single nesting calculation or production task; preset upper limit. This refers to the maximum number of independent orders included in a single sorting task. When the termination condition is triggered, the optimization calculation server locks the current pool of orders awaiting processing. .

[0055] See attached document Figures 1-3 After constructing the pool of orders to be processed and locking the set of orders in the pool, the optimization computing server 100 executes step S50, which is to extract the synthetic spectrum topology features corresponding to the pool of orders to be processed and adaptively match the nesting solver according to the synthetic spectrum topology features.

[0056] The optimized computing server 100 first determines the composite spectrum corresponding to the pool of orders to be processed. The composite spectrum refers to the spectrum that can characterize the entire pool of orders to be processed. The vector representing the overall distribution density of all parts across the discrete interval of the standard raw material length. In this embodiment, the system directly reads the mixed fill degree spectrum of the order pool to be processed, which was finally updated in step S40. Use it as a synthetic spectrum ,Right now: .

[0057] To quantify the waveform morphology of the synthesized spectrum, the optimized computing server 100 extracted two key dimensions of the synthesized spectrum's topological features: spectral entropy value features. Spectral peak characteristics .

[0058] Optimize computing server 100 to calculate spectral entropy characteristics of synthesized spectra Spectral entropy characteristics This value is used to quantify the discreteness and disorder of part length requirements in the order pool. A higher spectral entropy value indicates a more uniform and dispersed distribution of part length specifications within the order, resulting in extremely high freedom in layout combinations. This increases the risk of combinatorial explosion and consequently increases the problem's solution complexity. The calculation formula is as follows: ; in, For the synthetic spectrum In the Component values ​​of a discrete interval; This represents the total number of discrete intervals determined in step S30. To prevent logarithmic operations from dealing with extremely small positive numbers (e.g., taking 10) -6 ); Represents the natural logarithm operation.

[0059] Optimize computing server 100 to calculate the spectral kurtosis characteristics of synthesized spectra Spectral peak characteristics This is used to quantify the central tendency of the dominant length specifications in the pending order pool. A higher kurtosis value indicates that one or more specific lengths of parts account for the main demand, the layout pattern is relatively simple and regular, and it is easy to fill using simple rules. The calculation formula is as follows: ; in, This indicates the maximum value operation, which extracts the maximum component value in the synthesized spectral vector.

[0060] After extracting the topological features of the synthesized spectrum, the optimization computing server 100 adaptively matches the nesting solver based on these features. The system has a pre-built nesting solver library, which is a collection of pre-written algorithm implementations for one-dimensional nesting problems stored in the server's memory. This collection contains at least two types of algorithm logic with different computational characteristics and optimization capabilities.

[0061] In this embodiment, the nesting solver library stores at least two types of nesting solvers: Constructive heuristic solvers This solver uses deterministic construction rules (such as the best-fit algorithm and the bottom-left corner filling algorithm) for fast sorting. Its characteristics are fast calculation speed, but relatively weak global optimization ability. It is suitable for simple scenarios with concentrated order specifications and obvious patterns.

[0062] Metaheuristic Iterative Solver This solver uses a population- or neighborhood-based random search mechanism (such as genetic algorithm, simulated annealing algorithm, particle swarm optimization, etc.) for iterative optimization. Its characteristics are high computational cost, but it has the ability to escape local optima and is suitable for difficult scenarios with dispersed order specifications and complex combinations.

[0063] To achieve adaptive matching, the optimization computing server 100 is based on the spectral entropy value feature. Spectral peak characteristics Construct a complexity discrimination function The calculation formula is as follows: ; in, This is the theoretical maximum entropy value, used to normalize the entropy value to the [0, 1] interval; and The preset feature weight coefficients (e.g., take...) , ), used to adjust the weights of "dispersion" and "concentration" on the algorithm selection. The larger the value, the higher the sorting complexity of the current task.

[0064] Optimize computing server 100 to calculate the results Switching to the preset complexity threshold For comparison, the preset complexity switching threshold is a pre-set dimensionless scalar value (e.g., set to 0.4). This value serves as a dividing line to distinguish whether the sorting task is dominated by simple rules or complex combinations. Its specific value can be obtained through training statistics of historical order data.

[0065] Optimize computing server 100 to execute the following adaptive matching logic: like This indicates that the length distribution of the current order pool exhibits a high-entropy, low-peak discrete characteristic, leading the system to determine it as a complex sorting task and automatically select a metaheuristic iterative solver. As the sorting execution engine for the current batch.

[0066] like This indicates that the length distribution of the current order pool exhibits a concentrated characteristic of low-entropy peaks, leading the system to determine it as a simple sorting task and automatically select a constructivist heuristic solver. As the sorting execution engine for the current batch.

[0067] Through the above steps, the optimization computing server 100 completes the algorithm adaptation for the order pool to be processed, and calls the selected nesting solver to perform nesting calculations on the parts in the order pool to be processed, and outputs the nesting scheme.

[0068] See attached document Figure 1 and Figure 3 After the algorithm configuration is completed using the adaptive matching nesting solver, the optimization computing server 100 executes step S60, which is to use the nesting solver to generate a cutting scheme for the order pool to be processed, and to perform a pre-execution verification on the cutting scheme. After the pre-execution verification passes, the processing instruction is generated.

[0069] The optimization computing server 100 first checks the current availability status of the device obtained in step S10 above. Perform a judgment. If The system triggers an alarm and terminates the process; if The optimized computing server 100 calls the adaptive matching selected nesting solver (i.e., the constructive heuristic solver) from step S50 above. or metaheuristic iterative solver Then, perform the subsequent nesting calculation steps.

[0070] Optimize computing server 100 to process the order pool All parts are used as layout objects, based on standard raw material length. To constrain the boundaries, perform nesting calculations and output the cutting scheme. Cutting scheme It refers to including A set of sub-solutions Each of the sub-schemes The cutting layout corresponding to a standard raw material is recorded in detail, including the part numbers arranged on the raw material, the starting coordinates of the cutting of each part on the raw material, the ending coordinates of the cutting, and the length of the remaining waste material after cutting.

[0071] After generating the cutting plan, the optimization computing server 100 performs a pre-execution verification of the cutting plan. The optimization computing server 100 calculates the current cutting plan. Actual material utilization rate The calculation formula is as follows: ; in, This represents the sum of the effective occupied lengths of all parts contained in the pending order pool (i.e., the cumulative effective occupied length defined in step S40 above). ); The effective length occupied by the part; This represents the total number of standard raw materials used in the cutting scheme. This refers to the standard length of the raw material.

[0072] The optimized computing server 100 will calculate the actual material utilization rate. Compared with the preset utilization qualification threshold A comparison is made. The preset utilization rate qualification threshold is used. This refers to the minimum material utilization standard allowed by the production process (e.g., set at 92%).

[0073] like This indicates that the order combinations in the current pending order pool cannot achieve compact filling in geometric space, and the pre-execution verification fails. The optimized computing server 100 triggers the nesting rollback and reassembly mechanism. The optimized computing server 100 calculates each order in the pending order pool. Outlier contribution value The outlier contribution value is used to assess the degree to which the length characteristic of a single order deviates from the overall distribution, thereby identifying outlier orders that lead to inefficient sorting. The calculation formula is as follows: ; in, For orders The arithmetic mean of the lengths of all the parts included; This is the arithmetic mean of the lengths of all parts in the current pool of pending orders; This represents the standard deviation of the lengths of all parts in the current pool of pending orders. This indicates the absolute value operation. Optimized computing server 100 will have the maximum... Value orders are from the pending order pool Remove it from the list and return it to the remaining order collection. Subsequently, the optimization computing server 100 re-invokes the nesting solver to generate new cutting schemes for the updated pending order pool, and performs pre-execution verification again until the verification passes or the preset retry limit is reached.

[0074] like This indicates that the current cutting scheme meets the economic requirements, and the pre-execution verification has passed. The optimization calculation server 100 generates processing instructions. The optimization calculation server 100 then applies the cutting scheme... The geometric data is converted into control codes that the device can recognize. The system analyzes each sub-scheme. Based on the coordinate data, processing instructions are generated according to the motion control logic of the cutting equipment. Processing instructions This includes instructions for controlling rapid tool positioning (such as GOO), instructions for controlling linear cutting feed (such as GO1), and auxiliary instructions for controlling coolant switching and clamp tightness (such as M-codes). The optimization calculation server 100 sends the generated machining instructions to the copper busbar cutting equipment, driving the equipment to perform the physical cutting operation. The above-mentioned layout calculation and instruction generation process can be implemented by those skilled in the art using computer programming languages ​​combined with numerical calculation libraries; this is a conventional technical method in the field of industrial control and will not be elaborated further here.

[0075] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing material utilization and merging orders during copper busbar processing, characterized in that, Includes the following steps: Obtain the copper busbar processing order set and the status parameters of the cutting equipment in the workshop; Based on the status parameters of the cutting equipment, the parts included in the copper busbar processing order set are preprocessed to calculate the effective occupied length of the parts; The effective occupied length is mapped to a discrete interval of the standard raw material length to generate an effective virtual fill degree spectrum for each order; Using a comprehensive utility function and a seed adsorption strategy, orders are iteratively filtered from the copper busbar processing order set to construct a pool of orders to be processed; Extract the synthetic spectrum topology features corresponding to the order pool to be processed, and adaptively match the sorting solver according to the synthetic spectrum topology features; The nesting solver is used to generate a cutting scheme for the pool of orders to be processed, and the cutting scheme is pre-executed and verified. After the pre-execution verification is passed, a processing instruction is generated.

2. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 1, characterized in that, The step of performing feature preprocessing on the parts included in the copper busbar processing order set based on the status parameters of the cutting equipment, and calculating the effective occupied length of the parts, includes: The digital geometric description file of the part is parsed to identify process features, and the physical distance from the edge boundary of the process feature to the nearest end face of the part is calculated as the feature distance; The minimum processable length of the cutting equipment in the status parameters is obtained as the safety clamping threshold, and the feature distance is compared with the safety clamping threshold to determine the feature safety margin. The real-time kerf width of the cutting equipment is obtained from the status parameters of the cutting equipment. The effective occupied length of the part is calculated by linearly summing the physical geometric length of the part, the characteristic safety margin, and the real-time kerf width of the equipment.

3. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 1, characterized in that, The step of mapping the effective occupied length to a discrete interval of the standard raw material length to generate an effective virtual fill degree spectrum for each order includes: The standard raw material length is divided into multiple equally spaced discrete intervals according to a preset discretization resolution; Calculate the index of the discrete interval to which the effective occupied length of the part belongs, and accumulate the effective occupied length into the components of the corresponding original length distribution vector; The original length distribution vector is normalized using the L1 norm criterion to generate the effective virtual fill degree spectrum in the form of a probability density function.

4. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 1, characterized in that, The step of iteratively filtering orders from the copper busbar processing order set to construct a pool of orders to be processed using a comprehensive utility function and a seed adsorption strategy includes: The dynamic urgency index of each order in the copper busbar processing order set is calculated based on the order priority weight and the order's dwell time in the buffer. The order with the highest dynamic urgency index is selected as the seed order and moved into the newly created pool of pending orders; The effective virtual fill spectrum corresponding to the seed order is assigned to the mixed fill spectrum of the order pool to be processed, and the sum of the effective occupied lengths of all parts contained in the seed order is assigned to the cumulative effective occupied length of the order pool to be processed.

5. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 4, characterized in that, The step of iteratively filtering orders from the copper busbar processing order set to construct a pool of orders to be processed using a comprehensive utility function and a seed adsorption strategy further includes: Calculate the spectral complementarity index between the effective virtual fill degree spectrum of candidate orders in the remaining order set and the mixed fill degree spectrum of the order pool to be processed; The comprehensive utility value of the candidate order is calculated based on the weighted sum of the spectral complementarity index and the normalized dynamic urgency index. The candidate order with the largest comprehensive utility value is selected as the target order and moved into the pending order pool. The mixed fill degree spectrum and the cumulative effective occupied length of the pending order pool are updated based on the characteristics of the target order.

6. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 1, characterized in that, The step of extracting the synthetic spectrum topological features corresponding to the order pool to be processed includes: The mixed fill degree spectrum of the order pool to be processed is determined as the composite spectrum; Calculate the spectral entropy value feature of the synthesized spectrum, which is used to quantify the discrete disorder of the part length requirements in the order pool to be processed; Calculate the kurtosis feature of the synthesized spectrum. The kurtosis feature is used to quantify the central tendency of the dominant length specifications in the order pool to be processed. The kurtosis feature is the maximum value among the vector components of the synthesized spectrum.

7. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 6, characterized in that, The steps of the adaptive matching and sorting solver based on the topological features of the synthetic spectrum include: A complexity discrimination function is constructed by calculating the difference between the weighted normalized spectral entropy feature and the weighted spectral kurtosis feature. The value of the complexity discrimination function is compared with the preset complexity switching threshold; If the value is greater than the complexity switching threshold, the metaheuristic iterative solver is selected as the sorting solver. If the value is less than or equal to the complexity switching threshold, the constructive heuristic solver is selected as the nesting solver.

8. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 1, characterized in that, The steps of generating cutting schemes for the order pool using the nesting solver and performing pre-execution verification on the cutting schemes include: The current availability status of the cutting equipment is determined in the status parameters. If the current availability status indicates that the equipment is normal, the nesting solver is invoked. Using all parts in the order pool to be processed as the nesting objects and the standard raw material length as the constraint boundary, the nesting solver is run to output the cutting scheme; Calculate the actual material utilization rate of the cutting scheme, which is the ratio of the sum of the effective occupied lengths of all parts in the order pool to the total length of the standard raw materials used in the cutting scheme; The actual material utilization rate is compared with a preset utilization rate qualification threshold.

9. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 8, characterized in that, The step of generating processing instructions after the pre-execution verification passes includes: If the actual material utilization rate is greater than or equal to the qualified utilization rate threshold, the pre-execution verification is deemed to have passed. The geometric coordinate data in the cutting scheme is analyzed, and the machining instructions, including tool positioning instructions, linear cutting feed instructions and auxiliary control instructions, are generated according to the motion control logic of the cutting equipment. The processing instructions are sent to the copper busbar cutting equipment to drive the copper busbar cutting equipment to perform physical cutting operations.

10. The method for optimizing material utilization and merging orders during copper busbar processing according to claim 8, characterized in that, It also includes performing the following steps when the pre-execution check fails: If the actual material utilization rate is less than the qualified utilization rate threshold, calculate the outlier contribution value of each order in the order pool to be processed; The order with the largest outlier contribution value is removed from the pool of pending orders, and the nesting solver is called again for the updated pool of pending orders.

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