Intelligent scheduling method and device for copper foil production, computer equipment and storage medium

Abstract

The invention relates to the technical field of copper foil production, and provides an intelligent scheduling method and device for copper foil production, computer equipment and a storage medium. An initial intelligent scheduling strategy is simulated and generated based on production data of each piece of copper foil production equipment on a copper foil production line, and primary simulation optimization is performed by using a pre-constructed optimization model; calculating production data and product images before and after optimization to obtain an optimization stopping factor value; when the optimization stopping factor value is greater than a threshold value, performing simulation optimization again by using a pre-constructed optimization model; when the optimization stopping factor value is smaller than a threshold value, iteration simulation optimization is stopped, and a target intelligent scheduling strategy is obtained; and generating an intelligent scheduling instruction based on the target intelligent scheduling strategy and sending the intelligent scheduling instruction to the corresponding copper foil production equipment in real time so as to realize intelligent scheduling of copper foil production. According to the invention, the operation sequence and the operation time of each production device can be automatically adjusted, and intelligent and automatic scheduling of copper foil production is realized.

Description

Technical Field

[0001] The present application relates to the technical field of copper foil production, and in particular to a method, device, computer equipment and storage medium for intelligent scheduling of copper foil production. Background Art

[0002] In the PCB manufacturing process, multiple production equipment (such as laser drilling machines, electroplating lines, etching machines, etc.) need to work closely together to ensure the continuity of the production line and product quality. However, due to factors such as equipment performance differences, maintenance requirements and raw material supply, production time differences often exist, affecting overall production efficiency. Traditional monitoring methods rely on manual inspections or simple sensor feedback, which makes it difficult to accurately grasp the status of equipment in real time and make effective scheduling. Summary of the invention

[0003] Based on this, an intelligent scheduling method, device, computer equipment and storage medium for copper foil production are proposed, aiming to automatically adjust the operation sequence and operation time of each production equipment through the production data of each production equipment, so as to realize intelligent and automated scheduling of copper foil production.

[0004] A first aspect of the present application provides a method for intelligent scheduling of copper foil production, the method comprising:

[0005] Acquire first production data of each copper foil production equipment on the copper foil production line and a first product image of the produced copper foil;

[0006] Simulating and generating an initial intelligent scheduling strategy according to the first production data;

[0007] Using a pre-built optimization model to perform a first simulation optimization on the initial intelligent scheduling strategy, wherein the optimization model uses the equipment operation sequence and operation time as decision variables, and aims to maximize production efficiency, minimize energy consumption, and maximize equipment utilization under multiple preset constraints;

[0008] Acquire the second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and the second product image of the copper foil produced by simulation;

[0009] Obtaining a stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image;

[0010] Determine whether the stop optimization factor value is less than a preset stop optimization factor threshold;

[0011] When the stop optimization factor value is greater than the preset stop optimization factor threshold, the initial intelligent scheduling strategy is simulated and optimized again using the pre-built optimization model;

[0012] When the stop optimization factor value is less than the preset stop optimization factor threshold, the iterative simulation optimization is stopped to obtain the target intelligent scheduling strategy;

[0013] Based on the target intelligent scheduling strategy, an intelligent scheduling instruction is generated, and the instruction is sent to the corresponding copper foil production equipment in real time to realize intelligent scheduling of copper foil production.

[0014] Optionally, the optimization model is expressed as follows:

[0015] F(x) = α·Production efficiency (x) - β·Energy consumption (x) + γ·Equipment utilization (x);

[0016] Wherein, α, β and γ are weight coefficients; x is the vector of decision variables [x1, x2, ..., xn], and xi represents the operation sequence and operation time of the i-th copper foil production equipment;

[0017] The multiple constraints are:

[0018] g1(x)=device dependency constraint(x)≤0;

[0019] g2(x) = time window constraint (x) ≤ 0;

[0020] g3(x) = resource limit constraint (x) ≤ 0;

[0021] h(x)=capacity demand constraint(x)=0.

[0022] Optionally, obtaining a stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image includes:

[0023] Evaluate the impact of the simulated optimized scheduling strategy on the production environment according to the first production data and the second production data;

[0024] Evaluate the impact of the simulated optimized scheduling strategy on product quality according to the first product image and the second product image;

[0025] Using a preset stop optimization factor calculation model, the stop optimization factor value is calculated based on the impact degree of the production environment and the impact degree of the product quality;

[0026] The preset stop optimization factor calculation model is expressed as follows:

[0027] F=1 / (1+a×exp(-b×ΔQ)×(ΔI)^c),

[0028] ΔQ is the impact of product quality, ΔI is the impact of production environment, and a, b, and c are preset adjustment coefficients.

[0029] Optionally, evaluating the impact of the scheduling strategy after simulation optimization on the production environment according to the first production data and the second production data includes:

[0030] Obtaining a first production efficiency, a first energy consumption and a first equipment utilization rate based on the first production data;

[0031] Obtaining a second production efficiency, a second energy consumption and a second equipment utilization rate based on the second production data;

[0032] Calculate a production efficiency change based on the first production efficiency and the second production efficiency;

[0033] Calculate the energy consumption change based on the first energy consumption and the second energy consumption;

[0034] Calculate the equipment utilization change based on the first equipment utilization and the second equipment utilization;

[0035] The degree of influence of the scheduling strategy after simulation optimization on the production environment is evaluated based on the change in production efficiency, the change in energy consumption and the change in equipment utilization.

[0036] Optionally, the simulating and generating an initial intelligent scheduling strategy according to the first production data includes:

[0037] Generate an equipment relationship map according to the layout diagram of the copper foil production line and the dependency relationship between various copper foil production equipment;

[0038] Selecting a scheduling rule from a rule library based on the first production data and the equipment relationship map;

[0039] The initial intelligent scheduling strategy is simulated and generated based on the selected scheduling rules.

[0040] Optionally, the method further includes:

[0041] A copper foil production prediction model is obtained based on historical production data and historical copper foil production data;

[0042] Using the copper foil production prediction model to predict future copper foil production data based on real-time production data;

[0043] Production resources are scheduled based on the future copper foil output data.

[0044] Optionally, the method further includes:

[0045] Obtaining the operating parameter values ​​of each copper foil production equipment and the corresponding parameter threshold range;

[0046] comparing the operating parameter value with a corresponding parameter threshold range;

[0047] When the operating parameter value exceeds the corresponding parameter threshold range, an alarm instruction carrying the copper foil production equipment identification is generated.

[0048] A second aspect of the present application provides a copper foil production intelligent scheduling device, the device comprising:

[0049] A first acquisition module is used to acquire first production data of each copper foil production equipment on the copper foil production line and a first product image of the produced copper foil;

[0050] A strategy generation module, used to simulate and generate an initial intelligent scheduling strategy according to the first production data;

[0051] A simulation optimization module, used to perform a preliminary simulation optimization on the initial intelligent scheduling strategy using a pre-built optimization model, wherein the optimization model uses the equipment operation sequence and operation time as decision variables, and aims to maximize production efficiency, minimize energy consumption, and maximize equipment utilization under multiple preset constraints;

[0052] A second acquisition module is used to acquire second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and a second product image of the copper foil produced in simulation;

[0053] a factor calculation module, configured to obtain a stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image;

[0054] A threshold judgment module, for judging whether the stop optimization factor value is less than a preset stop optimization factor threshold;

[0055] The simulation optimization module is further used to simulate and optimize the initial intelligent scheduling strategy again using a pre-built optimization model when the stop optimization factor value is greater than the preset stop optimization factor threshold;

[0056] A target generation module, used to stop iterative simulation optimization and obtain a target intelligent scheduling strategy when the stop optimization factor value is less than the preset stop optimization factor threshold;

[0057] The intelligent scheduling module is used to generate intelligent scheduling instructions based on the target intelligent scheduling strategy, and send the instructions to the corresponding copper foil production equipment in real time to achieve intelligent scheduling of copper foil production.

[0058] The third aspect of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent scheduling method for copper foil production when executing the computer program.

[0059] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the intelligent scheduling method for copper foil production are implemented.

[0060] The copper foil production intelligent scheduling method provided in the embodiment of the present application obtains the first production data of each copper foil production equipment on the copper foil production line and the first product image of the copper foil produced, and generates an initial intelligent scheduling strategy based on the first production data, which provides basic data support for subsequent simulation optimization and ensures the pertinence and accuracy of the scheduling strategy. The initial intelligent scheduling strategy is simulated and optimized using a pre-built optimization model for the first time, with the goal of maximizing production efficiency, minimizing energy consumption and maximizing equipment utilization, laying the foundation for subsequent optimization iterations. The second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and the second product image of the simulated copper foil are obtained, and the stop optimization factor value is obtained based on the first production data, the second production data, the first product image and the second product image, and the optimization effect of the current scheduling strategy is quantitatively evaluated. By comparing the stop optimization factor value with the preset stop optimization factor threshold, it is decided whether to continue iterative optimization. If the stop optimization factor value is less than the threshold, it indicates that the optimization effect has reached or is close to the expected target (i.e.), and no additional iteration is required, thereby saving computing resources and time. On the contrary, if the stop optimization factor value is greater than the threshold, indicating that the optimization effect still has room for improvement, the iterative optimization will continue to be performed to further approach the optimal solution. Finally, the target intelligent scheduling strategy is obtained when the iteration is stopped. Through iterative optimization, the optimal scheduling strategy is gradually approached, and the comprehensive optimization of production efficiency, energy consumption and equipment utilization is achieved. Based on the target intelligent scheduling strategy, intelligent scheduling instructions are generated, and the instructions are sent to the corresponding copper foil production equipment in real time, so as to realize the precise control of the copper foil production process, ensure the effective execution of the scheduling strategy, and improve production efficiency and product quality. This application realizes the precise scheduling of the copper foil production process by integrating production data, product images and optimization model iteration, which not only improves production efficiency and reduces energy consumption, but also ensures the stability of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 It is a flow chart of the intelligent scheduling method for copper foil production provided in an embodiment of the present application.

[0063] Figure 2 It is a functional module diagram of the intelligent scheduling device for copper foil production provided in an embodiment of the present application.

[0064] Figure 3 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] Figure 1 A schematic flow chart of a method for intelligent scheduling of copper foil production provided in an embodiment of the present application, wherein the method for intelligent scheduling of copper foil production comprises the following steps.

[0067] S11, acquiring first production data of each copper foil production equipment on a copper foil production line and a first product image of the produced copper foil.

[0068] Various production equipment will be used in the copper foil production process, including but not limited to copper melting equipment, foil raw machine, surface treatment machine, slitting machine, etc.

[0069] The first production data may refer to the initial state data of the copper foil production equipment at the beginning of production or in the current production cycle, including but not limited to the equipment's operating status data (such as startup, shutdown, maintenance, etc.), production parameter data (such as temperature, pressure, speed, etc.), and production progress data (such as the number, type, priority, completion progress, etc. of the current production task). Sensors and monitoring equipment are installed on the copper foil production line. These sensors and monitoring equipment can monitor the production data of the production equipment in real time. Through industrial control systems (such as PLC, DCS, etc.) or Internet of Things technology, the data of sensors and monitoring equipment are integrated into a unified monitoring platform. The monitoring platform can display the production data of each copper foil production equipment in real time.

[0070] The first product image refers to the image of the first batch of copper foil products produced by the copper foil production equipment. Online inspection equipment such as high-resolution industrial cameras, laser imaging cameras or machine vision systems are installed at the end or key positions of the copper foil production line to capture images of copper foil products in real time.

[0071] S12: Generate an initial intelligent scheduling strategy based on the first production data simulation.

[0072] After obtaining the first production data of each production equipment, a preliminary and intelligent production scheduling strategy is generated based on the first production data with the help of simulation or emulation technology. This production scheduling strategy is intended to guide how each production equipment on the production line can operate optimally to achieve the established production goals. By simulating and generating initial scheduling strategies, these strategies can be preliminarily verified and evaluated without interfering with actual production, thereby effectively avoiding potential risks.

[0073] In an optional implementation, the simulating and generating an initial intelligent scheduling strategy according to the first production data includes:

[0074] Generate an equipment relationship map according to the layout diagram of the copper foil production line and the dependency relationship between various copper foil production equipment;

[0075] Selecting a scheduling rule from a rule library based on the first production data and the equipment relationship map;

[0076] The initial intelligent scheduling strategy is simulated and generated based on the selected scheduling rules.

[0077] Obtain the layout diagram of the copper foil production line through on-site investigation or consulting design documents. The layout diagram depicts the location, connection method and material flow path of all equipment on the production line, and is usually used to represent the physical layout of the production line and the relative position of the equipment.

[0078] The dependencies between copper foil production equipment can be drawn by analyzing the production process, equipment functions, and material flow paths. The dependencies between copper foil production equipment refer to the functional or physical interdependencies between the equipment on the production line. For example, a certain equipment may require materials or semi-finished products provided by another equipment as input, or the output of a certain equipment needs to be further processed by another equipment.

[0079] Based on the layout diagram and device dependencies, a device relationship graph is generated using a graphical modeling tool (such as Graphviz, Neo4j, etc.). In the device relationship graph, each device is assigned a unique identifier and is connected to the devices it depends on through edges or arrows. That is, a device is represented as a node in the relationship graph, and the dependencies between devices are represented as edges or arrows connecting the nodes.

[0080] The rule base is a database that stores a variety of scheduling rules and strategies. These rules may be formulated based on various factors such as equipment production capacity, production priority, production cycle, material flow direction, etc., in the form of conditional statements, logical expressions or algorithms, and are used to guide production line scheduling decisions.

[0081] According to the current production data (such as equipment status, production progress, etc.) and equipment relationship map, select the applicable scheduling rules from the rule library. Use simulation software or algorithms to simulate the production line and generate the initial intelligent scheduling strategy based on the selected scheduling rules.

[0082] For example, suppose a copper foil production enterprise has a production line including multiple electrolytic equipment, rolling equipment, surface treatment equipment, cutting equipment and testing equipment. These equipment are interdependent in the production process and jointly complete the manufacturing process from electrolytic copper to copper foil. Assume that the current first production data is: electrolytic equipment A is running, but the production capacity is insufficient; rolling equipment B is shut down due to maintenance; other equipment is idle. Based on the current production data and equipment relationship map, applicable scheduling rules can be selected from the rule base. For example, you can choose to give priority to idle equipment and avoid overload scheduling rules. Through simulation and calculation, an initial intelligent scheduling strategy can be generated. In this strategy, electrolytic equipment A may continue to operate, but its output will be limited to within its production capacity; rolling equipment B will be given priority in production tasks after maintenance; other idle equipment will be arranged to handle subsequent production tasks.

[0083] The above optional implementation method, since the equipment relationship map can accurately depict the physical layout and functional dependencies between the various devices in the copper foil production line, enables production managers to intuitively understand the overall structure of the production line and the interaction between the equipment, which helps to fully consider the coordination and constraints between the equipment when formulating the scheduling strategy, and ensure the feasibility and effectiveness of the scheduling plan. By combining the first production data and the equipment relationship map, the most appropriate scheduling rules can be intelligently selected from the rule base, making the formulation of the scheduling strategy more scientific, reasonable and efficient. Based on the simulation of the selected scheduling rules, the initial intelligent scheduling strategy can dynamically adjust the distribution of production tasks among the equipment to ensure the efficient operation of the production process.

[0084] S13, using a pre-built optimization model to perform a first simulation optimization on the initial intelligent scheduling strategy.

[0085] Although the initial intelligent scheduling strategy is generated based on the current production data and equipment relationships, it may not fully consider all possible variables and constraints, such as uneven equipment utilization, accumulated production tasks, and excessive production costs. These problems may cause reduced production efficiency, waste of resources, or product quality problems in actual production. Therefore, the initial intelligent scheduling strategy generated by simulation is not directly applied to actual production, but is first simulated and optimized through a pre-designed optimization model.

[0086] In the intelligent scheduling system for copper foil production, the optimization model uses the equipment operation sequence and operation time as decision variables, and aims to maximize production efficiency, minimize energy consumption, and maximize equipment utilization under multiple preset constraints. The optimization model will serve as the basis for the formulation of subsequent scheduling strategies.

[0087] In an optional embodiment, the optimization model is expressed as follows:

[0088] F(x)=α·production efficiency(x)-β·energy consumption(x)+γ·equipment utilization(x).

[0089] The optimization model is represented by the function F(x), which integrates the three objectives of production efficiency, energy consumption and equipment utilization, and balances the importance of the three by adjusting the weight coefficients α, β and γ. Among them, x is the vector of decision variables [x1, x2, ..., xn], which includes the operation sequence and operation time of all equipment on the copper foil production line, and xi represents the operation sequence and operation time of the i-th copper foil production equipment.

[0090] Production efficiency (x) measures the overall output efficiency of the equipment on the production line under a given operation sequence and time. Energy consumption (x) reflects the energy consumption in the production process and is an important indicator for evaluating production costs and environmental impacts. Equipment utilization (x) reflects the ratio of the actual working time of the equipment to the total available time and is the key to evaluating equipment efficiency and production flexibility.

[0091] The optimization model includes multiple constraints to ensure that the generated scheduling strategy is consistent with production reality and meets the company's production goals.

[0092] In an optional implementation, the multiple constraints are:

[0093] g1(x)=device dependency constraint(x)≤0;

[0094] g2(x) = time window constraint (x) ≤ 0;

[0095] g3(x) = resource limit constraint (x) ≤ 0;

[0096] h(x)=capacity demand constraint(x)=0.

[0097] The g1(x) constraint ensures that the order and timing of operations between devices conform to the physical and functional dependencies between them, avoiding conflicts and delays in the production process.

[0098] The g2(x) constraint specifies the time frame for equipment operation, ensuring that production tasks are completed within a predetermined time window to meet order delivery and customer demand.

[0099] The g3(x) constraint takes into account the resource limitations in the production process, such as raw material supply, human resources, etc., to ensure that the scheduling strategy is within the feasible range of resources.

[0100] The h(x) constraint ensures that the total output of the production line can meet the company's production capacity requirements and avoids overproduction or underproduction.

[0101] The optimization model not only provides the basis for the initial simulation optimization of the initial intelligent scheduling strategy, but also provides guidance for the subsequent scheduling strategy formulation. By continuously iterating and adjusting the parameters and constraints in the model, the optimal scheduling strategy can be found.

[0102] In order to more specifically describe the specific process of optimizing the model based on F(x) and several constraints, the following is an actual example of a copper foil production line. Assume that a copper foil production line contains three production equipment: equipment A, equipment B, and equipment C. These equipment need to follow a certain operation sequence and time when producing copper foil, and are constrained by equipment dependencies, time windows, resource constraints, and production capacity requirements.

[0103] Equipment A: responsible for the initial processing of copper foil, each operation takes 2 hours. Equipment B: responsible for the further processing of copper foil, each operation takes 3 hours, and must be operated after equipment A. Equipment C: responsible for the final shaping of copper foil, each operation takes 1 hour, and must be operated after equipment B. Weight coefficient: α = 0.5 (weight of production efficiency), β = 0.3 (weight of energy consumption), γ = 0.2 (weight of equipment utilization). Preset stop optimization factor threshold: 0.01.

[0104] Assume that the initial intelligent scheduling strategy is: equipment A operates in 0-2 hours, equipment B operates in 2-5 hours, and equipment C operates in 5-6 hours. According to the F(x) formula, calculate the production efficiency, energy consumption, and equipment utilization under the current scheduling strategy, and sum them up to get the value of F(x). Equipment dependency constraint: Ensure that equipment B operates after equipment A, and equipment C operates after equipment B. Time window constraint: Ensure that the operating time of each device does not exceed its maximum allowed operating time. Resource limitation constraint: Ensure that the resources on the production line (such as raw materials, energy, etc.) are sufficient to support the current scheduling strategy. Capacity demand constraint: Ensure that the capacity under the current scheduling strategy meets production needs.

[0105] The simulation system is used to obtain production data and product images after the initial simulation optimization. The stop optimization factor value is calculated based on the first production data, the second production data, the first product image, and the second product image. If the stop optimization factor value is greater than the preset stop optimization factor threshold, the optimization model is continued to be used for simulation optimization. The equipment operation sequence and operation time are adjusted, the value of F(x) is recalculated, and the constraint conditions are checked.

[0106] Repeat the above steps until the stop optimization factor value is less than the preset stop optimization factor threshold, and obtain the target intelligent scheduling strategy.

[0107] After multiple iterations of simulation and optimization, the target intelligent scheduling strategy is: Equipment A operates at 0-2 hours, Equipment B operates at 2.5-5.5 hours (the start time is slightly adjusted to optimize resource utilization), and Equipment C operates at 5.75-6.75 hours (also to optimize resource utilization and capacity requirements). The target intelligent scheduling strategy maximizes production efficiency, minimizes energy consumption, and maximizes equipment utilization while meeting all constraints.

[0108] S14, obtaining second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and a second product image of the copper foil produced in simulation.

[0109] The second production data reflects the working status of the copper foil production equipment under the equipment operation sequence and operation time adjusted by the optimization model. By comparing the first production data with the second production data, the impact of the optimization model on the production process can be evaluated, and by comparing the first product image with the second product image, the impact of the optimization strategy on the quality of the copper foil can be evaluated.

[0110] S15, obtaining a stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image.

[0111] In the intelligent scheduling and optimization system of copper foil production, the calculation of the stop optimization factor value involves dual considerations of the production environment and product quality. By simulating production and obtaining product images, production strategies can be evaluated and compared without increasing actual production costs to select the optimal production strategy and minimize costs.

[0112] In an optional implementation, obtaining the stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image includes:

[0113] Evaluate the impact of the simulated optimized scheduling strategy on the production environment according to the first production data and the second production data;

[0114] Evaluate the impact of the simulated optimized scheduling strategy on product quality according to the first product image and the second product image;

[0115] The preset stop optimization factor calculation model is used to calculate the stop optimization factor value based on the impact degree of the production environment and the impact degree of the product quality.

[0116] Compare the first production data with the second production data, and analyze the improvement of the scheduling strategy after simulation optimization on the production environment (such as equipment, energy consumption, etc.). According to the improvement, quantitatively evaluate the impact of the production environment. The impact of the production environment can be expressed by percentage, index or other appropriate quantitative indicators.

[0117] Use image processing technology to compare and analyze the first product image and the second product image to identify points for improvement in product quality, such as surface quality, thickness uniformity, microstructure, etc. Based on the results of the comparative analysis, score each product quality indicator, and then calculate a comprehensive quality score. Finally, based on the comprehensive quality score, quantify the impact of product quality. Percentages, indices, or other suitable quantitative indicators can be used to indicate the impact of product quality.

[0118] A stop optimization factor calculation model is established in advance, which comprehensively considers the impact of the production environment and the product quality, and evaluates the overall effect of the scheduling strategy optimization. The quantitatively evaluated production environment impact and product quality impact are used as input data and input into the preset stop optimization factor calculation model. The stop optimization factor value is calculated according to the preset stop optimization factor calculation model. The stop optimization factor value is a number between 0 and 1, which is used to quantify the distance between the current optimization strategy and the optimal state.

[0119] In an optional implementation manner, the preset stop optimization factor calculation model is expressed as follows:

[0120] F=1 / (1+a×exp(-b×ΔQ)×(ΔI)^c),

[0121] ΔQ is the impact of product quality, ΔI is the impact of production environment, and a, b, and c are preset adjustment coefficients.

[0122] When ΔQ increases, exp(-b×ΔQ) decreases, thereby reducing the denominator of F. As F increases, the more the product quality improves, the greater F becomes. When ΔI increases, (ΔI)c increases, thereby increasing the denominator of F. As F decreases, F decreases, indicating that when the instability or impact of the production environment increases, F decreases.

[0123] The larger the F is, the more favorable or closer to the ideal state the optimization process may be under the current ΔQ and ΔI. On the contrary, the smaller the F is, the optimization process may be hindered or affected by certain unfavorable factors.

[0124] In the above optional implementation, in the intelligent scheduling and optimization process of copper foil production, the degree of influence of product quality (ΔQ) and the degree of influence of the production environment (ΔI) are used to determine whether the model continues to optimize. By evaluating the degree of influence of product quality, it can be determined whether the current optimization strategy has effectively improved product performance, thereby ensuring that the product meets market demand and customer expectations. By evaluating the degree of influence of the production environment, it can be understood whether the current optimization strategy has improved the production process, reduced unnecessary waste, and improved overall production efficiency. By comprehensively considering the degree of influence of product quality and production environment, resources can be allocated more reasonably to avoid wasting too many resources on a production process that is already close to the optimal state.

[0125] In an optional implementation, evaluating the impact of the scheduling strategy after simulation optimization on the production environment according to the first production data and the second production data includes:

[0126] Obtaining a first production efficiency, a first energy consumption and a first equipment utilization rate based on the first production data;

[0127] Obtaining a second production efficiency, a second energy consumption and a second equipment utilization rate based on the second production data;

[0128] Calculate a production efficiency change based on the first production efficiency and the second production efficiency;

[0129] Calculate the energy consumption change based on the first energy consumption and the second energy consumption;

[0130] Calculate the equipment utilization change based on the first equipment utilization and the second equipment utilization;

[0131] The degree of influence of the scheduling strategy after simulation optimization on the production environment is evaluated based on the change in production efficiency, the change in energy consumption and the change in equipment utilization.

[0132] Production efficiency is the quantity or quality of copper foil produced per unit time, reflecting the output capacity of the production line. Energy consumption is the amount of energy consumed in the production process, usually measured in electricity, gas, etc. Equipment utilization is the actual use ratio of production equipment during working hours, reflecting the equipment's use efficiency and production efficiency.

[0133] The difference between the production efficiency before and after optimization is calculated to obtain the change in production efficiency, which is used to evaluate the degree to which the optimization strategy improves production efficiency. The difference between the energy consumption before and after optimization is calculated to obtain the change in energy consumption, which is used to evaluate the degree to which the optimization strategy reduces energy consumption. The difference between the equipment utilization before and after optimization is calculated to obtain the change in equipment utilization, which is used to evaluate the degree to which the optimization strategy improves equipment utilization efficiency.

[0134] The degree of impact on the production environment can be obtained by weighting and calculating the change in production efficiency, the change in energy consumption, and the change in equipment utilization. A high degree of impact on the production environment indicates that the scheduling strategy after simulation optimization has a significant impact on the production environment, which may be positive (such as increased production efficiency, reduced energy consumption, and increased equipment utilization) or negative (such as decreased production efficiency, increased energy consumption, and reduced equipment utilization). When the degree of impact is positive and significant, it means that the optimization strategy is effective; when the degree of impact is negative and significant, it means that the optimization strategy is invalid. A low degree of impact on the production environment indicates that the scheduling strategy after simulation optimization has little impact on the production environment, and the production environment remains relatively stable before and after optimization. It means that the effect of the optimization strategy is not obvious, or the production environment itself is already in a relatively optimized state. In an optional embodiment, the degree of impact of the scheduling strategy after simulation optimization on product quality evaluated based on the first product image and the second product image includes:

[0135] extracting a plurality of first quality indicator values ​​from the first product image;

[0136] extracting a plurality of second quality indicator values ​​from the second product image;

[0137] A quality indicator difference is calculated based on each first quality indicator value and the corresponding second quality indicator value;

[0138] The degree of influence of the scheduling strategy after simulation optimization on product quality is evaluated based on the difference values ​​of the multiple quality indicators.

[0139] A plurality of first quality index values, such as texture features, defect rate, uniformity, surface finish, etc., are extracted from the first product image before optimization.

[0140] Similarly, a quality index value corresponding to the first product image is extracted from the optimized second product image. The second quality index value is consistent with the first quality index value to facilitate accurate comparison.

[0141] For each extracted quality index, the difference before and after optimization is calculated, that is, the second quality index value minus the first quality index value. The quality index difference is the change in the copper foil product quality index before and after optimization based on product image analysis, reflecting the degree of influence of the optimization strategy on the quality index. A positive difference indicates an improvement in quality after optimization, and a negative difference indicates a decrease in quality.

[0142] According to the calculated differences of multiple quality indicators, the degree of influence of the scheduling strategy after simulation optimization on product quality is comprehensively evaluated. The weighted average method can be used to assign a certain weight to each quality indicator difference (determined according to the importance of the indicator to product quality), and then calculate the weighted total difference. The positive and negative and size of the total difference can intuitively reflect the direction and degree of influence of the optimization strategy on product quality. If the total difference is positive and large, it means that the optimization strategy has significantly improved product quality; if the total difference is negative, it means that the optimization strategy may have led to a decline in product quality.

[0143] S16, determining whether the stop optimization factor value is less than a preset stop optimization factor threshold.

[0144] Before or during the optimization process, a reasonable preset stop optimization factor threshold is set according to actual needs and expected goals. The preset stop optimization factor threshold is used to reflect whether the optimization process has achieved the expected effect.

[0145] Compare the calculated stop optimization factor value with the preset stop optimization factor threshold. When the stop optimization factor value is less than or equal to the preset stop optimization factor threshold, it means that the current optimization strategy has achieved significant results and the production data has reached or is close to the expected target. At this point, further optimization can be stopped to avoid additional costs or risks caused by over-optimization. When the stop optimization factor value is not less than the preset stop optimization factor threshold, it means that the current optimization strategy has not achieved the expected results. At this point, it is necessary to continue to adjust the optimization strategy and re-evaluate the stop optimization factor value until the stop condition is met.

[0146] When the stop optimization factor value is greater than the preset stop optimization factor threshold, S17 is executed; when the stop optimization factor value is less than the preset stop optimization factor threshold, S18 is executed.

[0147] S17, using the pre-built optimization model to simulate and optimize the initial intelligent scheduling strategy again.

[0148] In the optimization process of the intelligent scheduling strategy, when the stop optimization factor value is greater than the preset stop optimization factor threshold, it indicates that the current optimization result has not yet reached the expected goal, or there is still room for further optimization. At this time, the pre-built optimization model can be used to simulate and optimize the current simulated optimized intelligent scheduling strategy again.

[0149] In specific implementation, after each optimization iteration, the current stop optimization factor value is calculated, and the calculated stop optimization factor value is compared with the preset stop optimization factor threshold. If the stop optimization factor value is greater than the preset stop optimization factor threshold, it means that the current optimization result has not met the requirements, and the re-optimization process is triggered.

[0150] During the simulation, the optimization model will output one or more optimal scheduling strategies as candidate solutions. The optimal scheduling strategy can be selected from the candidate solutions based on the preset evaluation criteria (such as production efficiency, product quality, energy consumption, etc.). The selected optimal strategy is used as the initial strategy for the next iteration for the next round of optimization. Repeat the above steps until the stop optimization factor value is less than or equal to the preset stop optimization factor threshold, or the preset upper limit of the number of iterations is reached.

[0151] S18, stop iterative simulation optimization and obtain the target intelligent scheduling strategy.

[0152] During the optimization process of the intelligent scheduling strategy, when the stop optimization factor value is less than the preset stop optimization factor threshold, it indicates that the current optimization result is good enough, or the optimization process has reached the expected convergence state, and continued optimization may not bring significant performance improvement, but may increase computing cost and time. Therefore, in this case, the process of stopping optimization is triggered, and the next round of iterative simulation optimization is no longer performed, and the intelligent scheduling strategy obtained by the current iteration is used as the target intelligent scheduling strategy.

[0153] It should be understood that, for the case where the stop optimization factor value is equal to the preset stop optimization factor threshold, it can be applicable to the case where the stop optimization factor value is greater than the preset stop optimization factor threshold, and it can also be applicable to the case where the stop optimization factor value is less than the preset stop optimization factor threshold, and the present application does not impose any limitations here.

[0154] S19, generating intelligent scheduling instructions based on the target intelligent scheduling strategy, and sending the instructions to the corresponding copper foil production equipment in real time to achieve intelligent scheduling of copper foil production.

[0155] Based on the obtained target intelligent scheduling strategy, corresponding intelligent scheduling instructions are generated, and these instructions are sent to the corresponding copper foil production equipment in real time.

[0156] In a specific embodiment, key scheduling parameters can be extracted from the strategy, such as production batches, equipment allocation, production sequence, process parameters, etc. Based on the extracted scheduling parameters, specific intelligent scheduling instructions are constructed to instruct each copper foil production equipment when to start or stop production, what specifications of copper foil to produce, what process parameters to use, etc.

[0157] The instruction is sent to the corresponding copper foil production equipment in real time. After receiving the instruction, the production equipment performs production operations according to the requirements of the instruction.

[0158] During the production process, the operating status and production data of the equipment are continuously monitored to ensure the smooth execution of instructions. At the same time, feedback data from the equipment is collected to continuously optimize the subsequent intelligent scheduling strategy.

[0159] In an optional embodiment, the method further comprises:

[0160] A copper foil production prediction model is obtained based on historical production data and historical copper foil production data;

[0161] Using the copper foil production prediction model to predict future copper foil production data based on real-time production data;

[0162] Production resources are scheduled based on the future copper foil output data.

[0163] In the intelligent scheduling system of copper foil production, in order to more accurately predict future output and rationally schedule production resources based on it, the copper foil output prediction model can be trained based on historical production data and historical copper foil output data.

[0164] In a specific embodiment, first, historical production data in the copper foil production process is collected, including production time, equipment status, process parameters, raw material quality, etc., as well as corresponding historical copper foil production data. The collected data is cleaned and sorted, and outliers and missing values ​​are removed to ensure the accuracy and completeness of the data. Machine learning or deep learning algorithms, such as linear regression, decision trees, random forests, neural networks, etc., are used to train a copper foil production prediction model based on processed historical data. During the production process, various data on copper foil production, such as equipment operating status, process parameter adjustment, etc., are collected in real time. The real-time data is preprocessed to ensure that it is consistent with the data format used in model training. The preprocessed real-time data is input into the copper foil production prediction model to obtain copper foil production prediction data for a period of time in the future.

[0165] According to the forecast data of future copper foil production, evaluate the adequacy and rationality of current production resources (such as equipment, manpower, raw materials, etc.). According to the results of resource evaluation, formulate a production resource scheduling plan for the next period of time, including equipment allocation, production batch arrangement, personnel deployment, etc.

[0166] The above optional implementation method, through the copper foil production prediction model obtained by training based on historical production data and historical copper foil production data, can more accurately predict future copper foil production, provide a strong basis for the rational scheduling of production resources, and ensure that production resources can be used efficiently and reasonably, thereby improving production efficiency and reducing costs.

[0167] In an optional embodiment, the method further comprises:

[0168] Obtaining the operating parameter values ​​of each copper foil production equipment and the corresponding parameter threshold range;

[0169] comparing the operating parameter value with a corresponding parameter threshold range;

[0170] When the operating parameter value exceeds the corresponding parameter threshold range, an alarm instruction carrying the copper foil production equipment identification is generated.

[0171] In the intelligent scheduling system for copper foil production, in order to ensure the normal operation of production equipment and respond to potential faults in a timely manner, the operating parameter values ​​of each copper foil production equipment are obtained in real time and compared with the preset parameter threshold range. When it is found that the operating parameter value exceeds the threshold range, an alarm instruction is automatically generated so that timely measures can be taken to deal with it.

[0172] In a specific embodiment, the operating parameter values ​​of each copper foil production equipment, such as temperature, pressure, current, vibration, etc., are collected in real time through sensors, monitoring equipment or production management systems. A reasonable threshold range is set for each operating parameter based on the technical specifications, historical operating data and industry standards of the equipment. The threshold range includes an upper limit and a lower limit, which are used to determine whether the parameter value is normal.

[0173] The operating parameter values ​​collected in real time are compared with the corresponding parameter threshold range. If a certain operating parameter value exceeds its corresponding threshold range (greater than the upper limit, or less than the lower limit), the device is judged to be abnormal, and an alarm instruction carrying the copper foil production equipment identification (such as equipment number, name, etc.) is automatically generated. The alarm instruction may include information such as the name of the abnormal parameter, the current value, the threshold range, and the alarm time.

[0174] The generated alarm instruction is sent to the relevant management personnel or maintenance personnel, which can be sent by SMS, email, system notification, etc., so that the management personnel or maintenance personnel can go to the site to conduct a detailed inspection of the abnormal equipment to determine the specific cause of the problem.

[0175] The above optional implementation method can issue an alarm command in time when the equipment operation status is abnormal by continuously acquiring the operating parameter values ​​of each copper foil production equipment and comparing them with the preset parameter threshold range, which helps enterprises to timely discover and deal with equipment failures, prevent the failures from further expanding, and ensure the continuity and stability of production. The alarm command carries the copper foil production equipment identification, which is convenient for quickly locating the faulty equipment, thereby shortening the time for troubleshooting and repairing, improving the maintenance efficiency of production equipment, and reducing production interruptions caused by equipment failures.

[0176] The copper foil production intelligent scheduling method provided in the embodiment of the present application obtains the first production data of each copper foil production equipment on the copper foil production line and the first product image of the copper foil produced, and generates an initial intelligent scheduling strategy based on the first production data, which provides basic data support for subsequent simulation optimization and ensures the pertinence and accuracy of the scheduling strategy. The initial intelligent scheduling strategy is simulated and optimized using a pre-built optimization model for the first time, with the goal of maximizing production efficiency, minimizing energy consumption and maximizing equipment utilization, laying the foundation for subsequent optimization iterations. The second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and the second product image of the simulated copper foil are obtained, and the stop optimization factor value is obtained based on the first production data, the second production data, the first product image and the second product image, and the optimization effect of the current scheduling strategy is quantitatively evaluated. By comparing the stop optimization factor value with the preset stop optimization factor threshold, it is decided whether to continue iterative optimization. If the stop optimization factor value is less than the threshold, it indicates that the optimization effect has reached or is close to the expected target (i.e.), and no additional iteration is required, thereby saving computing resources and time. On the contrary, if the stop optimization factor value is greater than the threshold, indicating that the optimization effect still has room for improvement, the iterative optimization will continue to be performed to further approach the optimal solution. Finally, the target intelligent scheduling strategy is obtained when the iteration is stopped. Through iterative optimization, the optimal scheduling strategy is gradually approached, and the comprehensive optimization of production efficiency, energy consumption and equipment utilization is achieved. Based on the target intelligent scheduling strategy, intelligent scheduling instructions are generated, and the instructions are sent to the corresponding copper foil production equipment in real time, so as to realize the precise control of the copper foil production process, ensure the effective execution of the scheduling strategy, and improve production efficiency and product quality. This application realizes the precise scheduling of the copper foil production process by integrating production data, product images and optimization model iteration, which not only improves production efficiency and reduces energy consumption, but also ensures the stability of product quality.

[0177] Figure 2 It is a functional module diagram of the intelligent scheduling device for copper foil production provided in an embodiment of the present application.

[0178] In some embodiments, the copper foil production intelligent scheduling device 20 may include a plurality of functional modules composed of program code segments. The program code of each program segment in the copper foil production intelligent scheduling device 20 may be stored in a memory of a computer device and executed by at least one processor to execute (see Figure 1 Description) Function of intelligent scheduling of copper foil production.

[0179] In this embodiment, the copper foil production intelligent scheduling device 20 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a first acquisition module 201, a strategy generation module 202, a simulation optimization module 203, a second acquisition module 204, a factor calculation module 205, a threshold judgment module 206, a target generation module 207, an intelligent scheduling module 208, a resource scheduling module 209 and a monitoring alarm module 210. The module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0180] The first acquisition module 201 is used to acquire first production data of each copper foil production equipment on the copper foil production line and a first product image of the produced copper foil.

[0181] The strategy generation module 202 is used to simulate and generate an initial intelligent scheduling strategy according to the first production data.

[0182] The simulation optimization module 203 is used to perform initial simulation optimization on the initial intelligent scheduling strategy using a pre-built optimization model, wherein the optimization model uses the equipment operation sequence and operation time as decision variables, and aims to maximize production efficiency, minimize energy consumption, and maximize equipment utilization under multiple preset constraints.

[0183] The second acquisition module 204 is used to acquire the second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and the second product image of the copper foil produced in simulation.

[0184] The factor calculation module 205 is used to obtain a stop optimization factor value according to the first production data, the second production data, the first product image and the second product image.

[0185] The threshold determination module 206 determines whether the stop optimization factor value is less than a preset stop optimization factor threshold.

[0186] The simulation optimization module 203 is further used to simulate and optimize the initial intelligent scheduling strategy again using a pre-built optimization model when the stop optimization factor value is greater than the preset stop optimization factor threshold.

[0187] The target generation module 207 is used to stop iterative simulation optimization and obtain a target intelligent scheduling strategy when the stop optimization factor value is less than the preset stop optimization factor threshold.

[0188] The intelligent scheduling module 208 is used to generate intelligent scheduling instructions based on the target intelligent scheduling strategy, and send the instructions to the corresponding copper foil production equipment in real time to achieve intelligent scheduling of copper foil production.

[0189] The resource scheduling module 209 is used to train a copper foil production prediction model based on historical production data and historical copper foil production data; use the copper foil production prediction model to predict future copper foil production data based on real-time production data; and schedule production resources based on the future copper foil production data.

[0190] The monitoring alarm module 210 is used to obtain the operating parameter value of each copper foil production equipment and the corresponding parameter threshold range; compare the operating parameter value with the corresponding parameter threshold range; when the operating parameter value exceeds the corresponding parameter threshold range, generate an alarm instruction carrying the copper foil production equipment identification.

[0191] It should be understood that the various variations and specific embodiments of the intelligent scheduling method for copper foil production provided in the above-mentioned embodiments are also applicable to the intelligent scheduling device for copper foil production in this embodiment. Through the detailed description of the aforementioned intelligent scheduling method for copper foil production, those skilled in the art can clearly understand the implementation process of the intelligent scheduling device for copper foil production in this embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0192] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the steps of the intelligent scheduling method for copper foil production are implemented.

[0193] See also Figure 3 FIG. 2 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. In a preferred embodiment of the present application, the computer device 3 includes a memory 301 , at least one processor 302 , and at least one communication bus 303 .

[0194] Those skilled in the art should understand that Figure 3 The structure of the computer device shown does not constitute a limitation of the embodiments of the present application. The computer device 3 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0195] In some embodiments, the computer device 3 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices. The computer device 3 may also include client devices, which include but are not limited to any electronic product that can interact with the client through a keyboard, mouse, remote control, touchpad, or voice control device, such as a personal computer, tablet computer, smart phone, digital camera, etc.

[0196] It should be noted that the computer device 3 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0197] In some embodiments, a computer program is stored in the memory 301, and when the computer program is executed by the at least one processor 302, all or part of the steps in the intelligent scheduling method for copper foil production as described above are implemented. The memory 301 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data. Further, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, and the like.

[0198] In some embodiments, the at least one processor 302 is the control core (CrolUnit) of the computer device 3, and uses various interfaces and lines to connect the various components of the entire computer device 3, and executes various functions and processes data of the computer device 3 by running or executing the program or module stored in the memory 301, and calling the data stored in the memory 301. For example, when the at least one processor 302 executes the computer program stored in the memory, it implements all or part of the steps of the intelligent scheduling method for copper foil production described in the embodiment of the present application; or implements all or part of the functions of the intelligent scheduling device for copper foil production. The at least one processor 302 can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same function or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips.

[0199] In some embodiments, the at least one communication bus 303 is configured to realize connection and communication between the memory 301 and the at least one processor 302, etc. Although not shown, the computer device 3 may also include a power supply (such as a battery) for powering each component. Preferably, the power supply may be logically connected to the at least one processor 302 through a power management device, so as to realize functions such as management of charging, discharging, and power consumption management through the power management device. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The computer device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0200] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0202] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for intelligent scheduling of copper foil production, characterized in that: The copper foil production intelligent scheduling method comprises: Acquire first production data of each copper foil production equipment on the copper foil production line and a first product image of the produced copper foil; Simulating and generating an initial intelligent scheduling strategy according to the first production data; Using a pre-built optimization model to perform a first simulation optimization on the initial intelligent scheduling strategy, wherein the optimization model uses the equipment operation sequence and operation time as decision variables, and aims to maximize production efficiency, minimize energy consumption, and maximize equipment utilization under multiple preset constraints; Acquire the second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and the second product image of the copper foil produced by simulation; Obtaining a stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image; Determine whether the stop optimization factor value is less than a preset stop optimization factor threshold; When the stop optimization factor value is greater than the preset stop optimization factor threshold, the initial intelligent scheduling strategy is simulated and optimized again using the pre-built optimization model; When the stop optimization factor value is less than the preset stop optimization factor threshold, the iterative simulation optimization is stopped to obtain the target intelligent scheduling strategy; Based on the target intelligent scheduling strategy, an intelligent scheduling instruction is generated, and the instruction is sent to the corresponding copper foil production equipment in real time to realize intelligent scheduling of copper foil production.

2. The intelligent scheduling method for copper foil production according to claim 1, characterized in that: The optimization model is expressed as follows: F(x) = α·Production efficiency (x) - β·Energy consumption (x) + γ·Equipment utilization (x); Wherein, α, β and γ are weight coefficients; x is the vector of decision variables [x1, x2, ..., xn], and xi represents the operation sequence and operation time of the i-th copper foil production equipment; The multiple constraints are: g1(x)=device dependency constraint(x)≤0; g2(x) = time window constraint (x) ≤ 0; g3(x) = resource limit constraint (x) ≤ 0; h(x)=capacity demand constraint(x)=0.

3. The intelligent scheduling method for copper foil production according to claim 2, characterized in that: The obtaining of the stop optimization factor value according to the first production data, the second production data, the first product image and the second product image comprises: Evaluate the impact of the simulated optimized scheduling strategy on the production environment according to the first production data and the second production data; Evaluate the impact of the simulated optimized scheduling strategy on product quality according to the first product image and the second product image; Using a preset stop optimization factor calculation model, the stop optimization factor value is calculated based on the impact degree of the production environment and the impact degree of the product quality; The preset stop optimization factor calculation model is expressed as follows: F=1 / (1+a×exp(-b×ΔQ)×(ΔI)^c), ΔQ is the impact of product quality, ΔI is the impact of production environment, and a, b, and c are preset adjustment coefficients.

4. The intelligent scheduling method for copper foil production according to claim 3, characterized in that: The evaluating, based on the first production data and the second production data, the impact of the scheduling strategy after simulation optimization on the production environment comprises: Obtaining a first production efficiency, a first energy consumption and a first equipment utilization rate based on the first production data; Obtaining a second production efficiency, a second energy consumption and a second equipment utilization rate based on the second production data; Calculate a production efficiency change based on the first production efficiency and the second production efficiency; Calculate the energy consumption change based on the first energy consumption and the second energy consumption; Calculate the equipment utilization change based on the first equipment utilization and the second equipment utilization; The degree of influence of the scheduling strategy after simulation optimization on the production environment is evaluated based on the change in production efficiency, the change in energy consumption and the change in equipment utilization.

5. The intelligent scheduling method for copper foil production according to claim 1, characterized in that: The simulating and generating an initial intelligent scheduling strategy according to the first production data includes: Generate an equipment relationship map according to the layout diagram of the copper foil production line and the dependency relationship between various copper foil production equipment; Selecting a scheduling rule from a rule library based on the first production data and the equipment relationship map; The initial intelligent scheduling strategy is simulated and generated based on the selected scheduling rules.

6. The intelligent scheduling method for copper foil production according to claim 1, characterized in that: The method further comprises: A copper foil production prediction model is obtained based on historical production data and historical copper foil production data; Using the copper foil production prediction model to predict future copper foil production data based on real-time production data; Production resources are scheduled based on the future copper foil output data.

7. The intelligent scheduling method for copper foil production according to claim 1, characterized in that: The method further comprises: Obtaining the operating parameter values ​​of each copper foil production equipment and the corresponding parameter threshold range; comparing the operating parameter value with a corresponding parameter threshold range; When the operating parameter value exceeds the corresponding parameter threshold range, an alarm instruction carrying the copper foil production equipment identification is generated.

8. An intelligent scheduling device for copper foil production, characterized in that: The device comprises: A first acquisition module is used to acquire first production data of each copper foil production equipment on the copper foil production line and a first product image of the produced copper foil; A strategy generation module, used to simulate and generate an initial intelligent scheduling strategy according to the first production data; A simulation optimization module, used to perform a preliminary simulation optimization on the initial intelligent scheduling strategy using a pre-built optimization model, wherein the optimization model uses the equipment operation sequence and operation time as decision variables, and aims to maximize production efficiency, minimize energy consumption, and maximize equipment utilization under multiple preset constraints; A second acquisition module is used to acquire second production data of each copper foil production equipment on the copper foil production line after the initial simulation optimization and a second product image of the copper foil produced in simulation; a factor calculation module, configured to obtain a stop optimization factor value according to the first production data, the second production data, the first product image, and the second product image; A threshold judgment module, for judging whether the stop optimization factor value is less than a preset stop optimization factor threshold; The simulation optimization module is further used to simulate and optimize the initial intelligent scheduling strategy again using a pre-built optimization model when the stop optimization factor value is greater than the preset stop optimization factor threshold; A target generation module, used to stop iterative simulation optimization and obtain a target intelligent scheduling strategy when the stop optimization factor value is less than the preset stop optimization factor threshold; The intelligent scheduling module is used to generate intelligent scheduling instructions based on the target intelligent scheduling strategy, and send the instructions to the corresponding copper foil production equipment in real time to achieve intelligent scheduling of copper foil production.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent scheduling method for copper foil production as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the intelligent scheduling method for copper foil production as described in any one of claims 1 to 7 are implemented.

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